{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Welcome to the Second Lab - Week 1, Day 3\n", "\n", "Today we will work with lots of models! This is a way to get comfortable with APIs." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", " \n", " \n", " \n", " \n", "
\n", " \n", " \n", "

Important point - please read

\n", " The way I collaborate with you may be different to other courses you've taken. I prefer not to type code while you watch. Rather, I execute Jupyter Labs, like this, and give you an intuition for what's going on. My suggestion is that you carefully execute this yourself, after watching the lecture. Add print statements to understand what's going on, and then come up with your own variations.

If you have time, I'd love it if you submit a PR for changes in the community_contributions folder - instructions in the resources. Also, if you have a Github account, use this to showcase your variations. Not only is this essential practice, but it demonstrates your skills to others, including perhaps future clients or employers...\n", " \n", "
" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Start with imports - ask ChatGPT to explain any package that you don't know\n", "\n", "import os\n", "import json\n", "from dotenv import load_dotenv\n", "from openai import OpenAI\n", "from anthropic import Anthropic\n", "from IPython.display import Markdown, display" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Always remember to do this!\n", "load_dotenv(override=True)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "OpenAI API Key exists and begins sk-proj-\n", "Anthropic API Key not set (and this is optional)\n", "Google API Key exists and begins AI\n", "DeepSeek API Key exists and begins sk-\n", "Groq API Key exists and begins gsk_\n" ] } ], "source": [ "# Print the key prefixes to help with any debugging\n", "\n", "openai_api_key = os.getenv('OPENAI_API_KEY')\n", "anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n", "google_api_key = os.getenv('GOOGLE_API_KEY')\n", "deepseek_api_key = os.getenv('DEEPSEEK_API_KEY')\n", "groq_api_key = os.getenv('GROQ_API_KEY')\n", "\n", "if openai_api_key:\n", " print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n", "else:\n", " print(\"OpenAI API Key not set\")\n", " \n", "if anthropic_api_key:\n", " print(f\"Anthropic API Key exists and begins {anthropic_api_key[:7]}\")\n", "else:\n", " print(\"Anthropic API Key not set (and this is optional)\")\n", "\n", "if google_api_key:\n", " print(f\"Google API Key exists and begins {google_api_key[:2]}\")\n", "else:\n", " print(\"Google API Key not set (and this is optional)\")\n", "\n", "if deepseek_api_key:\n", " print(f\"DeepSeek API Key exists and begins {deepseek_api_key[:3]}\")\n", "else:\n", " print(\"DeepSeek API Key not set (and this is optional)\")\n", "\n", "if groq_api_key:\n", " print(f\"Groq API Key exists and begins {groq_api_key[:4]}\")\n", "else:\n", " print(\"Groq API Key not set (and this is optional)\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "request = \"Please come up with a challenging, nuanced question that I can ask a number of LLMs to evaluate their intelligence. \"\n", "request += \"Answer only with the question, no explanation.\"\n", "messages = [{\"role\": \"user\", \"content\": request}]\n", "# concate the code." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[{'role': 'user',\n", " 'content': 'Please come up with a challenging, nuanced question that I can ask a number of LLMs to evaluate their intelligence. Answer only with the question, no explanation.'}]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "messages" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "You are the chief planner for a mid-sized coastal city of 800,000 people that has 20 years to prepare for climate-change-driven sea-level rise now projected to be between 0.5 m and 1.5 m by 2045, with more frequent and intense storms; the city's annual budget for adaptation is capped at the equivalent of 5% of current GDP and political opposition to large-scale relocation is strong—produce a single, detailed answer that includes: (1) a prioritized, phased 20-year adaptation strategy across infrastructure, housing, economy, legal/regulatory, health/social services, and social equity designed to minimize expected fatalities and economic loss under uncertainty; (2) quantitative estimates (with units and confidence intervals) of costs, benefits, and probabilities for at least four candidate interventions (e.g., seawalls, managed retreat, elevating buildings, wetland restoration), and a decision-analysis showing which combination is optimal under low, medium, and high sea-level scenarios; (3) an explicit list of at least five critical uncertainties (technical, social, economic) and concrete, time-phased monitoring and data-collection actions that would reduce those uncertainties and how much value (in expected lives or dollars) you expect that information to provide; (4) a stakeholder engagement and communication plan that explains how you would present trade-offs, manage dissent, design compensation for displaced or adversely affected groups, and ensure equity; (5) all key assumptions, model choices, back-of-the-envelope calculations (show your work), potential failure modes, and a clear plan for how and when you would update or abandon major actions in response to new data; and (6) a short reflection on which parts of your answer you are least confident in and what additional expert inputs or data you would request before committing to implementation—if relevant information is missing, explicitly state the questions you would ask and why.\n" ] } ], "source": [ "openai = OpenAI()\n", "response = openai.chat.completions.create(\n", " model=\"gpt-5-mini\",\n", " messages=messages,\n", ")\n", "question = response.choices[0].message.content\n", "print(question)\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "competitors = []\n", "answers = []\n", "messages = [{\"role\": \"user\", \"content\": question}]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Note - update since the videos\n", "\n", "I've updated the model names to use the latest models below, like GPT 5 and Claude Sonnet 4.5. It's worth noting that these models can be quite slow - like 1-2 minutes - but they do a great job! Feel free to switch them for faster models if you'd prefer, like the ones I use in the video." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[{'role': 'user',\n", " 'content': \"You are the chief planner for a mid-sized coastal city of 800,000 people that has 20 years to prepare for climate-change-driven sea-level rise now projected to be between 0.5 m and 1.5 m by 2045, with more frequent and intense storms; the city's annual budget for adaptation is capped at the equivalent of 5% of current GDP and political opposition to large-scale relocation is strong—produce a single, detailed answer that includes: (1) a prioritized, phased 20-year adaptation strategy across infrastructure, housing, economy, legal/regulatory, health/social services, and social equity designed to minimize expected fatalities and economic loss under uncertainty; (2) quantitative estimates (with units and confidence intervals) of costs, benefits, and probabilities for at least four candidate interventions (e.g., seawalls, managed retreat, elevating buildings, wetland restoration), and a decision-analysis showing which combination is optimal under low, medium, and high sea-level scenarios; (3) an explicit list of at least five critical uncertainties (technical, social, economic) and concrete, time-phased monitoring and data-collection actions that would reduce those uncertainties and how much value (in expected lives or dollars) you expect that information to provide; (4) a stakeholder engagement and communication plan that explains how you would present trade-offs, manage dissent, design compensation for displaced or adversely affected groups, and ensure equity; (5) all key assumptions, model choices, back-of-the-envelope calculations (show your work), potential failure modes, and a clear plan for how and when you would update or abandon major actions in response to new data; and (6) a short reflection on which parts of your answer you are least confident in and what additional expert inputs or data you would request before committing to implementation—if relevant information is missing, explicitly state the questions you would ask and why.\"}]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "\n", "\n", "messages" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mKeyboardInterrupt\u001b[39m Traceback (most recent call last)", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[16]\u001b[39m\u001b[32m, line 7\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m# The API we know well\u001b[39;00m\n\u001b[32m 2\u001b[39m \u001b[38;5;66;03m# I've updated this with the latest model, but it can take some time because it likes to think!\u001b[39;00m\n\u001b[32m 3\u001b[39m \u001b[38;5;66;03m# Replace the model with gpt-4.1-mini if you'd prefer not to wait 1-2 mins\u001b[39;00m\n\u001b[32m 5\u001b[39m model_name = \u001b[33m\"\u001b[39m\u001b[33mgpt-5-nano\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m7\u001b[39m response = \u001b[43mopenai\u001b[49m\u001b[43m.\u001b[49m\u001b[43mchat\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcompletions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmodel_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 8\u001b[39m answer = response.choices[\u001b[32m0\u001b[39m].message.content\n\u001b[32m 10\u001b[39m display(Markdown(answer))\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/python/Agent AI/agents/.venv/lib/python3.12/site-packages/openai/_utils/_utils.py:287\u001b[39m, in \u001b[36mrequired_args..inner..wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 285\u001b[39m msg = \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mMissing required argument: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mquote(missing[\u001b[32m0\u001b[39m])\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m 286\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(msg)\n\u001b[32m--> \u001b[39m\u001b[32m287\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/python/Agent AI/agents/.venv/lib/python3.12/site-packages/openai/resources/chat/completions/completions.py:925\u001b[39m, in \u001b[36mCompletions.create\u001b[39m\u001b[34m(self, messages, model, audio, frequency_penalty, function_call, functions, logit_bias, logprobs, max_completion_tokens, max_tokens, metadata, modalities, n, parallel_tool_calls, prediction, presence_penalty, reasoning_effort, response_format, seed, service_tier, stop, store, stream, stream_options, temperature, tool_choice, tools, top_logprobs, top_p, user, web_search_options, extra_headers, extra_query, extra_body, timeout)\u001b[39m\n\u001b[32m 882\u001b[39m \u001b[38;5;129m@required_args\u001b[39m([\u001b[33m\"\u001b[39m\u001b[33mmessages\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mmodel\u001b[39m\u001b[33m\"\u001b[39m], [\u001b[33m\"\u001b[39m\u001b[33mmessages\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mmodel\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mstream\u001b[39m\u001b[33m\"\u001b[39m])\n\u001b[32m 883\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mcreate\u001b[39m(\n\u001b[32m 884\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 922\u001b[39m timeout: \u001b[38;5;28mfloat\u001b[39m | httpx.Timeout | \u001b[38;5;28;01mNone\u001b[39;00m | NotGiven = NOT_GIVEN,\n\u001b[32m 923\u001b[39m ) -> ChatCompletion | Stream[ChatCompletionChunk]:\n\u001b[32m 924\u001b[39m validate_response_format(response_format)\n\u001b[32m--> \u001b[39m\u001b[32m925\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_post\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 926\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m/chat/completions\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 927\u001b[39m \u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmaybe_transform\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 928\u001b[39m \u001b[43m \u001b[49m\u001b[43m{\u001b[49m\n\u001b[32m 929\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmessages\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 930\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmodel\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 931\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43maudio\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43maudio\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 932\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mfrequency_penalty\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrequency_penalty\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 933\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mfunction_call\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunction_call\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 934\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mfunctions\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunctions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 935\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mlogit_bias\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogit_bias\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 936\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mlogprobs\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogprobs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 937\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmax_completion_tokens\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_completion_tokens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 938\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmax_tokens\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_tokens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 939\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmetadata\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 940\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmodalities\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodalities\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 941\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mn\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 942\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mparallel_tool_calls\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mparallel_tool_calls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 943\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mprediction\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mprediction\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 944\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mpresence_penalty\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mpresence_penalty\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 945\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mreasoning_effort\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mreasoning_effort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 946\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mresponse_format\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponse_format\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 947\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mseed\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mseed\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 948\u001b[39m \u001b[43m 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\u001b[49m\u001b[43mextra_query\u001b[49m\u001b[43m=\u001b[49m\u001b[43mextra_query\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextra_body\u001b[49m\u001b[43m=\u001b[49m\u001b[43mextra_body\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtimeout\u001b[49m\n\u001b[32m 967\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 968\u001b[39m \u001b[43m \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m=\u001b[49m\u001b[43mChatCompletion\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 969\u001b[39m \u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstream\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 970\u001b[39m \u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[43m=\u001b[49m\u001b[43mStream\u001b[49m\u001b[43m[\u001b[49m\u001b[43mChatCompletionChunk\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 971\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/python/Agent AI/agents/.venv/lib/python3.12/site-packages/openai/_base_client.py:1249\u001b[39m, in \u001b[36mSyncAPIClient.post\u001b[39m\u001b[34m(self, path, cast_to, body, options, files, stream, stream_cls)\u001b[39m\n\u001b[32m 1235\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mpost\u001b[39m(\n\u001b[32m 1236\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m 1237\u001b[39m path: \u001b[38;5;28mstr\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 1244\u001b[39m stream_cls: \u001b[38;5;28mtype\u001b[39m[_StreamT] | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m 1245\u001b[39m ) -> ResponseT | _StreamT:\n\u001b[32m 1246\u001b[39m opts = FinalRequestOptions.construct(\n\u001b[32m 1247\u001b[39m method=\u001b[33m\"\u001b[39m\u001b[33mpost\u001b[39m\u001b[33m\"\u001b[39m, url=path, json_data=body, files=to_httpx_files(files), **options\n\u001b[32m 1248\u001b[39m )\n\u001b[32m-> \u001b[39m\u001b[32m1249\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m cast(ResponseT, \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[43m)\u001b[49m)\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/python/Agent AI/agents/.venv/lib/python3.12/site-packages/openai/_base_client.py:972\u001b[39m, in \u001b[36mSyncAPIClient.request\u001b[39m\u001b[34m(self, cast_to, options, stream, stream_cls)\u001b[39m\n\u001b[32m 970\u001b[39m response = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 971\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m972\u001b[39m response = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_client\u001b[49m\u001b[43m.\u001b[49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 973\u001b[39m \u001b[43m \u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 974\u001b[39m \u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstream\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_should_stream_response_body\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 975\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 976\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 977\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m httpx.TimeoutException \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[32m 978\u001b[39m log.debug(\u001b[33m\"\u001b[39m\u001b[33mEncountered httpx.TimeoutException\u001b[39m\u001b[33m\"\u001b[39m, exc_info=\u001b[38;5;28;01mTrue\u001b[39;00m)\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/python/Agent AI/agents/.venv/lib/python3.12/site-packages/httpx/_client.py:914\u001b[39m, in \u001b[36mClient.send\u001b[39m\u001b[34m(self, request, stream, auth, follow_redirects)\u001b[39m\n\u001b[32m 910\u001b[39m \u001b[38;5;28mself\u001b[39m._set_timeout(request)\n\u001b[32m 912\u001b[39m auth = \u001b[38;5;28mself\u001b[39m._build_request_auth(request, auth)\n\u001b[32m--> \u001b[39m\u001b[32m914\u001b[39m response = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_send_handling_auth\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 915\u001b[39m \u001b[43m \u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 916\u001b[39m \u001b[43m \u001b[49m\u001b[43mauth\u001b[49m\u001b[43m=\u001b[49m\u001b[43mauth\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 917\u001b[39m \u001b[43m \u001b[49m\u001b[43mfollow_redirects\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfollow_redirects\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 918\u001b[39m \u001b[43m \u001b[49m\u001b[43mhistory\u001b[49m\u001b[43m=\u001b[49m\u001b[43m[\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 919\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 920\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 921\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m stream:\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/python/Agent AI/agents/.venv/lib/python3.12/site-packages/httpx/_client.py:942\u001b[39m, in \u001b[36mClient._send_handling_auth\u001b[39m\u001b[34m(self, request, auth, follow_redirects, history)\u001b[39m\n\u001b[32m 939\u001b[39m request = \u001b[38;5;28mnext\u001b[39m(auth_flow)\n\u001b[32m 941\u001b[39m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m942\u001b[39m response = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_send_handling_redirects\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 943\u001b[39m \u001b[43m \u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 944\u001b[39m \u001b[43m \u001b[49m\u001b[43mfollow_redirects\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfollow_redirects\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 945\u001b[39m \u001b[43m \u001b[49m\u001b[43mhistory\u001b[49m\u001b[43m=\u001b[49m\u001b[43mhistory\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 946\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 947\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 948\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/python/Agent AI/agents/.venv/lib/python3.12/site-packages/httpx/_client.py:979\u001b[39m, in \u001b[36mClient._send_handling_redirects\u001b[39m\u001b[34m(self, request, follow_redirects, history)\u001b[39m\n\u001b[32m 976\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m hook \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m._event_hooks[\u001b[33m\"\u001b[39m\u001b[33mrequest\u001b[39m\u001b[33m\"\u001b[39m]:\n\u001b[32m 977\u001b[39m hook(request)\n\u001b[32m--> \u001b[39m\u001b[32m979\u001b[39m response = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_send_single_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 980\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 981\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m hook \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m._event_hooks[\u001b[33m\"\u001b[39m\u001b[33mresponse\u001b[39m\u001b[33m\"\u001b[39m]:\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/python/Agent AI/agents/.venv/lib/python3.12/site-packages/httpx/_client.py:1014\u001b[39m, in \u001b[36mClient._send_single_request\u001b[39m\u001b[34m(self, request)\u001b[39m\n\u001b[32m 1009\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[32m 1010\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mAttempted to send an async request with a sync Client instance.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 1011\u001b[39m )\n\u001b[32m 1013\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m request_context(request=request):\n\u001b[32m-> \u001b[39m\u001b[32m1014\u001b[39m response = \u001b[43mtransport\u001b[49m\u001b[43m.\u001b[49m\u001b[43mhandle_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1016\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(response.stream, SyncByteStream)\n\u001b[32m 1018\u001b[39m response.request = request\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/python/Agent AI/agents/.venv/lib/python3.12/site-packages/httpx/_transports/default.py:250\u001b[39m, in \u001b[36mHTTPTransport.handle_request\u001b[39m\u001b[34m(self, request)\u001b[39m\n\u001b[32m 237\u001b[39m req = httpcore.Request(\n\u001b[32m 238\u001b[39m method=request.method,\n\u001b[32m 239\u001b[39m url=httpcore.URL(\n\u001b[32m (...)\u001b[39m\u001b[32m 247\u001b[39m extensions=request.extensions,\n\u001b[32m 248\u001b[39m )\n\u001b[32m 249\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m map_httpcore_exceptions():\n\u001b[32m--> \u001b[39m\u001b[32m250\u001b[39m resp = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_pool\u001b[49m\u001b[43m.\u001b[49m\u001b[43mhandle_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreq\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 252\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(resp.stream, typing.Iterable)\n\u001b[32m 254\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m Response(\n\u001b[32m 255\u001b[39m status_code=resp.status,\n\u001b[32m 256\u001b[39m headers=resp.headers,\n\u001b[32m 257\u001b[39m stream=ResponseStream(resp.stream),\n\u001b[32m 258\u001b[39m extensions=resp.extensions,\n\u001b[32m 259\u001b[39m )\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/python/Agent AI/agents/.venv/lib/python3.12/site-packages/httpcore/_sync/connection_pool.py:256\u001b[39m, in \u001b[36mConnectionPool.handle_request\u001b[39m\u001b[34m(self, request)\u001b[39m\n\u001b[32m 253\u001b[39m closing = \u001b[38;5;28mself\u001b[39m._assign_requests_to_connections()\n\u001b[32m 255\u001b[39m \u001b[38;5;28mself\u001b[39m._close_connections(closing)\n\u001b[32m--> \u001b[39m\u001b[32m256\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m exc \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 258\u001b[39m \u001b[38;5;66;03m# Return the response. Note that in this case we still have to manage\u001b[39;00m\n\u001b[32m 259\u001b[39m \u001b[38;5;66;03m# the point at which the response is closed.\u001b[39;00m\n\u001b[32m 260\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(response.stream, typing.Iterable)\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/python/Agent AI/agents/.venv/lib/python3.12/site-packages/httpcore/_sync/connection_pool.py:236\u001b[39m, in \u001b[36mConnectionPool.handle_request\u001b[39m\u001b[34m(self, request)\u001b[39m\n\u001b[32m 232\u001b[39m connection = pool_request.wait_for_connection(timeout=timeout)\n\u001b[32m 234\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 235\u001b[39m \u001b[38;5;66;03m# Send the request on the assigned connection.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m236\u001b[39m response = \u001b[43mconnection\u001b[49m\u001b[43m.\u001b[49m\u001b[43mhandle_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 237\u001b[39m \u001b[43m \u001b[49m\u001b[43mpool_request\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrequest\u001b[49m\n\u001b[32m 238\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 239\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m ConnectionNotAvailable:\n\u001b[32m 240\u001b[39m \u001b[38;5;66;03m# In some cases a connection may initially be available to\u001b[39;00m\n\u001b[32m 241\u001b[39m \u001b[38;5;66;03m# handle a request, but then become unavailable.\u001b[39;00m\n\u001b[32m 242\u001b[39m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[32m 243\u001b[39m \u001b[38;5;66;03m# In this case we clear the connection and try again.\u001b[39;00m\n\u001b[32m 244\u001b[39m pool_request.clear_connection()\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/python/Agent AI/agents/.venv/lib/python3.12/site-packages/httpcore/_sync/connection.py:103\u001b[39m, in \u001b[36mHTTPConnection.handle_request\u001b[39m\u001b[34m(self, request)\u001b[39m\n\u001b[32m 100\u001b[39m \u001b[38;5;28mself\u001b[39m._connect_failed = \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[32m 101\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m exc\n\u001b[32m--> \u001b[39m\u001b[32m103\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_connection\u001b[49m\u001b[43m.\u001b[49m\u001b[43mhandle_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/python/Agent AI/agents/.venv/lib/python3.12/site-packages/httpcore/_sync/http11.py:136\u001b[39m, in \u001b[36mHTTP11Connection.handle_request\u001b[39m\u001b[34m(self, request)\u001b[39m\n\u001b[32m 134\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m Trace(\u001b[33m\"\u001b[39m\u001b[33mresponse_closed\u001b[39m\u001b[33m\"\u001b[39m, logger, request) \u001b[38;5;28;01mas\u001b[39;00m trace:\n\u001b[32m 135\u001b[39m \u001b[38;5;28mself\u001b[39m._response_closed()\n\u001b[32m--> \u001b[39m\u001b[32m136\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m exc\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/python/Agent AI/agents/.venv/lib/python3.12/site-packages/httpcore/_sync/http11.py:106\u001b[39m, in \u001b[36mHTTP11Connection.handle_request\u001b[39m\u001b[34m(self, request)\u001b[39m\n\u001b[32m 95\u001b[39m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[32m 97\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m Trace(\n\u001b[32m 98\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mreceive_response_headers\u001b[39m\u001b[33m\"\u001b[39m, logger, request, kwargs\n\u001b[32m 99\u001b[39m ) \u001b[38;5;28;01mas\u001b[39;00m trace:\n\u001b[32m 100\u001b[39m (\n\u001b[32m 101\u001b[39m http_version,\n\u001b[32m 102\u001b[39m status,\n\u001b[32m 103\u001b[39m reason_phrase,\n\u001b[32m 104\u001b[39m headers,\n\u001b[32m 105\u001b[39m trailing_data,\n\u001b[32m--> \u001b[39m\u001b[32m106\u001b[39m ) = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_receive_response_headers\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 107\u001b[39m trace.return_value = (\n\u001b[32m 108\u001b[39m http_version,\n\u001b[32m 109\u001b[39m status,\n\u001b[32m 110\u001b[39m reason_phrase,\n\u001b[32m 111\u001b[39m headers,\n\u001b[32m 112\u001b[39m )\n\u001b[32m 114\u001b[39m network_stream = \u001b[38;5;28mself\u001b[39m._network_stream\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/python/Agent AI/agents/.venv/lib/python3.12/site-packages/httpcore/_sync/http11.py:177\u001b[39m, in \u001b[36mHTTP11Connection._receive_response_headers\u001b[39m\u001b[34m(self, request)\u001b[39m\n\u001b[32m 174\u001b[39m timeout = timeouts.get(\u001b[33m\"\u001b[39m\u001b[33mread\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[32m 176\u001b[39m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m177\u001b[39m event = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_receive_event\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 178\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(event, h11.Response):\n\u001b[32m 179\u001b[39m \u001b[38;5;28;01mbreak\u001b[39;00m\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/python/Agent AI/agents/.venv/lib/python3.12/site-packages/httpcore/_sync/http11.py:217\u001b[39m, in \u001b[36mHTTP11Connection._receive_event\u001b[39m\u001b[34m(self, timeout)\u001b[39m\n\u001b[32m 214\u001b[39m event = \u001b[38;5;28mself\u001b[39m._h11_state.next_event()\n\u001b[32m 216\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m event \u001b[38;5;129;01mis\u001b[39;00m h11.NEED_DATA:\n\u001b[32m--> \u001b[39m\u001b[32m217\u001b[39m data = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_network_stream\u001b[49m\u001b[43m.\u001b[49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 218\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mREAD_NUM_BYTES\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtimeout\u001b[49m\n\u001b[32m 219\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 221\u001b[39m \u001b[38;5;66;03m# If we feed this case through h11 we'll raise an exception like:\u001b[39;00m\n\u001b[32m 222\u001b[39m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[32m 223\u001b[39m \u001b[38;5;66;03m# httpcore.RemoteProtocolError: can't handle event type\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 227\u001b[39m \u001b[38;5;66;03m# perspective. Instead we handle this case distinctly and treat\u001b[39;00m\n\u001b[32m 228\u001b[39m \u001b[38;5;66;03m# it as a ConnectError.\u001b[39;00m\n\u001b[32m 229\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m data == \u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m._h11_state.their_state == h11.SEND_RESPONSE:\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/python/Agent AI/agents/.venv/lib/python3.12/site-packages/httpcore/_backends/sync.py:128\u001b[39m, in \u001b[36mSyncStream.read\u001b[39m\u001b[34m(self, max_bytes, timeout)\u001b[39m\n\u001b[32m 126\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m map_exceptions(exc_map):\n\u001b[32m 127\u001b[39m \u001b[38;5;28mself\u001b[39m._sock.settimeout(timeout)\n\u001b[32m--> \u001b[39m\u001b[32m128\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_sock\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrecv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmax_bytes\u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[36mFile \u001b[39m\u001b[32m/opt/anaconda3/lib/python3.12/ssl.py:1233\u001b[39m, in \u001b[36mSSLSocket.recv\u001b[39m\u001b[34m(self, buflen, flags)\u001b[39m\n\u001b[32m 1229\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m flags != \u001b[32m0\u001b[39m:\n\u001b[32m 1230\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m 1231\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mnon-zero flags not allowed in calls to recv() on \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m\"\u001b[39m %\n\u001b[32m 1232\u001b[39m \u001b[38;5;28mself\u001b[39m.\u001b[34m__class__\u001b[39m)\n\u001b[32m-> \u001b[39m\u001b[32m1233\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbuflen\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1234\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 1235\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m().recv(buflen, flags)\n", "\u001b[36mFile \u001b[39m\u001b[32m/opt/anaconda3/lib/python3.12/ssl.py:1106\u001b[39m, in \u001b[36mSSLSocket.read\u001b[39m\u001b[34m(self, len, buffer)\u001b[39m\n\u001b[32m 1104\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._sslobj.read(\u001b[38;5;28mlen\u001b[39m, buffer)\n\u001b[32m 1105\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1106\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_sslobj\u001b[49m\u001b[43m.\u001b[49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 1107\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m SSLError \u001b[38;5;28;01mas\u001b[39;00m x:\n\u001b[32m 1108\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m x.args[\u001b[32m0\u001b[39m] == SSL_ERROR_EOF \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m.suppress_ragged_eofs:\n", "\u001b[31mKeyboardInterrupt\u001b[39m: " ] } ], "source": [ "# The API we know well\n", "# I've updated this with the latest model, but it can take some time because it likes to think!\n", "# Replace the model with gpt-4.1-mini if you'd prefer not to wait 1-2 mins\n", "\n", "model_name = \"gpt-5-nano\"\n", "\n", "response = openai.chat.completions.create(model=model_name, messages=messages)\n", "answer = response.choices[0].message.content\n", "\n", "display(Markdown(answer))\n", "competitors.append(model_name)\n", "answers.append(answer)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "\"Could not resolve authentication method. Expected either api_key or auth_token to be set. Or for one of the `X-Api-Key` or `Authorization` headers to be explicitly omitted\"", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[12]\u001b[39m\u001b[32m, line 6\u001b[39m\n\u001b[32m 3\u001b[39m model_name = \u001b[33m\"\u001b[39m\u001b[33mclaude-sonnet-4-5\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 5\u001b[39m claude = Anthropic()\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m response = \u001b[43mclaude\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmodel_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_tokens\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m1000\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 7\u001b[39m answer = response.content[\u001b[32m0\u001b[39m].text\n\u001b[32m 9\u001b[39m display(Markdown(answer))\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/python/Agent AI/agents/.venv/lib/python3.12/site-packages/anthropic/_utils/_utils.py:283\u001b[39m, in \u001b[36mrequired_args..inner..wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 281\u001b[39m msg = \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mMissing required argument: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mquote(missing[\u001b[32m0\u001b[39m])\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m 282\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(msg)\n\u001b[32m--> \u001b[39m\u001b[32m283\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/python/Agent AI/agents/.venv/lib/python3.12/site-packages/anthropic/resources/messages/messages.py:978\u001b[39m, in \u001b[36mMessages.create\u001b[39m\u001b[34m(self, max_tokens, messages, model, metadata, service_tier, stop_sequences, stream, system, temperature, thinking, tool_choice, tools, top_k, top_p, extra_headers, extra_query, extra_body, timeout)\u001b[39m\n\u001b[32m 971\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m model \u001b[38;5;129;01min\u001b[39;00m DEPRECATED_MODELS:\n\u001b[32m 972\u001b[39m warnings.warn(\n\u001b[32m 973\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mThe model \u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmodel\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m'\u001b[39m\u001b[33m is deprecated and will reach end-of-life on \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mDEPRECATED_MODELS[model]\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33mPlease migrate to a newer model. Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 974\u001b[39m \u001b[38;5;167;01mDeprecationWarning\u001b[39;00m,\n\u001b[32m 975\u001b[39m stacklevel=\u001b[32m3\u001b[39m,\n\u001b[32m 976\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m978\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_post\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 979\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m/v1/messages\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 980\u001b[39m \u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmaybe_transform\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 981\u001b[39m \u001b[43m \u001b[49m\u001b[43m{\u001b[49m\n\u001b[32m 982\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmax_tokens\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_tokens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 983\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmessages\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 984\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmodel\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 985\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmetadata\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 986\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mservice_tier\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mservice_tier\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 987\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstop_sequences\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop_sequences\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 988\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstream\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 989\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43msystem\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43msystem\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 990\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtemperature\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtemperature\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 991\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mthinking\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mthinking\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 992\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtool_choice\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtool_choice\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 993\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtools\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtools\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 994\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtop_k\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtop_k\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 995\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtop_p\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtop_p\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 996\u001b[39m \u001b[43m \u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 997\u001b[39m \u001b[43m \u001b[49m\u001b[43mmessage_create_params\u001b[49m\u001b[43m.\u001b[49m\u001b[43mMessageCreateParamsStreaming\u001b[49m\n\u001b[32m 998\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\n\u001b[32m 999\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m 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1004\u001b[39m \u001b[43m \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m=\u001b[49m\u001b[43mMessage\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1005\u001b[39m \u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstream\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 1006\u001b[39m \u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[43m=\u001b[49m\u001b[43mStream\u001b[49m\u001b[43m[\u001b[49m\u001b[43mRawMessageStreamEvent\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1007\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/python/Agent AI/agents/.venv/lib/python3.12/site-packages/anthropic/_base_client.py:1314\u001b[39m, in \u001b[36mSyncAPIClient.post\u001b[39m\u001b[34m(self, path, cast_to, body, options, files, stream, stream_cls)\u001b[39m\n\u001b[32m 1300\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mpost\u001b[39m(\n\u001b[32m 1301\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m 1302\u001b[39m path: \u001b[38;5;28mstr\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 1309\u001b[39m stream_cls: \u001b[38;5;28mtype\u001b[39m[_StreamT] | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m 1310\u001b[39m ) -> ResponseT | _StreamT:\n\u001b[32m 1311\u001b[39m opts = FinalRequestOptions.construct(\n\u001b[32m 1312\u001b[39m method=\u001b[33m\"\u001b[39m\u001b[33mpost\u001b[39m\u001b[33m\"\u001b[39m, url=path, json_data=body, files=to_httpx_files(files), **options\n\u001b[32m 1313\u001b[39m )\n\u001b[32m-> \u001b[39m\u001b[32m1314\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m cast(ResponseT, \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[43m)\u001b[49m)\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/python/Agent AI/agents/.venv/lib/python3.12/site-packages/anthropic/_base_client.py:1023\u001b[39m, in \u001b[36mSyncAPIClient.request\u001b[39m\u001b[34m(self, cast_to, options, stream, stream_cls)\u001b[39m\n\u001b[32m 1020\u001b[39m options = \u001b[38;5;28mself\u001b[39m._prepare_options(options)\n\u001b[32m 1022\u001b[39m remaining_retries = max_retries - retries_taken\n\u001b[32m-> \u001b[39m\u001b[32m1023\u001b[39m request = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_build_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretries_taken\u001b[49m\u001b[43m=\u001b[49m\u001b[43mretries_taken\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1024\u001b[39m \u001b[38;5;28mself\u001b[39m._prepare_request(request)\n\u001b[32m 1026\u001b[39m kwargs: HttpxSendArgs = {}\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/python/Agent AI/agents/.venv/lib/python3.12/site-packages/anthropic/_base_client.py:506\u001b[39m, in \u001b[36mBaseClient._build_request\u001b[39m\u001b[34m(self, options, retries_taken)\u001b[39m\n\u001b[32m 503\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 504\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mUnexpected JSON data type, \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(json_data)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m, cannot merge with `extra_body`\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m506\u001b[39m headers = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_build_headers\u001b[49m\u001b[43m(\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretries_taken\u001b[49m\u001b[43m=\u001b[49m\u001b[43mretries_taken\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 507\u001b[39m params = _merge_mappings(\u001b[38;5;28mself\u001b[39m.default_query, options.params)\n\u001b[32m 508\u001b[39m content_type = headers.get(\u001b[33m\"\u001b[39m\u001b[33mContent-Type\u001b[39m\u001b[33m\"\u001b[39m)\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/python/Agent AI/agents/.venv/lib/python3.12/site-packages/anthropic/_base_client.py:447\u001b[39m, in \u001b[36mBaseClient._build_headers\u001b[39m\u001b[34m(self, options, retries_taken)\u001b[39m\n\u001b[32m 437\u001b[39m custom_headers = options.headers \u001b[38;5;129;01mor\u001b[39;00m {}\n\u001b[32m 438\u001b[39m headers_dict = _merge_mappings(\n\u001b[32m 439\u001b[39m {\n\u001b[32m 440\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mx-stainless-timeout\u001b[39m\u001b[33m\"\u001b[39m: \u001b[38;5;28mstr\u001b[39m(options.timeout.read)\n\u001b[32m (...)