diff --git a/Snakefile b/Snakefile
index f586a2a7..92ff9e76 100644
--- a/Snakefile
+++ b/Snakefile
@@ -159,7 +159,8 @@ rule build_industrial_demand_per_country:
 
 rule build_industrial_demand:
     input:
-        clustered_pop_layout="resources/pop_layout_{network}_s{simpl}_{clusters}.csv"
+        clustered_pop_layout="resources/pop_layout_{network}_s{simpl}_{clusters}.csv",
+        industrial_demand_per_country="resources/industrial_demand_per_country.csv"
     output:
         industrial_demand="resources/industrial_demand_{network}_s{simpl}_{clusters}.csv"
     threads: 1
diff --git a/config.yaml b/config.yaml
index ca93a08c..148a09d8 100644
--- a/config.yaml
+++ b/config.yaml
@@ -7,10 +7,10 @@ run: '190712-test_district'
 scenario:
   sectors: [E] # ,E+EV,E+BEV,E+BEV+V2G] # [ E+EV, E+BEV, E+BEV+V2G ]
   simpl: ['']
-  lv: [1.0,1.25,2.0]#, 1.125, 1.25, 1.5, 2.0]# or opt
+  lv: [1.0]#, 1.125, 1.25, 1.5, 2.0]# or opt
   clusters: [128] #[90, 128, 181] #[45, 64, 90, 128, 181, 256] #, 362] # (2**np.r_[5.5:9:.5]).astype(int) minimum is 37
   opts: ['']  #for pypsa-eur
-  sector_opts: [Co2L0-3H-T-H-B-I,Co2L0-3H-T-H-B-I-nodistrict]#,Co2L0p1-3H-T-H-B-I,Co2L0p25-3H-T-H-B-I,Co2L0p5-3H-T-H-B-I]#[Co2L0-3H-T-H-B-I-onwind0-solar3,Co2L0-3H-T-H-B-I-onwind0p125-solar3,Co2L0-3H-T-H-B-I-onwind0p25-solar3,Co2L0-3H-T-H-B-I-onwind0p50-solar3,Co2L0-3H-T-H-B-I-solar3]#,Co2L0-3H-T-H-B-I-onwind0p25-solar3]#,Co2L0p05-3H-T-H-B-I,Co2L0p10-3H-T-H-B-I,Co2L0p20-3H-T-H-B-I,Co2L0p30-3H-T-H-B-I,Co2L0p50-3H-T-H-B-I]#[Co2L-3H-T-H,Co2L0p10-3H-T-H,Co2L0-3H-T-H,Co2L0p20-3H-T-H]   #Co2L-3H-T-H,Co2L0p10-3H-T-H,Co2L0p20-3H-T-HCo2L-3H-T-H,Co2L0p10-3H-T-H,Co2L0p30-3H-T-H,Co2L0p50-3H-T-H] #Co2L-3H,Co2L-3H-T,, LC-FL, LC-T, Ep-T, Co2L-T]
+  sector_opts: [Co2L0-3H-T-H-B-I]#,Co2L0p1-3H-T-H-B-I,Co2L0p25-3H-T-H-B-I,Co2L0p5-3H-T-H-B-I]#[Co2L0-3H-T-H-B-I-onwind0-solar3,Co2L0-3H-T-H-B-I-onwind0p125-solar3,Co2L0-3H-T-H-B-I-onwind0p25-solar3,Co2L0-3H-T-H-B-I-onwind0p50-solar3,Co2L0-3H-T-H-B-I-solar3]#,Co2L0-3H-T-H-B-I-onwind0p25-solar3]#,Co2L0p05-3H-T-H-B-I,Co2L0p10-3H-T-H-B-I,Co2L0p20-3H-T-H-B-I,Co2L0p30-3H-T-H-B-I,Co2L0p50-3H-T-H-B-I]#[Co2L-3H-T-H,Co2L0p10-3H-T-H,Co2L0-3H-T-H,Co2L0p20-3H-T-H]   #Co2L-3H-T-H,Co2L0p10-3H-T-H,Co2L0p20-3H-T-HCo2L-3H-T-H,Co2L0p10-3H-T-H,Co2L0p30-3H-T-H,Co2L0p50-3H-T-H] #Co2L-3H,Co2L-3H-T,, LC-FL, LC-T, Ep-T, Co2L-T]
   # Co2L will give default (5%); Co2L0p25 will give 25% CO2 emissions; Co2Lm0p05 will give 5% negative emissions
 
 
diff --git a/scripts/build_industrial_demand.py b/scripts/build_industrial_demand.py
index ca4aa934..fce7b6ad 100644
--- a/scripts/build_industrial_demand.py
+++ b/scripts/build_industrial_demand.py
@@ -4,15 +4,18 @@ import pandas as pd
 idx = pd.IndexSlice
 
 def build_industrial_demand():
+    pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout,index_col=0)
+    pop_layout["ct"] = pop_layout.index.str[:2]
+    ct_total = pop_layout.total.groupby(pop_layout["ct"]).sum()
+    pop_layout["ct_total"] = pop_layout["ct"].map(ct_total.get)
+    pop_layout["fraction"] = pop_layout["total"]/pop_layout["ct_total"]
 
-    population = pd.read_csv(snakemake.input.clustered_pop_layout,
-                             index_col=0)
+    industrial_demand_per_country = pd.read_csv(snakemake.input.industrial_demand_per_country,index_col=0)
 
-    totals = pd.Series(data=[1100.,1814.,586.,400.,580.,186.],
-                       index=["industry new electricity","industry process heat",
-                              "naphtha feedstock","shipping H2","aviation kerosene","process emissions"])
+    industrial_demand = industrial_demand_per_country.loc[pop_layout.ct].fillna(0.)
+    industrial_demand.index = pop_layout.index
+    industrial_demand = industrial_demand.multiply(pop_layout.fraction,axis=0)
 
-    industrial_demand = pd.DataFrame({i : population["total"]*totals[i]*1e6/population["total"].sum() for i in totals.index })
 
     industrial_demand.to_csv(snakemake.output.industrial_demand)
 
@@ -27,6 +30,7 @@ if __name__ == "__main__":
         snakemake = Dict()
         snakemake.input = Dict()
         snakemake.input['clustered_pop_layout'] = "resources/pop_layout_elec_s_128.csv"
+        snakemake.input['industrial_demand_per_country']="resources/industrial_demand_per_country.csv"
         snakemake.output = Dict()
         snakemake.output['industrial_demand'] = "resources/industrial_demand_elec_s_128.csv"
         with open('config.yaml') as f: