Upload 44 files

First upload of the V2 model. pending further fixes

.streamlit/config.toml

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+[theme]
+base = "light"
+primaryColor = "#3d86e9"
+backgroundColor = "#ffffff"
+secondaryBackgroundColor = "#c6d7f0e1"
+textColor = "#262730"
+font = "monospace"

README.md

@@ -1,20 +1,14 @@
----
-title: CRRA Optimization Tool
-emoji: 🚀
-colorFrom: red
-colorTo: red
-sdk: docker
-app_port: 8501
-tags:
-- streamlit
-pinned: false
-short_description: Otpimization tool for the creation of scenarios for CRRA
-license: mit
----
-
-# Welcome to Streamlit!
-
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
-
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+---
+title: Carbon Dioxide Removal Toolkit
+emoji: 🌍
+colorFrom: green
+colorTo: blue
+sdk: streamlit
+sdk_version: "1.32.0"
+app_file: app.py
+pinned: false
+---
+
+# Carbon Dioxide Removal Optimizer
+
+A Streamlit app for optimizing carbon removal portfolios across different methods and constraints.

app.py

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+import streamlit as st
+from streamlit_option_menu import option_menu
+
+from tabs import (tab0_welcome, tab1_inputs, tab2_constraints,
+                  tab3_coefficients, tab4_optimization, tab5_sensitivity,
+                  tab6_scenarios)
+from utils.data_utils import load_slider_data
+
+st.set_page_config("Carbon Portfolio Optimizer", layout="wide")
+st.title("Toolkit: CDR Portfolio Optimization")
+
+with st.sidebar:
+    selected = option_menu(
+        "Navigation",
+        options=[
+            "Welcome",
+            "Resource inputs",
+            "Method constraints",
+            "Resource coefficients",
+            "Portfolio generation",
+            "Sensitivity analysis",
+            "Scenario comparison",
+        ],
+        icons=["house", "upload", "gear", "sliders", "graph-up-arrow", "card-checklist", "search"],
+        default_index=0,
+        orientation="vertical"
+    )
+
+# Dispatch to appropriate tab
+cost_sliders = load_slider_data()
+
+
+if selected == "Welcome":
+    tab0_welcome.render_tab()
+elif selected == "Resource inputs":
+    tab1_inputs.render_tab()
+elif selected == "Method constraints":
+    tab2_constraints.render_tab()
+elif selected == "Resource coefficients":
+    tab3_coefficients.render_tab(cost_sliders)
+elif selected == "Portfolio generation":
+    tab4_optimization.render_tab()
+elif selected == "Sensitivity analysis":
+    tab5_sensitivity.render_tab()
+elif selected == "Scenario comparison":
+    tab6_scenarios.render_tab()

config/config.py

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+import pandas as pd
+
+method_groups = {
+    "Forestry": [
+        "Afforestation", "Reforestation", "Forest management", "Agroforestry"],
+    "Soil carbon sequestration (crop, grasslands)": [
+        "Cropland management", "Pasture management"],
+    "Blue carbon": [
+        "Coastal revegetation (mangrove, seaweeds)"],
+    "Durable biobased products": [
+        "Durable biobased products"],
+    "Bio-char": [
+        "Bio-char - Forest biomass", "Bio-char - Non-forest biomass", "Bio-char - Unspecified biomass"
+    ],
+    "Bio-CCS": [
+        "BioCCS with combustion", "BioCCS with combustion & growing biomass", "BioCCS with anaerobic digestion/fermentation", "BioCCS with anaerobic digestion/fermentation & growing biomass"
+    ],
+    "Enhanced weathering": [
+        "Enhanced weathering - Basalt mineral", "Enhanced weathering - Olivine mineral", "Enhanced weathering - Wollastonite mineral", "Enhanced weathering - Unspecified mineral"
+    ],
+    "Ocean alkalinity enhancement": [
+        "Mineral OAE / Ocean liming", "Electrochemical OAE"
+    ],
+    "Direct air carbon capture and storage (DACCS)": [
+        "Electrochemical DACCS", "Low-temp DACCS (solid-sorbent/liquid-solvent)",
+        "High-temp DACCS (solid-sorbent/liquid-solvent)", "Mineral looping DACCS",
+        "Moisture swing DACCS"
+    ],
+    "Direct ocean carbon captureand storage (DOCCS)": ["pH swing"],
+    "Mineralization": ["Mineralization ex-situ"]
+}
+
+resource_groups = {
+    "Minerals": ["Basalt mineral (t/tCO2)", "Olivine mineral (t/tCO2)", "Wollastonite mineral (t/tCO2)", "Unspecified mineral (t/tCO2)"],
+    "Biomass": ["Forest biomass (t/tCO2)", "Non-forest biomass (t/tCO2)", "Unspecified biomass (t/tCO2)"],
+    "Land": ["Arable land (ha/tCO2)", "Non-arable land (ha/tCO2)", "Unspecified land (ha/tCO2)"],
+    "Chemicals": ["Unspecified chemical (t/tCO2)"],
+    "Energy": ["Electrical energy (MWh/tCO2)", "Thermal energy unspecified grade (MWh/tCO2)", "Thermal energy low grade (MWh/tCO2)", "Thermal energy high grade (MWh/tCO2)"],
+    "Water": ["Salt water (Liters/tCO2)", "Unspecified water (Liters/tCO2)"],
+    "Shoreline": ["Shoreline (km/tCO2)"],
+}
+
+method_order = sum(method_groups.values(), [])
+
+resource_order = sum(resource_groups.values(), [])
+
+# names used for display
+resource_unit_map = {
+    "Land": "ha",
+    "Energy": "MWh",
+    "Biomass": "t",
+    "Minerals": "t",
+    "Chemicals": "t",
+    "Shoreline": "km",
+    "Water": "L"
+}
+
+resource_units = {
+    "ha/tCO2": ["Arable land (ha/tCO2)", "Non-arable land (ha/tCO2)", "Unspecified land (ha/tCO2)"],
+    "MWh/tCO2": ["Electrical energy (MWh/tCO2)", "Thermal energy unspecified grade (MWh/tCO2)", "Thermal energy low grade (MWh/tCO2)", "Thermal energy high grade (MWh/tCO2)"],
+    "t/tCO2": ["Forest biomass (t/tCO2)", "Non-forest biomass (t/tCO2)", "Unspecified biomass (t/tCO2)", "Basalt mineral (t/tCO2)", "Olivine mineral (t/tCO2)", "Wollastonite mineral (t/tCO2)", "Unspecified mineral (t/tCO2)", "Unspecified chemical (t/tCO2)"],
+    "km/tCO2": ["Shoreline (km/tCO2)"],
+    "Liters/tCO2": ["Salt water (Liters/tCO2)", "Unspecified water (Liters/tCO2)"]
+}
+
+resource_grouped = {
+    "Land": ["Arable land (ha/tCO2)", "Non-arable land (ha/tCO2)", "Unspecified land (ha/tCO2)"],
+    "Energy": ["Electrical energy (MWh/tCO2)", "Thermal energy unspecified grade (MWh/tCO2)", "Thermal energy low grade (MWh/tCO2)", "Thermal energy high grade (MWh/tCO2)"],
+    "Biomass": ["Forest biomass (t/tCO2)", "Non-forest biomass (t/tCO2)", "Unspecified biomass (t/tCO2)"],
+    "Minerals": ["Basalt mineral (t/tCO2)", "Olivine mineral (t/tCO2)", "Wollastonite mineral (t/tCO2)", "Unspecified mineral (t/tCO2)"],
+    "Chemicals": ["Unspecified chemical (t/tCO2)"],
+    "Shoreline": ["Shoreline (km/tCO2)"],
+    "Water": ["Salt water (Liters/tCO2)", "Unspecified water (Liters/tCO2)"]
+}
+
+# secondary resources
+secondary_df = pd.read_csv("data/secondary_resources.csv")
+secondary_resources = set(zip(secondary_df["method"], secondary_df["resource"]))
+
+# tooltips
+resource_tooltips = {
+    # "Arable land (ha/tCO2)": "Land used for growing crops.",
+    # "Non-arable land (ha/tCO2)": "Land not suitable for crop production, e.g., pastures.",
+    # "Unspecified land (ha/tCO2)": "General land use not specified.",
+    # "Electrical energy (MWh/tCO2)": "Electricity consumption per ton of CO2 captured.",
+    # "Thermal energy unspecified grade (MWh/tCO2)": "Heat energy consumption per ton of CO2 captured.",
+    # "Thermal energy low grade (MWh/tCO2)": "Heat energy consumption per ton of CO2 captured.",
+    # "Thermal energy high grade (MWh/tCO2)": "Heat energy consumption per ton of CO2 captured.",
+    "Forest biomass (t/tCO2)": "Dry quantities of biomass sourced from forests.",
+    "Non-forest biomass (t/tCO2)": "Dry quantities of biomass sourced from non-forest areas.",
+    "Unspecified biomass (t/tCO2)": "Dry quantities of general biomass, not specified.",
+    # "Basalt mineral (t/tCO2)": "Basalt mineral used in enhanced weathering.",
+    # "Olivine mineral (t/tCO2)": "Olivine mineral used in enhanced weathering.",
+    # "Wollastonite mineral (t/tCO2)": "Wollastonite mineral used in enhanced weathering.",
+    # "Unspecified mineral (t/tCO2)": "General mineral not specified for enhanced weathering.",
+    # "Unspecified chemical (t/tCO2)": "General chemical resource not specified.",
+    # "Salt water (Liters/tCO2)": "Salt water used in ocean-based methods.",
+    # "Unspecified water (Liters/tCO2)": "General water resource not specified.",
+    # "Shoreline (km/tCO2)": "Length of shoreline affected by coastal methods."
+}
+
+method_tooltips = {
+    "Afforestation": "Methods related to afforestation practices.",
+    "Reforestation": "Methods related to reforestation practices.",
+    "Forest management": "Sustainable management of forest resources.",
+    "Agroforestry": "Integration of trees and shrubs into agricultural systems.",
+    "Cropland management": "Practices to enhance carbon storage in croplands.",
+    "Pasture management": "Sustainable practices for managing pastures.",
+    "Coastal revegetation (mangrove, seaweeds)": "Restoration of coastal ecosystems using mangroves and seaweeds.",
+    "Durable biobased products": "Production of long-lasting biobased materials.",
+    "Bio-char - Forest biomass": "Production of bio-char from forest biomass.",
+    "Bio-char - Non-forest biomass": "Production of bio-char from non-forest biomass.",
+    "Bio-char - Unspecified biomass": "Production of bio-char from unspecified biomass sources.",
+    "BioCCS with combustion": "Bioenergy with carbon capture and storage using combustion.",
+    "BioCCS with combustion & growing biomass": "Bioenergy with carbon capture and storage using combustion and growing biomass.",
+    "BioCCS with anaerobic digestion/fermentation": "Bioenergy with carbon capture and storage using anaerobic digestion or fermentation.",
+    "BioCCS with anaerobic digestion/fermentation & growing biomass": "Bioenergy with carbon capture and storage using anaerobic digestion or fermentation and growing biomass.",
+    "Enhanced weathering - Basalt mineral": "Use of basalt mineral for enhanced weathering.",
+    "Enhanced weathering - Olivine mineral": "Use of olivine mineral for enhanced weathering.",
+    "Enhanced weathering - Wollastonite mineral": "Use of wollastonite mineral for enhanced weathering.",
+    "Enhanced weathering - Unspecified mineral": "Use of unspecified mineral for enhanced weathering.",
+    "Mineral OAE / Ocean liming": "Ocean alkalinity enhancement using mineral or ocean liming.",
+    "Electrochemical OAE": "Ocean alkalinity enhancement using electrochemical methods.",
+    "Electrochemical DACCS": "Direct air carbon capture and storage using electrochemical methods.",
+    "Low-temp DACCS (solid-sorbent/liquid-solvent)": "Direct air carbon capture and storage using low-temperature solid sorbents or liquid solvents.",
+    "High-temp DACCS (solid-sorbent/liquid-solvent)": "Direct air carbon capture and storage using high-temperature solid sorbents or liquid solvents.",
+    "Mineral looping DACCS": "Direct air carbon capture and storage using mineral looping.",
+    "Moisture swing DACCS": "Direct air carbon capture and storage using moisture swing methods.",
+    "pH swing": "Direct ocean carbon capture and storage using pH swing methods.",
+    "Mineralization ex-situ": "Ex-situ mineralization methods for carbon capture."
+}

