[FEAT] Add OR-substitutable resource family pools and rename inputs.csv

app.py

@@ -12,10 +12,10 @@ from utils.data_utils import load_slider_data, strip_unit_suffix
 from utils.state_utils import init_custom_resources
 from config.config import method_order, resource_order, secondary_resources
 
-# ── Auto-update: regenerate JSON files if inputs_aggregated.csv has changed ───
+# ── Auto-update: regenerate JSON files if inputs.csv has changed ───
 # Uses a hash file to detect changes — works both locally and on Hugging Face.
 # Must run before importing config/tabs, which read the generated JSON files.
-csv = "data/inputs_aggregated.csv"
+csv = "data/inputs.csv"
 hash_file = "data/.csv_hash"
 generated = [
     "data/cost_sliders.json",
@@ -35,7 +35,7 @@ if os.path.exists(csv):
     missing = any(not os.path.exists(f) for f in generated)
 
     if current_hash != stored_hash or missing:
-        print("inputs_aggregated.csv has changed — running data/update.py ...")
+        print("inputs.csv has changed — running data/update.py ...")
         subprocess.run([sys.executable, "data/update.py"], check=True)
         with open(hash_file, "w") as file:
             file.write(current_hash)

data/.csv_hash

@@ -1 +1 @@
-3d6bb195ae77cbc0aaa32556f2219657
+954594a32cc4a1765412a40054f7e4fb

data/cost_sliders.json

@@ -1,10 +1,15 @@
 {
-  "Afforestation": {
+  "Afforestation/reforestation": {
     "Other land (Mha/MtCO2)": {
       "min": 0.002,
       "median": 0.094927,
       "max": 0.384615
     },
+    "Arable land (Mha/MtCO2)": {
+      "min": 0.002,
+      "median": 0.094927,
+      "max": 0.384615
+    },
     "Other water (bcm/MtCO2)": {
       "min": 0.046,
       "median": 0.092,
@@ -25,98 +30,29 @@
       "max": 500000.0
     },
     "Forest biomass (Mt/MtCO2)": {
-      "min": 0.33,
-      "median": 0.37,
-      "max": 0.82
-    },
-    "Other land (Mha/MtCO2)": {
-      "min": 0.056402,
-      "median": 0.163201,
-      "max": 0.27
-    },
-    "Other water (bcm/MtCO2)": {
-      "min": -0.0005,
-      "median": 0.0001,
-      "max": 0.003
-    }
-  },
-  "Bio-char - Non-forest biomass": {
-    "Electrical energy (MWh/MtCO2)": {
-      "min": 0.0,
-      "median": 350.0,
-      "max": 500000.0
-    },
-    "Non-forest biomass (Mt/MtCO2)": {
-      "min": 0.818,
-      "median": 1.3395,
-      "max": 1.667
-    },
-    "Other land (Mha/MtCO2)": {
-      "min": 0.056402,
-      "median": 0.163201,
-      "max": 0.27
-    },
-    "Other water (bcm/MtCO2)": {
-      "min": -0.0005,
-      "median": 0.0001,
-      "max": 0.003
+      "min": 0.3,
+      "median": 1.5,
+      "max": 2.2
     }
   },
   "Bio-char - Other biomass": {
     "Electrical energy (MWh/MtCO2)": {
       "min": 0.0,
-      "median": 350.0,
+      "median": 350000.0,
       "max": 500000.0
     },
     "Other biomass (Mt/MtCO2)": {
       "min": 0.5,
       "median": 1.209221,
       "max": 12.86
-    },
-    "Other land (Mha/MtCO2)": {
-      "min": 0.056402,
-      "median": 0.163201,
-      "max": 0.27
-    },
-    "Other water (bcm/MtCO2)": {
-      "min": -0.0005,
-      "median": 0.0001,
-      "max": 0.003
     }
   },
   "BioCCS with anaerobic digestion/fermentation": {
-    "Non-forest biomass (Mt/MtCO2)": {
+    "Other biomass (Mt/MtCO2)": {
       "min": 1.315,
       "median": 1.561743,
       "max": 8.0
     },
-    "Geologic storage (Mt/MtCO2)": {
-      "min": 1.0,
-      "median": 1.0,
-      "max": 1.0
-    },
-    "Other biomass (Mt/MtCO2)": {
-      "min": 0.998094,
-      "median": 1.14,
-      "max": 2.0
-    },
-    "Other water (bcm/MtCO2)": {
-      "min": 0.368182,
-      "median": 0.565909,
-      "max": 0.913636
-    }
-  },
-  "BioCCS with anaerobic digestion/fermentation & growing biomass": {
-    "Arable land (Mha/MtCO2)": {
-      "min": 0.022803,
-      "median": 0.058844,
-      "max": 0.454545
-    },
-    "Other water (bcm/MtCO2)": {
-      "min": 0.368182,
-      "median": 0.565909,
-      "max": 0.913636
-    },
     "Geologic storage (Mt/MtCO2)": {
       "min": 1.0,
       "median": 1.0,
@@ -148,55 +84,6 @@
       "min": 0.666667,
       "median": 1.14,
       "max": 2.0
-    },
-    "Other chemical (Mt/MtCO2)": {
-      "min": 0.00013,
-      "median": 0.00316,
-      "max": 20.2
-    },
-    "Other water (bcm/MtCO2)": {
-      "min": 0.00063,
-      "median": 0.201625,
-      "max": 1.296342
-    }
-  },
-  "BioCCS with combustion & growing biomass": {
-    "Arable land (Mha/MtCO2)": {
-      "min": 0.022803,
-      "median": 0.0584,
-      "max": 1.9451
-    },
-    "Electrical energy (MWh/MtCO2)": {
-      "min": 0.0,
-      "median": 101000.0,
-      "max": 500000.0
-    },
-    "Thermal energy other grade (MWh/MtCO2)": {
-      "min": 0.0,
-      "median": 930611.1,
-      "max": 4000000.0
-    },
-    "Geologic storage (Mt/MtCO2)": {
-      "min": 1.0,
-      "median": 1.0,
-      "max": 1.0
-    },
-    "Other chemical (Mt/MtCO2)": {
-      "min": 0.00013,
-      "median": 0.00316,
-      "max": 20.2
-    },
-    "Other water (bcm/MtCO2)": {
-      "min": 0.00063,
-      "median": 0.201625,
-      "max": 1.296342
-    }
-  },
-  "Coastal revegetation (mangrove, seaweeds, etc.)": {
-    "Non-arable land (Mha/MtCO2)": {
-      "min": 0.0078,
-      "median": 0.120566,
-      "max": 0.328289
     }
   },
   "Cropland management": {
@@ -212,11 +99,6 @@
       "median": 4000000.0,
       "max": 4000000.0
     },
-    "Non-arable land (Mha/MtCO2)": {
-      "min": 3.0,
-      "median": 6.0,
-      "max": 6.0
-    },
     "Other biomass (Mt/MtCO2)": {
       "min": 0.0,
       "median": 3.571429,
@@ -234,7 +116,7 @@
       "median": 784125.0,
       "max": 6222222.0
     },
-    "Non-arable land (Mha/MtCO2)": {
+    "Other land (Mha/MtCO2)": {
       "min": 0.000223,
       "median": 0.000324,
       "max": 0.016
@@ -260,16 +142,6 @@
       "min": 0.00071,
       "median": 0.00101,
       "max": 0.00131
-    },
-    "Shoreline (km/MtCO2)": {
-      "min": 0.006,
-      "median": 0.012,
-      "max": 0.012
-    },
-    "Other mineral (Mt/MtCO2)": {
-      "min": 2.4,
-      "median": 2.95,
-      "max": 3.5
     }
   },
   "Enhanced weathering - Basalt mineral": {
@@ -285,7 +157,7 @@
     },
     "Electrical energy (MWh/MtCO2)": {
       "min": 9250.0,
-      "median": 388750.0,
+      "median": 64795.9,
       "max": 2775000.0
     },
     "Non-arable land (Mha/MtCO2)": {
@@ -307,7 +179,7 @@
     },
     "Electrical energy (MWh/MtCO2)": {
       "min": 9250.0,
-      "median": 388750.0,
+      "median": 64795.9,
       "max": 2775000.0
     },
     "Non-arable land (Mha/MtCO2)": {
@@ -334,7 +206,7 @@
     },
     "Electrical energy (MWh/MtCO2)": {
       "min": 9250.0,
-      "median": 388750.0,
+      "median": 64795.9,
       "max": 2775000.0
     },
     "Non-arable land (Mha/MtCO2)": {
@@ -361,7 +233,7 @@
     },
     "Electrical energy (MWh/MtCO2)": {
       "min": 9250.0,
-      "median": 388750.0,
+      "median": 64795.9,
       "max": 2775000.0
     },
     "Non-arable land (Mha/MtCO2)": {
@@ -461,11 +333,6 @@
       "min": 1.4,
       "median": 1.743,
       "max": 5.0
-    },
-    "Other water (bcm/MtCO2)": {
-      "min": 0.000339,
-      "median": 0.000678,
-      "max": 0.000678
     }
   },
   "Mineral looping DACCS": {
@@ -511,11 +378,6 @@
       "median": 0.000436,
       "max": 0.000991
     },
-    "Olivine mineral (Mt/MtCO2)": {
-      "min": 0.4,
-      "median": 0.8,
-      "max": 0.8
-    },
     "Thermal energy other grade (MWh/MtCO2)": {
       "min": 400000.0,
       "median": 450000.0,
@@ -561,18 +423,6 @@
       "max": 2.289744
     }
   },
-  "Reforestation": {
-    "Other land (Mha/MtCO2)": {
-      "min": 0.002,
-      "median": 0.094927,
-      "max": 38.8
-    },
-    "Other water (bcm/MtCO2)": {
-      "min": 0.046,
-      "median": 0.092,
-      "max": 0.092
-    }
-  },
   "pH swing": {
     "Electrical energy (MWh/MtCO2)": {
       "min": 660000.0,

data/generate_model_data.py

@@ -11,7 +11,7 @@ from utils.data_utils import strip_unit_suffix
 
 def build_method_groups(df):
     """Build the method_groups dict from the CSV Method group and Implementation columns.
-    Group and method order follows first appearance in inputs_aggregated.csv."""
+    Group and method order follows first appearance in inputs.csv."""
     method_groups = {}
     for _, row in df.drop_duplicates(subset=["Implementation"]).iterrows():
         m, g = row["Implementation"], row["Method group"]
@@ -30,7 +30,7 @@ def build_secondary_resources(df):
 
 
 def create_sliders_inputs():
-    df = pd.read_csv("data/inputs_aggregated.csv")
+    df = pd.read_csv("data/inputs.csv")
 
