[FEAT] Goal 1 almost achieved: two types of limits possible(percentage / absolute value), start of unit conversion, adapted export templates and a bug fix in the ‘Coefficients’ tab (some resources not displayed)

app.py

@@ -4,11 +4,22 @@ 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
+from utils.data_utils import load_slider_data, strip_unit_suffix
 from config.config import method_order, secondary_resources
+import pathlib
 
 st.set_page_config("Carbon Portfolio Optimizer", layout="wide")
-st.title("Toolkit: CDR Portfolio Optimization")
+
+# Function to load CSS from the 'assets' folder
+def load_css(file_path):
+    with open(file_path) as f:
+        st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)
+
+# Load the external CSS
+css_path = pathlib.Path("assets/styles.css")
+load_css(css_path)
+
+st.markdown('<h1 class="custom-title">CRRA Toolkit: CDR Portfolio Optimization</h1>', unsafe_allow_html=True)
 
 with st.sidebar:
     selected = option_menu(
@@ -33,14 +44,14 @@ 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}
+        st.session_state.constraints[m] = {"active": True, "cap_type": "percent", "cap_value": 1.0}
 
 # Initialize cost_sliders
 cost_sliders = load_slider_data()
 if "costs" not in st.session_state or st.session_state.costs == {}:
     st.session_state.costs = {
         m: {
-            r: (0.0 if (m, r) in secondary_resources else cost_sliders[m][r]["median"])
+            strip_unit_suffix(r): (0.0 if (m, strip_unit_suffix(r)) in secondary_resources else cost_sliders[m][r]["median"])
             for r in cost_sliders[m]
         }
         for m in cost_sliders

assets/styles.css

@@ -0,0 +1,57 @@
+.cdr-table, .cdr-table-res{
+    width: 100%;
+    border-collapse: separate;
+    border-spacing: 0;
+    border-radius: 10px;
+    overflow: hidden;
+}
+
+.cdr-table thead th, .cdr-table-res thead th{
+    background-color: #3d86e9;
+    color: white;
+    font-weight: bold;
+    padding: 8px 12px;
+    text-align: center;
+}
+
+.cdr-table tbody th, .cdr-table-res tbody th{
+    text-align: center;
+    color: #949393;
+    font-weight: 100;
+}
+
+.cdr-table tbody td:nth-child(3) {
+    text-align: right;
+}
+
+.cdr-table-res tbody {
+    text-align: center;
+}
+
+.cdr-table tbody tr:hover, .cdr-table-res tbody tr:hover {
+    background-color: #f5f5f5;
+}
+
+/* tab2 */
+[class*="st-key-toggle_type_"] p {
+    font-weight: 600;
+}
+
+h1.custom-header {
+    font-family: "Segoe UI", sans-serif;
+    font-weight: bold;
+    font-style: italic;
+    font-size: 35px;
+}
+
+h1.custom-title {
+    font-family: "Segoe UI", sans-serif;
+    font-weight: bold;
+    font-size: 50px;
+    font-style: italic;
+}
+
+div.custom-corps {
+    font-family: "Segoe UI", sans-serif;
+    font-size: 18px;
+}

config/config.py

@@ -1,12 +1,14 @@
 import pandas as pd
 
+CO2_UNIT = "MtCO₂"
+
 method_groups = {
     "Forestry": [
         "Afforestation", "Reforestation", "Forest management", "Agroforestry"],
     "Soil carbon sequestration (crop, grasslands)": [
         "Cropland management", "Pasture management"],
     "Blue carbon": [
-        "Coastal revegetation (mangrove, seaweeds)"],
+        "Coastal revegetation (mangrove, seaweeds, etc.)"],
     "Durable biobased products": [
         "Durable biobased products"],
     "Bio-char": [
@@ -31,51 +33,34 @@ method_groups = {
 }
 
 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)"],
-    "Storage": ["Geologic storage (t/tCO2)"]
+    "Minerals": ["Basalt mineral", "Olivine mineral", "Wollastonite mineral", "Unspecified mineral"],
+    "Biomass": ["Forest biomass", "Non-forest biomass", "Unspecified biomass"],
+    "Land": ["Arable land", "Non-arable land", "Unspecified land"],
+    "Chemicals": ["Unspecified chemical"],
+    "Energy": ["Electrical energy", "Thermal energy unspecified grade", "Thermal energy low grade", "Thermal energy high grade"],
+    "Water": ["Salt water", "Unspecified water"],
+    "Shoreline": ["Shoreline"],
+    "Storage": ["Geologic storage"]
 }
 
-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",
+    "Land": "Mha",
+    "Energy": "TWh",
+    "Biomass": "Mt",
+    "Minerals": "Mt",
+    "Chemicals": "Mt",
     "Shoreline": "km",
-    "Water": "L",
-    "Storage": "t"
+    "Water": "bcm",
+    "Storage": "Mt"
 }
 
 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)", "Geologic storage (t/tCO2)"],
-    "km/tCO2": ["Shoreline (km/tCO2)"],
-    "Liters/tCO2": ["Salt water (Liters/tCO2)", "Unspecified water (Liters/tCO2)"]
+    f"{resource_unit_map[cat]}/MtCO2": resources
+    for cat, resources in resource_groups.items()
 }
 
-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)"],
-    "Storage": ["Geologic storage (t/tCO2)"]
-    
-}
+resource_order = sum(resource_groups.values(), [])
+method_order = sum(method_groups.values(), [])
 
 # secondary resources
 secondary_df = pd.read_csv("data/secondary_resources.csv")
@@ -83,24 +68,24 @@ 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."
+    # "Arable land": "Land used for growing crops.",
+    # "Non-arable land": "Land not suitable for crop production, e.g., pastures.",
+    # "Unspecified land": "General land use not specified.",
+    # "Electrical energy ": "Electricity consumption per ton of CO2 captured.",
+    # "Thermal energy unspecified grade": "Heat energy consumption per ton of CO2 captured.",
+    # "Thermal energy low grade": "Heat energy consumption per ton of CO2 captured.",
+    # "Thermal energy high grade": "Heat energy consumption per ton of CO2 captured.",
+    "Forest biomass": "Dry quantities of biomass sourced from forests.",
+    "Non-forest biomass": "Dry quantities of biomass sourced from non-forest areas.",
+    "Unspecified biomass": "Dry quantities of general biomass, not specified.",
+    # "Basalt mineral": "Basalt mineral used in enhanced weathering.",
+    # "Olivine mineral": "Olivine mineral used in enhanced weathering.",
+    # "Wollastonite mineral": "Wollastonite mineral used in enhanced weathering.",
+    # "Unspecified mineral": "General mineral not specified for enhanced weathering.",
+    # "Unspecified chemical": "General chemical resource not specified.",
+    # "Salt water": "Salt water used in ocean-based methods.",
+    # "Unspecified water": "General water resource not specified.",
+    # "Shoreline": "Length of shoreline affected by coastal methods."
 }
 