data/cost_sliders.json

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+{
+  "Afforestation": {
+    "Unspecified land (ha/tCO2)": {
+      "min": 0.002,
+      "median": 0.094926862556845,
+      "max": 0.38461538
+    },
+    "Unspecified water (Liters/tCO2)": {
+      "min": 46000.0,
+      "median": 92000.0,
+      "max": 92000.0
+    }
+  },
+  "Agroforestry": {
+    "Arable land (ha/tCO2)": {
+      "min": 0.0025,
+      "median": 0.06410128,
+      "max": 3.03030303
+    }
+  },
+  "BioCCS with anaerobic digestion/fermentation": {
+    "Non-forest biomass (t/tCO2)": {
+      "min": 1.315,
+      "median": 1.5617433414042998,
+      "max": 8.0
+    },
+    "Unspecified biomass (t/tCO2)": {
+      "min": 0.99809364,
+      "median": 1.14,
+      "max": 2.0
+    },
+    "Unspecified water (Liters/tCO2)": {
+      "min": 368182.0,
+      "median": 565909.0,
+      "max": 913636.0
+    }
+  },
+  "BioCCS with combustion": {
+    "Electrical energy (MWh/tCO2)": {
+      "min": 0.0,
+      "median": 0.101,
+      "max": 0.5
+    },
+    "Forest biomass (t/tCO2)": {
+      "min": 0.55555555,
+      "median": 0.59375,
+      "max": 0.8547
+    },
+    "Thermal energy unspecified grade (MWh/tCO2)": {
+      "min": 0.0,
+      "median": 0.9306111111111112,
+      "max": 4.0
+    },
+    "Unspecified biomass (t/tCO2)": {
+      "min": 0.66666667,
+      "median": 1.14,
+      "max": 2.0
+    },
+    "Unspecified chemical (t/tCO2)": {
+      "min": 0.00013,
+      "median": 0.00316,
+      "max": 20.2
+    },
+    "Unspecified water (Liters/tCO2)": {
+      "min": 630.0,
+      "median": 201625.2165,
+      "max": 1296342.17
+    }
+  },
+  "Coastal revegetation (mangrove, seaweeds)": {
+    "Non-arable land (ha/tCO2)": {
+      "min": 0.0078,
+      "median": 0.12056618,
+      "max": 0.32828863
+    }
+  },
+  "Cropland management": {
+    "Arable land (ha/tCO2)": {
+      "min": 0.08264463,
+      "median": 0.4206349227777499,
+      "max": 9.0
+    }
+  },
+  "Durable biobased products": {
+    "Electrical energy (MWh/tCO2)": {
+      "min": 2.0,
+      "median": 4.0,
+      "max": 4.0
+    },
+    "Non-arable land (ha/tCO2)": {
+      "min": 3.0,
+      "median": 6.0,
+      "max": 6.0
+    },
+    "Unspecified biomass (t/tCO2)": {
+      "min": 0.0,
+      "median": 3.57142857,
+      "max": 4.0
+    },
+    "Unspecified water (Liters/tCO2)": {
+      "min": 100.0,
+      "median": 300.0,
+      "max": 500.0
+    }
+  },
+  "Electrochemical DACCS": {
+    "Electrical energy (MWh/tCO2)": {
+      "min": 0.252556,
+      "median": 0.784125,
+      "max": 6.2222222222
+    },
+    "Non-arable land (ha/tCO2)": {
+      "min": 0.0002225771032,
+      "median": 0.000323748514,
+      "max": 0.016
+    },
+    "Unspecified water (Liters/tCO2)": {
+      "min": 1000.0,
+      "median": 5000.0,
+      "max": 27000.0
+    }
+  },
+  "Electrochemical OAE": {
+    "Electrical energy (MWh/tCO2)": {
+      "min": 0.83,
+      "median": 2.15,
+      "max": 5.0
+    },
+    "Non-arable land (ha/tCO2)": {
+      "min": 0.00071,
+      "median": 0.00101,
+      "max": 0.00131
+    },
+    "Shoreline (km/tCO2)": {
+      "min": 6e-09,
+      "median": 1.2e-08,
+      "max": 1.2e-08
+    },
+    "Unspecified mineral (t/tCO2)": {
+      "min": 2.4,
+      "median": 2.95,
+      "max": 3.5
+    }
+  },
+  "Forest management": {
+    "Unspecified land (ha/tCO2)": {
+      "min": 0.1087,
+      "median": 0.76865,
+      "max": 1.4286
+    }
+  },
+  "High-temp DACCS (solid-sorbent/liquid-solvent)": {
+    "Electrical energy (MWh/tCO2)": {
+      "min": 0.077,
+      "median": 0.46296111111111,
+      "max": 0.77
+    },
+    "Non-arable land (ha/tCO2)": {
+      "min": 1.5e-10,
+      "median": 0.0002225771032,
+      "max": 0.0009914798235
+    },
+    "Thermal energy high grade (MWh/tCO2)": {
+      "min": 1.125,
+      "median": 1.75,
+      "max": 3.8
+    },
+    "Unspecified water (Liters/tCO2)": {
+      "min": 0.005,
+      "median": 4700.0,
+      "max": 50000.0
+    }
+  },
+  "Low-temp DACCS (solid-sorbent/liquid-solvent)": {
+    "Electrical energy (MWh/tCO2)": {
+      "min": 0.13,
+      "median": 0.46296111111111,
+      "max": 4.6111111111
+    },
+    "Non-arable land (ha/tCO2)": {
+      "min": 1e-11,
+      "median": 0.0002225771032,
+      "max": 0.0009914798235
+    },
+    "Thermal energy low grade (MWh/tCO2)": {
+      "min": 0.6388888889,
+      "median": 1.5,
+      "max": 2.7222222222
+    },
+    "Unspecified water (Liters/tCO2)": {
+      "min": -2000.0,
+      "median": 1008.17439,
+      "max": 15000.0
+    }
+  },
+  "Mineral OAE / Ocean liming": {
+    "Electrical energy (MWh/tCO2)": {
+      "min": 0.027,
+      "median": 0.6065,
+      "max": 4.0
+    },
+    "Non-arable land (ha/tCO2)": {
+      "min": 0.0,
+      "median": 0.000355,
+      "max": 0.00131
+    },
+    "Thermal energy unspecified grade (MWh/tCO2)": {
+      "min": 0.16,
+      "median": 1.4589444444444444,
+      "max": 1.55
+    },
+    "Unspecified mineral (t/tCO2)": {
+      "min": 1.4,
+      "median": 1.743,
+      "max": 5.0
+    },
+    "Unspecified water (Liters/tCO2)": {
+      "min": 339.1655,
+      "median": 678.331,
+      "max": 678.331
+    }
+  },
+  "Mineral looping DACCS": {
+    "Electrical energy (MWh/tCO2)": {
+      "min": 0.0,
+      "median": 0.0833333333333333,
+      "max": 4.0
+    },
+    "Non-arable land (ha/tCO2)": {
+      "min": 0.0002225771032,
+      "median": 0.00063889,
+      "max": 0.0033
+    },
+    "Thermal energy unspecified grade (MWh/tCO2)": {
+      "min": 1.0,
+      "median": 1.81944444445,
+      "max": 2.2222222222
+    },
+    "Unspecified mineral (t/tCO2)": {
+      "min": 0.0,
+      "median": 0.2,
+      "max": 0.5
+    },
+    "Unspecified water (Liters/tCO2)": {
+      "min": 0.0,
+      "median": 200.0,
+      "max": 26714.00689
+    }
+  },
+  "Mineralization ex-situ": {
+    "Electrical energy (MWh/tCO2)": {
+      "min": 0.0,
+      "median": 0.75,
+      "max": 3.0
+    },
+    "Non-arable land (ha/tCO2)": {
+      "min": 0.0002225771032,
+      "median": 0.00043604877962,
+      "max": 0.0009914798235
+    },
+    "Olivine mineral (t/tCO2)": {
+      "min": 0.4,
+      "median": 0.8,
+      "max": 0.8
+    },
+    "Thermal energy unspecified grade (MWh/tCO2)": {
+      "min": 0.4,
+      "median": 0.45,
+      "max": 1.0
+    },
+    "Unspecified mineral (t/tCO2)": {
+      "min": 0.04,
+      "median": 3.0,
+      "max": 10.0
+    },
+    "Unspecified water (Liters/tCO2)": {
+      "min": 0.0,
+      "median": 5450.0,
+      "max": 300000.0
+    }
+  },
+  "Moisture swing DACCS": {
+    "Electrical energy (MWh/tCO2)": {
+      "min": 0.316,
+      "median": 0.32466666665,
+      "max": 0.3333333333
+    },
+    "Non-arable land (ha/tCO2)": {
+      "min": 8e-06,
+      "median": 0.00027248,
+      "max": 0.0009914798235
+    },
+    "Unspecified water (Liters/tCO2)": {
+      "min": -146.0,
+      "median": 427.0,
+      "max": 1000.0
+    }
+  },
+  "Pasture management": {
+    "Arable land (ha/tCO2)": {
+      "min": 0.07189434,
+      "median": 0.25242204,
+      "max": 2.28974424
+    }
+  },
+  "Reforestation": {
+    "Unspecified land (ha/tCO2)": {
+      "min": 0.002,
+      "median": 0.094926862556845,
+      "max": 38.8
+    },
+    "Unspecified water (Liters/tCO2)": {
+      "min": 46000.0,
+      "median": 92000.0,
+      "max": 92000.0
+    }
+  },
+  "pH swing": {
+    "Electrical energy (MWh/tCO2)": {
+      "min": 0.66,
+      "median": 1.768,
+      "max": 4.67
+    },
+    "Salt water (Liters/tCO2)": {
+      "min": 435000.0,
+      "median": 4780000.0,
+      "max": 9125000.0
+    },
+    "Unspecified water (Liters/tCO2)": {
+      "min": 217391.304,
+      "median": 6608695.652,
+      "max": 13000000.0
+    }
+  },
+  "Bio-char - Forest biomass": {
+    "Electrical energy (MWh/tCO2)": {
+      "min": 0.0,
+      "median": 0.00035,
+      "max": 0.5
+    },
+    "Forest biomass (t/tCO2)": {
+      "min": 0.33,
+      "median": 0.37,
+      "max": 0.82
+    },
+    "Unspecified land (ha/tCO2)": {
+      "min": 0.05640158,
+      "median": 0.16320079,
+      "max": 0.27
+    },
+    "Unspecified water (Liters/tCO2)": {
+      "min": -500.0,
+      "median": 100.0,
+      "max": 3000.0
+    }
+  },
+  "Bio-char - Non-forest biomass": {
+    "Electrical energy (MWh/tCO2)": {
+      "min": 0.0,
+      "median": 0.00035,
+      "max": 0.5
+    },
+    "Non-forest biomass (t/tCO2)": {
+      "min": 0.818,
+      "median": 1.3395,
+      "max": 1.667
+    },
+    "Unspecified land (ha/tCO2)": {
+      "min": 0.05640158,
+      "median": 0.16320079,
+      "max": 0.27
+    },
+    "Unspecified water (Liters/tCO2)": {
+      "min": -500.0,
+      "median": 100.0,
+      "max": 3000.0
+    }
+  },
+  "Bio-char - Unspecified biomass": {
+    "Electrical energy (MWh/tCO2)": {
+      "min": 0.0,
+      "median": 0.00035,
+      "max": 0.5
+    },
+    "Unspecified biomass (t/tCO2)": {
+      "min": 0.5,
+      "median": 1.20922073,
+      "max": 12.86
+    },
+    "Unspecified land (ha/tCO2)": {
+      "min": 0.05640158,
+      "median": 0.16320079,
+      "max": 0.27
+    },
+    "Unspecified water (Liters/tCO2)": {
+      "min": -500.0,
+      "median": 100.0,
+      "max": 3000.0
+    }
+  },
+  "Enhanced weathering - Basalt mineral": {
+    "Arable land (ha/tCO2)": {
+      "min": 1e-06,
+      "median": 0.25,
+      "max": 2.5
+    },
+    "Basalt mineral (t/tCO2)": {
+      "min": 1.0,
+      "median": 4.3,
+      "max": 19.0
+    },
+    "Electrical energy (MWh/tCO2)": {
+      "min": 0.00925,
+      "median": 0.38875,
+      "max": 2.775
+    },
+    "Non-arable land (ha/tCO2)": {
+      "min": 0.0009,
+      "median": 0.00906504,
+      "max": 40.0
+    },
+    "Unspecified water (Liters/tCO2)": {
+      "min": 100.0,
+      "median": 866.0,
+      "max": 50000.0
+    }
+  },
+  "Enhanced weathering - Olivine mineral": {
+    "Arable land (ha/tCO2)": {
+      "min": 1e-06,
+      "median": 0.25,
+      "max": 2.5
+    },
+    "Electrical energy (MWh/tCO2)": {
+      "min": 0.00925,
+      "median": 0.38875,
+      "max": 2.775
+    },
+    "Non-arable land (ha/tCO2)": {
+      "min": 0.0009,
+      "median": 0.00906504,
+      "max": 40.0
+    },
+    "Olivine mineral (t/tCO2)": {
+      "min": 0.27027027,
+      "median": 1.25,
+      "max": 3.7
+    },
+    "Unspecified water (Liters/tCO2)": {
+      "min": 100.0,
+      "median": 866.0,
+      "max": 50000.0
+    }
+  },
+  "Enhanced weathering - Wollastonite mineral": {
+    "Arable land (ha/tCO2)": {
+      "min": 1e-06,
+      "median": 0.25,
+      "max": 2.5
+    },
+    "Electrical energy (MWh/tCO2)": {
+      "min": 0.00925,
+      "median": 0.38875,
+      "max": 2.775
+    },
+    "Non-arable land (ha/tCO2)": {
+      "min": 0.0009,
+      "median": 0.00906504,
+      "max": 40.0
+    },
+    "Unspecified water (Liters/tCO2)": {
+      "min": 100.0,
+      "median": 866.0,
+      "max": 50000.0
+    },
+    "Wollastonite mineral (t/tCO2)": {
+      "min": 0.56,
+      "median": 1.5642857142856998,
+      "max": 3.0
+    }
+  },
+  "Enhanced weathering - Unspecified mineral": {
+    "Arable land (ha/tCO2)": {
+      "min": 1e-06,
+      "median": 0.25,
+      "max": 2.5
+    },
+    "Electrical energy (MWh/tCO2)": {
+      "min": 0.00925,
+      "median": 0.38875,
+      "max": 2.775
+    },
+    "Non-arable land (ha/tCO2)": {
+      "min": 0.0009,
+      "median": 0.00906504,
+      "max": 40.0
+    },
+    "Unspecified mineral (t/tCO2)": {
+      "min": 0.5,
+      "median": 2.5,
+      "max": 25.0
+    },
+    "Unspecified water (Liters/tCO2)": {
+      "min": 100.0,
+      "median": 866.0,
+      "max": 50000.0
+    }
+  },
+  "BioCCS with combustion & growing biomass": {
+    "Arable land (ha/tCO2)": {
+      "min": 0.022803284,
+      "median": 0.058399844,
+      "max": 1.9451
+    },
+    "Electrical energy (MWh/tCO2)": {
+      "min": 0.0,
+      "median": 0.101,
+      "max": 0.5
+    },
+    "Thermal energy unspecified grade (MWh/tCO2)": {
+      "min": 0.0,
+      "median": 0.9306111111111112,
+      "max": 4.0
+    },
+    "Unspecified chemical (t/tCO2)": {
+      "min": 0.00013,
+      "median": 0.00316,
+      "max": 20.2
+    },
+    "Unspecified water (Liters/tCO2)": {
+      "min": 630.0,
+      "median": 201625.2165,
+      "max": 1296342.17
+    }
+  },
+  "BioCCS with anaerobic digestion/fermentation & growing biomass": {
+    "Arable land (ha/tCO2)": {
+      "min": 0.022803284,
+      "median": 0.058844191,
+      "max": 0.454545455
+    },
+    "Unspecified water (Liters/tCO2)": {
+      "min": 368182.0,
+      "median": 565909.0,
+      "max": 913636.0
+    }
+  }
+}