     cost_sliders = {}
 

data/{inputs_aggregated.csv → inputs.csv}

@@ -1,109 +1,81 @@
 Implementation,Method group,Resource,Resource group,Type,Unit,count,quantile_25,mean,median,quantile_75,std,min,max,Is secondary
-Afforestation,Forestry,Other land,Land,Physical,Mha/MtCO2,36,0.024593,0.115652,0.094927,0.197075,0.112368,0.002,0.384615,False
-Afforestation,Forestry,Other water,Water,Physical,bcm/MtCO2,2,0.092,0.092,0.092,0.092,0.0,0.092,0.092,True
-Agroforestry,Forestry,Arable land,Land,Physical,Mha/MtCO2,12,0.039953,0.450767,0.064101,0.2141,0.90114,0.0025,3.030303,False
-Biochar - Forest biomass,Biochar,Electrical energy,Energy,Energy,MWh/MtCO2,6,87.5,167000.0,350.0,375000.0,258000.0,0.0,500000.0,True
-Biochar - Forest biomass,Biochar,Forest biomass,Biomass,Physical,Mt/MtCO2,6,0.34,0.472833,0.37,0.55525,0.201624,0.33,0.82,False
-Biochar - Forest biomass,Biochar,Other land,Land,Physical,Mha/MtCO2,4,0.056402,0.163201,0.163201,0.27,0.123321,0.056402,0.27,True
-Biochar - Forest biomass,Biochar,Other water,Water,Physical,bcm/MtCO2,9,0.0,0.000633,0.0001,0.0005,0.001377,-0.0005,0.003,True
-Biochar - Non-forest biomass,Biochar,Electrical energy,Energy,Energy,MWh/MtCO2,6,87.5,167000.0,350.0,375000.0,258000.0,0.0,500000.0,True
-Biochar - Non-forest biomass,Biochar,Non-forest biomass,Biomass,Physical,Mt/MtCO2,8,1.142,1.291,1.3395,1.4885,0.33202,0.818,1.667,False
-Biochar - Non-forest biomass,Biochar,Other land,Land,Physical,Mha/MtCO2,4,0.056402,0.163201,0.163201,0.27,0.123321,0.056402,0.27,True
-Biochar - Non-forest biomass,Biochar,Other water,Water,Physical,bcm/MtCO2,9,0.0,0.000633,0.0001,0.0005,0.001377,-0.0005,0.003,True
-Biochar - Other biomass,Biochar,Electrical energy,Energy,Energy,MWh/MtCO2,6,87.5,167000.0,350.0,375000.0,258000.0,0.0,500000.0,True
-Biochar - Other biomass,Biochar,Other biomass,Biomass,Physical,Mt/MtCO2,48,0.943396,1.924255,1.209221,2.522177,1.937824,0.5,12.86,False
-Biochar - Other biomass,Biochar,Other land,Land,Physical,Mha/MtCO2,4,0.056402,0.163201,0.163201,0.27,0.123321,0.056402,0.27,True
-Biochar - Other biomass,Biochar,Other water,Water,Physical,bcm/MtCO2,9,0.0,0.000633,0.0001,0.0005,0.001377,-0.0005,0.003,True
-BioCCS with anaerobic digestion/fermentation,Bio-CCS,Non-forest biomass,Biomass,Physical,Mt/MtCO2,6,1.428571,2.689964,1.561743,2.128274,2.62425,1.315,8.0,False
-BioCCS with anaerobic digestion/fermentation,Bio-CCS,Geologic storage,Storage,Physical,Mt/MtCO2,1,1.0,1.0,1.0,1.0,0.0,1.0,1.0,False
-BioCCS with anaerobic digestion/fermentation,Bio-CCS,Other biomass,Biomass,Physical,Mt/MtCO2,6,1.03357,1.379365,1.14,1.785,0.484913,0.998094,2.0,False
-BioCCS with anaerobic digestion/fermentation,Bio-CCS,Other water,Water,Physical,bcm/MtCO2,14,0.395455,0.569805,0.565909,0.654546,0.183277,0.368182,0.913636,True
-BioCCS with anaerobic digestion/fermentation & growing biomass,Bio-CCS,Arable land,Land,Physical,Mha/MtCO2,16,0.037937,0.100867,0.058844,0.103034,0.113758,0.022803,0.454545,False
-BioCCS with anaerobic digestion/fermentation & growing biomass,Bio-CCS,Other water,Water,Physical,bcm/MtCO2,14,0.395455,0.569805,0.565909,0.654546,0.183277,0.368182,0.913636,True
-BioCCS with anaerobic digestion/fermentation & growing biomass,Bio-CCS,Geologic storage,Storage,Physical,Mt/MtCO2,1,1.0,1.0,1.0,1.0,0.0,1.0,1.0,False
-BioCCS with combustion,Bio-CCS,Electrical energy,Energy,Energy,MWh/MtCO2,18,27777.78,130975.3,101000.0,111111.1,162410.3,0.0,500000.0,True
-BioCCS with combustion,Bio-CCS,Forest biomass,Biomass,Physical,Mt/MtCO2,8,0.560764,0.675481,0.59375,0.838675,0.144419,0.555556,0.8547,False
-BioCCS with combustion,Bio-CCS,Thermal energy other grade,Energy,Energy,MWh/MtCO2,22,347222.2,1006242.0,930611.1,1025000.0,1052440.0,0.0,4000000.0,True
-BioCCS with combustion,Bio-CCS,Geologic storage,Storage,Physical,Mt/MtCO2,1,1.0,1.0,1.0,1.0,0.0,1.0,1.0,False
-BioCCS with combustion,Bio-CCS,Other biomass,Biomass,Physical,Mt/MtCO2,8,0.916667,1.264167,1.14,1.625,0.528225,0.666667,2.0,False
-BioCCS with combustion,Bio-CCS,Other chemical,Chemicals,Physical,Mt/MtCO2,20,0.00027,4.044119,0.00316,0.016,8.287794,0.00013,20.2,True
-BioCCS with combustion,Bio-CCS,Other water,Water,Physical,bcm/MtCO2,32,0.006475,0.312937,0.201625,0.542046,0.343155,0.00063,1.296342,True
-BioCCS with combustion & growing biomass,Bio-CCS,Arable land,Land,Physical,Mha/MtCO2,32,0.038,0.20054,0.0584,0.094595,0.46566,0.022803,1.9451,False
-BioCCS with combustion & growing biomass,Bio-CCS,Electrical energy,Energy,Energy,MWh/MtCO2,18,27777.78,130975.3,101000.0,111111.1,162410.3,0.0,500000.0,True
-BioCCS with combustion & growing biomass,Bio-CCS,Thermal energy other grade,Energy,Energy,MWh/MtCO2,22,347222.2,1006242.0,930611.1,1025000.0,1052440.0,0.0,4000000.0,True
-BioCCS with combustion & growing biomass,Bio-CCS,Geologic storage,Storage,Physical,Mt/MtCO2,1,1.0,1.0,1.0,1.0,0.0,1.0,1.0,False
-BioCCS with combustion & growing biomass,Bio-CCS,Other chemical,Chemicals,Physical,Mt/MtCO2,20,0.00027,4.044119,0.00316,0.016,8.287794,0.00013,20.2,True
-BioCCS with combustion & growing biomass,Bio-CCS,Other water,Water,Physical,bcm/MtCO2,32,0.006475,0.312937,0.201625,0.542046,0.343155,0.00063,1.296342,True
-"Coastal revegetation (mangrove, seaweeds, etc.)",Blue carbon,Non-arable land,Land,Physical,Mha/MtCO2,11,0.043295,0.132018,0.120566,0.197449,0.117064,0.0078,0.328289,False
-Cropland management,"Soil carbon sequestration (crop, grasslands)",Arable land,Land,Physical,Mha/MtCO2,14,0.27062,1.277946,0.420635,1.052335,2.329779,0.082645,9.0,False
-Durable biobased products,Durable biobased products,Electrical energy,Energy,Energy,MWh/MtCO2,2,4000000.0,4000000.0,4000000.0,4000000.0,0.0,4000000.0,4000000.0,True
-Durable biobased products,Durable biobased products,Non-arable land,Land,Physical,Mha/MtCO2,2,6.0,6.0,6.0,6.0,0.0,6.0,6.0,True
-Durable biobased products,Durable biobased products,Other biomass,Biomass,Physical,Mt/MtCO2,6,1.267857,2.607143,3.571429,3.892857,1.842663,0.0,4.0,False
-Durable biobased products,Durable biobased products,Other water,Water,Physical,bcm/MtCO2,2,0.0002,0.0003,0.0003,0.0004,0.000283,0.0001,0.0005,True
-Electrochemical DACCS,Direct air carbon capture and storage (DACCS),Electrical energy,Energy,Energy,MWh/MtCO2,10,333333.3,2032081.0,784125.0,2750000.0,2378833.0,252556.0,6222222.0,False
-Electrochemical DACCS,Direct air carbon capture and storage (DACCS),Non-arable land,Land,Physical,Mha/MtCO2,14,0.000272,0.002662,0.000324,0.000881,0.005657,0.000223,0.016,True
-Electrochemical DACCS,Direct air carbon capture and storage (DACCS),Geologic storage,Storage,Physical,Mt/MtCO2,1,1.0,1.0,1.0,1.0,0.0,1.0,1.0,False
-Electrochemical DACCS,Direct air carbon capture and storage (DACCS),Other water,Water,Physical,bcm/MtCO2,6,0.002,0.011,0.005,0.0215,0.012522,0.001,0.027,False
-Electrochemical OAE,Ocean alkalinity enhancement,Electrical energy,Energy,Energy,MWh/MtCO2,8,1600000.0,2241250.0,2150000.0,2500000.0,1283594.0,830000.0,5000000.0,False
-Electrochemical OAE,Ocean alkalinity enhancement,Non-arable land,Land,Physical,Mha/MtCO2,2,0.00086,0.00101,0.00101,0.00116,0.000424,0.00071,0.00131,True
-Electrochemical OAE,Ocean alkalinity enhancement,Shoreline,Shoreline,Physical,km/MtCO2,2,0.012,0.012,0.012,0.012,0.0,0.012,0.012,False
-Electrochemical OAE,Ocean alkalinity enhancement,Other mineral,Minerals,Physical,Mt/MtCO2,4,2.4,2.95,2.95,3.5,0.635085,2.4,3.5,False
-Enhanced weathering - Basalt mineral,Enhanced weathering,Arable land,Land,Physical,Mha/MtCO2,28,0.12875,0.548974,0.25,0.5,0.709916,1E-06,2.5,True
-Enhanced weathering - Basalt mineral,Enhanced weathering,Basalt mineral,Minerals,Physical,Mt/MtCO2,25,3.33,5.773042,4.3,5.3,4.967901,1.0,19.0,False
-Enhanced weathering - Basalt mineral,Enhanced weathering,Electrical energy,Energy,Energy,MWh/MtCO2,8,92850.0,593431.3,388750.0,500000.0,904565.7,9250.0,2775000.0,True
-Enhanced weathering - Basalt mineral,Enhanced weathering,Non-arable land,Land,Physical,Mha/MtCO2,12,0.004429,11.43247,0.009065,28.57143,17.2264,0.0009,40.0,True
-Enhanced weathering - Basalt mineral,Enhanced weathering,Other water,Water,Physical,bcm/MtCO2,5,0.0005,0.010466,0.000866,0.000866,0.022102,0.0001,0.05,True
-Enhanced weathering - Olivine mineral,Enhanced weathering,Arable land,Land,Physical,Mha/MtCO2,28,0.12875,0.548974,0.25,0.5,0.709916,1E-06,2.5,True
-Enhanced weathering - Olivine mineral,Enhanced weathering,Electrical energy,Energy,Energy,MWh/MtCO2,8,92850.0,593431.3,388750.0,500000.0,904565.7,9250.0,2775000.0,True
-Enhanced weathering - Olivine mineral,Enhanced weathering,Non-arable land,Land,Physical,Mha/MtCO2,12,0.004429,11.43247,0.009065,28.57143,17.2264,0.0009,40.0,True
-Enhanced weathering - Olivine mineral,Enhanced weathering,Olivine mineral,Minerals,Physical,Mt/MtCO2,26,0.909091,1.429511,1.25,1.58,0.941247,0.27027,3.7,False
-Enhanced weathering - Olivine mineral,Enhanced weathering,Other water,Water,Physical,bcm/MtCO2,5,0.0005,0.010466,0.000866,0.000866,0.022102,0.0001,0.05,True
-Enhanced weathering - Wollastonite mineral,Enhanced weathering,Arable land,Land,Physical,Mha/MtCO2,28,0.12875,0.548974,0.25,0.5,0.709916,1E-06,2.5,True
-Enhanced weathering - Wollastonite mineral,Enhanced weathering,Electrical energy,Energy,Energy,MWh/MtCO2,8,92850.0,593431.3,388750.0,500000.0,904565.7,9250.0,2775000.0,True
-Enhanced weathering - Wollastonite mineral,Enhanced weathering,Non-arable land,Land,Physical,Mha/MtCO2,12,0.004429,11.43247,0.009065,28.57143,17.2264,0.0009,40.0,True
-Enhanced weathering - Wollastonite mineral,Enhanced weathering,Other water,Water,Physical,bcm/MtCO2,5,0.0005,0.010466,0.000866,0.000866,0.022102,0.0001,0.05,True
-Enhanced weathering - Wollastonite mineral,Enhanced weathering,Wollastonite mineral,Minerals,Physical,Mt/MtCO2,8,1.321429,1.629643,1.564286,1.83,0.742366,0.56,3.0,False
-Enhanced weathering - Other mineral,Enhanced weathering,Arable land,Land,Physical,Mha/MtCO2,28,0.12875,0.548974,0.25,0.5,0.709916,1E-06,2.5,True