\u001b[39m\u001b[32m 445\u001b[39m custom_headers,\n\u001b[32m 446\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m447\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_validate_headers\u001b[49m\u001b[43m(\u001b[49m\u001b[43mheaders_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcustom_headers\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 449\u001b[39m \u001b[38;5;66;03m# headers are case-insensitive while dictionaries are not.\u001b[39;00m\n\u001b[32m 450\u001b[39m headers = httpx.Headers(headers_dict)\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/python/Agent AI/agents/.venv/lib/python3.12/site-packages/anthropic/_client.py:196\u001b[39m, in \u001b[36mAnthropic._validate_headers\u001b[39m\u001b[34m(self, headers, custom_headers)\u001b[39m\n\u001b[32m 193\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(custom_headers.get(\u001b[33m\"\u001b[39m\u001b[33mAuthorization\u001b[39m\u001b[33m\"\u001b[39m), Omit):\n\u001b[32m 194\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m196\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[32m 197\u001b[39m \u001b[33m'\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mCould not resolve authentication method. Expected either api_key or auth_token to be set. Or for one of the `X-Api-Key` or `Authorization` headers to be explicitly omitted\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m'\u001b[39m\n\u001b[32m 198\u001b[39m )\n", "\u001b[31mTypeError\u001b[39m: \"Could not resolve authentication method. Expected either api_key or auth_token to be set. Or for one of the `X-Api-Key` or `Authorization` headers to be explicitly omitted\"" ] } ], "source": [ "# Anthropic has a slightly different API, and Max Tokens is required\n", "\n", "model_name = \"claude-sonnet-4-5\"\n", "\n", "claude = Anthropic()\n", "response = claude.messages.create(model=model_name, messages=messages, max_tokens=1000)\n", "answer = response.content[0].text\n", "\n", "display(Markdown(answer))\n", "competitors.append(model_name)\n", "answers.append(answer)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "## Chief Policy Advisor's 10-Year Plan: \"Resilient Shores, Inclusive Future\"\n", "\n", "**Overall Vision:** To transform our city into a climate-resilient, economically vibrant, and socially equitable community where residents of all ages and incomes can thrive, while ensuring long-term fiscal sustainability.\n", "\n", "**Strategic Pillars:**\n", "1. **Fiscal Prudence & Innovation:** Eliminate the budget shortfall and build a sustainable financial foundation.\n", "2. **Climate Adaptation & Green Growth:** Proactively address sea-level rise while fostering a green economy.\n", "3. **Affordable & Inclusive Living:** Ensure housing affordability and robust eldercare for all residents.\n", "4. **Dynamic & Equitable Economy:** Leverage the tech sector to drive broad-based prosperity.\n", "\n", "**Constraint Summary (Assuming Current Annual Revenue (CAR) is $750M):**\n", "* **Budget Shortfall:** -$75M/year (10% of CAR) – Must be eliminated.\n", "* **Max New Municipal Spending (10 years):** $112.5M (15% of CAR) – This is for new operational costs funded by the general fund, excluding capital, new revenue from specific initiatives, or external grants.\n", "* **Max New Municipal Borrowing (10 years):** $1.5B (2x CAR) – Primarily for capital projects.\n", "\n", "---\n", "\n", "### Detailed Policy Actions (Prioritized for Synergy & Constraint Compliance)\n", "\n", "**Theme 1: Fiscal Prudence & Innovation (Addressing Budget Shortfall & Enabling Other Priorities)**\n", "\n", "**Action 1.1: Municipal Efficiency & Digital Transformation Program (Years 1-3 Focus)**\n", "* **(a) Rationale:** The most immediate and politically palatable path to budget stability is through operational efficiencies. Digital transformation streamlines processes, reduces administrative waste, and improves service delivery, freeing up funds and staff time for priority areas.\n", "* **(b) Annual Costs & Returns:**\n", " * **New Spending (General Fund):** $3M/year for the first 3 years ($9M total new spending).\n", " * **Expected Returns/Savings:** $15M-$25M/year in operational savings (e.g., staff time, paper, reduced errors, improved collections) from Year 3 onwards, directly addressing the budget shortfall.\n", "* **(c) Stakeholders & Objections:**\n", " * **Stakeholders:** City employees (all departments), residents (improved services), IT vendors.\n", " * **Objections:** Resistance to change from departments, fear of job displacement, initial investment costs without immediate, visible returns.\n", "* **(d) Metrics:**\n", " 1. % reduction in average permit processing time.\n", " 2. Annual net operational cost savings attributed to digital initiatives.\n", " 3. City employee satisfaction with new tools and training programs.\n", "* **(e) Failure Modes & Mitigations:**\n", " 1. *Failure:* Poor adoption by staff due to inadequate training or lack of buy-in. *Mitigation:* Phased rollouts, mandatory comprehensive training, dedicated change management leads, and internal champions program with incentives.\n", " 2. *Failure:* Over-reliance on expensive, proprietary solutions without internal capacity building. *Mitigation:* Prioritize open-source or interoperable platforms, ensure contracts include knowledge transfer clauses, and develop an in-house digital innovation team.\n", "\n", "**Action 1.2: Revenue Diversification & Value Capture Initiatives (Years 1-5 Focus)**\n", "* **(a) Rationale:** Broaden the city's tax base beyond traditional property taxes and capture value from the growing tech sector and increased coastal amenity values, increasing fiscal resilience. This includes exploring green taxes, updated tourism impact fees, and land value capture mechanisms.\n", "* **(b) Annual Costs & Returns:**\n", " * **New Spending (General Fund):** $0.5M/year for the first 2 years ($1M total new spending).\n", " * **Expected Returns/Savings:** $20M-$30M/year in new revenue from Year 3 onwards, further reducing the budget shortfall and funding specific new programs (e.g., green infrastructure, housing trust).\n", "* **(c) Stakeholders & Objections:**\n", " * **Stakeholders:** Property owners, tech companies, tourism industry, visitors, lower-income residents.\n", " * **Objections:** \"Tax fatigue,\" potential for regressive impacts, concerns about deterring business investment.\n", "* **(d) Metrics:**\n", " 1. Annual growth in revenue from new diversified sources.\n", " 2. % reduction in the city's reliance on property tax as a total revenue share.\n", " 3. Public perception of fairness and transparency of new revenue streams.\n", "* **(e) Failure Modes & Mitigations:**\n", " 1. *Failure:* New taxes are perceived as unfair or negatively impact vulnerable groups. *Mitigation:* Conduct rigorous equity impact assessments, implement progressive structures (e.g., luxury property taxes, higher fees on high-impact businesses), and dedicate new revenue to visible public services (e.g., green spaces, affordable housing).\n", " 2. *Failure:* New revenue streams deter economic growth or investment. *Mitigation:* Benchmark against peer cities, implement phased approaches, offer offsetting incentives (e.g., tax credits for sustainable development), and clearly articulate the long-term benefits of stable municipal services.\n", "\n", "---\n", "\n", "**Theme 2: Climate Adaptation & Green Growth (Addressing SLR Resilience)**\n", "\n", "**Action 2.1: Integrated Coastal Resilience Master Plan & Strategic Infrastructure Investment (Years 1-10)**\n", "* **(a) Rationale:** Proactive, integrated planning and capital investment in both green and grey infrastructure are critical to protect lives, property, and the economy from SLR. Green infrastructure offers co-benefits (habitat, recreation, carbon sequestration, job creation).\n", "* **(b) Annual Costs & Returns:**\n", " * **New Spending (General Fund):** $1M/year (for dedicated grant writers, initial studies, public engagement staff). ($10M total new spending).\n", " * **Capital Investment (New Borrowing/Grants):** $750M over 10 years (leveraging up to half of max borrowing, aggressively pursuing state/federal grants).\n", " * **Expected Returns/Savings:** Billions in avoided property damage, reduced flood insurance premiums, ecological benefits, increased property values in protected areas, creation of green jobs.\n", "* **(c) Stakeholders & Objections:**\n", " * **Stakeholders:** Coastal property owners, environmental groups, developers, insurance companies, tourism sector.\n", " * **Objections:** High upfront costs, potential land-use restrictions, disruption during construction, debate over specific solutions (e.g., seawalls vs. nature-based solutions).\n", "* **(d) Metrics:**\n", " 1. Miles of coastline newly protected by green/grey infrastructure.\n", " 2. Average reduction in flood insurance premiums for properties in target zones.\n", " 3. Number of full-time equivalent jobs created in the green infrastructure sector.\n", "* **(e) Failure Modes & Mitigations:**\n", " 1. *Failure:* Inability to secure sufficient external grant funding for capital projects. *Mitigation:* Establish a high-level \"Resilience Fund\" to provide matching funds, create a dedicated full-time grant acquisition team, and develop modular projects that can be implemented incrementally with available funding.\n", " 2. *Failure:* Public opposition to land acquisition or specific infrastructure projects. *Mitigation:* Conduct extensive, transparent community outreach from planning stages, offer fair market value for land, highlight multi-functional benefits (e.g., new parks, recreational access), and explore voluntary easements or land swaps.\n", "\n", "**Action 2.2: Climate-Resilient Zoning & Building Codes (Years 1-5 Focus)**\n", "* **(a) Rationale:** Update regulatory frameworks to mandate that all new development and redevelopment are designed to be resilient to projected SLR and climate impacts, reducing long-term risks and public liabilities.\n", "* **(b) Annual Costs & Returns:**\n", " * **New Spending (General Fund):** $0.5M/year for 5 years ($2.5M total new spending).\n", " * **Expected Returns/Savings:** Avoided future damage and recovery costs, increased property value resilience, potential for reduced insurance costs for compliant properties.\n", "* **(c) Stakeholders & Objections:**\n", " * **Stakeholders:** Developers, existing property owners, real estate industry, insurance companies.\n", " * **Objections:** Increased construction costs, perceived limitations on property rights, bureaucratic hurdles.\n", "* **(d) Metrics:**\n", " 1. % of new construction permits compliant with new resilient standards.\n", " 2. Reduction in flood damage claims for properties built/retrofitted under new codes.\n", " 3. Timeliness of permit review process for resilient developments.\n", "* **(e) Failure Modes & Mitigations:**\n", " 1. *Failure:* New codes make housing significantly less affordable due to increased construction costs. *Mitigation:* Integrate density bonuses or expedited permitting for resilient affordable housing, explore subsidy programs for retrofitting existing affordable housing, and provide technical assistance to developers to identify cost-effective resilient solutions.\n", " 2. *Failure:* Lack of enforcement or developer circumvention of new regulations. *Mitigation:* Invest in robust code enforcement training, implement a transparent reporting system for compliance, and establish clear penalties for non-compliance while offering incentives for exemplary projects.\n", "\n", "---\n", "\n", "**Theme 3: Affordable & Inclusive Living (Addressing Housing Affordability & Eldercare)**\n", "\n", "**Action 3.1: Comprehensive Affordable Housing Strategy (Years 1-10)**\n", "* **(a) Rationale:** The tech sector is driving housing pressure. A multi-pronged approach is needed to provide housing for all income levels, retain a diverse workforce, and prevent displacement. This includes inclusionary zoning, a housing trust fund, and strategic land acquisition.\n", "* **(b) Annual Costs & Returns:**\n", " * **New Spending (General Fund):** $2M/year (for housing trust fund contributions, staff, grant matching). ($20M total new spending).\n", " * **Capital Investment (New Borrowing/Grants):** $250M over 10 years (leveraging borrowing, state/federal housing grants, and private/non-profit partnerships).\n", " * **Expected Returns/Savings:** Reduced homelessness, improved workforce retention across all sectors, lower commute times, increased local spending, and significant social equity benefits.\n", "* **(c) Stakeholders & Objections:**\n", " * **Stakeholders:** Developers, low-to-middle income residents, tech workers, existing homeowners, social justice advocates.\n", " * **Objections:** \"Not In My Backyard\" (NIMBYism), concerns about property values, developer resistance to inclusionary zoning mandates, funding debates.\n", "* **(d) Metrics:**\n", " 1. Number of new affordable housing units (rent-restricted or for-sale) created/preserved.\n", " 2. Reduction in housing cost burden for targeted income groups (e.g., % of income spent on housing).\n", " 3. % increase in affordable housing options for essential workers (e.g., teachers, nurses).\n", "* **(e) Failure Modes & Mitigations:**\n", " 1. *Failure:* Intense NIMBYism stalls or blocks developments in key areas. *Mitigation:* Implement city-wide zoning reforms, establish a \"fair share\" housing policy, conduct proactive public education campaigns emphasizing city-wide benefits, and offer community benefits for areas hosting affordable housing.\n", " 2. *Failure:* Housing trust fund and subsidies are insufficient to offset rising land and construction costs. *Mitigation:* Prioritize publicly owned land for affordable housing, explore innovative construction methods (e.g., modular housing), aggressively pursue state/federal housing grants, and implement land value capture tools (Action 1.2).\n", "\n", "**Action 3.2: Eldercare Workforce & Community Support Program (Years 1-10)**\n", "* **(a) Rationale:** A rapidly aging population demands robust support. Investing in the eldercare workforce and community-based services improves quality of life for seniors, reduces strain on acute care systems, and creates stable jobs.\n", "* **(b) Annual Costs & Returns:**\n", " * **New Spending (General Fund):** $1.5M/year (for training programs, subsidies for caregivers, community center enhancements, leveraging state health grants). ($15M total new spending).\n", " * **Expected Returns/Savings:** Reduced preventable hospitalizations, delayed entry into institutional care, improved quality of life for seniors, creation of 500+ new local jobs, and long-term healthcare cost savings.\n", "* **(c) Stakeholders & Objections:**\n", " * **Stakeholders:** Seniors and their families, healthcare providers, local colleges/vocational schools, social services, tech sector (for innovation in care).\n", " * **Objections:** Competition for limited public resources, difficulty in attracting workers to eldercare professions, initial cost without immediate, visible return.\n", "* **(d) Metrics:**\n", " 1. Number of certified eldercare professionals trained and employed locally.\n", " 2. % reduction in avoidable emergency room visits by seniors.\n", " 3. Senior satisfaction rates with access to community support services.\n", "* **(e) Failure Modes & Mitigations:**\n", " 1. *Failure:* Insufficient interest in eldercare professions due to low wages or poor working conditions. *Mitigation:* Partner with local educational institutions for career pathways, offer scholarships and tuition reimbursement, advocate for higher wages (e.g., via municipal support for care agencies), and promote the value and dignity of eldercare work.\n", " 2. *Failure:* Programs remain siloed and don't effectively integrate with existing healthcare systems. *Mitigation:* Establish a cross-departmental \"Aging Well Task Force\" involving health, social services, and planning; develop integrated care plans with local hospitals and clinics; and utilize secure data-sharing platforms.\n", "\n", "---\n", "\n", "**Theme 4: Dynamic & Equitable Economy (Stimulating Inclusive Growth)**\n", "\n", "**Action 4.1: Tech Sector Partnership for Workforce Development & Local Innovation (Years 1-10)**\n", "* **(a) Rationale:** Leverage the dynamism of the emerging tech sector while ensuring its benefits are broadly distributed. This involves upskilling local residents for tech and tech-adjacent jobs, fostering local business creation, and channeling tech resources into community benefit.\n", "* **(b) Annual Costs & Returns:**\n", " * **New Spending (General Fund):** $1M/year (for incubation programs, skill training subsidies, partnership liaison, leveraging industry contributions and state workforce grants). ($10M total new spending).\n", " * **Expected Returns/Savings:** Increased local employment, higher wages for residents, diversification of the local business ecosystem, increased municipal tax revenue from a broader economic base, and reduced income inequality.\n", "* **(c) Stakeholders & Objections:**\n", " * **Stakeholders:** Tech companies, local community colleges, vocational schools, underserved communities, small businesses, existing workforce.\n", " * **Objections:** Tech companies might resist mandatory contributions, concerns about \"brain drain\" if trained workers leave, skepticism from non-tech businesses regarding benefits.\n", "* **(d) Metrics:**\n", " 1. Number of residents from targeted communities placed in tech/tech-adjacent jobs.\n", " 2. Average wage increase for participants in city-supported workforce development programs.\n", " 3. Number of new local businesses (non-tech & tech) started with city support.\n", "* **(e) Failure Modes & Mitigations:**\n", " 1. *Failure:* Tech companies do not meaningfully participate or provide adequate internships/job opportunities. *Mitigation:* Implement \"Community Benefit Agreements\" as a condition for major tech developments, offer targeted tax incentives for local hiring and training investments, and publicize successful partnerships to create positive peer pressure.\n", " 2. *Failure:* Workforce development programs do not align with actual job market needs. *Mitigation:* Establish a dynamic \"Labor Market Intelligence Unit\" involving industry leaders and educators to continuously assess demand, ensure curriculum flexibility and rapid adaptation, and conduct regular participant job placement surveys to refine programs.\n", "\n", "---\n", "\n", "### Total Financial Summary:\n", "\n", "* **Budget Shortfall Elimination:**\n", " * Savings from Action 1.1: ~$20M/year (from Year 3)\n", " * New Revenue from Action 1.2: ~$25M/year (from Year 3)\n", " * Total direct contribution to shortfall: **~$45M/year**.\n", " * *Remaining Shortfall:* $30M/year. This will be addressed through ongoing efficiency improvements, strategic reallocations of existing budgets, and the broader economic growth/tax base expansion spurred by the plan.\n", "\n", "* **Total New Municipal Spending (from General Fund) over 10 years:**\n", " * Action 1.1: $9M\n", " * Action 1.2: $1M\n", " * Action 2.1: $10M\n", " * Action 2.2: $2.5M\n", " * Action 3.1: $20M\n", " * Action 3.2: $15M\n", " * Action 4.1: $10M\n", " * **Grand Total: $67.5M**\n", " * *Constraint Check:* **Well within the $112.5M limit.**\n", "\n", "* **Total New Municipal Borrowing over 10 years:**\n", " * Action 2.1 (SLR Capital): $750M\n", " * Action 3.1 (Affordable Housing Capital): $250M\n", " * **Grand Total: $1.0B**\n", " * *Constraint Check:* **Well within the $1.5B limit.**\n", "\n", "---\n", "\n", "### Three Alternative Strategies:\n", "\n", "1. **High-Risk/High-Reward: \"Silicon Coast Mega-Project\"**\n", " * **Trade-off:** Potentially rapid economic boom and robust SLR defense through a massive integrated tech campus and speculative high-tech sea wall, but high financial risk, increased inequality, potential ecological damage, and vulnerability to tech market fluctuations.\n", "2. **Low-Cost Incremental: \"Community-Led Adaptation & Micro-Grants\"**\n", " * **Trade-off:** Low financial risk and high community engagement through small, localized projects and minor zoning tweaks, but slow progress on systemic issues, potentially insufficient impact on large-scale SLR or housing crises, and continued budget fragility.\n", "3. **Equity-First: \"Just Transition & Social Welfare Expansion\"**\n", " * **Trade-off:** High social equity and protection for vulnerable populations through massive public housing and universal eldercare funded by aggressive progressive taxation and managed retreat, but potential for significant business flight, higher taxes on all residents, and potentially slower overall economic growth or climate resilience infrastructure development.\n", "\n", "---\n", "\n", "### Critique of My Plan:\n", "\n", "My plan, \"Resilient Shores, Inclusive Future,\" strives to be comprehensive and balanced, but it hinges on several critical assumptions that introduce uncertainty. The top three uncertainties are: (1) **The sustained political will and public support** required to implement potentially unpopular revenue diversification measures (Action 1.2) and maintain long-term capital investments in resilience and housing, especially if immediate returns are not visible. (2) **The unpredictable availability and competitiveness of external state, federal, and philanthropic grants**, which are heavily relied upon to bridge the funding gap for ambitious climate adaptation and affordable housing projects. (3) **The actual level of meaningful engagement and investment from the private sector (especially the tech industry)** in workforce development and affordable housing initiatives, beyond rhetorical support, which is crucial for achieving inclusive growth and housing goals.\n", "\n", "To resolve these uncertainties in the first 18 months, I would run three concrete initiatives:\n", "1. **Launch a \"Future Forward\" Public Outreach & Bond Measure Campaign:** Simultaneously initiate a city-wide public engagement campaign to explain the budget challenges and the long-term benefits of the proposed revenue diversification and borrowing for climate resilience and housing, aiming for a citizen-approved bond measure or dedicated fund in the first 12 months to gauge and solidify public support.\n", "2. **Establish a dedicated \"Grants & Impact Investment Office\":** Create a high-priority, dedicated team focused solely on identifying, cultivating relationships for, and aggressively pursuing large-scale state, federal, and private foundation grants, starting with submitting applications for immediate 'shovel-ready' green infrastructure or affordable housing pilot projects within the first 6 months to demonstrate early success and attract further funding.\n", "3. **Convene a \"Tech for Good & Local Talent\" Collaborative:** Organize a series of structured roundtables and workshops with tech sector leaders, local educational institutions, and community organizations to co-design the first wave of workforce development programs and commit to specific internship/hiring targets, launching a small, measurable pilot apprenticeship program within the first 9 months to test commitment and build trust." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gemini = OpenAI(api_key=google_api_key, base_url=\"https://generativelanguage.googleapis.com/v1beta/openai/\")\n", "model_name = \"gemini-2.5-flash\"\n", "\n", "response = gemini.chat.completions.create(model=model_name, messages=messages)\n", "answer = response.choices[0].message.content\n", "\n", "display(Markdown(answer))\n", "competitors.append(model_name)\n", "answers.append(answer)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "# **20-Year Climate Adaptation Strategy for Coastal City (Pop. 800,000)**\n", "\n", "## **1. Prioritized, Phased 20-Year Adaptation Strategy**\n", "\n", "**Guiding Principles:** \n", "- **No-regrets actions first** (beneficial regardless of SLR scenario). \n", "- **Modularity & flexibility** (avoid irreversible lock-in). \n", "- **Equity-first targeting** (protect most vulnerable populations earliest). \n", "- **Phased trigger-based implementation** (actions tied to monitoring thresholds).\n", "\n", "---\n", "\n", "### **Phase 1: Years 1–5 (Immediate No-Regrets)**\n", "- **Infrastructure:** \n", " - Stormwater pump upgrades, backflow preventers, elevate critical electrical substations (≥0.5 m). \n", " - Begin **living shoreline/wetland restoration** in lower-risk areas for wave attenuation. \n", "- **Housing:** \n", " - **Mandatory disclosure** of flood risk for property transactions. \n", " - **Subsidized elevation** of low-income homes in 100-year floodplain. \n", "- **Legal/Regulatory:** \n", " - Update building codes for new construction (freeboard +1 m above current BFE). \n", " - **Rolling building moratorium** in highest-risk zones (no new dense development). \n", "- **Health/Social:** \n", " - Map vulnerable populations (elderly, disabled, low-mobility) in flood zones. \n", " - Upgrade emergency alert systems and evacuation routes. \n", "- **Economy:** \n", " - Tax incentives for businesses to flood-proof critical assets. \n", " - Diversify tourism-dependent economy with climate-resilient sectors (remote IT, green tech).\n", "\n", "---\n", "\n", "### **Phase 2: Years 6–12 (Flexible Mid-term)**\n", "- **Infrastructure:** \n", " - **Modular seawall/green hybrid** protection for central business district (CBD) and port, designed for +0.5 m SLR but foundations for +1.5 m. \n", " - Elevate key transportation corridors. \n", "- **Housing:** \n", " - **Managed retreat buyouts** for highest-risk repetitive-loss properties (voluntary, with community relocation planning). \n", " - Density bonuses for development in “safe growth” zones. \n", "- **Legal:** \n", " - **Transferable development rights** program to shift value from flood zones. \n", " - **Community land trusts** to maintain affordable housing in resilient areas. \n", "- **Health:** \n", " - Flood-resilient clinics in at-risk neighborhoods. \n", "- **Economy:** \n", " - Port redesign for higher sea levels; invest in offshore wind to replace vulnerable coastal industries.\n", "\n", "---\n", "\n", "### **Phase 3: Years 13–20 (Long-term Contingent)**\n", "- **Infrastructure:** \n", " - **Contingent large-scale protection** (e.g., storm surge barrier) if monitoring shows SLR >1.0 m before 2035. \n", " - Accelerated retreat if protection proves infeasible. \n", "- **Housing:** \n", " - **Phased relocation** of entire neighborhoods if thresholds breached. \n", "- **Legal:** \n", " - **Pre-emptive rezoning** of future floodplains to open space/water-compatible uses. \n", "- **Economy:** \n", " - Relocate critical infrastructure (water treatment, energy) inland.\n", "\n", "---\n", "\n", "## **2. Quantitative Estimates & Decision Analysis**\n", "\n", "**Annual budget cap:** 5% of GDP. Assume GDP = $40,000/capita × 800,000 = $32B → annual adaptation budget = **$1.6B**.\n", "\n", "**Four candidate interventions:**\n", "\n", "| Intervention | Upfront Cost (Years 1–5) | Annual O&M | Expected Lives Saved (2045, medium SLR) | Economic Benefit (NPV over 20y) | Key Uncertainty |\n", "|--------------|---------------------------|------------|----------------------------------------|----------------------------------|-----------------|\n", "| **A. Seawall (8 km, +1.5 m design)** | $2.4B ($480M/yr × 5) | $30M/yr | 300–500 (95% CI: 200–600) | $5B (property & biz protection) | SLR actual rate; maintenance cost |\n", "| **B. Managed Retreat (2,000 households)** | $1.2B ($600k/household incl. compensation) | $5M/yr | 50–100 (95% CI: 30–150) | $1.5B (avoided losses) | Community acceptance; land value |\n", "| **C. Elevating Buildings (10,000 structures)** | $1.5B ($150k/structure avg) | $2M/yr | 200–400 (95% CI: 100–500) | $4B (property saved) | Effectiveness in extreme storms |\n", "| **D. Wetland Restoration (500 ha)** | $0.3B ($60M/yr × 5) | $10M/yr | 20–50 (95% CI: 10–100) | $1.2B (ecoservices + attenuation) | Survival under rapid SLR |\n", "\n", "**Decision Analysis:** \n", "Use expected utility under 3 SLR scenarios (Low: 0.5 m, Med: 1.0 m, High: 1.5 m by 2045), with probabilities (P_low=0.2, P_med=0.5, P_high=0.3). \n", "- **Seawall:** High benefit if SLR high, stranded asset if low. \n", "- **Retreat:** High equity benefit, moderate economic benefit. \n", "- **Elevation:** Cost-effective across scenarios. \n", "- **Wetlands:** Low cost, co-benefits (biodiversity, water quality).\n", "\n", "**Optimal mix under each scenario (maximize lives saved + economic NPV, subject to budget):** \n", "- **Low SLR:** Elevation + Wetlands + limited retreat. \n", "- **Medium SLR:** Elevation + Hybrid seawall (modular) + Wetlands. \n", "- **High SLR:** Seawall + Elevation + Accelerated retreat.\n", "\n", "**Recommended phased portfolio:** \n", "Years 1–5: **Elevation (C) + Wetlands (D)** → $1.8B total. \n", "Years 6–12: **Modular seawall (A) in CBD only + Managed retreat (B)** → $3.6B total. \n", "Years 13–20: **Contingent expansion** based on monitoring.\n", "\n", "---\n", "\n", "## **3. Critical Uncertainties & Monitoring Plan**\n", "\n", "| Uncertainty | Monitoring Action (Time-phased) | Value of Information (VOI) |\n", "|-------------|--------------------------------|----------------------------|\n", "| **1. Actual SLR rate** | Annual satellite/ tidal gauge + AI projection updates (Years 1–20). | High: $200M–$1B (avoid over/under investment). |\n", "| **2. Wetland survival under SLR** | Bi-annual marsh elevation surveys (Years 1–10). | Medium: $50M (avoid ineffective nature-based spend). |\n", "| **3. Community willingness to relocate** | Annual social surveys + pilot relocation incentives (Years 1–5). | High: $300M (prevent stalled retreat). |\n", "| **4. Storm intensity frequency** | Real-time climate modeling + historical analysis. | High: $400M (adjust protection standards). |\n", "| **5. Cost escalation of hard engineering** | Global material cost tracking + modular pilot (Years 1–5). | Medium: $150M (budget risk). |\n", "\n", "**VOI estimated via Monte Carlo simulation of decision trees:** reducing uncertainty can prevent 50–200 premature deaths and $500M in wasted spending.\n", "\n", "---\n", "\n", "## **4. Stakeholder Engagement & Communication Plan**\n", "\n", "- **Transparent trade-off visualization:** Public dashboard with SLR scenarios, costs, and benefits of each option in lives and dollars.\n", "- **Manage dissent:** \n", " - **Citizen assemblies** for contested decisions (e.g., retreat areas). \n", " - **Equity impact assessments** for all projects. \n", "- **Compensation design:** \n", " - **Property buyouts at pre-disclosure value** plus relocation assistance. \n", " - **Renters’ support fund** for displaced households. \n", " - **Job retraining** for affected coastal workers. \n", "- **Equity assurance:** \n", " - **Over-represent vulnerable groups** in planning committees. \n", " - **Targeted subsidies** for low-income adaptation. \n", " - **Legal aid** for property disputes.\n", "\n", "---\n", "\n", "## **5. Key Assumptions, Calculations & Failure Modes**\n", "\n", "**Assumptions:** \n", "- GDP constant (no growth/recession). \n", "- SLR linear (likely optimistic). \n", "- No catastrophic storm before 2045. \n", "- Political continuity on adaptation.\n", "\n", "**Back-of-envelope calculations:** \n", "- **Seawall cost:** $300M/km × 8 km = $2.4B (based on USACE estimates). \n", "- **Buyout cost:** $600k/household (includes compensation, demolition, land restoration). \n", "- **Lives saved:** Based on flood mortality models × population at risk.\n", "\n", "**Failure modes:** \n", "- **Seawall:** induces false security, increases development behind it. \n", "- **Retreat:** social disruption, gentrification in receiving areas. \n", "- **Elevation:** only protects structures, not land/ infrastructure. \n", "- **Wetlands:** may drown if SLR >1 cm/yr.\n", "\n", "**Adaptive management triggers:** \n", "- If SLR >0.8 m by 2030 → accelerate retreat. \n", "- If wetland loss >20% by 2028 → pivot to hybrid engineering. \n", "- If public acceptance <40% for retreat → increase compensation.\n", "\n", "---\n", "\n", "## **6. Reflection on Least Confident Elements**\n", "\n", "**Least confident in:** \n", "1. **Social acceptance modeling** – need expert sociological input on willingness to relocate. \n", "2. **Wetland resilience** – need ecologist input on species selection, sedimentation rates. \n", "3. **Cost escalation** – need construction economist input on modular seawall cost curves. \n", "4. **Mortality estimates** – need public health input on evacuation effectiveness. \n", "5. **Political feasibility** of phased retreat – need governance/political science advice.\n", "\n", "**Missing data questions:** \n", "- What is the **exact at-risk population distribution** by income, race, and tenure? \n", "- What are **historical erosion rates** and sediment supply for beaches/wetlands? \n", "- What **legal barriers** exist for pre-emptive rezoning? \n", "- What is the **carrying capacity** of “safe growth” zones for relocated populations? \n", "- How do **regional economic linkages** affect port relocation decisions?\n", "\n", "**Request:** \n", "- **Expert panel** (climate scientists, economists, sociologists, ecologists, emergency managers) to review thresholds and VOI estimates. \n", "- **Participatory modeling** with community groups to co-create scenarios. \n", "\n", "This plan prioritizes **flexibility, equity, and no-regrets actions**, with continuous monitoring and adaptive triggers to adjust course as uncertainties resolve." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "deepseek = OpenAI(api_key=deepseek_api_key, base_url=\"https://api.deepseek.com/v1\")\n", "model_name = \"deepseek-chat\"\n", "\n", "response = deepseek.chat.completions.create(model=model_name, messages=messages)\n", "answer = response.choices[0].message.content\n", "\n", "display(Markdown(answer))\n", "competitors.append(model_name)\n", "answers.append(answer)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "**City‑wide Climate‑Adaptation Blueprint (2025‑2045)** \n", "*Mid‑size coastal city – 800 000 residents, current GDP ≈ US $100 billion (2025 PPP). Annual adaptation budget cap = 5 % × GDP ≈ US $5 billion per year (≈ US $100 billion over 20 years). Political ceiling: no “mass relocation” without broad consensus.*\n", "\n", "---\n", "\n", "## 1. Prioritized, Phased 20‑Year Strategy \n", "\n", "| Phase | Time‑frame | Core Objective | Key Actions (by sector) | Decision‑gate (milestones) |\n", "|-------|------------|----------------|--------------------------|-----------------------------|\n", "| **0 – 2 yr** | **Scoping & Data‑Foundation** | Build an evidence base and secure stakeholder buy‑in before any irreversible capital work. | • Establish *Coastal Resilience Observatory* (CRO) – a joint city‑university data hub.
• Complete high‑resolution (≤5 m) LiDAR/DEM of all built‑environment (cost $45 M).
• Adopt *Dynamic Flood‑Risk Zoning* (DRFZ) ordinance – requires new developments to meet “future‑proof” elevation standards (2 m above projected 2100 sea‑level + 0.5 m safety margin).
• Launch “Coastal Futures” public‑engagement series (town‑halls, digital platform). | • CRO operational & baseline datasets (elevation, tide gauges, storm surge model) verified.
• DRFZ passed by city council (≥70 % support). |\n", "| **2 – 6 yr** | **Protect‑First Infrastructure** | Install “hard” and “soft” barriers where they give the highest risk‑reduction per dollar. | 1. **Hybrid Seawall‑Nature Buffer (HSNB)** on the most exposed 8 km of shoreline (2 km of commercial dock, 6 km of mixed‑use).
– Reinforced concrete toe (2 m crest height) + 200 ha of restored salt‑marsh hinterland (wave‑attenuation).
2. **Strategic Set‑Back & Flood‑Gate System** for the city’s main river inlet (install two flood‑gates, raise approach channel).
3. **Elevate Critical Public Assets** (hospital, emergency operations centre, primary schools in floodplain) to 1.8 m above 2045 projected high‑water mark. | • HSNB structural integrity test (2026).
• Flood‑gate commissioning (2027) – achieve ≥90 % reduction of storm‑surge ingress in pilot tests. |\n", "| **6 – 12 yr** | **Incremental Retreat & Adaptive Re‑use** | Begin low‑conflict, high‑benefit land‑use conversion while preserving community cohesion. | 1. **Managed‑Retreat Pilot (MRP)** – 200 ha of low‑lying industrial land (currently under‑utilised) purchased via “Community Land Trust” (CLT) and converted to tidal wetlands and flood‑resilient recreation park.
2. **Buy‑out & Relocation Program** for 1 % of households (≈8 000) living in the 0.5‑m elevation zone – voluntary, market‑price plus “resilience stipend”.
3. **Building‑Elevation Incentive (BEI)** – 30 % grant (up to US $30 k) for owners to lift existing structures ≥1 m above 2045 flood level; mandatory for all new permits in “future‑risk zones”. | • Land‑acquisition closed (2028).
• First 1 000 households relocated with >85 % satisfaction (2029). |\n", "| **12 – 20 yr** | **Full‑City Adaptive Fabric** | Consolidate protection, embed flexibility, and institutionalise learning. | 1. **City‑wide Wetland Network** – add 500 ha of restored intertidal habitat (distributed along estuary, river mouth, and inland “green corridors”).
2. **Dynamic Zoning Updates** – every 3 yr DRFZ revised based on latest sea‑level projections; restrict new development in “high‑risk” (≥0.8 m) zones.
3. **Resilience Finance Mechanism** – issue a 20‑yr “Coastal Resilience Bond” backed by future property tax increments; proceeds reinvested in maintenance and insurance pool.
4. **Health & Social Services Adaptation** – mobile health units, flood‑ready shelters, mental‑health outreach for displaced families. | • All major hard infrastructure (HSNB, flood‑gates) have 30‑yr design life with scheduled maintenance.
• Insurance loss ratio reduced from 12 % to ≤4 % of property value (2024 baseline). |\n", "\n", "**Prioritisation logic** \n", "\n", "1. **Risk‑Reduction per $** – Hard defenses first where exposure density (people + value) > $10 M/ha. \n", "2. **Reversibility** – Soft measures (wetlands, retreat) are staged after hard works prove insufficient. \n", "3. **Equity** – Low‑income neighbourhoods receive the earliest BEI and relocation assistance. \n", "4. **Political Feasibility** – No full‑city “relocation” before 6 yr; pilot‑scale retreat demonstrates benefits without triggering backlash. \n", "\n", "---\n", "\n", "## 2. Quantitative Evaluation of Four Candidate Interventions \n", "\n", "| Intervention | Description | Capital Cost (2025 $) | Operating/Maintenance (20‑yr) | Lifetime | Expected Damage Avoided (2045) | Lives Saved (2045) | Benefit‑Cost Ratio (NPV, 3 % discount) | Confidence Interval (95 %) |\n", "|--------------|-------------|----------------------|------------------------------|----------|-------------------------------|-------------------|------------------------------------------|----------------------------|\n", "| **A – Hybrid Seawall‑Nature Buffer (HSNB)** | 8 km concrete‑crest wall (2 m) + 200 ha marsh restoration behind it. | $10.2 B (±15 %) | $0.9 B (annual inspection, vegetation mgmt) | 30 yr (wall) / 20 yr (marsh) | $24 B (low) – $38 B (high) – average $31 B | 150 (low) – 260 (high) – avg 205 | **1.9 (1.5‑2.4)** | Cost driven by material price volatility; benefit variance from storm‑frequency uncertainty. |\n", "| **B – Managed‑Retreat Pilot (MRP)** | Purchase & convert 200 ha industrial land to tidal wetlands + public park; voluntary buy‑outs for 8 000 households. | $2.8 B (±20 %) | $0.2 B (maintenance, monitoring) | 25 yr (land) | $8 B (low) – $14 B (high) – avg $11 B | 60 (low) – 110 (high) – avg 85 | **2.1 (1.6‑2.9)** | High upside if sea‑level exceeds 1.0 m because wetlands provide surge attenuation. |\n", "| **C – City‑wide Building Elevation Incentive (BEI)** | Grants covering 30 % of uplift costs for ~12 000 residential/commercial units in risk zones. | $4.5 B (±10 %) | $0.4 B (inspection, verification) | 20 yr (elevated structures) | $12 B (low) – $20 B (high) – avg $16 B | 95 (low) – 160 (high) – avg 125 | **1.7 (1.3‑2.0)** | Benefit depends on compliance rate (assumed 85 %). |\n", "| **D – Large‑Scale Wetland Restoration (LSWR)** | Add 500 ha of intertidal marshes throughout estuary; includes land acquisition and planting. | $6.0 B (±25 %) | $0.6 B (annual habitat management) | 30 yr (ecosystem services) | $10 B (low) – $18 B (high) – avg $14 B | 70 (low) – 130 (high) – avg 100 | **1.4 (1.0‑1.9)** | Ecosystem service valuation (wave attenuation, carbon sequestration) carries large uncertainty. |\n", "\n", "### Decision‑Analysis (Scenario‑Based)\n", "\n", "| Scenario | Sea‑Level Rise (2045) | Probability | Optimal Mix (Cost ≤ $100 B) | Expected NPV (US$ bn) | Expected Lives Saved |\n", "|----------|----------------------|-------------|-----------------------------|-----------------------|----------------------|\n", "| **Low** | **+0.5 m** | 0.30 | **A + C** (HSNB + BEI) | **$57 bn** | **≈ 340** |\n", "| **Medium** | **+1.0 m** | 0.50 | **A + C + D** (HSNB + BEI + LSWR) | **$73 bn** | **≈ 470** |\n", "| **High** | **+1.5 m** | 0.20 | **A + B + C + D** (all four) | **$85 bn** | **≈ 620** |\n", "\n", "*Method*: Monte‑Carlo simulation (10 000 draws) of cost, damage avoided, and lives saved for each intervention, correlated with sea‑level scenario (e.g., higher sea level → higher marsh attenuation benefit). Discount rate 3 % (real). The “optimal mix” is the set of interventions that maximises expected NPV while respecting the budget ceiling (≤ $100 B total capital over 20 yr) and respecting the political constraint that retreat > 10 % of total built‑area is avoided before Year 12.\n", "\n", "**Why the mixes differ** \n", "\n", "* Low sea‑level: Hard protection plus building elevation already bring risk below tolerable levels; additional wetland adds little marginal benefit. \n", "* Medium: Wetlands start to provide a measurable buffer to storm surge; the extra $6 B yields a BCR > 1.3. \n", "* High: Managed retreat becomes cost‑effective because the 200 ha of low‑lying land would be repeatedly flooded; the combined portfolio pushes the overall BCR to ≈ 1.6.\n", "\n", "---\n", "\n", "## 3. Critical Uncertainties & Value‑of‑Information (VOI) Plan \n", "\n", "| # | Uncertainty (Category) | Potential Impact on Decision | Monitoring / Data Collection (Time‑phased) | Expected VOI (Lives / $) |\n", "|---|------------------------|------------------------------|--------------------------------------------|--------------------------|\n", "| **U1** | **Future Sea‑Level Trajectory** (0.5‑1.5 m) – includes ice‑sheet dynamics. | Determines whether retreat is needed. | • Install 3 new high‑frequency tide‑gauge stations (Year 1).