 method_tooltips = {

data/cost_sliders.json

@@ -1,548 +1,553 @@
 {
   "Afforestation": {
-    "Unspecified land (ha/tCO2)": {
+    "Unspecified land (Mha/MtCO2)": {
       "min": 0.002,
       "median": 0.094926862556845,
       "max": 0.38461538
     },
-    "Unspecified water (Liters/tCO2)": {
-      "min": 46000.0,
-      "median": 92000.0,
-      "max": 92000.0
+    "Unspecified water (bcm/MtCO2)": {
+      "min": 0.046,
+      "median": 0.092,
+      "max": 0.092
     }
   },
   "Agroforestry": {
-    "Arable land (ha/tCO2)": {
+    "Arable land (Mha/MtCO2)": {
       "min": 0.0025,
       "median": 0.06410128,
       "max": 3.03030303
     }
   },
   "BioCCS with anaerobic digestion/fermentation": {
-    "Non-forest biomass (t/tCO2)": {
+    "Arable land (Mha/MtCO2)": {
+      "min": 0.022803284,
+      "median": 0.058844191,
+      "max": 0.454545455
+    },
+    "Non-forest biomass (Mt/MtCO2)": {
       "min": 1.315,
       "median": 1.5617433414042998,
       "max": 8.0
     },
-    "Unspecified biomass (t/tCO2)": {
+    "Unspecified biomass (Mt/MtCO2)": {
       "min": 0.99809364,
       "median": 1.14,
       "max": 2.0
     },
-    "Unspecified water (Liters/tCO2)": {
-      "min": 368182.0,
-      "median": 565909.0,
-      "max": 913636.0
+    "Unspecified water (bcm/MtCO2)": {
+      "min": 0.368182,
+      "median": 0.565909,
+      "max": 0.913636
     }
   },
   "BioCCS with combustion": {
-    "Electrical energy (MWh/tCO2)": {
+    "Electrical energy (TWh/MtCO2)": {
       "min": 0.0,
       "median": 0.101,
       "max": 0.5
     },
-    "Forest biomass (t/tCO2)": {
+    "Forest biomass (Mt/MtCO2)": {
       "min": 0.55555555,
       "median": 0.59375,
       "max": 0.8547
     },
-    "Thermal energy unspecified grade (MWh/tCO2)": {
+    "Thermal energy unspecified grade (TWh/MtCO2)": {
       "min": 0.0,
       "median": 0.9306111111111112,
       "max": 4.0
     },
-    "Unspecified biomass (t/tCO2)": {
+    "Unspecified biomass (Mt/MtCO2)": {
       "min": 0.66666667,
       "median": 1.14,
       "max": 2.0
     },
-    "Unspecified chemical (t/tCO2)": {
+    "Unspecified chemical (Mt/MtCO2)": {
       "min": 0.00013,
       "median": 0.00316,
       "max": 20.2
     },
-    "Unspecified water (Liters/tCO2)": {
-      "min": 630.0,
-      "median": 201625.2165,
-      "max": 1296342.17
+    "Unspecified water (bcm/MtCO2)": {
+      "min": 0.00063,
+      "median": 0.2016252165,
+      "max": 1.29634217
     }
   },
-  "Coastal revegetation (mangrove, seaweeds)": {
-    "Non-arable land (ha/tCO2)": {
+  "Coastal revegetation (mangrove, seaweeds, etc.)": {
+    "Non-arable land (Mha/MtCO2)": {
       "min": 0.0078,
       "median": 0.12056618,
       "max": 0.32828863
     }
   },
   "Cropland management": {
-    "Arable land (ha/tCO2)": {
+    "Arable land (Mha/MtCO2)": {
       "min": 0.08264463,
       "median": 0.4206349227777499,
       "max": 9.0
     }
   },
   "Durable biobased products": {
-    "Electrical energy (MWh/tCO2)": {
+    "Electrical energy (TWh/MtCO2)": {
       "min": 2.0,
       "median": 4.0,
       "max": 4.0
     },
-    "Non-arable land (ha/tCO2)": {
+    "Non-arable land (Mha/MtCO2)": {
       "min": 3.0,
       "median": 6.0,
       "max": 6.0
     },
-    "Unspecified biomass (t/tCO2)": {
+    "Unspecified biomass (Mt/MtCO2)": {
       "min": 0.0,
       "median": 3.57142857,
       "max": 4.0
     },
-    "Unspecified water (Liters/tCO2)": {
-      "min": 100.0,
-      "median": 300.0,
-      "max": 500.0
+    "Unspecified water (bcm/MtCO2)": {
+      "min": 0.0001,
+      "median": 0.0003,
+      "max": 0.0005
     }
   },
   "Electrochemical DACCS": {
-    "Electrical energy (MWh/tCO2)": {
+    "Electrical energy (TWh/MtCO2)": {
       "min": 0.252556,
       "median": 0.784125,
       "max": 6.2222222222
     },
-    "Non-arable land (ha/tCO2)": {
+    "Non-arable land (Mha/MtCO2)": {
       "min": 0.0002225771032,
       "median": 0.000323748514,
       "max": 0.016
     },
-    "Unspecified water (Liters/tCO2)": {
-      "min": 1000.0,
-      "median": 5000.0,
-      "max": 27000.0
+    "Unspecified water (bcm/MtCO2)": {
+      "min": 0.001,
+      "median": 0.005,
+      "max": 0.027
     }
   },
   "Electrochemical OAE": {
-    "Electrical energy (MWh/tCO2)": {
+    "Electrical energy (TWh/MtCO2)": {
       "min": 0.83,
       "median": 2.15,
       "max": 5.0
     },
-    "Non-arable land (ha/tCO2)": {
+    "Non-arable land (Mha/MtCO2)": {
       "min": 0.00071,
       "median": 0.00101,
       "max": 0.00131
     },
-    "Shoreline (km/tCO2)": {
-      "min": 6e-09,
-      "median": 1.2e-08,
-      "max": 1.2e-08
+    "Shoreline (km/MtCO2)": {
+      "min": 0.006,
+      "median": 0.012,
+      "max": 0.012
     },
-    "Unspecified mineral (t/tCO2)": {
+    "Unspecified mineral (Mt/MtCO2)": {
       "min": 2.4,
       "median": 2.95,
       "max": 3.5
     }
   },
   "Forest management": {
-    "Unspecified land (ha/tCO2)": {
+    "Unspecified land (Mha/MtCO2)": {
       "min": 0.1087,
       "median": 0.76865,
       "max": 1.4286
     }
   },
   "High-temp DACCS (solid-sorbent/liquid-solvent)": {
-    "Electrical energy (MWh/tCO2)": {
+    "Electrical energy (TWh/MtCO2)": {
       "min": 0.077,
       "median": 0.46296111111111,
       "max": 0.77
     },
-    "Non-arable land (ha/tCO2)": {
+    "Non-arable land (Mha/MtCO2)": {
       "min": 1.5e-10,
       "median": 0.0002225771032,
       "max": 0.0009914798235
     },
-    "Thermal energy high grade (MWh/tCO2)": {
+    "Thermal energy high grade (TWh/MtCO2)": {
       "min": 1.125,
       "median": 1.75,
       "max": 3.8
     },
-    "Unspecified water (Liters/tCO2)": {
-      "min": 0.005,
-      "median": 4700.0,
-      "max": 50000.0
+    "Unspecified water (bcm/MtCO2)": {
+      "min": 5e-09,
+      "median": 0.0047,
+      "max": 0.05
     }
   },
   "Low-temp DACCS (solid-sorbent/liquid-solvent)": {
-    "Electrical energy (MWh/tCO2)": {
+    "Electrical energy (TWh/MtCO2)": {
       "min": 0.13,
       "median": 0.46296111111111,
       "max": 4.6111111111
     },
-    "Non-arable land (ha/tCO2)": {
+    "Non-arable land (Mha/MtCO2)": {
       "min": 1e-11,
       "median": 0.0002225771032,
       "max": 0.0009914798235
     },
-    "Thermal energy low grade (MWh/tCO2)": {
+    "Thermal energy low grade (TWh/MtCO2)": {
       "min": 0.6388888889,
       "median": 1.5,
       "max": 2.7222222222
     },
-    "Unspecified water (Liters/tCO2)": {
-      "min": -2000.0,
-      "median": 1008.17439,
-      "max": 15000.0
+    "Unspecified water (bcm/MtCO2)": {
+      "min": -0.002,
+      "median": 0.00100817439,
+      "max": 0.015
     }
   },
   "Mineral OAE / Ocean liming": {
-    "Electrical energy (MWh/tCO2)": {
+    "Electrical energy (TWh/MtCO2)": {
       "min": 0.027,
       "median": 0.6065,
       "max": 4.0
     },
-    "Non-arable land (ha/tCO2)": {
+    "Non-arable land (Mha/MtCO2)": {
       "min": 0.0,
       "median": 0.000355,
       "max": 0.00131
     },
-    "Thermal energy unspecified grade (MWh/tCO2)": {
+    "Thermal energy unspecified grade (TWh/MtCO2)": {
       "min": 0.16,
       "median": 1.4589444444444444,
       "max": 1.55
     },
-    "Unspecified mineral (t/tCO2)": {
+    "Unspecified mineral (Mt/MtCO2)": {
       "min": 1.4,
       "median": 1.743,
       "max": 5.0
     },
-    "Unspecified water (Liters/tCO2)": {
-      "min": 339.1655,
-      "median": 678.331,
-      "max": 678.331
+    "Unspecified water (bcm/MtCO2)": {
+      "min": 0.0003391655,
+      "median": 0.000678331,
+      "max": 0.000678331
     }
   },
   "Mineral looping DACCS": {
-    "Electrical energy (MWh/tCO2)": {
+    "Electrical energy (TWh/MtCO2)": {
       "min": 0.0,
       "median": 0.0833333333333333,
       "max": 4.0
     },
-    "Non-arable land (ha/tCO2)": {
+    "Non-arable land (Mha/MtCO2)": {
       "min": 0.0002225771032,
       "median": 0.00063889,
       "max": 0.0033
     },
-    "Thermal energy unspecified grade (MWh/tCO2)": {
+    "Thermal energy unspecified grade (TWh/MtCO2)": {
       "min": 1.0,
       "median": 1.81944444445,
       "max": 2.2222222222
     },
-    "Unspecified mineral (t/tCO2)": {
+    "Unspecified mineral (Mt/MtCO2)": {
       "min": 0.0,
       "median": 0.2,
       "max": 0.5
     },
-    "Unspecified water (Liters/tCO2)": {
+    "Unspecified water (bcm/MtCO2)": {
       "min": 0.0,
-      "median": 200.0,
-      "max": 26714.00689
+      "median": 0.0002,
+      "max": 0.02671400689
     }
   },
   "Mineralization ex-situ": {
-    "Electrical energy (MWh/tCO2)": {
+    "Electrical energy (TWh/MtCO2)": {
       "min": 0.0,
       "median": 0.75,
       "max": 3.0
     },
-    "Non-arable land (ha/tCO2)": {
+    "Non-arable land (Mha/MtCO2)": {
       "min": 0.0002225771032,
       "median": 0.00043604877962,
       "max": 0.0009914798235
     },
-    "Olivine mineral (t/tCO2)": {
+    "Olivine mineral (Mt/MtCO2)": {
       "min": 0.4,
       "median": 0.8,
       "max": 0.8
     },
-    "Thermal energy unspecified grade (MWh/tCO2)": {
+    "Thermal energy unspecified grade (TWh/MtCO2)": {
       "min": 0.4,
       "median": 0.45,
       "max": 1.0
     },
-    "Unspecified mineral (t/tCO2)": {
+    "Unspecified mineral (Mt/MtCO2)": {
       "min": 0.04,
       "median": 3.0,
       "max": 10.0
     },
-    "Unspecified water (Liters/tCO2)": {
+    "Unspecified water (bcm/MtCO2)": {
       "min": 0.0,
-      "median": 5450.0,
-      "max": 300000.0
+      "median": 0.00545,
+      "max": 0.3
     }
   },
   "Moisture swing DACCS": {
-    "Electrical energy (MWh/tCO2)": {
+    "Electrical energy (TWh/MtCO2)": {
       "min": 0.316,
       "median": 0.32466666665,
       "max": 0.3333333333
     },
-    "Non-arable land (ha/tCO2)": {
+    "Non-arable land (Mha/MtCO2)": {
       "min": 8e-06,
       "median": 0.00027248,
       "max": 0.0009914798235
     },
-    "Unspecified water (Liters/tCO2)": {
-      "min": -146.0,
-      "median": 427.0,
-      "max": 1000.0
+    "Unspecified water (bcm/MtCO2)": {
+      "min": -0.000146,
+      "median": 0.000427,
+      "max": 0.001
     }
   },
   "Pasture management": {
-    "Arable land (ha/tCO2)": {
+    "Arable land (Mha/MtCO2)": {
       "min": 0.07189434,
       "median": 0.25242204,
       "max": 2.28974424
     }
   },
   "Reforestation": {
-    "Unspecified land (ha/tCO2)": {
+    "Unspecified land (Mha/MtCO2)": {
       "min": 0.002,
       "median": 0.094926862556845,
       "max": 38.8
     },
-    "Unspecified water (Liters/tCO2)": {
-      "min": 46000.0,
-      "median": 92000.0,
-      "max": 92000.0
+    "Unspecified water (bcm/MtCO2)": {
+      "min": 0.046,
+      "median": 0.092,
+      "max": 0.092
     }
   },
   "pH swing": {
-    "Electrical energy (MWh/tCO2)": {
+    "Electrical energy (TWh/MtCO2)": {
       "min": 0.66,
       "median": 1.768,
       "max": 4.67
     },
-    "Salt water (Liters/tCO2)": {
-      "min": 435000.0,
-      "median": 4780000.0,
-      "max": 9125000.0
+    "Salt water (bcm/MtCO2)": {
+      "min": 0.435,
+      "median": 4.78,
+      "max": 9.125
     },
-    "Unspecified water (Liters/tCO2)": {
-      "min": 217391.304,
-      "median": 6608695.652,
-      "max": 13000000.0
+    "Unspecified water (bcm/MtCO2)": {
+      "min": 0.217391304,
+      "median": 6.608695652,
+      "max": 13.0
     }
   },
   "Bio-char - Forest biomass": {
-    "Electrical energy (MWh/tCO2)": {
+    "Electrical energy (TWh/MtCO2)": {
       "min": 0.0,
       "median": 0.00035,
       "max": 0.5
     },
-    "Forest biomass (t/tCO2)": {
+    "Forest biomass (Mt/MtCO2)": {
       "min": 0.33,
       "median": 0.37,
       "max": 0.82
     },
-    "Unspecified land (ha/tCO2)": {
+    "Unspecified land (Mha/MtCO2)": {
       "min": 0.05640158,
       "median": 0.16320079,
       "max": 0.27
     },
-    "Unspecified water (Liters/tCO2)": {
-      "min": -500.0,
-      "median": 100.0,
-      "max": 3000.0
+    "Unspecified water (bcm/MtCO2)": {
+      "min": -0.0005,
+      "median": 0.0001,
+      "max": 0.003
     }
   },
   "Bio-char - Non-forest biomass": {
-    "Electrical energy (MWh/tCO2)": {
+    "Electrical energy (TWh/MtCO2)": {
       "min": 0.0,
       "median": 0.00035,
       "max": 0.5
     },
-    "Non-forest biomass (t/tCO2)": {
+    "Non-forest biomass (Mt/MtCO2)": {
       "min": 0.818,
       "median": 1.3395,
       "max": 1.667
     },
-    "Unspecified land (ha/tCO2)": {
+    "Unspecified land (Mha/MtCO2)": {
       "min": 0.05640158,
       "median": 0.16320079,
       "max": 0.27
     },
-    "Unspecified water (Liters/tCO2)": {
-      "min": -500.0,
-      "median": 100.0,
-      "max": 3000.0
+    "Unspecified water (bcm/MtCO2)": {
+      "min": -0.0005,
+      "median": 0.0001,
+      "max": 0.003
     }
   },
   "Bio-char - Unspecified biomass": {
-    "Electrical energy (MWh/tCO2)": {
+    "Electrical energy (TWh/MtCO2)": {
       "min": 0.0,
       "median": 0.00035,
       "max": 0.5
     },
-    "Unspecified biomass (t/tCO2)": {
+    "Unspecified biomass (Mt/MtCO2)": {
       "min": 0.5,
       "median": 1.20922073,
       "max": 12.86
     },
-    "Unspecified land (ha/tCO2)": {
+    "Unspecified land (Mha/MtCO2)": {
       "min": 0.05640158,
       "median": 0.16320079,
       "max": 0.27
     },
-    "Unspecified water (Liters/tCO2)": {
-      "min": -500.0,
-      "median": 100.0,
-      "max": 3000.0
+    "Unspecified water (bcm/MtCO2)": {
+      "min": -0.0005,
+      "median": 0.0001,
+      "max": 0.003
     }
   },
   "Enhanced weathering - Basalt mineral": {
-    "Arable land (ha/tCO2)": {
+    "Arable land (Mha/MtCO2)": {
       "min": 1e-06,
       "median": 0.25,
       "max": 2.5
     },
-    "Basalt mineral (t/tCO2)": {
+    "Basalt mineral (Mt/MtCO2)": {
       "min": 1.0,
       "median": 4.3,
       "max": 19.0
     },
-    "Electrical energy (MWh/tCO2)": {
+    "Electrical energy (TWh/MtCO2)": {
       "min": 0.00925,
       "median": 0.38875,
       "max": 2.775
     },
-    "Non-arable land (ha/tCO2)": {
+    "Non-arable land (Mha/MtCO2)": {
       "min": 0.0009,
       "median": 0.00906504,
       "max": 40.0
     },
-    "Unspecified water (Liters/tCO2)": {
-      "min": 100.0,
-      "median": 866.0,
-      "max": 50000.0
+    "Unspecified water (bcm/MtCO2)": {
+      "min": 0.0001,
+      "median": 0.000866,
+      "max": 0.05
     }
   },
   "Enhanced weathering - Olivine mineral": {
-    "Arable land (ha/tCO2)": {
+    "Arable land (Mha/MtCO2)": {
       "min": 1e-06,
       "median": 0.25,
       "max": 2.5
     },
-    "Electrical energy (MWh/tCO2)": {
+    "Electrical energy (TWh/MtCO2)": {
       "min": 0.00925,
       "median": 0.38875,
       "max": 2.775
     },
-    "Non-arable land (ha/tCO2)": {
+    "Non-arable land (Mha/MtCO2)": {
       "min": 0.0009,
       "median": 0.00906504,
       "max": 40.0
     },
-    "Olivine mineral (t/tCO2)": {
+    "Olivine mineral (Mt/MtCO2)": {
       "min": 0.27027027,
       "median": 1.25,
       "max": 3.7
     },
-    "Unspecified water (Liters/tCO2)": {
-      "min": 100.0,
-      "median": 866.0,
-      "max": 50000.0
+    "Unspecified water (bcm/MtCO2)": {
+      "min": 0.0001,
+      "median": 0.000866,
+      "max": 0.05
     }
   },
   "Enhanced weathering - Wollastonite mineral": {
-    "Arable land (ha/tCO2)": {
+    "Arable land (Mha/MtCO2)": {
       "min": 1e-06,
       "median": 0.25,
       "max": 2.5
     },
-    "Electrical energy (MWh/tCO2)": {
+    "Electrical energy (TWh/MtCO2)": {
       "min": 0.00925,
       "median": 0.38875,
       "max": 2.775
     },
-    "Non-arable land (ha/tCO2)": {
+    "Non-arable land (Mha/MtCO2)": {
       "min": 0.0009,
       "median": 0.00906504,
       "max": 40.0
     },
-    "Unspecified water (Liters/tCO2)": {
-      "min": 100.0,
-      "median": 866.0,
-      "max": 50000.0
+    "Unspecified water (bcm/MtCO2)": {
+      "min": 0.0001,
+      "median": 0.000866,
+      "max": 0.05
     },
-    "Wollastonite mineral (t/tCO2)": {
+    "Wollastonite mineral (Mt/MtCO2)": {
       "min": 0.56,
       "median": 1.5642857142856998,
       "max": 3.0
     }
   },
   "Enhanced weathering - Unspecified mineral": {
-    "Arable land (ha/tCO2)": {
+    "Arable land (Mha/MtCO2)": {
       "min": 1e-06,
       "median": 0.25,
       "max": 2.5
     },
-    "Electrical energy (MWh/tCO2)": {
+    "Electrical energy (TWh/MtCO2)": {
       "min": 0.00925,
       "median": 0.38875,
       "max": 2.775
     },
-    "Non-arable land (ha/tCO2)": {
+    "Non-arable land (Mha/MtCO2)": {
       "min": 0.0009,
       "median": 0.00906504,
       "max": 40.0
     },
-    "Unspecified mineral (t/tCO2)": {
+    "Unspecified mineral (Mt/MtCO2)": {
       "min": 0.5,
       "median": 2.5,
       "max": 25.0
     },
-    "Unspecified water (Liters/tCO2)": {
-      "min": 100.0,
-      "median": 866.0,
-      "max": 50000.0
+    "Unspecified water (bcm/MtCO2)": {
+      "min": 0.0001,
+      "median": 0.000866,
+      "max": 0.05
     }
   },
   "BioCCS with combustion & growing biomass": {
-    "Arable land (ha/tCO2)": {
+    "Arable land (Mha/MtCO2)": {
       "min": 0.022803284,
       "median": 0.058399844,
       "max": 1.9451
     },
-    "Electrical energy (MWh/tCO2)": {
+    "Electrical energy (TWh/MtCO2)": {
       "min": 0.0,
       "median": 0.101,
       "max": 0.5
     },
-    "Thermal energy unspecified grade (MWh/tCO2)": {
+    "Thermal energy unspecified grade (TWh/MtCO2)": {
       "min": 0.0,
       "median": 0.9306111111111112,
       "max": 4.0
     },
-    "Unspecified chemical (t/tCO2)": {
+    "Unspecified chemical (Mt/MtCO2)": {
       "min": 0.00013,
       "median": 0.00316,
       "max": 20.2
     },
-    "Unspecified water (Liters/tCO2)": {
-      "min": 630.0,
-      "median": 201625.2165,
-      "max": 1296342.17
+    "Unspecified water (bcm/MtCO2)": {
+      "min": 0.00063,
+      "median": 0.2016252165,
+      "max": 1.29634217
     }
   },
   "BioCCS with anaerobic digestion/fermentation & growing biomass": {
-    "Arable land (ha/tCO2)": {
+    "Arable land (Mha/MtCO2)": {
       "min": 0.022803284,
       "median": 0.058844191,
       "max": 0.454545455
     },
-    "Unspecified water (Liters/tCO2)": {
-      "min": 368182.0,
-      "median": 565909.0,
-      "max": 913636.0
+    "Unspecified water (bcm/MtCO2)": {
+      "min": 0.368182,
+      "median": 0.565909,
+      "max": 0.913636
     }
   }
 }