-Enhanced weathering - Other mineral,Enhanced weathering,Electrical energy,Energy,Energy,MWh/MtCO2,8,92850.0,593431.3,388750.0,500000.0,904565.7,9250.0,2775000.0,True
-Enhanced weathering - Other mineral,Enhanced weathering,Non-arable land,Land,Physical,Mha/MtCO2,12,0.004429,11.43247,0.009065,28.57143,17.2264,0.0009,40.0,True
-Enhanced weathering - Other mineral,Enhanced weathering,Other mineral,Minerals,Physical,Mt/MtCO2,19,1.315789,6.356736,2.5,8.625,7.726415,0.5,25.0,False
-Enhanced weathering - Other mineral,Enhanced weathering,Other water,Water,Physical,bcm/MtCO2,5,0.0005,0.010466,0.000866,0.000866,0.022102,0.0001,0.05,True
-Forest management,Forestry,Other land,Land,Physical,Mha/MtCO2,2,0.438675,0.76865,0.76865,1.098625,0.93331,0.1087,1.4286,False
-High-temp DACCS (solid-sorbent/liquid-solvent),Direct air carbon capture and storage (DACCS),Electrical energy,Energy,Energy,MWh/MtCO2,32,361111.1,423787.8,462961.1,500000.0,163178.3,77000.0,770000.0,False
-High-temp DACCS (solid-sorbent/liquid-solvent),Direct air carbon capture and storage (DACCS),Non-arable land,Land,Physical,Mha/MtCO2,23,2.2e-08,0.000229,0.000223,0.000298,0.000299,1.5e-10,0.000991,False
-High-temp DACCS (solid-sorbent/liquid-solvent),Direct air carbon capture and storage (DACCS),Thermal energy high grade,Energy,Energy,MWh/MtCO2,38,1472222.0,1954835.0,1750000.0,2314815.0,643937.6,1125000.0,3800000.0,False
-High-temp DACCS (solid-sorbent/liquid-solvent),Direct air carbon capture and storage (DACCS),Geologic storage,Storage,Physical,Mt/MtCO2,1,1.0,1.0,1.0,1.0,0.0,1.0,1.0,False
-High-temp DACCS (solid-sorbent/liquid-solvent),Direct air carbon capture and storage (DACCS),Other water,Water,Physical,bcm/MtCO2,16,0.002502,0.010221,0.0047,0.00818,0.015887,5E-09,0.05,True
-Low-temp DACCS (solid-sorbent/liquid-solvent),Direct air carbon capture and storage (DACCS),Electrical energy,Energy,Energy,MWh/MtCO2,44,218000.0,895320.2,462961.1,690000.0,1255695.0,130000.0,4611111.0,False
-Low-temp DACCS (solid-sorbent/liquid-solvent),Direct air carbon capture and storage (DACCS),Non-arable land,Land,Physical,Mha/MtCO2,23,6.25e-08,0.000238,0.000223,0.000298,0.000294,1E-11,0.000991,False
-Low-temp DACCS (solid-sorbent/liquid-solvent),Direct air carbon capture and storage (DACCS),Thermal energy low grade,Energy,Energy,MWh/MtCO2,30,1432500.0,1678215.0,1500000.0,2062500.0,516794.3,638888.9,2722222.0,False
-Low-temp DACCS (solid-sorbent/liquid-solvent),Direct air carbon capture and storage (DACCS),Geologic storage,Storage,Physical,Mt/MtCO2,1,1.0,1.0,1.0,1.0,0.0,1.0,1.0,False
-Low-temp DACCS (solid-sorbent/liquid-solvent),Direct air carbon capture and storage (DACCS),Other water,Water,Physical,bcm/MtCO2,14,7.5e-10,0.003187,0.001008,0.004925,0.005238,-0.002,0.015,True
-Mineral OAE / Ocean liming,Ocean alkalinity enhancement,Electrical energy,Energy,Energy,MWh/MtCO2,12,323625.0,1153583.0,606500.0,1755000.0,1286024.0,27000.0,4000000.0,True
-Mineral OAE / Ocean liming,Ocean alkalinity enhancement,Non-arable land,Land,Physical,Mha/MtCO2,4,0.0,0.000505,0.000355,0.00086,0.000632,0.0,0.00131,True
-Mineral OAE / Ocean liming,Ocean alkalinity enhancement,Thermal energy other grade,Energy,Energy,MWh/MtCO2,6,1388889.0,1257630.0,1458944.0,1529000.0,542588.8,160000.0,1550000.0,True
-Mineral OAE / Ocean liming,Ocean alkalinity enhancement,Other mineral,Minerals,Physical,Mt/MtCO2,8,1.62,2.489,1.743,2.5895,1.554598,1.4,5.0,False
-Mineral OAE / Ocean liming,Ocean alkalinity enhancement,Other water,Water,Physical,bcm/MtCO2,2,0.000678,0.000678,0.000678,0.000678,0.0,0.000678,0.000678,True
-Mineral looping DACCS,Direct air carbon capture and storage (DACCS),Electrical energy,Energy,Energy,MWh/MtCO2,8,83333.33,1104167.0,83333.33,1375000.0,1793811.0,0.0,4000000.0,False
-Mineral looping DACCS,Direct air carbon capture and storage (DACCS),Non-arable land,Land,Physical,Mha/MtCO2,12,0.000587,0.001065,0.000639,0.000873,0.001064,0.000223,0.0033,False
-Mineral looping DACCS,Direct air carbon capture and storage (DACCS),Thermal energy other grade,Energy,Energy,MWh/MtCO2,4,1479167.0,1715278.0,1819444.0,2055556.0,534015.3,1000000.0,2222222.0,False
-Mineral looping DACCS,Direct air carbon capture and storage (DACCS),Other mineral,Minerals,Physical,Mt/MtCO2,4,0.15,0.225,0.2,0.275,0.206155,0.0,0.5,False
-Mineral looping DACCS,Direct air carbon capture and storage (DACCS),Geologic storage,Storage,Physical,Mt/MtCO2,1,1.0,1.0,1.0,1.0,0.0,1.0,1.0,False
-Mineral looping DACCS,Direct air carbon capture and storage (DACCS),Other water,Water,Physical,bcm/MtCO2,6,0.000125,0.006762,0.0002,0.010068,0.011116,0.0,0.026714,True
-Mineralisation ex-situ,Mineralisation,Electrical energy,Energy,Energy,MWh/MtCO2,8,500000.0,1062500.0,750000.0,1250000.0,979704.8,0.0,3000000.0,False
-Mineralisation ex-situ,Mineralisation,Non-arable land,Land,Physical,Mha/MtCO2,4,0.000298,0.000522,0.000436,0.000659,0.000342,0.000223,0.000991,False
-Mineralisation ex-situ,Mineralisation,Olivine mineral,Minerals,Physical,Mt/MtCO2,2,0.8,0.8,0.8,0.8,0.0,0.8,0.8,False
-Mineralisation ex-situ,Mineralisation,Thermal energy other grade,Energy,Energy,MWh/MtCO2,4,400000.0,575000.0,450000.0,625000.0,287228.1,400000.0,1000000.0,False
-Mineralisation ex-situ,Mineralisation,Other mineral,Minerals,Physical,Mt/MtCO2,22,0.4375,3.935,3.0,7.0,3.647722,0.04,10.0,False
-Mineralisation ex-situ,Mineralisation,Other water,Water,Physical,bcm/MtCO2,14,0.0002,0.050004,0.00545,0.0145,0.106908,0.0,0.3,False
-Moisture swing DACCS,Direct air carbon capture and storage (DACCS),Electrical energy,Energy,Energy,MWh/MtCO2,4,316000.0,324666.7,324666.7,333333.3,10007.4,316000.0,333333.3,False
-Moisture swing DACCS,Direct air carbon capture and storage (DACCS),Non-arable land,Land,Physical,Mha/MtCO2,8,0.000169,0.000331,0.000272,0.00038,0.000319,8E-06,0.000991,False
-Moisture swing DACCS,Direct air carbon capture and storage (DACCS),Geologic storage,Storage,Physical,Mt/MtCO2,1,1.0,1.0,1.0,1.0,0.0,1.0,1.0,False
-Moisture swing DACCS,Direct air carbon capture and storage (DACCS),Other water,Water,Physical,bcm/MtCO2,4,-0.00015,0.000427,0.000427,0.001,0.000662,-0.00015,0.001,False
-Pasture management,"Soil carbon sequestration (crop, grasslands)",Arable land,Land,Physical,Mha/MtCO2,24,0.102253,0.442368,0.252422,0.522974,0.522145,0.071894,2.289744,False
-Reforestation,Forestry,Other land,Land,Physical,Mha/MtCO2,34,0.044459,1.564102,0.094927,0.1887,6.825803,0.002,38.8,False
-Reforestation,Forestry,Other water,Water,Physical,bcm/MtCO2,2,0.092,0.092,0.092,0.092,0.0,0.092,0.092,True
-pH swing,Direct ocean carbon capture and storage (DOCCS),Electrical energy,Energy,Energy,MWh/MtCO2,28,980000.0,2205112.0,1768000.0,3302500.0,1381909.0,660000.0,4670000.0,False
-pH swing,Direct ocean carbon capture and storage (DOCCS),Salt water,Water,Physical,bcm/MtCO2,4,0.435,4.78,4.78,9.125,5.017174,0.435,9.125,False
-pH swing,Direct ocean carbon capture and storage (DOCCS),Geologic storage,Storage,Physical,Mt/MtCO2,1,1.0,1.0,1.0,1.0,0.0,1.0,1.0,False
-pH swing,Direct ocean carbon capture and storage (DOCCS),Other water,Water,Physical,bcm/MtCO2,4,0.217391,6.608696,6.608696,13.0,7.380043,0.217391,13.0,False
+Afforestation/reforestation,Forestry,Other land,Land,Physical,Mha/MtCO2,36,0.024593,0.115652,0.094927,0.197075,0.112368,0.002,0.384615,FALSE
+Afforestation/reforestation,Forestry,Arable land,Land,Physical,Mha/MtCO2,36,0.024593,0.115652,0.094927,0.197075,0.112368,0.002,0.384615,FALSE
+Afforestation/reforestation,Forestry,Other water,Water,Physical,bcm/MtCO2,2,0.092,0.092,0.092,0.092,0,0.092,0.092,TRUE
+Agroforestry,Forestry,Arable land,Land,Physical,Mha/MtCO2,12,0.039953,0.450767,0.064101,0.2141,0.90114,0.0025,3.030303,FALSE
+Bio-char - Forest biomass,Bio-char,Electrical energy,Electrical,Energy,MWh/MtCO2,6,87.5,167000,350,375000,258000,0,500000,TRUE
+Bio-char - Forest biomass,Bio-char,Forest biomass,Biomass,Physical,Mt/MtCO2,6,0.5,1.5,1.5,2,0.4,0.3,2.2,FALSE
+Bio-char - Other biomass,Bio-char,Electrical energy,Electrical,Energy,MWh/MtCO2,6,87.5,167000,350000,375000,258000,0,500000,TRUE
+Bio-char - Other biomass,Bio-char,Other biomass,Biomass,Physical,Mt/MtCO2,48,0.943396,1.924255,1.209221,2.522177,1.937824,0.5,12.86,FALSE
+BioCCS with anaerobic digestion/fermentation,Bio-CCS,Other biomass,Biomass,Physical,Mt/MtCO2,6,1.428571,2.689964,1.561743,2.128274,2.62425,1.315,8,FALSE
+BioCCS with anaerobic digestion/fermentation,Bio-CCS,Geologic storage,Storage,Physical,Mt/MtCO2,1,1,1,1,1,0,1,1,FALSE
+BioCCS with combustion,Bio-CCS,Electrical energy,Electrical,Energy,MWh/MtCO2,18,27777.78,130975.3,101000,111111.1,162410.3,0,500000,TRUE
+BioCCS with combustion,Bio-CCS,Forest biomass,Biomass,Physical,Mt/MtCO2,8,0.560764,0.675481,0.59375,0.838675,0.144419,0.555556,0.8547,FALSE
+BioCCS with combustion,Bio-CCS,Thermal energy other grade,Thermal,Energy,MWh/MtCO2,22,347222.2,1006242,930611.1,1025000,1052440,0,4000000,TRUE
+BioCCS with combustion,Bio-CCS,Geologic storage,Storage,Physical,Mt/MtCO2,1,1,1,1,1,0,1,1,FALSE
+BioCCS with combustion,Bio-CCS,Other biomass,Biomass,Physical,Mt/MtCO2,8,0.916667,1.264167,1.14,1.625,0.528225,0.666667,2,FALSE
+Cropland management,"Soil carbon sequestration (crop, grasslands)",Arable land,Land,Physical,Mha/MtCO2,14,0.27062,1.277946,0.420635,1.052335,2.329779,0.082645,9,FALSE
+Durable biobased products,Durable biobased products,Electrical energy,Electrical,Energy,MWh/MtCO2,2,4000000,4000000,4000000,4000000,0,4000000,4000000,TRUE
+Durable biobased products,Durable biobased products,Other biomass,Biomass,Physical,Mt/MtCO2,6,1.267857,2.607143,3.571429,3.892857,1.842663,0,4,FALSE
+Durable biobased products,Durable biobased products,Other water,Water,Physical,bcm/MtCO2,2,0.0002,0.0003,0.0003,0.0004,0.000283,0.0001,0.0005,TRUE
