remindmodel · lecfab · Feb 24, 2026 · Feb 24, 2026 · Feb 24, 2026 · Feb 24, 2026
diff --git a/core/bounds.gms b/core/bounds.gms
@@ -150,10 +150,6 @@ vm_deltaCap.lo(t,regi,"bioh2c","1") $ (t.val <= 2030) = 0;
 vm_costTeCapital.fx(ttot,regi,teNoLearn) = pm_inco0_t("2005",regi,teNoLearn); !! use 2005 value for the past
 vm_costTeCapital.fx(t,   regi,teNoLearn) = pm_inco0_t(t,regi,teNoLearn);
 
-*** RP: theoretically, floor costs represent the lower bound of investment costs for learnTe. However, with regional 
-*** variations of 2015 costs and long-term costs being high in SSP3/SSP5, this can be different -> set lower bound to 0.2
-vm_costTeCapital.lo(t,regi,teLearn) = 0.2 * pm_data(regi,"floorcost",teLearn);
-
 *' No battery storage in 2010
 vm_cap.up("2010",regi,teStor,"1") = 0;
 
@@ -530,6 +526,8 @@ loop(all_te $ (
   );
 );
 
+*** remove legacy values for timesteps and technologies that are not required 
+vm_capCum(tall,all_regi,all_te).l $ (not (ttot(tall) and regi(all_regi) and te(all_te))) = 0;
 
 *** H2 Curtailment (TODO: RLDC removal)
 *** Fixing h2curt value to zero to avoid the model to generate SE out of nothing.

diff --git a/core/datainput.gms b/core/datainput.gms
diff --git a/core/declarations.gms b/core/declarations.gms
@@ -43,7 +43,7 @@ pm_taxCO2eq_anchor_iterationdiff(ttot)               "difference in global ancho
 pm_cesdata(tall,all_regi,all_in,cesParameter)        "parameters of the CES function: efficiency parameters (xi, eff, effgr) [unitless], target quantities of CES calibration (quantity) [unit of CES node, see set all_in], CES prices resulting from calibration (price) [T$/unit of CES node]"
 f_pop(tall,all_regi,all_GDPpopScen)                  "population data for all possible scenarios [million people]"
 pm_pop(tall,all_regi)                                "population data [bn people]"
-pm_gdp(tall,all_regi)                                "GDP data [trn US$ 2005]"
+pm_gdp(tall,all_regi)                                "GDP MER data [trn US$ 2005]"
 p_developmentState(tall,all_regi)                    "level of development based on GDP per capita, 0 is low income, 1 is high income"
 f_lab(tall,all_regi,all_GDPpopScen)                  "labour data for all possible scenarios [million people]"
 pm_lab(tall,all_regi)                                "data for labour [bn people]"
@@ -210,17 +210,17 @@ pm_teAnnuity(all_te)                                 "Annuity factor of a techno
 *** parameters used for floor costs calculation
 p_maxRegTechCost2015(all_te)                         "highest historical regional investment cost in 2015, used to calculate regionally-differentiated floor costs of learning technologies"
 p_maxRegTechCost2020(all_te)                         "highest historical regional investment cost in 2020, used to calculate regionally-differentiated floor costs of learning technologies"
-p_gdppcap2050_PPP(all_regi)                          "regional GDP PPP per capita in 2050 [thousand $/capita]"
-p_maxPPP2050                                         "maximum income GDP PPP among regions in 2050 [T$]"
+p_GDPpCap2050(all_regi)                              "regional GDP per capita in 2050 [$/capita]"
+p_GDPpCap2050_world                                  "global average GDP per capita in 2050 [$/capita]"
 p_maxSpvCost                                         "maximum spv investment cost among regions [T$/TW]" 
 p_oldFloorCostdata(all_regi,all_te)                  "print old floor cost data [T$/TW]"
 
 *** parameters for capacity equations
 pm_tsu2opTimeYr(ttot,opTimeYr)                       "auxiliary parameter to map time steps to past time steps: counts the number of model timesteps between years ttot-opTimeYr and ttot, used for q_transPe2se and q_cap equations [unitless]" 
 