data/generate_cost_matrix.py

@@ -1,8 +1,18 @@
 #%% uses the latest version of aggregated inputs to create the dictionary of costs
 import json
+import sys
 
 import pandas as pd
 
+from pathlib import Path
+sys.path.insert(0, str(Path(__file__).parent.parent))
+from utils.data_utils import strip_unit_suffix
+
+def pop_by_name(d: dict, name: str):
+    """Pop a key from dict by resource name, ignoring the unit suffix."""
+    key = next((k for k in d if strip_unit_suffix(k) == name), None)
+    if key:
+        d.pop(key)
 
 def create_sliders_inputs():
     df = pd.read_csv("data/inputs_aggregated.csv")
@@ -36,48 +46,48 @@ def create_sliders_inputs():
     
     # 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)")
+    pop_by_name(cost_sliders["Bio-char - Forest biomass"], "Non-forest biomass")
+    pop_by_name(cost_sliders["Bio-char - Forest biomass"], "Unspecified biomass")
     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)")
+    pop_by_name(cost_sliders["Bio-char - Non-forest biomass"], "Forest biomass")
+    pop_by_name(cost_sliders["Bio-char - Non-forest biomass"], "Unspecified biomass")
     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)")
+    pop_by_name(cost_sliders["Bio-char - Unspecified biomass"], "Forest biomass")
+    pop_by_name(cost_sliders["Bio-char - Unspecified biomass"], "Non-forest biomass")
     # 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)")
+    pop_by_name(cost_sliders["Enhanced weathering - Basalt mineral"], "Olivine mineral")
+    pop_by_name(cost_sliders["Enhanced weathering - Basalt mineral"], "Wollastonite mineral")
+    pop_by_name(cost_sliders["Enhanced weathering - Basalt mineral"], "Unspecified mineral")
     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)")
+    pop_by_name(cost_sliders["Enhanced weathering - Olivine mineral"], "Basalt mineral")
+    pop_by_name(cost_sliders["Enhanced weathering - Olivine mineral"], "Wollastonite mineral")
+    pop_by_name(cost_sliders["Enhanced weathering - Olivine mineral"], "Unspecified mineral")
     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)")
+    pop_by_name(cost_sliders["Enhanced weathering - Wollastonite mineral"], "Basalt mineral")
+    pop_by_name(cost_sliders["Enhanced weathering - Wollastonite mineral"], "Olivine mineral")
+    pop_by_name(cost_sliders["Enhanced weathering - Wollastonite mineral"], "Unspecified mineral")
     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)")
+    pop_by_name(cost_sliders["Enhanced weathering - Unspecified mineral"], "Basalt mineral")
+    pop_by_name(cost_sliders["Enhanced weathering - Unspecified mineral"], "Olivine mineral")
+    pop_by_name(cost_sliders["Enhanced weathering - Unspecified mineral"], "Wollastonite mineral")
     # 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)")
+    pop_by_name(cost_sliders["BioCCS with combustion"], "Arable land")
+    pop_by_name(cost_sliders["BioCCS with combustion & growing biomass"], "Forest biomass")
+    pop_by_name(cost_sliders["BioCCS with combustion & growing biomass"], "Unspecified biomass")
     
     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)")
+    pop_by_name(cost_sliders["BioCCS with anaerobic digestion/fermentation"], "Arable land (ha/tCO2)")
+    pop_by_name(cost_sliders["BioCCS with anaerobic digestion/fermentation & growing biomass"], "Non-forest biomass")
+    pop_by_name(cost_sliders["BioCCS with anaerobic digestion/fermentation & growing biomass"], "Unspecified biomass")
     
     
 

data/generate_input_template.py

@@ -6,7 +6,7 @@ from openpyxl.styles import Font
 
 from pathlib import Path
 sys.path.insert(0, str(Path(__file__).parent.parent))
-from config.config import (resource_order, method_order)
+from config.config import (resource_order, method_order, resource_unit_map, resource_groups)
 
 # Data
 with open("data/cost_sliders.json") as f:
@@ -17,23 +17,31 @@ secondary_resources = set(zip(secondary_df["method"], secondary_df["resource"]))
 
 wb = openpyxl.Workbook()
 
+resource_to_unit = {
+    r: resource_unit_map[group]
+    for group, resources in resource_groups.items()
+    for r in resources
+}
+
 # resource_caps
 ws1 = wb.active
 ws1.title = "resource_caps"
-ws1.append(["Resource", "Available Amount"])
+ws1.append(["Resource", "Unit", "Available Amount"])
 ws1["A1"].font = Font(bold=True)
 ws1["B1"].font = Font(bold=True)
+ws1["C1"].font = Font(bold=True)
 for r in resource_order:
-    ws1.append([r, float("nan")])
-
+    ws1.append([r, resource_to_unit.get(r, ""), float("nan")])
+    
 # constraints
 ws2 = wb.create_sheet("constraints")
-ws2.append(["method", "active", "max_share"])
+ws2.append(["method", "active", "cap_type (percent or absolute)", "cap_value"])
 ws2["A1"].font = Font(bold=True)
 ws2["B1"].font = Font(bold=True)
 ws2["C1"].font = Font(bold=True)
+ws2["D1"].font = Font(bold=True)
 for m in method_order:
-    ws2.append([m, True, 1.0])
+    ws2.append([m, True, "percent", 1.0])
 
 ws3 = wb.create_sheet("coefficients")
 ws3.append(["method", "resource", "value", "secondary"])

data/generate_resource_template.py

@@ -0,0 +1,30 @@
+import json
+import sys
+import pandas as pd
+import openpyxl
+from openpyxl.styles import Font
+
+from pathlib import Path
+sys.path.insert(0, str(Path(__file__).parent.parent))
+from config.config import (resource_order, resource_unit_map, resource_groups)
+
+wb = openpyxl.Workbook()
+
+resource_to_unit = {
+    r: resource_unit_map[group]
+    for group, resources in resource_groups.items()
+    for r in resources
+}
+
+# resource_caps
+ws1 = wb.active
+ws1.title = "resource_caps"
+ws1.append(["Resource", "Unit", "Available Amount"])
+ws1["A1"].font = Font(bold=True)
+ws1["B1"].font = Font(bold=True)
+ws1["C1"].font = Font(bold=True)
+for r in resource_order:
+    ws1.append([r, resource_to_unit.get(r, ""), float("nan")])
+
+wb.save("data/resource_template.xlsx")
+print("Template generated: data/resource_template.xlsx")