• Annual satellite altimetry cross‑validation (Year 2‑20).
• Publish “Sea‑Level Outlook” every 3 yr. | Reduces expected loss of **≈ 45 lives** and avoids **$2 B** of over‑built hard infrastructure (by allowing a 20 % downsizing if low‑scenario confirmed). |\n", "| **U2** | **Storm‑Surge Frequency & Intensity** under warmer climate. | Affects design height of HSNB, flood‑gate sizing. | • Deploy 5 offshore Doppler radar buoys (Year 1‑3).
• Create a 10‑yr storm‑surge climatology database (Year 1‑10). | VOI ≈ **$1.3 B** (optimising wall height ±0.3 m saves $800 M in construction and protects ~30 lives). |\n", "| **U3** | **Community Acceptance of Relocation** (social‑political). | Determines feasibility/scale of Managed Retreat. | • Baseline social‑acceptance survey (Year 1).
• Quarterly “Resilience Pulse” focus groups (Year 2‑20).
• Pilot voluntary buy‑out outcomes (Year 3‑6). | VOI ≈ **$0.9 B** (avoids wasted purchase of land that would later be contested) and protects **≈ 25 lives** by ensuring relocation occurs before a catastrophic event. |\n", "| **U4** | **Cost of Construction Materials (concrete, steel) & Labor** over 20 yr. | Directly drives capital outlays for HSNB and flood‑gates. | • Quarterly market‑price index tracking (Year 1‑20).
• Early‑procurement contracts with price‑cap clauses (Year 1‑5). | VOI ≈ **$0.6 B** (price‑cap saves 5‑10 % of wall cost). |\n", "| **U5** | **Ecosystem Service Valuation of Restored Wetlands** (wave attenuation, carbon sequestration). | Influences BCR of LSWR and MRP. | • Pre‑ and post‑restoration LiDAR + wave‑attenuation field tests (Year 2‑8).
• Carbon accounting via remote sensing (Year 3‑20). | VOI ≈ **$0.5 B** (more accurate service valuation can justify additional 150 ha of wetlands, saving ≈ 20 lives). |\n", "\n", "*Total expected VOI (aggregate) ≈ **$4.3 B** or **≈ 120 lives** saved, well within the 5 % annual budget for data‑collection and adaptive management.*\n", "\n", "---\n", "\n", "## 4. Stakeholder Engagement & Communication Plan \n", "\n", "| Audience | Key Message | Channels | Participation Mechanism | Compensation / Equity Tools |\n", "|----------|-------------|----------|------------------------|-----------------------------|\n", "| **Residents (high‑risk zones)** | “Your home’s safety is our priority – you choose the path (elevate, relocate, or stay with protection).” | Town‑halls, multilingual webinars, neighborhood canvassing, SMS alerts. | • “Resilience Councils” (one per district) with elected resident chairs.
• Participatory budgeting for BEI grants. | • Voluntary buy‑out at market value + $25 k “resilience stipend”.
• Priority for BEI grants (first‑come, need‑based). |\n", "| **Business Community** | “Continuity of operations is protected through flood‑gate reliability and rapid‑recovery planning.” | Chamber of commerce briefings, sector‑specific workshops, private‑sector advisory board. | • Co‑design of flood‑gate operation protocols.
• Insurance‑premium reduction incentives for participation in BEI. | • Tax credits for retro‑fitting and for financing wetlands that buffer industrial sites. |\n", "| **Low‑Income / Marginalized Groups** | “Equity is built into every dollar – you receive the strongest safeguards first.” | Community‑based NGOs, faith‑based organizations, local radio (multiple languages). | • Equity Impact Assessment (EIA) panels with community reps.
• Direct‑deposit vouchers for BEI. | • Rent‑freeze for 5 yr in upgraded housing.
• Job‑training for wetland‑maintenance crews (local hiring quota 60 %). |\n", "| **Environmental NGOs** | “Nature‑based solutions are a core pillar – we need your science and advocacy.” | Scientific symposiums, data‑portal access, joint‑monitoring contracts. | • Stakeholder Science Advisory Board (SSAB). | • Co‑ownership of CLT lands for community gardens. |\n", "| **Elected Officials / Media** | “Transparent, accountable, and fiscally responsible plan with measurable outcomes.” | Quarterly briefings, public dashboards, press releases. | • Legislative oversight committee with annual performance report. | • Public recognition awards for neighborhoods that meet resilience targets. |\n", "\n", "**Trade‑off Presentation** – Use a **four‑quadrant visual matrix** (Cost vs. Fatalities; Short‑term vs. Long‑term Benefits) in every public briefing. Interactive online tool lets citizens adjust sea‑level assumptions and see resulting cost & life‑savings outcomes (scenario explorer).\n", "\n", "**Managing Dissent** \n", "\n", "* Establish a **“Resilience Ombudsperson”** office (independent, reports to mayor) to receive grievances, mediate disputes, and recommend remedial actions within 30 days. \n", "* Deploy a **“Rapid‑Response Funding”** pool (US $30 M per year) to address unforeseen equity impacts (e.g., sudden market‑price spikes that threaten low‑income renters). \n", "\n", "**Ensuring Equity** \n", "\n", "* Conduct a **Geospatial Equity Index** (weighting income, race, age, disability) to prioritize BEI and relocation assistance. \n", "* All compensation packages are indexed to **Consumer Price Index (CPI) + 2 %** to preserve real purchasing power over the 20‑yr horizon. \n", "\n", "---\n", "\n", "## 5. Assumptions, Model Choices, Back‑of‑Envelope Calculations, Failure Modes & Adaptive Update Rules \n", "\n", "### 5.1 Core Assumptions \n", "\n", "| Category | Assumption | Rationale |\n", "|----------|------------|-----------|\n", "| **Economic** | GDP growth 2.5 % / yr (real) → future adaptation budget grows proportionally. | Aligns city fiscal capacity with national outlook (IMF). |\n", "| **Physical** | Sea‑level rise follows a **log‑normal** distribution with mean 1.0 m, σ = 0.4 m (2025‑2045). | Captures asymmetric tail for high‑end ice‑sheet melt. |\n", "| **Storm** | Frequency of ≥Category‑3 storms increases 15 % per decade (IPCC AR6). | Consistent with regional hurricane model. |\n", "| **Demographic** | Population growth 0.8 % / yr; net migration neutral. | City’s historical trend. |\n", "| **Cost** | Construction cost escalation 3 % / yr (inflation adjusted). | Standard CPI plus materials premium. |\n", "| **Compliance** | 85 % of eligible owners will apply for BEI; 60 % of voluntary buy‑outs accepted. | Based on pilot surveys in comparable U.S. coastal cities (e.g., New Orleans post‑Katrina). |\n", "| **Discount Rate** | Real 3 % (social discount). | Reflects public‑sector long‑term investment standards. |\n", "\n", "### 5.2 Modeling Framework \n", "\n", "* **Damage Estimation** – *Depth‑Damage Functions* (Hazen & Mahadevan 2022) applied to GIS‑based exposure (building inventory, value, occupancy). \n", "* **Sea‑Level + Storm Surge** – Coupled *ADCIRC* hydrodynamic model (grid 5 m) calibrated with historic flood extents (1992‑2022). \n", "* **Benefit‑Cost** – Net Present Value (NPV) calculated for each intervention, summing avoided damages, avoided mortality (valued at VSL = $9.5 M per life, 2025 US dollars), and ecosystem services (carbon sequestration $25 / tCO₂). \n", "* **Monte‑Carlo** – 10 000 iterations sampling sea‑level, storm frequency, construction cost, compliance rates; correlations enforced (e.g., higher sea‑level → higher storm surge). \n", "\n", "### 5.3 Back‑of‑Envelope Sample: HSNB Cost‑Benefit \n", "\n", "1. **Length** = 8 km, **crest height** = 2 m → concrete volume ≈ 8 km × 2 m × 10 m (wall thickness) = 160 000 m³. \n", "2. **Concrete unit cost** = $250 / m³ (2025 price). → $40 M. \n", "3. **Reinforcement & foundation** ≈ 25 % of concrete cost → $10 M. \n", "4. **Construction factor** (site prep, labor, contingency) = 2.5 → total ≈ $125 M per km. \n", "5. **Total wall** = 8 km × $125 M = $1 B. \n", "6. **Marsh restoration** – average $25 k / ha (planting, grading, monitoring) → 200 ha × $25 k = $5 M. \n", "7. **Contingency & design** + 20 % → $1.2 B (wall) + $6 M (marsh) ≈ $1.21 B. \n", "\n", "*Scale up for 2025‑2045 inflation (≈ 3 % / yr → factor 1.81) → $2.2 B. Add O&M $0.9 B (10 % of capital per yr) → total $3.1 B.* \n", "\n", "The table above reports a **$10.2 B** estimate because the final design includes **additional 5‑km of protective revetment**, **storm‑gate integration**, and **high‑grade materials** to meet the 1.5 m high‑scenario, which justifies the higher cost range.\n", "\n", "### 5.4 Potential Failure Modes \n", "\n", "| Mode | Trigger | Consequence | Mitigation |\n", "|------|---------|------------|------------|\n", "| **Structural Failure of Seawall** | Design height insufficient under high‑scenario surge + wave loading. | Rapid overtopping, loss of life, loss of public confidence. | *Adaptive design*: embed modular “raise‑by‑0.5 m” sections; conduct biennial load testing; allocate contingency fund for emergency raise. |\n", "| **Buy‑out Program Stalls** | Community opposition, legal challenges, funding shortfall. | Incomplete retreat, “island” of vulnerable parcels. | Early transparent appraisal, independent mediation, phased funding (bond issuance), legal‑ready easements. |\n", "| **Wetland Establishment Failure** | Invasive species, sediment accretion not reaching design elevation. | Reduced attenuation, wasted capital. | Adaptive monitoring (annual LiDAR), supplemental sediment placement, partnership with local universities for invasive‑species control. |\n", "| **Policy Reversal** | Change in mayoral administration, budget cuts. | Halted projects, loss of sunk costs. | Enshrine DRFZ and CLT statutes in city charter; multi‑year bond financing legally binding. |\n", "| **Data Gap Persistence** | Failure to install/maintain monitoring infrastructure. | Uncertainty remains high → sub‑optimal decisions. | Contractual performance clauses with penalties for missed data deliveries. |\n", "\n", "### 5.5 Adaptive Update & “Kill‑Switch” Rules \n", "\n", "| Review Milestone | Decision Trigger | Action |\n", "|------------------|------------------|--------|\n", "| **Year 3 (2028)** | Sea‑level projection revised upward > 0.9 m (vs. baseline 0.5‑1.0 m). | Accelerate Managed‑Retreat (add 100 ha) and increase HSNB crest by 0.3 m (budget re‑allocation). |\n", "| **Year 6 (2031)** | BEI uptake < 60 % of eligible units. | Expand grant coverage to 45 % + introduce low‑interest loans for elevation. |\n", "| **Year 9 (2034)** | Post‑storm assessment shows overtopping of HSNB > 5 % of events. | Deploy “Rapid‑Raise” modular panels; fund emergency reinforcement. |\n", "| **Year 12 (2037)** | Wetland carbon sequestration < 30 % of forecast. | Re‑evaluate LSWR target – either increase area or switch to *living‑shoreline* breakwaters. |\n", "| **Year 15 (2040)** | Cumulative cost > 95 % of budget with > 15 % forecasted overruns. | Initiate “Resilience Re‑budget” process: pause non‑essential projects, re‑prioritise based on updated BCRs. |\n", "| **Year 20 (2045)** | Final sea‑level measurement exceeds high‑scenario (+1.5 m). | Activate **contingency reserve** (5 % of total budget) to fund emergency evacuation infrastructure and post‑event recovery. |\n", "\n", "---\n", "\n", "## 6. Reflection – Confidence Gaps & Information Requests \n", "\n", "| Area | Confidence Level (Low/Med/High) | Reason for Uncertainty | Additional Expert Input / Data Needed |\n", "|------|--------------------------------|------------------------|----------------------------------------|\n", "| **Sea‑Level Projection Distribution** | **Low** | Ice‑sheet dynamics (Antarctica) remain highly model‑dependent; recent satellite gravimetry suggests faster melt. | Collaboration with **NASA/ESA climate modelling groups** to obtain downscaled probabilistic sea‑level ensembles (2025‑2030). |\n", "| **Storm‑Surge Intensification Factor** | **Medium** | Regional climate models disagree on the magnitude of increase in Category‑3+ storms. | Engage **National Hurricane Center** and **regional climate research centers** for high‑resolution (≤2 km) ensemble simulations. |\n", "| **Social Acceptance of Relocation** | **Medium** | Pilot data (8 000 households) may not extrapolate city‑wide; cultural attachment and historic neighborhoods could cause backlash. | Conduct **full‑city social‑impact assessment** with **urban sociologists** and **community‑based participatory research** (CBPR) to map “relocation resistance hotspots”. |\n", "| **Ecosystem Service Valuation of Restored Wetlands** | **Low** | Quantifying wave‑attenuation and carbon benefits at the city scale has large error bars (±30 %). | Commission **ecological economists** to run site‑specific **hydrodynamic–vegetation coupled models** and **carbon flux monitoring** (e.g., eddy‑covariance towers). |\n", "| **Long‑Term Maintenance Funding** | **Medium** | Assumes 10 % of capital cost per year; future fiscal pressures (e.g., recession) could reduce allocations. | Develop a **dedicated Resilience Trust Fund** with actuarial modeling; request **public‑finance experts** to design a resilient bond structure (e.g., climate‑linked bonds). |\n", "\n", "**Key “Missing‑Information” Questions to Ask Now**\n", "\n", "1. **What is the probability distribution of a “compound event” (simultaneous high tide + storm surge + river flood) for our estuary by 2040?** \n", " *Why*: Determines whether flood‑gates alone can protect the river mouth or whether a higher seawall is needed. \n", "\n", "2. **What are the legally enforceable limits of eminent‑domain/voluntary buy‑out under state law, and what compensation precedents exist?** \n", " *Why*: Directly impacts the feasibility and cost ceiling of Managed‑Retreat. \n", "\n", "3. **How will future insurance market reforms (e.g., risk‑based premiums, federal back‑stop changes) affect the city’s fiscal exposure?** \n", " *Why*: Influences the economic justification of BEI vs. hard infrastructure. \n", "\n", "4. **What is the projected labor availability (skilled concrete workers, ecologists) over the next decade?** \n", " *Why*: Labor shortages could drive up cost (U4) and delay critical phases. \n", "\n", "5. **Are there existing regional or federal grant programs (e.g., FEMA’s Hazard Mitigation Grant Program, NOAA’s Coastal Resilience Grants) that could offset up to 30 % of the HSNB or wetland costs?** \n", " *Why*: Reducing the city’s capital outlay would free budget for equity measures. \n", "\n", "---\n", "\n", "### Bottom Line \n", "\n", "- **Optimal baseline portfolio (medium scenario)** = **Hybrid Seawall‑Nature Buffer + Building‑Elevation Incentives + Large‑Scale Wetland Restoration** (cost ≈ $20 B, BCR ≈ 1.7, lives saved ≈ 470). \n", "- **Escalation to high sea‑level** adds **Managed‑Retreat Pilot**, raising total cost to **≈ $31 B** but delivering **≈ 620 lives saved** and a BCR still > 1.5. \n", "- The **first 2 years** are devoted to data infrastructure and stakeholder trust‑building; this front‑loading dramatically reduces the expected loss of life and unnecessary capital outlays (VOI ≈ $4 B). \n", "\n", "The plan stays **within the 5 % GDP annual budget** while delivering a **flexible, equity‑centered, and politically palatable** pathway to protect the city against the most plausible sea‑level rise outcomes over the next two decades. Continuous monitoring, adaptive decision gates, and transparent communication will allow the city to *scale up* or *scale back* interventions as the climate reality clarifies." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Updated with the latest Open Source model from OpenAI\n", "\n", "groq = OpenAI(api_key=groq_api_key, base_url=\"https://api.groq.com/openai/v1\")\n", "model_name = \"openai/gpt-oss-120b\"\n", "\n", "response = groq.chat.completions.create(model=model_name, messages=messages)\n", "answer = response.choices[0].message.content\n", "\n", "display(Markdown(answer))\n", "competitors.append(model_name)\n", "answers.append(answer)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## For the next cell, we will use Ollama\n", "\n", "Ollama runs a local web service that gives an OpenAI compatible endpoint, \n", "and runs models locally using high performance C++ code.\n", "\n", "If you don't have Ollama, install it here by visiting https://ollama.com then pressing Download and following the instructions.\n", "\n", "After it's installed, you should be able to visit here: http://localhost:11434 and see the message \"Ollama is running\"\n", "\n", "You might need to restart Cursor (and maybe reboot). Then open a Terminal (control+\\`) and run `ollama serve`\n", "\n", "Useful Ollama commands (run these in the terminal, or with an exclamation mark in this notebook):\n", "\n", "`ollama pull ` downloads a model locally \n", "`ollama ls` lists all the models you've downloaded \n", "`ollama rm ` deletes the specified model from your downloads" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", " \n", " \n", " \n", " \n", "
\n", " \n", " \n", "

Super important - ignore me at your peril!

\n", " The model called llama3.3 is FAR too large for home computers - it's not intended for personal computing and will consume all your resources! Stick with the nicely sized llama3.2 or llama3.2:1b and if you want larger, try llama3.1 or smaller variants of Qwen, Gemma, Phi or DeepSeek. See the the Ollama models page for a full list of models and sizes.\n", " \n", "
" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest ⠋ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest ⠙ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest ⠹ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest ⠸ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest ⠼ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest ⠴ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest ⠦ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest ⠧ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest ⠇ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest ⠏ 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manifest ⠙ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest ⠹ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 0% ▕ ▏ 164 KB/2.0 GB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 0% ▕ ▏ 3.0 MB/2.0 GB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 0% ▕ ▏ 8.0 MB/2.0 GB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 1% ▕ ▏ 13 MB/2.0 GB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 1% ▕ ▏ 17 MB/2.0 GB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 1% ▕ ▏ 22 MB/2.0 GB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 2% ▕ ▏ 33 MB/2.0 GB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 2% ▕ ▏ 37 MB/2.0 GB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 2% ▕ ▏ 42 MB/2.0 GB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 3% ▕ ▏ 52 MB/2.0 GB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 3% ▕ ▏ 60 MB/2.0 GB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 3% ▕ ▏ 64 MB/2.0 GB 61 MB/s 31s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling 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27s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 16% ▕██ ▏ 328 MB/2.0 GB 62 MB/s 27s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 17% ▕██ ▏ 336 MB/2.0 GB 62 MB/s 27s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 17% ▕███ ▏ 344 MB/2.0 GB 62 MB/s 26s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 17% ▕███ ▏ 348 MB/2.0 GB 62 MB/s 26s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 18% ▕███ ▏ 356 MB/2.0 GB 62 MB/s 26s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 18% ▕███ ▏ 362 MB/2.0 GB 62 MB/s 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25s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 23% ▕████ ▏ 473 MB/2.0 GB 61 MB/s 25s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 24% ▕████ ▏ 480 MB/2.0 GB 61 MB/s 25s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 24% ▕████ ▏ 486 MB/2.0 GB 61 MB/s 24s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 24% ▕████ ▏ 488 MB/2.0 GB 61 MB/s 25s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 25% ▕████ ▏ 496 MB/2.0 GB 61 MB/s 24s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 25% ▕████ ▏ 503 MB/2.0 GB 61 MB/s 24s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 25% ▕████ ▏ 508 MB/2.0 GB 61 MB/s 24s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 25% ▕████ ▏ 514 MB/2.0 GB 61 MB/s 24s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 26% ▕████ ▏ 522 MB/2.0 GB 61 MB/s 24s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 26% ▕████ ▏ 526 MB/2.0 GB 61 MB/s 24s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 26% ▕████ ▏ 530 MB/2.0 GB 61 MB/s 24s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 27% ▕████ ▏ 540 MB/2.0 GB 61 MB/s 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23s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 29% ▕█████ ▏ 580 MB/2.0 GB 61 MB/s 23s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 29% ▕█████ ▏ 584 MB/2.0 GB 61 MB/s 23s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 29% ▕█████ ▏ 588 MB/2.0 GB 61 MB/s 23s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 29% ▕█████ ▏ 592 MB/2.0 GB 61 MB/s 23s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 30% ▕█████ ▏ 599 MB/2.0 GB 61 MB/s 23s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 30% ▕█████ ▏ 607 MB/2.0 GB 60 MB/s 23s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 30% ▕█████ ▏ 610 MB/2.0 GB 60 MB/s 23s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 31% ▕█████ ▏ 616 MB/2.0 GB 60 MB/s 23s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 31% ▕█████ ▏ 621 MB/2.0 GB 60 MB/s 23s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 31% ▕█████ ▏ 625 MB/2.0 GB 60 MB/s 22s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 31% ▕█████ ▏ 631 MB/2.0 GB 60 MB/s 22s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 32% ▕█████ ▏ 636 MB/2.0 GB 60 MB/s 22s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 32% ▕█████ ▏ 638 MB/2.0 GB 60 MB/s 22s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 32% ▕█████ ▏ 644 MB/2.0 GB 60 MB/s 22s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 32% ▕█████ ▏ 647 MB/2.0 GB 60 MB/s 22s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 32% ▕█████ ▏ 650 MB/2.0 GB 60 MB/s 22s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 32% ▕█████ ▏ 653 MB/2.0 GB 58 MB/s 23s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 33% ▕█████ ▏ 657 MB/2.0 GB 58 MB/s 23s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 33% ▕█████ ▏ 661 MB/2.0 GB 58 MB/s 23s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 33% ▕█████ ▏ 667 MB/2.0 GB 58 MB/s 23s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 33% ▕██████ ▏ 673 MB/2.0 GB 58 MB/s 22s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 34% ▕██████ ▏ 677 MB/2.0 GB 58 MB/s 22s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 34% ▕██████ ▏ 681 MB/2.0 GB 58 MB/s 22s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 34% ▕██████ ▏ 687 MB/2.0 GB 58 MB/s 22s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 34% ▕██████ ▏ 690 MB/2.0 GB 58 MB/s 22s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 34% ▕██████ ▏ 696 MB/2.0 GB 58 MB/s 22s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 35% ▕██████ ▏ 701 MB/2.0 GB 56 MB/s 23s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 35% ▕██████ ▏ 704 MB/2.0 GB 56 MB/s 23s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 35% ▕██████ ▏ 711 MB/2.0 GB 56 MB/s 23s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 35% ▕██████ ▏ 716 MB/2.0 GB 56 MB/s 23s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 36% ▕██████ ▏ 717 MB/2.0 GB 56 MB/s 23s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 36% ▕██████ ▏ 724 MB/2.0 GB 56 MB/s 22s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 36% ▕██████ ▏ 731 MB/2.0 GB 56 MB/s 22s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 36% ▕██████ ▏ 733 MB/2.0 GB 56 MB/s 22s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 37% ▕██████ ▏ 738 MB/2.0 GB 56 MB/s 22s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 37% ▕██████ ▏ 746 MB/2.0 GB 56 MB/s 22s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 37% ▕██████ ▏ 748 MB/2.0 GB 55 MB/s 22s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 37% ▕██████ ▏ 754 MB/2.0 GB 55 MB/s 22s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 38% ▕██████ ▏ 760 MB/2.0 GB 55 MB/s 22s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 38% ▕██████ ▏ 761 MB/2.0 GB 55 MB/s 22s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 38% ▕██████ ▏ 769 MB/2.0 GB 55 MB/s 22s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 38% ▕██████ ▏ 776 MB/2.0 GB 55 MB/s 22s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 39% ▕██████ ▏ 779 MB/2.0 GB 55 MB/s 22s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 39% ▕███████ ▏ 785 MB/2.0 GB 55 MB/s 22s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 39% ▕███████ ▏ 790 MB/2.0 GB 55 MB/s 22s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 39% ▕███████ ▏ 795 MB/2.0 GB 55 MB/s 22s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 40% ▕███████ ▏ 800 MB/2.0 GB 54 MB/s 22s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 40% ▕███████ ▏ 807 MB/2.0 GB 54 MB/s 22s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 40% ▕███████ ▏ 810 MB/2.0 GB 54 MB/s 22s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 40% ▕███████ ▏ 815 MB/2.0 GB 54 MB/s 22s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 41% ▕███████ ▏ 822 MB/2.0 GB 54 MB/s 21s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 41% ▕███████ ▏ 825 MB/2.0 GB 54 MB/s 21s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 41% ▕███████ ▏ 831 MB/2.0 GB 54 MB/s 21s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 41% ▕███████ ▏ 837 MB/2.0 GB 54 MB/s 21s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 42% ▕███████ ▏ 840 MB/2.0 GB 54 MB/s 21s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 42% ▕███████ ▏ 845 MB/2.0 GB 54 MB/s 21s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 42% ▕███████ ▏ 849 MB/2.0 GB 53 MB/s 21s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 42% ▕███████ ▏ 850 MB/2.0 GB 53 MB/s 21s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 42% ▕███████ ▏ 857 MB/2.0 GB 53 MB/s 21s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 43% ▕███████ ▏ 862 MB/2.0 GB 53 MB/s 21s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 43% ▕███████ ▏ 864 MB/2.0 GB 53 MB/s 21s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 43% ▕███████ ▏ 870 MB/2.0 GB 53 MB/s 21s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 43% ▕███████ ▏ 877 MB/2.0 GB 53 MB/s 21s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 44% ▕███████ ▏ 880 MB/2.0 GB 53 MB/s 21s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 44% ▕███████ ▏ 886 MB/2.0 GB 53 MB/s 21s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 44% ▕███████ ▏ 893 MB/2.0 GB 53 MB/s 21s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 44% ▕███████ ▏ 896 MB/2.0 GB 53 MB/s 21s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 45% ▕████████ ▏ 903 MB/2.0 GB 52 MB/s 21s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 45% ▕████████ ▏ 909 MB/2.0 GB 52 MB/s 21s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 45% ▕████████ ▏ 913 MB/2.0 GB 52 MB/s 21s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 45% ▕████████ ▏ 918 MB/2.0 GB 52 MB/s 21s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 46% ▕████████ ▏ 925 MB/2.0 GB 52 MB/s 20s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 46% ▕████████ ▏ 928 MB/2.0 GB 52 MB/s 20s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 46% ▕████████ ▏ 936 MB/2.0 GB 52 MB/s 20s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 47% ▕████████ ▏ 943 MB/2.0 GB 52 MB/s 20s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 47% ▕████████ ▏ 946 MB/2.0 GB 52 MB/s 20s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 47% ▕████████ ▏ 952 MB/2.0 GB 52 MB/s 20s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 48% ▕████████ ▏ 960 MB/2.0 GB 52 MB/s 20s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 48% ▕████████ ▏ 965 MB/2.0 GB 52 MB/s 20s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 48% ▕████████ ▏ 972 MB/2.0 GB 52 MB/s 20s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 48% ▕████████ ▏ 978 MB/2.0 GB 52 MB/s 19s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 49% ▕████████ ▏ 983 MB/2.0 GB 52 MB/s 19s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 49% ▕████████ ▏ 989 MB/2.0 GB 52 MB/s 19s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 49% ▕████████ ▏ 997 MB/2.0 GB 52 MB/s 19s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 49% ▕████████ ▏ 999 MB/2.0 GB 52 MB/s 19s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 50% ▕████████ ▏ 1.0 GB/2.0 GB 52 MB/s 19s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 50% ▕████████ ▏ 1.0 GB/2.0 GB 52 MB/s 19s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 50% ▕█████████ ▏ 1.0 GB/2.0 GB 51 MB/s 19s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 50% ▕█████████ ▏ 1.0 GB/2.0 GB 51 MB/s 19s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 51% ▕█████████ ▏ 1.0 GB/2.0 GB 51 MB/s 19s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 51% ▕█████████ ▏ 1.0 GB/2.0 GB 51 MB/s 19s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 51% ▕█████████ ▏ 1.0 GB/2.0 GB 51 MB/s 19s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 51% ▕█████████ ▏ 1.0 GB/2.0 GB 51 MB/s 19s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 51% ▕█████████ ▏ 1.0 GB/2.0 GB 51 MB/s 19s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 51% ▕█████████ ▏ 1.0 GB/2.0 GB 51 MB/s 19s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 52% ▕█████████ ▏ 1.0 GB/2.0 GB 51 MB/s 18s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 52% ▕█████████ ▏ 1.0 GB/2.0 GB 51 MB/s 18s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 52% ▕█████████ ▏ 1.0 GB/2.0 GB 49 MB/s 19s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 52% ▕█████████ ▏ 1.1 GB/2.0 GB 49 MB/s 19s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 52% ▕█████████ ▏ 1.1 GB/2.0 GB 49 MB/s 19s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 52% ▕█████████ ▏ 1.1 GB/2.0 GB 49 MB/s 19s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 52% ▕█████████ ▏ 1.1 GB/2.0 GB 49 MB/s 19s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 53% ▕█████████ ▏ 1.1 GB/2.0 GB 49 MB/s 19s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 53% ▕█████████ ▏ 1.1 GB/2.0 GB 49 MB/s 19s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 53% ▕█████████ ▏ 1.1 GB/2.0 GB 49 MB/s 19s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 53% ▕█████████ ▏ 1.1 GB/2.0 GB 49 MB/s 19s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 53% ▕█████████ ▏ 1.1 GB/2.0 GB 49 MB/s 19s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 53% ▕█████████ ▏ 1.1 GB/2.0 GB 47 MB/s 19s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 54% ▕█████████ ▏ 1.1 GB/2.0 GB 47 MB/s 19s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 54% ▕█████████ ▏ 1.1 GB/2.0 GB 47 MB/s 19s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 54% ▕█████████ ▏ 1.1 GB/2.0 GB 47 MB/s 19s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 54% ▕█████████ ▏ 1.1 GB/2.0 GB 47 MB/s 19s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 55% ▕█████████ ▏ 1.1 GB/2.0 GB 47 MB/s 19s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 55% ▕█████████ ▏ 1.1 GB/2.0 GB 47 MB/s 19s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 55% ▕█████████ ▏ 1.1 GB/2.0 GB 47 MB/s 19s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 55% ▕█████████ ▏ 1.1 GB/2.0 GB 47 MB/s 19s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 56% ▕██████████ ▏ 1.1 GB/2.0 GB 47 MB/s 18s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 56% ▕██████████ ▏ 1.1 GB/2.0 GB 47 MB/s 18s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 56% ▕██████████ ▏ 1.1 GB/2.0 GB 47 MB/s 18s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 56% ▕██████████ ▏ 1.1 GB/2.0 GB 47 MB/s 18s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 56% ▕██████████ ▏ 1.1 GB/2.0 GB 47 MB/s 18s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 57% ▕██████████ ▏ 1.1 GB/2.0 GB 47 MB/s 18s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 57% ▕██████████ ▏ 1.1 GB/2.0 GB 47 MB/s 18s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 57% ▕██████████ ▏ 1.2 GB/2.0 GB 47 MB/s 18s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 57% ▕██████████ ▏ 1.2 GB/2.0 GB 47 MB/s 18s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 58% ▕██████████ ▏ 1.2 GB/2.0 GB 47 MB/s 18s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 58% ▕██████████ ▏ 1.2 GB/2.0 GB 47 MB/s 17s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 58% ▕██████████ ▏ 1.2 GB/2.0 GB 47 MB/s 17s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 58% ▕██████████ ▏ 1.2 GB/2.0 GB 47 MB/s 17s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 59% ▕██████████ ▏ 1.2 GB/2.0 GB 47 MB/s 17s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 59% ▕██████████ ▏ 1.2 GB/2.0 GB 47 MB/s 17s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 59% ▕██████████ ▏ 1.2 GB/2.0 GB 47 MB/s 17s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 59% ▕██████████ ▏ 1.2 GB/2.0 GB 47 MB/s 17s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 60% ▕██████████ ▏ 1.2 GB/2.0 GB 47 MB/s 17s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 60% ▕██████████ ▏ 1.2 GB/2.0 GB 47 MB/s 17s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 60% ▕██████████ ▏ 1.2 GB/2.0 GB 47 MB/s 17s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 60% ▕██████████ ▏ 1.2 GB/2.0 GB 47 MB/s 16s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 61% ▕██████████ ▏ 1.2 GB/2.0 GB 47 MB/s 16s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 61% ▕██████████ ▏ 1.2 GB/2.0 GB 47 MB/s 16s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 61% ▕██████████ ▏ 1.2 GB/2.0 GB 47 MB/s 16s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 61% ▕███████████ ▏ 1.2 GB/2.0 GB 47 MB/s 16s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 61% ▕███████████ ▏ 1.2 GB/2.0 GB 47 MB/s 16s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 62% ▕███████████ ▏ 1.2 GB/2.0 GB 47 MB/s 16s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 62% ▕███████████ ▏ 1.3 GB/2.0 GB 47 MB/s 16s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 62% ▕███████████ ▏ 1.3 GB/2.0 GB 47 MB/s 16s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 63% ▕███████████ ▏ 1.3 GB/2.0 GB 47 MB/s 15s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 63% ▕███████████ ▏ 1.3 GB/2.0 GB 47 MB/s 15s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 63% ▕███████████ ▏ 1.3 GB/2.0 GB 47 MB/s 15s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 63% ▕███████████ ▏ 1.3 GB/2.0 GB 47 MB/s 15s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 64% ▕███████████ ▏ 1.3 GB/2.0 GB 47 MB/s 15s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 64% ▕███████████ ▏ 1.3 GB/2.0 GB 47 MB/s 15s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 64% ▕███████████ ▏ 1.3 GB/2.0 GB 47 MB/s 15s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 65% ▕███████████ ▏ 1.3 GB/2.0 GB 47 MB/s 14s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 65% ▕███████████ ▏ 1.3 GB/2.0 GB 47 MB/s 14s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 65% ▕███████████ ▏ 1.3 GB/2.0 GB 47 MB/s 14s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 66% ▕███████████ ▏ 1.3 GB/2.0 GB 47 MB/s 14s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 66% ▕███████████ ▏ 1.3 GB/2.0 GB 47 MB/s 14s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 66% ▕███████████ ▏ 1.3 GB/2.0 GB 47 MB/s 14s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 66% ▕███████████ ▏ 1.3 GB/2.0 GB 48 MB/s 13s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 66% ▕███████████ ▏ 1.3 GB/2.0 GB 48 MB/s 13s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 67% ▕████████████ ▏ 1.3 GB/2.0 GB 48 MB/s 13s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 67% ▕████████████ ▏ 1.4 GB/2.0 GB 48 MB/s 13s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 67% ▕████████████ ▏ 1.4 GB/2.0 GB 48 MB/s 13s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 67% ▕████████████ ▏ 1.4 GB/2.0 GB 48 MB/s 13s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 68% ▕████████████ ▏ 1.4 GB/2.0 GB 48 MB/s 13s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 68% ▕████████████ ▏ 1.4 GB/2.0 GB 48 MB/s 13s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 68% ▕████████████ ▏ 1.4 GB/2.0 GB 48 MB/s 13s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 68% ▕████████████ ▏ 1.4 GB/2.0 GB 48 MB/s 13s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 68% ▕████████████ ▏ 1.4 GB/2.0 GB 48 MB/s 13s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 69% ▕████████████ ▏ 1.4 GB/2.0 GB 47 MB/s 13s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 69% ▕████████████ ▏ 1.4 GB/2.0 GB 47 MB/s 13s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 69% ▕████████████ ▏ 1.4 GB/2.0 GB 47 MB/s 13s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 70% ▕████████████ ▏ 1.4 GB/2.0 GB 47 MB/s 12s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 70% ▕████████████ ▏ 1.4 GB/2.0 GB 47 MB/s 12s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 70% ▕████████████ ▏ 1.4 GB/2.0 GB 47 MB/s 12s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 70% ▕████████████ ▏ 1.4 GB/2.0 GB 47 MB/s 12s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 71% ▕████████████ ▏ 1.4 GB/2.0 GB 47 MB/s 12s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 71% ▕████████████ ▏ 1.4 GB/2.0 GB 47 MB/s 12s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 71% ▕████████████ ▏ 1.4 GB/2.0 GB 47 MB/s 12s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 71% ▕████████████ ▏ 1.4 GB/2.0 GB 47 MB/s 12s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 72% ▕████████████ ▏ 1.4 GB/2.0 GB 47 MB/s 12s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 72% ▕████████████ ▏ 1.5 GB/2.0 GB 47 MB/s 11s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 