+Electrochemical DACCS,Direct air carbon capture and storage (DACCS),Electrical energy,Electrical,Energy,MWh/MtCO2,10,333333.3,2032081,784125,2750000,2378833,252556,6222222,FALSE
+Electrochemical DACCS,Direct air carbon capture and storage (DACCS),Other land,Land,Physical,Mha/MtCO2,14,0.000272,0.002662,0.000324,0.000881,0.005657,0.000223,0.016,TRUE
+Electrochemical DACCS,Direct air carbon capture and storage (DACCS),Geologic storage,Storage,Physical,Mt/MtCO2,1,1,1,1,1,0,1,1,FALSE
+Electrochemical DACCS,Direct air carbon capture and storage (DACCS),Other water,Water,Physical,bcm/MtCO2,6,0.002,0.011,0.005,0.0215,0.012522,0.001,0.027,FALSE
+Electrochemical OAE,Ocean alkalinity enhancement,Electrical energy,Electrical,Energy,MWh/MtCO2,8,1600000,2241250,2150000,2500000,1283594,830000,5000000,FALSE
+Electrochemical OAE,Ocean alkalinity enhancement,Non-arable land,Land,Physical,Mha/MtCO2,2,0.00086,0.00101,0.00101,0.00116,0.000424,0.00071,0.00131,TRUE
+Enhanced weathering - Basalt mineral,Enhanced weathering,Arable land,Land,Physical,Mha/MtCO2,28,0.12875,0.548974,0.25,0.5,0.709916,1.00E-06,2.5,TRUE
+Enhanced weathering - Basalt mineral,Enhanced weathering,Basalt mineral,Minerals,Physical,Mt/MtCO2,25,3.33,5.773042,4.3,5.3,4.967901,1,19,FALSE
+Enhanced weathering - Basalt mineral,Enhanced weathering,Electrical energy,Electrical,Energy,MWh/MtCO2,8,37000,150000,64795.9,92591.8,200000,9250,2775000,FALSE
+Enhanced weathering - Basalt mineral,Enhanced weathering,Non-arable land,Land,Physical,Mha/MtCO2,12,0.004429,11.43247,0.009065,28.57143,17.2264,0.0009,40,TRUE
+Enhanced weathering - Basalt mineral,Enhanced weathering,Other water,Water,Physical,bcm/MtCO2,5,0.0005,0.010466,0.000866,0.000866,0.022102,0.0001,0.05,TRUE
+Enhanced weathering - Olivine mineral,Enhanced weathering,Arable land,Land,Physical,Mha/MtCO2,28,0.12875,0.548974,0.25,0.5,0.709916,1.00E-06,2.5,TRUE
+Enhanced weathering - Olivine mineral,Enhanced weathering,Electrical energy,Electrical,Energy,MWh/MtCO2,8,37000,150000,64795.9,92591.8,200000,9250,2775000,FALSE
+Enhanced weathering - Olivine mineral,Enhanced weathering,Non-arable land,Land,Physical,Mha/MtCO2,12,0.004429,11.43247,0.009065,28.57143,17.2264,0.0009,40,TRUE
+Enhanced weathering - Olivine mineral,Enhanced weathering,Olivine mineral,Minerals,Physical,Mt/MtCO2,26,0.909091,1.429511,1.25,1.58,0.941247,0.27027,3.7,FALSE
+Enhanced weathering - Olivine mineral,Enhanced weathering,Other water,Water,Physical,bcm/MtCO2,5,0.0005,0.010466,0.000866,0.000866,0.022102,0.0001,0.05,TRUE
+Enhanced weathering - Wollastonite mineral,Enhanced weathering,Arable land,Land,Physical,Mha/MtCO2,28,0.12875,0.548974,0.25,0.5,0.709916,1.00E-06,2.5,TRUE
+Enhanced weathering - Wollastonite mineral,Enhanced weathering,Electrical energy,Electrical,Energy,MWh/MtCO2,8,37000,150000,64795.9,92591.8,200000,9250,2775000,FALSE
+Enhanced weathering - Wollastonite mineral,Enhanced weathering,Non-arable land,Land,Physical,Mha/MtCO2,12,0.004429,11.43247,0.009065,28.57143,17.2264,0.0009,40,TRUE
+Enhanced weathering - Wollastonite mineral,Enhanced weathering,Other water,Water,Physical,bcm/MtCO2,5,0.0005,0.010466,0.000866,0.000866,0.022102,0.0001,0.05,TRUE
+Enhanced weathering - Wollastonite mineral,Enhanced weathering,Wollastonite mineral,Minerals,Physical,Mt/MtCO2,8,1.321429,1.629643,1.564286,1.83,0.742366,0.56,3,FALSE
+Enhanced weathering - Other mineral,Enhanced weathering,Arable land,Land,Physical,Mha/MtCO2,28,0.12875,0.548974,0.25,0.5,0.709916,1.00E-06,2.5,TRUE
+Enhanced weathering - Other mineral,Enhanced weathering,Electrical energy,Electrical,Energy,MWh/MtCO2,8,37000,150000,64795.9,92591.8,200000,9250,2775000,FALSE
+Enhanced weathering - Other mineral,Enhanced weathering,Non-arable land,Land,Physical,Mha/MtCO2,12,0.004429,11.43247,0.009065,28.57143,17.2264,0.0009,40,TRUE
+Enhanced weathering - Other mineral,Enhanced weathering,Other mineral,Minerals,Physical,Mt/MtCO2,19,1.315789,6.356736,2.5,8.625,7.726415,0.5,25,FALSE
+Enhanced weathering - Other mineral,Enhanced weathering,Other water,Water,Physical,bcm/MtCO2,5,0.0005,0.010466,0.000866,0.000866,0.022102,0.0001,0.05,TRUE
+Forest management,Forestry,Other land,Land,Physical,Mha/MtCO2,2,0.438675,0.76865,0.76865,1.098625,0.93331,0.1087,1.4286,FALSE
+High-temp DACCS (solid-sorbent/liquid-solvent),Direct air carbon capture and storage (DACCS),Electrical energy,Electrical,Energy,MWh/MtCO2,32,361111.1,423787.8,462961.1,500000,163178.3,77000,770000,FALSE
+High-temp DACCS (solid-sorbent/liquid-solvent),Direct air carbon capture and storage (DACCS),Non-arable land,Land,Physical,Mha/MtCO2,23,2.20E-08,0.000229,0.000223,0.000298,0.000299,1.50E-10,0.000991,FALSE
+High-temp DACCS (solid-sorbent/liquid-solvent),Direct air carbon capture and storage (DACCS),Thermal energy high grade,Thermal,Energy,MWh/MtCO2,38,1472222,1954835,1750000,2314815,643937.6,1125000,3800000,FALSE
+High-temp DACCS (solid-sorbent/liquid-solvent),Direct air carbon capture and storage (DACCS),Geologic storage,Storage,Physical,Mt/MtCO2,1,1,1,1,1,0,1,1,FALSE
+High-temp DACCS (solid-sorbent/liquid-solvent),Direct air carbon capture and storage (DACCS),Other water,Water,Physical,bcm/MtCO2,16,0.002502,0.010221,0.0047,0.00818,0.015887,5.00E-09,0.05,TRUE
+Low-temp DACCS (solid-sorbent/liquid-solvent),Direct air carbon capture and storage (DACCS),Electrical energy,Electrical,Energy,MWh/MtCO2,44,218000,895320.2,462961.1,690000,1255695,130000,4611111,FALSE
+Low-temp DACCS (solid-sorbent/liquid-solvent),Direct air carbon capture and storage (DACCS),Non-arable land,Land,Physical,Mha/MtCO2,23,6.25E-08,0.000238,0.000223,0.000298,0.000294,1.00E-11,0.000991,FALSE
+Low-temp DACCS (solid-sorbent/liquid-solvent),Direct air carbon capture and storage (DACCS),Thermal energy low grade,Thermal,Energy,MWh/MtCO2,30,1432500,1678215,1500000,2062500,516794.3,638888.9,2722222,FALSE
+Low-temp DACCS (solid-sorbent/liquid-solvent),Direct air carbon capture and storage (DACCS),Geologic storage,Storage,Physical,Mt/MtCO2,1,1,1,1,1,0,1,1,FALSE
+Low-temp DACCS (solid-sorbent/liquid-solvent),Direct air carbon capture and storage (DACCS),Other water,Water,Physical,bcm/MtCO2,14,7.50E-10,0.003187,0.001008,0.004925,0.005238,-0.002,0.015,TRUE
+Mineral OAE / Ocean liming,Ocean alkalinity enhancement,Electrical energy,Electrical,Energy,MWh/MtCO2,12,323625,1153583,606500,1755000,1286024,27000,4000000,FALSE
+Mineral OAE / Ocean liming,Ocean alkalinity enhancement,Non-arable land,Land,Physical,Mha/MtCO2,4,0,0.000505,0.000355,0.00086,0.000632,0,0.00131,TRUE
+Mineral OAE / Ocean liming,Ocean alkalinity enhancement,Thermal energy other grade,Thermal,Energy,MWh/MtCO2,6,1388889,1257630,1458944,1529000,542588.8,160000,1550000,TRUE
+Mineral OAE / Ocean liming,Ocean alkalinity enhancement,Other mineral,Minerals,Physical,Mt/MtCO2,8,1.62,2.489,1.743,2.5895,1.554598,1.4,5,FALSE
+Mineral looping DACCS,Direct air carbon capture and storage (DACCS),Electrical energy,Electrical,Energy,MWh/MtCO2,8,83333.33,1104167,83333.33,1375000,1793811,0,4000000,FALSE
+Mineral looping DACCS,Direct air carbon capture and storage (DACCS),Non-arable land,Land,Physical,Mha/MtCO2,12,0.000587,0.001065,0.000639,0.000873,0.001064,0.000223,0.0033,FALSE
+Mineral looping DACCS,Direct air carbon capture and storage (DACCS),Thermal energy other grade,Thermal,Energy,MWh/MtCO2,4,1479167,1715278,1819444,2055556,534015.3,1000000,2222222,FALSE
+Mineral looping DACCS,Direct air carbon capture and storage (DACCS),Other mineral,Minerals,Physical,Mt/MtCO2,4,0.15,0.225,0.2,0.275,0.206155,0,0.5,FALSE
+Mineral looping DACCS,Direct air carbon capture and storage (DACCS),Geologic storage,Storage,Physical,Mt/MtCO2,1,1,1,1,1,0,1,1,FALSE
+Mineral looping DACCS,Direct air carbon capture and storage (DACCS),Other water,Water,Physical,bcm/MtCO2,6,0.000125,0.006762,0.0002,0.010068,0.011116,0,0.026714,TRUE
+Mineralization ex-situ,Mineralization,Electrical energy,Electrical,Energy,MWh/MtCO2,8,500000,1062500,750000,1250000,979704.8,0,3000000,FALSE
+Mineralization ex-situ,Mineralization,Non-arable land,Land,Physical,Mha/MtCO2,4,0.000298,0.000522,0.000436,0.000659,0.000342,0.000223,0.000991,FALSE
+Mineralization ex-situ,Mineralization,Thermal energy other grade,Thermal,Energy,MWh/MtCO2,4,400000,575000,450000,625000,287228.1,400000,1000000,FALSE
+Mineralization ex-situ,Mineralization,Other mineral,Minerals,Physical,Mt/MtCO2,22,0.4375,3.935,3,7,3.647722,0.04,10,FALSE
+Mineralization ex-situ,Mineralization,Other water,Water,Physical,bcm/MtCO2,14,0.0002,0.050004,0.00545,0.0145,0.106908,0,0.3,FALSE
+Moisture swing DACCS,Direct air carbon capture and storage (DACCS),Electrical energy,Electrical,Energy,MWh/MtCO2,4,316000,324666.7,324666.7,333333.3,10007.4,316000,333333.3,FALSE
+Moisture swing DACCS,Direct air carbon capture and storage (DACCS),Non-arable land,Land,Physical,Mha/MtCO2,8,0.000169,0.000331,0.000272,0.00038,0.000319,8.00E-06,0.000991,FALSE
+Moisture swing DACCS,Direct air carbon capture and storage (DACCS),Geologic storage,Storage,Physical,Mt/MtCO2,1,1,1,1,1,0,1,1,FALSE
+Moisture swing DACCS,Direct air carbon capture and storage (DACCS),Other water,Water,Physical,bcm/MtCO2,4,-0.00015,0.000427,0.000427,0.001,0.000662,-0.00015,0.001,FALSE
+Pasture management,"Soil carbon sequestration (crop, grasslands)",Arable land,Land,Physical,Mha/MtCO2,24,0.102253,0.442368,0.252422,0.522974,0.522145,0.071894,2.289744,FALSE
+pH swing,Direct ocean carbon capture and storage (DOCCS),Electrical energy,Electrical,Energy,MWh/MtCO2,28,980000,2205112,1768000,3302500,1381909,660000,4670000,FALSE
+pH swing,Direct ocean carbon capture and storage (DOCCS),Salt water,Water,Physical,bcm/MtCO2,4,0.435,4.78,4.78,9.125,5.017174,0.435,9.125,FALSE
+pH swing,Direct ocean carbon capture and storage (DOCCS),Geologic storage,Storage,Physical,Mt/MtCO2,1,1,1,1,1,0,1,1,FALSE
+pH swing,Direct ocean carbon capture and storage (DOCCS),Other water,Water,Physical,bcm/MtCO2,4,0.217391,6.608696,6.608696,13,7.380043,0.217391,13,FALSE