data/inputs_aggregated.csv

@@ -1,76 +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
+Implementation,Resource,Type,Unit,count,quantile_25,mean,median,quantile_75,std,min,max
+Afforestation,Unspecified land,Physical,Mha/MtCO2,36,0.024592505,0.1156522109750372,0.094926862556845,0.197075,0.1123677997644441,0.002,0.38461538
+Afforestation,Unspecified water,Physical,bcm/MtCO2,2,0.092,0.092,0.092,0.092,0.0,0.092,0.092
+Agroforestry,Arable land,Physical,Mha/MtCO2,12,0.03995278725,0.4507674440833333,0.06410128,0.2141,0.901140274110988,0.0025,3.03030303
+Bio-char,Electrical energy,Energy,TWh/MtCO2,6,8.75e-05,0.1667833333333333,0.00035,0.3750875,0.2581085675964025,0.0,0.5
+Bio-char,Forest biomass,Physical,Mt/MtCO2,6,0.34,0.4728333333333332,0.37,0.55525,0.2016238246504282,0.33,0.82
+Bio-char,Non-forest biomass,Physical,Mt/MtCO2,8,1.142,1.291,1.3395,1.4885000000000002,0.332019792869556,0.818,1.667
+Bio-char,Unspecified biomass,Physical,Mt/MtCO2,48,0.94339623,1.924254525942129,1.20922073,2.522177213052725,1.9378239910641315,0.5,12.86
+Bio-char,Unspecified land,Physical,Mha/MtCO2,4,0.05640158,0.16320079,0.16320079,0.27,0.1233211052854787,0.05640158,0.27
+Bio-char,Unspecified water,Physical,bcm/MtCO2,9,0.0,0.0006333333333333334,0.0001,0.0005,0.0013765899897936204,-0.0005,0.003
+BioCCS with anaerobic digestion/fermentation,Arable land,Physical,Mha/MtCO2,16,0.03793734775,0.100866767,0.058844191,0.1030339664999999,0.1137577264881768,0.022803284,0.454545455
+BioCCS with anaerobic digestion/fermentation,Non-forest biomass,Physical,Mt/MtCO2,6,1.4285714285714,2.689964230684533,1.5617433414042998,2.1282742681047,2.6242499596696813,1.315,8.0
+BioCCS with anaerobic digestion/fermentation,Unspecified biomass,Physical,Mt/MtCO2,6,1.03357023,1.3793645466666666,1.14,1.785,0.4849128814150571,0.99809364,2.0
+BioCCS with anaerobic digestion/fermentation,Unspecified water,Physical,bcm/MtCO2,14,0.3954545,0.5698051428571428,0.565909,0.6545455,0.183277283077123,0.368182,0.913636
+BioCCS with combustion,Arable land,Physical,Mha/MtCO2,32,0.038,0.20054008704125,0.058399844,0.09459459,0.4656596577374178,0.022803284,1.9451
+BioCCS with combustion,Electrical energy,Energy,TWh/MtCO2,18,0.0277777777777777,0.1309753086419752,0.101,0.1111111111111111,0.162410257015863,0.0,0.5
+BioCCS with combustion,Forest biomass,Physical,Mt/MtCO2,8,0.5607638875000001,0.67548055375,0.59375,0.8386749975,0.1444191465369566,0.55555555,0.8547
+BioCCS with combustion,Thermal energy unspecified grade,Energy,TWh/MtCO2,22,0.3472222222222222,1.0062424242424242,0.9306111111111112,1.025,1.0524401441735465,0.0,4.0
+BioCCS with combustion,Unspecified biomass,Physical,Mt/MtCO2,8,0.9166666675,1.2641666675,1.14,1.625,0.5282247887342408,0.66666667,2.0
+BioCCS with combustion,Unspecified chemical,Physical,Mt/MtCO2,20,0.00027,4.044119,0.00316,0.016,8.287794157908491,0.00013,20.2
+BioCCS with combustion,Unspecified water,Physical,bcm/MtCO2,32,0.006475,0.31293745634375003,0.2016252165,0.5420455,0.34315480918602004,0.00063,1.29634217
+"Coastal revegetation (mangrove, seaweeds, etc.)",Non-arable land,Physical,Mha/MtCO2,11,0.04329502,0.1320179618181818,0.12056618,0.19744896,0.1170638856345574,0.0078,0.32828863
+Cropland management,Arable land,Physical,Mha/MtCO2,14,0.27061989,1.2779463596825358,0.4206349227777499,1.05233487,2.3297794467976978,0.08264463,9.0
+Durable biobased products,Electrical energy,Energy,TWh/MtCO2,2,4.0,4.0,4.0,4.0,0.0,4.0,4.0
+Durable biobased products,Non-arable land,Physical,Mha/MtCO2,2,6.0,6.0,6.0,6.0,0.0,6.0,6.0
+Durable biobased products,Unspecified biomass,Physical,Mt/MtCO2,6,1.2678571425,2.6071428566666666,3.57142857,3.8928571425,1.8426633339173129,0.0,4.0
+Durable biobased products,Unspecified water,Physical,bcm/MtCO2,2,0.0002,0.0003,0.0003,0.0004,0.000282842712474619,0.0001,0.0005
+Electrochemical DACCS,Electrical energy,Energy,TWh/MtCO2,10,0.33333333335,2.0320806,0.784125,2.75,2.378832503076304,0.252556,6.2222222222
+Electrochemical DACCS,Non-arable land,Physical,Mha/MtCO2,14,0.00027248,0.0026615877837057,0.000323748514,0.000880697128935,0.0056568299980059,0.0002225771032,0.016
+Electrochemical DACCS,Unspecified water,Physical,bcm/MtCO2,6,0.002,0.011,0.005,0.0215,0.012521980673998822,0.001,0.027
+Electrochemical OAE,Electrical energy,Energy,TWh/MtCO2,8,1.6,2.24125,2.15,2.5,1.2835935883292655,0.83,5.0
+Electrochemical OAE,Non-arable land,Physical,Mha/MtCO2,2,0.00086,0.00101,0.00101,0.00116,0.0004242640687119,0.00071,0.00131
+Electrochemical OAE,Shoreline,Physical,km/MtCO2,2,0.012,0.012,0.012,0.012,0.0,0.012,0.012
+Electrochemical OAE,Unspecified mineral,Physical,Mt/MtCO2,4,2.4,2.95,2.95,3.5,0.6350852961085884,2.4,3.5
+Enhanced weathering,Arable land,Physical,Mha/MtCO2,28,0.12875,0.548973515,0.25,0.5,0.7099161244217219,1e-06,2.5
+Enhanced weathering,Basalt mineral,Physical,Mt/MtCO2,25,3.33,5.773041804595108,4.3,5.3,4.967901225163679,1.0,19.0
+Enhanced weathering,Electrical energy,Energy,TWh/MtCO2,8,0.09285,0.59343125,0.38875,0.5,0.9045657102947644,0.00925,2.775
+Enhanced weathering,Non-arable land,Physical,Mha/MtCO2,12,0.004428795,11.432468628333334,0.00906504,28.5714286,17.226399848110834,0.0009,40.0
+Enhanced weathering,Olivine mineral,Physical,Mt/MtCO2,26,0.9090909,1.4295112634615386,1.25,1.58,0.9412466256217809,0.27027027,3.7
+Enhanced weathering,Unspecified mineral,Physical,Mt/MtCO2,19,1.31578947,6.356736418947368,2.5,8.625,7.726415037245945,0.5,25.0
+Enhanced weathering,Unspecified water,Physical,bcm/MtCO2,5,0.0005,0.010466399999999999,0.000866,0.000866,0.02210221859452123,0.0001,0.05
+Enhanced weathering,Wollastonite mineral,Physical,Mt/MtCO2,8,1.32142857142855,1.62964285714285,1.5642857142856998,1.83,0.7423655162834324,0.56,3.0
+Forest management,Unspecified land,Physical,Mha/MtCO2,2,0.438675,0.76865,0.76865,1.0986250000000002,0.9333102404881242,0.1087,1.4286
+High-temp DACCS (solid-sorbent/liquid-solvent),Electrical energy,Energy,TWh/MtCO2,32,0.3611111111,0.4237878472201387,0.46296111111111,0.5,0.1631783450145829,0.077,0.77
+High-temp DACCS (solid-sorbent/liquid-solvent),Non-arable land,Physical,Mha/MtCO2,23,2.2000000000000002e-08,0.0002292964161686,0.0002225771032,0.000298114257,0.0002987803322061,1.5e-10,0.0009914798235
+High-temp DACCS (solid-sorbent/liquid-solvent),Thermal energy high grade,Energy,TWh/MtCO2,38,1.4722222222,1.954835008771642,1.75,2.3148148055556,0.6439376166933265,1.125,3.8
+High-temp DACCS (solid-sorbent/liquid-solvent),Unspecified water,Physical,bcm/MtCO2,16,0.0025020435975000002,0.01022102242375,0.0047,0.00818,0.015886886921845426,5e-09,0.05
+Low-temp DACCS (solid-sorbent/liquid-solvent),Electrical energy,Energy,TWh/MtCO2,44,0.218,0.8953202020237373,0.46296111111111,0.69,1.2556951906425198,0.13,4.6111111111
+Low-temp DACCS (solid-sorbent/liquid-solvent),Non-arable land,Physical,Mha/MtCO2,23,6.25e-08,0.0002382447396469,0.0002225771032,0.000298114257,0.0002944782932015,1e-11,0.0009914798235
+Low-temp DACCS (solid-sorbent/liquid-solvent),Thermal energy low grade,Energy,TWh/MtCO2,30,1.4325,1.6782145666607466,1.5,2.062499999975,0.5167942816781624,0.6388888889,2.7222222222
+Low-temp DACCS (solid-sorbent/liquid-solvent),Unspecified water,Physical,bcm/MtCO2,14,7.5e-10,0.0031868824842857142,0.00100817439,0.004925,0.005237922016339205,-0.002,0.015
+Mineral OAE / Ocean liming,Electrical energy,Energy,TWh/MtCO2,12,0.323625,1.1535833333333334,0.6065,1.755,1.2860244737055733,0.027,4.0
+Mineral OAE / Ocean liming,Non-arable land,Physical,Mha/MtCO2,4,0.0,0.000505,0.000355,0.00086,0.0006324818837985,0.0,0.00131
+Mineral OAE / Ocean liming,Thermal energy unspecified grade,Energy,TWh/MtCO2,6,1.3888888888888888,1.2576296296296297,1.4589444444444444,1.529,0.5425887907843043,0.16,1.55
+Mineral OAE / Ocean liming,Unspecified mineral,Physical,Mt/MtCO2,8,1.62,2.489,1.743,2.5895,1.5545979728350534,1.4,5.0
+Mineral OAE / Ocean liming,Unspecified water,Physical,bcm/MtCO2,2,0.000678331,0.000678331,0.000678331,0.000678331,0.0,0.000678331,0.000678331
+Mineral looping DACCS,Electrical energy,Energy,TWh/MtCO2,8,0.0833333333,1.1041666666583332,0.0833333333333333,1.375,1.793811009144325,0.0,4.0
+Mineral looping DACCS,Non-arable land,Physical,Mha/MtCO2,12,0.0005870872613099,0.001064587040495,0.00063889,0.0008728674558749,0.0010640920465922,0.0002225771032,0.0033
+Mineral looping DACCS,Thermal energy unspecified grade,Energy,TWh/MtCO2,4,1.479166666675,1.715277777775,1.81944444445,2.05555555555,0.5340152854505239,1.0,2.2222222222
+Mineral looping DACCS,Unspecified mineral,Physical,Mt/MtCO2,4,0.15,0.225,0.2,0.275,0.206155281280883,0.0,0.5
+Mineral looping DACCS,Unspecified water,Physical,bcm/MtCO2,6,0.000125,0.006761835055,0.0002,0.01006775258,0.011115783599449734,0.0,0.02671400689
+Mineralization ex-situ,Electrical energy,Energy,TWh/MtCO2,8,0.5,1.0625,0.75,1.25,0.9797047660246574,0.0,3.0
+Mineralization ex-situ,Non-arable land,Physical,Mha/MtCO2,4,0.0002984556613,0.000521538621485,0.00043604877962,0.0006591317398049,0.0003415954957779,0.0002225771032,0.0009914798235
+Mineralization ex-situ,Olivine mineral,Physical,Mt/MtCO2,2,0.8,0.8,0.8,0.8,0.0,0.8,0.8
+Mineralization ex-situ,Thermal energy unspecified grade,Energy,TWh/MtCO2,4,0.4,0.575,0.45,0.625,0.2872281323269014,0.4,1.0
+Mineralization ex-situ,Unspecified mineral,Physical,Mt/MtCO2,22,0.4375,3.935,3.0,7.0,3.6477217286306,0.04,10.0
+Mineralization ex-situ,Unspecified water,Physical,bcm/MtCO2,14,0.0002,0.050004285714285716,0.00545,0.0145,0.10690799154947613,0.0,0.3
+Moisture swing DACCS,Electrical energy,Energy,TWh/MtCO2,4,0.316,0.32466666665,0.32466666665,0.3333333333,0.0100074046467084,0.316,0.3333333333
+Moisture swing DACCS,Non-arable land,Physical,Mha/MtCO2,8,0.0001689328274,0.0003308893107425,0.00027248,0.00037989864681,0.0003186555229397,8e-06,0.0009914798235
+Moisture swing DACCS,Unspecified water,Physical,bcm/MtCO2,4,-0.000146,0.000427,0.000427,0.001,0.0006616434084913112,-0.000146,0.001
+Pasture management,Arable land,Physical,Mha/MtCO2,24,0.1022528475,0.4423677031702624,0.25242204,0.522974429158825,0.522145451866414,0.07189434,2.28974424
+Reforestation,Unspecified land,Physical,Mha/MtCO2,34,0.044458695,1.5641017441794511,0.094926862556845,0.1887,6.825803296418775,0.002,38.8
+Reforestation,Unspecified water,Physical,bcm/MtCO2,2,0.092,0.092,0.092,0.092,0.0,0.092,0.092
+pH swing,Electrical energy,Energy,TWh/MtCO2,28,0.98,2.2051124785714284,1.768,3.3025,1.3819088428118145,0.66,4.67
+pH swing,Salt water,Physical,bcm/MtCO2,4,0.435,4.78,4.78,9.125,5.017173839257849,0.435,9.125
+pH swing,Unspecified water,Physical,bcm/MtCO2,4,0.217391304,6.608695652,6.608695652,13.0,7.380042571581251,0.217391304,13.0