data/generate_cost_matrix.py

@@ -0,0 +1,92 @@
+#%% uses the latest version of aggregated inputs to create the dictionary of costs
+import json
+
+import pandas as pd
+
+
+def create_sliders_inputs():
+    df = pd.read_csv("inputs_aggregated.csv")
+
+    cost_sliders = {}
+
+    # extract data from inputs and create final dictionary for sliders
+    for _, row in df.iterrows():
+        method = row["Implementation"]
+        # break bio-char into 3 different cases, based on which biomass is used
+        """
+        For the Implementation "Biochar", we must create three different cases:
+        1. Bio-char - Forest biomass
+        2. Bio-char - Non-forest biomass
+        3. Bio-char - Unspecified biomass
+        Where we have to keep the value for the corresponding resource in:
+            [Forest biomass OR Non-forest biomass OR Unspecified biomass]
+        But set the other two to zero, and keep the values in the other resources intact
+        """
+        resource = row["Resource"] + " (" + row["Unit"] + ")"
+        if row["min"] == row["max"]:
+            row["min"] = row["min"] * .5
+        entry = {
+            "min": float(row["min"]),
+            "median": float(row["median"]),
+            "max": float(row["max"])
+        }
+        cost_sliders.setdefault(method, {})[resource] = entry
+        
+    print(cost_sliders)
+    
+    # create the three cases for bio-char
+    cost_sliders["Bio-char - Forest biomass"] = cost_sliders["Bio-char"].copy()
+    cost_sliders["Bio-char - Forest biomass"].pop("Non-forest biomass (t/tCO2)")
+    cost_sliders["Bio-char - Forest biomass"].pop("Unspecified biomass (t/tCO2)")
+    cost_sliders["Bio-char - Non-forest biomass"] = cost_sliders["Bio-char"].copy()
+    cost_sliders["Bio-char - Non-forest biomass"].pop("Forest biomass (t/tCO2)")
+    cost_sliders["Bio-char - Non-forest biomass"].pop("Unspecified biomass (t/tCO2)")
+    cost_sliders["Bio-char - Unspecified biomass"] = cost_sliders["Bio-char"].copy()
+    cost_sliders["Bio-char - Unspecified biomass"].pop("Forest biomass (t/tCO2)")
+    cost_sliders["Bio-char - Unspecified biomass"].pop("Non-forest biomass (t/tCO2)")
+    # remove "Bio-char" from the dictionary
+    if "Bio-char" in cost_sliders:
+        del cost_sliders["Bio-char"]
+        
+    # create all cases for enhanced weathering
+    cost_sliders["Enhanced weathering - Basalt mineral"] = cost_sliders["Enhanced weathering"].copy()
+    cost_sliders["Enhanced weathering - Basalt mineral"].pop("Olivine mineral (t/tCO2)")
+    cost_sliders["Enhanced weathering - Basalt mineral"].pop("Wollastonite mineral (t/tCO2)")
+    cost_sliders["Enhanced weathering - Basalt mineral"].pop("Unspecified mineral (t/tCO2)")
+    cost_sliders["Enhanced weathering - Olivine mineral"] = cost_sliders["Enhanced weathering"].copy()
+    cost_sliders["Enhanced weathering - Olivine mineral"].pop("Basalt mineral (t/tCO2)")
+    cost_sliders["Enhanced weathering - Olivine mineral"].pop("Wollastonite mineral (t/tCO2)")
+    cost_sliders["Enhanced weathering - Olivine mineral"].pop("Unspecified mineral (t/tCO2)")
+    cost_sliders["Enhanced weathering - Wollastonite mineral"] = cost_sliders["Enhanced weathering"].copy()
+    cost_sliders["Enhanced weathering - Wollastonite mineral"].pop("Basalt mineral (t/tCO2)")
+    cost_sliders["Enhanced weathering - Wollastonite mineral"].pop("Olivine mineral (t/tCO2)")
+    cost_sliders["Enhanced weathering - Wollastonite mineral"].pop("Unspecified mineral (t/tCO2)")
+    cost_sliders["Enhanced weathering - Unspecified mineral"] = cost_sliders["Enhanced weathering"].copy()
+    cost_sliders["Enhanced weathering - Unspecified mineral"].pop("Basalt mineral (t/tCO2)")
+    cost_sliders["Enhanced weathering - Unspecified mineral"].pop("Olivine mineral (t/tCO2)")
+    cost_sliders["Enhanced weathering - Unspecified mineral"].pop("Wollastonite mineral (t/tCO2)")
+    # remove "Enhanced weathering" from the dictionary
+    if "Enhanced weathering" in cost_sliders:
+        del cost_sliders["Enhanced weathering"]    
+    # create case for growing biomass in bioccs
+    cost_sliders["BioCCS with combustion & growing biomass"] = cost_sliders["BioCCS with combustion"].copy()
+    cost_sliders["BioCCS with combustion"].pop("Arable land (ha/tCO2)")
+    cost_sliders["BioCCS with combustion & growing biomass"].pop("Forest biomass (t/tCO2)")
+    cost_sliders["BioCCS with combustion & growing biomass"].pop("Unspecified biomass (t/tCO2)")
+    
+    cost_sliders["BioCCS with anaerobic digestion/fermentation & growing biomass"] = cost_sliders["BioCCS with anaerobic digestion/fermentation"].copy()
+    cost_sliders["BioCCS with anaerobic digestion/fermentation"].pop("Arable land (ha/tCO2)")
+    cost_sliders["BioCCS with anaerobic digestion/fermentation & growing biomass"].pop("Non-forest biomass (t/tCO2)")
+    cost_sliders["BioCCS with anaerobic digestion/fermentation & growing biomass"].pop("Unspecified biomass (t/tCO2)")
+    
+    
+
+    with open("cost_sliders.json", "w") as f:
+        json.dump(cost_sliders, f, indent=2)
+
+    print("Saved slider configuration to cost_sliders.json")
+
+if __name__ == "__main__":
+    create_sliders_inputs()
+    print("Script executed successfully.")
+# %%

data/inputs_aggregated.csv

@@ -0,0 +1,76 @@
+Implementation,Resource,Type,Unit,count,quantile_25,mean,median,quantile_75,std,min,max
+Afforestation,Unspecified land,Physical,ha/tCO2,36,0.024592505,0.11565221097503724,0.094926862556845,0.197075,0.11236779976444418,0.002,0.38461538
+Afforestation,Unspecified water,Physical,Liters/tCO2,2,92000.0,92000.0,92000.0,92000.0,0.0,92000.0,92000.0
+Agroforestry,Arable land,Physical,ha/tCO2,12,0.03995278725,0.4507674440833333,0.06410128000000001,0.2141,0.901140274110988,0.0025,3.03030303
+Bio-char,Electrical energy,Energy,MWh/tCO2,6,8.75e-05,0.1667833333333333,0.00035,0.3750875,0.2581085675964025,0.0,0.5
+Bio-char,Forest biomass,Physical,t/tCO2,6,0.34,0.47283333333333327,0.37,0.55525,0.20162382465042825,0.33,0.82
+Bio-char,Non-forest biomass,Physical,t/tCO2,8,1.142,1.291,1.3395000000000001,1.4885000000000002,0.332019792869556,0.818,1.667
+Bio-char,Unspecified biomass,Physical,t/tCO2,48,0.94339623,1.924254525942129,1.20922073,2.522177213052725,1.9378239910641315,0.5,12.86
+Bio-char,Unspecified land,Physical,ha/tCO2,4,0.05640158,0.16320079,0.16320079,0.27,0.12332110528547877,0.05640158,0.27
+Bio-char,Unspecified water,Physical,Liters/tCO2,9,0.0,633.3333333333334,100.0,500.0,1376.5899897936204,-500.0,3000.0
+BioCCS with anaerobic digestion/fermentation,Arable land,Physical,ha/tCO2,16,0.03793734775,0.100866767,0.058844191000000004,0.10303396649999999,0.11375772648817686,0.022803284,0.454545455
+BioCCS with anaerobic digestion/fermentation,Non-forest biomass,Physical,t/tCO2,6,1.4285714285714,2.689964230684533,1.5617433414042998,2.1282742681047,2.6242499596696813,1.315,8.0
+BioCCS with anaerobic digestion/fermentation,Unspecified biomass,Physical,t/tCO2,6,1.03357023,1.3793645466666666,1.14,1.785,0.4849128814150571,0.99809364,2.0
+BioCCS with anaerobic digestion/fermentation,Unspecified water,Physical,Liters/tCO2,14,395454.5,569805.1428571428,565909.0,654545.5,183277.283077123,368182.0,913636.0
+BioCCS with combustion,Arable land,Physical,ha/tCO2,32,0.038,0.20054008704125,0.058399844,0.09459459,0.4656596577374178,0.022803284,1.9451
+BioCCS with combustion,Electrical energy,Energy,MWh/tCO2,18,0.0277777777777777,0.13097530864197526,0.101,0.1111111111111111,0.16241025701586306,0.0,0.5
+BioCCS with combustion,Forest biomass,Physical,t/tCO2,8,0.5607638875000001,0.67548055375,0.59375,0.8386749975,0.14441914653695664,0.55555555,0.8547
+BioCCS with combustion,Thermal energy unspecified grade,Energy,MWh/tCO2,22,0.3472222222222222,1.0062424242424242,0.9306111111111111,1.025,1.0524401441735463,0.0,4.0
+BioCCS with combustion,Unspecified biomass,Physical,t/tCO2,8,0.9166666675,1.2641666675,1.14,1.625,0.5282247887342408,0.66666667,2.0
+BioCCS with combustion,Unspecified chemical,Physical,t/tCO2,20,0.00027,4.044119,0.00316,0.016,8.287794157908491,0.00013,20.2
+BioCCS with combustion,Unspecified water,Physical,Liters/tCO2,32,6475.0,312937.45634375,201625.2165,542045.5,343154.80918602005,630.0,1296342.17
+"Coastal revegetation (mangrove, seaweeds…)",Non-arable land,Physical,ha/tCO2,11,0.043295020000000004,0.13201796181818182,0.12056618,0.19744896,0.11706388563455744,0.0078,0.32828863
+Cropland management,Arable land,Physical,ha/tCO2,14,0.27061989000000003,1.2779463596825358,0.42063492277774994,1.05233487,2.3297794467976978,0.08264463,9.0
+Durable biobased products,Electrical energy,Energy,MWh/tCO2,2,4.0,4.0,4.0,4.0,0.0,4.0,4.0
+Durable biobased products,Non-arable land,Physical,ha/tCO2,2,6.0,6.0,6.0,6.0,0.0,6.0,6.0
+Durable biobased products,Unspecified biomass,Physical,t/tCO2,6,1.2678571425,2.6071428566666666,3.57142857,3.8928571425,1.8426633339173129,0.0,4.0
+Durable biobased products,Unspecified water,Physical,Liters/tCO2,2,200.0,300.0,300.0,400.0,282.842712474619,100.0,500.0
+Electrochemical DACCS,Electrical energy,Energy,MWh/tCO2,10,0.33333333335000004,2.0320806,0.784125,2.75,2.3788325030763033,0.252556,6.2222222222
+Electrochemical DACCS,Non-arable land,Physical,ha/tCO2,14,0.00027248,0.0026615877837057145,0.000323748514,0.000880697128935,0.005656829998005935,0.0002225771032,0.016
+Electrochemical DACCS,Unspecified water,Physical,Liters/tCO2,6,2000.0,11000.0,5000.0,21500.0,12521.980673998822,1000.0,27000.0
+Electrochemical OAE,Electrical energy,Energy,MWh/tCO2,8,1.6,2.24125,2.15,2.5,1.2835935883292655,0.83,5.0
+Electrochemical OAE,Non-arable land,Physical,ha/tCO2,2,0.00086,0.00101,0.00101,0.00116,0.00042426406871192844,0.00071,0.00131
+Electrochemical OAE,Shoreline,Physical,km/tCO2,2,1.2e-08,1.2e-08,1.2e-08,1.2e-08,0.0,1.2e-08,1.2e-08
+Electrochemical OAE,Unspecified mineral,Physical,t/tCO2,4,2.4,2.95,2.95,3.5,0.6350852961085884,2.4,3.5
+Enhanced weathering,Arable land,Physical,ha/tCO2,28,0.12875,0.548973515,0.25,0.5,0.7099161244217219,1e-06,2.5
+Enhanced weathering,Basalt mineral,Physical,t/tCO2,25,3.33,5.773041804595108,4.3,5.3,4.967901225163679,1.0,19.0
+Enhanced weathering,Electrical energy,Energy,MWh/tCO2,8,0.09285,0.59343125,0.38875000000000004,0.5,0.9045657102947644,0.00925,2.775
+Enhanced weathering,Non-arable land,Physical,ha/tCO2,12,0.004428795,11.432468628333334,0.00906504,28.5714286,17.226399848110834,0.0009,40.0
+Enhanced weathering,Olivine mineral,Physical,t/tCO2,26,0.9090909,1.4295112634615386,1.25,1.58,0.9412466256217807,0.27027027,3.7
+Enhanced weathering,Unspecified mineral,Physical,t/tCO2,19,1.31578947,6.356736418947368,2.5,8.625,7.726415037245945,0.5,25.0
+Enhanced weathering,Unspecified water,Physical,Liters/tCO2,5,500.0,10466.4,866.0,866.0,22102.21859452123,100.0,50000.0
+Enhanced weathering,Wollastonite mineral,Physical,t/tCO2,8,1.3214285714285499,1.62964285714285,1.5642857142856998,1.83,0.7423655162834324,0.56,3.0
+Forest management,Unspecified land,Physical,ha/tCO2,2,0.43867500000000004,0.76865,0.76865,1.0986250000000002,0.9333102404881242,0.1087,1.4286
+High-temp DACCS (solid-sorbent/liquid-solvent),Electrical energy,Energy,MWh/tCO2,32,0.3611111111,0.42378784722013874,0.46296111111111,0.5,0.16317834501458298,0.077,0.77
+High-temp DACCS (solid-sorbent/liquid-solvent),Non-arable land,Physical,ha/tCO2,23,2.2000000000000002e-08,0.00022929641616869565,0.0002225771032,0.000298114257,0.0002987803322061786,1.5e-10,0.0009914798235
+High-temp DACCS (solid-sorbent/liquid-solvent),Thermal energy high grade,Energy,MWh/tCO2,38,1.4722222222,1.954835008771642,1.75,2.3148148055556,0.6439376166933265,1.125,3.8
+High-temp DACCS (solid-sorbent/liquid-solvent),Unspecified water,Physical,Liters/tCO2,16,2502.0435975,10221.02242375,4700.0,8180.0,15886.886921845426,0.005,50000.0
+Low-temp DACCS (solid-sorbent/liquid-solvent),Electrical energy,Energy,MWh/tCO2,44,0.218,0.8953202020237373,0.46296111111111,0.69,1.2556951906425198,0.13,4.6111111111
+Low-temp DACCS (solid-sorbent/liquid-solvent),Non-arable land,Physical,ha/tCO2,23,6.25e-08,0.00023824473964695653,0.0002225771032,0.000298114257,0.00029447829320152126,1e-11,0.0009914798235
+Low-temp DACCS (solid-sorbent/liquid-solvent),Thermal energy low grade,Energy,MWh/tCO2,30,1.4324999999999999,1.6782145666607466,1.5,2.062499999975,0.5167942816781624,0.6388888889,2.7222222222
+Low-temp DACCS (solid-sorbent/liquid-solvent),Unspecified water,Physical,Liters/tCO2,14,0.00075,3186.8824842857143,1008.17439,4925.0,5237.922016339205,-2000.0,15000.0
+Mineral OAE / Ocean liming,Electrical energy,Energy,MWh/tCO2,12,0.323625,1.1535833333333334,0.6065,1.755,1.2860244737055733,0.027,4.0
+Mineral OAE / Ocean liming,Non-arable land,Physical,ha/tCO2,4,0.0,0.000505,0.000355,0.00086,0.0006324818837985268,0.0,0.00131
+Mineral OAE / Ocean liming,Thermal energy unspecified grade,Energy,MWh/tCO2,6,1.3888888888888888,1.2576296296296297,1.4589444444444444,1.529,0.5425887907843043,0.16,1.55
+Mineral OAE / Ocean liming,Unspecified mineral,Physical,t/tCO2,8,1.62,2.489,1.7429999999999999,2.5895,1.5545979728350534,1.4,5.0
+Mineral OAE / Ocean liming,Unspecified water,Physical,Liters/tCO2,2,678.331,678.331,678.331,678.331,0.0,678.331,678.331
+Mineral looping DACCS,Electrical energy,Energy,MWh/tCO2,8,0.0833333333,1.1041666666583332,0.0833333333333333,1.375,1.793811009144325,0.0,4.0
+Mineral looping DACCS,Non-arable land,Physical,ha/tCO2,12,0.0005870872613099999,0.001064587040495,0.00063889,0.0008728674558749999,0.0010640920465922832,0.0002225771032,0.0033
+Mineral looping DACCS,Thermal energy unspecified grade,Energy,MWh/tCO2,4,1.4791666666749999,1.715277777775,1.81944444445,2.05555555555,0.5340152854505239,1.0,2.2222222222
+Mineral looping DACCS,Unspecified mineral,Physical,t/tCO2,4,0.15000000000000002,0.225,0.2,0.275,0.20615528128088303,0.0,0.5
+Mineral looping DACCS,Unspecified water,Physical,Liters/tCO2,6,125.0,6761.8350550000005,200.0,10067.75258,11115.783599449733,0.0,26714.00689
+Mineralization ex-situ,Electrical energy,Energy,MWh/tCO2,8,0.5,1.0625,0.75,1.25,0.9797047660246574,0.0,3.0
+Mineralization ex-situ,Non-arable land,Physical,ha/tCO2,4,0.0002984556613,0.000521538621485,0.00043604877962,0.0006591317398049999,0.00034159549577798685,0.0002225771032,0.0009914798235
+Mineralization ex-situ,Olivine mineral,Physical,t/tCO2,2,0.8,0.8,0.8,0.8,0.0,0.8,0.8
+Mineralization ex-situ,Thermal energy unspecified grade,Energy,MWh/tCO2,4,0.4,0.575,0.45,0.625,0.2872281323269014,0.4,1.0
+Mineralization ex-situ,Unspecified mineral,Physical,t/tCO2,22,0.4375,3.9349999999999996,3.0,7.0,3.6477217286306,0.04,10.0
+Mineralization ex-situ,Unspecified water,Physical,Liters/tCO2,14,200.0,50004.28571428572,5450.0,14500.0,106907.99154947611,0.0,300000.0
+Moisture swing DACCS,Electrical energy,Energy,MWh/tCO2,4,0.316,0.32466666665,0.32466666665,0.3333333333,0.01000740464670849,0.316,0.3333333333
+Moisture swing DACCS,Non-arable land,Physical,ha/tCO2,8,0.00016893282740000002,0.0003308893107425,0.00027248,0.00037989864681,0.0003186555229397746,8e-06,0.0009914798235
+Moisture swing DACCS,Unspecified water,Physical,Liters/tCO2,4,-146.0,427.0,427.0,1000.0,661.6434084913111,-146.0,1000.0
+Pasture management,Arable land,Physical,ha/tCO2,24,0.10225284750000001,0.44236770317026247,0.25242204,0.522974429158825,0.522145451866414,0.07189434,2.28974424
+Reforestation,Unspecified land,Physical,ha/tCO2,34,0.044458695,1.5641017441794511,0.094926862556845,0.1887,6.8258032964187745,0.002,38.8
+Reforestation,Unspecified water,Physical,Liters/tCO2,2,92000.0,92000.0,92000.0,92000.0,0.0,92000.0,92000.0
+pH swing,Electrical energy,Energy,MWh/tCO2,28,0.98,2.2051124785714284,1.768,3.3024999999999998,1.3819088428118145,0.66,4.67
+pH swing,Salt water,Physical,Liters/tCO2,4,435000.0,4780000.0,4780000.0,9125000.0,5017173.8392578475,435000.0,9125000.0
+pH swing,Unspecified water,Physical,Liters/tCO2,4,217391.304,6608695.652,6608695.652,13000000.0,7380042.571581251,217391.304,13000000.0