data/method_groups.json

@@ -1,23 +1,16 @@
 {
   "Forestry": [
-    "Afforestation",
+    "Afforestation/reforestation",
     "Agroforestry",
-    "Forest management",
-    "Reforestation"
+    "Forest management"
   ],
   "Bio-char": [
     "Bio-char - Forest biomass",
-    "Bio-char - Non-forest biomass",
     "Bio-char - Other biomass"
   ],
   "Bio-CCS": [
     "BioCCS with anaerobic digestion/fermentation",
-    "BioCCS with anaerobic digestion/fermentation & growing biomass",
-    "BioCCS with combustion",
-    "BioCCS with combustion & growing biomass"
-  ],
-  "Blue carbon": [
-    "Coastal revegetation (mangrove, seaweeds, etc.)"
+    "BioCCS with combustion"
   ],
   "Soil carbon sequestration (crop, grasslands)": [
     "Cropland management",

data/secondary_resources.json

@@ -1,99 +1,35 @@
 [
   [
-    "Afforestation",
+    "Afforestation/reforestation",
     "Other water"
   ],
   [
     "Bio-char - Forest biomass",
     "Electrical energy"
   ],
-  [
-    "Bio-char - Forest biomass",
-    "Other land"
-  ],
-  [
-    "Bio-char - Forest biomass",
-    "Other water"
-  ],
-  [
-    "Bio-char - Non-forest biomass",
-    "Electrical energy"
-  ],
-  [
-    "Bio-char - Non-forest biomass",
-    "Other land"
-  ],
-  [
-    "Bio-char - Non-forest biomass",
-    "Other water"
-  ],
   [
     "Bio-char - Other biomass",
     "Electrical energy"
   ],
-  [
-    "Bio-char - Other biomass",
-    "Other land"
-  ],
-  [
-    "Bio-char - Other biomass",
-    "Other water"
-  ],
-  [
-    "BioCCS with anaerobic digestion/fermentation",
-    "Other water"
-  ],
-  [
-    "BioCCS with anaerobic digestion/fermentation & growing biomass",
-    "Other water"
-  ],
   [
     "BioCCS with combustion",
     "Electrical energy"
   ],
   [
     "BioCCS with combustion",
-    "Other chemical"
-  ],
-  [
-    "BioCCS with combustion",
-    "Other water"
-  ],
-  [
-    "BioCCS with combustion",
-    "Thermal energy other grade"
-  ],
-  [
-    "BioCCS with combustion & growing biomass",
-    "Electrical energy"
-  ],
-  [
-    "BioCCS with combustion & growing biomass",
-    "Other chemical"
-  ],
-  [
-    "BioCCS with combustion & growing biomass",
-    "Other water"
-  ],
-  [
-    "BioCCS with combustion & growing biomass",
     "Thermal energy other grade"
   ],
   [
     "Durable biobased products",
     "Electrical energy"
   ],
-  [
-    "Durable biobased products",
-    "Non-arable land"
-  ],
   [
     "Durable biobased products",
     "Other water"
   ],
   [
     "Electrochemical DACCS",
-    "Non-arable land"
+    "Other land"
   ],
   [
     "Electrochemical OAE",
@@ -103,10 +39,6 @@
     "Enhanced weathering - Basalt mineral",
     "Arable land"
   ],
-  [
-    "Enhanced weathering - Basalt mineral",
-    "Electrical energy"
-  ],
   [
     "Enhanced weathering - Basalt mineral",
     "Non-arable land"
@@ -119,10 +51,6 @@
     "Enhanced weathering - Olivine mineral",
     "Arable land"
   ],
-  [
-    "Enhanced weathering - Olivine mineral",
-    "Electrical energy"
-  ],
   [
     "Enhanced weathering - Olivine mineral",
     "Non-arable land"
@@ -135,10 +63,6 @@
     "Enhanced weathering - Other mineral",
     "Arable land"
   ],
-  [
-    "Enhanced weathering - Other mineral",
-    "Electrical energy"
-  ],
   [
     "Enhanced weathering - Other mineral",
     "Non-arable land"
@@ -151,10 +75,6 @@
     "Enhanced weathering - Wollastonite mineral",
     "Arable land"
   ],
-  [
-    "Enhanced weathering - Wollastonite mineral",
-    "Electrical energy"
-  ],
   [
     "Enhanced weathering - Wollastonite mineral",
     "Non-arable land"
@@ -171,18 +91,10 @@
     "Low-temp DACCS (solid-sorbent/liquid-solvent)",
     "Other water"
   ],
-  [
-    "Mineral OAE / Ocean liming",
-    "Electrical energy"
-  ],
   [
     "Mineral OAE / Ocean liming",
     "Non-arable land"
   ],
-  [
-    "Mineral OAE / Ocean liming",
-    "Other water"
-  ],
   [
     "Mineral OAE / Ocean liming",
     "Thermal energy other grade"
@@ -190,9 +102,5 @@
   [
     "Mineral looping DACCS",
     "Other water"
-  ],
-  [
-    "Reforestation",
-    "Other water"
   ]
 ]

data/update.py

@@ -1,5 +1,5 @@
 """
-Run this script after any change to inputs_aggregated.csv to update all
+Run this script after any change to inputs.csv to update all
 generated data files and Excel templates.
 
 Usage:
@@ -41,7 +41,7 @@ print("All files updated successfully.")
 print("=" * 60)
 
 # Update the hash file so the app knows the CSV is in sync
-csv_path = ROOT / "data/inputs_aggregated.csv"
+csv_path = ROOT / "data/inputs.csv"
 hash_path = ROOT / "data/.csv_hash"
 hasher = hashlib.md5()
 with open(csv_path, "rb") as f:

optimization/optimization.py

@@ -65,7 +65,7 @@ def build_custom_batch_structures(method_costs, resource_caps, custom_resources)
     return custom_batch_specs, group_to_custom_batches, custom_batch_names
 
 
-def build_lp_indices(active_methods, num_methods, custom_batch_specs, resource_caps, method_costs):
+def build_lp_indices(active_methods, num_methods, custom_batch_specs, resource_caps, method_costs, pool_of_entity=None):
     """Build the LP variable index for custom-batch draw variables z_{m,n}.
 
     When the user adds a custom resource batch (e.g. "degraded land patch A"),
@@ -81,6 +81,9 @@ def build_lp_indices(active_methods, num_methods, custom_batch_specs, resource_c
         custom_batch_specs (dict): {batch_name: {group, amount, primary_methods}}.
         resource_caps (dict): {resource: cap} — used to skip zero-cap batches.
         method_costs (dict): {method: {resource: coefficient}}.
+        pool_of_entity (dict | None): {(method, entity_name): pool_id} — batches
+            already covered by a family-resource pool (see build_family_pools)
+            are excluded here since they get a w_{m,e} variable instead.
 
     Returns:
         tuple[list, dict, int]:
@@ -88,6 +91,7 @@ def build_lp_indices(active_methods, num_methods, custom_batch_specs, resource_c
             custom_pair_to_index – {(method, batch_name): lp_column_index}.
             num_lp_vars          – total number of LP variables.
     """
+    pool_of_entity = pool_of_entity or {}
     custom_usage_pairs = []
     for batch_name, specs in custom_batch_specs.items():
         primary_methods = specs["primary_methods"]
@@ -96,6 +100,8 @@ def build_lp_indices(active_methods, num_methods, custom_batch_specs, resource_c
         for m in active_methods:
             if m not in primary_methods:
                 continue
+            if (m, batch_name) in pool_of_entity:
+                continue
             if method_costs.get(m, {}).get(batch_name, 0.0) > 0:
                 custom_usage_pairs.append((m, batch_name))
 
@@ -104,20 +110,105 @@ def build_lp_indices(active_methods, num_methods, custom_batch_specs, resource_c
     return custom_usage_pairs, custom_pair_to_index, num_lp_vars
 