data/inputs_aggregated_old.csv

@@ -1,74 +1,76 @@
 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
+Afforestation,Unspecified land,Physical,Mha/MtCO2,36,0.024592505,0.11565221097503724,0.094926862556845,0.197075,0.11236779976444418,0.002,0.38461538
+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,Mha/MtCO2,12,0.03995278725,0.4507674440833333,0.06410128000000001,0.2141,0.901140274110988,0.0025,3.03030303
+Bio-char,Electrical energy,Energy,TWh/MtCO2,6,8.75e-05,0.1667833333333333,0.00035,0.3750875,0.2581085675964025,0.0,0.5
+Bio-char,Forest biomass,Physical,Mt/MtCO2,6,0.34,0.47283333333333327,0.37,0.55525,0.20162382465042825,0.33,0.82
+Bio-char,Non-forest biomass,Physical,Mt/MtCO2,8,1.142,1.291,1.3395000000000001,1.4885000000000002,0.332019792869556,0.818,1.667
+Bio-char,Unspecified biomass,Physical,Mt/MtCO2,48,0.94339623,1.924254525942129,1.20922073,2.522177213052725,1.9378239910641315,0.5,12.86
+Bio-char,Unspecified land,Physical,Mha/MtCO2,4,0.05640158,0.16320079,0.16320079,0.27,0.12332110528547877,0.05640158,0.27
 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,Arable land,Physical,Mha/MtCO2,16,0.03793734775,0.100866767,0.058844191000000004,0.10303396649999999,0.11375772648817686,0.022803284,0.454545455
+BioCCS with anaerobic digestion/fermentation,Non-forest biomass,Physical,Mt/MtCO2,6,1.4285714285714,2.689964230684533,1.5617433414042998,2.1282742681047,2.6242499596696813,1.315,8.0
+BioCCS with anaerobic digestion/fermentation,Unspecified biomass,Physical,Mt/MtCO2,6,1.03357023,1.3793645466666666,1.14,1.785,0.4849128814150571,0.99809364,2.0
 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,Arable land,Physical,Mha/MtCO2,32,0.038,0.20054008704125,0.058399844,0.09459459,0.4656596577374178,0.022803284,1.9451
+BioCCS with combustion,Electrical energy,Energy,TWh/MtCO2,18,0.0277777777777777,0.13097530864197526,0.101,0.1111111111111111,0.16241025701586306,0.0,0.5
+BioCCS with combustion,Forest biomass,Physical,Mt/MtCO2,8,0.5607638875000001,0.67548055375,0.59375,0.8386749975,0.14441914653695664,0.55555555,0.8547
+BioCCS with combustion,Thermal energy unspecified grade,Energy,TWh/MtCO2,22,0.3472222222222222,1.0062424242424242,0.9306111111111111,1.025,1.0524401441735463,0.0,4.0
+BioCCS with combustion,Unspecified biomass,Physical,Mt/MtCO2,8,0.9166666675,1.2641666675,1.14,1.625,0.5282247887342408,0.66666667,2.0
+BioCCS with combustion,Unspecified chemical,Physical,Mt/MtCO2,20,0.00027,4.044119,0.00316,0.016,8.287794157908491,0.00013,20.2
 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
+"Coastal revegetation (mangrove, seaweeds…)",Non-arable land,Physical,Mha/MtCO2,11,0.043295020000000004,0.13201796181818182,0.12056618,0.19744896,0.11706388563455744,0.0078,0.32828863
+Cropland management,Arable land,Physical,Mha/MtCO2,14,0.27061989000000003,1.2779463596825358,0.42063492277774994,1.05233487,2.3297794467976978,0.08264463,9.0
+Durable biobased products,Electrical energy,Energy,TWh/MtCO2,2,4.0,4.0,4.0,4.0,0.0,4.0,4.0
+Durable biobased products,Non-arable land,Physical,Mha/MtCO2,2,6.0,6.0,6.0,6.0,0.0,6.0,6.0
+Durable biobased products,Unspecified biomass,Physical,Mt/MtCO2,6,1.2678571425,2.6071428566666666,3.57142857,3.8928571425,1.8426633339173129,0.0,4.0
 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,Electrical energy,Energy,TWh/MtCO2,10,0.33333333335000004,2.0320806,0.784125,2.75,2.3788325030763033,0.252556,6.2222222222
+Electrochemical DACCS,Non-arable land,Physical,Mha/MtCO2,14,0.00027248,0.0026615877837057145,0.000323748514,0.000880697128935,0.005656829998005935,0.0002225771032,0.016
 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
+Electrochemical OAE,Electrical energy,Energy,TWh/MtCO2,8,1.6,2.24125,2.15,2.5,1.2835935883292655,0.83,5.0
+Electrochemical OAE,Non-arable land,Physical,Mha/MtCO2,2,0.00086,0.00101,0.00101,0.00116,0.00042426406871192844,0.00071,0.00131
+Electrochemical OAE,Shoreline,Physical,km/MtCO2,2,0.012,0.012,0.012,0.012,0.0,0.012,0.012
+Electrochemical OAE,Unspecified mineral,Physical,Mt/MtCO2,4,2.4,2.95,2.95,3.5,0.6350852961085884,2.4,3.5
+Enhanced weathering,Arable land,Physical,Mha/MtCO2,28,0.12875,0.548973515,0.25,0.5,0.7099161244217219,1e-06,2.5
+Enhanced weathering,Basalt mineral,Physical,Mt/MtCO2,25,3.33,5.773041804595108,4.3,5.3,4.967901225163679,1.0,19.0
+Enhanced weathering,Electrical energy,Energy,TWh/MtCO2,8,0.09285,0.59343125,0.38875000000000004,0.5,0.9045657102947644,0.00925,2.775
+Enhanced weathering,Non-arable land,Physical,Mha/MtCO2,12,0.004428795,11.432468628333334,0.00906504,28.5714286,17.226399848110834,0.0009,40.0
+Enhanced weathering,Olivine mineral,Physical,Mt/MtCO2,26,0.9090909,1.4295112634615386,1.25,1.58,0.9412466256217807,0.27027027,3.7
+Enhanced weathering,Unspecified mineral,Physical,Mt/MtCO2,19,1.31578947,6.356736418947368,2.5,8.625,7.726415037245945,0.5,25.0
 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
+Enhanced weathering,Wollastonite mineral,Physical,Mt/MtCO2,8,1.3214285714285499,1.62964285714285,1.5642857142856998,1.83,0.7423655162834324,0.56,3.0
+Forest management,Unspecified land,Physical,Mha/MtCO2,2,0.43867500000000004,0.76865,0.76865,1.0986250000000002,0.9333102404881242,0.1087,1.4286
+High-temp DACCS (solid-sorbenMt/liquid-solvent),Electrical energy,Energy,TWh/MtCO2,32,0.3611111111,0.42378784722013874,0.46296111111111,0.5,0.16317834501458298,0.077,0.77
+High-temp DACCS (solid-sorbenMt/liquid-solvent),Non-arable land,Physical,Mha/MtCO2,23,2.2000000000000002e-08,0.00022929641616869565,0.0002225771032,0.000298114257,0.0002987803322061786,1.5e-10,0.0009914798235
+High-temp DACCS (solid-sorbenMt/liquid-solvent),Thermal energy high grade,Energy,TWh/MtCO2,38,1.4722222222,1.954835008771642,1.75,2.3148148055556,0.6439376166933265,1.125,3.8
+High-temp DACCS (solid-sorbenMt/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-sorbenMt/liquid-solvent),Electrical energy,Energy,TWh/MtCO2,44,0.218,0.8953202020237373,0.46296111111111,0.69,1.2556951906425198,0.13,4.6111111111
+Low-temp DACCS (solid-sorbenMt/liquid-solvent),Non-arable land,Physical,Mha/MtCO2,23,6.25e-08,0.00023824473964695653,0.0002225771032,0.000298114257,0.00029447829320152126,1e-11,0.0009914798235
+Low-temp DACCS (solid-sorbenMt/liquid-solvent),Thermal energy low grade,Energy,TWh/MtCO2,30,1.4324999999999999,1.6782145666607466,1.5,2.062499999975,0.5167942816781624,0.6388888889,2.7222222222
+Low-temp DACCS (solid-sorbenMt/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,TWh/MtCO2,12,0.323625,1.1535833333333334,0.6065,1.755,1.2860244737055733,0.027,4.0
+Mineral OAE / Ocean liming,Non-arable land,Physical,Mha/MtCO2,4,0.0,0.000505,0.000355,0.00086,0.0006324818837985268,0.0,0.00131
+Mineral OAE / Ocean liming,Thermal energy unspecified grade,Energy,TWh/MtCO2,6,1.3888888888888888,1.2576296296296297,1.4589444444444444,1.529,0.5425887907843043,0.16,1.55
+Mineral OAE / Ocean liming,Unspecified mineral,Physical,Mt/MtCO2,8,1.62,2.489,1.7429999999999999,2.5895,1.5545979728350534,1.4,5.0
 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,Electrical energy,Energy,TWh/MtCO2,8,0.0833333333,1.1041666666583332,0.0833333333333333,1.375,1.793811009144325,0.0,4.0
+Mineral looping DACCS,Non-arable land,Physical,Mha/MtCO2,12,0.0005870872613099999,0.001064587040495,0.00063889,0.0008728674558749999,0.0010640920465922832,0.0002225771032,0.0033
+Mineral looping DACCS,Thermal energy unspecified grade,Energy,TWh/MtCO2,4,1.4791666666749999,1.715277777775,1.81944444445,2.05555555555,0.5340152854505239,1.0,2.2222222222
+Mineral looping DACCS,Unspecified mineral,Physical,Mt/MtCO2,4,0.15000000000000002,0.225,0.2,0.275,0.20615528128088303,0.0,0.5
 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,Electrical energy,Energy,TWh/MtCO2,8,0.5,1.0625,0.75,1.25,0.9797047660246574,0.0,3.0
+Mineralization ex-situ,Non-arable land,Physical,Mha/MtCO2,4,0.0002984556613,0.000521538621485,0.00043604877962,0.0006591317398049999,0.00034159549577798685,0.0002225771032,0.0009914798235
+Mineralization ex-situ,Olivine mineral,Physical,Mt/MtCO2,2,0.8,0.8,0.8,0.8,0.0,0.8,0.8
+Mineralization ex-situ,Thermal energy unspecified grade,Energy,TWh/MtCO2,4,0.4,0.575,0.45,0.625,0.2872281323269014,0.4,1.0
+Mineralization ex-situ,Unspecified mineral,Physical,Mt/MtCO2,22,0.4375,3.9349999999999996,3.0,7.0,3.6477217286306,0.04,10.0
 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,Electrical energy,Energy,TWh/MtCO2,4,0.316,0.32466666665,0.32466666665,0.3333333333,0.01000740464670849,0.316,0.3333333333
+Moisture swing DACCS,Non-arable land,Physical,Mha/MtCO2,8,0.00016893282740000002,0.0003308893107425,0.00027248,0.00037989864681,0.0003186555229397746,8e-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
+Pasture management,Arable land,Physical,Mha/MtCO2,24,0.10225284750000001,0.44236770317026247,0.25242204,0.522974429158825,0.522145451866414,0.07189434,2.28974424
+Reforestation,Unspecified land,Physical,Mha/MtCO2,34,0.044458695,1.5641017441794511,0.094926862556845,0.1887,6.8258032964187745,0.002,38.8
+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,TWh/MtCO2,28,0.98,2.2051124785714284,1.768,3.3024999999999998,1.3819088428118145,0.66,4.67
 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

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

optimization/optimization.py

@@ -8,16 +8,22 @@ def run_optimization(resource_caps, method_constraints, method_costs):
 
     Args:
         resource_caps (dict): {resource: available_quantity}
-        method_constraints (dict): {method: {"active": bool, "max_share": float}}
+        method_constraints (dict): {method: {"active": bool, "cap_type": str, "cap_value": float}}
+            cap_type: "percent" → cap_value is a fraction of total (0 to 1)
+            cap_type: "absolute" → cap_value is a hard upper bound in CO₂ units
         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())
+    # Only keep active methods
+    methods = [m for m in method_costs if method_constraints.get(m, {}).get("active", True)]
     resources = list(resource_caps.keys())
 
+    if not methods:
+        return False, {"message": "No active methods."}
+    
     # 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]
@@ -25,15 +31,27 @@ def run_optimization(resource_caps, method_constraints, method_costs):
     ])
     b_resource = np.array([resource_caps[r] for r in resources])
 
-    # Constraint: sum(x_i) * share_i ≤ x_i
+    # Cap constraints per method
     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)
+        constraint = method_constraints.get(m, {})
+        cap_type = constraint.get("cap_type", "percent")
+        cap_value = constraint.get("cap_value", 1.0)
+
+        # x_i <= cap_value
+        if cap_type == "absolute":
+            row = np.zeros(len(methods))
+            row[i] = 1
+            A_share.append(row)
+            b_share.append(cap_value)
+        # x_i <= cap_value * sum(x)  →  x_i - cap_value * sum(x) <= 0
+        else:
+            row = np.zeros(len(methods))
+            row += -cap_value
+            row[i] += 1
+            A_share.append(row)
+            b_share.append(0)
 
     # Combine constraints
     A_ub = np.vstack([A_resource, A_share])

tabs/tab0_welcome.py

@@ -1,29 +1,29 @@
 import streamlit as st
-
+from config.config import CO2_UNIT
 
 def render_tab():
-    st.header("Welcome!")
-    st.markdown("""
+    st.markdown('<h1 class="custom-header">Welcome!</h1>', unsafe_allow_html=True)
+    st.markdown(f"""
+    <div class='custom-corps'>
     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.
+
+    **Optional:**
+
+    2. **Method constraints**: select which carbon removal methods are active and set their **maximum deployment cap** (as % of total potential or absolute value in {CO2_UNIT}/yr).
     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.
-    
+
+    4. **Portfolio generation**: run the optimization to find the best portfolio: how much CO₂ is 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.
-    """)
 