 *** parameters used for endogenous technology learning implementation
-pm_capCum0(tall,all_regi,all_te)                     "Total cumulated capacity of learning technologies from last iteration used for learning curves based on vm_capCum[TW]"
-p_capCum(tall, all_regi,all_te)                      "Total cumulated capacity of learning technologies from input.gdx used for learning curves based on vm_capCum[TW]"
+pm_capCum0(tall,all_regi,all_te)                     "Total cumulated capacity of learning technologies from last iteration used for learning curves based on vm_capCum [TW]"
+p_capCum(tall, all_regi,all_te)                      "Total cumulated capacity of learning technologies from input.gdx used for learning curves based on vm_capCum [TW]"
 pm_capCumForeign(ttot,all_regi,all_te)               "Total cumulated capacity of learning technologies of all other regions except regi [TW]"
 
 *** early retirement parameters
@@ -635,19 +635,20 @@ sm_giga_2_non                "giga to non"                             /1e+9/,
 sm_trillion_2_non            "trillion to non"                         /1e+12/,
 
 *** energy units
-pm_conv_TWa_EJ               "conversion from TWa to EJ"                          /31.536/,
-s_zj_2_twa                   "zeta joule to tw year"                              /31.7098/,
-sm_EJ_2_TWa                  "multiplicative factor to convert from EJ to TWa"    /31.71e-03/,
-sm_GJ_2_TWa                  "multiplicative factor to convert from GJ to TWa"    /31.71e-12/,
-sm_TWa_2_TWh                 "tera Watt year to Tera Watt hour"                    /8.76e+3/,
-sm_TWa_2_MWh                 "tera Watt year to Mega Watt hour"                    /8.76e+9/,
-sm_TWa_2_kWh                 "tera Watt year to kilo Watt hour"                    /8.76e+12/,
-sm_h2kg_2_h2kWh              "convert kilogramme of hydrogen to kwh energy value." /32.5/,
-sm_DptCO2_2_TDpGtC           "Conversion multiplier to go from $/tCO2 to T$/GtC: 44/12/1000"     /0.00366667/,
-sm_tBC_2_TWa                  "t biochar to TWa biochar (28700 [MJ/tBC]*10^-12[EJ/MJ]/31.536[EJ/TWa])" /9.101e-10/,
+s_ZJ_2_TWa                   "convert from Zeta Joule to Tera Watt annum"   /31.71/,
+sm_EJ_2_TWa                  "convert from Exa Joule to Tera Watt annum"    /31.71e-03/,
+sm_GJ_2_TWa                  "convert from Giga Joule to Tera Watt annum"   /31.71e-12/,
+sm_TWa_2_EJ                  "convert from Tera Watt annum to Exa Joule"    /31.54/,
+sm_DpGJ_2_TDpTWa             "convert $/GJ to T$/TWa"                       /31.54e-03/
+sm_TWa_2_TWh                 "convert Tera Watt annum to Tera Wh"           /8.76e+3/,
+sm_TWa_2_MWh                 "convert Tera Watt annum to Mega Wh"           /8.76e+9/,
+sm_TWa_2_kWh                 "convert Tera Watt annum to kilo Wh"           /8.76e+12/,
+sm_h2kg_2_h2kWh              "convert kg of hydrogen to kWh energy value"   /32.5/,
+sm_tBC_2_TWa                 "t biochar to TWa biochar (28700 [MJ/tBC]*10^-12[EJ/MJ]/31.536[EJ/TWa])" /9.101e-10/,
 
 *** emissions units
-sm_c_2_co2                   "conversion from c to co2"                /3.666666666667/,
+sm_c_2_co2                   "convert mass from carbon to CO2 (44/12)" /3.66667/,
+sm_DptCO2_2_TDpGtC           "convert $/tCO2 to T$/GtC: 44/12/1000"    /0.00366667/,
 s_NO2_2_N                    "convert NO2 to N [14 / (14 + 2 * 16)]"   / .304 /
 sm_tgn_2_pgc                 "conversion factor 100-yr GWP from TgN to PgCeq"
 sm_tgch4_2_pgc               "conversion factor 100-yr GWP from TgCH4 to PgCeq"
@@ -659,11 +660,10 @@ s_gwpCH4_AR4                 "Global Warming Potentials of CH4 as in the AR4, us
 s_gwpN2O_AR4                 "Global Warming Potentials of N2O as in the AR4, used in the MACCs"     /298/
 