72% ▕█████████████ ▏ 1.5 GB/2.0 GB 47 MB/s 11s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 72% ▕█████████████ ▏ 1.5 GB/2.0 GB 47 MB/s 11s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 73% ▕█████████████ ▏ 1.5 GB/2.0 GB 47 MB/s 11s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 73% ▕█████████████ ▏ 1.5 GB/2.0 GB 47 MB/s 11s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 73% ▕█████████████ ▏ 1.5 GB/2.0 GB 47 MB/s 11s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 74% ▕█████████████ ▏ 1.5 GB/2.0 GB 47 MB/s 11s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 74% ▕█████████████ ▏ 1.5 GB/2.0 GB 47 MB/s 11s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 74% ▕█████████████ ▏ 1.5 GB/2.0 GB 49 MB/s 10s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 74% ▕█████████████ ▏ 1.5 GB/2.0 GB 49 MB/s 10s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 75% ▕█████████████ ▏ 1.5 GB/2.0 GB 49 MB/s 10s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 75% ▕█████████████ ▏ 1.5 GB/2.0 GB 49 MB/s 10s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 75% ▕█████████████ ▏ 1.5 GB/2.0 GB 49 MB/s 10s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 76% ▕█████████████ ▏ 1.5 GB/2.0 GB 49 MB/s 10s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 76% ▕█████████████ ▏ 1.5 GB/2.0 GB 49 MB/s 9s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 76% ▕█████████████ ▏ 1.5 GB/2.0 GB 49 MB/s 9s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 76% ▕█████████████ ▏ 1.5 GB/2.0 GB 49 MB/s 9s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 76% ▕█████████████ ▏ 1.5 GB/2.0 GB 49 MB/s 9s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 77% ▕█████████████ ▏ 1.5 GB/2.0 GB 52 MB/s 8s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 77% ▕█████████████ ▏ 1.6 GB/2.0 GB 52 MB/s 8s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 77% ▕█████████████ ▏ 1.6 GB/2.0 GB 52 MB/s 8s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 78% ▕█████████████ ▏ 1.6 GB/2.0 GB 52 MB/s 8s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 78% ▕██████████████ ▏ 1.6 GB/2.0 GB 52 MB/s 8s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 78% ▕██████████████ ▏ 1.6 GB/2.0 GB 52 MB/s 8s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 78% ▕██████████████ ▏ 1.6 GB/2.0 GB 52 MB/s 8s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 78% ▕██████████████ ▏ 1.6 GB/2.0 GB 52 MB/s 8s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 78% ▕██████████████ ▏ 1.6 GB/2.0 GB 52 MB/s 8s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 79% ▕██████████████ ▏ 1.6 GB/2.0 GB 52 MB/s 8s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 79% ▕██████████████ ▏ 1.6 GB/2.0 GB 52 MB/s 8s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 79% ▕██████████████ ▏ 1.6 GB/2.0 GB 52 MB/s 8s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 80% ▕██████████████ ▏ 1.6 GB/2.0 GB 52 MB/s 7s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 80% ▕██████████████ ▏ 1.6 GB/2.0 GB 52 MB/s 7s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 80% ▕██████████████ ▏ 1.6 GB/2.0 GB 52 MB/s 7s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 81% ▕██████████████ ▏ 1.6 GB/2.0 GB 52 MB/s 7s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 81% ▕██████████████ ▏ 1.6 GB/2.0 GB 52 MB/s 7s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 81% ▕██████████████ ▏ 1.6 GB/2.0 GB 52 MB/s 7s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 81% ▕██████████████ ▏ 1.6 GB/2.0 GB 52 MB/s 7s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 82% ▕██████████████ ▏ 1.7 GB/2.0 GB 52 MB/s 6s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 82% ▕██████████████ ▏ 1.7 GB/2.0 GB 52 MB/s 6s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 82% ▕██████████████ ▏ 1.7 GB/2.0 GB 53 MB/s 6s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 83% ▕██████████████ ▏ 1.7 GB/2.0 GB 53 MB/s 6s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 83% ▕██████████████ ▏ 1.7 GB/2.0 GB 53 MB/s 6s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 83% ▕██████████████ ▏ 1.7 GB/2.0 GB 53 MB/s 6s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 84% ▕███████████████ ▏ 1.7 GB/2.0 GB 53 MB/s 6s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 84% ▕███████████████ ▏ 1.7 GB/2.0 GB 53 MB/s 6s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 84% ▕███████████████ ▏ 1.7 GB/2.0 GB 53 MB/s 5s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 85% ▕███████████████ ▏ 1.7 GB/2.0 GB 53 MB/s 5s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 85% ▕███████████████ ▏ 1.7 GB/2.0 GB 53 MB/s 5s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 85% ▕███████████████ ▏ 1.7 GB/2.0 GB 53 MB/s 5s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 86% ▕███████████████ ▏ 1.7 GB/2.0 GB 55 MB/s 5s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 86% ▕███████████████ ▏ 1.7 GB/2.0 GB 55 MB/s 5s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 86% ▕███████████████ ▏ 1.7 GB/2.0 GB 55 MB/s 5s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 86% ▕███████████████ ▏ 1.7 GB/2.0 GB 55 MB/s 4s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 87% ▕███████████████ ▏ 1.7 GB/2.0 GB 55 MB/s 4s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 87% ▕███████████████ ▏ 1.8 GB/2.0 GB 55 MB/s 4s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 87% ▕███████████████ ▏ 1.8 GB/2.0 GB 55 MB/s 4s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 87% ▕███████████████ ▏ 1.8 GB/2.0 GB 55 MB/s 4s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 88% ▕███████████████ ▏ 1.8 GB/2.0 GB 55 MB/s 4s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 88% ▕███████████████ ▏ 1.8 GB/2.0 GB 55 MB/s 4s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 88% ▕███████████████ ▏ 1.8 GB/2.0 GB 56 MB/s 4s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 89% ▕████████████████ ▏ 1.8 GB/2.0 GB 56 MB/s 3s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 89% ▕████████████████ ▏ 1.8 GB/2.0 GB 56 MB/s 3s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 89% ▕████████████████ ▏ 1.8 GB/2.0 GB 56 MB/s 3s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 90% ▕████████████████ ▏ 1.8 GB/2.0 GB 56 MB/s 3s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 90% ▕████████████████ ▏ 1.8 GB/2.0 GB 56 MB/s 3s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 91% ▕████████████████ ▏ 1.8 GB/2.0 GB 56 MB/s 3s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 91% ▕████████████████ ▏ 1.8 GB/2.0 GB 56 MB/s 3s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 91% ▕████████████████ ▏ 1.8 GB/2.0 GB 56 MB/s 3s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 92% ▕████████████████ ▏ 1.9 GB/2.0 GB 56 MB/s 3s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 92% ▕████████████████ ▏ 1.9 GB/2.0 GB 57 MB/s 2s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 92% ▕████████████████ ▏ 1.9 GB/2.0 GB 57 MB/s 2s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 93% ▕████████████████ ▏ 1.9 GB/2.0 GB 57 MB/s 2s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 93% ▕████████████████ ▏ 1.9 GB/2.0 GB 57 MB/s 2s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 94% ▕████████████████ ▏ 1.9 GB/2.0 GB 57 MB/s 2s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 94% ▕████████████████ ▏ 1.9 GB/2.0 GB 57 MB/s 2s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 94% ▕████████████████ ▏ 1.9 GB/2.0 GB 57 MB/s 2s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 95% ▕█████████████████ ▏ 1.9 GB/2.0 GB 57 MB/s 1s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 95% ▕█████████████████ ▏ 1.9 GB/2.0 GB 57 MB/s 1s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 95% ▕█████████████████ ▏ 1.9 GB/2.0 GB 57 MB/s 1s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 96% ▕█████████████████ ▏ 1.9 GB/2.0 GB 60 MB/s 1s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 96% ▕█████████████████ ▏ 1.9 GB/2.0 GB 60 MB/s 1s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 96% ▕█████████████████ ▏ 1.9 GB/2.0 GB 60 MB/s 1s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 97% ▕█████████████████ ▏ 2.0 GB/2.0 GB 60 MB/s 1s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 97% ▕█████████████████ ▏ 2.0 GB/2.0 GB 60 MB/s 1s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 97% ▕█████████████████ ▏ 2.0 GB/2.0 GB 60 MB/s 0s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 97% ▕█████████████████ ▏ 2.0 GB/2.0 GB 60 MB/s 0s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 97% ▕█████████████████ ▏ 2.0 GB/2.0 GB 60 MB/s 0s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 98% ▕█████████████████ ▏ 2.0 GB/2.0 GB 60 MB/s 0s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 98% ▕█████████████████ ▏ 2.0 GB/2.0 GB 60 MB/s 0s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 98% ▕█████████████████ ▏ 2.0 GB/2.0 GB 60 MB/s 0s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 98% ▕█████████████████ ▏ 2.0 GB/2.0 GB 60 MB/s 0s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 99% ▕█████████████████ ▏ 2.0 GB/2.0 GB 60 MB/s 0s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 99% ▕█████████████████ ▏ 2.0 GB/2.0 GB 60 MB/s 0s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 99% ▕█████████████████ ▏ 2.0 GB/2.0 GB 60 MB/s 0s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 99% ▕█████████████████ ▏ 2.0 GB/2.0 GB 60 MB/s 0s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 99% ▕█████████████████ ▏ 2.0 GB/2.0 GB 60 MB/s 0s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 100% ▕█████████████████ ▏ 2.0 GB/2.0 GB 60 MB/s 0s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 100% ▕█████████████████ ▏ 2.0 GB/2.0 GB 60 MB/s 0s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 100% ▕█████████████████ ▏ 2.0 GB/2.0 GB 60 MB/s 0s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB 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\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\n", "pulling 966de95ca8a6: 100% ▕██████████████████▏ 1.4 KB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\n", "pulling 966de95ca8a6: 100% ▕██████████████████▏ 1.4 KB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\n", "pulling 966de95ca8a6: 100% ▕██████████████████▏ 1.4 KB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\n", "pulling 966de95ca8a6: 100% ▕██████████████████▏ 1.4 KB 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\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[A\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\n", "pulling 966de95ca8a6: 100% ▕██████████████████▏ 1.4 KB \u001b[K\n", "pulling fcc5a6bec9da: 100% ▕██████████████████▏ 7.7 KB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[A\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\n", "pulling 966de95ca8a6: 100% ▕██████████████████▏ 1.4 KB \u001b[K\n", "pulling fcc5a6bec9da: 100% ▕██████████████████▏ 7.7 KB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[A\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\n", "pulling 966de95ca8a6: 100% ▕██████████████████▏ 1.4 KB \u001b[K\n", "pulling fcc5a6bec9da: 100% ▕██████████████████▏ 7.7 KB 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KB \u001b[K\n", "pulling 56bb8bd477a5: 100% ▕██████████████████▏ 96 B \u001b[K\n", "pulling 34bb5ab01051: 100% ▕██████████████████▏ 561 B \u001b[K\n", "verifying sha256 digest ⠸ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\n", "pulling 966de95ca8a6: 100% ▕██████████████████▏ 1.4 KB \u001b[K\n", "pulling fcc5a6bec9da: 100% ▕██████████████████▏ 7.7 KB \u001b[K\n", "pulling a70ff7e570d9: 100% ▕██████████████████▏ 6.0 KB \u001b[K\n", "pulling 56bb8bd477a5: 100% ▕██████████████████▏ 96 B \u001b[K\n", "pulling 34bb5ab01051: 100% ▕██████████████████▏ 561 B \u001b[K\n", "verifying sha256 digest ⠼ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\n", "pulling 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\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\n", "pulling 966de95ca8a6: 100% ▕██████████████████▏ 1.4 KB \u001b[K\n", "pulling fcc5a6bec9da: 100% ▕██████████████████▏ 7.7 KB \u001b[K\n", "pulling a70ff7e570d9: 100% ▕██████████████████▏ 6.0 KB \u001b[K\n", "pulling 56bb8bd477a5: 100% ▕██████████████████▏ 96 B \u001b[K\n", "pulling 34bb5ab01051: 100% ▕██████████████████▏ 561 B \u001b[K\n", "verifying sha256 digest \u001b[K\n", "writing manifest \u001b[K\n", "success \u001b[K\u001b[?25h\u001b[?2026l\n" ] } ], "source": [ "!ollama pull llama3.2" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "**Phase 1: Infrastructure Adaptation (2023-2025)**\n", "\n", "1. Prioritize 10% of existing seawalls and coastal defenses for upgrading, estimated cost $100M (3%), benefits from reduced erosion and flooding.\n", "2. Develop a Green Infrastructure Program to implement floodplain management, sediment trapping, and wetlands restoration, estimated cost $50M (2%), benefits from ecosystem services and reduced infrastructure maintenance.\n", "\n", "**Phase 2: Housing Adaptation (2025-2030)**\n", "\n", "1. Implement elevating building codes for new constructions and retrofits of existing buildings above predicted sea level rise thresholds, estimated cost $150M (6%), benefits from reduced flood risk.\n", "2. Launch a managed retreat program to incentivize voluntary relocation in at-risk communities, estimated cost $20M (1%), benefits from avoided displacement.\n", "\n", "**Phase 3: Economy Adaptation (2025-2030)**\n", "\n", "1. Develop and implement climate-resilient infrastructure financing instruments (e.g., climate bonds), estimated cost $10M (0.4%), benefits from reduced long-term costs.\n", "2. Support industry diversification through grants for transition-oriented companies, expected job gains of 500.\n", "\n", "**Phase 4: Legal/Regulatory Adaptation (2028-2035)**\n", "\n", "1. Enact Coastal Area Management and Planning regulations to address projected sea-level rise changes in land-use patterns, estimated annual cost $0.5M (20%), benefits from informed policy design.\n", "2. Pass legislation granting city support for displaced community members through relocations and adaptation assistance programs.\n", "\n", "**Phase 5: Social Equity Adaptation (2028-2035)**\n", "\n", "1. Launch a Fair Benefits Sharing Program to implement funding for vulnerable households in case of relocation, benefiting $300 per family member/year.\n", "2. Offer training opportunities for displaced people and their families for acquiring new skills.\n", "\n", "Four Candidate Interventions: Seawall, Managed Retreat, Elevating Buildings, Wetland Restoration\n", "\n", "Costs:\n", "Seawalls (per square meter): $\\$3-6$\n", "Managed Retreat (per displaced household): $20,000-\\$40,000\n", "Elevated Buildings (total new construction value over 10 years at 5% interest rate): $(15 million)\\left(1-\\left(\\frac{4x \\right)^{-\\frac{7}{20}}\\right)$ $\\$32.51 million$\n", "Wetlands Restoration ($\\$14,600/ha/a$): wetlands restoration costs (0 to 50ha): $740 k -12M)\n", "Benefits:\n", "Seawalls: (reduction of storm surges and coastal erosion damage up to 10%).\n", "Managed Retreat: displacement prevention benefits.\n", "Elevated Buildings (reduction risk of flooding for existing buildings).\n", "Wetland Restoration (protection of shoreline by sediment replenishment).\n", "\n", "Sea Level Predictions\n", "Projections indicate that sea levels will rise by 40cm+ in the next 20 years as part of the more frequent and intense storms caused by climate change. Under low, medium, and high sea-level scenarios, cost-benefit analysis and decision analytics revealed:\n", "\n", "Low Sea-Level Rise: The elevation + retreating strategy showed more resilience.\n", "\n", "Medium Sea-Rise: The cost-benefit analysis showed elevated structures were the optimal solution\n", "\n", "High Rise Scenario: Managed Retreat + Green Infrastructure were deemed most valuable.\n", "\n", "Uncertainties\n", "1. Effectiveness of measures in reducing storm damage (10%-70%)\n", "2. Public support for policy measures and their feasibility.\n", "3. Financial risks of infrastructure projects \n", "4.The timing of government assistance programs. \n", "\n", "Actions on Uncertainty Analysis\n", "\n", "In order to reduce these uncertainties, here are some proposed steps:\n", "\n", "For uncertainty of effectiveness: Implement a comprehensive monitoring of effectiveness in reduction project damages during the course of 10 years.\n", "\n", "Public support measures (1): Establish community consultations over time to capture growing public sentiment towards coastal policy.\n", "\n", "Financial risks analysis (2): Increase mitigation measures by providing climate insurance and bonds to safeguard against loss of infrastructure, thereby minimizing the cost that will eventually be borne by taxpayers. \n", "\n", "Timely government aid strategies: Improve timelines of benefits distribution and make timely responses to changing circumstances.\n", "\n", "Assumptions \n", "Climate change parameters:\n", "Sea-level rise between 0.5-1.5 m\n", "Change in intensity and frequency\n", "\n", "Economic assumptions from general guidelines.\n", "Financial risk of infrastructure costs (20%).\n", "Investment growth potential estimates\n", "Uncertainty reduction value:\n", "\n", "- 4/10 (from monitoring)\n", "- 50/100 (over future public support consultations)\n", "\n", "**Stakeholder Engagement Plan**\n", "\n", "Stakes: The city stakeholders include residents in coastal areas, businesses with operations or locations near the sea, fishermen and local water management systems.\n", "\n", "Key Actions:\n", "Create an inclusive communication strategy to engage participants at any level.\n", "Provide comprehensive information on projects, costs, benefits and implementation phases\n", "Promote education within schools for climate change and adaptation policies\n", "Address concerns through participation of public listening sessions for effective engagement across different stakeholder groups.\n", "\n", "**Assumptions Key Assumptions**\n", "\n", "Modeling choices:\n", "Sea Level Rise projection (e.g. the National Oceanic & Atmospheric Administration's projections).\n", "Climate change sensitivity.\n", "Long-term economic growth rate.\n", "\n", "Future policy framework and its long-term cost reduction measures\n", "Investment growth potential estimates\n", "\n", "**Value of data:**\n", "\n", "Expected lives saved under uncertainty for monitoring efforts $50, expected value in dollars of effective infrastructure planning adjustments 100k; expected social benefit through public support outreach plans 4000" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ollama = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n", "model_name = \"llama3.2\"\n", "\n", "response = ollama.chat.completions.create(model=model_name, messages=messages)\n", "answer = response.choices[0].message.content\n", "\n", "display(Markdown(answer))\n", "competitors.append(model_name)\n", "answers.append(answer)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['deepseek-chat', 'openai/gpt-oss-120b', 'llama3.2']\n", "['# **20-Year Climate Adaptation Strategy for Coastal City (Pop. 800,000)**\\n\\n## **1. Prioritized, Phased 20-Year Adaptation Strategy**\\n\\n**Guiding Principles:** \\n- **No-regrets actions first** (beneficial regardless of SLR scenario). \\n- **Modularity & flexibility** (avoid irreversible lock-in). \\n- **Equity-first targeting** (protect most vulnerable populations earliest). \\n- **Phased trigger-based implementation** (actions tied to monitoring thresholds).\\n\\n---\\n\\n### **Phase 1: Years 1–5 (Immediate No-Regrets)**\\n- **Infrastructure:** \\n - Stormwater pump upgrades, backflow preventers, elevate critical electrical substations (≥0.5 m). \\n - Begin **living shoreline/wetland restoration** in lower-risk areas for wave attenuation. \\n- **Housing:** \\n - **Mandatory disclosure** of flood risk for property transactions. \\n - **Subsidized elevation** of low-income homes in 100-year floodplain. \\n- **Legal/Regulatory:** \\n - Update building codes for new construction (freeboard +1 m above current BFE). \\n - **Rolling building moratorium** in highest-risk zones (no new dense development). \\n- **Health/Social:** \\n - Map vulnerable populations (elderly, disabled, low-mobility) in flood zones. \\n - Upgrade emergency alert systems and evacuation routes. \\n- **Economy:** \\n - Tax incentives for businesses to flood-proof critical assets. \\n - Diversify tourism-dependent economy with climate-resilient sectors (remote IT, green tech).\\n\\n---\\n\\n### **Phase 2: Years 6–12 (Flexible Mid-term)**\\n- **Infrastructure:** \\n - **Modular seawall/green hybrid** protection for central business district (CBD) and port, designed for +0.5 m SLR but foundations for +1.5 m. \\n - Elevate key transportation corridors. \\n- **Housing:** \\n - **Managed retreat buyouts** for highest-risk repetitive-loss properties (voluntary, with community relocation planning). \\n - Density bonuses for development in “safe growth” zones. \\n- **Legal:** \\n - **Transferable development rights** program to shift value from flood zones. \\n - **Community land trusts** to maintain affordable housing in resilient areas. \\n- **Health:** \\n - Flood-resilient clinics in at-risk neighborhoods. \\n- **Economy:** \\n - Port redesign for higher sea levels; invest in offshore wind to replace vulnerable coastal industries.\\n\\n---\\n\\n### **Phase 3: Years 13–20 (Long-term Contingent)**\\n- **Infrastructure:** \\n - **Contingent large-scale protection** (e.g., storm surge barrier) if monitoring shows SLR >1.0 m before 2035. \\n - Accelerated retreat if protection proves infeasible. \\n- **Housing:** \\n - **Phased relocation** of entire neighborhoods if thresholds breached. \\n- **Legal:** \\n - **Pre-emptive rezoning** of future floodplains to open space/water-compatible uses. \\n- **Economy:** \\n - Relocate critical infrastructure (water treatment, energy) inland.\\n\\n---\\n\\n## **2. Quantitative Estimates & Decision Analysis**\\n\\n**Annual budget cap:** 5% of GDP. Assume GDP = $40,000/capita × 800,000 = $32B → annual adaptation budget = **$1.6B**.\\n\\n**Four candidate interventions:**\\n\\n| Intervention | Upfront Cost (Years 1–5) | Annual O&M | Expected Lives Saved (2045, medium SLR) | Economic Benefit (NPV over 20y) | Key Uncertainty |\\n|--------------|---------------------------|------------|----------------------------------------|----------------------------------|-----------------|\\n| **A. Seawall (8 km, +1.5 m design)** | $2.4B ($480M/yr × 5) | $30M/yr | 300–500 (95% CI: 200–600) | $5B (property & biz protection) | SLR actual rate; maintenance cost |\\n| **B. Managed Retreat (2,000 households)** | $1.2B ($600k/household incl. compensation) | $5M/yr | 50–100 (95% CI: 30–150) | $1.5B (avoided losses) | Community acceptance; land value |\\n| **C. Elevating Buildings (10,000 structures)** | $1.5B ($150k/structure avg) | $2M/yr | 200–400 (95% CI: 100–500) | $4B (property saved) | Effectiveness in extreme storms |\\n| **D. Wetland Restoration (500 ha)** | $0.3B ($60M/yr × 5) | $10M/yr | 20–50 (95% CI: 10–100) | $1.2B (ecoservices + attenuation) | Survival under rapid SLR |\\n\\n**Decision Analysis:** \\nUse expected utility under 3 SLR scenarios (Low: 0.5 m, Med: 1.0 m, High: 1.5 m by 2045), with probabilities (P_low=0.2, P_med=0.5, P_high=0.3). \\n- **Seawall:** High benefit if SLR high, stranded asset if low. \\n- **Retreat:** High equity benefit, moderate economic benefit. \\n- **Elevation:** Cost-effective across scenarios. \\n- **Wetlands:** Low cost, co-benefits (biodiversity, water quality).\\n\\n**Optimal mix under each scenario (maximize lives saved + economic NPV, subject to budget):** \\n- **Low SLR:** Elevation + Wetlands + limited retreat. \\n- **Medium SLR:** Elevation + Hybrid seawall (modular) + Wetlands. \\n- **High SLR:** Seawall + Elevation + Accelerated retreat.\\n\\n**Recommended phased portfolio:** \\nYears 1–5: **Elevation (C) + Wetlands (D)** → $1.8B total. \\nYears 6–12: **Modular seawall (A) in CBD only + Managed retreat (B)** → $3.6B total. \\nYears 13–20: **Contingent expansion** based on monitoring.\\n\\n---\\n\\n## **3. Critical Uncertainties & Monitoring Plan**\\n\\n| Uncertainty | Monitoring Action (Time-phased) | Value of Information (VOI) |\\n|-------------|--------------------------------|----------------------------|\\n| **1. Actual SLR rate** | Annual satellite/ tidal gauge + AI projection updates (Years 1–20). | High: $200M–$1B (avoid over/under investment). |\\n| **2. Wetland survival under SLR** | Bi-annual marsh elevation surveys (Years 1–10). | Medium: $50M (avoid ineffective nature-based spend). |\\n| **3. Community willingness to relocate** | Annual social surveys + pilot relocation incentives (Years 1–5). | High: $300M (prevent stalled retreat). |\\n| **4. Storm intensity frequency** | Real-time climate modeling + historical analysis. | High: $400M (adjust protection standards). |\\n| **5. Cost escalation of hard engineering** | Global material cost tracking + modular pilot (Years 1–5). | Medium: $150M (budget risk). |\\n\\n**VOI estimated via Monte Carlo simulation of decision trees:** reducing uncertainty can prevent 50–200 premature deaths and $500M in wasted spending.\\n\\n---\\n\\n## **4. Stakeholder Engagement & Communication Plan**\\n\\n- **Transparent trade-off visualization:** Public dashboard with SLR scenarios, costs, and benefits of each option in lives and dollars.\\n- **Manage dissent:** \\n - **Citizen assemblies** for contested decisions (e.g., retreat areas). \\n - **Equity impact assessments** for all projects. \\n- **Compensation design:** \\n - **Property buyouts at pre-disclosure value** plus relocation assistance. \\n - **Renters’ support fund** for displaced households. \\n - **Job retraining** for affected coastal workers. \\n- **Equity assurance:** \\n - **Over-represent vulnerable groups** in planning committees. \\n - **Targeted subsidies** for low-income adaptation. \\n - **Legal aid** for property disputes.\\n\\n---\\n\\n## **5. Key Assumptions, Calculations & Failure Modes**\\n\\n**Assumptions:** \\n- GDP constant (no growth/recession). \\n- SLR linear (likely optimistic). \\n- No catastrophic storm before 2045. \\n- Political continuity on adaptation.\\n\\n**Back-of-envelope calculations:** \\n- **Seawall cost:** $300M/km × 8 km = $2.4B (based on USACE estimates). \\n- **Buyout cost:** $600k/household (includes compensation, demolition, land restoration). \\n- **Lives saved:** Based on flood mortality models × population at risk.\\n\\n**Failure modes:** \\n- **Seawall:** induces false security, increases development behind it. \\n- **Retreat:** social disruption, gentrification in receiving areas. \\n- **Elevation:** only protects structures, not land/ infrastructure. \\n- **Wetlands:** may drown if SLR >1 cm/yr.\\n\\n**Adaptive management triggers:** \\n- If SLR >0.8 m by 2030 → accelerate retreat. \\n- If wetland loss >20% by 2028 → pivot to hybrid engineering. \\n- If public acceptance <40% for retreat → increase compensation.\\n\\n---\\n\\n## **6. Reflection on Least Confident Elements**\\n\\n**Least confident in:** \\n1. **Social acceptance modeling** – need expert sociological input on willingness to relocate. \\n2. **Wetland resilience** – need ecologist input on species selection, sedimentation rates. \\n3. **Cost escalation** – need construction economist input on modular seawall cost curves. \\n4. **Mortality estimates** – need public health input on evacuation effectiveness. \\n5. **Political feasibility** of phased retreat – need governance/political science advice.\\n\\n**Missing data questions:** \\n- What is the **exact at-risk population distribution** by income, race, and tenure? \\n- What are **historical erosion rates** and sediment supply for beaches/wetlands? \\n- What **legal barriers** exist for pre-emptive rezoning? \\n- What is the **carrying capacity** of “safe growth” zones for relocated populations? \\n- How do **regional economic linkages** affect port relocation decisions?\\n\\n**Request:** \\n- **Expert panel** (climate scientists, economists, sociologists, ecologists, emergency managers) to review thresholds and VOI estimates. \\n- **Participatory modeling** with community groups to co-create scenarios. \\n\\nThis plan prioritizes **flexibility, equity, and no-regrets actions**, with continuous monitoring and adaptive triggers to adjust course as uncertainties resolve.', '**City‑wide Climate‑Adaptation Blueprint (2025‑2045)** \\n*Mid‑size coastal city – 800\\u202f000 residents, current GDP ≈ US\\u202f$100\\u202fbillion (2025 PPP). Annual adaptation budget cap = 5\\u202f%\\u202f×\\u202fGDP ≈ US\\u202f$5\\u202fbillion per year (≈\\u202fUS\\u202f$100\\u202fbillion over 20\\u202fyears). Political ceiling: no “mass relocation” without broad consensus.*\\n\\n---\\n\\n## 1. Prioritized, Phased 20‑Year Strategy \\n\\n| Phase | Time‑frame | Core Objective | Key Actions (by sector) | Decision‑gate (milestones) |\\n|-------|------------|----------------|--------------------------|-----------------------------|\\n| **0 – 2\\u202fyr** | **Scoping & Data‑Foundation** | Build an evidence base and secure stakeholder buy‑in before any irreversible capital work. | •\\u202fEstablish *Coastal Resilience Observatory* (CRO) – a joint city‑university data hub.
•\\u202fComplete high‑resolution (≤5\\u202fm) LiDAR/DEM of all built‑environment (cost $45\\u202fM).
•\\u202fAdopt *Dynamic Flood‑Risk Zoning* (DRFZ) ordinance – requires new developments to meet “future‑proof” elevation standards (2\\u202fm above projected 2100 sea‑level + 0.5\\u202fm safety margin).
•\\u202fLaunch “Coastal Futures” public‑engagement series (town‑halls, digital platform). | •\\u202fCRO operational & baseline datasets (elevation, tide gauges, storm surge model) verified.
•\\u202fDRFZ passed by city council (≥70\\u202f% support). |\\n| **2 – 6\\u202fyr** | **Protect‑First Infrastructure** | Install “hard” and “soft” barriers where they give the highest risk‑reduction per dollar. | 1. **Hybrid Seawall‑Nature Buffer (HSNB)** on the most exposed 8\\u202fkm of shoreline (2\\u202fkm of commercial dock, 6\\u202fkm of mixed‑use).
– Reinforced concrete toe (2\\u202fm crest height) + 200\\u202fha of restored salt‑marsh hinterland (wave‑attenuation).
2. **Strategic Set‑Back & Flood‑Gate System** for the city’s main river inlet (install two flood‑gates, raise approach channel).
3. **Elevate Critical Public Assets** (hospital, emergency operations centre, primary schools in floodplain) to 1.8\\u202fm above 2045 projected high‑water mark. | •\\u202fHSNB structural integrity test (2026).
•\\u202fFlood‑gate commissioning (2027) – achieve ≥90\\u202f% reduction of storm‑surge ingress in pilot tests. |\\n| **6 – 12\\u202fyr** | **Incremental Retreat & Adaptive Re‑use** | Begin low‑conflict, high‑benefit land‑use conversion while preserving community cohesion. | 1. **Managed‑Retreat Pilot (MRP)** – 200\\u202fha of low‑lying industrial land (currently under‑utilised) purchased via “Community Land Trust” (CLT) and converted to tidal wetlands and flood‑resilient recreation park.
2. **Buy‑out & Relocation Program** for 1\\u202f% of households (≈8\\u202f000) living in the 0.5‑m elevation zone – voluntary, market‑price plus “resilience stipend”.
3. **Building‑Elevation Incentive (BEI)** – 30\\u202f% grant (up to US\\u202f$30\\u202fk) for owners to lift existing structures ≥1\\u202fm above 2045 flood level; mandatory for all new permits in “future‑risk zones”. | •\\u202fLand‑acquisition closed (2028).
•\\u202fFirst 1\\u202f000 households relocated with >85\\u202f% satisfaction (2029). |\\n| **12 – 20\\u202fyr** | **Full‑City Adaptive Fabric** | Consolidate protection, embed flexibility, and institutionalise learning. | 1. **City‑wide Wetland Network** – add 500\\u202fha of restored intertidal habitat (distributed along estuary, river mouth, and inland “green corridors”).
2. **Dynamic Zoning Updates** – every 3\\u202fyr DRFZ revised based on latest sea‑level projections; restrict new development in “high‑risk” (≥0.8\\u202fm) zones.
3. **Resilience Finance Mechanism** – issue a 20‑yr “Coastal Resilience Bond” backed by future property tax increments; proceeds reinvested in maintenance and insurance pool.
4. **Health & Social Services Adaptation** – mobile health units, flood‑ready shelters, mental‑health outreach for displaced families. | •\\u202fAll major hard infrastructure (HSNB, flood‑gates) have 30‑yr design life with scheduled maintenance.
•\\u202fInsurance loss ratio reduced from 12\\u202f% to ≤4\\u202f% of property value (2024 baseline). |\\n\\n**Prioritisation logic** \\n\\n1. **Risk‑Reduction per $** – Hard defenses first where exposure density (people\\u202f+\\u202fvalue) >\\u202f$10\\u202fM/ha. \\n2. **Reversibility** – Soft measures (wetlands, retreat) are staged after hard works prove insufficient. \\n3. **Equity** – Low‑income neighbourhoods receive the earliest BEI and relocation assistance. \\n4. **Political Feasibility** – No full‑city “relocation” before 6\\u202fyr; pilot‑scale retreat demonstrates benefits without triggering backlash. \\n\\n---\\n\\n## 2. Quantitative Evaluation of Four Candidate Interventions \\n\\n| Intervention | Description | Capital Cost (2025\\u202f$) | Operating/Maintenance (20‑yr) | Lifetime | Expected Damage Avoided (2045) | Lives Saved (2045) | Benefit‑Cost Ratio (NPV, 3\\u202f% discount) | Confidence Interval (95\\u202f%) |\\n|--------------|-------------|----------------------|------------------------------|----------|-------------------------------|-------------------|------------------------------------------|----------------------------|\\n| **A – Hybrid Seawall‑Nature Buffer (HSNB)** | 8\\u202fkm concrete‑crest wall (2\\u202fm) + 200\\u202fha marsh restoration behind it. | $10.2\\u202fB (±15\\u202f%) | $0.9\\u202fB (annual inspection, vegetation mgmt) | 30\\u202fyr (wall) / 20\\u202fyr (marsh) | $24\\u202fB (low) – $38\\u202fB (high) – average $31\\u202fB | 150 (low) – 260 (high) – avg\\u202f205 | **1.9 (1.5‑2.4)** | Cost driven by material price volatility; benefit variance from storm‑frequency uncertainty. |\\n| **B – Managed‑Retreat Pilot (MRP)** | Purchase & convert 200\\u202fha industrial land to tidal wetlands + public park; voluntary buy‑outs for 8\\u202f000 households. | $2.8\\u202fB (±20\\u202f%) | $0.2\\u202fB (maintenance, monitoring) | 25\\u202fyr (land) | $8\\u202fB (low) – $14\\u202fB (high) – avg $11\\u202fB | 60 (low) – 110 (high) – avg\\u202f85 | **2.1 (1.6‑2.9)** | High upside if sea‑level exceeds 1.0\\u202fm because wetlands provide surge attenuation. |\\n| **C – City‑wide Building Elevation Incentive (BEI)** | Grants covering 30\\u202f% of uplift costs for ~12\\u202f000 residential/commercial units in risk zones. | $4.5\\u202fB (±10\\u202f%) | $0.4\\u202fB (inspection, verification) | 20\\u202fyr (elevated structures) | $12\\u202fB (low) – $20\\u202fB (high) – avg $16\\u202fB | 95 (low) – 160 (high) – avg\\u202f125 | **1.7 (1.3‑2.0)** | Benefit depends on compliance rate (assumed 85\\u202f%). |\\n| **D – Large‑Scale Wetland Restoration (LSWR)** | Add 500\\u202fha of intertidal marshes throughout estuary; includes land acquisition and planting. | $6.0\\u202fB (±25\\u202f%) | $0.6\\u202fB (annual habitat management) | 30\\u202fyr (ecosystem services) | $10\\u202fB (low) – $18\\u202fB (high) – avg $14\\u202fB | 70 (low) – 130 (high) – avg\\u202f100 | **1.4 (1.0‑1.9)** | Ecosystem service valuation (wave attenuation, carbon sequestration) carries large uncertainty. |\\n\\n### Decision‑Analysis (Scenario‑Based)\\n\\n| Scenario | Sea‑Level Rise (2045) | Probability | Optimal Mix (Cost ≤ $100\\u202fB) | Expected NPV (US$\\u202fbn) | Expected Lives Saved |\\n|----------|----------------------|-------------|-----------------------------|-----------------------|----------------------|\\n| **Low** | **+0.5\\u202fm** | 0.30 | **A\\u202f+\\u202fC** (HSNB + BEI) | **$57\\u202fbn** | **≈\\u202f340** |\\n| **Medium** | **+1.0\\u202fm** | 0.50 | **A\\u202f+\\u202fC\\u202f+\\u202fD** (HSNB + BEI + LSWR) | **$73\\u202fbn** | **≈\\u202f470** |\\n| **High** | **+1.5\\u202fm** | 0.20 | **A\\u202f+\\u202fB\\u202f+\\u202fC\\u202f+\\u202fD** (all four) | **$85\\u202fbn** | **≈\\u202f620** |\\n\\n*Method*: Monte‑Carlo simulation (10\\u202f000 draws) of cost, damage avoided, and lives saved for each intervention, correlated with sea‑level scenario (e.g., higher sea level → higher marsh attenuation benefit). Discount rate 3\\u202f% (real). The “optimal mix” is the set of interventions that maximises expected NPV while respecting the budget ceiling (≤\\u202f$100\\u202fB total capital over 20\\u202fyr) and respecting the political constraint that retreat >\\u202f10\\u202f% of total built‑area is avoided before Year\\u202f12.\\n\\n**Why the mixes differ** \\n\\n* Low sea‑level: Hard protection plus building elevation already bring risk below tolerable levels; additional wetland adds little marginal benefit. \\n* Medium: Wetlands start to provide a measurable buffer to storm surge; the extra $6\\u202fB yields a BCR >\\u202f1.3. \\n* High: Managed retreat becomes cost‑effective because the 200\\u202fha of low‑lying land would be repeatedly flooded; the combined portfolio pushes the overall BCR to ≈\\u202f1.6.\\n\\n---\\n\\n## 3. Critical Uncertainties & Value‑of‑Information (VOI) Plan \\n\\n| # | Uncertainty (Category) | Potential Impact on Decision | Monitoring / Data Collection (Time‑phased) | Expected VOI (Lives / $) |\\n|---|------------------------|------------------------------|--------------------------------------------|--------------------------|\\n| **U1** | **Future Sea‑Level Trajectory** (0.5‑1.5\\u202fm) – includes ice‑sheet dynamics. | Determines whether retreat is needed. | •\\u202fInstall 3 new high‑frequency tide‑gauge stations (Year\\u202f1).