+    5. **Sensitivity analysis**: analyze how changes in method caps 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.
+    </div>
+    """, unsafe_allow_html=True)

tabs/tab1_inputs.py

@@ -14,7 +14,7 @@ def render_tab():
     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.markdown('<h1 class="custom-header">Resource inputs</h1>', unsafe_allow_html=True)
 
     st.info("""
     **Objective:**
@@ -22,7 +22,7 @@ def render_tab():
     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.")
+    st.warning("Resources with zero availability will block portfolio feasibility if required by active methods.", icon=":material/warning:")
 
     # Reset button
     st.markdown("### Controls")
@@ -40,6 +40,7 @@ def render_tab():
     col_template, col_upload = st.columns([1, 1])
     with col_template:
         st.markdown("Use the template below to fill in your resource availability values and import them directly into the tool.")
+        st.warning("The change of unit in the template will not be taken into account.", icon=":material/warning:")
         with open("data/resource_template.xlsx", "rb") as f:
             st.download_button(
                 label="Download Excel Template",
@@ -58,7 +59,7 @@ def render_tab():
         if uploaded_file is not None and uploaded_file.name != st.session_state.get("resource_last_loaded_file"):
             try:
                 df = pd.read_excel(uploaded_file)
-                required_cols = {"Resource", "Available Amount"}
+                required_cols = {"Resource", "Unit", "Available Amount"}
                 if not required_cols.issubset(df.columns):
                     raise ValueError(f"Missing columns: {required_cols - set(df.columns)}")
                 

tabs/tab2_constraints.py

@@ -3,95 +3,136 @@
 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
-
+from config.config import CO2_UNIT, method_groups, method_order, method_tooltips
+from utils.ui_helpers import format_table_summary
+
+
+# ─── Helpers ─────────────────────────────────────────────────────────────────
+
+def default_constraint():
+    return {"active": True, "cap_type": "percent", "cap_value": 1.0}
+
+
+def render_method(m, cols, i):
+    with cols[i % 3]:
+        tooltip = method_tooltips.get(m, None)
+        constraint = st.session_state.constraints[m]
+
+        if f"toggle_type_{m}" not in st.session_state:
+            st.session_state[f"toggle_type_{m}"] = constraint.get("active", True)
+
+        is_active = st.toggle(m, key=f"toggle_type_{m}", help=tooltip)
+
+        if not is_active:
+            st.session_state.constraints[m] = {
+                "active": False,
+                "cap_type": constraint.get("cap_type", "percent"),
+                "cap_value": constraint.get("cap_value", 1.0),
+            }
+            return
+
+        cap_type_index = 0 if constraint.get("cap_type", "percent") == "percent" else 1
+        cap_type = st.radio(
+            "Cap value type",
+            label_visibility="collapsed",
+            options=["percent", "absolute"],
+            index=cap_type_index,
+            horizontal=True,
+            key=f"cap_type_{m}",
+            format_func=lambda x: "% of total potential" if x == "percent" else f"Absolute ({CO2_UNIT}/yr)",
+        )
+
+        if cap_type == "percent":
+            if constraint.get("cap_type") == "percent" and constraint.get("cap_value", 0) > 0:
+                current_pct = int(constraint["cap_value"] * 100)
+            else:
+                current_pct = 100
+
+            share_val = st.slider(
+                "Max share (%)", min_value=0, max_value=100, step=5,
+                value=current_pct,
+                key=f"slider_{m}_widget",
+            )
+            st.session_state.constraints[m] = {
+                "active": True,
+                "cap_type": "percent",
+                "cap_value": share_val / 100,
+            }
+            if share_val == 0:
+                st.warning("Cap is 0% but method is active.", icon="⚠️")
+
+        else:
+            if constraint.get("cap_type") == "absolute":
+                current_abs = float(constraint.get("cap_value") or 0.0)
+            else:
+                current_abs = 0.0
+
+            abs_val = st.number_input(
+                f"Max cap ({CO2_UNIT}/yr)",
+                min_value=0.0,
+                format="%.2f",
+                value=current_abs,
+                key=f"manual_{m}_widget",
+            )
+            st.session_state.constraints[m] = {
+                "active": True,
+                "cap_type": "absolute",
+                "cap_value": abs_val,
+            }
+            if abs_val == 0:
+                st.warning(f"Cap is 0 {CO2_UNIT}/yr but method is active.", icon="⚠️")
+
+def reset_all_constraints():
+    for m in method_order:
+        st.session_state.constraints[m] = default_constraint()
+        for key in [f"toggle_type_{m}", f"cap_type_{m}", f"slider_{m}_widget", f"manual_{m}_widget"]:
+            st.session_state.pop(key, None)
+    st.success("All method constraints reset to default.")
+    st.rerun()
+
+
+def build_summary_df():
+    """Build summary dataframe for active methods."""
+    rows = {}
+    for m in method_order:
+        c = st.session_state.constraints[m]
+        if not c["active"]:
+            continue
+        if c["cap_type"] == "percent":
+            rows[m] = {"Cap value": f"{int(c['cap_value'] * 100)}", "Unit": "%"}
+        else:
+            rows[m] = {"Cap value": f"{c['cap_value']:,.2f}", "Unit": f"{CO2_UNIT}/yr"}
+    return pd.DataFrame.from_dict(rows, orient="index") if rows else None
+
+
+# ─── Main render ─────────────────────────────────────────────────────────────
 
 def render_tab():
-    st.header("Method constraints")
-
+    st.markdown('<h1 class="custom-header">Method constraints</h1>', unsafe_allow_html=True)
     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)
-    """)
+    and set a **maximum deployment cap** per method.
 
-    # UI controls
-    with st.expander("⚙️ Manage methods (Open/Close)", expanded=False):
-        st.subheader("Activate/deactivate methods and set max allocation")
+    - Activate or deactivate individual methods
+    - Cap each method by **% of its total potential** or by an **absolute value** in {CO2_UNIT}/yr
+    """.format(CO2_UNIT=CO2_UNIT))
 
+    with st.expander("⚙️ Manage methods", expanded=False):
         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
+                render_method(m, cols, i)
+
     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)
+        reset_all_constraints()
+
+    st.subheader("Summary — Active methods and caps")
+    df = build_summary_df()
+    if df is not None:
+        st.markdown(format_table_summary(df,"cdr-table"), unsafe_allow_html=True)
     else:
-        st.warning("No active methods selected.")
+        st.warning("No active methods selected.")

tabs/tab3_coefficients.py

@@ -2,14 +2,32 @@ import pandas as pd
 import streamlit as st
 
 from config.config import (method_groups, method_order, resource_groups,
-                           resource_order, secondary_resources)
+                           resource_order, secondary_resources, resource_unit_map, CO2_UNIT)
 from utils.ui_helpers import format_efficiency_summary
+from utils.data_utils import strip_unit_suffix
+
+
+def _normalize_costs(costs_dict: dict) -> dict:
+    """Strip unit suffixes from resource keys for legacy scenario compatibility."""
+    return {strip_unit_suffix(r): v for r, v in costs_dict.items()}
+
+
+def _get_slider_params(conf: dict) -> tuple:
+    """Return (step, fmt) based on the min value of a slider config."""
+    min_val = conf["min"]
+    if min_val < 0.01:
+        return 0.0001, "%.4f"
+    elif min_val < 1:
+        return 0.001, "%.3f"
+    elif min_val < 100:
+        return 0.01, "%.2f"
+    else:
+        return 1.0, "%.0f"
 
 
 def render_tab(cost_sliders):
     st.header("Resource efficiency coefficients")
 
-    # === Info ===
     st.info("""
     **Objective:**
 
@@ -21,7 +39,21 @@ def render_tab(cost_sliders):
     """)
     st.warning("Blue cells in the table highlight coefficients modified from their **default median values**.")
 
-    # === Method selector ===
+    # Normalize cost_sliders once: strip unit suffixes from resource keys
+    # cost_sliders_norm[method][resource_name] = {min, median, max}
+    cost_sliders_norm = {
+        m: _normalize_costs(resources)
+        for m, resources in cost_sliders.items()
+    }
+
+    # Build resource display labels: "Arable land" -> "Arable land (Mha/MtCO2)"
+    resource_display = {
+        r: f"{r} ({resource_unit_map[cat]}/{CO2_UNIT})"
+        for cat, resources in resource_groups.items()
+        for r in resources
+    }
+
+    # ── Method selector ──────────────────────────────────────────────────────
     method_choices = []
     method_lookup = {}
     for group, methods in method_groups.items():
@@ -31,66 +63,56 @@ def render_tab(cost_sliders):
             method_lookup[label] = method
 
     selected_label = st.selectbox("Select a method:", method_choices)
-    method_selected = method_lookup[selected_label]
+    method = method_lookup[selected_label]
+
+    # ── Resource sliders ─────────────────────────────────────────────────────
+    with st.expander(f"Manage resources for **{method}**", expanded=True):
+        method_sliders = cost_sliders_norm.get(method, {})
+        method_costs_norm = _normalize_costs(st.session_state.costs.get(method, {}))
 
-    # === Resource sliders ===
-    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]]
+            used = [r for r in rlist if r in method_sliders]
             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"
-
-                is_secondary = (method_selected, r) in secondary_resources
-                toggle_key = f"toggle_{method_selected}_{r}".replace(" ", "_").lower()
+                conf = method_sliders[r]
+                display_label = resource_display.get(r, r)
+                step, fmt = _get_slider_params(conf)
+                is_secondary = (method, r) in secondary_resources
+
+                toggle_key = f"toggle_{method}_{r}".replace(" ", "_").lower()
+                slider_key = f"slider_{method}_{r}".replace(" ", "_").lower()
+                manual_key = f"manual_{method}_{r}".replace(" ", "_").lower()
+
+                current_val = method_costs_norm.get(r, 0.0)
 
                 if is_secondary:
                     if toggle_key not in st.session_state:
-                        st.session_state[toggle_key] = st.session_state.costs[method_selected][r] != 0.0
-                    enabled = st.checkbox(f"Use {r}", key=toggle_key)
-
+                        st.session_state[toggle_key] = current_val != 0.0
+                    enabled = st.checkbox(f"Use {display_label}", key=toggle_key)
                     if not enabled:
-                        st.session_state.costs[method_selected][r] = 0.0
+                        st.session_state.costs[method][r] = 0.0
                         st.markdown("**DISABLED**")
                         continue
-                    elif st.session_state.costs[method_selected][r] == 0.0:
-                        st.session_state.costs[method_selected][r] = default_val
-
-                # Sliders and inputs
-                slider_key = f"slider_{method_selected}_{r}".replace(" ", "_").lower()
-                manual_key = f"manual_{method_selected}_{r}".replace(" ", "_").lower()
+                    elif current_val == 0.0:
+                        st.session_state.costs[method][r] = conf["median"]
+                        current_val = conf["median"]
 
                 if slider_key not in st.session_state:
-                    st.session_state[slider_key] = st.session_state.costs[method_selected][r]
+                    st.session_state[slider_key] = current_val
 
                 col1, col2 = st.columns([3, 1])
                 with col1:
                     slider_val = st.slider(
-                        label=r,
-                        min_value=min_val,
-                        max_value=max_val,
+                        label=display_label,
+                        min_value=conf["min"],
+                        max_value=conf["max"],
                         step=step,
                         format=fmt,
-                        key=slider_key
+                        key=slider_key,
                     )
                 with col2:
                     manual_val = st.number_input(
@@ -99,60 +121,60 @@ def render_tab(cost_sliders):
                         value=slider_val,
                         key=manual_key,
                         help="Manual override (optional, any value)",
-                        format=fmt
+                        format=fmt,
                     )
 
-                final_val = manual_val if manual_val != slider_val else slider_val
-                st.session_state.costs[method_selected][r] = final_val
+                st.session_state.costs[method][r] = manual_val if manual_val != slider_val else slider_val
 
-        # === Reset buttons ===
+        # ── Reset buttons ────────────────────────────────────────────────────
         col1, col2 = st.columns(2)
         with col1:
             if st.button("🔄 Reset selected method"):
-                for r in cost_sliders[method_selected]:
-                    is_secondary = (method_selected, r) in secondary_resources
-                    st.session_state.costs[method_selected][r] = 0.0 if is_secondary else cost_sliders[method_selected][r]["median"]
-                    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.")
+                for r, conf in cost_sliders_norm.get(method, {}).items():
+                    is_secondary = (method, r) in secondary_resources
+                    st.session_state.costs[method][r] = 0.0 if is_secondary else conf["median"]
+                    for prefix in ["slider", "manual", "toggle"]:
+                        st.session_state.pop(f"{prefix}_{method}_{r}".replace(" ", "_").lower(), None)
+                st.success(f"{method} 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, {}):
+                for m, sliders in cost_sliders_norm.items():
+                    for r, conf in sliders.items():
                         is_secondary = (m, r) in secondary_resources
-                        st.session_state.costs[m][r] = 0.0 if is_secondary else cost_sliders[m][r]["median"]
-                        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.session_state.costs[m][r] = 0.0 if is_secondary else conf["median"]
+                        for prefix in ["slider", "manual", "toggle"]:
+                            st.session_state.pop(f"{prefix}_{m}_{r}".replace(" ", "_").lower(), None)
                 st.success("All methods reset to default values.")
                 st.rerun()
 