data/inputs_aggregated_old.csv

@@ -0,0 +1,74 @@
+Implementation,Resource,Type,Unit,count,quantile_25,mean,median,quantile_75,std,min,max
+Agroforestry,Arable land,Physical,ha/tCO2,12,0.03995278725,0.4507674440833333,0.06410128000000001,0.2141,0.901140274110988,0.0025,3.03030303
+Bio-char,Electrical energy,Energy,MWh/tCO2,6,8.75e-05,0.1667833333333333,0.00035,0.3750875,0.2581085675964025,0.0,0.5
+Bio-char,Forest biomass,Physical,t/tCO2,6,0.34,0.47283333333333327,0.37,0.55525,0.20162382465042825,0.33,0.82
+Bio-char,Non-forest biomass,Physical,t/tCO2,8,1.142,1.291,1.3395000000000001,1.4885000000000002,0.332019792869556,0.818,1.667
+Bio-char,Unspecified biomass,Physical,t/tCO2,48,0.94339623,1.924254525942129,1.20922073,2.522177213052725,1.9378239910641315,0.5,12.86
+Bio-char,Unspecified land,Physical,ha/tCO2,4,0.05640158,0.16320079,0.16320079,0.27,0.12332110528547877,0.05640158,0.27
+Bio-char,Unspecified water,Physical,Liters/tCO2,9,0.0,633.3333333333334,100.0,500.0,1376.5899897936204,-500.0,3000.0
+BioCCS with anaerobic digestion/fermentation,Arable land,Physical,ha/tCO2,16,0.03793734775,0.100866767,0.058844191000000004,0.10303396649999999,0.11375772648817686,0.022803284,0.454545455
+BioCCS with anaerobic digestion/fermentation,Non-forest biomass,Physical,t/tCO2,6,1.4285714285714,2.689964230684533,1.5617433414042998,2.1282742681047,2.6242499596696813,1.315,8.0
+BioCCS with anaerobic digestion/fermentation,Unspecified biomass,Physical,t/tCO2,6,1.03357023,1.3793645466666666,1.14,1.785,0.4849128814150571,0.99809364,2.0
+BioCCS with anaerobic digestion/fermentation,Unspecified water,Physical,Liters/tCO2,14,395454.5,569805.1428571428,565909.0,654545.5,183277.283077123,368182.0,913636.0
+BioCCS with combustion,Arable land,Physical,ha/tCO2,32,0.038,0.20054008704125,0.058399844,0.09459459,0.4656596577374178,0.022803284,1.9451
+BioCCS with combustion,Electrical energy,Energy,MWh/tCO2,18,0.0277777777777777,0.13097530864197526,0.101,0.1111111111111111,0.16241025701586306,0.0,0.5
+BioCCS with combustion,Forest biomass,Physical,t/tCO2,8,0.5607638875000001,0.67548055375,0.59375,0.8386749975,0.14441914653695664,0.55555555,0.8547
+BioCCS with combustion,Thermal energy,Energy,MWh/tCO2,22,0.3472222222222222,1.0062424242424242,0.9306111111111111,1.025,1.0524401441735463,0.0,4.0
+BioCCS with combustion,Unspecified biomass,Physical,t/tCO2,8,0.9166666675,1.2641666675,1.14,1.625,0.5282247887342408,0.66666667,2.0
+BioCCS with combustion,Unspecified chemical,Physical,t/tCO2,20,0.00027,4.044119,0.00316,0.016,8.287794157908491,0.00013,20.2
+BioCCS with combustion,Unspecified water,Physical,Liters/tCO2,32,6475.0,312937.45634375,201625.2165,542045.5,343154.80918602005,630.0,1296342.17
+"Coastal revegetation (mangrove, seaweeds…)",Non-arable land,Physical,ha/tCO2,11,0.043295020000000004,0.13201796181818182,0.12056618,0.19744896,0.11706388563455744,0.0078,0.32828863
+Cropland management,Arable land,Physical,ha/tCO2,14,0.27061989000000003,1.2779463596825358,0.42063492277774994,1.05233487,2.3297794467976978,0.08264463,9.0
+Durable biobased products,Electrical energy,Energy,MWh/tCO2,2,4.0,4.0,4.0,4.0,0.0,4.0,4.0
+Durable biobased products,Non-arable land,Physical,ha/tCO2,2,6.0,6.0,6.0,6.0,0.0,6.0,6.0
+Durable biobased products,Unspecified biomass,Physical,t/tCO2,6,1.2678571425,2.6071428566666666,3.57142857,3.8928571425,1.8426633339173129,0.0,4.0
+Durable biobased products,Unspecified water,Physical,Liters/tCO2,2,200.0,300.0,300.0,400.0,282.842712474619,100.0,500.0
+Electrochemical DACCS,Electrical energy,Energy,MWh/tCO2,10,0.33333333335000004,2.0320806,0.784125,2.75,2.3788325030763033,0.252556,6.2222222222
+Electrochemical DACCS,Non-arable land,Physical,ha/tCO2,14,0.00027248,0.0026615877837057145,0.000323748514,0.000880697128935,0.005656829998005935,0.0002225771032,0.016
+Electrochemical DACCS,Unspecified water,Physical,Liters/tCO2,6,2000.0,11000.0,5000.0,21500.0,12521.980673998822,1000.0,27000.0
+Electrochemical OAE,Electrical energy,Energy,MWh/tCO2,8,1.6,2.24125,2.15,2.5,1.2835935883292655,0.83,5.0
+Electrochemical OAE,Non-arable land,Physical,ha/tCO2,2,0.00086,0.00101,0.00101,0.00116,0.00042426406871192844,0.00071,0.00131
+Electrochemical OAE,Shoreline,Physical,km/tCO2,2,1.2e-08,1.2e-08,1.2e-08,1.2e-08,0.0,1.2e-08,1.2e-08
+Electrochemical OAE,Unspecified mineral,Physical,t/tCO2,4,2.4,2.95,2.95,3.5,0.6350852961085884,2.4,3.5
+Enhanced weathering,Arable land,Physical,ha/tCO2,28,0.12875,0.548973515,0.25,0.5,0.7099161244217219,1e-06,2.5
+Enhanced weathering,Basalt mineral,Physical,t/tCO2,25,3.33,5.773041804595108,4.3,5.3,4.967901225163679,1.0,19.0
+Enhanced weathering,Electrical energy,Energy,MWh/tCO2,8,0.09285,0.59343125,0.38875000000000004,0.5,0.9045657102947644,0.00925,2.775
+Enhanced weathering,Non-arable land,Physical,ha/tCO2,12,0.004428795,11.432468628333334,0.00906504,28.5714286,17.226399848110834,0.0009,40.0
+Enhanced weathering,Olivine mineral,Physical,t/tCO2,26,0.9090909,1.4295112634615386,1.25,1.58,0.9412466256217807,0.27027027,3.7
+Enhanced weathering,Unspecified mineral,Physical,t/tCO2,19,1.31578947,6.356736418947368,2.5,8.625,7.726415037245945,0.5,25.0
+Enhanced weathering,Unspecified water,Physical,Liters/tCO2,5,500.0,10466.4,866.0,866.0,22102.21859452123,100.0,50000.0
+Enhanced weathering,Wollastonite mineral,Physical,t/tCO2,8,1.3214285714285499,1.62964285714285,1.5642857142856998,1.83,0.7423655162834324,0.56,3.0
+Forest management,Unspecified land,Physical,ha/tCO2,2,0.43867500000000004,0.76865,0.76865,1.0986250000000002,0.9333102404881242,0.1087,1.4286
+Forestry,Unspecified land,Physical,ha/tCO2,70,0.0264,0.8191848413886097,0.094926862556845,0.1887,4.777133871507938,0.002,38.8
+Forestry,Unspecified water,Physical,Liters/tCO2,4,92000.0,92000.0,92000.0,92000.0,0.0,92000.0,92000.0
+High-temp DACCS (solid-sorbent/liquid-solvent),Electrical energy,Energy,MWh/tCO2,32,0.3611111111,0.42378784722013874,0.46296111111111,0.5,0.16317834501458298,0.077,0.77
+High-temp DACCS (solid-sorbent/liquid-solvent),Non-arable land,Physical,ha/tCO2,23,2.2000000000000002e-08,0.00022929641616869565,0.0002225771032,0.000298114257,0.0002987803322061786,1.5e-10,0.0009914798235
+High-temp DACCS (solid-sorbent/liquid-solvent),Thermal energy,Energy,MWh/tCO2,38,1.4722222222,1.954835008771642,1.75,2.3148148055556,0.6439376166933265,1.125,3.8
+High-temp DACCS (solid-sorbent/liquid-solvent),Unspecified water,Physical,Liters/tCO2,16,2502.0435975,10221.02242375,4700.0,8180.0,15886.886921845426,0.005,50000.0
+Low-temp DACCS (solid-sorbent/liquid-solvent),Electrical energy,Energy,MWh/tCO2,44,0.218,0.8953202020237373,0.46296111111111,0.69,1.2556951906425198,0.13,4.6111111111
+Low-temp DACCS (solid-sorbent/liquid-solvent),Non-arable land,Physical,ha/tCO2,23,6.25e-08,0.00023824473964695653,0.0002225771032,0.000298114257,0.00029447829320152126,1e-11,0.0009914798235
+Low-temp DACCS (solid-sorbent/liquid-solvent),Thermal energy,Energy,MWh/tCO2,30,1.4324999999999999,1.6782145666607466,1.5,2.062499999975,0.5167942816781624,0.6388888889,2.7222222222
+Low-temp DACCS (solid-sorbent/liquid-solvent),Unspecified water,Physical,Liters/tCO2,14,0.00075,3186.8824842857143,1008.17439,4925.0,5237.922016339205,-2000.0,15000.0
+Mineral OAE / Ocean liming,Electrical energy,Energy,MWh/tCO2,12,0.323625,1.1535833333333334,0.6065,1.755,1.2860244737055733,0.027,4.0
+Mineral OAE / Ocean liming,Non-arable land,Physical,ha/tCO2,4,0.0,0.000505,0.000355,0.00086,0.0006324818837985268,0.0,0.00131
+Mineral OAE / Ocean liming,Thermal energy,Energy,MWh/tCO2,6,1.3888888888888888,1.2576296296296297,1.4589444444444444,1.529,0.5425887907843043,0.16,1.55
+Mineral OAE / Ocean liming,Unspecified mineral,Physical,t/tCO2,8,1.62,2.489,1.7429999999999999,2.5895,1.5545979728350534,1.4,5.0
+Mineral OAE / Ocean liming,Unspecified water,Physical,Liters/tCO2,2,678.331,678.331,678.331,678.331,0.0,678.331,678.331
+Mineral looping DACCS,Electrical energy,Energy,MWh/tCO2,8,0.0833333333,1.1041666666583332,0.0833333333333333,1.375,1.793811009144325,0.0,4.0
+Mineral looping DACCS,Non-arable land,Physical,ha/tCO2,12,0.0005870872613099999,0.001064587040495,0.00063889,0.0008728674558749999,0.0010640920465922832,0.0002225771032,0.0033
+Mineral looping DACCS,Thermal energy,Energy,MWh/tCO2,4,1.4791666666749999,1.715277777775,1.81944444445,2.05555555555,0.5340152854505239,1.0,2.2222222222
+Mineral looping DACCS,Unspecified mineral,Physical,t/tCO2,4,0.15000000000000002,0.225,0.2,0.275,0.20615528128088303,0.0,0.5
+Mineral looping DACCS,Unspecified water,Physical,Liters/tCO2,6,125.0,6761.8350550000005,200.0,10067.75258,11115.783599449733,0.0,26714.00689
+Mineralization ex-situ,Electrical energy,Energy,MWh/tCO2,8,0.5,1.0625,0.75,1.25,0.9797047660246574,0.0,3.0
+Mineralization ex-situ,Non-arable land,Physical,ha/tCO2,4,0.0002984556613,0.000521538621485,0.00043604877962,0.0006591317398049999,0.00034159549577798685,0.0002225771032,0.0009914798235
+Mineralization ex-situ,Olivine mineral,Physical,t/tCO2,2,0.8,0.8,0.8,0.8,0.0,0.8,0.8
+Mineralization ex-situ,Thermal energy,Energy,MWh/tCO2,4,0.4,0.575,0.45,0.625,0.2872281323269014,0.4,1.0
+Mineralization ex-situ,Unspecified mineral,Physical,t/tCO2,22,0.4375,3.9349999999999996,3.0,7.0,3.6477217286306,0.04,10.0
+Mineralization ex-situ,Unspecified water,Physical,Liters/tCO2,14,200.0,50004.28571428572,5450.0,14500.0,106907.99154947611,0.0,300000.0
+Moisture swing DACCS,Electrical energy,Energy,MWh/tCO2,4,0.316,0.32466666665,0.32466666665,0.3333333333,0.01000740464670849,0.316,0.3333333333
+Moisture swing DACCS,Non-arable land,Physical,ha/tCO2,8,0.00016893282740000002,0.0003308893107425,0.00027248,0.00037989864681,0.0003186555229397746,8e-06,0.0009914798235
+Moisture swing DACCS,Unspecified water,Physical,Liters/tCO2,4,-146.0,427.0,427.0,1000.0,661.6434084913111,-146.0,1000.0
+Pasture management,Arable land,Physical,ha/tCO2,24,0.10225284750000001,0.44236770317026247,0.25242204,0.522974429158825,0.522145451866414,0.07189434,2.28974424
+pH swing,Electrical energy,Energy,MWh/tCO2,28,0.98,2.2051124785714284,1.768,3.3024999999999998,1.3819088428118145,0.66,4.67
+pH swing,Salt water,Physical,Liters/tCO2,4,435000.0,4780000.0,4780000.0,9125000.0,5017173.8392578475,435000.0,9125000.0
+pH swing,Unspecified water,Physical,Liters/tCO2,4,217391.304,6608695.652,6608695.652,13000000.0,7380042.571581251,217391.304,13000000.0

data/secondary_resources.csv

@@ -0,0 +1,50 @@
+method,resource
+Afforestation,Unspecified water (Liters/tCO2)
+Reforestation,Unspecified water (Liters/tCO2)
+Durable biobased products,Non-arable land (ha/tCO2)
+Durable biobased products,Electrical energy (MWh/tCO2)
+Durable biobased products,Unspecified water (Liters/tCO2)
+Bio-char - Forest biomass,Unspecified land (ha/tCO2)
+Bio-char - Forest biomass,Electrical energy (MWh/tCO2)
+Bio-char - Forest biomass,Unspecified water (Liters/tCO2)
+Bio-char - Non-forest biomass,Unspecified land (ha/tCO2)
+Bio-char - Non-forest biomass,Electrical energy (MWh/tCO2)
+Bio-char - Non-forest biomass,Unspecified water (Liters/tCO2)
+Bio-char - Unspecified biomass,Unspecified land (ha/tCO2)
+Bio-char - Unspecified biomass,Electrical energy (MWh/tCO2)
+Bio-char - Unspecified biomass,Unspecified water (Liters/tCO2)
+BioCCS with combustion,Unspecified chemical (t/tCO2)
+BioCCS with combustion,Electrical energy (MWh/tCO2)
+BioCCS with combustion,Thermal energy (MWh/tCO2)
+BioCCS with combustion,Unspecified water (Liters/tCO2)
+BioCCS with combustion & growing biomass,Unspecified chemical (t/tCO2)
+BioCCS with combustion & growing biomass,Electrical energy (MWh/tCO2)
+BioCCS with combustion & growing biomass,Thermal energy (MWh/tCO2)
+BioCCS with combustion & growing biomass,Unspecified water (Liters/tCO2)
+BioCCS with anaerobic digestion/fermentation,Unspecified water (Liters/tCO2)
+BioCCS with anaerobic digestion/fermentation & growing biomass,Unspecified water (Liters/tCO2)
+Enhanced weathering - Basalt mineral,Arable land (ha/tCO2)
+Enhanced weathering - Basalt mineral,Non-arable land (ha/tCO2)
+Enhanced weathering - Basalt mineral,Electrical energy (MWh/tCO2)
+Enhanced weathering - Basalt mineral,Unspecified water (Liters/tCO2)
+Enhanced weathering - Olivine mineral,Arable land (ha/tCO2)
+Enhanced weathering - Olivine mineral,Non-arable land (ha/tCO2)
+Enhanced weathering - Olivine mineral,Electrical energy (MWh/tCO2)
+Enhanced weathering - Olivine mineral,Unspecified water (Liters/tCO2)
+Enhanced weathering - Wollastonite mineral,Arable land (ha/tCO2)
+Enhanced weathering - Wollastonite mineral,Non-arable land (ha/tCO2)
+Enhanced weathering - Wollastonite mineral,Electrical energy (MWh/tCO2)
+Enhanced weathering - Wollastonite mineral,Unspecified water (Liters/tCO2)
+Enhanced weathering - Unspecified mineral,Arable land (ha/tCO2)
+Enhanced weathering - Unspecified mineral,Non-arable land (ha/tCO2)
+Enhanced weathering - Unspecified mineral,Electrical energy (MWh/tCO2)
+Enhanced weathering - Unspecified mineral,Unspecified water (Liters/tCO2)
+Mineral OAE / Ocean liming,Non-arable land (ha/tCO2)
+Mineral OAE / Ocean liming,Electrical energy (MWh/tCO2)
+Mineral OAE / Ocean liming,Thermal energy (MWh/tCO2)
+Mineral OAE / Ocean liming,Unspecified water (Liters/tCO2)
+Electrochemical OAE,Non-arable land (ha/tCO2)
+Electrochemical DACCS,Non-arable land (ha/tCO2)
+Low-temp DACCS (solid-sorbent/liquid-solvent),Unspecified water (Liters/tCO2)
+High-temp DACCS (solid-sorbent/liquid-solvent),Unspecified water (Liters/tCO2)
+Mineral looping DACCS,Unspecified water (Liters/tCO2)