 
+def build_family_pools(active_methods, method_costs, resource_to_group, group_to_custom_batches, custom_batch_specs):
+    """Group a method's own resources into OR-substitutable pools.
+
+    Two (or more) resources are pooled for a given method when they belong to
+    the same higher-level resource family (e.g. "Arable land" and "Other land"
+    are both "Land") and both currently have a non-zero coefficient for that
+    method — i.e. both are active (primary, or a secondary resource the user
+    switched on). Pooling means the method can satisfy its need from either
+    one (or a mix) instead of requiring all of them at once (AND), the same
+    way a custom batch already complements its reference standard resource.
+
+    Any custom batch attached to a pooled standard resource joins the same
+    pool, so the batch and its reference resource stay complementary exactly
+    as they already do outside a family pool.
+
+    Args:
+        active_methods (list[str]): ordered active CDR method names.
+        method_costs (dict): {method: {resource: coefficient}}.
+        resource_to_group (dict | None): {resource: family group name}. When
+            None or empty, no method has any pool (fully backward compatible).
+        group_to_custom_batches (dict): {reference_resource: [batch_name, ...]}.
+        custom_batch_specs (dict): {batch_name: {group, amount, primary_methods}}.
+
+    Returns:
+        tuple[dict, dict]:
+            pool_of_entity – {(method, entity_name): pool_id} for every entity
+                (standard resource or custom batch) that belongs to a real
+                pool (2+ standard resources sharing a family for that method).
+            pools          – {pool_id: [entity_name, ...]}.
+    """
+    resource_to_group = resource_to_group or {}
+    pool_of_entity = {}
+    pools = {}
+    for m in active_methods:
+        by_group = {}
+        for r, c in method_costs.get(m, {}).items():
+            if c <= 0:
+                continue
+            g = resource_to_group.get(r)
+            if g is None:
+                continue
+            by_group.setdefault(g, []).append(r)
+        for g, members in by_group.items():
+            if len(members) < 2:
+                continue
+            pool_id = (m, g)
+            entities = list(members)
+            for r in members:
+                for batch_name in group_to_custom_batches.get(r, []):
+                    if m in custom_batch_specs.get(batch_name, {}).get("primary_methods", set()):
+                        entities.append(batch_name)
+            pools[pool_id] = entities
+            for e in entities:
+                pool_of_entity[(m, e)] = pool_id
+    return pool_of_entity, pools
+
+
+def build_pool_indices(offset, pool_of_entity, resource_caps):
+    """Build the LP variable index for family-pool draw variables w_{m,e}.
+
+    Analogous to build_lp_indices's z_{m,n}, but for resources pooled because
+    they share a resource family (see build_family_pools), not because a
+    custom batch substitutes a single standard resource.
+
+    Args:
+        offset (int): first free LP column index (after y_m and any z_{m,n}).
+        pool_of_entity (dict): {(method, entity_name): pool_id}.
+        resource_caps (dict): {resource: cap} — used to skip zero-cap entities.
+
+    Returns:
+        tuple[list, dict, int]:
+            pool_pairs         – [(method, entity_name), ...].
+            pool_pair_to_index – {(method, entity_name): lp_column_index}.
+            num_lp_vars        – total LP variable count including these columns.
+    """
+    pool_pairs = sorted(
+        pair for pair in pool_of_entity if resource_caps.get(pair[1], 0.0) > 0
+    )
+    pool_pair_to_index = {pair: offset + i for i, pair in enumerate(pool_pairs)}
+    num_lp_vars = offset + len(pool_pairs)
+    return pool_pairs, pool_pair_to_index, num_lp_vars
+
+
 def build_constraint_matrix(
     active_methods, method_to_index, num_lp_vars, num_methods,
     resource_caps, method_costs, method_constraints,
     custom_batch_specs, group_to_custom_batches,
     resources_no_batches, resources_with_batches,
     custom_usage_pairs, custom_pair_to_index,
+    pool_of_entity=None, pools=None, pool_pair_to_index=None,
 ):
     """Assemble the LP inequality constraint matrix (A_ub · x ≤ b_ub).
 
-    Builds five groups of constraints:
+    Builds six groups of constraints:
       A – standard resources with no custom substitutes.
       B – standard resources that have custom-batch substitutes.
       C – custom batch capacity limits.
       D1/D2 – coupling constraints between y_m and z_{m,n}.
+      F – family-pool entity capacity limits, and their own D1/D2 coupling to y_m.
       E – per-method output caps (percent or absolute).
 
     Args:
@@ -134,6 +225,11 @@ def build_constraint_matrix(
         resources_with_batches (list[str]): standard resources that have substitutes.
         custom_usage_pairs (list): [(method, batch_name)].
         custom_pair_to_index (dict): {(method, batch_name): lp_column_index}.
+        pool_of_entity (dict | None): {(method, entity_name): pool_id} — see
+            build_family_pools. Entities here are excluded from A/B/C's direct
+            y_m contribution; they get their own w_{m,e} variable instead.
+        pools (dict | None): {pool_id: [entity_name, ...]}.
+        pool_pair_to_index (dict | None): {(method, entity_name): lp_column_index}.
 
     Returns:
         tuple[np.ndarray, np.ndarray, list]:
@@ -141,10 +237,20 @@ def build_constraint_matrix(
             b_ub               – constraint right-hand-side vector.
             constraint_labels  – list of (type_str, identifier) per row.
     """
+    pool_of_entity = pool_of_entity or {}
+    pools = pools or {}
+    pool_pair_to_index = pool_pair_to_index or {}
+
     A_rows, b_rows, constraint_labels = [], [], []
+    resource_rows: dict[str, np.ndarray] = {}
+
+    def get_row(name):
+        if name not in resource_rows:
+            resource_rows[name] = np.zeros(num_lp_vars)
+        return resource_rows[name]
 
     # ── CONSTRAINT GROUPS ─────────────────────────────────────────────────────
-    # Each group adds one row per resource/method to the matrix A_ub.
+    # Each group adds to one row per resource/method in the matrix A_ub.
     # The solver will find the y_m values that maximise total CO₂ removal while
     # keeping every A_ub · x ≤ b_ub inequality satisfied.
     #
@@ -155,52 +261,63 @@ def build_constraint_matrix(
     #   D – "Book-keeping: custom-batch draw variables can't exceed the method's
     #        total output, and if a method has no standard resource it must draw
     #        entirely from custom batches."
+    #   F – "A method with two resources from the same family (e.g. two kinds
+    #        of land) can draw from either, not both at once — each still can't
+    #        exceed its own declared quantity."
     #   E – "Respect each method's user-defined cap (% or absolute MtCO₂/yr)."
     # ─────────────────────────────────────────────────────────────────────────
 
     # (A) Standard resources — no custom substitutes.
     # Rule: total resource consumed across all methods ≤ available amount.
+    # A method whose use of r is covered by a family pool (F) is skipped here —
+    # its contribution comes from its w_{m,r} variable instead.
     for r in resources_no_batches:
-        row = np.zeros(num_lp_vars)
+        row = get_row(r)
         for j, m in enumerate(active_methods):
-            row[j] = method_costs.get(m, {}).get(r, 0.0)
-        if np.any(row[:num_methods] != 0):
-            A_rows.append(row)
-            b_rows.append(resource_caps[r])
-            constraint_labels.append(("resource", r))
+            if (m, r) in pool_of_entity:
+                continue
+            row[j] += method_costs.get(m, {}).get(r, 0.0)
 
     # (B) Standard resources that have custom-batch substitutes.
     # Rule: only the portion of the resource NOT already covered by custom batches
     # counts against the standard pool cap. Formula: Σ cost(m,r)·(y_m − Σ z_{m,n}) ≤ cap(r)
     for r in resources_with_batches:
-        row = np.zeros(num_lp_vars)
+        row = get_row(r)
         for j, m in enumerate(active_methods):
+            if (m, r) in pool_of_entity:
+                continue
             c_s_m = method_costs.get(m, {}).get(r, 0.0)   # coefficient for standard resource r
-            row[j] = c_s_m
+            row[j] += c_s_m
             for batch_name in group_to_custom_batches.get(r, []):
                 if (m, batch_name) in custom_pair_to_index:
                     row[custom_pair_to_index[(m, batch_name)]] -= c_s_m   # subtract batch draw
-        if np.any(row != 0):
-            A_rows.append(row)
-            b_rows.append(resource_caps[r])
-            constraint_labels.append(("resource", r))
 
     # (C) Custom batch capacity.
     # Rule: total resource drawn from this specific batch ≤ batch quantity Q_n.
     for batch_name, specs in custom_batch_specs.items():
         if resource_caps.get(batch_name, 0.0) <= 0:
             continue
-        row = np.zeros(num_lp_vars)
+        row = get_row(batch_name)
         for j, m in enumerate(active_methods):
             if (m, batch_name) not in custom_pair_to_index:
                 continue
             c_n_m = method_costs.get(m, {}).get(batch_name, 0.0)   # coefficient for batch n
             if c_n_m > 0:
-                row[custom_pair_to_index[(m, batch_name)]] = c_n_m
+                row[custom_pair_to_index[(m, batch_name)]] += c_n_m
+
+    # (F) Family-pool entities — each pooled entity (standard resource or batch)
+    # is drawn from via its own w_{m,e} variable instead of y_m directly.
+    # Rule: total resource drawn from entity e ≤ its own declared quantity —
+    # exactly like A/B/C, just keyed by the pool draw variable.
+    for (m, e), col in pool_pair_to_index.items():
+        row = get_row(e)
+        row[col] += method_costs.get(m, {}).get(e, 0.0)
+
+    for name, row in resource_rows.items():
         if np.any(row != 0):
             A_rows.append(row)
-            b_rows.append(resource_caps.get(batch_name, 0.0))
-            constraint_labels.append(("custom_batch", batch_name))
+            b_rows.append(resource_caps.get(name, 0.0))
+            constraint_labels.append(("resource", name))
 
     # (D) Coupling constraints — link z_{m,n} draw variables to y_m output.
     # D1: Σ z_{m,n} ≤ y_m  — can't draw more from batches than the method produces.
@@ -237,6 +354,34 @@ def build_constraint_matrix(
             b_rows.append(0.0)
             constraint_labels.append(("coupling_D2", (m, ref)))
 
+    # (F, coupling) Every unit a method produces must come from exactly one (or
+    # a mix) of its pool's entities: Σ w_{m,e} == y_m, expressed as two ≤ rows.
+    # Unlike D1/D2 above, both directions always apply — a family pool has no
+    # single "reference" resource to silently absorb the remainder.
+    for pool_id, entities in pools.items():
+        m = pool_id[0]
+        j = method_to_index.get(m)
+        if j is None:
+            continue
+        cols = [pool_pair_to_index[(m, e)] for e in entities if (m, e) in pool_pair_to_index]
+        if not cols:
+            continue
+        row1 = np.zeros(num_lp_vars)
+        row1[j] = -1.0
+        for col in cols:
+            row1[col] = 1.0
+        A_rows.append(row1)
+        b_rows.append(0.0)
+        constraint_labels.append(("pool_D1", pool_id))
+
+        row2 = np.zeros(num_lp_vars)
+        row2[j] = 1.0
+        for col in cols:
+            row2[col] = -1.0
+        A_rows.append(row2)
+        b_rows.append(0.0)
+        constraint_labels.append(("pool_D2", pool_id))
+
     # (E) Method output caps — respect the user's per-method limits from Tab 2.
     # Absolute cap: y_m ≤ cap_value (in MtCO₂/yr).
     # Percent cap:  y_m ≤ cap_ratio · Σ y_all  (fraction of total portfolio output).
@@ -274,6 +419,7 @@ def extract_resource_usage(
     resource_caps, custom_batch_specs, group_to_custom_batches,
     custom_pair_to_index, standard_resources,
     resources_no_batches, resources_with_batches, custom_batch_names,
+    pool_of_entity=None, pool_pair_to_index=None,
 ):
     """Compute per-method resource consumption from the LP solution.
 
@@ -291,6 +437,10 @@ def extract_resource_usage(
         resources_no_batches (list[str]): standard resources without substitutes.
         resources_with_batches (list[str]): standard resources with substitutes.
         custom_batch_names (set[str]): set of all custom batch names.
+        pool_of_entity (dict | None): {(method, entity_name): pool_id} — see
+            build_family_pools. Entities here report usage via their own
+            w_{m,e} variable instead of the formulas below.
+        pool_pair_to_index (dict | None): {(method, entity_name): lp_column_index}.
 