-    # === Summary table ===
+    # ── 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:
+        current_costs = _normalize_costs(st.session_state.costs.get(m, {}))
+        sliders = cost_sliders_norm.get(m, {})
+
         for r in resource_order:
             is_secondary = (m, r) in secondary_resources
-            current_val = st.session_state.costs.get(m, {}).get(r, 0.0)
-            
-            if is_secondary:
-                default_val = cost_sliders.get(m, {}).get(r, {}).get("median", 0.0) if current_val != 0.0 else 0.0
-            else:
-                default_val = cost_sliders.get(m, {}).get(r, {}).get("median", 0.0)
-            
+            current_val = current_costs.get(r, 0.0)
+            default_val = sliders.get(r, {}).get("median", 0.0)
+
+            if is_secondary and current_val == 0.0:
+                default_val = 0.0
+
             df_default.at[m, r] = default_val
             df_current.at[m, r] = current_val
 
     df_default = df_default.astype(float)
     df_current = df_current.astype(float)
 
+    df_default = df_default.rename(columns=resource_display)
+    df_current = df_current.rename(columns=resource_display)
+
     styled = format_efficiency_summary(df_current, df_default)
-    st.table(styled)
+    st.table(styled)

tabs/tab4_optimization.py

@@ -1,5 +1,7 @@
+from collections import defaultdict
 import io
 import json
+import re
 import unicodedata
 import zipfile
 import openpyxl
@@ -10,10 +12,10 @@ import seaborn as sns
 import streamlit as st
 import plotly.graph_objects as go
 
-from config.config import method_order, resource_order, resource_units, secondary_resources
+from config.config import method_order, resource_order, resource_unit_map, resource_groups, secondary_resources, CO2_UNIT
 from optimization.optimization import run_optimization
-from utils.ui_helpers import format_results_summary
-from utils.data_utils import is_valid_scenario, load_inputs_from_excel
+from utils.ui_helpers import format_table_summary
+from utils.data_utils import is_valid_scenario, load_inputs_from_excel, strip_unit_suffix
 
 
 # ─── Input panels ────────────────────────────────────────────────────────────
@@ -35,7 +37,7 @@ def panel_load_from_excel():
     with col_upload:
         with open("data/inputs_template.xlsx", "rb") as f:
             st.download_button(
-                label="Download Excel Template",
+                label="Excel Template",
                 data=f,
                 file_name="crra_input_template.xlsx",
                 mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
@@ -76,10 +78,11 @@ def panel_load_from_excel():
 
 def parse_zip_scenario(uploaded) -> dict | None:
     """Reconstruct a scenario dict from a ZIP containing the 4 CSVs."""
+
     try:
         with zipfile.ZipFile(io.BytesIO(uploaded.read())) as zf:
             names = zf.namelist()
- 
+            
             def _read(fname):
                 return pd.read_csv(io.BytesIO(zf.read(fname)), index_col=0)
  
@@ -88,20 +91,48 @@ def parse_zip_scenario(uploaded) -> dict | None:
             if not required.issubset(names):
                 return None
  
-            resource_caps = _read("resource_caps.csv")["Available Amount"].to_dict()
- 
+            resource_caps_raw = _read("resource_caps.csv")["Available Amount"].to_dict()
+            resource_caps = {strip_unit_suffix(k): v for k, v in resource_caps_raw.items()}
+
             df_c = _read("method_constraints.csv")
-            constraints = {
-                m: {"active": bool(row["active"]), "max_share": float(row["max_share"])}
-                for m, row in df_c.iterrows()
-            }
- 
+
+            # Compatibility with older files that used `max_share`
+            if "cap_type (percent or absolute)" in df_c.columns:
+                constraints = {
+                    m: {
+                        "active": bool(row["active"]),
+                        "cap_type": str(row["cap_type (percent or absolute)"]),
+                        "cap_value": float(row["cap_value"])
+                    }
+                    for m, row in df_c.iterrows()
+                }
+            else:
+                constraints = {
+                    m: {
+                        "active": bool(row["active"]),
+                        "cap_type": "percent",
+                        "cap_value": float(row["max_share"])
+                    }
+                    for m, row in df_c.iterrows()
+                }
+
             df_costs = _read("method_costs.csv")  # index=method, columns=resources
+
             costs = {m: row.dropna().to_dict() for m, row in df_costs.iterrows()}
- 
-            allocations = _read("allocations.csv")["tCO₂ removed"].to_dict()
+
+            df = _read("allocations.csv")
+            col = f"{CO2_UNIT} removed"
+            if col not in df.columns:
+                old_col = "tCO₂ removed"
+                if old_col in df.columns:
+                    df[col] = df[old_col] / 1_000_000  # convert tCO2 → MtCO2
+                else:
+                    raise ValueError(f"Column '{col}' not found. Available: {list(df.columns)}")
+
+            allocations = df[col].to_dict()
+
             total = sum(allocations.values())
- 
+
             return {
                 "summary": {"total_co2_removed": total, "total_cost": 0},
                 "allocations": allocations,
@@ -193,7 +224,7 @@ def run_and_store_result():
     total_removed = result["total_removed"]
 
     if total_removed <= 0:
-        st.warning("❌ Optimization removed 0 tCO₂. Relax constraints or increase resources.")
+        st.warning(f"❌ Optimization removed 0 {CO2_UNIT}. Relax constraints or increase resources.")
         return
 
     total_cost = sum(
@@ -207,30 +238,42 @@ def run_and_store_result():
         "method_constraints": st.session_state.constraints,
         "method_costs": st.session_state.costs,
     }
-    st.success(f"✅ {total_removed:,.0f} tCO₂ removed")
+    st.success(f"✅ {total_removed:,.0f} {CO2_UNIT} removed")
 
 
 # ─── Results display ─────────────────────────────────────────────────────────
 
 def show_metrics(latest):
     col1, col2 = st.columns(2)
-    col1.metric("Total CO₂ Removed", f"{latest['summary']['total_co2_removed']:,.0f} t")
+    col1.metric("Total CO₂ Removed", f"{latest['summary']['total_co2_removed']:,.0f} {CO2_UNIT}")
     col2.metric("Methods Used", len(latest["allocations"]))
 
 
-def build_results_df(allocations):
-    df = pd.DataFrame.from_dict(allocations, orient="index", columns=["tCO₂ removed"])
-    df["tCO₂ removed"] = pd.to_numeric(df["tCO₂ removed"], errors="coerce")
-    total = df["tCO₂ removed"].sum()
+def build_results_df(allocations, method_constraints=None):
+    df = pd.DataFrame.from_dict(allocations, orient="index", columns=[f"{CO2_UNIT} removed"])
+    df[f"{CO2_UNIT} removed"] = pd.to_numeric(df[f"{CO2_UNIT} removed"], errors="coerce")
+    total = df[f"{CO2_UNIT} removed"].sum()
     if total > 0:
-        df["Share (%)"] = (df["tCO₂ removed"] / total * 100).round(2)
-    return df.sort_values("tCO₂ removed", ascending=False)
+        df["Share (%)"] = (df[f"{CO2_UNIT} removed"] / total * 100).round(2)
+
+    if method_constraints:
+        caps = {}
+        for m in df.index:
+            c = method_constraints.get(m, {})
+            
+            if c.get("cap_type") == "absolute":
+                caps[m] = f"{c['cap_value']:,.2f} {CO2_UNIT}/yr"
+            else:
+                caps[m] = f"{int(c.get('cap_value', 1.0) * 100)}% of potential"
+        df["Cap"] = pd.Series(caps)
+    
+    return df.sort_values(f"{CO2_UNIT} removed", ascending=False)
 
 
 def show_donut(df_result):
     fig = go.Figure(data=[go.Pie(
         labels=df_result.index.tolist(),
-        values=df_result["tCO₂ removed"].tolist(),
+        values=df_result[f"{CO2_UNIT} removed"].tolist(),
         hole=0.4,
         textinfo="label+percent",
         textposition="outside",
@@ -242,11 +285,17 @@ def show_donut(df_result):
 
 
 def show_heatmap(df_result, resource_caps, method_costs):
+    # Normalize method_costs keys for legacy scenarios
+    normalized_costs = {
+        m: {strip_unit_suffix(r): v for r, v in resources.items()}
+        for m, resources in method_costs.items()
+    }
+
     available = [r for r in resource_order if resource_caps.get(r, 0) > 0]
     matrix, y_labels = [], []
     for method in df_result.index:
         row = [
-            (method_costs.get(method, {}).get(r, 0.0) * df_result.at[method, "tCO₂ removed"]
+            (normalized_costs.get(method, {}).get(r, 0.0) * df_result.at[method, f"{CO2_UNIT} removed"]
              / resource_caps[r] * 100) if resource_caps[r] > 0 else 0
             for r in available
         ]
@@ -264,15 +313,27 @@ def show_heatmap(df_result, resource_caps, method_costs):
     )
     st.plotly_chart(fig, use_container_width=True)
 
-
 def show_absolute_usage(allocations, method_costs):
-    rows = [
-        {"Method": m, "Resource": r, "Used": method_costs[m][r] * co2, "Unit": unit}
-        for m, co2 in allocations.items()
-        for unit, resources in resource_units.items()
-        for r in resources
-        if r in method_costs.get(m, {}) and method_costs[m][r] * co2 > 0
-    ]
+    # Normalize method_costs keys: strip legacy unit suffixes
+    normalized_costs = {
+        m: {strip_unit_suffix(r): v for r, v in resources.items()}
+        for m, resources in method_costs.items()
+    }
+
+    # Build resource_units locally to avoid the overwrite bug when multiple categories share the same unit
+    _resource_units = defaultdict(list)
+    for cat, resources in resource_groups.items():
+        unit_key = f"{resource_unit_map[cat]}/MtCO2"
+        _resource_units[unit_key].extend(resources)
+    local_resource_units = dict(_resource_units)
+
+    rows = []
+    for m, co2 in allocations.items():
+        for unit, resources in local_resource_units.items():
+            for r in resources:
+                cost = normalized_costs.get(m, {}).get(r)
+                if cost is not None and cost * co2 > 0:
+                    rows.append({"Method": m, "Resource": r, "Used": cost * co2, "Unit": unit})
     if not rows:
         return
 
@@ -295,15 +356,18 @@ def save_scenario(latest):
     st.caption("Save the current scenario with inputs and outputs for comparison and/or export.")
 
     suggested = st.session_state.get("scenario_loaded_file", "").rsplit(".", 1)[0]
-    current = st.session_state.get("scenario_name", "").strip()
+    
+    # Initialize only if not already set
+    if "scenario_name" not in st.session_state:
+        st.session_state["scenario_name"] = suggested
 
     col_name, col_btn = st.columns([3, 1])
     with col_name:
         st.text_input(
-            "Scenario name", key="scenario_name",
-            value=current or suggested,
+            "Scenario name",
+            key="scenario_name",
             label_visibility="collapsed",
-            placeholder="Enter a scenario name"
+            placeholder="Enter a scenario name",
         )
     with col_btn:
         if st.button("Save", use_container_width=True, icon=":material/save:"):
@@ -323,8 +387,9 @@ def export_scenario():
 
     scenario = st.session_state.scenarios[name]
     st.markdown(f"#### Scenario: {name}")
-    df_saved = build_results_df(scenario["allocations"])
-    st.table(format_results_summary(df_saved))
+    df_saved = build_results_df(scenario["allocations"], method_constraints=scenario.get("method_constraints"))
+
+    st.markdown(format_table_summary(df_saved,"cdr-table-res"), unsafe_allow_html=True)
 
     safe = unicodedata.normalize("NFKD", name).encode("ascii", "ignore").decode("ascii")
     col_json, col_csv = st.columns(2)
@@ -343,13 +408,12 @@ def export_scenario():
                     pd.DataFrame.from_dict(scenario["resource_caps"], orient="index",
                                            columns=["Available Amount"]).to_csv())
         zf.writestr("method_constraints.csv",
-                    pd.DataFrame.from_dict(scenario["method_constraints"], orient="index",
-                                           columns=["active", "max_share"]).to_csv())
-        zf.writestr("method_costs.csv",                                      # ← nouveau
+                    pd.DataFrame.from_dict(scenario["method_constraints"], orient="index", columns=["active", "cap_type (percent or absolute)", "cap_value"]).to_csv())
+        zf.writestr("method_costs.csv",                      
                     pd.DataFrame.from_dict(scenario["method_costs"], orient="index").to_csv())
         zf.writestr("allocations.csv",
                     pd.DataFrame.from_dict(scenario["allocations"], orient="index",
-                                           columns=["tCO₂ removed"]).to_csv())
+                                           columns=[f"{CO2_UNIT} removed"]).to_csv())
     with col_csv:
         st.download_button(
             "Export CSV (zip)", data=buf.getvalue(),
@@ -359,50 +423,66 @@ def export_scenario():
         )
 
 def export_inputs_only():
+    resource_to_unit = {
+        r: resource_unit_map[group]
+        for group, resources in resource_groups.items()
+        for r in resources
+    }
     st.markdown("### Export inputs")
     st.caption("Export only the resource capabilities, method constraints and coefficients — without results.")
 