data/update_cost_sliders_with_priority.py

@@ -0,0 +1,32 @@
+import json
+
+import pandas as pd
+
+# === Load JSON data ===
+with open("cost_sliders.json", "r") as f:
+    cost_sliders = json.load(f)
+
+# === Load resource priority CSV ===
+df_priority = pd.read_csv("resource_priority.csv")
+
+# === Validate columns ===
+required_cols = {"method", "resource", "priority"}
+if not required_cols.issubset(df_priority.columns):
+    raise ValueError(f"CSV must contain the columns: {required_cols}")
+
+# === Update the JSON structure ===
+for _, row in df_priority.iterrows():
+    method = row["method"]
+    resource = row["resource"]
+    priority = row["priority"]
+
+    if method in cost_sliders and resource in cost_sliders[method]:
+        cost_sliders[method][resource]["priority"] = priority
+    else:
+        print(f"⚠️ Warning: ({method} / {resource}) not found in JSON.")
+
+# === Save the updated JSON ===
+with open("cost_sliders_with_priority.json", "w") as f:
+    json.dump(cost_sliders, f, indent=2)
+
+print("✅ Updated JSON saved to cost_sliders_with_priority.json")

optimization/optimization.py

@@ -0,0 +1,56 @@
+import numpy as np
+from scipy.optimize import linprog
+
+
+def run_optimization(resource_caps, method_constraints, method_costs):
+    """
+    Run linear optimization to maximize CO₂ removal while respecting resource limits and method constraints.
+
+    Args:
+        resource_caps (dict): {resource: available_quantity}
+        method_constraints (dict): {method: {"active": bool, "max_share": float}}
+        method_costs (dict): {method: {resource: usage_per_tCO2}}
+
+    Returns:
+        (bool, dict): (success_flag, result_dict)
+            result_dict includes "total_removed", "method_usage"
+    """
+    methods = list(method_costs.keys())
+    resources = list(resource_caps.keys())
+
+    # A matrix: each row is a resource, each column a method
+    A_resource = np.array([
+        [method_costs[m].get(r, 0.0) for m in methods]
+        for r in resources
+    ])
+    b_resource = np.array([resource_caps[r] for r in resources])
+
+    # Constraint: sum(x_i) * share_i ≤ x_i
+    A_share = []
+    b_share = []
+    for i, m in enumerate(methods):
+        row = np.zeros(len(methods))
+        row += -method_constraints[m]["max_share"]
+        row[i] += 1
+        A_share.append(row)
+        b_share.append(0)
+
+    # Combine constraints
+    A_ub = np.vstack([A_resource, A_share])
+    b_ub = np.concatenate([b_resource, b_share])
+
+    # Objective: maximize sum(x_i), hence minimize -1 * sum(x_i)
+    c = -1 * np.ones(len(methods))
+    bounds = [(0, None)] * len(methods)
+
+    try:
+        result = linprog(c, A_ub=A_ub, b_ub=b_ub, bounds=bounds, method="highs")
+        if result.success:
+            x = np.floor(result.x).astype(int)
+            total_removed = int(x.sum())
+            method_usage = {m: int(x[i]) for i, m in enumerate(methods) if x[i] > 0}
+            return True, {"total_removed": total_removed, "method_usage": method_usage}
+        else:
+            return False, {"message": result.message}
+    except Exception as e:
+        return False, {"message": str(e)}

tabs/tab0_welcome.py

@@ -0,0 +1,29 @@
+import streamlit as st
+
+
+def render_tab():
+    st.header("Welcome!")
+    st.markdown("""
+    This tool helps you optimize your carbon direct removal portfolio by adjusting resource usage and constraints.
+    
+    **Required:**
+    1. **Resource inputs**: provide the total available quantities of resources for 1 year.
+    
+    **Optionnal:**
+    
+    2. **Method constraints**: select which carbon removal methods are active and set their maximum portfolio share.
+    3. **Resource efficiency coefficients**: adjust the resource usage for each method.
+    
+    **Outputs:**
+    
+    4. **Portfolio generation**: run the optimization to find the best portfolio: how much CO2 removed, what methods are used, and in what proportions.
+    
+    **Explore:**
+    
+    5. **Sensitivity analysis**: analyze how changes in method shares or resource availability affect the portfolio.
+    6. **Scenario comparison**: compare different scenarios based on the optimization results.
+    
+    Let's get started! Use the side tabs to navigate through the sections.
+    """)
+
+

tabs/tab1_inputs.py

@@ -0,0 +1,62 @@
+import pandas as pd
+import streamlit as st
+
+from config.config import (resource_groups, resource_order, resource_tooltips,
+                           resource_unit_map)
+from utils.data_utils import render_resource_input
+from utils.ui_helpers import format_resource_summary
+
+
+def render_tab():
+    # Safe session init
+    if "resource_caps" not in st.session_state:
+        st.session_state.resource_caps = {}
+    for r in resource_order:
+        if r not in st.session_state.resource_caps:
+            st.session_state.resource_caps[r] = 0.0
+
+    st.header("Resource inputs")
+
+    st.info("""
+    **Objective:**
+
+    This section lets you define the **total available quantities of each resource** for the optimization model.
+    """)
+
+    st.warning("⚠️ Resources with zero availability will block portfolio feasibility if required by active methods.")
+
+    # Reset button
+    st.markdown("### Controls")
+    col_reset, _ = st.columns([1, 5])
+    with col_reset:
+        if st.button("🔄 Reset All", type="secondary"):
+            for r in st.session_state.resource_caps:
+                st.session_state.resource_caps[r] = 0.0
+            st.success("All resource values reset to 0.")
+            st.rerun()
+
+    # Inputs by resource group
+    st.markdown("### Set available resources for one year")
+    for group, rlist in resource_groups.items():
+        unit = resource_unit_map.get(group, "")
+        with st.expander(f"**{group}** ({unit})", expanded=True):
+            cols = st.columns(4)
+            for i, r in enumerate(rlist):
+                tooltip = resource_tooltips.get(r, None)
+                name_clean = r.split(" (")[0]
+                with cols[i % 4]:
+                    render_resource_input(name_clean, r, tooltip=tooltip)
+
+    # Summary table
+    st.markdown("### Summary of available resources")
+    df_caps = pd.DataFrame.from_dict({
+        r: st.session_state.resource_caps[r]
+        for r in resource_order if st.session_state.resource_caps.get(r, 0.0) > 0
+    }, orient="index", columns=["Available Amount"]).reindex(resource_order).dropna()
+
+    if not df_caps.empty:
+        df_caps.columns = ["Available Amount"]
+        styled = format_resource_summary(df_caps)
+        st.table(styled)
+    else:
+        st.error("No resources defined yet. Set availability above to populate the summary table.")

tabs/tab2_constraints.py

@@ -0,0 +1,104 @@
+# tabs/tab2_constraints.py
+
+import pandas as pd
+import streamlit as st
+
+from config.config import method_groups, method_order, method_tooltips
+from utils.ui_helpers import format_method_summary
+
+
+def render_tab():
+    st.header("Method constraints")
+
+    st.info("""
+    **Objective:**
+
+    This section lets you control which CDR methods are included in the optimization
+    and assign their **maximum portfolio share**.
+
+    - Activate or deactivate individual methods  
+    - Set each method's **max share** (as % of the total portfolio)
+    """)
+
+    # Initialize constraints state
+    if "constraints" not in st.session_state:
+        st.session_state.constraints = {}
+    for m in method_order:
+        if m not in st.session_state.constraints:
+            st.session_state.constraints[m] = {"active": True, "max_share": 1.0}
+
+    # UI controls
+    with st.expander("⚙️ Manage methods (Open/Close)", expanded=False):
+        st.subheader("Activate/deactivate methods and set max allocation")
+
+        for group, method_list in method_groups.items():
+            st.markdown(f"#### {group}")
+            cols = st.columns(3)
+
+            for i, m in enumerate(method_list):
+                with cols[i % 3]:
+                    toggle_key = f"toggle_{m}"
+                    slider_key = f"slider_{m}_widget"
+                    tooltip = method_tooltips.get(m, None)
+
+                    # Set default slider value only on first render
+                    if slider_key not in st.session_state:
+                        st.session_state[slider_key] = int(st.session_state.constraints[m]["max_share"] * 100)
+
+                    # Toggle
+                    is_active = st.toggle(
+                        m,
+                        value=st.session_state.constraints[m]["active"],
+                        key=toggle_key,
+                        help=tooltip
+                    )
+
+                    if is_active:
+                        share_val = st.slider(
+                            "Max share (%)",
+                            min_value=0,
+                            max_value=100,
+                            step=5,
+                            key=slider_key
+                        )
+                        st.session_state.constraints[m] = {
+                            "active": True,
+                            "max_share": share_val / 100
+                        }
+
+                        if share_val == 0:
+                            st.warning("Max share is 0% but method is active", icon="⚠️")
+                    else:
+                        st.session_state.constraints[m] = {
+                            "active": False,
+                            "max_share": 0.0
+                        }
+
+    # Reset all constraints
+    st.divider()
+    if st.button("🔄 Reset All Method Constraints"):
+        for m in method_order:
+            st.session_state.constraints[m] = {"active": True, "max_share": 1.0}
+            slider_key = f"slider_{m}_widget"
+            if slider_key in st.session_state:
+                del st.session_state[slider_key]
+        st.success("All method constraints reset to default.")
+        st.rerun()
+
+    # Summary table
+    st.subheader("Summary table: Active methods in the model")
+
+    active_methods_summary = {
+        m: int(st.session_state.constraints[m]["max_share"] * 100)
+        for m in method_order if st.session_state.constraints[m]["active"]
+    }
+
+    if active_methods_summary:
+        df_methods = pd.DataFrame.from_dict(
+            active_methods_summary, orient="index", columns=["Max allocation (%)"]
+        ).reindex(method_order).dropna()
+
+        styled = format_method_summary(df_methods)
+        st.table(styled)
+    else:
+        st.warning("No active methods selected.")

tabs/tab3_coefficients.py

@@ -0,0 +1,164 @@
+import pandas as pd
+import streamlit as st
+
+from config.config import (method_groups, method_order, resource_groups,
+                           resource_order, secondary_resources)
+from utils.ui_helpers import format_efficiency_summary
+
+
+def render_tab(cost_sliders):
+    # Initialize costs
+    if "costs" not in st.session_state:
+        st.session_state.costs = {}
+    for m in cost_sliders:
+        if m not in st.session_state.costs:
+            st.session_state.costs[m] = {}
+        for r in cost_sliders[m]:
+            if r not in st.session_state.costs[m]:
+                st.session_state.costs[m][r] = cost_sliders[m][r]["median"]
+
+    st.header("Resource efficiency coefficients")
+
+    st.info("""
+    **Objective:**
+
+    This section lets you adjust the resource usage coefficients for each CDR method to best fit your country conditions.
+
+    - Select a method using the dropdown
+    - Adjust the resource usage coefficients (e.g., land, energy) using sliders
+    - Optionally override the slider using a manual input (even outside bounds)
+    - Disable secondary resources (e.g., water, chemicals) if needed
+    - Reset a single method or all methods with the reset buttons
+    - View a summary table of coefficients used in the model
+    """)
+    st.info("""
+    **Information:**
+
+    Data used in this section is based on 
+    """)
+    st.warning("Blue cells in the table highlight coefficients modified from their **default median values**.")
+
+    # Combined dropdown for group + method
+    method_choices = []
+    method_lookup = {}
+    for group, methods in method_groups.items():
+        for method in methods:
+            label = f"{group} | {method}"
+            method_choices.append(label)
+            method_lookup[label] = method
+
+    selected_label = st.selectbox("Select a method:", method_choices)
+    method_selected = method_lookup[selected_label]
+
+    with st.expander(f"Manage resources for the {method_selected} method", expanded=True):
+        for group_name, rlist in resource_groups.items():
+            used = [r for r in rlist if r in cost_sliders[method_selected]]
+            if not used:
+                continue
+
+            st.markdown(f"### {group_name}")
+            for r in used:
+                conf = cost_sliders[method_selected][r]
+                default_val = conf["median"]
+                min_val = conf["min"]
+                max_val = conf["max"]
+                if min_val < 0.01:
+                    step = 0.0001
+                    fmt = "%.4f"
+                elif min_val < 1:
+                    step = 0.001
+                    fmt = "%.3f"
+                elif min_val < 100:
+                    step = 0.01
+                    fmt = "%.2f"
+                else:
+                    step = 1.0
+                    fmt = "%.0f"
+
+                slider_key = f"slider_{method_selected}_{r}".replace(" ", "_").lower()
+                manual_key = f"manual_{method_selected}_{r}".replace(" ", "_").lower()
+                toggle_key = f"toggle_{method_selected}_{r}".replace(" ", "_").lower()
+
+                is_secondary = (method_selected, r) in secondary_resources
+                if is_secondary and toggle_key not in st.session_state:
+                    st.session_state[toggle_key] = False
+
+                enabled = True
+                if is_secondary:
+                    enabled = st.checkbox(f"Use {r}", key=toggle_key)
+
+                if enabled:
+                    col1, col2 = st.columns([3, 1])
+                    with col1:
+                        slider_val = st.slider(
+                            label=r,
+                            min_value=min_val,
+                            max_value=max_val,
+                            value=st.session_state.costs[method_selected].get(r, default_val),
+                            step=step,
+                            format=fmt,
+                            key=slider_key
+                        )
+                    with col2:
+                        manual_val = st.number_input(
+                            label="",
+                            value=slider_val,
+                            key=manual_key,
+                            help="Manual override (optional, any value)"
+                        )
+
+                    # Manual overrides slider if changed
+                    final_val = manual_val if manual_val != slider_val else slider_val
+                    st.session_state.costs[method_selected][r] = final_val
+                else:
+                    st.session_state.costs[method_selected][r] = 0.0
+                    st.markdown("**DISABLED**")
+
+        # Reset buttons
+        col1, col2 = st.columns(2)
+        with col1:
+            if st.button("🔄 Reset selected method"):
+                for r in cost_sliders[method_selected]:
+                    default_val = cost_sliders[method_selected][r]["median"]
+                    st.session_state.costs[method_selected][r] = default_val
+
+                    # Clear UI state
+                    for suffix in ["slider", "manual", "toggle"]:
+                        key = f"{suffix}_{method_selected}_{r}".replace(" ", "_").lower()
+                        if key in st.session_state:
+                            del st.session_state[key]
+
+                st.success(f"{method_selected} reset to default values.")
+                st.rerun()
+
+        with col2:
+            if st.button("🔄 Reset all methods"):
+                for m in method_order:
+                    for r in cost_sliders.get(m, {}):
+                        default_val = cost_sliders[m][r]["median"]
+                        st.session_state.costs[m][r] = default_val
+
+                        for suffix in ["slider", "manual", "toggle"]:
+                            key = f"{suffix}_{m}_{r}".replace(" ", "_").lower()
+                            if key in st.session_state:
+                                del st.session_state[key]
+
+                st.success("All methods reset to default values.")
+                st.rerun()
+
+    # Summary table
+    st.subheader("Summary table: resource usage coefficients in the model")
+
+    df_default = pd.DataFrame(index=method_order, columns=resource_order)
+    df_current = pd.DataFrame(index=method_order, columns=resource_order)
+
+    for m in method_order:
+        for r in resource_order:
+            df_default.at[m, r] = cost_sliders.get(m, {}).get(r, {}).get("median", 0.0)
+            df_current.at[m, r] = st.session_state.costs.get(m, {}).get(r, 0.0)
+
+    df_default = df_default.astype(float)
+    df_current = df_current.astype(float)
+
+    styled = format_efficiency_summary(df_current, df_default)
+    st.table(styled)