 *** monetary units
-s_DpKWa_2_TDpTWa             "convert Dollar per kWa to TeraDollar per TeraWattYear"       /0.001/
-s_DpKW_2_TDpTW               "convert Dollar per kW to TeraDollar per TeraWatt"            /0.001/
-sm_DpGJ_2_TDpTWa             "multipl. factor to convert (Dollar per GJoule) to (TerraDollar per TWyear)"    / 31.54e-03/
+s_DpKWa_2_TDpTWa             "convert Dollar per kWa to Tera Dollar per TWa" /0.001/
+s_DpKW_2_TDpTW               "convert Dollar per kW to Tera Dollar per TW"   /0.001/
 s_D2010_2_D2017              "Convert US$2010 to US$2017"      /1.1491/
-sm_D2015_2_D2017              "Convert US$2015 to US$2017"      /1.0292/
+sm_D2015_2_D2017             "Convert US$2015 to US$2017"      /1.0292/
 sm_D2005_2_D2017             "Convert US$2005 to US$2017"      /1.231/
 sm_D2020_2_D2017             "Convert US$2020 to US$2017"      /0.9469/
 sm_EURO2023_2_D2017          "Convert EURO 2023 to US$2017"    /0.8915/

diff --git a/core/equations.gms b/core/equations.gms
@@ -375,7 +375,7 @@ qm_deltaCapCumNet(ttot,regi,teLearn)$(ord(ttot) lt card(ttot) AND pm_ttot_val(tt
 *' Initial values for cumulated capacities (learning technologies only):
 *' (except for tech_stat 4 technologies that have no standing capacities in 2005 and ccap0 refers to another year)
 ***---------------------------------------------------------------------------
-q_capCumNet(t0,regi,teLearn)$(NOT (pm_data(regi,"tech_stat",teLearn) eq 4))..
+q_capCumNet(t0,regi,teLearn)$(pm_data(regi,"tech_stat",teLearn) < 4)..
   vm_capCum(t0,regi,teLearn)
   =e=
   pm_data(regi,"ccap0",teLearn);
@@ -455,92 +455,36 @@ q_limitGeopot(t,regi,peReComp(enty),rlf)..
 *' In equations.gms, the investment costs equation `q_costTeCapital` corresponds to $I = a'\times C^{b'} + F$,
 *' with variations depending on time period and floor cost scenarios.
 
-q_costTeCapital(t,regi,teLearn)$(NOT (pm_data(regi,"tech_stat",teLearn) eq 4 AND t.val le 2020)) ..
+
+$macro macro_capCumGlob (sum(regi2, vm_capCum(t,regi2,teLearn)) + pm_capCumForeign(t,regi,teLearn))
+$macro macro_costRegi   (pm_data(regi,"floorcost",teLearn) + pm_data(regi,"learnMult_wFC",teLearn) * macro_capCumGlob ** pm_data(regi,"learnExp_wFC",teLearn))
+$macro macro_costGlob   (fm_dataglob("floorcost",teLearn) + fm_dataglob("learnMult_wFC",teLearn) * macro_capCumGlob ** fm_dataglob("learnExp_wFC",teLearn))
+
+q_costTeCapital(t,regi,teLearn) $ (pm_data(regi,"tech_stat",teLearn) < 4 or t.val > 2020) ..
   vm_costTeCapital(t,regi,teLearn)
   =e=
 *** until 2005: using global estimates better matches historic values
-  + ( fm_dataglob("floorcost",teLearn)
-      + ( fm_dataglob("learnMult_wFC",teLearn)
-          * ( sum(regi2, vm_capCum(t,regi2,teLearn))
-              + pm_capCumForeign(t,regi,teLearn)
-          ) ** fm_dataglob("learnExp_wFC",teLearn)
-      )
-  )$( t.val le 2005 )
+  macro_costGlob $ (t.val <= 2005)
 