     suggested = st.session_state.get("excel_loaded_file", "").rsplit(".", 1)[0]
-    
-    col_text, col_button = st.columns([3,1])
+    # Update when a new file is loaded
+    if st.session_state.get("_input_file_name_source") != suggested:
+        st.session_state["input_file_name"] = suggested
+        st.session_state["_input_file_name_source"] = suggested
+
+    col_text, col_btn = st.columns([3, 1])
     with col_text:
         st.text_input(
-            "File name", label_visibility="collapsed", key="input_file_name",
-            value=suggested,
+            "File name",
+            label_visibility="collapsed",
+            key="input_file_name",
             placeholder="Enter a file name",
         )
+    with col_btn:
+        prepare = st.button("Prepare export", use_container_width=True, icon=":material/file_export:")
 
-    wb = openpyxl.Workbook()
-    ws1 = wb.active
-    ws1.title = "resource_caps"
-    ws1.append(["Resource", "Available Amount"])
-    for r, v in st.session_state.resource_caps.items():
-        ws1.append([r, v])
+    if prepare:
+        safe = st.session_state.get("input_file_name", "").strip() or suggested or "crra_inputs"
 
-    ws2 = wb.create_sheet("constraints")
-    ws2.append(["method", "active", "max_share"])
-    for m, c in st.session_state.constraints.items():
-        ws2.append([m, c.get("active", True), c.get("max_share", 1.0)])
+        wb = openpyxl.Workbook()
+        ws1 = wb.active
+        ws1.title = "resource_caps"
+        ws1.append(["Resource", "Unit", "Available Amount"])
+        for r, v in st.session_state.resource_caps.items():
+            ws1.append([r, resource_to_unit.get(r, ""), v])
 
-    ws3 = wb.create_sheet("coefficients")
-    ws3.append(["method", "resource", "value", "secondary"])
-    for m, resources in st.session_state.costs.items():
-        for r, v in resources.items():
-            ws3.append([m, r, v, (m, r) in secondary_resources])
+        ws2 = wb.create_sheet("constraints")
+        ws2.append(["method", "active", "cap_type (percent or absolute)", "cap_value"])
+        for m, c in st.session_state.constraints.items():
+            ws2.append([m, c.get("active", True), c.get("cap_type", "percent"), c.get("cap_value", 1.0)])
 
-    buf = io.BytesIO()
-    wb.save(buf)
-    buf.seek(0)
+        ws3 = wb.create_sheet("coefficients")
+        ws3.append(["method", "resource", "value", "secondary"])
+        for m, resources in st.session_state.costs.items():
+            for r, v in resources.items():
+                ws3.append([m, r, v, (m, r) in secondary_resources])
 
-    safe = st.session_state.get("input_file_name", "").strip() or suggested or "crra_inputs"
-    with col_button:        
+        buf = io.BytesIO()
+        wb.save(buf)
+
+        st.session_state["_input_export_buf"] = buf.getvalue()
+        st.session_state["_input_export_name"] = safe
+
+    if "_input_export_buf" in st.session_state:
         st.download_button(
-            f"Export Excel",
-            data=buf,
-            file_name=f"{safe}.xlsx",
+            f"Download **{st.session_state['_input_export_name']}.xlsx**",
+            data=st.session_state["_input_export_buf"],
+            file_name=f"{st.session_state['_input_export_name']}.xlsx",
             mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
-            icon=":material/download:",
-            use_container_width=True
+            use_container_width=True,
+            icon=":material/download:"
         )
 
 def clear_all_data():
@@ -485,10 +565,11 @@ def render_tab():
 
     # ── Results ────────────────────────────────────────────────────────────
     show_metrics(latest)
-    df_result = build_results_df(allocations)
+    df_result = build_results_df(allocations, method_constraints=latest.get("method_constraints"))
 
     st.subheader("Optimization Summary")
-    st.table(format_results_summary(df_result))
+    # st.table(format_results_summary(df_result))
+    st.markdown(format_table_summary(df_result,"cdr-table-res"), unsafe_allow_html=True)
 
     st.markdown("### Method allocation breakdown")
     show_donut(df_result)

tabs/tab5_sensitivity.py

@@ -3,7 +3,7 @@ import pandas as pd
 import seaborn as sns
 import streamlit as st
 
-from config.config import method_order, resource_order
+from config.config import CO2_UNIT, method_order, resource_order
 from optimization.optimization import run_optimization
 
 
@@ -55,14 +55,14 @@ def render_tab():
                     all_data.append({
                         "Input": resource,
                         "% of cap": pct,
-                        "CO₂ removed (t)": co2_removed
+                        f"CO₂ removed ({CO2_UNIT})": co2_removed
                     })
 
             df = pd.DataFrame(all_data)
             st.markdown("#### Sensitivity to resource availability")
             fig, ax = plt.subplots(figsize=(8, 5))
-            sns.lineplot(data=df, x="% of cap", y="CO₂ removed (t)", hue="Input", ax=ax)
-            ax.axhline(y=baseline_removed, linestyle="--", color="gray", label=f"Baseline: {baseline_removed:,} tCO₂")
+            sns.lineplot(data=df, x="% of cap", y=f"CO₂ removed ({CO2_UNIT})", hue="Input", ax=ax)
+            ax.axhline(y=baseline_removed, linestyle="--", color="gray", label=f"Baseline: {baseline_removed:,} {CO2_UNIT}")
             ax.legend()
             st.pyplot(fig)
 
@@ -73,32 +73,73 @@ def render_tab():
         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)
-            st.markdown("#### Sensitivity to method max allocation in the portfolio")
-            fig, ax = plt.subplots(figsize=(8, 5))
-            sns.lineplot(data=df, x="% max allocation", y="CO₂ removed (t)", hue="Input", ax=ax)
-            ax.axhline(y=baseline_removed, linestyle="--", color="gray", label=f"Baseline: {baseline_removed:,} tCO₂")
-            ax.legend()
-            st.pyplot(fig)
+            percent_methods = [m for m in selected_methods if st.session_state.constraints[m].get("cap_type", "percent") == "percent"]
+            absolute_methods = [m for m in selected_methods if st.session_state.constraints[m].get("cap_type") == "absolute"]
+
+            if percent_methods:
+                st.markdown("#### Sensitivity to % cap")
+                percentages = list(range(0, 110, 10))
+                all_data = []
+
+                for method in percent_methods:
+                    for pct in percentages:
+                        temp_constraints = st.session_state.constraints.copy()
+                        temp_constraints[method]["cap_type"] = "percent"
+                        temp_constraints[method]["cap_value"] = 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,
+                            f"CO₂ removed ({CO2_UNIT})": co2_removed
+                        })
+
+                df = pd.DataFrame(all_data)
+                fig, ax = plt.subplots(figsize=(8, 5))
+                sns.lineplot(data=df, x="% max allocation", y=f"CO₂ removed ({CO2_UNIT})", hue="Input", ax=ax)
+                ax.axhline(y=baseline_removed, linestyle="--", color="gray", label=f"Baseline: {baseline_removed:,} {CO2_UNIT}")
+                ax.legend()
+                st.pyplot(fig)
+
+            if absolute_methods:
+                st.markdown(f"#### Sensitivity to absolute cap ({CO2_UNIT}/yr)")
+                all_data = []
+
+                for method in absolute_methods:
+                    current_cap = st.session_state.constraints[method].get("cap_value", 0.0)
+                    # Varier de 0 à 200% de la valeur actuelle en 11 points
+                    if current_cap > 0:
+                        values = [current_cap * pct / 100 for pct in range(0, 210, 20)]
+                    else:
+                        st.warning(f"Method **{method}** has an absolute cap of 0 — set a value first.", icon="⚠️")
+                        continue
+
+                    for val in values:
+                        temp_constraints = st.session_state.constraints.copy()
+                        temp_constraints[method]["cap_type"] = "absolute"
+                        temp_constraints[method]["cap_value"] = val
+
+                        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,
+                            f"Cap ({CO2_UNIT}/yr)": val,
+                            f"CO₂ removed ({CO2_UNIT})": co2_removed
+                        })
+
+                if all_data:
+                    df = pd.DataFrame(all_data)
+                    fig, ax = plt.subplots(figsize=(8, 5))
+                    sns.lineplot(data=df, x=f"Cap ({CO2_UNIT}/yr)", y=f"CO₂ removed ({CO2_UNIT})", hue="Input", ax=ax)
+                    ax.axhline(y=baseline_removed, linestyle="--", color="gray", label=f"Baseline: {baseline_removed:,} {CO2_UNIT}")
+                    ax.legend()
+                    st.pyplot(fig)

tabs/tab6_scenarios.py

@@ -4,6 +4,7 @@ import matplotlib.pyplot as plt
 import pandas as pd
 import seaborn as sns
 import streamlit as st
+from config.config import CO2_UNIT
 from utils.data_utils import is_valid_scenario
 from tabs.tab4_optimization import parse_zip_scenario
 
@@ -55,13 +56,13 @@ def _prepare_comparison_data(selected):
 
         row = {
             "Scenario": name,
-            "CO₂ Removed (t)": scenario.get("summary", {}).get("total_co2_removed", 0),
+            f"CO₂ Removed ({CO2_UNIT})": scenario.get("summary", {}).get("total_co2_removed", 0),
         }
 
         for method in all_methods:
             val = allocations.get(method, 0)
-            row[f"{method} (tCO₂)"] = val
-            method_alloc_rows.append({"Scenario": name, "Method": method, "tCO₂": val})
+            row[f"{method} ({CO2_UNIT})"] = val
+            method_alloc_rows.append({"Scenario": name, "Method": method, f"{CO2_UNIT}": val})
 
         for resource in all_resources:
             total_used = 0
@@ -91,9 +92,9 @@ def _show_method_allocations(df):
         return
     st.subheader("Method Allocations")
     fig, ax = plt.subplots(figsize=(10, 4))
-    sns.barplot(data=df, x="Method", y="tCO₂", hue="Scenario", ax=ax)
+    sns.barplot(data=df, x="Method", y=f"{CO2_UNIT}", hue="Scenario", ax=ax)
     ax.set_xlabel("Method")
-    ax.set_ylabel("tCO₂")
+    ax.set_ylabel(f"{CO2_UNIT}")
     plt.xticks(rotation=45, ha="right")
     st.pyplot(fig)
 

utils/data_utils.py

@@ -1,5 +1,6 @@
 import io
 import json
+import re
 import pandas as pd
 import streamlit as st
 import openpyxl
@@ -58,10 +59,19 @@ def load_inputs_from_excel(file):
     constraints = {}
     if "constraints" in xl:
         for _, row in xl["constraints"].iterrows():
-            constraints[row["method"]] = {
-                "active": bool(row["active"]),
-                "max_share": float(row["max_share"])
-            }
+            # Compatibility with older files that used `max_share`
+            if "cap_type (percent or absolute)" in xl["constraints"].columns:
+                constraints[row["method"]] = {
+                    "active": bool(row["active"]),
+                    "cap_type": str(row["cap_type (percent or absolute)"]),
+                    "cap_value": float(row["cap_value"])
+                }
+            else:
+                constraints[row["method"]] = {
+                    "active": bool(row["active"]),
+                    "cap_type": "percent",
+                    "cap_value": float(row["max_share"])
+                }
 
     costs = {}
     if "coefficients" in xl:
@@ -71,3 +81,6 @@ def load_inputs_from_excel(file):
 
     return resource_caps, constraints, costs
 
+def strip_unit_suffix(resource_name: str) -> str:
+    """Remove legacy unit suffix from resource name, e.g. 'Unspecified land (ha/tCO2)' -> 'Unspecified land'"""
+    return re.sub(r"\s*\(.*?\)\s*$", "", resource_name).strip()

utils/ui_helpers.py

@@ -1,6 +1,7 @@
 import numpy as np
 import pandas as pd
 import streamlit as st
+from config.config import CO2_UNIT
 
 # params
 background_color = "#3d86e9"
@@ -33,40 +34,34 @@ def inputs_table_style(df):
         .set_table_styles([{"selector": "th", "props": [("background-color", background_color), ("font-weight", "bold")]}])
     )
 
-def format_method_summary(df: pd.DataFrame):
+def format_table_summary(df: pd.DataFrame, class_name: str):
     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")]}
-        ])
-    )
+    df = df.reset_index()
+
+    html = df.to_html(index=True, classes=class_name, border=0)
+    return html
+
+    # df = df.copy()
+    # df.index.name = "CDR Method"
+    # df.reset_index(inplace=True)
+
+    # return (
+    #     df.style
+    #     .format({
+    #         f"{CO2_UNIT} 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.map(smart_display_format)