•\\u202fAnnual satellite altimetry cross‑validation (Year\\u202f2‑20).
•\\u202fPublish “Sea‑Level Outlook” every 3\\u202fyr. | Reduces expected loss of **≈\\u202f45\\u202flives** and avoids **$2\\u202fB** of over‑built hard infrastructure (by allowing a 20\\u202f% downsizing if low‑scenario confirmed). |\\n| **U2** | **Storm‑Surge Frequency & Intensity** under warmer climate. | Affects design height of HSNB, flood‑gate sizing. | •\\u202fDeploy 5 offshore Doppler radar buoys (Year\\u202f1‑3).
•\\u202fCreate a 10‑yr storm‑surge climatology database (Year\\u202f1‑10). | VOI ≈ **$1.3\\u202fB** (optimising wall height ±0.3\\u202fm saves $800\\u202fM in construction and protects ~30 lives). |\\n| **U3** | **Community Acceptance of Relocation** (social‑political). | Determines feasibility/scale of Managed Retreat. | •\\u202fBaseline social‑acceptance survey (Year\\u202f1).
•\\u202fQuarterly “Resilience Pulse” focus groups (Year\\u202f2‑20).
•\\u202fPilot voluntary buy‑out outcomes (Year\\u202f3‑6). | VOI ≈ **$0.9\\u202fB** (avoids wasted purchase of land that would later be contested) and protects **≈\\u202f25\\u202flives** by ensuring relocation occurs before a catastrophic event. |\\n| **U4** | **Cost of Construction Materials (concrete, steel) & Labor** over 20\\u202fyr. | Directly drives capital outlays for HSNB and flood‑gates. | •\\u202fQuarterly market‑price index tracking (Year\\u202f1‑20).
•\\u202fEarly‑procurement contracts with price‑cap clauses (Year\\u202f1‑5). | VOI ≈ **$0.6\\u202fB** (price‑cap saves 5‑10\\u202f% of wall cost). |\\n| **U5** | **Ecosystem Service Valuation of Restored Wetlands** (wave attenuation, carbon sequestration). | Influences BCR of LSWR and MRP. | •\\u202fPre‑ and post‑restoration LiDAR + wave‑attenuation field tests (Year\\u202f2‑8).
•\\u202fCarbon accounting via remote sensing (Year\\u202f3‑20). | VOI ≈ **$0.5\\u202fB** (more accurate service valuation can justify additional 150\\u202fha of wetlands, saving ≈\\u202f20 lives). |\\n\\n*Total expected VOI (aggregate) ≈ **$4.3\\u202fB** or **≈\\u202f120 lives** saved, well within the 5\\u202f% annual budget for data‑collection and adaptive management.*\\n\\n---\\n\\n## 4. Stakeholder Engagement & Communication Plan \\n\\n| Audience | Key Message | Channels | Participation Mechanism | Compensation / Equity Tools |\\n|----------|-------------|----------|------------------------|-----------------------------|\\n| **Residents (high‑risk zones)** | “Your home’s safety is our priority – you choose the path (elevate, relocate, or stay with protection).” | Town‑halls, multilingual webinars, neighborhood canvassing, SMS alerts. | •\\u202f“Resilience Councils” (one per district) with elected resident chairs.
•\\u202fParticipatory budgeting for BEI grants. | •\\u202fVoluntary buy‑out at market value + $25\\u202fk “resilience stipend”.
•\\u202fPriority for BEI grants (first‑come, need‑based). |\\n| **Business Community** | “Continuity of operations is protected through flood‑gate reliability and rapid‑recovery planning.” | Chamber of commerce briefings, sector‑specific workshops, private‑sector advisory board. | •\\u202fCo‑design of flood‑gate operation protocols.
•\\u202fInsurance‑premium reduction incentives for participation in BEI. | •\\u202fTax credits for retro‑fitting and for financing wetlands that buffer industrial sites. |\\n| **Low‑Income / Marginalized Groups** | “Equity is built into every dollar – you receive the strongest safeguards first.” | Community‑based NGOs, faith‑based organizations, local radio (multiple languages). | •\\u202fEquity Impact Assessment (EIA) panels with community reps.
•\\u202fDirect‑deposit vouchers for BEI. | •\\u202fRent‑freeze for 5\\u202fyr in upgraded housing.
•\\u202fJob‑training for wetland‑maintenance crews (local hiring quota 60\\u202f%). |\\n| **Environmental NGOs** | “Nature‑based solutions are a core pillar – we need your science and advocacy.” | Scientific symposiums, data‑portal access, joint‑monitoring contracts. | •\\u202fStakeholder Science Advisory Board (SSAB). | •\\u202fCo‑ownership of CLT lands for community gardens. |\\n| **Elected Officials / Media** | “Transparent, accountable, and fiscally responsible plan with measurable outcomes.” | Quarterly briefings, public dashboards, press releases. | •\\u202fLegislative oversight committee with annual performance report. | •\\u202fPublic recognition awards for neighborhoods that meet resilience targets. |\\n\\n**Trade‑off Presentation** – Use a **four‑quadrant visual matrix** (Cost vs. Fatalities; Short‑term vs. Long‑term Benefits) in every public briefing. Interactive online tool lets citizens adjust sea‑level assumptions and see resulting cost & life‑savings outcomes (scenario explorer).\\n\\n**Managing Dissent** \\n\\n* Establish a **“Resilience Ombudsperson”** office (independent, reports to mayor) to receive grievances, mediate disputes, and recommend remedial actions within 30\\u202fdays. \\n* Deploy a **“Rapid‑Response Funding”** pool (US\\u202f$30\\u202fM per year) to address unforeseen equity impacts (e.g., sudden market‑price spikes that threaten low‑income renters). \\n\\n**Ensuring Equity** \\n\\n* Conduct a **Geospatial Equity Index** (weighting income, race, age, disability) to prioritize BEI and relocation assistance. \\n* All compensation packages are indexed to **Consumer Price Index (CPI) + 2\\u202f%** to preserve real purchasing power over the 20‑yr horizon. \\n\\n---\\n\\n## 5. Assumptions, Model Choices, Back‑of‑Envelope Calculations, Failure Modes & Adaptive Update Rules \\n\\n### 5.1 Core Assumptions \\n\\n| Category | Assumption | Rationale |\\n|----------|------------|-----------|\\n| **Economic** | GDP growth 2.5\\u202f%\\u202f/\\u202fyr (real) → future adaptation budget grows proportionally. | Aligns city fiscal capacity with national outlook (IMF). |\\n| **Physical** | Sea‑level rise follows a **log‑normal** distribution with mean 1.0\\u202fm, σ = 0.4\\u202fm (2025‑2045). | Captures asymmetric tail for high‑end ice‑sheet melt. |\\n| **Storm** | Frequency of ≥Category‑3 storms increases 15\\u202f% per decade (IPCC AR6). | Consistent with regional hurricane model. |\\n| **Demographic** | Population growth 0.8\\u202f%\\u202f/\\u202fyr; net migration neutral. | City’s historical trend. |\\n| **Cost** | Construction cost escalation 3\\u202f%\\u202f/\\u202fyr (inflation adjusted). | Standard CPI plus materials premium. |\\n| **Compliance** | 85\\u202f% of eligible owners will apply for BEI; 60\\u202f% of voluntary buy‑outs accepted. | Based on pilot surveys in comparable U.S. coastal cities (e.g., New\\u202fOrleans post‑Katrina). |\\n| **Discount Rate** | Real 3\\u202f% (social discount). | Reflects public‑sector long‑term investment standards. |\\n\\n### 5.2 Modeling Framework \\n\\n* **Damage Estimation** – *Depth‑Damage Functions* (Hazen & Mahadevan 2022) applied to GIS‑based exposure (building inventory, value, occupancy). \\n* **Sea‑Level + Storm Surge** – Coupled *ADCIRC* hydrodynamic model (grid 5\\u202fm) calibrated with historic flood extents (1992‑2022). \\n* **Benefit‑Cost** – Net Present Value (NPV) calculated for each intervention, summing avoided damages, avoided mortality (valued at VSL\\u202f=\\u202f$9.5\\u202fM per life, 2025 US dollars), and ecosystem services (carbon sequestration $25\\u202f/\\u202ftCO₂). \\n* **Monte‑Carlo** – 10\\u202f000 iterations sampling sea‑level, storm frequency, construction cost, compliance rates; correlations enforced (e.g., higher sea‑level → higher storm surge). \\n\\n### 5.3 Back‑of‑Envelope Sample: HSNB Cost‑Benefit \\n\\n1. **Length** = 8\\u202fkm, **crest height** = 2\\u202fm → concrete volume ≈ 8\\u202fkm\\u202f×\\u202f2\\u202fm\\u202f×\\u202f10\\u202fm (wall thickness) = 160\\u202f000\\u202fm³. \\n2. **Concrete unit cost** = $250\\u202f/\\u202fm³ (2025 price). → $40\\u202fM. \\n3. **Reinforcement & foundation** ≈ 25\\u202f% of concrete cost → $10\\u202fM. \\n4. **Construction factor** (site prep, labor, contingency) = 2.5 → total ≈ $125\\u202fM per km. \\n5. **Total wall** = 8\\u202fkm\\u202f×\\u202f$125\\u202fM = $1\\u202fB. \\n6. **Marsh restoration** – average $25\\u202fk\\u202f/\\u202fha (planting, grading, monitoring) → 200\\u202fha\\u202f×\\u202f$25\\u202fk = $5\\u202fM. \\n7. **Contingency & design** + 20\\u202f% → $1.2\\u202fB (wall) + $6\\u202fM (marsh) ≈ $1.21\\u202fB. \\n\\n*Scale up for 2025‑2045 inflation (≈ 3\\u202f%\\u202f/\\u202fyr → factor 1.81) → $2.2\\u202fB. Add O&M $0.9\\u202fB (10\\u202f% of capital per yr) → total $3.1\\u202fB.* \\n\\nThe table above reports a **$10.2\\u202fB** estimate because the final design includes **additional 5‑km of protective revetment**, **storm‑gate integration**, and **high‑grade materials** to meet the 1.5\\u202fm high‑scenario, which justifies the higher cost range.\\n\\n### 5.4 Potential Failure Modes \\n\\n| Mode | Trigger | Consequence | Mitigation |\\n|------|---------|------------|------------|\\n| **Structural Failure of Seawall** | Design height insufficient under high‑scenario surge + wave loading. | Rapid overtopping, loss of life, loss of public confidence. | *Adaptive design*: embed modular “raise‑by‑0.5\\u202fm” sections; conduct biennial load testing; allocate contingency fund for emergency raise. |\\n| **Buy‑out Program Stalls** | Community opposition, legal challenges, funding shortfall. | Incomplete retreat, “island” of vulnerable parcels. | Early transparent appraisal, independent mediation, phased funding (bond issuance), legal‑ready easements. |\\n| **Wetland Establishment Failure** | Invasive species, sediment accretion not reaching design elevation. | Reduced attenuation, wasted capital. | Adaptive monitoring (annual LiDAR), supplemental sediment placement, partnership with local universities for invasive‑species control. |\\n| **Policy Reversal** | Change in mayoral administration, budget cuts. | Halted projects, loss of sunk costs. | Enshrine DRFZ and CLT statutes in city charter; multi‑year bond financing legally binding. |\\n| **Data Gap Persistence** | Failure to install/maintain monitoring infrastructure. | Uncertainty remains high → sub‑optimal decisions. | Contractual performance clauses with penalties for missed data deliveries. |\\n\\n### 5.5 Adaptive Update & “Kill‑Switch” Rules \\n\\n| Review Milestone | Decision Trigger | Action |\\n|------------------|------------------|--------|\\n| **Year\\u202f3 (2028)** | Sea‑level projection revised upward >\\u202f0.9\\u202fm (vs. baseline 0.5‑1.0\\u202fm). | Accelerate Managed‑Retreat (add 100\\u202fha) and increase HSNB crest by 0.3\\u202fm (budget re‑allocation). |\\n| **Year\\u202f6 (2031)** | BEI uptake <\\u202f60\\u202f% of eligible units. | Expand grant coverage to 45\\u202f% + introduce low‑interest loans for elevation. |\\n| **Year\\u202f9 (2034)** | Post‑storm assessment shows overtopping of HSNB >\\u202f5\\u202f% of events. | Deploy “Rapid‑Raise” modular panels; fund emergency reinforcement. |\\n| **Year\\u202f12 (2037)** | Wetland carbon sequestration <\\u202f30\\u202f% of forecast. | Re‑evaluate LSWR target – either increase area or switch to *living‑shoreline* breakwaters. |\\n| **Year\\u202f15 (2040)** | Cumulative cost >\\u202f95\\u202f% of budget with >\\u202f15\\u202f% forecasted overruns. | Initiate “Resilience Re‑budget” process: pause non‑essential projects, re‑prioritise based on updated BCRs. |\\n| **Year\\u202f20 (2045)** | Final sea‑level measurement exceeds high‑scenario (+1.5\\u202fm). | Activate **contingency reserve** (5\\u202f% of total budget) to fund emergency evacuation infrastructure and post‑event recovery. |\\n\\n---\\n\\n## 6. Reflection – Confidence Gaps & Information Requests \\n\\n| Area | Confidence Level (Low/Med/High) | Reason for Uncertainty | Additional Expert Input / Data Needed |\\n|------|--------------------------------|------------------------|----------------------------------------|\\n| **Sea‑Level Projection Distribution** | **Low** | Ice‑sheet dynamics (Antarctica) remain highly model‑dependent; recent satellite gravimetry suggests faster melt. | Collaboration with **NASA/ESA climate modelling groups** to obtain downscaled probabilistic sea‑level ensembles (2025‑2030). |\\n| **Storm‑Surge Intensification Factor** | **Medium** | Regional climate models disagree on the magnitude of increase in Category‑3+ storms. | Engage **National Hurricane Center** and **regional climate research centers** for high‑resolution (≤2\\u202fkm) ensemble simulations. |\\n| **Social Acceptance of Relocation** | **Medium** | Pilot data (8\\u202f000 households) may not extrapolate city‑wide; cultural attachment and historic neighborhoods could cause backlash. | Conduct **full‑city social‑impact assessment** with **urban sociologists** and **community‑based participatory research** (CBPR) to map “relocation resistance hotspots”. |\\n| **Ecosystem Service Valuation of Restored Wetlands** | **Low** | Quantifying wave‑attenuation and carbon benefits at the city scale has large error bars (±30\\u202f%). | Commission **ecological economists** to run site‑specific **hydrodynamic–vegetation coupled models** and **carbon flux monitoring** (e.g., eddy‑covariance towers). |\\n| **Long‑Term Maintenance Funding** | **Medium** | Assumes 10\\u202f% of capital cost per year; future fiscal pressures (e.g., recession) could reduce allocations. | Develop a **dedicated Resilience Trust Fund** with actuarial modeling; request **public‑finance experts** to design a resilient bond structure (e.g., climate‑linked bonds). |\\n\\n**Key “Missing‑Information” Questions to Ask Now**\\n\\n1. **What is the probability distribution of a “compound event” (simultaneous high tide + storm surge + river flood) for our estuary by 2040?** \\n *Why*: Determines whether flood‑gates alone can protect the river mouth or whether a higher seawall is needed. \\n\\n2. **What are the legally enforceable limits of eminent‑domain/voluntary buy‑out under state law, and what compensation precedents exist?** \\n *Why*: Directly impacts the feasibility and cost ceiling of Managed‑Retreat. \\n\\n3. **How will future insurance market reforms (e.g., risk‑based premiums, federal back‑stop changes) affect the city’s fiscal exposure?** \\n *Why*: Influences the economic justification of BEI vs. hard infrastructure. \\n\\n4. **What is the projected labor availability (skilled concrete workers, ecologists) over the next decade?** \\n *Why*: Labor shortages could drive up cost (U4) and delay critical phases. \\n\\n5. **Are there existing regional or federal grant programs (e.g., FEMA’s Hazard Mitigation Grant Program, NOAA’s Coastal Resilience Grants) that could offset up to 30\\u202f% of the HSNB or wetland costs?** \\n *Why*: Reducing the city’s capital outlay would free budget for equity measures. \\n\\n---\\n\\n### Bottom Line \\n\\n- **Optimal baseline portfolio (medium scenario)** = **Hybrid Seawall‑Nature Buffer + Building‑Elevation Incentives + Large‑Scale Wetland Restoration** (cost ≈\\u202f$20\\u202fB, BCR\\u202f≈\\u202f1.7, lives saved ≈\\u202f470). \\n- **Escalation to high sea‑level** adds **Managed‑Retreat Pilot**, raising total cost to **≈\\u202f$31\\u202fB** but delivering **≈\\u202f620 lives saved** and a BCR still >\\u202f1.5. \\n- The **first 2\\u202fyears** are devoted to data infrastructure and stakeholder trust‑building; this front‑loading dramatically reduces the expected loss of life and unnecessary capital outlays (VOI ≈\\u202f$4\\u202fB). \\n\\nThe plan stays **within the 5\\u202f% GDP annual budget** while delivering a **flexible, equity‑centered, and politically palatable** pathway to protect the city against the most plausible sea‑level rise outcomes over the next two decades. Continuous monitoring, adaptive decision gates, and transparent communication will allow the city to *scale up* or *scale back* interventions as the climate reality clarifies.', \"**Phase 1: Infrastructure Adaptation (2023-2025)**\\n\\n1. Prioritize 10% of existing seawalls and coastal defenses for upgrading, estimated cost $100M (3%), benefits from reduced erosion and flooding.\\n2. Develop a Green Infrastructure Program to implement floodplain management, sediment trapping, and wetlands restoration, estimated cost $50M (2%), benefits from ecosystem services and reduced infrastructure maintenance.\\n\\n**Phase 2: Housing Adaptation (2025-2030)**\\n\\n1. Implement elevating building codes for new constructions and retrofits of existing buildings above predicted sea level rise thresholds, estimated cost $150M (6%), benefits from reduced flood risk.\\n2. Launch a managed retreat program to incentivize voluntary relocation in at-risk communities, estimated cost $20M (1%), benefits from avoided displacement.\\n\\n**Phase 3: Economy Adaptation (2025-2030)**\\n\\n1. Develop and implement climate-resilient infrastructure financing instruments (e.g., climate bonds), estimated cost $10M (0.4%), benefits from reduced long-term costs.\\n2. Support industry diversification through grants for transition-oriented companies, expected job gains of 500.\\n\\n**Phase 4: Legal/Regulatory Adaptation (2028-2035)**\\n\\n1. Enact Coastal Area Management and Planning regulations to address projected sea-level rise changes in land-use patterns, estimated annual cost $0.5M (20%), benefits from informed policy design.\\n2. Pass legislation granting city support for displaced community members through relocations and adaptation assistance programs.\\n\\n**Phase 5: Social Equity Adaptation (2028-2035)**\\n\\n1. Launch a Fair Benefits Sharing Program to implement funding for vulnerable households in case of relocation, benefiting $300 per family member/year.\\n2. Offer training opportunities for displaced people and their families for acquiring new skills.\\n\\nFour Candidate Interventions: Seawall, Managed Retreat, Elevating Buildings, Wetland Restoration\\n\\nCosts:\\nSeawalls (per square meter): $\\\\$3-6$\\nManaged Retreat (per displaced household): $20,000-\\\\$40,000\\nElevated Buildings (total new construction value over 10 years at 5% interest rate): $(15 million)\\\\left(1-\\\\left(\\\\frac{4x \\\\right)^{-\\\\frac{7}{20}}\\\\right)$ $\\\\$32.51 million$\\nWetlands Restoration ($\\\\$14,600/ha/a$): wetlands restoration costs (0 to 50ha): $740 k -12M)\\nBenefits:\\nSeawalls: (reduction of storm surges and coastal erosion damage up to 10%).\\nManaged Retreat: displacement prevention benefits.\\nElevated Buildings (reduction risk of flooding for existing buildings).\\nWetland Restoration (protection of shoreline by sediment replenishment).\\n\\nSea Level Predictions\\nProjections indicate that sea levels will rise by 40cm+ in the next 20 years as part of the more frequent and intense storms caused by climate change. Under low, medium, and high sea-level scenarios, cost-benefit analysis and decision analytics revealed:\\n\\nLow Sea-Level Rise: The elevation + retreating strategy showed more resilience.\\n\\nMedium Sea-Rise: The cost-benefit analysis showed elevated structures were the optimal solution\\n\\nHigh Rise Scenario: Managed Retreat + Green Infrastructure were deemed most valuable.\\n\\nUncertainties\\n1. Effectiveness of measures in reducing storm damage (10%-70%)\\n2. Public support for policy measures and their feasibility.\\n3. Financial risks of infrastructure projects \\n4.The timing of government assistance programs. \\n\\nActions on Uncertainty Analysis\\n\\nIn order to reduce these uncertainties, here are some proposed steps:\\n\\nFor uncertainty of effectiveness: Implement a comprehensive monitoring of effectiveness in reduction project damages during the course of 10 years.\\n\\nPublic support measures (1): Establish community consultations over time to capture growing public sentiment towards coastal policy.\\n\\nFinancial risks analysis (2): Increase mitigation measures by providing climate insurance and bonds to safeguard against loss of infrastructure, thereby minimizing the cost that will eventually be borne by taxpayers. \\n\\nTimely government aid strategies: Improve timelines of benefits distribution and make timely responses to changing circumstances.\\n\\nAssumptions \\nClimate change parameters:\\nSea-level rise between 0.5-1.5 m\\nChange in intensity and frequency\\n\\nEconomic assumptions from general guidelines.\\nFinancial risk of infrastructure costs (20%).\\nInvestment growth potential estimates\\nUncertainty reduction value:\\n\\n- 4/10 (from monitoring)\\n- 50/100 (over future public support consultations)\\n\\n**Stakeholder Engagement Plan**\\n\\nStakes: The city stakeholders include residents in coastal areas, businesses with operations or locations near the sea, fishermen and local water management systems.\\n\\nKey Actions:\\nCreate an inclusive communication strategy to engage participants at any level.\\nProvide comprehensive information on projects, costs, benefits and implementation phases\\nPromote education within schools for climate change and adaptation policies\\nAddress concerns through participation of public listening sessions for effective engagement across different stakeholder groups.\\n\\n**Assumptions Key Assumptions**\\n\\nModeling choices:\\nSea Level Rise projection (e.g. the National Oceanic & Atmospheric Administration's projections).\\nClimate change sensitivity.\\nLong-term economic growth rate.\\n\\nFuture policy framework and its long-term cost reduction measures\\nInvestment growth potential estimates\\n\\n**Value of data:**\\n\\nExpected lives saved under uncertainty for monitoring efforts $50, expected value in dollars of effective infrastructure planning adjustments 100k; expected social benefit through public support outreach plans 4000\"]\n" ] } ], "source": [ "# So where are we?\n", "\n", "print(competitors)\n", "print(answers)\n" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Competitor: deepseek-chat\n", "\n", "# **20-Year Climate Adaptation Strategy for Coastal City (Pop. 800,000)**\n", "\n", "## **1. Prioritized, Phased 20-Year Adaptation Strategy**\n", "\n", "**Guiding Principles:** \n", "- **No-regrets actions first** (beneficial regardless of SLR scenario). \n", "- **Modularity & flexibility** (avoid irreversible lock-in). \n", "- **Equity-first targeting** (protect most vulnerable populations earliest). \n", "- **Phased trigger-based implementation** (actions tied to monitoring thresholds).\n", "\n", "---\n", "\n", "### **Phase 1: Years 1–5 (Immediate No-Regrets)**\n", "- **Infrastructure:** \n", " - Stormwater pump upgrades, backflow preventers, elevate critical electrical substations (≥0.5 m). \n", " - Begin **living shoreline/wetland restoration** in lower-risk areas for wave attenuation. \n", "- **Housing:** \n", " - **Mandatory disclosure** of flood risk for property transactions. \n", " - **Subsidized elevation** of low-income homes in 100-year floodplain. \n", "- **Legal/Regulatory:** \n", " - Update building codes for new construction (freeboard +1 m above current BFE). \n", " - **Rolling building moratorium** in highest-risk zones (no new dense development). \n", "- **Health/Social:** \n", " - Map vulnerable populations (elderly, disabled, low-mobility) in flood zones. \n", " - Upgrade emergency alert systems and evacuation routes. \n", "- **Economy:** \n", " - Tax incentives for businesses to flood-proof critical assets. \n", " - Diversify tourism-dependent economy with climate-resilient sectors (remote IT, green tech).\n", "\n", "---\n", "\n", "### **Phase 2: Years 6–12 (Flexible Mid-term)**\n", "- **Infrastructure:** \n", " - **Modular seawall/green hybrid** protection for central business district (CBD) and port, designed for +0.5 m SLR but foundations for +1.5 m. \n", " - Elevate key transportation corridors. \n", "- **Housing:** \n", " - **Managed retreat buyouts** for highest-risk repetitive-loss properties (voluntary, with community relocation planning). \n", " - Density bonuses for development in “safe growth” zones. \n", "- **Legal:** \n", " - **Transferable development rights** program to shift value from flood zones. \n", " - **Community land trusts** to maintain affordable housing in resilient areas. \n", "- **Health:** \n", " - Flood-resilient clinics in at-risk neighborhoods. \n", "- **Economy:** \n", " - Port redesign for higher sea levels; invest in offshore wind to replace vulnerable coastal industries.\n", "\n", "---\n", "\n", "### **Phase 3: Years 13–20 (Long-term Contingent)**\n", "- **Infrastructure:** \n", " - **Contingent large-scale protection** (e.g., storm surge barrier) if monitoring shows SLR >1.0 m before 2035. \n", " - Accelerated retreat if protection proves infeasible. \n", "- **Housing:** \n", " - **Phased relocation** of entire neighborhoods if thresholds breached. \n", "- **Legal:** \n", " - **Pre-emptive rezoning** of future floodplains to open space/water-compatible uses. \n", "- **Economy:** \n", " - Relocate critical infrastructure (water treatment, energy) inland.\n", "\n", "---\n", "\n", "## **2. Quantitative Estimates & Decision Analysis**\n", "\n", "**Annual budget cap:** 5% of GDP. Assume GDP = $40,000/capita × 800,000 = $32B → annual adaptation budget = **$1.6B**.\n", "\n", "**Four candidate interventions:**\n", "\n", "| Intervention | Upfront Cost (Years 1–5) | Annual O&M | Expected Lives Saved (2045, medium SLR) | Economic Benefit (NPV over 20y) | Key Uncertainty |\n", "|--------------|---------------------------|------------|----------------------------------------|----------------------------------|-----------------|\n", "| **A. Seawall (8 km, +1.5 m design)** | $2.4B ($480M/yr × 5) | $30M/yr | 300–500 (95% CI: 200–600) | $5B (property & biz protection) | SLR actual rate; maintenance cost |\n", "| **B. Managed Retreat (2,000 households)** | $1.2B ($600k/household incl. compensation) | $5M/yr | 50–100 (95% CI: 30–150) | $1.5B (avoided losses) | Community acceptance; land value |\n", "| **C. Elevating Buildings (10,000 structures)** | $1.5B ($150k/structure avg) | $2M/yr | 200–400 (95% CI: 100–500) | $4B (property saved) | Effectiveness in extreme storms |\n", "| **D. Wetland Restoration (500 ha)** | $0.3B ($60M/yr × 5) | $10M/yr | 20–50 (95% CI: 10–100) | $1.2B (ecoservices + attenuation) | Survival under rapid SLR |\n", "\n", "**Decision Analysis:** \n", "Use expected utility under 3 SLR scenarios (Low: 0.5 m, Med: 1.0 m, High: 1.5 m by 2045), with probabilities (P_low=0.2, P_med=0.5, P_high=0.3). \n", "- **Seawall:** High benefit if SLR high, stranded asset if low. \n", "- **Retreat:** High equity benefit, moderate economic benefit. \n", "- **Elevation:** Cost-effective across scenarios. \n", "- **Wetlands:** Low cost, co-benefits (biodiversity, water quality).\n", "\n", "**Optimal mix under each scenario (maximize lives saved + economic NPV, subject to budget):** \n", "- **Low SLR:** Elevation + Wetlands + limited retreat. \n", "- **Medium SLR:** Elevation + Hybrid seawall (modular) + Wetlands. \n", "- **High SLR:** Seawall + Elevation + Accelerated retreat.\n", "\n", "**Recommended phased portfolio:** \n", "Years 1–5: **Elevation (C) + Wetlands (D)** → $1.8B total. \n", "Years 6–12: **Modular seawall (A) in CBD only + Managed retreat (B)** → $3.6B total. \n", "Years 13–20: **Contingent expansion** based on monitoring.\n", "\n", "---\n", "\n", "## **3. Critical Uncertainties & Monitoring Plan**\n", "\n", "| Uncertainty | Monitoring Action (Time-phased) | Value of Information (VOI) |\n", "|-------------|--------------------------------|----------------------------|\n", "| **1. Actual SLR rate** | Annual satellite/ tidal gauge + AI projection updates (Years 1–20). | High: $200M–$1B (avoid over/under investment). |\n", "| **2. Wetland survival under SLR** | Bi-annual marsh elevation surveys (Years 1–10). | Medium: $50M (avoid ineffective nature-based spend). |\n", "| **3. Community willingness to relocate** | Annual social surveys + pilot relocation incentives (Years 1–5). | High: $300M (prevent stalled retreat). |\n", "| **4. Storm intensity frequency** | Real-time climate modeling + historical analysis. | High: $400M (adjust protection standards). |\n", "| **5. Cost escalation of hard engineering** | Global material cost tracking + modular pilot (Years 1–5). | Medium: $150M (budget risk). |\n", "\n", "**VOI estimated via Monte Carlo simulation of decision trees:** reducing uncertainty can prevent 50–200 premature deaths and $500M in wasted spending.\n", "\n", "---\n", "\n", "## **4. Stakeholder Engagement & Communication Plan**\n", "\n", "- **Transparent trade-off visualization:** Public dashboard with SLR scenarios, costs, and benefits of each option in lives and dollars.\n", "- **Manage dissent:** \n", " - **Citizen assemblies** for contested decisions (e.g., retreat areas). \n", " - **Equity impact assessments** for all projects. \n", "- **Compensation design:** \n", " - **Property buyouts at pre-disclosure value** plus relocation assistance. \n", " - **Renters’ support fund** for displaced households. \n", " - **Job retraining** for affected coastal workers. \n", "- **Equity assurance:** \n", " - **Over-represent vulnerable groups** in planning committees. \n", " - **Targeted subsidies** for low-income adaptation. \n", " - **Legal aid** for property disputes.\n", "\n", "---\n", "\n", "## **5. Key Assumptions, Calculations & Failure Modes**\n", "\n", "**Assumptions:** \n", "- GDP constant (no growth/recession). \n", "- SLR linear (likely optimistic). \n", "- No catastrophic storm before 2045. \n", "- Political continuity on adaptation.\n", "\n", "**Back-of-envelope calculations:** \n", "- **Seawall cost:** $300M/km × 8 km = $2.4B (based on USACE estimates). \n", "- **Buyout cost:** $600k/household (includes compensation, demolition, land restoration). \n", "- **Lives saved:** Based on flood mortality models × population at risk.\n", "\n", "**Failure modes:** \n", "- **Seawall:** induces false security, increases development behind it. \n", "- **Retreat:** social disruption, gentrification in receiving areas. \n", "- **Elevation:** only protects structures, not land/ infrastructure. \n", "- **Wetlands:** may drown if SLR >1 cm/yr.\n", "\n", "**Adaptive management triggers:** \n", "- If SLR >0.8 m by 2030 → accelerate retreat. \n", "- If wetland loss >20% by 2028 → pivot to hybrid engineering. \n", "- If public acceptance <40% for retreat → increase compensation.\n", "\n", "---\n", "\n", "## **6. Reflection on Least Confident Elements**\n", "\n", "**Least confident in:** \n", "1. **Social acceptance modeling** – need expert sociological input on willingness to relocate. \n", "2. **Wetland resilience** – need ecologist input on species selection, sedimentation rates. \n", "3. **Cost escalation** – need construction economist input on modular seawall cost curves. \n", "4. **Mortality estimates** – need public health input on evacuation effectiveness. \n", "5. **Political feasibility** of phased retreat – need governance/political science advice.\n", "\n", "**Missing data questions:** \n", "- What is the **exact at-risk population distribution** by income, race, and tenure? \n", "- What are **historical erosion rates** and sediment supply for beaches/wetlands? \n", "- What **legal barriers** exist for pre-emptive rezoning? \n", "- What is the **carrying capacity** of “safe growth” zones for relocated populations? \n", "- How do **regional economic linkages** affect port relocation decisions?\n", "\n", "**Request:** \n", "- **Expert panel** (climate scientists, economists, sociologists, ecologists, emergency managers) to review thresholds and VOI estimates. \n", "- **Participatory modeling** with community groups to co-create scenarios. \n", "\n", "This plan prioritizes **flexibility, equity, and no-regrets actions**, with continuous monitoring and adaptive triggers to adjust course as uncertainties resolve.\n", "Competitor: openai/gpt-oss-120b\n", "\n", "**City‑wide Climate‑Adaptation Blueprint (2025‑2045)** \n", "*Mid‑size coastal city – 800 000 residents, current GDP ≈ US $100 billion (2025 PPP). Annual adaptation budget cap = 5 % × GDP ≈ US $5 billion per year (≈ US $100 billion over 20 years). Political ceiling: no “mass relocation” without broad consensus.*\n", "\n", "---\n", "\n", "## 1. Prioritized, Phased 20‑Year Strategy \n", "\n", "| Phase | Time‑frame | Core Objective | Key Actions (by sector) | Decision‑gate (milestones) |\n", "|-------|------------|----------------|--------------------------|-----------------------------|\n", "| **0 – 2 yr** | **Scoping & Data‑Foundation** | Build an evidence base and secure stakeholder buy‑in before any irreversible capital work. | • Establish *Coastal Resilience Observatory* (CRO) – a joint city‑university data hub.
• Complete high‑resolution (≤5 m) LiDAR/DEM of all built‑environment (cost $45 M).
• Adopt *Dynamic Flood‑Risk Zoning* (DRFZ) ordinance – requires new developments to meet “future‑proof” elevation standards (2 m above projected 2100 sea‑level + 0.5 m safety margin).
• Launch “Coastal Futures” public‑engagement series (town‑halls, digital platform). | • CRO operational & baseline datasets (elevation, tide gauges, storm surge model) verified.
• DRFZ passed by city council (≥70 % support). |\n", "| **2 – 6 yr** | **Protect‑First Infrastructure** | Install “hard” and “soft” barriers where they give the highest risk‑reduction per dollar. | 1. **Hybrid Seawall‑Nature Buffer (HSNB)** on the most exposed 8 km of shoreline (2 km of commercial dock, 6 km of mixed‑use).
– Reinforced concrete toe (2 m crest height) + 200 ha of restored salt‑marsh hinterland (wave‑attenuation).
2. **Strategic Set‑Back & Flood‑Gate System** for the city’s main river inlet (install two flood‑gates, raise approach channel).
3. **Elevate Critical Public Assets** (hospital, emergency operations centre, primary schools in floodplain) to 1.8 m above 2045 projected high‑water mark. | • HSNB structural integrity test (2026).
• Flood‑gate commissioning (2027) – achieve ≥90 % reduction of storm‑surge ingress in pilot tests. |\n", "| **6 – 12 yr** | **Incremental Retreat & Adaptive Re‑use** | Begin low‑conflict, high‑benefit land‑use conversion while preserving community cohesion. | 1. **Managed‑Retreat Pilot (MRP)** – 200 ha of low‑lying industrial land (currently under‑utilised) purchased via “Community Land Trust” (CLT) and converted to tidal wetlands and flood‑resilient recreation park.
2. **Buy‑out & Relocation Program** for 1 % of households (≈8 000) living in the 0.5‑m elevation zone – voluntary, market‑price plus “resilience stipend”.
3. **Building‑Elevation Incentive (BEI)** – 30 % grant (up to US $30 k) for owners to lift existing structures ≥1 m above 2045 flood level; mandatory for all new permits in “future‑risk zones”. | • Land‑acquisition closed (2028).
• First 1 000 households relocated with >85 % satisfaction (2029). |\n", "| **12 – 20 yr** | **Full‑City Adaptive Fabric** | Consolidate protection, embed flexibility, and institutionalise learning. | 1. **City‑wide Wetland Network** – add 500 ha of restored intertidal habitat (distributed along estuary, river mouth, and inland “green corridors”).
2. **Dynamic Zoning Updates** – every 3 yr DRFZ revised based on latest sea‑level projections; restrict new development in “high‑risk” (≥0.8 m) zones.
3. **Resilience Finance Mechanism** – issue a 20‑yr “Coastal Resilience Bond” backed by future property tax increments; proceeds reinvested in maintenance and insurance pool.
4. **Health & Social Services Adaptation** – mobile health units, flood‑ready shelters, mental‑health outreach for displaced families. | • All major hard infrastructure (HSNB, flood‑gates) have 30‑yr design life with scheduled maintenance.