tabs/tab4_optimization.py

@@ -0,0 +1,206 @@
+import json
+
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+import streamlit as st
+
+from config.config import method_order, resource_order, resource_units
+from optimization.optimization import run_optimization
+from utils.ui_helpers import format_results_summary
+
+
+def render_tab():
+    st.header("Portfolio generation")
+    st.info("""
+    **Objective:**
+    Run the optimization tool, explore results, and save scenarios for comparison or export.
+    """)
+
+    # Input check - ensure all required inputs are available
+    if "constraints" not in st.session_state or "costs" not in st.session_state or "resource_caps" not in st.session_state:
+        st.error("Missing inputs. Please complete previous steps.")
+        return
+
+    if "scenarios" not in st.session_state:
+        st.session_state.scenarios = {}
+
+    if sum(st.session_state.resource_caps.values()) <= 0:
+        st.warning("⚠️ All resources are currently set to 0. Please increase availability.")
+        return
+
+    # Optimization
+    if st.button("Run Optimization"):
+        success, result = run_optimization(
+            resource_caps=st.session_state.resource_caps,
+            method_constraints=st.session_state.constraints,
+            method_costs=st.session_state.costs,
+        )
+
+        if success:
+            allocations = result["method_usage"]
+            total_removed = result["total_removed"]
+
+            if total_removed <= 0:
+                st.warning("❌ Optimization removed 0 tCO₂. Relax constraints or increase resources.")
+                return
+
+            total_cost = sum(
+                allocations[m] * sum(st.session_state.costs[m].values())
+                for m in allocations
+            )
+
+            st.session_state.latest_result = {
+                "summary": {
+                    "total_co2_removed": total_removed,
+                    "total_cost": total_cost
+                },
+                "allocations": allocations,
+                "resource_caps": st.session_state.resource_caps.copy(),
+                "method_constraints": st.session_state.constraints,
+                "method_costs": st.session_state.costs,
+            }
+
+            st.success(f"✅ Optimization successful: {total_removed:,.0f} tCO₂ removed")
+        else:
+            st.error("❌ Optimization failed")
+            if "message" in result:
+                st.error(result["message"])
+
+    # --- Display Section ---
+    if "latest_result" not in st.session_state:
+        return
+
+    latest = st.session_state.latest_result
+    allocations = latest["allocations"]
+    resource_caps = latest["resource_caps"]
+    method_costs = latest["method_costs"]
+
+    if not allocations or sum(allocations.values()) == 0:
+        return
+
+    # --- Metrics ---
+    col1, col2 = st.columns(2)
+    col1.metric("Total CO₂ Removed", f"{latest['summary']['total_co2_removed']:,.0f} t")
+    col2.metric("Methods Used", len(allocations))
+
+    # --- Summary Table ---
+    st.subheader("Optimization Summary")
+    df_result = pd.DataFrame.from_dict(allocations, orient="index", columns=["tCO₂ removed"])
+    df_result["tCO₂ removed"] = pd.to_numeric(df_result["tCO₂ removed"], errors="coerce")
+    total = df_result["tCO₂ removed"].sum()
+    if total > 0:
+        df_result["Share (%)"] = (df_result["tCO₂ removed"] / total * 100).round(2)
+    df_result = df_result.sort_values(by="tCO₂ removed", ascending=False)
+    st.table(format_results_summary(df_result))
+
+    # --- Donut Chart ---
+    st.markdown("### Method allocation breakdown")
+    fig_donut = px.pie(
+        df_result.reset_index(),
+        names="index",
+        values="tCO₂ removed",
+        hole=0.4,
+        labels={"index": "Method", "tCO₂ removed": "tCO₂ Removed"},
+    )
+    fig_donut.update_traces(
+        textinfo="label+percent",
+        textposition="outside",
+        pull=[0.02] * len(df_result),
+        marker=dict(line=dict(color="#000000", width=1)),
+        showlegend=False,
+    )
+    st.plotly_chart(fig_donut, use_container_width=True)
+
+    # --- Heatmap of Resource Use ---
+    st.markdown("### Resource usage heatmap (% of cap)")
+    heatmap_matrix, heatmap_y = [], []
+    available_resources = [r for r in resource_order if resource_caps.get(r, 0) > 0]
+
+    for method in df_result.index:
+        row = []
+        for r in available_resources:
+            usage = method_costs.get(method, {}).get(r, 0.0) * allocations.get(method, 0.0)
+            row.append((usage / resource_caps[r]) * 100 if resource_caps[r] > 0 else 0)
+        heatmap_matrix.append(row)
+        heatmap_y.append(method)
+
+    fig_heatmap = go.Figure(data=go.Heatmap(
+        z=heatmap_matrix,
+        x=available_resources,
+        y=heatmap_y,
+        colorscale="Blues",
+        hovertemplate="Method: %{y}<br>Resource: %{x}<br>% Used: %{z:.2f}%<extra></extra>",
+    ))
+    fig_heatmap.update_layout(
+        title="Resource Usage vs. Cap",
+        xaxis_title="Resource",
+        yaxis_title="Method",
+        height=400,
+    )
+    st.plotly_chart(fig_heatmap, use_container_width=True)
+
+    # --- Absolute Resource Use ---
+    st.markdown("### Absolute resource usage by unit")
+    df_abs = []
+    for m, co2_removed in allocations.items():
+        for unit, resources in resource_units.items():
+            for r in resources:
+                if r in method_costs.get(m, {}):
+                    used = method_costs[m][r] * co2_removed
+                    if used > 0:
+                        df_abs.append({
+                            "Method": m,
+                            "Resource": r,
+                            "Used": used,
+                            "Unit": unit
+                        })
+
+    if df_abs:
+        df_abs = pd.DataFrame(df_abs)
+        for unit in df_abs["Unit"].unique():
+            df_unit = df_abs[df_abs["Unit"] == unit]
+            st.markdown(f"#### Resources in {unit}")
+            fig = px.bar(
+                df_unit,
+                x="Resource",
+                y="Used",
+                color="Method",
+                barmode="group",
+                labels={"Used": f"Used ({unit})"},
+            )
+            fig.update_layout(
+                height=300 + 30 * df_unit["Resource"].nunique(),
+                margin=dict(t=30, b=30),
+                legend_title_text="",
+            )
+            st.plotly_chart(fig, use_container_width=True)
+
+    # --- Save Scenario ---
+    st.markdown("### Save scenario")
+    st.text_input("Scenario name", key="scenario_name")
+
+    if st.button("Save Scenario"):
+        name = st.session_state.get("scenario_name", "").strip()
+        if not name:
+            st.warning("Please enter a name before saving.")
+        elif name in st.session_state.scenarios:
+            st.warning("Scenario name already exists.")
+        else:
+            st.session_state.scenarios[name] = latest
+            st.success(f"Scenario '{name}' saved.")
+
+    # --- Preview Saved Scenario ---
+    current_name = st.session_state.get("scenario_name", "").strip()
+    if current_name and current_name in st.session_state.scenarios:
+        scenario = st.session_state.scenarios[current_name]
+        st.markdown(f"#### Scenario: {current_name}")
+        df_saved = pd.DataFrame.from_dict(
+            scenario["allocations"], orient="index", columns=["tCO₂ removed"]
+        )
+        df_saved["tCO₂ removed"] = pd.to_numeric(df_saved["tCO₂ removed"], errors="coerce")
+        total = df_saved["tCO₂ removed"].sum()
+        if total > 0:
+            df_saved["Share (%)"] = (df_saved["tCO₂ removed"] / total * 100).round(2)
+        df_saved = df_saved.sort_values(by="tCO₂ removed", ascending=False)
+        st.table(format_results_summary(df_saved))

tabs/tab5_sensitivity.py

@@ -0,0 +1,107 @@
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+
+from config.config import method_order, resource_order
+from optimization.optimization import run_optimization
+
+
+def render_tab():
+    st.header("Sensitivity Analysis")
+    st.info("""
+        **Objective**:
+        
+        - Explore how varying **one or multiple resources' availability** or **methods' maximum allocation** affects total CO₂ removed.
+    """)
+
+    if "resource_caps" not in st.session_state or "constraints" not in st.session_state or "costs" not in st.session_state:
+        st.warning("Please complete the setup in previous tabs before running sensitivity analysis.")
+        return
+
+    # Compute baseline once
+    baseline_success, baseline_result = run_optimization(
+        resource_caps=st.session_state.resource_caps,
+        method_constraints=st.session_state.constraints,
+        method_costs=st.session_state.costs,
+    )
+    baseline_removed = baseline_result["total_removed"] if baseline_success else 0
+
+    tab1, tab2 = st.tabs(["🔁 Resource Sensitivity", "🔁 Method Sensitivity"])
+
+    with tab1:
+        st.subheader("Impact of resource availability")
+        selected_resources = st.multiselect("Select one or more resources:", resource_order)
+
+        if not selected_resources:
+            st.warning("Please select at least one resource to run the sensitivity analysis.")
+        else:
+            percentages = list(range(0, 210, 10))
+            all_data = []
+
+            for resource in selected_resources:
+                for pct in percentages:
+                    temp_caps = st.session_state.resource_caps.copy()
+                    base_cap = st.session_state.resource_caps.get(resource, 1)
+                    temp_caps[resource] = base_cap * pct / 100
+
+                    success, result = run_optimization(
+                        resource_caps=temp_caps,
+                        method_constraints=st.session_state.constraints,
+                        method_costs=st.session_state.costs,
+                    )
+
+                    co2_removed = result["total_removed"] if success else 0
+                    all_data.append({
+                        "Input": resource,
+                        "% of cap": pct,
+                        "CO₂ removed (t)": co2_removed
+                    })
+
+            df = pd.DataFrame(all_data)
+            fig = px.line(df, x="% of cap", y="CO₂ removed (t)", color="Input",
+                          title="Sensitivity to Resource Availability")
+
+            fig.add_hline(y=baseline_removed, line_dash="dash", line_color="gray",
+                          annotation_text=f"Baseline: {baseline_removed:,} tCO₂",
+                          annotation_position="top left")
+
+            st.plotly_chart(fig, use_container_width=True)
+
+    with tab2:
+        st.subheader("Impact of max allocation per method")
+        selected_methods = st.multiselect("Select one or more methods:", method_order)
+
+        if not selected_methods:
+            st.warning("Please select at least one method to run the sensitivity analysis.")
+        else:
+            percentages = list(range(0, 110, 10))
+            all_data = []
+
+            for method in selected_methods:
+                for pct in percentages:
+                    temp_constraints = st.session_state.constraints.copy()
+                    temp_constraints[method] = temp_constraints.get(method, {}).copy()
+                    temp_constraints[method]["max_share"] = pct / 100
+
+                    success, result = run_optimization(
+                        resource_caps=st.session_state.resource_caps,
+                        method_constraints=temp_constraints,
+                        method_costs=st.session_state.costs,
+                    )
+
+                    co2_removed = result["total_removed"] if success else 0
+                    all_data.append({
+                        "Input": method,
+                        "% max allocation": pct,
+                        "CO₂ removed (t)": co2_removed
+                    })
+
+            df = pd.DataFrame(all_data)
+            fig = px.line(df, x="% max allocation", y="CO₂ removed (t)", color="Input",
+                          title="Sensitivity to Method Max Allocation")
+
+            fig.add_hline(y=baseline_removed, line_dash="dash", line_color="gray",
+                          annotation_text=f"Baseline: {baseline_removed:,} tCO₂",
+                          annotation_position="top left")
+
+            st.plotly_chart(fig, use_container_width=True)