     Returns:
         tuple[dict, dict, dict]:
@@ -298,6 +448,9 @@ def extract_resource_usage(
             resource_used_total  – {resource: total_qty_used}.
             resource_remaining   – {resource: qty_remaining}.
     """
+    pool_of_entity = pool_of_entity or {}
+    pool_pair_to_index = pool_pair_to_index or {}
+
     all_resource_names = standard_resources + list(custom_batch_names)
     resource_usage = {r: {} for r in all_resource_names}
 
@@ -307,12 +460,16 @@ def extract_resource_usage(
     _USAGE_EPS = 1e-14
     for r in resources_no_batches:
         for j, m in enumerate(active_methods):
+            if (m, r) in pool_of_entity:
+                continue
             used = method_costs.get(m, {}).get(r, 0.0) * method_outputs[j]
             if used > _USAGE_EPS:
                 resource_usage[r][m] = float(used)
 
     for r in resources_with_batches:
         for j, m in enumerate(active_methods):
+            if (m, r) in pool_of_entity:
+                continue
             c_s_m = method_costs.get(m, {}).get(r, 0.0)
             z_sum = sum(
                 lp_solution[custom_pair_to_index[(m, batch_name)]]
@@ -328,6 +485,8 @@ def extract_resource_usage(
         for j, m in enumerate(active_methods):
             if m not in primary_methods:
                 continue
+            if (m, batch_name) in pool_of_entity:
+                continue
             c_n_m = method_costs.get(m, {}).get(batch_name, 0.0)
             if c_n_m <= 0:
                 continue
@@ -339,6 +498,13 @@ def extract_resource_usage(
             if used > _USAGE_EPS:
                 resource_usage[batch_name][m] = float(used)
 
+    # Family-pool entities (standard resources or batches pooled across a
+    # method's own resource family) — usage comes directly from w_{m,e}.
+    for (m, e), col in pool_pair_to_index.items():
+        used = method_costs.get(m, {}).get(e, 0.0) * lp_solution[col]
+        if used > _USAGE_EPS:
+            resource_usage.setdefault(e, {})[m] = float(used)
+
     _EPS = 1e-14
     resource_used_total = {
         r: round(sum(v.values()), 10) for r, v in resource_usage.items()
@@ -360,19 +526,25 @@ def extract_resource_usage(
 def resolve_pooling_tiebreak(
     num_methods, num_lp_vars, method_outputs, custom_usage_pairs,
     custom_pair_to_index, custom_batch_specs, method_costs, A_ub, b_ub, bounds,
+    pool_pair_to_index=None,
 ):
     """Re-solve the LP with each method's output pinned, preferring the more
-    efficient resource within each standard/custom pooling pair.
+    efficient resource within each standard/custom pooling pair or family pool.
 
     The main LP (run_optimization) maximises total CO2 removed only — nothing
     in its objective distinguishes a standard resource from a custom batch
-    that substitutes for it, so when both could satisfy the same demand the
-    solver picks an arbitrary split. This second pass fixes that: it pins
-    every active method's output y_m to its pass-1 value (so total_removed and
-    method_usage never change) and minimises Σ (c_n - c_s) · z_{m,n} across all
-    (method, batch) pooling pairs — i.e. it prefers fully using whichever
-    resource (standard or custom) has the lower coefficient (more MtCO2
-    removed per unit of resource) before drawing on the less efficient one.
+    that substitutes for it (or from a same-family alternative resource), so
+    when several could satisfy the same demand the solver picks an arbitrary
+    split. This second pass fixes that: it pins every active method's output
+    y_m to its pass-1 value (so total_removed and method_usage never change)
+    and minimises Σ (c_n - c_s) · z_{m,n} across all (method, batch) pooling
+    pairs, plus Σ c_e · w_{m,e} across all family-pool entities — i.e. it
+    prefers fully using whichever resource has the lower coefficient (more
+    MtCO2 removed per unit of resource) before drawing on the less efficient
+    one. For a two-entity pool this reduces exactly to the batch-pooling
+    formula (pinning w_other = y_m - w_e turns Σ c_e·w_e into a (c_e - c_other)
+    term up to a constant), so it generalises the original tie-break rather
+    than replacing it.
 
     Args:
         num_methods (int): number of y_m variables.
@@ -385,13 +557,16 @@ def resolve_pooling_tiebreak(
         A_ub, b_ub: pass-1 constraint matrix/vector (reused unchanged — pass-1's
             own solution already satisfies them, so this second LP is always feasible).
         bounds: pass-1 variable bounds (reused unchanged).
+        pool_pair_to_index (dict | None): {(method, entity_name): lp_column_index}
+            for family-pool draw variables — see build_family_pools.
 
     Returns:
         np.ndarray | None: the tie-broken solution vector, or None if there are
             no pooling pairs to break ties on, or if the second solve fails
             (in which case the caller should fall back to the pass-1 solution).
     """
-    if not custom_usage_pairs:
+    pool_pair_to_index = pool_pair_to_index or {}
+    if not custom_usage_pairs and not pool_pair_to_index:
         return None
 
     A_eq = np.zeros((num_methods, num_lp_vars))
@@ -406,6 +581,9 @@ def resolve_pooling_tiebreak(
         c_n = method_costs.get(m, {}).get(batch_name, 0.0)
         objective2[custom_pair_to_index[(m, batch_name)]] = c_n - c_s
 
+    for (m, e), col in pool_pair_to_index.items():
+        objective2[col] = method_costs.get(m, {}).get(e, 0.0)
+
     try:
         result = linprog(
             objective2, A_ub=A_ub, b_ub=b_ub, A_eq=A_eq, b_eq=b_eq,
@@ -452,7 +630,7 @@ def run_perturbation(constraint_labels, A_ub, b_ub, objective_coefficients, boun
     return resource_actual_gain
 
 
-def compute_standalone_potential(method, resource_caps, method_costs, custom_resources, enabled_methods):
+def compute_standalone_potential(method, resource_caps, method_costs, custom_resources, enabled_methods, resource_to_group=None):
     """Compute a method's own maximum achievable output, in isolation.
 
     "Isolation" means solving as if this were the only active method — no
@@ -471,6 +649,10 @@ def compute_standalone_potential(method, resource_caps, method_costs, custom_res
             full list — batches tied to other methods simply produce no
             usage pairs once every other method is excluded).
         enabled_methods (dict | None): {resource: {method: bool}}.
+        resource_to_group (dict | None): {resource: family group name} — see
+            build_family_pools. Must match what the outer run_optimization
+            call uses, so the method's standalone potential already reflects
+            OR-availability across its own same-family resources.
 
     Returns:
         float: the method's standalone potential in MtCO₂ (0.0 if it can't
@@ -483,6 +665,7 @@ def compute_standalone_potential(method, resource_caps, method_costs, custom_res
         method_costs={method: dict(method_costs.get(method, {}))},
         custom_resources=custom_resources,
         enabled_methods=enabled_methods,
+        resource_to_group=resource_to_group,
     )
     return result["total_removed"] if success else 0.0
 
@@ -493,6 +676,7 @@ def run_optimization(
     method_costs,
     custom_resources=None,
     enabled_methods=None,
+    resource_to_group=None,
 ):
     """Maximize total CO₂ removal subject to resource caps and method constraints.
 
@@ -500,12 +684,17 @@ def run_optimization(
     Also runs a perturbation analysis: for each binding resource constraint,
     re-solves with cap + 1 to compute the marginal CDR gain per unit.
 
-    LP variables (two types, packed into one vector x):
+    LP variables (three types, packed into one vector x):
       y_m     = MtCO₂ removed by CDR method m — the main "answer" (indices 0..num_methods-1).
       z_{m,n} = MtCO₂ that method m draws specifically from custom batch n — a helper
                 variable introduced when the user adds a custom resource batch; it lets the
-                solver track which batch supplies which method without double-counting
-                (indices num_methods..num_lp_vars-1).
+                solver track which batch supplies which method without double-counting.
+      w_{m,e} = MtCO₂ that method m draws specifically from same-family entity e (a
+                standard resource or custom batch inside an OR-substitutable resource
+                pool — see build_family_pools); same bookkeeping role as z_{m,n}, for
+                resources that compete with each other rather than with the standard/
+                custom-batch split.
+                (z and w columns together occupy indices num_methods..num_lp_vars-1.)
 
     Args:
         resource_caps (dict): {resource: available amount (float)}.
@@ -515,6 +704,13 @@ def run_optimization(
                                          name, group, amount, methods.
         enabled_methods (dict | None): {resource: {method: bool}} — False entries
                                         zero-out the corresponding coefficient before solving.
+        resource_to_group (dict | None): {resource: family group name} (e.g.
+            "Arable land" → "Land"). When a method has 2+ resources from the
+            same family with a non-zero coefficient, they're treated as
+            OR-substitutable (draw from either, or a mix) instead of both
+            being required at once — see build_family_pools. None (default)
+            disables this and preserves the original AND-of-all-resources
+            behaviour.
 
     Returns:
         tuple[bool, dict]:
@@ -556,6 +752,7 @@ def run_optimization(
             cap_value = max(0.0, min(100.0, c.get("cap_value", 100)))
             potential = compute_standalone_potential(
                 m, resource_caps, method_costs, custom_resources, enabled_methods,
+                resource_to_group=resource_to_group,
             )
             resolved_constraints[m] = {**c, "cap_type": "absolute", "cap_value": potential * cap_value / 100.0}
         else:
@@ -605,8 +802,15 @@ def run_optimization(
         }
         return True, empty_result
 
+    pool_of_entity, pools = build_family_pools(
+        active_methods, method_costs, resource_to_group, group_to_custom_batches, custom_batch_specs
+    )
+
     custom_usage_pairs, custom_pair_to_index, num_lp_vars = build_lp_indices(
-        active_methods, num_methods, custom_batch_specs, resource_caps, method_costs
+        active_methods, num_methods, custom_batch_specs, resource_caps, method_costs, pool_of_entity
+    )
+    pool_pairs, pool_pair_to_index, num_lp_vars = build_pool_indices(
+        num_lp_vars, pool_of_entity, resource_caps
     )
 
     standard_resources     = [r for r in resource_caps if r not in custom_batch_names]
@@ -620,6 +824,7 @@ def run_optimization(
             custom_batch_specs, group_to_custom_batches,
             resources_no_batches, resources_with_batches,
             custom_usage_pairs, custom_pair_to_index,
+            pool_of_entity, pools, pool_pair_to_index,
         )
     except ValueError as e:
         return False, {"message": str(e)}
@@ -645,6 +850,7 @@ def run_optimization(
         tiebreak_solution = resolve_pooling_tiebreak(
             num_methods, num_lp_vars, method_outputs, custom_usage_pairs,
             custom_pair_to_index, custom_batch_specs, method_costs, A_ub, b_ub, bounds,
+            pool_pair_to_index,
         )
         lp_solution_for_usage = tiebreak_solution if tiebreak_solution is not None else lp_solution
 
@@ -653,6 +859,7 @@ def run_optimization(
             resource_caps, custom_batch_specs, group_to_custom_batches,
             custom_pair_to_index, standard_resources,
             resources_no_batches, resources_with_batches, custom_batch_names,
+            pool_of_entity, pool_pair_to_index,
         )
 
         resource_actual_gain = run_perturbation(

tabs/tab2_constraints.py

@@ -3,7 +3,7 @@ import streamlit as st
 
 from config.config import CO2_UNIT, method_groups, method_order, method_tooltips, method_notes, secondary_resources, hidden_resource_names, section_intros
 from utils.ui_helpers import show_bar
-from utils.data_utils import strip_unit_suffix, get_other_method_variants, make_variant_name, default_constraint
+from utils.data_utils import strip_unit_suffix, get_other_method_variants, make_variant_name, default_constraint, get_resource_to_group
 
 
 def _make_slider_cb(m):
@@ -56,27 +56,55 @@ def get_all_missing_required(m, cost_sliders_norm):
     "No active methods" once every resource of a method is disabled this way.
     Secondary resources and resources with median <= 0 are excluded.
 