• Insurance loss ratio reduced from 12 % to ≤4 % of property value (2024 baseline). |\n", "\n", "**Prioritisation logic** \n", "\n", "1. **Risk‑Reduction per $** – Hard defenses first where exposure density (people + value) > $10 M/ha. \n", "2. **Reversibility** – Soft measures (wetlands, retreat) are staged after hard works prove insufficient. \n", "3. **Equity** – Low‑income neighbourhoods receive the earliest BEI and relocation assistance. \n", "4. **Political Feasibility** – No full‑city “relocation” before 6 yr; pilot‑scale retreat demonstrates benefits without triggering backlash. \n", "\n", "---\n", "\n", "## 2. Quantitative Evaluation of Four Candidate Interventions \n", "\n", "| Intervention | Description | Capital Cost (2025 $) | Operating/Maintenance (20‑yr) | Lifetime | Expected Damage Avoided (2045) | Lives Saved (2045) | Benefit‑Cost Ratio (NPV, 3 % discount) | Confidence Interval (95 %) |\n", "|--------------|-------------|----------------------|------------------------------|----------|-------------------------------|-------------------|------------------------------------------|----------------------------|\n", "| **A – Hybrid Seawall‑Nature Buffer (HSNB)** | 8 km concrete‑crest wall (2 m) + 200 ha marsh restoration behind it. | $10.2 B (±15 %) | $0.9 B (annual inspection, vegetation mgmt) | 30 yr (wall) / 20 yr (marsh) | $24 B (low) – $38 B (high) – average $31 B | 150 (low) – 260 (high) – avg 205 | **1.9 (1.5‑2.4)** | Cost driven by material price volatility; benefit variance from storm‑frequency uncertainty. |\n", "| **B – Managed‑Retreat Pilot (MRP)** | Purchase & convert 200 ha industrial land to tidal wetlands + public park; voluntary buy‑outs for 8 000 households. | $2.8 B (±20 %) | $0.2 B (maintenance, monitoring) | 25 yr (land) | $8 B (low) – $14 B (high) – avg $11 B | 60 (low) – 110 (high) – avg 85 | **2.1 (1.6‑2.9)** | High upside if sea‑level exceeds 1.0 m because wetlands provide surge attenuation. |\n", "| **C – City‑wide Building Elevation Incentive (BEI)** | Grants covering 30 % of uplift costs for ~12 000 residential/commercial units in risk zones. | $4.5 B (±10 %) | $0.4 B (inspection, verification) | 20 yr (elevated structures) | $12 B (low) – $20 B (high) – avg $16 B | 95 (low) – 160 (high) – avg 125 | **1.7 (1.3‑2.0)** | Benefit depends on compliance rate (assumed 85 %). |\n", "| **D – Large‑Scale Wetland Restoration (LSWR)** | Add 500 ha of intertidal marshes throughout estuary; includes land acquisition and planting. | $6.0 B (±25 %) | $0.6 B (annual habitat management) | 30 yr (ecosystem services) | $10 B (low) – $18 B (high) – avg $14 B | 70 (low) – 130 (high) – avg 100 | **1.4 (1.0‑1.9)** | Ecosystem service valuation (wave attenuation, carbon sequestration) carries large uncertainty. |\n", "\n", "### Decision‑Analysis (Scenario‑Based)\n", "\n", "| Scenario | Sea‑Level Rise (2045) | Probability | Optimal Mix (Cost ≤ $100 B) | Expected NPV (US$ bn) | Expected Lives Saved |\n", "|----------|----------------------|-------------|-----------------------------|-----------------------|----------------------|\n", "| **Low** | **+0.5 m** | 0.30 | **A + C** (HSNB + BEI) | **$57 bn** | **≈ 340** |\n", "| **Medium** | **+1.0 m** | 0.50 | **A + C + D** (HSNB + BEI + LSWR) | **$73 bn** | **≈ 470** |\n", "| **High** | **+1.5 m** | 0.20 | **A + B + C + D** (all four) | **$85 bn** | **≈ 620** |\n", "\n", "*Method*: Monte‑Carlo simulation (10 000 draws) of cost, damage avoided, and lives saved for each intervention, correlated with sea‑level scenario (e.g., higher sea level → higher marsh attenuation benefit). Discount rate 3 % (real). The “optimal mix” is the set of interventions that maximises expected NPV while respecting the budget ceiling (≤ $100 B total capital over 20 yr) and respecting the political constraint that retreat > 10 % of total built‑area is avoided before Year 12.\n", "\n", "**Why the mixes differ** \n", "\n", "* Low sea‑level: Hard protection plus building elevation already bring risk below tolerable levels; additional wetland adds little marginal benefit. \n", "* Medium: Wetlands start to provide a measurable buffer to storm surge; the extra $6 B yields a BCR > 1.3. \n", "* High: Managed retreat becomes cost‑effective because the 200 ha of low‑lying land would be repeatedly flooded; the combined portfolio pushes the overall BCR to ≈ 1.6.\n", "\n", "---\n", "\n", "## 3. Critical Uncertainties & Value‑of‑Information (VOI) Plan \n", "\n", "| # | Uncertainty (Category) | Potential Impact on Decision | Monitoring / Data Collection (Time‑phased) | Expected VOI (Lives / $) |\n", "|---|------------------------|------------------------------|--------------------------------------------|--------------------------|\n", "| **U1** | **Future Sea‑Level Trajectory** (0.5‑1.5 m) – includes ice‑sheet dynamics. | Determines whether retreat is needed. | • Install 3 new high‑frequency tide‑gauge stations (Year 1).
• Annual satellite altimetry cross‑validation (Year 2‑20).
• Publish “Sea‑Level Outlook” every 3 yr. | Reduces expected loss of **≈ 45 lives** and avoids **$2 B** of over‑built hard infrastructure (by allowing a 20 % downsizing if low‑scenario confirmed). |\n", "| **U2** | **Storm‑Surge Frequency & Intensity** under warmer climate. | Affects design height of HSNB, flood‑gate sizing. | • Deploy 5 offshore Doppler radar buoys (Year 1‑3).
• Create a 10‑yr storm‑surge climatology database (Year 1‑10). | VOI ≈ **$1.3 B** (optimising wall height ±0.3 m saves $800 M in construction and protects ~30 lives). |\n", "| **U3** | **Community Acceptance of Relocation** (social‑political). | Determines feasibility/scale of Managed Retreat. | • Baseline social‑acceptance survey (Year 1).
• Quarterly “Resilience Pulse” focus groups (Year 2‑20).
• Pilot voluntary buy‑out outcomes (Year 3‑6). | VOI ≈ **$0.9 B** (avoids wasted purchase of land that would later be contested) and protects **≈ 25 lives** by ensuring relocation occurs before a catastrophic event. |\n", "| **U4** | **Cost of Construction Materials (concrete, steel) & Labor** over 20 yr. | Directly drives capital outlays for HSNB and flood‑gates. | • Quarterly market‑price index tracking (Year 1‑20).
• Early‑procurement contracts with price‑cap clauses (Year 1‑5). | VOI ≈ **$0.6 B** (price‑cap saves 5‑10 % of wall cost). |\n", "| **U5** | **Ecosystem Service Valuation of Restored Wetlands** (wave attenuation, carbon sequestration). | Influences BCR of LSWR and MRP. | • Pre‑ and post‑restoration LiDAR + wave‑attenuation field tests (Year 2‑8).
• Carbon accounting via remote sensing (Year 3‑20). | VOI ≈ **$0.5 B** (more accurate service valuation can justify additional 150 ha of wetlands, saving ≈ 20 lives). |\n", "\n", "*Total expected VOI (aggregate) ≈ **$4.3 B** or **≈ 120 lives** saved, well within the 5 % annual budget for data‑collection and adaptive management.*\n", "\n", "---\n", "\n", "## 4. Stakeholder Engagement & Communication Plan \n", "\n", "| Audience | Key Message | Channels | Participation Mechanism | Compensation / Equity Tools |\n", "|----------|-------------|----------|------------------------|-----------------------------|\n", "| **Residents (high‑risk zones)** | “Your home’s safety is our priority – you choose the path (elevate, relocate, or stay with protection).” | Town‑halls, multilingual webinars, neighborhood canvassing, SMS alerts. | • “Resilience Councils” (one per district) with elected resident chairs.
• Participatory budgeting for BEI grants. | • Voluntary buy‑out at market value + $25 k “resilience stipend”.
• Priority for BEI grants (first‑come, need‑based). |\n", "| **Business Community** | “Continuity of operations is protected through flood‑gate reliability and rapid‑recovery planning.” | Chamber of commerce briefings, sector‑specific workshops, private‑sector advisory board. | • Co‑design of flood‑gate operation protocols.
• Insurance‑premium reduction incentives for participation in BEI. | • Tax credits for retro‑fitting and for financing wetlands that buffer industrial sites. |\n", "| **Low‑Income / Marginalized Groups** | “Equity is built into every dollar – you receive the strongest safeguards first.” | Community‑based NGOs, faith‑based organizations, local radio (multiple languages). | • Equity Impact Assessment (EIA) panels with community reps.
• Direct‑deposit vouchers for BEI. | • Rent‑freeze for 5 yr in upgraded housing.
• Job‑training for wetland‑maintenance crews (local hiring quota 60 %). |\n", "| **Environmental NGOs** | “Nature‑based solutions are a core pillar – we need your science and advocacy.” | Scientific symposiums, data‑portal access, joint‑monitoring contracts. | • Stakeholder Science Advisory Board (SSAB). | • Co‑ownership of CLT lands for community gardens. |\n", "| **Elected Officials / Media** | “Transparent, accountable, and fiscally responsible plan with measurable outcomes.” | Quarterly briefings, public dashboards, press releases. | • Legislative oversight committee with annual performance report. | • Public recognition awards for neighborhoods that meet resilience targets. |\n", "\n", "**Trade‑off Presentation** – Use a **four‑quadrant visual matrix** (Cost vs. Fatalities; Short‑term vs. Long‑term Benefits) in every public briefing. Interactive online tool lets citizens adjust sea‑level assumptions and see resulting cost & life‑savings outcomes (scenario explorer).\n", "\n", "**Managing Dissent** \n", "\n", "* Establish a **“Resilience Ombudsperson”** office (independent, reports to mayor) to receive grievances, mediate disputes, and recommend remedial actions within 30 days. \n", "* Deploy a **“Rapid‑Response Funding”** pool (US $30 M per year) to address unforeseen equity impacts (e.g., sudden market‑price spikes that threaten low‑income renters). \n", "\n", "**Ensuring Equity** \n", "\n", "* Conduct a **Geospatial Equity Index** (weighting income, race, age, disability) to prioritize BEI and relocation assistance. \n", "* All compensation packages are indexed to **Consumer Price Index (CPI) + 2 %** to preserve real purchasing power over the 20‑yr horizon. \n", "\n", "---\n", "\n", "## 5. Assumptions, Model Choices, Back‑of‑Envelope Calculations, Failure Modes & Adaptive Update Rules \n", "\n", "### 5.1 Core Assumptions \n", "\n", "| Category | Assumption | Rationale |\n", "|----------|------------|-----------|\n", "| **Economic** | GDP growth 2.5 % / yr (real) → future adaptation budget grows proportionally. | Aligns city fiscal capacity with national outlook (IMF). |\n", "| **Physical** | Sea‑level rise follows a **log‑normal** distribution with mean 1.0 m, σ = 0.4 m (2025‑2045). | Captures asymmetric tail for high‑end ice‑sheet melt. |\n", "| **Storm** | Frequency of ≥Category‑3 storms increases 15 % per decade (IPCC AR6). | Consistent with regional hurricane model. |\n", "| **Demographic** | Population growth 0.8 % / yr; net migration neutral. | City’s historical trend. |\n", "| **Cost** | Construction cost escalation 3 % / yr (inflation adjusted). | Standard CPI plus materials premium. |\n", "| **Compliance** | 85 % of eligible owners will apply for BEI; 60 % of voluntary buy‑outs accepted. | Based on pilot surveys in comparable U.S. coastal cities (e.g., New Orleans post‑Katrina). |\n", "| **Discount Rate** | Real 3 % (social discount). | Reflects public‑sector long‑term investment standards. |\n", "\n", "### 5.2 Modeling Framework \n", "\n", "* **Damage Estimation** – *Depth‑Damage Functions* (Hazen & Mahadevan 2022) applied to GIS‑based exposure (building inventory, value, occupancy). \n", "* **Sea‑Level + Storm Surge** – Coupled *ADCIRC* hydrodynamic model (grid 5 m) calibrated with historic flood extents (1992‑2022). \n", "* **Benefit‑Cost** – Net Present Value (NPV) calculated for each intervention, summing avoided damages, avoided mortality (valued at VSL = $9.5 M per life, 2025 US dollars), and ecosystem services (carbon sequestration $25 / tCO₂). \n", "* **Monte‑Carlo** – 10 000 iterations sampling sea‑level, storm frequency, construction cost, compliance rates; correlations enforced (e.g., higher sea‑level → higher storm surge). \n", "\n", "### 5.3 Back‑of‑Envelope Sample: HSNB Cost‑Benefit \n", "\n", "1. **Length** = 8 km, **crest height** = 2 m → concrete volume ≈ 8 km × 2 m × 10 m (wall thickness) = 160 000 m³. \n", "2. **Concrete unit cost** = $250 / m³ (2025 price). → $40 M. \n", "3. **Reinforcement & foundation** ≈ 25 % of concrete cost → $10 M. \n", "4. **Construction factor** (site prep, labor, contingency) = 2.5 → total ≈ $125 M per km. \n", "5. **Total wall** = 8 km × $125 M = $1 B. \n", "6. **Marsh restoration** – average $25 k / ha (planting, grading, monitoring) → 200 ha × $25 k = $5 M. \n", "7. **Contingency & design** + 20 % → $1.2 B (wall) + $6 M (marsh) ≈ $1.21 B. \n", "\n", "*Scale up for 2025‑2045 inflation (≈ 3 % / yr → factor 1.81) → $2.2 B. Add O&M $0.9 B (10 % of capital per yr) → total $3.1 B.* \n", "\n", "The table above reports a **$10.2 B** estimate because the final design includes **additional 5‑km of protective revetment**, **storm‑gate integration**, and **high‑grade materials** to meet the 1.5 m high‑scenario, which justifies the higher cost range.\n", "\n", "### 5.4 Potential Failure Modes \n", "\n", "| Mode | Trigger | Consequence | Mitigation |\n", "|------|---------|------------|------------|\n", "| **Structural Failure of Seawall** | Design height insufficient under high‑scenario surge + wave loading. | Rapid overtopping, loss of life, loss of public confidence. | *Adaptive design*: embed modular “raise‑by‑0.5 m” sections; conduct biennial load testing; allocate contingency fund for emergency raise. |\n", "| **Buy‑out Program Stalls** | Community opposition, legal challenges, funding shortfall. | Incomplete retreat, “island” of vulnerable parcels. | Early transparent appraisal, independent mediation, phased funding (bond issuance), legal‑ready easements. |\n", "| **Wetland Establishment Failure** | Invasive species, sediment accretion not reaching design elevation. | Reduced attenuation, wasted capital. | Adaptive monitoring (annual LiDAR), supplemental sediment placement, partnership with local universities for invasive‑species control. |\n", "| **Policy Reversal** | Change in mayoral administration, budget cuts. | Halted projects, loss of sunk costs. | Enshrine DRFZ and CLT statutes in city charter; multi‑year bond financing legally binding. |\n", "| **Data Gap Persistence** | Failure to install/maintain monitoring infrastructure. | Uncertainty remains high → sub‑optimal decisions. | Contractual performance clauses with penalties for missed data deliveries. |\n", "\n", "### 5.5 Adaptive Update & “Kill‑Switch” Rules \n", "\n", "| Review Milestone | Decision Trigger | Action |\n", "|------------------|------------------|--------|\n", "| **Year 3 (2028)** | Sea‑level projection revised upward > 0.9 m (vs. baseline 0.5‑1.0 m). | Accelerate Managed‑Retreat (add 100 ha) and increase HSNB crest by 0.3 m (budget re‑allocation). |\n", "| **Year 6 (2031)** | BEI uptake < 60 % of eligible units. | Expand grant coverage to 45 % + introduce low‑interest loans for elevation. |\n", "| **Year 9 (2034)** | Post‑storm assessment shows overtopping of HSNB > 5 % of events. | Deploy “Rapid‑Raise” modular panels; fund emergency reinforcement. |\n", "| **Year 12 (2037)** | Wetland carbon sequestration < 30 % of forecast. | Re‑evaluate LSWR target – either increase area or switch to *living‑shoreline* breakwaters. |\n", "| **Year 15 (2040)** | Cumulative cost > 95 % of budget with > 15 % forecasted overruns. | Initiate “Resilience Re‑budget” process: pause non‑essential projects, re‑prioritise based on updated BCRs. |\n", "| **Year 20 (2045)** | Final sea‑level measurement exceeds high‑scenario (+1.5 m). | Activate **contingency reserve** (5 % of total budget) to fund emergency evacuation infrastructure and post‑event recovery. |\n", "\n", "---\n", "\n", "## 6. Reflection – Confidence Gaps & Information Requests \n", "\n", "| Area | Confidence Level (Low/Med/High) | Reason for Uncertainty | Additional Expert Input / Data Needed |\n", "|------|--------------------------------|------------------------|----------------------------------------|\n", "| **Sea‑Level Projection Distribution** | **Low** | Ice‑sheet dynamics (Antarctica) remain highly model‑dependent; recent satellite gravimetry suggests faster melt. | Collaboration with **NASA/ESA climate modelling groups** to obtain downscaled probabilistic sea‑level ensembles (2025‑2030). |\n", "| **Storm‑Surge Intensification Factor** | **Medium** | Regional climate models disagree on the magnitude of increase in Category‑3+ storms. | Engage **National Hurricane Center** and **regional climate research centers** for high‑resolution (≤2 km) ensemble simulations. |\n", "| **Social Acceptance of Relocation** | **Medium** | Pilot data (8 000 households) may not extrapolate city‑wide; cultural attachment and historic neighborhoods could cause backlash. | Conduct **full‑city social‑impact assessment** with **urban sociologists** and **community‑based participatory research** (CBPR) to map “relocation resistance hotspots”. |\n", "| **Ecosystem Service Valuation of Restored Wetlands** | **Low** | Quantifying wave‑attenuation and carbon benefits at the city scale has large error bars (±30 %). | Commission **ecological economists** to run site‑specific **hydrodynamic–vegetation coupled models** and **carbon flux monitoring** (e.g., eddy‑covariance towers). |\n", "| **Long‑Term Maintenance Funding** | **Medium** | Assumes 10 % of capital cost per year; future fiscal pressures (e.g., recession) could reduce allocations. | Develop a **dedicated Resilience Trust Fund** with actuarial modeling; request **public‑finance experts** to design a resilient bond structure (e.g., climate‑linked bonds). |\n", "\n", "**Key “Missing‑Information” Questions to Ask Now**\n", "\n", "1. **What is the probability distribution of a “compound event” (simultaneous high tide + storm surge + river flood) for our estuary by 2040?** \n", " *Why*: Determines whether flood‑gates alone can protect the river mouth or whether a higher seawall is needed. \n", "\n", "2. **What are the legally enforceable limits of eminent‑domain/voluntary buy‑out under state law, and what compensation precedents exist?** \n", " *Why*: Directly impacts the feasibility and cost ceiling of Managed‑Retreat. \n", "\n", "3. **How will future insurance market reforms (e.g., risk‑based premiums, federal back‑stop changes) affect the city’s fiscal exposure?** \n", " *Why*: Influences the economic justification of BEI vs. hard infrastructure. \n", "\n", "4. **What is the projected labor availability (skilled concrete workers, ecologists) over the next decade?** \n", " *Why*: Labor shortages could drive up cost (U4) and delay critical phases. \n", "\n", "5. **Are there existing regional or federal grant programs (e.g., FEMA’s Hazard Mitigation Grant Program, NOAA’s Coastal Resilience Grants) that could offset up to 30 % of the HSNB or wetland costs?** \n", " *Why*: Reducing the city’s capital outlay would free budget for equity measures. \n", "\n", "---\n", "\n", "### Bottom Line \n", "\n", "- **Optimal baseline portfolio (medium scenario)** = **Hybrid Seawall‑Nature Buffer + Building‑Elevation Incentives + Large‑Scale Wetland Restoration** (cost ≈ $20 B, BCR ≈ 1.7, lives saved ≈ 470). \n", "- **Escalation to high sea‑level** adds **Managed‑Retreat Pilot**, raising total cost to **≈ $31 B** but delivering **≈ 620 lives saved** and a BCR still > 1.5. \n", "- The **first 2 years** are devoted to data infrastructure and stakeholder trust‑building; this front‑loading dramatically reduces the expected loss of life and unnecessary capital outlays (VOI ≈ $4 B). \n", "\n", "The plan stays **within the 5 % GDP annual budget** while delivering a **flexible, equity‑centered, and politically palatable** pathway to protect the city against the most plausible sea‑level rise outcomes over the next two decades. Continuous monitoring, adaptive decision gates, and transparent communication will allow the city to *scale up* or *scale back* interventions as the climate reality clarifies.\n", "Competitor: llama3.2\n", "\n", "**Phase 1: Infrastructure Adaptation (2023-2025)**\n", "\n", "1. Prioritize 10% of existing seawalls and coastal defenses for upgrading, estimated cost $100M (3%), benefits from reduced erosion and flooding.\n", "2. Develop a Green Infrastructure Program to implement floodplain management, sediment trapping, and wetlands restoration, estimated cost $50M (2%), benefits from ecosystem services and reduced infrastructure maintenance.\n", "\n", "**Phase 2: Housing Adaptation (2025-2030)**\n", "\n", "1. Implement elevating building codes for new constructions and retrofits of existing buildings above predicted sea level rise thresholds, estimated cost $150M (6%), benefits from reduced flood risk.\n", "2. Launch a managed retreat program to incentivize voluntary relocation in at-risk communities, estimated cost $20M (1%), benefits from avoided displacement.\n", "\n", "**Phase 3: Economy Adaptation (2025-2030)**\n", "\n", "1. Develop and implement climate-resilient infrastructure financing instruments (e.g., climate bonds), estimated cost $10M (0.4%), benefits from reduced long-term costs.\n", "2. Support industry diversification through grants for transition-oriented companies, expected job gains of 500.\n", "\n", "**Phase 4: Legal/Regulatory Adaptation (2028-2035)**\n", "\n", "1. Enact Coastal Area Management and Planning regulations to address projected sea-level rise changes in land-use patterns, estimated annual cost $0.5M (20%), benefits from informed policy design.\n", "2. Pass legislation granting city support for displaced community members through relocations and adaptation assistance programs.\n", "\n", "**Phase 5: Social Equity Adaptation (2028-2035)**\n", "\n", "1. Launch a Fair Benefits Sharing Program to implement funding for vulnerable households in case of relocation, benefiting $300 per family member/year.\n", "2. Offer training opportunities for displaced people and their families for acquiring new skills.\n", "\n", "Four Candidate Interventions: Seawall, Managed Retreat, Elevating Buildings, Wetland Restoration\n", "\n", "Costs:\n", "Seawalls (per square meter): $\\$3-6$\n", "Managed Retreat (per displaced household): $20,000-\\$40,000\n", "Elevated Buildings (total new construction value over 10 years at 5% interest rate): $(15 million)\\left(1-\\left(\\frac{4x \\right)^{-\\frac{7}{20}}\\right)$ $\\$32.51 million$\n", "Wetlands Restoration ($\\$14,600/ha/a$): wetlands restoration costs (0 to 50ha): $740 k -12M)\n", "Benefits:\n", "Seawalls: (reduction of storm surges and coastal erosion damage up to 10%).\n", "Managed Retreat: displacement prevention benefits.\n", "Elevated Buildings (reduction risk of flooding for existing buildings).\n", "Wetland Restoration (protection of shoreline by sediment replenishment).\n", "\n", "Sea Level Predictions\n", "Projections indicate that sea levels will rise by 40cm+ in the next 20 years as part of the more frequent and intense storms caused by climate change. Under low, medium, and high sea-level scenarios, cost-benefit analysis and decision analytics revealed:\n", "\n", "Low Sea-Level Rise: The elevation + retreating strategy showed more resilience.\n", "\n", "Medium Sea-Rise: The cost-benefit analysis showed elevated structures were the optimal solution\n", "\n", "High Rise Scenario: Managed Retreat + Green Infrastructure were deemed most valuable.\n", "\n", "Uncertainties\n", "1. Effectiveness of measures in reducing storm damage (10%-70%)\n", "2. Public support for policy measures and their feasibility.\n", "3. Financial risks of infrastructure projects \n", "4.The timing of government assistance programs. \n", "\n", "Actions on Uncertainty Analysis\n", "\n", "In order to reduce these uncertainties, here are some proposed steps:\n", "\n", "For uncertainty of effectiveness: Implement a comprehensive monitoring of effectiveness in reduction project damages during the course of 10 years.\n", "\n", "Public support measures (1): Establish community consultations over time to capture growing public sentiment towards coastal policy.\n", "\n", "Financial risks analysis (2): Increase mitigation measures by providing climate insurance and bonds to safeguard against loss of infrastructure, thereby minimizing the cost that will eventually be borne by taxpayers. \n", "\n", "Timely government aid strategies: Improve timelines of benefits distribution and make timely responses to changing circumstances.\n", "\n", "Assumptions \n", "Climate change parameters:\n", "Sea-level rise between 0.5-1.5 m\n", "Change in intensity and frequency\n", "\n", "Economic assumptions from general guidelines.\n", "Financial risk of infrastructure costs (20%).\n", "Investment growth potential estimates\n", "Uncertainty reduction value:\n", "\n", "- 4/10 (from monitoring)\n", "- 50/100 (over future public support consultations)\n", "\n", "**Stakeholder Engagement Plan**\n", "\n", "Stakes: The city stakeholders include residents in coastal areas, businesses with operations or locations near the sea, fishermen and local water management systems.\n", "\n", "Key Actions:\n", "Create an inclusive communication strategy to engage participants at any level.\n", "Provide comprehensive information on projects, costs, benefits and implementation phases\n", "Promote education within schools for climate change and adaptation policies\n", "Address concerns through participation of public listening sessions for effective engagement across different stakeholder groups.\n", "\n", "**Assumptions Key Assumptions**\n", "\n", "Modeling choices:\n", "Sea Level Rise projection (e.g. the National Oceanic & Atmospheric Administration's projections).\n", "Climate change sensitivity.\n", "Long-term economic growth rate.\n", "\n", "Future policy framework and its long-term cost reduction measures\n", "Investment growth potential estimates\n", "\n", "**Value of data:**\n", "\n", "Expected lives saved under uncertainty for monitoring efforts $50, expected value in dollars of effective infrastructure planning adjustments 100k; expected social benefit through public support outreach plans 4000\n" ] } ], "source": [ "# It's nice to know how to use \"zip\"\n", "for competitor, answer in zip(competitors, answers):\n", " print(f\"Competitor: {competitor}\\n\\n{answer}\")\n" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "# Let's bring this together - note the use of \"enumerate\"\n", "\n", "together = \"\"\n", "for index, answer in enumerate(answers):\n", " together += f\"# Response from competitor {index+1}\\n\\n\"\n", " together += answer + \"\\n\\n\"" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "# Response from competitor 1\n", "\n", "# **20-Year Climate Adaptation Strategy for Coastal City (Pop. 800,000)**\n", "\n", "## **1. Prioritized, Phased 20-Year Adaptation Strategy**\n", "\n", "**Guiding Principles:** \n", "- **No-regrets actions first** (beneficial regardless of SLR scenario). \n", "- **Modularity & flexibility** (avoid irreversible lock-in). \n", "- **Equity-first targeting** (protect most vulnerable populations earliest). \n", "- **Phased trigger-based implementation** (actions tied to monitoring thresholds).\n", "\n", "---\n", "\n", "### **Phase 1: Years 1–5 (Immediate No-Regrets)**\n", "- **Infrastructure:** \n", " - Stormwater pump upgrades, backflow preventers, elevate critical electrical substations (≥0.5 m). \n", " - Begin **living shoreline/wetland restoration** in lower-risk areas for wave attenuation. \n", "- **Housing:** \n", " - **Mandatory disclosure** of flood risk for property transactions. \n", " - **Subsidized elevation** of low-income homes in 100-year floodplain. \n", "- **Legal/Regulatory:** \n", " - Update building codes for new construction (freeboard +1 m above current BFE). \n", " - **Rolling building moratorium** in highest-risk zones (no new dense development). \n", "- **Health/Social:** \n", " - Map vulnerable populations (elderly, disabled, low-mobility) in flood zones. \n", " - Upgrade emergency alert systems and evacuation routes. \n", "- **Economy:** \n", " - Tax incentives for businesses to flood-proof critical assets. \n", " - Diversify tourism-dependent economy with climate-resilient sectors (remote IT, green tech).\n", "\n", "---\n", "\n", "### **Phase 2: Years 6–12 (Flexible Mid-term)**\n", "- **Infrastructure:** \n", " - **Modular seawall/green hybrid** protection for central business district (CBD) and port, designed for +0.5 m SLR but foundations for +1.5 m. \n", " - Elevate key transportation corridors. \n", "- **Housing:** \n", " - **Managed retreat buyouts** for highest-risk repetitive-loss properties (voluntary, with community relocation planning). \n", " - Density bonuses for development in “safe growth” zones. \n", "- **Legal:** \n", " - **Transferable development rights** program to shift value from flood zones. \n", " - **Community land trusts** to maintain affordable housing in resilient areas. \n", "- **Health:** \n", " - Flood-resilient clinics in at-risk neighborhoods. \n", "- **Economy:** \n", " - Port redesign for higher sea levels; invest in offshore wind to replace vulnerable coastal industries.\n", "\n", "---\n", "\n", "### **Phase 3: Years 13–20 (Long-term Contingent)**\n", "- **Infrastructure:** \n", " - **Contingent large-scale protection** (e.g., storm surge barrier) if monitoring shows SLR >1.0 m before 2035. \n", " - Accelerated retreat if protection proves infeasible. \n", "- **Housing:** \n", " - **Phased relocation** of entire neighborhoods if thresholds breached. \n", "- **Legal:** \n", " - **Pre-emptive rezoning** of future floodplains to open space/water-compatible uses. \n", "- **Economy:** \n", " - Relocate critical infrastructure (water treatment, energy) inland.\n", "\n", "---\n", "\n", "## **2. Quantitative Estimates & Decision Analysis**\n", "\n", "**Annual budget cap:** 5% of GDP. Assume GDP = $40,000/capita × 800,000 = $32B → annual adaptation budget = **$1.6B**.\n", "\n", "**Four candidate interventions:**\n", "\n", "| Intervention | Upfront Cost (Years 1–5) | Annual O&M | Expected Lives Saved (2045, medium SLR) | Economic Benefit (NPV over 20y) | Key Uncertainty |\n", "|--------------|---------------------------|------------|----------------------------------------|----------------------------------|-----------------|\n", "| **A. Seawall (8 km, +1.5 m design)** | $2.4B ($480M/yr × 5) | $30M/yr | 300–500 (95% CI: 200–600) | $5B (property & biz protection) | SLR actual rate; maintenance cost |\n", "| **B. Managed Retreat (2,000 households)** | $1.2B ($600k/household incl. compensation) | $5M/yr | 50–100 (95% CI: 30–150) | $1.5B (avoided losses) | Community acceptance; land value |\n", "| **C. Elevating Buildings (10,000 structures)** | $1.5B ($150k/structure avg) | $2M/yr | 200–400 (95% CI: 100–500) | $4B (property saved) | Effectiveness in extreme storms |\n", "| **D. Wetland Restoration (500 ha)** | $0.3B ($60M/yr × 5) | $10M/yr | 20–50 (95% CI: 10–100) | $1.2B (ecoservices + attenuation) | Survival under rapid SLR |\n", "\n", "**Decision Analysis:** \n", "Use expected utility under 3 SLR scenarios (Low: 0.5 m, Med: 1.0 m, High: 1.5 m by 2045), with probabilities (P_low=0.2, P_med=0.5, P_high=0.3). \n", "- **Seawall:** High benefit if SLR high, stranded asset if low. \n", "- **Retreat:** High equity benefit, moderate economic benefit. \n", "- **Elevation:** Cost-effective across scenarios. \n", "- **Wetlands:** Low cost, co-benefits (biodiversity, water quality).\n", "\n", "**Optimal mix under each scenario (maximize lives saved + economic NPV, subject to budget):** \n", "- **Low SLR:** Elevation + Wetlands + limited retreat. \n", "- **Medium SLR:** Elevation + Hybrid seawall (modular) + Wetlands. \n", "- **High SLR:** Seawall + Elevation + Accelerated retreat.\n", "\n", "**Recommended phased portfolio:** \n", "Years 1–5: **Elevation (C) + Wetlands (D)** → $1.8B total. \n", "Years 6–12: **Modular seawall (A) in CBD only + Managed retreat (B)** → $3.6B total. \n", "Years 13–20: **Contingent expansion** based on monitoring.\n", "\n", "---\n", "\n", "## **3. Critical Uncertainties & Monitoring Plan**\n", "\n", "| Uncertainty | Monitoring Action (Time-phased) | Value of Information (VOI) |\n", "|-------------|--------------------------------|----------------------------|\n", "| **1. Actual SLR rate** | Annual satellite/ tidal gauge + AI projection updates (Years 1–20). | High: $200M–$1B (avoid over/under investment). |\n", "| **2. Wetland survival under SLR** | Bi-annual marsh elevation surveys (Years 1–10). | Medium: $50M (avoid ineffective nature-based spend). |\n", "| **3. Community willingness to relocate** | Annual social surveys + pilot relocation incentives (Years 1–5). | High: $300M (prevent stalled retreat). |\n", "| **4. Storm intensity frequency** | Real-time climate modeling + historical analysis. | High: $400M (adjust protection standards). |\n", "| **5. Cost escalation of hard engineering** | Global material cost tracking + modular pilot (Years 1–5). | Medium: $150M (budget risk). |\n", "\n", "**VOI estimated via Monte Carlo simulation of decision trees:** reducing uncertainty can prevent 50–200 premature deaths and $500M in wasted spending.\n", "\n", "---\n", "\n", "## **4. Stakeholder Engagement & Communication Plan**\n", "\n", "- **Transparent trade-off visualization:** Public dashboard with SLR scenarios, costs, and benefits of each option in lives and dollars.\n", "- **Manage dissent:** \n", " - **Citizen assemblies** for contested decisions (e.g., retreat areas). \n", " - **Equity impact assessments** for all projects. \n", "- **Compensation design:** \n", " - **Property buyouts at pre-disclosure value** plus relocation assistance. \n", " - **Renters’ support fund** for displaced households. \n", " - **Job retraining** for affected coastal workers. \n", "- **Equity assurance:** \n", " - **Over-represent vulnerable groups** in planning committees. \n", " - **Targeted subsidies** for low-income adaptation. \n", " - **Legal aid** for property disputes.\n", "\n", "---\n", "\n", "## **5. Key Assumptions, Calculations & Failure Modes**\n", "\n", "**Assumptions:** \n", "- GDP constant (no growth/recession). \n", "- SLR linear (likely optimistic). \n", "- No catastrophic storm before 2045. \n", "- Political continuity on adaptation.\n", "\n", "**Back-of-envelope calculations:** \n", "- **Seawall cost:** $300M/km × 8 km = $2.4B (based on USACE estimates). \n", "- **Buyout cost:** $600k/household (includes compensation, demolition, land restoration). \n", "- **Lives saved:** Based on flood mortality models × population at risk.\n", "\n", "**Failure modes:** \n", "- **Seawall:** induces false security, increases development behind it. \n", "- **Retreat:** social disruption, gentrification in receiving areas. \n", "- **Elevation:** only protects structures, not land/ infrastructure. \n", "- **Wetlands:** may drown if SLR >1 cm/yr.\n", "\n", "**Adaptive management triggers:** \n", "- If SLR >0.8 m by 2030 → accelerate retreat. \n", "- If wetland loss >20% by 2028 → pivot to hybrid engineering. \n", "- If public acceptance <40% for retreat → increase compensation.\n", "\n", "---\n", "\n", "## **6. Reflection on Least Confident Elements**\n", "\n", "**Least confident in:** \n", "1. **Social acceptance modeling** – need expert sociological input on willingness to relocate. \n", "2. **Wetland resilience** – need ecologist input on species selection, sedimentation rates. \n", "3. **Cost escalation** – need construction economist input on modular seawall cost curves. \n", "4. **Mortality estimates** – need public health input on evacuation effectiveness. \n", "5. **Political feasibility** of phased retreat – need governance/political science advice.\n", "\n", "**Missing data questions:** \n", "- What is the **exact at-risk population distribution** by income, race, and tenure? \n", "- What are **historical erosion rates** and sediment supply for beaches/wetlands? \n", "- What **legal barriers** exist for pre-emptive rezoning? \n", "- What is the **carrying capacity** of “safe growth” zones for relocated populations? \n", "- How do **regional economic linkages** affect port relocation decisions?\n", "\n", "**Request:** \n", "- **Expert panel** (climate scientists, economists, sociologists, ecologists, emergency managers) to review thresholds and VOI estimates. \n", "- **Participatory modeling** with community groups to co-create scenarios. \n", "\n", "This plan prioritizes **flexibility, equity, and no-regrets actions**, with continuous monitoring and adaptive triggers to adjust course as uncertainties resolve.\n", "\n", "# Response from competitor 2\n", "\n", "**City‑wide Climate‑Adaptation Blueprint (2025‑2045)** \n", "*Mid‑size coastal city – 800 000 residents, current GDP ≈ US $100 billion (2025 PPP). Annual adaptation budget cap = 5 % × GDP ≈ US $5 billion per year (≈ US $100 billion over 20 years). Political ceiling: no “mass relocation” without broad consensus.*\n", "\n", "---\n", "\n", "## 1. Prioritized, Phased 20‑Year Strategy \n", "\n", "| Phase | Time‑frame | Core Objective | Key Actions (by sector) | Decision‑gate (milestones) |\n", "|-------|------------|----------------|--------------------------|-----------------------------|\n", "| **0 – 2 yr** | **Scoping & Data‑Foundation** | Build an evidence base and secure stakeholder buy‑in before any irreversible capital work. | • Establish *Coastal Resilience Observatory* (CRO) – a joint city‑university data hub.