tabs/tab6_scenarios.py

@@ -0,0 +1,107 @@
+import json
+
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+
+
+def is_valid_scenario(scenario):
+    return (
+        isinstance(scenario, dict)
+        and "allocations" in scenario
+        and "resource_caps" in scenario
+        and "method_costs" in scenario
+        and "method_constraints" in scenario
+        and "summary" in scenario
+    )
+
+def render_tab():
+    st.header("Scenario Comparison")
+
+    if "scenarios" not in st.session_state:
+        st.session_state.scenarios = {}
+
+    if not st.session_state.scenarios:
+        st.info("No scenarios have been saved yet.")
+        return
+
+    # Select scenarios from current workspace
+    st.markdown("### Select Scenarios to Compare")
+    scenario_names = list(st.session_state.scenarios.keys())
+    selected = st.multiselect("Select scenarios", scenario_names, default=scenario_names)
+
+    if not selected:
+        st.warning("Please select at least one scenario to display the comparison.")
+        return
+
+    # Remove buttons
+    for name in selected:
+        col1, col2 = st.columns([10, 1])
+        with col1:
+            st.markdown(f"**{name}**")
+        with col2:
+            if st.button("❌", key=f"remove_{name}"):
+                st.session_state.scenarios.pop(name, None)
+                st.rerun()
+
+    # Prepare data
+    all_methods, all_resources = set(), set()
+    for name in selected:
+        scenario = st.session_state.scenarios[name]
+        all_methods.update(scenario.get("allocations", {}).keys())
+        all_resources.update(scenario.get("resource_caps", {}).keys())
+    all_methods, all_resources = sorted(all_methods), sorted(all_resources)
+
+    summary_rows, method_alloc_df, resource_usage_df = [], [], []
+    for name in selected:
+        data = st.session_state.scenarios[name]
+        allocations = data.get("allocations", {})
+        resource_caps = data.get("resource_caps", {})
+        constraints = data.get("method_constraints", {})
+
+        row = {
+            "Scenario": name,
+            "CO₂ Removed (t)": data.get("summary", {}).get("total_co2_removed", 0),
+        }
+
+        for method in all_methods:
+            val = allocations.get(method, 0)
+            row[f"{method} (tCO₂)"] = val
+            method_alloc_df.append({"Scenario": name, "Method": method, "tCO₂": val})
+
+        for resource in all_resources:
+            total_used = 0
+            for method, tco2 in allocations.items():
+                coeffs = constraints.get(method, {}).get("resources", {})
+                if not isinstance(coeffs, dict):
+                    continue
+                unit_use = coeffs.get(resource, 0)
+                used = unit_use * tco2
+                total_used += used
+                if used > 0:
+                    resource_usage_df.append({"Scenario": name, "Method": method, "Resource": resource, "Used": used})
+            row[f"{resource} used"] = total_used
+
+        summary_rows.append(row)
+
+    # Comparison table
+    df_summary = pd.DataFrame(summary_rows)
+    st.subheader("Summary Table")
+    st.dataframe(df_summary.style.format(na_rep="-", thousands=","), use_container_width=True)
+    st.download_button("Download Comparison CSV", df_summary.to_csv(index=False).encode("utf-8"), "scenario_comparison.csv", "text/csv")
+
+    # Charts
+    if method_alloc_df:
+        st.subheader("Method Allocations")
+        fig1 = px.bar(pd.DataFrame(method_alloc_df), x="Method", y="tCO₂", color="Scenario", barmode="group")
+        st.plotly_chart(fig1, use_container_width=True)
+
+    if resource_usage_df:
+        st.subheader("Resource Use per Method")
+        fig2 = px.bar(pd.DataFrame(resource_usage_df), x="Resource", y="Used", color="Scenario", facet_col="Method", facet_col_wrap=3)
+        st.plotly_chart(fig2, use_container_width=True)
+
+        st.subheader("Total Resource Use per Scenario")
+        df_total = pd.DataFrame(resource_usage_df).groupby(["Scenario", "Resource"]).sum(numeric_only=True).reset_index()
+        fig3 = px.bar(df_total, x="Scenario", y="Used", color="Resource", barmode="stack")
+        st.plotly_chart(fig3, use_container_width=True)

utils/data_utils.py

@@ -0,0 +1,28 @@
+import json
+
+import streamlit as st
+
+
+@st.cache_data
+def load_slider_data():
+    """Load slider data from a JSON file and cache it for performance."""
+    with open("data/cost_sliders.json") as f:
+        return json.load(f)
+    
+def render_resource_input(name_clean, r, tooltip=None):
+    if tooltip:
+        st.markdown(
+            f"""<span title="{tooltip}" style="font-weight: 500;">{name_clean} <sup style="color:#999;">ℹ️</sup></span>""",
+            unsafe_allow_html=True
+        )
+    else:
+        st.markdown(f"**{name_clean}**")
+
+    cap = st.number_input(
+        label="",  # Label shown above
+        min_value=0.0,
+        value=float(st.session_state.resource_caps.get(r, 0.0)),
+        format="%.2f",
+        key=f"cap_{r}"
+    )
+    st.session_state.resource_caps[r] = cap

utils/ui_helpers.py

@@ -0,0 +1,244 @@
+import numpy as np
+import pandas as pd
+import streamlit as st
+
+# params
+background_color = "#3d86e9"
+
+def styled_table(df):
+    return (
+        df.style
+        .set_table_styles([{"selector": "th", "props": [("background-color", background_color), ("font-weight", "bold")]}])
+    )
+
+def highlight_differences(data, df_default):
+    styled = pd.DataFrame('', index=data.index, columns=data.columns)
+    for m in data.index:
+        for r in data.columns:
+            if m in df_default.index and r in df_default.columns:
+                current = data.at[m, r]
+                default = df_default.at[m, r]
+                if abs(current - default) > 1e-6:
+                    styled.at[m, r] = 'background-color: #cce5ff'
+    return styled
+
+def smart_format(val):
+    return f"{val:.2e}" if abs(val) < 0.01 else f"{val:.2f}"
+
+def inputs_table_style(df):
+    return(
+        df.style
+        .set_properties(**{'text-align': 'right'})
+        .format(smart_format)
+        .set_table_styles([{"selector": "th", "props": [("background-color", background_color), ("font-weight", "bold")]}])
+    )
+
+def format_method_summary(df: pd.DataFrame):
+    df = df.copy()
+    df.index.name = "CDR Method"
+    df.reset_index(inplace=True)
+
+    return (
+        df.style
+        .set_table_styles([
+            {"selector": "thead th", "props": [("background-color", background_color), ("color", "white"), ("font-weight", "bold")]},
+            {"selector": "td.col0", "props": [("text-align", "left"), ("font-weight", "bold")]},
+            {"selector": "td.col1", "props": [("text-align", "center")]}
+        ])
+    )
+    
+def format_results_summary(df: pd.DataFrame):
+    df = df.copy()
+    df.index.name = "CDR Method"
+    df.reset_index(inplace=True)
+
+    return (
+        df.style
+        .format({
+            "tCO₂ removed": "{:,.0f}",
+            "Share (%)": "{:.2f}"
+        })
+        .set_table_styles([
+            {"selector": "thead th", "props": [("background-color", background_color), ("color", "white"), ("font-weight", "bold")]},
+            {"selector": "td.col0", "props": [("text-align", "left"), ("font-weight", "bold")]},
+            {"selector": "td.col1", "props": [("text-align", "center")]}
+        ])
+    )
+    
+def format_efficiency_summary(df: pd.DataFrame, df_default: pd.DataFrame):
+    df_numeric = df.copy()
+    df_numeric.index.name = "Method"
+
+    df_display = df_numeric.applymap(smart_display_format)
+
+    # Highlight modified values
+    def highlight_changes(data):
+        styled = pd.DataFrame('', index=data.index, columns=data.columns)
+        for m in data.index:
+            for r in data.columns:
+                try:
+                    current = df_numeric.at[m, r]
+                    default = df_default.at[m, r]
+                    if abs(current - default) > 1e-6:
+                        styled.at[m, r] = 'background-color: #cce5ff'
+                except:
+                    continue
+        return styled
+
+    # Table style
+    num_columns = df.shape[1]
+    styles = [
+        {"selector": "thead th", "props": [("background-color", background_color), ("color", "white"), ("font-weight", "bold")]},
+        {"selector": "th.row_heading", "props": [("text-align", "left"), ("font-weight", "bold"), ("width", "240px")]}
+    ]
+    for i in range(num_columns):
+        styles.append({
+            "selector": f"td.col{i}",
+            "props": [("text-align", "center"), ("width", "160px")]
+        })
+
+    return df_display.style.apply(highlight_changes, axis=None).set_table_styles(styles)
+
+# -----------------------------
+# SLIDER GROUP HELPER
+# -----------------------------
+def render_slider_group(method_name, resource, slider_data, key_prefix="", enable_toggle=False, toggle_default=True, store_path=None):
+    """
+    Renders a slider for a method/resource pair with optional enable toggle.
+    Reads from and writes to st.session_state using store_path: ("costs", method, resource).
+    Avoids value reset issues by separating widget key from storage.
+    """
+    base_key = f"{key_prefix}{method_name}_{resource}".replace(" ", "_").lower()
+    toggle_key = f"{base_key}_toggle"
+    widget_key = f"{base_key}_slider"
+
+    # Validate data availability
+    if method_name not in slider_data or resource not in slider_data[method_name]:
+        return None
+
+    values = slider_data[method_name][resource]
+    min_val = values.get("min", 0.0)
+    med_val = values.get("median", 0.5)
+    max_val = values.get("max", 1.0)
+
+    # Handle toggle for secondary resources
+    enabled = True
+    if enable_toggle:
+        if toggle_key not in st.session_state:
+            st.session_state[toggle_key] = toggle_default
+        enabled = st.checkbox(f"Use {resource}", key=toggle_key)
+
+    if not enabled:
+        return None
+
+    # Determine slider format and step size
+    step = 0.01 if max_val < 100 else 1.0
+    fmt = "%.2f" if max_val < 100 else "%.0f"
+
+    # Ensure session state value exists
+    if store_path:
+        base, m, r = store_path
+        if base not in st.session_state:
+            st.session_state[base] = {}
+        if m not in st.session_state[base]:
+            st.session_state[base][m] = {}
+        if r not in st.session_state[base][m]:
+            st.session_state[base][m][r] = med_val
+
+        current_val = st.session_state[base][m][r]
+    else:
+        if base_key not in st.session_state:
+            st.session_state[base_key] = med_val
+        current_val = st.session_state[base_key]
+
+    # Render slider with separate widget key
+    val = st.slider(
+        label=f"{method_name} – {resource}",
+        min_value=float(min_val),
+        max_value=float(max_val),
+        value=float(current_val),
+        step=step,
+        format=fmt,
+        key=widget_key
+    )
+
+    # Sync slider output to session state
+    if store_path:
+        st.session_state[base][m][r] = val
+    else:
+        st.session_state[base_key] = val
+
+    return val
+
+# -----------------------------
+# TABLE FORMATTING HELPER
+# -----------------------------
+def format_results_table(df, highlight_changes=False, precision=2):
+    """
+    Applies consistent styling to result tables.
+    """
+    def bold_methods(val):
+        return f"font-weight: bold" if isinstance(val, str) else ""
+
+    styled = df.style.format(
+        {col: f"{{:.{precision}f}}" for col in df.select_dtypes(include=[np.number]).columns}
+    ).applymap(bold_methods, subset=[df.columns[0]])
+
+    if highlight_changes:
+        styled = styled.highlight_max(axis=0, color='lightgreen')
+
+    return styled
+
+def format_resource_summary(df: pd.DataFrame):
+    """
+    Format a resource availability summary table with styling and column width control.
+    """
+    df = df.copy()
+    col = df.columns[0]
+    df[col] = df[col].map(lambda x: f"{x:,.2f}")
+    df.index.name = "Resource"  # label the index for clarity
+
+    return (
+        df.style
+            .set_table_styles([
+                {
+                    "selector": "thead th:nth-child(1)",  # First column header (after index)
+                    "props": [
+                        ("background-color", background_color),
+                        ("color", "white"),
+                        ("font-weight", "bold")
+                    ]
+                },
+                {
+                    "selector": "thead th:nth-child(2)",  # Second column header
+                    "props": [
+                        ("background-color", background_color),
+                        ("color", "white"),
+                        ("font-weight", "bold")
+                    ]
+                }
+            ])
+    )
+
+def smart_display_format(val):
+    if abs(val) < 1e-8:
+        return ""
+    elif abs(val) < 0.001:
+        return f"{val:,.5f}"
+    elif abs(val) < 0.01:
+        return f"{val:,.4f}"
+    elif abs(val) < 1:
+        return f"{val:,.3f}"
+    elif abs(val) < 100:
+        return f"{val:,.2f}"
+    else:
+        return f"{val:,.0f}"
+
+# -----------------------------
+# CHART LABEL CLEANER (Optional)
+# -----------------------------
+def clean_labels(labels):
+    """
+    Cleans labels to remove zero-allocation items or clutter.
+    """
+    return [label for label in labels if label and label.strip() != ""]