+    Family pooling: when two or more of the method's resources share the same
+    resource family (e.g. "Arable land" and "Other land" are both "Land" — see
+    build_family_pools in optimization.py), they are OR-substitutable rather
+    than all-required. A resource in such a family is only reported as missing
+    if every member of that family is unavailable; a family member currently
+    enabled as a secondary resource (its slider toggled on in Tab 3) also
+    counts as available, since it becomes a real substitute in the LP.
+
     Returns:
         list[str]: missing required resource names (standard and hidden combined).
     """
     resource_caps = st.session_state.get("resource_caps", {})
     custom_resources = st.session_state.get("custom_resources", [])
     enabled_methods = st.session_state.get("enabled_methods", {})
+    method_costs = st.session_state.get("costs", {}).get(m, {})
     covered_by_batch = {
         b["group"] for b in custom_resources
         if m in b.get("methods", {})
     }
+    resource_to_group = get_resource_to_group()
+
+    def _available(r):
+        if r in covered_by_batch:
+            return True
+        if resource_caps.get(r, 0.0) <= 0:
+            return False
+        return enabled_methods.get(r, {}).get(m, True)
+
+    # Family membership includes this method's primary resources plus any
+    # secondary resource currently switched on (cost > 0) — both act as real
+    # substitutes for one another in the LP once active.
+    family_members: dict[str, list[str]] = {}
+    for r in cost_sliders_norm.get(m, {}):
+        is_secondary = (m, r) in secondary_resources
+        if is_secondary and method_costs.get(r, 0.0) <= 0:
+            continue
+        family_members.setdefault(resource_to_group.get(r, r), []).append(r)
+
     missing = []
     for r, coeff_dict in cost_sliders_norm.get(m, {}).items():
         if (m, r) in secondary_resources:
             continue
         if coeff_dict.get("median", 0.0) <= 0:
             continue
-        if r in covered_by_batch:
-            continue
-        if resource_caps.get(r, 0.0) <= 0:
-            missing.append(r)
-        elif not enabled_methods.get(r, {}).get(m, True):
+        siblings = family_members.get(resource_to_group.get(r, r), [r])
+        if len(siblings) > 1:
+            if not any(_available(s) for s in siblings):
+                missing.append(r)
+        elif not _available(r):
             missing.append(r)
     return missing
 
@@ -128,10 +156,18 @@ def _render_resource_status(m, cost_sliders_norm):
         batch covers it.
     Hidden resources:   ✅ if covered by a custom batch, ❌ if not.
     Secondary resources are shown in a separate section.
+
+    Family pooling: when 2+ of the method's required resources share the same
+    resource family (e.g. "Forest biomass" / "Other biomass" are both
+    "Biomass" — see build_family_pools in optimization.py), only one of them
+    is actually needed. A family member that isn't set is shown as ✅
+    "optional" (not a blocking ❌) once a sibling already satisfies the
+    family, since adding it is a bonus, not a requirement.
     """
     resource_caps   = st.session_state.get("resource_caps", {})
     custom_resources = st.session_state.get("custom_resources", [])
     enabled_methods = st.session_state.get("enabled_methods", {})
+    resource_to_group = get_resource_to_group()
     # Map resource group → list of batch names covering it for method m (any
     # named standard resource or hidden "Other X" resource can be substituted
     # by a custom batch referencing it).
@@ -141,6 +177,22 @@ def _render_resource_status(m, cost_sliders_norm):
             covered_by_batch.setdefault(b["group"], []).append(b["name"])
 
     all_res = cost_sliders_norm.get(m, {})
+
+    def _is_available(r):
+        batch_names = covered_by_batch.get(r)
+        if r in hidden_resource_names:
+            return bool(batch_names)
+        disabled_for_method = not enabled_methods.get(r, {}).get(m, True)
+        return (resource_caps.get(r, 0.0) > 0 and not disabled_for_method) or bool(batch_names)
+
+    # Group this method's required (non-secondary) resources by family so
+    # siblings can be recognised as OR-substitutable below.
+    primary_by_family: dict[str, list[str]] = {}
+    for r, coeff_dict in all_res.items():
+        if coeff_dict.get("median", 0.0) <= 0 or (m, r) in secondary_resources:
+            continue
+        primary_by_family.setdefault(resource_to_group.get(r, r), []).append(r)
+
     required_rows = []
     optional_rows = []
 
@@ -148,14 +200,13 @@ def _render_resource_status(m, cost_sliders_norm):
         if coeff_dict.get("median", 0.0) <= 0:
             continue
         batch_names = covered_by_batch.get(r)
+        available = _is_available(r)
         if r in hidden_resource_names:
-            available = bool(batch_names)
             # Show actual batch name(s) instead of the generic "Other X" group
             display_r = ", ".join(batch_names) if batch_names else r
             source = "Add as new resource in Resource inputs"
         else:
             disabled_for_method = not enabled_methods.get(r, {}).get(m, True)
-            available = (resource_caps.get(r, 0.0) > 0 and not disabled_for_method) or bool(batch_names)
             display_r = ", ".join(batch_names) if (not resource_caps.get(r, 0.0) > 0 and batch_names) else r
             source = (
                 "Re-enable this method in the Related CDR Methods popover (Resource inputs tab)"
@@ -163,10 +214,30 @@ def _render_resource_status(m, cost_sliders_norm):
                 else "Set quantity in Resource inputs"
             )
 
-        icon = "✅" if available else "❌"
-        row = (icon, display_r, "" if available else source)
+        is_secondary = (m, r) in secondary_resources
+        siblings = primary_by_family.get(resource_to_group.get(r, r), [r]) if not is_secondary else [r]
+        available_siblings = [s for s in siblings if s != r and _is_available(s)]
+
+        if available and len(siblings) > 1:
+            icon, hint, is_blocking = "✅", "", False
+        elif not available and available_siblings:
+            # Family already satisfied by a sibling — not a blocker, just a bonus.
+            icon, is_blocking = "✅", False
+            hint = (
+                f"{', '.join(available_siblings)} already covers this "
+                f"(only one of {', '.join(siblings)} is required). You can still add "
+                f"{r} too."
+            )
+        elif not available and len(siblings) > 1:
+            icon, is_blocking = "❌", True
+            hint = f"{source} (only one of {', '.join(siblings)} is required)"
+        else:
+            icon, is_blocking = ("✅", False) if available else ("❌", True)
+            hint = "" if available else source
 
-        if (m, r) in secondary_resources:
+        row = (icon, display_r, hint, is_blocking)
+
+        if is_secondary:
             optional_rows.append(row)
         else:
             required_rows.append(row)
@@ -177,16 +248,16 @@ def _render_resource_status(m, cost_sliders_norm):
             st.info(note, icon=":material/lightbulb:")
         if required_rows:
             st.markdown("**Required resources**")
-            for _, r, hint in required_rows:
-                color = "#55C95B" if not hint else "#FF5F4D"
+            for _, r, hint, is_blocking in required_rows:
+                color = "#FF5F4D" if is_blocking else "#55C95B"
                 label = f'<span style="color:{color};font-weight:600"><strong>{r}</strong></span>'
                 if hint:
                     label += f' <span style="color:#888;font-style:italic;font-weight:400">: {hint}</span>'
                 st.markdown(label, unsafe_allow_html=True)
         if optional_rows:
             st.markdown("**Optional resources**")
-            for _, r, hint in optional_rows:
-                color = "#55C95B" if not hint else "#FF5F4D"
+            for _, r, hint, is_blocking in optional_rows:
+                color = "#FF5F4D" if is_blocking else "#55C95B"
                 label = f'<span style="color:{color};font-weight:600"><strong>{r}</strong></span>'
                 if hint:
                     label += f' <span style="color:#888;font-style:italic;font-weight:400">: {hint}</span>'

tabs/tab4_optimization.py

@@ -8,7 +8,7 @@ import openpyxl
 import pandas as pd
 import streamlit as st
 import plotly.express as px
-from config.config import CRRA_COLORS, PLOTLY_COLOR_SEQUENCE, PLOTLY_HEATMAP_SCALE, resource_order, resource_unit_map, resource_groups, secondary_resources, CO2_UNIT, method_order
+from config.config import CRRA_COLORS, PLOTLY_COLOR_SEQUENCE, PLOTLY_HEATMAP_SCALE, resource_order, resource_unit_map, resource_groups, secondary_resources, CO2_UNIT, method_order, section_intros
 from optimization.optimization import run_optimization
 from utils.ui_helpers import apply_chart_style, register_chart_data, show_bar
 from utils.data_utils import (is_valid_scenario, is_complete_scenario, load_inputs_from_excel,
@@ -206,6 +206,7 @@ def parse_zip_scenario(uploaded) -> dict | None:
                 method_costs=costs,
                 custom_resources=custom_resources,
                 enabled_methods=enabled_methods,
+                resource_to_group=get_resource_to_group(),
             )
 
             if neutralized:
@@ -254,7 +255,7 @@ def panel_load_from_json_or_csv():
 
     if "scenario_loaded_file" in st.session_state:
         st.success(
-            f"Scenario **{st.session_state['scenario_loaded_file']}** loaded :\n\nAll tabs updated - Optimisation results displayed.",
+            f"Scenario **{st.session_state['scenario_loaded_file']}** loaded :\n\nAll tabs updated - Optimization results displayed.",
             icon="✅"
         )
     uploaded_json = st.file_uploader(
@@ -280,6 +281,7 @@ def panel_load_from_json_or_csv():
                 method_costs=data["method_costs"],
                 custom_resources=data.get("custom_resources", []),
                 enabled_methods=data.get("enabled_methods", {}),
+                resource_to_group=get_resource_to_group(),
             )
             if success:
                 data["resource_usage"] = result["resource_usage"]
@@ -507,10 +509,11 @@ def run_and_store_result():
         method_costs=applied_costs,
         custom_resources=custom_resources_for_lp,
         enabled_methods=enabled_methods,
+        resource_to_group=get_resource_to_group(),
     )
 
     if not success:
-        st.error("❌ Optimisation failed")
+        st.error("❌ Optimization failed")
         if "message" in result:
             st.error(result["message"])
         return
@@ -526,7 +529,7 @@ def run_and_store_result():
     total_removed = result["total_removed"]
 
     if total_removed <= 0:
-        st.warning(f"❌ Optimisation removed 0 {CO2_UNIT}. Relax constraints or increase resources.", icon=":material/warning:")
+        st.warning(f"❌ Optimization removed 0 {CO2_UNIT}. Relax constraints or increase resources.", icon=":material/warning:")
         return
 
     total_cost = sum(
@@ -1328,9 +1331,9 @@ def render_tab():
     )
 
     st.markdown(
-        """
+        f"""
         <div class="text_with_border">
-            <p>Run the optimisation tool, explore results and save scenarios for comparison or export.</p>
+            <p>{section_intros.get("tab4_optimization", "")}</p>
         </div>
         """,
         unsafe_allow_html=True
@@ -1355,7 +1358,7 @@ def render_tab():
 
     with tab_run:
         st.markdown("""
-            <p class="crra-sub-heading">Run optimisation</p>""",
+            <p class="crra-sub-heading">Run optimization</p>""",
             unsafe_allow_html=True,
         )
         col_inputs, col_scenario = st.columns(2, gap="large")
@@ -1365,7 +1368,7 @@ def render_tab():
                 <p class="crra-sub-sub-heading">From inputs</p>""",
                 unsafe_allow_html=True,
             )
-            run_clicked = st.button("Run Optimisation", icon=":material/play_arrow:", key="btn-run-optimization", type="primary")
+            run_clicked = st.button("Run Optimization", icon=":material/play_arrow:", key="btn-run-optimization", type="primary")
             run_msg_area = st.container()
             panel_run_from_tabs()
             panel_load_from_excel()
@@ -1381,7 +1384,7 @@ def render_tab():
             with run_msg_area:
                 run_and_store_result()
 
-    no_results = '<p class="caption">Run the optimisation first to see results here.</p>'
+    no_results = '<p class="caption">Run the optimization first to see results here.</p>'
 
     latest = st.session_state.get("latest_result")
     if not latest:
@@ -1415,7 +1418,7 @@ def render_tab():
 
     with tab_results:
         show_metrics(latest)
-        st.markdown('<p class="crra-sub-heading">Optimisation Summary</p>', unsafe_allow_html=True)
+        st.markdown('<p class="crra-sub-heading">Optimization Summary</p>', unsafe_allow_html=True)
         st.dataframe(df_display, width='stretch')
         st.markdown('<p class="crra-sub-heading">Method allocation breakdown</p>', unsafe_allow_html=True)
         st.markdown(

utils/data_utils.py

@@ -365,6 +365,7 @@ def run_expanded_optimization(resource_caps, method_constraints, costs, custom_r
         method_costs=applied_costs,
         custom_resources=custom_resources_for_lp,
         enabled_methods=enabled_methods,
+        resource_to_group=get_resource_to_group(),
     )