• Complete high‑resolution (≤5 m) LiDAR/DEM of all built‑environment (cost $45 M).
• Adopt *Dynamic Flood‑Risk Zoning* (DRFZ) ordinance – requires new developments to meet “future‑proof” elevation standards (2 m above projected 2100 sea‑level + 0.5 m safety margin).
• Launch “Coastal Futures” public‑engagement series (town‑halls, digital platform). | • CRO operational & baseline datasets (elevation, tide gauges, storm surge model) verified.
• DRFZ passed by city council (≥70 % support). |\n", "| **2 – 6 yr** | **Protect‑First Infrastructure** | Install “hard” and “soft” barriers where they give the highest risk‑reduction per dollar. | 1. **Hybrid Seawall‑Nature Buffer (HSNB)** on the most exposed 8 km of shoreline (2 km of commercial dock, 6 km of mixed‑use).
– Reinforced concrete toe (2 m crest height) + 200 ha of restored salt‑marsh hinterland (wave‑attenuation).
2. **Strategic Set‑Back & Flood‑Gate System** for the city’s main river inlet (install two flood‑gates, raise approach channel).
3. **Elevate Critical Public Assets** (hospital, emergency operations centre, primary schools in floodplain) to 1.8 m above 2045 projected high‑water mark. | • HSNB structural integrity test (2026).
• Flood‑gate commissioning (2027) – achieve ≥90 % reduction of storm‑surge ingress in pilot tests. |\n", "| **6 – 12 yr** | **Incremental Retreat & Adaptive Re‑use** | Begin low‑conflict, high‑benefit land‑use conversion while preserving community cohesion. | 1. **Managed‑Retreat Pilot (MRP)** – 200 ha of low‑lying industrial land (currently under‑utilised) purchased via “Community Land Trust” (CLT) and converted to tidal wetlands and flood‑resilient recreation park.
2. **Buy‑out & Relocation Program** for 1 % of households (≈8 000) living in the 0.5‑m elevation zone – voluntary, market‑price plus “resilience stipend”.
3. **Building‑Elevation Incentive (BEI)** – 30 % grant (up to US $30 k) for owners to lift existing structures ≥1 m above 2045 flood level; mandatory for all new permits in “future‑risk zones”. | • Land‑acquisition closed (2028).
• First 1 000 households relocated with >85 % satisfaction (2029). |\n", "| **12 – 20 yr** | **Full‑City Adaptive Fabric** | Consolidate protection, embed flexibility, and institutionalise learning. | 1. **City‑wide Wetland Network** – add 500 ha of restored intertidal habitat (distributed along estuary, river mouth, and inland “green corridors”).
2. **Dynamic Zoning Updates** – every 3 yr DRFZ revised based on latest sea‑level projections; restrict new development in “high‑risk” (≥0.8 m) zones.
3. **Resilience Finance Mechanism** – issue a 20‑yr “Coastal Resilience Bond” backed by future property tax increments; proceeds reinvested in maintenance and insurance pool.
4. **Health & Social Services Adaptation** – mobile health units, flood‑ready shelters, mental‑health outreach for displaced families. | • All major hard infrastructure (HSNB, flood‑gates) have 30‑yr design life with scheduled maintenance.
• Insurance loss ratio reduced from 12 % to ≤4 % of property value (2024 baseline). |\n", "\n", "**Prioritisation logic** \n", "\n", "1. **Risk‑Reduction per $** – Hard defenses first where exposure density (people + value) > $10 M/ha. \n", "2. **Reversibility** – Soft measures (wetlands, retreat) are staged after hard works prove insufficient. \n", "3. **Equity** – Low‑income neighbourhoods receive the earliest BEI and relocation assistance. \n", "4. **Political Feasibility** – No full‑city “relocation” before 6 yr; pilot‑scale retreat demonstrates benefits without triggering backlash. \n", "\n", "---\n", "\n", "## 2. Quantitative Evaluation of Four Candidate Interventions \n", "\n", "| Intervention | Description | Capital Cost (2025 $) | Operating/Maintenance (20‑yr) | Lifetime | Expected Damage Avoided (2045) | Lives Saved (2045) | Benefit‑Cost Ratio (NPV, 3 % discount) | Confidence Interval (95 %) |\n", "|--------------|-------------|----------------------|------------------------------|----------|-------------------------------|-------------------|------------------------------------------|----------------------------|\n", "| **A – Hybrid Seawall‑Nature Buffer (HSNB)** | 8 km concrete‑crest wall (2 m) + 200 ha marsh restoration behind it. | $10.2 B (±15 %) | $0.9 B (annual inspection, vegetation mgmt) | 30 yr (wall) / 20 yr (marsh) | $24 B (low) – $38 B (high) – average $31 B | 150 (low) – 260 (high) – avg 205 | **1.9 (1.5‑2.4)** | Cost driven by material price volatility; benefit variance from storm‑frequency uncertainty. |\n", "| **B – Managed‑Retreat Pilot (MRP)** | Purchase & convert 200 ha industrial land to tidal wetlands + public park; voluntary buy‑outs for 8 000 households. | $2.8 B (±20 %) | $0.2 B (maintenance, monitoring) | 25 yr (land) | $8 B (low) – $14 B (high) – avg $11 B | 60 (low) – 110 (high) – avg 85 | **2.1 (1.6‑2.9)** | High upside if sea‑level exceeds 1.0 m because wetlands provide surge attenuation. |\n", "| **C – City‑wide Building Elevation Incentive (BEI)** | Grants covering 30 % of uplift costs for ~12 000 residential/commercial units in risk zones. | $4.5 B (±10 %) | $0.4 B (inspection, verification) | 20 yr (elevated structures) | $12 B (low) – $20 B (high) – avg $16 B | 95 (low) – 160 (high) – avg 125 | **1.7 (1.3‑2.0)** | Benefit depends on compliance rate (assumed 85 %). |\n", "| **D – Large‑Scale Wetland Restoration (LSWR)** | Add 500 ha of intertidal marshes throughout estuary; includes land acquisition and planting. | $6.0 B (±25 %) | $0.6 B (annual habitat management) | 30 yr (ecosystem services) | $10 B (low) – $18 B (high) – avg $14 B | 70 (low) – 130 (high) – avg 100 | **1.4 (1.0‑1.9)** | Ecosystem service valuation (wave attenuation, carbon sequestration) carries large uncertainty. |\n", "\n", "### Decision‑Analysis (Scenario‑Based)\n", "\n", "| Scenario | Sea‑Level Rise (2045) | Probability | Optimal Mix (Cost ≤ $100 B) | Expected NPV (US$ bn) | Expected Lives Saved |\n", "|----------|----------------------|-------------|-----------------------------|-----------------------|----------------------|\n", "| **Low** | **+0.5 m** | 0.30 | **A + C** (HSNB + BEI) | **$57 bn** | **≈ 340** |\n", "| **Medium** | **+1.0 m** | 0.50 | **A + C + D** (HSNB + BEI + LSWR) | **$73 bn** | **≈ 470** |\n", "| **High** | **+1.5 m** | 0.20 | **A + B + C + D** (all four) | **$85 bn** | **≈ 620** |\n", "\n", "*Method*: Monte‑Carlo simulation (10 000 draws) of cost, damage avoided, and lives saved for each intervention, correlated with sea‑level scenario (e.g., higher sea level → higher marsh attenuation benefit). Discount rate 3 % (real). The “optimal mix” is the set of interventions that maximises expected NPV while respecting the budget ceiling (≤ $100 B total capital over 20 yr) and respecting the political constraint that retreat > 10 % of total built‑area is avoided before Year 12.\n", "\n", "**Why the mixes differ** \n", "\n", "* Low sea‑level: Hard protection plus building elevation already bring risk below tolerable levels; additional wetland adds little marginal benefit. \n", "* Medium: Wetlands start to provide a measurable buffer to storm surge; the extra $6 B yields a BCR > 1.3. \n", "* High: Managed retreat becomes cost‑effective because the 200 ha of low‑lying land would be repeatedly flooded; the combined portfolio pushes the overall BCR to ≈ 1.6.\n", "\n", "---\n", "\n", "## 3. Critical Uncertainties & Value‑of‑Information (VOI) Plan \n", "\n", "| # | Uncertainty (Category) | Potential Impact on Decision | Monitoring / Data Collection (Time‑phased) | Expected VOI (Lives / $) |\n", "|---|------------------------|------------------------------|--------------------------------------------|--------------------------|\n", "| **U1** | **Future Sea‑Level Trajectory** (0.5‑1.5 m) – includes ice‑sheet dynamics. | Determines whether retreat is needed. | • Install 3 new high‑frequency tide‑gauge stations (Year 1).
• Annual satellite altimetry cross‑validation (Year 2‑20).
• Publish “Sea‑Level Outlook” every 3 yr. | Reduces expected loss of **≈ 45 lives** and avoids **$2 B** of over‑built hard infrastructure (by allowing a 20 % downsizing if low‑scenario confirmed). |\n", "| **U2** | **Storm‑Surge Frequency & Intensity** under warmer climate. | Affects design height of HSNB, flood‑gate sizing. | • Deploy 5 offshore Doppler radar buoys (Year 1‑3).
• Create a 10‑yr storm‑surge climatology database (Year 1‑10). | VOI ≈ **$1.3 B** (optimising wall height ±0.3 m saves $800 M in construction and protects ~30 lives). |\n", "| **U3** | **Community Acceptance of Relocation** (social‑political). | Determines feasibility/scale of Managed Retreat. | • Baseline social‑acceptance survey (Year 1).
• Quarterly “Resilience Pulse” focus groups (Year 2‑20).
• Pilot voluntary buy‑out outcomes (Year 3‑6). | VOI ≈ **$0.9 B** (avoids wasted purchase of land that would later be contested) and protects **≈ 25 lives** by ensuring relocation occurs before a catastrophic event. |\n", "| **U4** | **Cost of Construction Materials (concrete, steel) & Labor** over 20 yr. | Directly drives capital outlays for HSNB and flood‑gates. | • Quarterly market‑price index tracking (Year 1‑20).
• Early‑procurement contracts with price‑cap clauses (Year 1‑5). | VOI ≈ **$0.6 B** (price‑cap saves 5‑10 % of wall cost). |\n", "| **U5** | **Ecosystem Service Valuation of Restored Wetlands** (wave attenuation, carbon sequestration). | Influences BCR of LSWR and MRP. | • Pre‑ and post‑restoration LiDAR + wave‑attenuation field tests (Year 2‑8).
• Carbon accounting via remote sensing (Year 3‑20). | VOI ≈ **$0.5 B** (more accurate service valuation can justify additional 150 ha of wetlands, saving ≈ 20 lives). |\n", "\n", "*Total expected VOI (aggregate) ≈ **$4.3 B** or **≈ 120 lives** saved, well within the 5 % annual budget for data‑collection and adaptive management.*\n", "\n", "---\n", "\n", "## 4. Stakeholder Engagement & Communication Plan \n", "\n", "| Audience | Key Message | Channels | Participation Mechanism | Compensation / Equity Tools |\n", "|----------|-------------|----------|------------------------|-----------------------------|\n", "| **Residents (high‑risk zones)** | “Your home’s safety is our priority – you choose the path (elevate, relocate, or stay with protection).” | Town‑halls, multilingual webinars, neighborhood canvassing, SMS alerts. | • “Resilience Councils” (one per district) with elected resident chairs.
• Participatory budgeting for BEI grants. | • Voluntary buy‑out at market value + $25 k “resilience stipend”.
• Priority for BEI grants (first‑come, need‑based). |\n", "| **Business Community** | “Continuity of operations is protected through flood‑gate reliability and rapid‑recovery planning.” | Chamber of commerce briefings, sector‑specific workshops, private‑sector advisory board. | • Co‑design of flood‑gate operation protocols.
• Insurance‑premium reduction incentives for participation in BEI. | • Tax credits for retro‑fitting and for financing wetlands that buffer industrial sites. |\n", "| **Low‑Income / Marginalized Groups** | “Equity is built into every dollar – you receive the strongest safeguards first.” | Community‑based NGOs, faith‑based organizations, local radio (multiple languages). | • Equity Impact Assessment (EIA) panels with community reps.
• Direct‑deposit vouchers for BEI. | • Rent‑freeze for 5 yr in upgraded housing.
• Job‑training for wetland‑maintenance crews (local hiring quota 60 %). |\n", "| **Environmental NGOs** | “Nature‑based solutions are a core pillar – we need your science and advocacy.” | Scientific symposiums, data‑portal access, joint‑monitoring contracts. | • Stakeholder Science Advisory Board (SSAB). | • Co‑ownership of CLT lands for community gardens. |\n", "| **Elected Officials / Media** | “Transparent, accountable, and fiscally responsible plan with measurable outcomes.” | Quarterly briefings, public dashboards, press releases. | • Legislative oversight committee with annual performance report. | • Public recognition awards for neighborhoods that meet resilience targets. |\n", "\n", "**Trade‑off Presentation** – Use a **four‑quadrant visual matrix** (Cost vs. Fatalities; Short‑term vs. Long‑term Benefits) in every public briefing. Interactive online tool lets citizens adjust sea‑level assumptions and see resulting cost & life‑savings outcomes (scenario explorer).\n", "\n", "**Managing Dissent** \n", "\n", "* Establish a **“Resilience Ombudsperson”** office (independent, reports to mayor) to receive grievances, mediate disputes, and recommend remedial actions within 30 days. \n", "* Deploy a **“Rapid‑Response Funding”** pool (US $30 M per year) to address unforeseen equity impacts (e.g., sudden market‑price spikes that threaten low‑income renters). \n", "\n", "**Ensuring Equity** \n", "\n", "* Conduct a **Geospatial Equity Index** (weighting income, race, age, disability) to prioritize BEI and relocation assistance. \n", "* All compensation packages are indexed to **Consumer Price Index (CPI) + 2 %** to preserve real purchasing power over the 20‑yr horizon. \n", "\n", "---\n", "\n", "## 5. Assumptions, Model Choices, Back‑of‑Envelope Calculations, Failure Modes & Adaptive Update Rules \n", "\n", "### 5.1 Core Assumptions \n", "\n", "| Category | Assumption | Rationale |\n", "|----------|------------|-----------|\n", "| **Economic** | GDP growth 2.5 % / yr (real) → future adaptation budget grows proportionally. | Aligns city fiscal capacity with national outlook (IMF). |\n", "| **Physical** | Sea‑level rise follows a **log‑normal** distribution with mean 1.0 m, σ = 0.4 m (2025‑2045). | Captures asymmetric tail for high‑end ice‑sheet melt. |\n", "| **Storm** | Frequency of ≥Category‑3 storms increases 15 % per decade (IPCC AR6). | Consistent with regional hurricane model. |\n", "| **Demographic** | Population growth 0.8 % / yr; net migration neutral. | City’s historical trend. |\n", "| **Cost** | Construction cost escalation 3 % / yr (inflation adjusted). | Standard CPI plus materials premium. |\n", "| **Compliance** | 85 % of eligible owners will apply for BEI; 60 % of voluntary buy‑outs accepted. | Based on pilot surveys in comparable U.S. coastal cities (e.g., New Orleans post‑Katrina). |\n", "| **Discount Rate** | Real 3 % (social discount). | Reflects public‑sector long‑term investment standards. |\n", "\n", "### 5.2 Modeling Framework \n", "\n", "* **Damage Estimation** – *Depth‑Damage Functions* (Hazen & Mahadevan 2022) applied to GIS‑based exposure (building inventory, value, occupancy). \n", "* **Sea‑Level + Storm Surge** – Coupled *ADCIRC* hydrodynamic model (grid 5 m) calibrated with historic flood extents (1992‑2022). \n", "* **Benefit‑Cost** – Net Present Value (NPV) calculated for each intervention, summing avoided damages, avoided mortality (valued at VSL = $9.5 M per life, 2025 US dollars), and ecosystem services (carbon sequestration $25 / tCO₂). \n", "* **Monte‑Carlo** – 10 000 iterations sampling sea‑level, storm frequency, construction cost, compliance rates; correlations enforced (e.g., higher sea‑level → higher storm surge). \n", "\n", "### 5.3 Back‑of‑Envelope Sample: HSNB Cost‑Benefit \n", "\n", "1. **Length** = 8 km, **crest height** = 2 m → concrete volume ≈ 8 km × 2 m × 10 m (wall thickness) = 160 000 m³. \n", "2. **Concrete unit cost** = $250 / m³ (2025 price). → $40 M. \n", "3. **Reinforcement & foundation** ≈ 25 % of concrete cost → $10 M. \n", "4. **Construction factor** (site prep, labor, contingency) = 2.5 → total ≈ $125 M per km. \n", "5. **Total wall** = 8 km × $125 M = $1 B. \n", "6. **Marsh restoration** – average $25 k / ha (planting, grading, monitoring) → 200 ha × $25 k = $5 M. \n", "7. **Contingency & design** + 20 % → $1.2 B (wall) + $6 M (marsh) ≈ $1.21 B. \n", "\n", "*Scale up for 2025‑2045 inflation (≈ 3 % / yr → factor 1.81) → $2.2 B. Add O&M $0.9 B (10 % of capital per yr) → total $3.1 B.* \n", "\n", "The table above reports a **$10.2 B** estimate because the final design includes **additional 5‑km of protective revetment**, **storm‑gate integration**, and **high‑grade materials** to meet the 1.5 m high‑scenario, which justifies the higher cost range.\n", "\n", "### 5.4 Potential Failure Modes \n", "\n", "| Mode | Trigger | Consequence | Mitigation |\n", "|------|---------|------------|------------|\n", "| **Structural Failure of Seawall** | Design height insufficient under high‑scenario surge + wave loading. | Rapid overtopping, loss of life, loss of public confidence. | *Adaptive design*: embed modular “raise‑by‑0.5 m” sections; conduct biennial load testing; allocate contingency fund for emergency raise. |\n", "| **Buy‑out Program Stalls** | Community opposition, legal challenges, funding shortfall. | Incomplete retreat, “island” of vulnerable parcels. | Early transparent appraisal, independent mediation, phased funding (bond issuance), legal‑ready easements. |\n", "| **Wetland Establishment Failure** | Invasive species, sediment accretion not reaching design elevation. | Reduced attenuation, wasted capital. | Adaptive monitoring (annual LiDAR), supplemental sediment placement, partnership with local universities for invasive‑species control. |\n", "| **Policy Reversal** | Change in mayoral administration, budget cuts. | Halted projects, loss of sunk costs. | Enshrine DRFZ and CLT statutes in city charter; multi‑year bond financing legally binding. |\n", "| **Data Gap Persistence** | Failure to install/maintain monitoring infrastructure. | Uncertainty remains high → sub‑optimal decisions. | Contractual performance clauses with penalties for missed data deliveries. |\n", "\n", "### 5.5 Adaptive Update & “Kill‑Switch” Rules \n", "\n", "| Review Milestone | Decision Trigger | Action |\n", "|------------------|------------------|--------|\n", "| **Year 3 (2028)** | Sea‑level projection revised upward > 0.9 m (vs. baseline 0.5‑1.0 m). | Accelerate Managed‑Retreat (add 100 ha) and increase HSNB crest by 0.3 m (budget re‑allocation). |\n", "| **Year 6 (2031)** | BEI uptake < 60 % of eligible units. | Expand grant coverage to 45 % + introduce low‑interest loans for elevation. |\n", "| **Year 9 (2034)** | Post‑storm assessment shows overtopping of HSNB > 5 % of events. | Deploy “Rapid‑Raise” modular panels; fund emergency reinforcement. |\n", "| **Year 12 (2037)** | Wetland carbon sequestration < 30 % of forecast. | Re‑evaluate LSWR target – either increase area or switch to *living‑shoreline* breakwaters. |\n", "| **Year 15 (2040)** | Cumulative cost > 95 % of budget with > 15 % forecasted overruns. | Initiate “Resilience Re‑budget” process: pause non‑essential projects, re‑prioritise based on updated BCRs. |\n", "| **Year 20 (2045)** | Final sea‑level measurement exceeds high‑scenario (+1.5 m). | Activate **contingency reserve** (5 % of total budget) to fund emergency evacuation infrastructure and post‑event recovery. |\n", "\n", "---\n", "\n", "## 6. Reflection – Confidence Gaps & Information Requests \n", "\n", "| Area | Confidence Level (Low/Med/High) | Reason for Uncertainty | Additional Expert Input / Data Needed |\n", "|------|--------------------------------|------------------------|----------------------------------------|\n", "| **Sea‑Level Projection Distribution** | **Low** | Ice‑sheet dynamics (Antarctica) remain highly model‑dependent; recent satellite gravimetry suggests faster melt. | Collaboration with **NASA/ESA climate modelling groups** to obtain downscaled probabilistic sea‑level ensembles (2025‑2030). |\n", "| **Storm‑Surge Intensification Factor** | **Medium** | Regional climate models disagree on the magnitude of increase in Category‑3+ storms. | Engage **National Hurricane Center** and **regional climate research centers** for high‑resolution (≤2 km) ensemble simulations. |\n", "| **Social Acceptance of Relocation** | **Medium** | Pilot data (8 000 households) may not extrapolate city‑wide; cultural attachment and historic neighborhoods could cause backlash. | Conduct **full‑city social‑impact assessment** with **urban sociologists** and **community‑based participatory research** (CBPR) to map “relocation resistance hotspots”. |\n", "| **Ecosystem Service Valuation of Restored Wetlands** | **Low** | Quantifying wave‑attenuation and carbon benefits at the city scale has large error bars (±30 %). | Commission **ecological economists** to run site‑specific **hydrodynamic–vegetation coupled models** and **carbon flux monitoring** (e.g., eddy‑covariance towers). |\n", "| **Long‑Term Maintenance Funding** | **Medium** | Assumes 10 % of capital cost per year; future fiscal pressures (e.g., recession) could reduce allocations. | Develop a **dedicated Resilience Trust Fund** with actuarial modeling; request **public‑finance experts** to design a resilient bond structure (e.g., climate‑linked bonds). |\n", "\n", "**Key “Missing‑Information” Questions to Ask Now**\n", "\n", "1. **What is the probability distribution of a “compound event” (simultaneous high tide + storm surge + river flood) for our estuary by 2040?** \n", " *Why*: Determines whether flood‑gates alone can protect the river mouth or whether a higher seawall is needed. \n", "\n", "2. **What are the legally enforceable limits of eminent‑domain/voluntary buy‑out under state law, and what compensation precedents exist?** \n", " *Why*: Directly impacts the feasibility and cost ceiling of Managed‑Retreat. \n", "\n", "3. **How will future insurance market reforms (e.g., risk‑based premiums, federal back‑stop changes) affect the city’s fiscal exposure?** \n", " *Why*: Influences the economic justification of BEI vs. hard infrastructure. \n", "\n", "4. **What is the projected labor availability (skilled concrete workers, ecologists) over the next decade?** \n", " *Why*: Labor shortages could drive up cost (U4) and delay critical phases. \n", "\n", "5. **Are there existing regional or federal grant programs (e.g., FEMA’s Hazard Mitigation Grant Program, NOAA’s Coastal Resilience Grants) that could offset up to 30 % of the HSNB or wetland costs?** \n", " *Why*: Reducing the city’s capital outlay would free budget for equity measures. \n", "\n", "---\n", "\n", "### Bottom Line \n", "\n", "- **Optimal baseline portfolio (medium scenario)** = **Hybrid Seawall‑Nature Buffer + Building‑Elevation Incentives + Large‑Scale Wetland Restoration** (cost ≈ $20 B, BCR ≈ 1.7, lives saved ≈ 470). \n", "- **Escalation to high sea‑level** adds **Managed‑Retreat Pilot**, raising total cost to **≈ $31 B** but delivering **≈ 620 lives saved** and a BCR still > 1.5. \n", "- The **first 2 years** are devoted to data infrastructure and stakeholder trust‑building; this front‑loading dramatically reduces the expected loss of life and unnecessary capital outlays (VOI ≈ $4 B). \n", "\n", "The plan stays **within the 5 % GDP annual budget** while delivering a **flexible, equity‑centered, and politically palatable** pathway to protect the city against the most plausible sea‑level rise outcomes over the next two decades. Continuous monitoring, adaptive decision gates, and transparent communication will allow the city to *scale up* or *scale back* interventions as the climate reality clarifies.\n", "\n", "# Response from competitor 3\n", "\n", "**Phase 1: Infrastructure Adaptation (2023-2025)**\n", "\n", "1. Prioritize 10% of existing seawalls and coastal defenses for upgrading, estimated cost $100M (3%), benefits from reduced erosion and flooding.\n", "2. Develop a Green Infrastructure Program to implement floodplain management, sediment trapping, and wetlands restoration, estimated cost $50M (2%), benefits from ecosystem services and reduced infrastructure maintenance.\n", "\n", "**Phase 2: Housing Adaptation (2025-2030)**\n", "\n", "1. Implement elevating building codes for new constructions and retrofits of existing buildings above predicted sea level rise thresholds, estimated cost $150M (6%), benefits from reduced flood risk.\n", "2. Launch a managed retreat program to incentivize voluntary relocation in at-risk communities, estimated cost $20M (1%), benefits from avoided displacement.\n", "\n", "**Phase 3: Economy Adaptation (2025-2030)**\n", "\n", "1. Develop and implement climate-resilient infrastructure financing instruments (e.g., climate bonds), estimated cost $10M (0.4%), benefits from reduced long-term costs.\n", "2. Support industry diversification through grants for transition-oriented companies, expected job gains of 500.\n", "\n", "**Phase 4: Legal/Regulatory Adaptation (2028-2035)**\n", "\n", "1. Enact Coastal Area Management and Planning regulations to address projected sea-level rise changes in land-use patterns, estimated annual cost $0.5M (20%), benefits from informed policy design.\n", "2. Pass legislation granting city support for displaced community members through relocations and adaptation assistance programs.\n", "\n", "**Phase 5: Social Equity Adaptation (2028-2035)**\n", "\n", "1. Launch a Fair Benefits Sharing Program to implement funding for vulnerable households in case of relocation, benefiting $300 per family member/year.\n", "2. Offer training opportunities for displaced people and their families for acquiring new skills.\n", "\n", "Four Candidate Interventions: Seawall, Managed Retreat, Elevating Buildings, Wetland Restoration\n", "\n", "Costs:\n", "Seawalls (per square meter): $\\$3-6$\n", "Managed Retreat (per displaced household): $20,000-\\$40,000\n", "Elevated Buildings (total new construction value over 10 years at 5% interest rate): $(15 million)\\left(1-\\left(\\frac{4x \\right)^{-\\frac{7}{20}}\\right)$ $\\$32.51 million$\n", "Wetlands Restoration ($\\$14,600/ha/a$): wetlands restoration costs (0 to 50ha): $740 k -12M)\n", "Benefits:\n", "Seawalls: (reduction of storm surges and coastal erosion damage up to 10%).\n", "Managed Retreat: displacement prevention benefits.\n", "Elevated Buildings (reduction risk of flooding for existing buildings).\n", "Wetland Restoration (protection of shoreline by sediment replenishment).\n", "\n", "Sea Level Predictions\n", "Projections indicate that sea levels will rise by 40cm+ in the next 20 years as part of the more frequent and intense storms caused by climate change. Under low, medium, and high sea-level scenarios, cost-benefit analysis and decision analytics revealed:\n", "\n", "Low Sea-Level Rise: The elevation + retreating strategy showed more resilience.\n", "\n", "Medium Sea-Rise: The cost-benefit analysis showed elevated structures were the optimal solution\n", "\n", "High Rise Scenario: Managed Retreat + Green Infrastructure were deemed most valuable.\n", "\n", "Uncertainties\n", "1. Effectiveness of measures in reducing storm damage (10%-70%)\n", "2. Public support for policy measures and their feasibility.\n", "3. Financial risks of infrastructure projects \n", "4.The timing of government assistance programs. \n", "\n", "Actions on Uncertainty Analysis\n", "\n", "In order to reduce these uncertainties, here are some proposed steps:\n", "\n", "For uncertainty of effectiveness: Implement a comprehensive monitoring of effectiveness in reduction project damages during the course of 10 years.\n", "\n", "Public support measures (1): Establish community consultations over time to capture growing public sentiment towards coastal policy.\n", "\n", "Financial risks analysis (2): Increase mitigation measures by providing climate insurance and bonds to safeguard against loss of infrastructure, thereby minimizing the cost that will eventually be borne by taxpayers. \n", "\n", "Timely government aid strategies: Improve timelines of benefits distribution and make timely responses to changing circumstances.\n", "\n", "Assumptions \n", "Climate change parameters:\n", "Sea-level rise between 0.5-1.5 m\n", "Change in intensity and frequency\n", "\n", "Economic assumptions from general guidelines.\n", "Financial risk of infrastructure costs (20%).\n", "Investment growth potential estimates\n", "Uncertainty reduction value:\n", "\n", "- 4/10 (from monitoring)\n", "- 50/100 (over future public support consultations)\n", "\n", "**Stakeholder Engagement Plan**\n", "\n", "Stakes: The city stakeholders include residents in coastal areas, businesses with operations or locations near the sea, fishermen and local water management systems.\n", "\n", "Key Actions:\n", "Create an inclusive communication strategy to engage participants at any level.\n", "Provide comprehensive information on projects, costs, benefits and implementation phases\n", "Promote education within schools for climate change and adaptation policies\n", "Address concerns through participation of public listening sessions for effective engagement across different stakeholder groups.\n", "\n", "**Assumptions Key Assumptions**\n", "\n", "Modeling choices:\n", "Sea Level Rise projection (e.g. the National Oceanic & Atmospheric Administration's projections).\n", "Climate change sensitivity.\n", "Long-term economic growth rate.\n", "\n", "Future policy framework and its long-term cost reduction measures\n", "Investment growth potential estimates\n", "\n", "**Value of data:**\n", "\n", "Expected lives saved under uncertainty for monitoring efforts $50, expected value in dollars of effective infrastructure planning adjustments 100k; expected social benefit through public support outreach plans 4000\n", "\n", "\n" ] } ], "source": [ "print(together)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "judge = f\"\"\"You are judging a competition between {len(competitors)} competitors.\n", "Each model has been given this question:\n", "\n", "{question}\n", "\n", "Your job is to evaluate each response for clarity and strength of argument, and rank them in order of best to worst.\n", "Respond with JSON, and only JSON, with the following format:\n", "{{\"results\": [\"best competitor number\", \"second best competitor number\", \"third best competitor number\", ...]}}\n", "\n", "Here are the responses from each competitor:\n", "\n", "{together}\n", "\n", "Now respond with the JSON with the ranked order of the competitors, nothing else. Do not include markdown formatting or code blocks.\"\"\"\n" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "You are judging a competition between 3 competitors.\n", "Each model has been given this question:\n", "\n", "You are the chief planner for a mid-sized coastal city of 800,000 people that has 20 years to prepare for climate-change-driven sea-level rise now projected to be between 0.5 m and 1.5 m by 2045, with more frequent and intense storms; the city's annual budget for adaptation is capped at the equivalent of 5% of current GDP and political opposition to large-scale relocation is strong—produce a single, detailed answer that includes: (1) a prioritized, phased 20-year adaptation strategy across infrastructure, housing, economy, legal/regulatory, health/social services, and social equity designed to minimize expected fatalities and economic loss under uncertainty; (2) quantitative estimates (with units and confidence intervals) of costs, benefits, and probabilities for at least four candidate interventions (e.g., seawalls, managed retreat, elevating buildings, wetland restoration), and a decision-analysis showing which combination is optimal under low, medium, and high sea-level scenarios; (3) an explicit list of at least five critical uncertainties (technical, social, economic) and concrete, time-phased monitoring and data-collection actions that would reduce those uncertainties and how much value (in expected lives or dollars) you expect that information to provide; (4) a stakeholder engagement and communication plan that explains how you would present trade-offs, manage dissent, design compensation for displaced or adversely affected groups, and ensure equity; (5) all key assumptions, model choices, back-of-the-envelope calculations (show your work), potential failure modes, and a clear plan for how and when you would update or abandon major actions in response to new data; and (6) a short reflection on which parts of your answer you are least confident in and what additional expert inputs or data you would request before committing to implementation—if relevant information is missing, explicitly state the questions you would ask and why.\n", "\n", "Your job is to evaluate each response for clarity and strength of argument, and rank them in order of best to worst.\n", "Respond with JSON, and only JSON, with the following format:\n", "{\"results\": [\"best competitor number\", \"second best competitor number\", \"third best competitor number\", ...]}\n", "\n", "Here are the responses from each competitor:\n", "\n", "# Response from competitor 1\n", "\n", "# **20-Year Climate Adaptation Strategy for Coastal City (Pop. 800,000)**\n", "\n", "## **1. Prioritized, Phased 20-Year Adaptation Strategy**\n", "\n", "**Guiding Principles:** \n", "- **No-regrets actions first** (beneficial regardless of SLR scenario). \n", "- **Modularity & flexibility** (avoid irreversible lock-in). \n", "- **Equity-first targeting** (protect most vulnerable populations earliest). \n", "- **Phased trigger-based implementation** (actions tied to monitoring thresholds).\n", "\n", "---\n", "\n", "### **Phase 1: Years 1–5 (Immediate No-Regrets)**\n", "- **Infrastructure:** \n", " - Stormwater pump upgrades, backflow preventers, elevate critical electrical substations (≥0.5 m). \n", " - Begin **living shoreline/wetland restoration** in lower-risk areas for wave attenuation. \n", "- **Housing:** \n", " - **Mandatory disclosure** of flood risk for property transactions. \n", " - **Subsidized elevation** of low-income homes in 100-year floodplain. \n", "- **Legal/Regulatory:** \n", " - Update building codes for new construction (freeboard +1 m above current BFE). \n", " - **Rolling building moratorium** in highest-risk zones (no new dense development). \n", "- **Health/Social:** \n", " - Map vulnerable populations (elderly, disabled, low-mobility) in flood zones. \n", " - Upgrade emergency alert systems and evacuation routes. \n", "- **Economy:** \n", " - Tax incentives for businesses to flood-proof critical assets. \n", " - Diversify tourism-dependent economy with climate-resilient sectors (remote IT, green tech).\n", "\n", "---\n", "\n", "### **Phase 2: Years 6–12 (Flexible Mid-term)**\n", "- **Infrastructure:** \n", " - **Modular seawall/green hybrid** protection for central business district (CBD) and port, designed for +0.5 m SLR but foundations for +1.5 m. \n", " - Elevate key transportation corridors. \n", "- **Housing:** \n", " - **Managed retreat buyouts** for highest-risk repetitive-loss properties (voluntary, with community relocation planning). \n", " - Density bonuses for development in “safe growth” zones. \n", "- **Legal:** \n", " - **Transferable development rights** program to shift value from flood zones. \n", " - **Community land trusts** to maintain affordable housing in resilient areas. \n", "- **Health:** \n", " - Flood-resilient clinics in at-risk neighborhoods. \n", "- **Economy:** \n", " - Port redesign for higher sea levels; invest in offshore wind to replace vulnerable coastal industries.\n", "\n", "---\n", "\n", "### **Phase 3: Years 13–20 (Long-term Contingent)**\n", "- **Infrastructure:** \n", " - **Contingent large-scale protection** (e.g., storm surge barrier) if monitoring shows SLR >1.0 m before 2035. \n", " - Accelerated retreat if protection proves infeasible. \n", "- **Housing:** \n", " - **Phased relocation** of entire neighborhoods if thresholds breached. \n", "- **Legal:** \n", " - **Pre-emptive rezoning** of future floodplains to open space/water-compatible uses. \n", "- **Economy:** \n", " - Relocate critical infrastructure (water treatment, energy) inland.\n", "\n", "---\n", "\n", "## **2. Quantitative Estimates & Decision Analysis**\n", "\n", "**Annual budget cap:** 5% of GDP. Assume GDP = $40,000/capita × 800,000 = $32B → annual adaptation budget = **$1.6B**.\n", "\n", "**Four candidate interventions:**\n", "\n", "| Intervention | Upfront Cost (Years 1–5) | Annual O&M | Expected Lives Saved (2045, medium SLR) | Economic Benefit (NPV over 20y) | Key Uncertainty |\n", "|--------------|---------------------------|------------|----------------------------------------|----------------------------------|-----------------|\n", "| **A. Seawall (8 km, +1.5 m design)** | $2.4B ($480M/yr × 5) | $30M/yr | 300–500 (95% CI: 200–600) | $5B (property & biz protection) | SLR actual rate; maintenance cost |\n", "| **B. Managed Retreat (2,000 households)** | $1.2B ($600k/household incl. compensation) | $5M/yr | 50–100 (95% CI: 30–150) | $1.5B (avoided losses) | Community acceptance; land value |\n", "| **C. Elevating Buildings (10,000 structures)** | $1.5B ($150k/structure avg) | $2M/yr | 200–400 (95% CI: 100–500) | $4B (property saved) | Effectiveness in extreme storms |\n", "| **D. Wetland Restoration (500 ha)** | $0.3B ($60M/yr × 5) | $10M/yr | 20–50 (95% CI: 10–100) | $1.2B (ecoservices + attenuation) | Survival under rapid SLR |\n", "\n", "**Decision Analysis:** \n", "Use expected utility under 3 SLR scenarios (Low: 0.5 m, Med: 1.0 m, High: 1.5 m by 2045), with probabilities (P_low=0.2, P_med=0.5, P_high=0.3). \n", "- **Seawall:** High benefit if SLR high, stranded asset if low. \n", "- **Retreat:** High equity benefit, moderate economic benefit. \n", "- **Elevation:** Cost-effective across scenarios. \n", "- **Wetlands:** Low cost, co-benefits (biodiversity, water quality).\n", "\n", "**Optimal mix under each scenario (maximize lives saved + economic NPV, subject to budget):** \n", "- **Low SLR:** Elevation + Wetlands + limited retreat. \n", "- **Medium SLR:** Elevation + Hybrid seawall (modular) + Wetlands. \n", "- **High SLR:** Seawall + Elevation + Accelerated retreat.\n", "\n", "**Recommended phased portfolio:** \n", "Years 1–5: **Elevation (C) + Wetlands (D)** → $1.8B total. \n", "Years 6–12: **Modular seawall (A) in CBD only + Managed retreat (B)** → $3.6B total. \n", "Years 13–20: **Contingent expansion** based on monitoring.\n", "\n", "---\n", "\n", "## **3. Critical Uncertainties & Monitoring Plan**\n", "\n", "| Uncertainty | Monitoring Action (Time-phased) | Value of Information (VOI) |\n", "|-------------|--------------------------------|----------------------------|\n", "| **1. Actual SLR rate** | Annual satellite/ tidal gauge + AI projection updates (Years 1–20). | High: $200M–$1B (avoid over/under investment). |\n", "| **2. Wetland survival under SLR** | Bi-annual marsh elevation surveys (Years 1–10). | Medium: $50M (avoid ineffective nature-based spend). |\n", "| **3. Community willingness to relocate** | Annual social surveys + pilot relocation incentives (Years 1–5). | High: $300M (prevent stalled retreat). |\n", "| **4. Storm intensity frequency** | Real-time climate modeling + historical analysis. | High: $400M (adjust protection standards). |\n", "| **5. Cost escalation of hard engineering** | Global material cost tracking + modular pilot (Years 1–5). | Medium: $150M (budget risk). |\n", "\n", "**VOI estimated via Monte Carlo simulation of decision trees:** reducing uncertainty can prevent 50–200 premature deaths and $500M in wasted spending.\n", "\n", "---\n", "\n", "## **4. Stakeholder Engagement & Communication Plan**\n", "\n", "- **Transparent trade-off visualization:** Public dashboard with SLR scenarios, costs, and benefits of each option in lives and dollars.\n", "- **Manage dissent:** \n", " - **Citizen assemblies** for contested decisions (e.g., retreat areas). \n", " - **Equity impact assessments** for all projects. \n", "- **Compensation design:** \n", " - **Property buyouts at pre-disclosure value** plus relocation assistance. \n", " - **Renters’ support fund** for displaced households. \n", " - **Job retraining** for affected coastal workers. \n", "- **Equity assurance:** \n", " - **Over-represent vulnerable groups** in planning committees. \n", " - **Targeted subsidies** for low-income adaptation. \n", " - **Legal aid** for property disputes.\n", "\n", "---\n", "\n", "## **5. Key Assumptions, Calculations & Failure Modes**\n", "\n", "**Assumptions:** \n", "- GDP constant (no growth/recession). \n", "- SLR linear (likely optimistic). \n", "- No catastrophic storm before 2045. \n", "- Political continuity on adaptation.\n", "\n", "**Back-of-envelope calculations:** \n", "- **Seawall cost:** $300M/km × 8 km = $2.4B (based on USACE estimates). \n", "- **Buyout cost:** $600k/household (includes compensation, demolition, land restoration). \n", "- **Lives saved:** Based on flood mortality models × population at risk.\n", "\n", "**Failure modes:** \n", "- **Seawall:** induces false security, increases development behind it. \n", "- **Retreat:** social disruption, gentrification in receiving areas. \n", "- **Elevation:** only protects structures, not land/ infrastructure. \n", "- **Wetlands:** may drown if SLR >1 cm/yr.\n", "\n", "**Adaptive management triggers:** \n", "- If SLR >0.8 m by 2030 → accelerate retreat. \n", "- If wetland loss >20% by 2028 → pivot to hybrid engineering. \n", "- If public acceptance <40% for retreat → increase compensation.\n", "\n", "---\n", "\n", "## **6. Reflection on Least Confident Elements**\n", "\n", "**Least confident in:** \n", "1. **Social acceptance modeling** – need expert sociological input on willingness to relocate. \n", "2. **Wetland resilience** – need ecologist input on species selection, sedimentation rates. \n", "3. **Cost escalation** – need construction economist input on modular seawall cost curves. \n", "4. **Mortality estimates** – need public health input on evacuation effectiveness. \n", "5. **Political feasibility** of phased retreat – need governance/political science advice.\n", "\n", "**Missing data questions:** \n", "- What is the **exact at-risk population distribution** by income, race, and tenure? \n", "- What are **historical erosion rates** and sediment supply for beaches/wetlands? \n", "- What **legal barriers** exist for pre-emptive rezoning? \n", "- What is the **carrying capacity** of “safe growth” zones for relocated populations? \n", "- How do **regional economic linkages** affect port relocation decisions?\n", "\n", "**Request:** \n", "- **Expert panel** (climate scientists, economists, sociologists, ecologists, emergency managers) to review thresholds and VOI estimates. \n", "- **Participatory modeling** with community groups to co-create scenarios. \n", "\n", "This plan prioritizes **flexibility, equity, and no-regrets actions**, with continuous monitoring and adaptive triggers to adjust course as uncertainties resolve.\n", "\n", "# Response from competitor 2\n", "\n", "**City‑wide Climate‑Adaptation Blueprint (2025‑2045)** \n", "*Mid‑size coastal city – 800 000 residents, current GDP ≈ US $100 billion (2025 PPP). Annual adaptation budget cap = 5 % × GDP ≈ US $5 billion per year (≈ US $100 billion over 20 years). Political ceiling: no “mass relocation” without broad consensus.*\n", "\n", "---\n", "\n", "## 1. Prioritized, Phased 20‑Year Strategy \n", "\n", "| Phase | Time‑frame | Core Objective | Key Actions (by sector) | Decision‑gate (milestones) |\n", "|-------|------------|----------------|--------------------------|-----------------------------|\n", "| **0 – 2 yr** | **Scoping & Data‑Foundation** | Build an evidence base and secure stakeholder buy‑in before any irreversible capital work. | • Establish *Coastal Resilience Observatory* (CRO) – a joint city‑university data hub.
• Complete high‑resolution (≤5 m) LiDAR/DEM of all built‑environment (cost $45 M).
• Adopt *Dynamic Flood‑Risk Zoning* (DRFZ) ordinance – requires new developments to meet “future‑proof” elevation standards (2 m above projected 2100 sea‑level + 0.5 m safety margin).
• Launch “Coastal Futures” public‑engagement series (town‑halls, digital platform). | • CRO operational & baseline datasets (elevation, tide gauges, storm surge model) verified.
• DRFZ passed by city council (≥70 % support). |\n", "| **2 – 6 yr** | **Protect‑First Infrastructure** | Install “hard” and “soft” barriers where they give the highest risk‑reduction per dollar. | 1. **Hybrid Seawall‑Nature Buffer (HSNB)** on the most exposed 8 km of shoreline (2 km of commercial dock, 6 km of mixed‑use).
– Reinforced concrete toe (2 m crest height) + 200 ha of restored salt‑marsh hinterland (wave‑attenuation).
2. **Strategic Set‑Back & Flood‑Gate System** for the city’s main river inlet (install two flood‑gates, raise approach channel).
3. **Elevate Critical Public Assets** (hospital, emergency operations centre, primary schools in floodplain) to 1.8 m above 2045 projected high‑water mark. | • HSNB structural integrity test (2026).
• Flood‑gate commissioning (2027) – achieve ≥90 % reduction of storm‑surge ingress in pilot tests. |\n", "| **6 – 12 yr** | **Incremental Retreat & Adaptive Re‑use** | Begin low‑conflict, high‑benefit land‑use conversion while preserving community cohesion. | 1. **Managed‑Retreat Pilot (MRP)** – 200 ha of low‑lying industrial land (currently under‑utilised) purchased via “Community Land Trust” (CLT) and converted to tidal wetlands and flood‑resilient recreation park.
2. **Buy‑out & Relocation Program** for 1 % of households (≈8 000) living in the 0.5‑m elevation zone – voluntary, market‑price plus “resilience stipend”.
3. **Building‑Elevation Incentive (BEI)** – 30 % grant (up to US $30 k) for owners to lift existing structures ≥1 m above 2045 flood level; mandatory for all new permits in “future‑risk zones”. | • Land‑acquisition closed (2028).
• First 1 000 households relocated with >85 % satisfaction (2029). |\n", "| **12 – 20 yr** | **Full‑City Adaptive Fabric** | Consolidate protection, embed flexibility, and institutionalise learning. | 1. **City‑wide Wetland Network** – add 500 ha of restored intertidal habitat (distributed along estuary, river mouth, and inland “green corridors”).
2. **Dynamic Zoning Updates** – every 3 yr DRFZ revised based on latest sea‑level projections; restrict new development in “high‑risk” (≥0.8 m) zones.
3. **Resilience Finance Mechanism** – issue a 20‑yr “Coastal Resilience Bond” backed by future property tax increments; proceeds reinvested in maintenance and insurance pool.
4. **Health & Social Services Adaptation** – mobile health units, flood‑ready shelters, mental‑health outreach for displaced families. | • All major hard infrastructure (HSNB, flood‑gates) have 30‑yr design life with scheduled maintenance.
• Insurance loss ratio reduced from 12 % to ≤4 % of property value (2024 baseline). |\n", "\n", "**Prioritisation logic** \n", "\n", "1. **Risk‑Reduction per $** – Hard defenses first where exposure density (people + value) > $10 M/ha. \n", "2. **Reversibility** – Soft measures (wetlands, retreat) are staged after hard works prove insufficient. \n", "3. **Equity** – Low‑income neighbourhoods receive the earliest BEI and relocation assistance. \n", "4. **Political Feasibility** – No full‑city “relocation” before 6 yr; pilot‑scale retreat demonstrates benefits without triggering backlash. \n", "\n", "---\n", "\n", "## 2. Quantitative Evaluation of Four Candidate Interventions \n", "\n", "| Intervention | Description | Capital Cost (2025 $) | Operating/Maintenance (20‑yr) | Lifetime | Expected Damage Avoided (2045) | Lives Saved (2045) | Benefit‑Cost Ratio (NPV, 3 % discount) | Confidence Interval (95 %) |\n", "|--------------|-------------|----------------------|------------------------------|----------|-------------------------------|-------------------|------------------------------------------|----------------------------|\n", "| **A – Hybrid Seawall‑Nature Buffer (HSNB)** | 8 km concrete‑crest wall (2 m) + 200 ha marsh restoration behind it. | $10.2 B (±15 %) | $0.9 B (annual inspection, vegetation mgmt) | 30 yr (wall) / 20 yr (marsh) | $24 B (low) – $38 B (high) – average $31 B | 150 (low) – 260 (high) – avg 205 | **1.9 (1.5‑2.4)** | Cost driven by material price volatility; benefit variance from storm‑frequency uncertainty. |\n", "| **B – Managed‑Retreat Pilot (MRP)** | Purchase & convert 200 ha industrial land to tidal wetlands + public park; voluntary buy‑outs for 8 000 households. | $2.8 B (±20 %) | $0.2 B (maintenance, monitoring) | 25 yr (land) | $8 B (low) – $14 B (high) – avg $11 B | 60 (low) – 110 (high) – avg 85 | **2.1 (1.6‑2.9)** | High upside if sea‑level exceeds 1.0 m because wetlands provide surge attenuation. |\n", "| **C – City‑wide Building Elevation Incentive (BEI)** | Grants covering 30 % of uplift costs for ~12 000 residential/commercial units in risk zones. | $4.5 B (±10 %) | $0.4 B (inspection, verification) | 20 yr (elevated structures) | $12 B (low) – $20 B (high) – avg $16 B | 95 (low) – 160 (high) – avg 125 | **1.7 (1.3‑2.0)** | Benefit depends on compliance rate (assumed 85 %). |\n", "| **D – Large‑Scale Wetland Restoration (LSWR)** | Add 500 ha of intertidal marshes throughout estuary; includes land acquisition and planting. | $6.0 B (±25 %) | $0.6 B (annual habitat management) | 30 yr (ecosystem services) | $10 B (low) – $18 B (high) – avg $14 B | 70 (low) – 130 (high) – avg 100 | **1.4 (1.0‑1.9)** | Ecosystem service valuation (wave attenuation, carbon sequestration) carries large uncertainty. |\n", "\n", "### Decision‑Analysis (Scenario‑Based)\n", "\n", "| Scenario | Sea‑Level Rise (2045) | Probability | Optimal Mix (Cost ≤ $100 B) | Expected NPV (US$ bn) | Expected Lives Saved |\n", "|----------|----------------------|-------------|-----------------------------|-----------------------|----------------------|\n", "| **Low** | **+0.5 m** | 0.30 | **A + C** (HSNB + BEI) | **$57 bn** | **≈ 340** |\n", "| **Medium** | **+1.0 m** | 0.50 | **A + C + D** (HSNB + BEI + LSWR) | **$73 bn** | **≈ 470** |\n", "| **High** | **+1.5 m** | 0.20 | **A + B + C + D** (all four) | **$85 bn** | **≈ 620** |\n", "\n", "*Method*: Monte‑Carlo simulation (10 000 draws) of cost, damage avoided, and lives saved for each intervention, correlated with sea‑level scenario (e.g., higher sea level → higher marsh attenuation benefit). Discount rate 3 % (real). The “optimal mix” is the set of interventions that maximises expected NPV while respecting the budget ceiling (≤ $100 B total capital over 20 yr) and respecting the political constraint that retreat > 10 % of total built‑area is avoided before Year 12.\n", "\n", "**Why the mixes differ** \n", "\n", "* Low sea‑level: Hard protection plus building elevation already bring risk below tolerable levels; additional wetland adds little marginal benefit. \n", "* Medium: Wetlands start to provide a measurable buffer to storm surge; the extra $6 B yields a BCR > 1.3. \n", "* High: Managed retreat becomes cost‑effective because the 200 ha of low‑lying land would be repeatedly flooded; the combined portfolio pushes the overall BCR to ≈ 1.6.\n", "\n", "---\n", "\n", "## 3. Critical Uncertainties & Value‑of‑Information (VOI) Plan \n", "\n", "| # | Uncertainty (Category) | Potential Impact on Decision | Monitoring / Data Collection (Time‑phased) | Expected VOI (Lives / $) |\n", "|---|------------------------|------------------------------|--------------------------------------------|--------------------------|\n", "| **U1** | **Future Sea‑Level Trajectory** (0.5‑1.5 m) – includes ice‑sheet dynamics. | Determines whether retreat is needed. | • Install 3 new high‑frequency tide‑gauge stations (Year 1).
• Annual satellite altimetry cross‑validation (Year 2‑20).
• Publish “Sea‑Level Outlook” every 3 yr. | Reduces expected loss of **≈ 45 lives** and avoids **$2 B** of over‑built hard infrastructure (by allowing a 20 % downsizing if low‑scenario confirmed). |\n", "| **U2** | **Storm‑Surge Frequency & Intensity** under warmer climate. | Affects design height of HSNB, flood‑gate sizing. | • Deploy 5 offshore Doppler radar buoys (Year 1‑3).
• Create a 10‑yr storm‑surge climatology database (Year 1‑10). | VOI ≈ **$1.3 B** (optimising wall height ±0.3 m saves $800 M in construction and protects ~30 lives). |\n", "| **U3** | **Community Acceptance of Relocation** (social‑political). | Determines feasibility/scale of Managed Retreat. | • Baseline social‑acceptance survey (Year 1).
• Quarterly “Resilience Pulse” focus groups (Year 2‑20).
• Pilot voluntary buy‑out outcomes (Year 3‑6). | VOI ≈ **$0.9 B** (avoids wasted purchase of land that would later be contested) and protects **≈ 25 lives** by ensuring relocation occurs before a catastrophic event. |\n", "| **U4** | **Cost of Construction Materials (concrete, steel) & Labor** over 20 yr. | Directly drives capital outlays for HSNB and flood‑gates. | • Quarterly market‑price index tracking (Year 1‑20).
• Early‑procurement contracts with price‑cap clauses (Year 1‑5). | VOI ≈ **$0.6 B** (price‑cap saves 5‑10 % of wall cost). |\n", "| **U5** | **Ecosystem Service Valuation of Restored Wetlands** (wave attenuation, carbon sequestration). | Influences BCR of LSWR and MRP. | • Pre‑ and post‑restoration LiDAR + wave‑attenuation field tests (Year 2‑8).
• Carbon accounting via remote sensing (Year 3‑20). | VOI ≈ **$0.5 B** (more accurate service valuation can justify additional 150 ha of wetlands, saving ≈ 20 lives). |\n", "\n", "*Total expected VOI (aggregate) ≈ **$4.3 B** or **≈ 120 lives** saved, well within the 5 % annual budget for data‑collection and adaptive management.*\n", "\n", "---\n", "\n", "## 4. Stakeholder Engagement & Communication Plan \n", "\n", "| Audience | Key Message | Channels | Participation Mechanism | Compensation / Equity Tools |\n", "|----------|-------------|----------|------------------------|-----------------------------|\n", "| **Residents (high‑risk zones)** | “Your home’s safety is our priority – you choose the path (elevate, relocate, or stay with protection).” | Town‑halls, multilingual webinars, neighborhood canvassing, SMS alerts. | • “Resilience Councils” (one per district) with elected resident chairs.
• Participatory budgeting for BEI grants. | • Voluntary buy‑out at market value + $25 k “resilience stipend”.
• Priority for BEI grants (first‑come, need‑based). |\n", "| **Business Community** | “Continuity of operations is protected through flood‑gate reliability and rapid‑recovery planning.” | Chamber of commerce briefings, sector‑specific workshops, private‑sector advisory board. | • Co‑design of flood‑gate operation protocols.
• Insurance‑premium reduction incentives for participation in BEI. | • Tax credits for retro‑fitting and for financing wetlands that buffer industrial sites. |\n", "| **Low‑Income / Marginalized Groups** | “Equity is built into every dollar – you receive the strongest safeguards first.” | Community‑based NGOs, faith‑based organizations, local radio (multiple languages). | • Equity Impact Assessment (EIA) panels with community reps.
• Direct‑deposit vouchers for BEI. | • Rent‑freeze for 5 yr in upgraded housing.
• Job‑training for wetland‑maintenance crews (local hiring quota 60 %). |\n", "| **Environmental NGOs** | “Nature‑based solutions are a core pillar – we need your science and advocacy.” | Scientific symposiums, data‑portal access, joint‑monitoring contracts. | • Stakeholder Science Advisory Board (SSAB). | • Co‑ownership of CLT lands for community gardens. |\n", "| **Elected Officials / Media** | “Transparent, accountable, and fiscally responsible plan with measurable outcomes.” | Quarterly briefings, public dashboards, press releases. | • Legislative oversight committee with annual performance report. | • Public recognition awards for neighborhoods that meet resilience targets. |\n", "\n", "**Trade‑off Presentation** – Use a **four‑quadrant visual matrix** (Cost vs. Fatalities; Short‑term vs. Long‑term Benefits) in every public briefing. Interactive online tool lets citizens adjust sea‑level assumptions and see resulting cost & life‑savings outcomes (scenario explorer).\n", "\n", "**Managing Dissent** \n", "\n", "* Establish a **“Resilience Ombudsperson”** office (independent, reports to mayor) to receive grievances, mediate disputes, and recommend remedial actions within 30 days. \n", "* Deploy a **“Rapid‑Response Funding”** pool (US $30 M per year) to address unforeseen equity impacts (e.g., sudden market‑price spikes that threaten low‑income renters). \n", "\n", "**Ensuring Equity** \n", "\n", "* Conduct a **Geospatial Equity Index** (weighting income, race, age, disability) to prioritize BEI and relocation assistance. \n", "* All compensation packages are indexed to **Consumer Price Index (CPI) + 2 %** to preserve real purchasing power over the 20‑yr horizon. \n", "\n", "---\n", "\n", "## 5. Assumptions, Model Choices, Back‑of‑Envelope Calculations, Failure Modes & Adaptive Update Rules \n", "\n", "### 5.1 Core Assumptions \n", "\n", "| Category | Assumption | Rationale |\n", "|----------|------------|-----------|\n", "| **Economic** | GDP growth 2.5 % / yr (real) → future adaptation budget grows proportionally. | Aligns city fiscal capacity with national outlook (IMF). |\n", "| **Physical** | Sea‑level rise follows a **log‑normal** distribution with mean 1.0 m, σ = 0.4 m (2025‑2045). | Captures asymmetric tail for high‑end ice‑sheet melt. |\n", "| **Storm** | Frequency of ≥Category‑3 storms increases 15 % per decade (IPCC AR6). | Consistent with regional hurricane model. |\n", "| **Demographic** | Population growth 0.8 % / yr; net migration neutral. | City’s historical trend. |\n", "| **Cost** | Construction cost escalation 3 % / yr (inflation adjusted). | Standard CPI plus materials premium. |\n", "| **Compliance** | 85 % of eligible owners will apply for BEI; 60 % of voluntary buy‑outs accepted. | Based on pilot surveys in comparable U.S. coastal cities (e.g., New Orleans post‑Katrina). |\n", "| **Discount Rate** | Real 3 % (social discount). | Reflects public‑sector long‑term investment standards. |\n", "\n", "### 5.2 Modeling Framework \n", "\n", "* **Damage Estimation** – *Depth‑Damage Functions* (Hazen & Mahadevan 2022) applied to GIS‑based exposure (building inventory, value, occupancy). \n", "* **Sea‑Level + Storm Surge** – Coupled *ADCIRC* hydrodynamic model (grid 5 m) calibrated with historic flood extents (1992‑2022). \n", "* **Benefit‑Cost** – Net Present Value (NPV) calculated for each intervention, summing avoided damages, avoided mortality (valued at VSL = $9.5 M per life, 2025 US dollars), and ecosystem services (carbon sequestration $25 / tCO₂). \n", "* **Monte‑Carlo** – 10 000 iterations sampling sea‑level, storm frequency, construction cost, compliance rates; correlations enforced (e.g., higher sea‑level → higher storm surge). \n", "\n", "### 5.3 Back‑of‑Envelope Sample: HSNB Cost‑Benefit \n", "\n", "1. **Length** = 8 km, **crest height** = 2 m → concrete volume ≈ 8 km × 2 m × 10 m (wall thickness) = 160 000 m³. \n", "2. **Concrete unit cost** = $250 / m³ (2025 price). → $40 M. \n", "3. **Reinforcement & foundation** ≈ 25 % of concrete cost → $10 M. \n", "4. **Construction factor** (site prep, labor, contingency) = 2.5 → total ≈ $125 M per km. \n", "5. **Total wall** = 8 km × $125 M = $1 B. \n", "6. **Marsh restoration** – average $25 k / ha (planting, grading, monitoring) → 200 ha × $25 k = $5 M. \n", "7. **Contingency & design** + 20 % → $1.2 B (wall) + $6 M (marsh) ≈ $1.21 B. \n", "\n", "*Scale up for 2025‑2045 inflation (≈ 3 % / yr → factor 1.81) → $2.2 B. Add O&M $0.9 B (10 % of capital per yr) → total $3.1 B.* \n", "\n", "The table above reports a **$10.2 B** estimate because the final design includes **additional 5‑km of protective revetment**, **storm‑gate integration**, and **high‑grade materials** to meet the 1.5 m high‑scenario, which justifies the higher cost range.\n", "\n", "### 5.4 Potential Failure Modes \n", "\n", "| Mode | Trigger | Consequence | Mitigation |\n", "|------|---------|------------|------------|\n", "| **Structural Failure of Seawall** | Design height insufficient under high‑scenario surge + wave loading. | Rapid overtopping, loss of life, loss of public confidence. | *Adaptive design*: embed modular “raise‑by‑0.5 m” sections; conduct biennial load testing; allocate contingency fund for emergency raise. |\n", "| **Buy‑out Program Stalls** | Community opposition, legal challenges, funding shortfall. | Incomplete retreat, “island” of vulnerable parcels. | Early transparent appraisal, independent mediation, phased funding (bond issuance), legal‑ready easements. |\n", "| **Wetland Establishment Failure** | Invasive species, sediment accretion not reaching design elevation. | Reduced attenuation, wasted capital. | Adaptive monitoring (annual LiDAR), supplemental sediment placement, partnership with local universities for invasive‑species control. |\n", "| **Policy Reversal** | Change in mayoral administration, budget cuts. | Halted projects, loss of sunk costs. | Enshrine DRFZ and CLT statutes in city charter; multi‑year bond financing legally binding. |\n", "| **Data Gap Persistence** | Failure to install/maintain monitoring infrastructure. | Uncertainty remains high → sub‑optimal decisions. | Contractual performance clauses with penalties for missed data deliveries. |\n", "\n", "### 5.5 Adaptive Update & “Kill‑Switch” Rules \n", "\n", "| Review Milestone | Decision Trigger | Action |\n", "|------------------|------------------|--------|\n", "| **Year 3 (2028)** | Sea‑level projection revised upward > 0.9 m (vs. baseline 0.5‑1.0 m). | Accelerate Managed‑Retreat (add 100 ha) and increase HSNB crest by 0.3 m (budget re‑allocation). |\n", "| **Year 6 (2031)** | BEI uptake < 60 % of eligible units. | Expand grant coverage to 45 % + introduce low‑interest loans for elevation. |\n", "| **Year 9 (2034)** | Post‑storm assessment shows overtopping of HSNB > 5 % of events. | Deploy “Rapid‑Raise” modular panels; fund emergency reinforcement. |\n", "| **Year 12 (2037)** | Wetland carbon sequestration < 30 % of forecast. | Re‑evaluate LSWR target – either increase area or switch to *living‑shoreline* breakwaters. |\n", "| **Year 15 (2040)** | Cumulative cost > 95 % of budget with > 15 % forecasted overruns. | Initiate “Resilience Re‑budget” process: pause non‑essential projects, re‑prioritise based on updated BCRs. |\n", "| **Year 20 (2045)** | Final sea‑level measurement exceeds high‑scenario (+1.5 m). | Activate **contingency reserve** (5 % of total budget) to fund emergency evacuation infrastructure and post‑event recovery. |\n", "\n", "---\n", "\n", "## 6. Reflection – Confidence Gaps & Information Requests \n", "\n", "| Area | Confidence Level (Low/Med/High) | Reason for Uncertainty | Additional Expert Input / Data Needed |\n", "|------|--------------------------------|------------------------|----------------------------------------|\n", "| **Sea‑Level Projection Distribution** | **Low** | Ice‑sheet dynamics (Antarctica) remain highly model‑dependent; recent satellite gravimetry suggests faster melt. | Collaboration with **NASA/ESA climate modelling groups** to obtain downscaled probabilistic sea‑level ensembles (2025‑2030). |\n", "| **Storm‑Surge Intensification Factor** | **Medium** | Regional climate models disagree on the magnitude of increase in Category‑3+ storms. | Engage **National Hurricane Center** and **regional climate research centers** for high‑resolution (≤2 km) ensemble simulations. |\n", "| **Social Acceptance of Relocation** | **Medium** | Pilot data (8 000 households) may not extrapolate city‑wide; cultural attachment and historic neighborhoods could cause backlash. | Conduct **full‑city social‑impact assessment** with **urban sociologists** and **community‑based participatory research** (CBPR) to map “relocation resistance hotspots”. |\n", "| **Ecosystem Service Valuation of Restored Wetlands** | **Low** | Quantifying wave‑attenuation and carbon benefits at the city scale has large error bars (±30 %). | Commission **ecological economists** to run site‑specific **hydrodynamic–vegetation coupled models** and **carbon flux monitoring** (e.g., eddy‑covariance towers). |\n", "| **Long‑Term Maintenance Funding** | **Medium** | Assumes 10 % of capital cost per year; future fiscal pressures (e.g., recession) could reduce allocations. | Develop a **dedicated Resilience Trust Fund** with actuarial modeling; request **public‑finance experts** to design a resilient bond structure (e.g., climate‑linked bonds). |\n", "\n", "**Key “Missing‑Information” Questions to Ask Now**\n", "\n", "1. **What is the probability distribution of a “compound event” (simultaneous high tide + storm surge + river flood) for our estuary by 2040?** \n", " *Why*: Determines whether flood‑gates alone can protect the river mouth or whether a higher seawall is needed. \n", "\n", "2. **What are the legally enforceable limits of eminent‑domain/voluntary buy‑out under state law, and what compensation precedents exist?** \n", " *Why*: Directly impacts the feasibility and cost ceiling of Managed‑Retreat. \n", "\n", "3. **How will future insurance market reforms (e.g., risk‑based premiums, federal back‑stop changes) affect the city’s fiscal exposure?** \n", " *Why*: Influences the economic justification of BEI vs. hard infrastructure. \n", "\n", "4. **What is the projected labor availability (skilled concrete workers, ecologists) over the next decade?** \n", " *Why*: Labor shortages could drive up cost (U4) and delay critical phases. \n", "\n", "5. **Are there existing regional or federal grant programs (e.g., FEMA’s Hazard Mitigation Grant Program, NOAA’s Coastal Resilience Grants) that could offset up to 30 % of the HSNB or wetland costs?** \n", " *Why*: Reducing the city’s capital outlay would free budget for equity measures. \n", "\n", "---\n", "\n", "### Bottom Line \n", "\n", "- **Optimal baseline portfolio (medium scenario)** = **Hybrid Seawall‑Nature Buffer + Building‑Elevation Incentives + Large‑Scale Wetland Restoration** (cost ≈ $20 B, BCR ≈ 1.7, lives saved ≈ 470). \n", "- **Escalation to high sea‑level** adds **Managed‑Retreat Pilot**, raising total cost to **≈ $31 B** but delivering **≈ 620 lives saved** and a BCR still > 1.5. \n", "- The **first 2 years** are devoted to data infrastructure and stakeholder trust‑building; this front‑loading dramatically reduces the expected loss of life and unnecessary capital outlays (VOI ≈ $4 B). \n", "\n", "The plan stays **within the 5 % GDP annual budget** while delivering a **flexible, equity‑centered, and politically palatable** pathway to protect the city against the most plausible sea‑level rise outcomes over the next two decades. Continuous monitoring, adaptive decision gates, and transparent communication will allow the city to *scale up* or *scale back* interventions as the climate reality clarifies.\n", "\n", "# Response from competitor 3\n", "\n", "**Phase 1: Infrastructure Adaptation (2023-2025)**\n", "\n", "1. Prioritize 10% of existing seawalls and coastal defenses for upgrading, estimated cost $100M (3%), benefits from reduced erosion and flooding.\n", "2. Develop a Green Infrastructure Program to implement floodplain management, sediment trapping, and wetlands restoration, estimated cost $50M (2%), benefits from ecosystem services and reduced infrastructure maintenance.\n", "\n", "**Phase 2: Housing Adaptation (2025-2030)**\n", "\n", "1. Implement elevating building codes for new constructions and retrofits of existing buildings above predicted sea level rise thresholds, estimated cost $150M (6%), benefits from reduced flood risk.\n", "2. Launch a managed retreat program to incentivize voluntary relocation in at-risk communities, estimated cost $20M (1%), benefits from avoided displacement.\n", "\n", "**Phase 3: Economy Adaptation (2025-2030)**\n", "\n", "1. Develop and implement climate-resilient infrastructure financing instruments (e.g., climate bonds), estimated cost $10M (0.4%), benefits from reduced long-term costs.\n", "2. Support industry diversification through grants for transition-oriented companies, expected job gains of 500.\n", "\n", "**Phase 4: Legal/Regulatory Adaptation (2028-2035)**\n", "\n", "1. Enact Coastal Area Management and Planning regulations to address projected sea-level rise changes in land-use patterns, estimated annual cost $0.5M (20%), benefits from informed policy design.\n", "2. Pass legislation granting city support for displaced community members through relocations and adaptation assistance programs.\n", "\n", "**Phase 5: Social Equity Adaptation (2028-2035)**\n", "\n", "1. Launch a Fair Benefits Sharing Program to implement funding for vulnerable households in case of relocation, benefiting $300 per family member/year.\n", "2. Offer training opportunities for displaced people and their families for acquiring new skills.\n", "\n", "Four Candidate Interventions: Seawall, Managed Retreat, Elevating Buildings, Wetland Restoration\n", "\n", "Costs:\n", "Seawalls (per square meter): $\\$3-6$\n", "Managed Retreat (per displaced household): $20,000-\\$40,000\n", "Elevated Buildings (total new construction value over 10 years at 5% interest rate): $(15 million)\\left(1-\\left(\\frac{4x \\right)^{-\\frac{7}{20}}\\right)$ $\\$32.51 million$\n", "Wetlands Restoration ($\\$14,600/ha/a$): wetlands restoration costs (0 to 50ha): $740 k -12M)\n", "Benefits:\n", "Seawalls: (reduction of storm surges and coastal erosion damage up to 10%).\n", "Managed Retreat: displacement prevention benefits.\n", "Elevated Buildings (reduction risk of flooding for existing buildings).\n", "Wetland Restoration (protection of shoreline by sediment replenishment).\n", "\n", "Sea Level Predictions\n", "Projections indicate that sea levels will rise by 40cm+ in the next 20 years as part of the more frequent and intense storms caused by climate change. Under low, medium, and high sea-level scenarios, cost-benefit analysis and decision analytics revealed:\n", "\n", "Low Sea-Level Rise: The elevation + retreating strategy showed more resilience.\n", "\n", "Medium Sea-Rise: The cost-benefit analysis showed elevated structures were the optimal solution\n", "\n", "High Rise Scenario: Managed Retreat + Green Infrastructure were deemed most valuable.\n", "\n", "Uncertainties\n", "1. Effectiveness of measures in reducing storm damage (10%-70%)\n", "2. Public support for policy measures and their feasibility.\n", "3. Financial risks of infrastructure projects \n", "4.The timing of government assistance programs. \n", "\n", "Actions on Uncertainty Analysis\n", "\n", "In order to reduce these uncertainties, here are some proposed steps:\n", "\n", "For uncertainty of effectiveness: Implement a comprehensive monitoring of effectiveness in reduction project damages during the course of 10 years.\n", "\n", "Public support measures (1): Establish community consultations over time to capture growing public sentiment towards coastal policy.\n", "\n", "Financial risks analysis (2): Increase mitigation measures by providing climate insurance and bonds to safeguard against loss of infrastructure, thereby minimizing the cost that will eventually be borne by taxpayers. \n", "\n", "Timely government aid strategies: Improve timelines of benefits distribution and make timely responses to changing circumstances.\n", "\n", "Assumptions \n", "Climate change parameters:\n", "Sea-level rise between 0.5-1.5 m\n", "Change in intensity and frequency\n", "\n", "Economic assumptions from general guidelines.\n", "Financial risk of infrastructure costs (20%).\n", "Investment growth potential estimates\n", "Uncertainty reduction value:\n", "\n", "- 4/10 (from monitoring)\n", "- 50/100 (over future public support consultations)\n", "\n", "**Stakeholder Engagement Plan**\n", "\n", "Stakes: The city stakeholders include residents in coastal areas, businesses with operations or locations near the sea, fishermen and local water management systems.\n", "\n", "Key Actions:\n", "Create an inclusive communication strategy to engage participants at any level.\n", "Provide comprehensive information on projects, costs, benefits and implementation phases\n", "Promote education within schools for climate change and adaptation policies\n", "Address concerns through participation of public listening sessions for effective engagement across different stakeholder groups.\n", "\n", "**Assumptions Key Assumptions**\n", "\n", "Modeling choices:\n", "Sea Level Rise projection (e.g. the National Oceanic & Atmospheric Administration's projections).\n", "Climate change sensitivity.\n", "Long-term economic growth rate.\n", "\n", "Future policy framework and its long-term cost reduction measures\n", "Investment growth potential estimates\n", "\n", "**Value of data:**\n", "\n", "Expected lives saved under uncertainty for monitoring efforts $50, expected value in dollars of effective infrastructure planning adjustments 100k; expected social benefit through public support outreach plans 4000\n", "\n", "\n", "\n", "Now respond with the JSON with the ranked order of the competitors, nothing else. Do not include markdown formatting or code blocks.\n" ] } ], "source": [ "print(judge)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "judge_messages = [{\"role\": \"user\", \"content\": judge}]" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\"results\": [\"2\", \"1\", \"3\"]}\n" ] } ], "source": [ "# Judgement time!\n", "\n", "openai = OpenAI()\n", "response = openai.chat.completions.create(\n", " model=\"gpt-5-mini\",\n", " messages=judge_messages,\n", ")\n", "results = response.choices[0].message.content\n", "print(results)\n" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Rank 1: openai/gpt-oss-120b\n", "Rank 2: deepseek-chat\n", "Rank 3: llama3.2\n" ] } ], "source": [ "# OK let's turn this into results!\n", "\n", "results_dict = json.loads(results)\n", "ranks = results_dict[\"results\"]\n", "for index, result in enumerate(ranks):\n", " competitor = competitors[int(result)-1]\n", " print(f\"Rank {index+1}: {competitor}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", " \n", " \n", " \n", " \n", "
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Exercise

\n", " Which pattern(s) did this use? Try updating this to add another Agentic design pattern.\n", " \n", "
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Commercial implications

\n", " These kinds of patterns - to send a task to multiple models, and evaluate results,\n", " are common where you need to improve the quality of your LLM response. This approach can be universally applied\n", " to business projects where accuracy is critical.\n", " \n", "
" ] } ], "metadata": { "kernelspec": { "display_name": ".venv", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.4" } }, "nbformat": 4, "nbformat_minor": 2 }