 *** 2005 to 2020: linear transition from global 2005 to regional 2020
 *** to phase-in the observed 2020 regional variation from input-data
-  + ( (2020 - t.val) / (2020-2005)
-      * ( fm_dataglob("floorcost",teLearn)
-          + fm_dataglob("learnMult_wFC",teLearn)
-            * ( sum(regi2, vm_capCum(t,regi2,teLearn))
-                + pm_capCumForeign(t,regi,teLearn)
-              ) ** fm_dataglob("learnExp_wFC",teLearn)
-      )
+  + macro_interpolate(t.val, 2005, 2020, macro_costGlob, macro_costRegi) $ (t.val > 2005 and t.val <= 2020)
 
-    + (t.val - 2005) / (2020-2005) 
-      * ( pm_data(regi,"floorcost",teLearn) 
-          + pm_data(regi,"learnMult_wFC",teLearn)
-            * ( sum(regi2, vm_capCum(t,regi2,teLearn))
-                + pm_capCumForeign(t,regi,teLearn)
-              ) ** pm_data(regi,"learnExp_wFC",teLearn)
-      )
-  )$( (t.val gt 2005) AND (t.val le 2020) )
-
-$ifthen.floorscen %cm_floorCostScen% == "default"
-*** from 2020 to c_LearnTeConvStartYear: use regional values
-  + ( pm_data(regi,"floorcost",teLearn) 
-        + pm_data(regi,"learnMult_wFC",teLearn)
-          * ( sum(regi2, vm_capCum(t,regi2,teLearn))
-              + pm_capCumForeign(t,regi,teLearn)
-            ) ** pm_data(regi,"learnExp_wFC",teLearn)
-  )$( (t.val gt 2020) AND (t.val lt c_LearnTeConvStartYear) )
-
-*** c_LearnTeConvStartYear to c_LearnTeConvEndYear: assuming linear convergence of regional learning curves to global values
-  + ( (pm_ttot_val(t) - c_LearnTeConvStartYear) / (c_LearnTeConvEndYear-c_LearnTeConvStartYear)  
-      * ( fm_dataglob("floorcost",teLearn) 
-          + fm_dataglob("learnMult_wFC",teLearn)
-            * ( sum(regi2, vm_capCum(t,regi2,teLearn))
-                + pm_capCumForeign(t,regi,teLearn)
-              ) ** fm_dataglob("learnExp_wFC",teLearn)
-      )
+*** after 2020 for specific cm_floorCostScen: regional capital costs
+$if %cm_floorCostScen% == "pricestruc"  + macro_costRegi $ (t.val > 2020)
+$if %cm_floorCostScen% == "gdpBased"    + macro_costRegi $ (t.val > 2020)
+
+$ifthen.default %cm_floorCostScen% == "default"
+*** from 2020 to c_teLearnConvStartYr: regional capital costs
+  + macro_costRegi $ (t.val > 2020 and t.val <= c_teLearnConvStartYr)
+
+*** c_teLearnConvStartYr to c_teLearnConvEndYr: linear convergence from regional costs to global costs
+  + macro_interpolate(t.val, c_teLearnConvStartYr, c_teLearnConvEndYr, macro_costRegi, macro_costGlob) $ (t.val > c_teLearnConvStartYr and t.val < c_teLearnConvEndYr)
+
+*** after c_teLearnConvEndYr: global capital costs
+  + macro_costGlob $ (t.val >= c_teLearnConvEndYr)
+$endif.default
 
-    + (c_LearnTeConvEndYear - pm_ttot_val(t)) / (c_LearnTeConvEndYear-c_LearnTeConvStartYear)  
-      * ( pm_data(regi,"floorcost",teLearn) 
-          + pm_data(regi,"learnMult_wFC",teLearn)
-            * ( sum(regi2, vm_capCum(t,regi2,teLearn))
-                + pm_capCumForeign(t,regi,teLearn)
-              ) ** pm_data(regi,"learnExp_wFC",teLearn)
-      )
-  )$( t.val ge c_LearnTeConvStartYear AND t.val le c_LearnTeConvEndYear )
-$endif.floorscen
-
-$ifthen.floorscen %cm_floorCostScen% == "pricestruc"
-  + ( pm_data(regi,"floorcost",teLearn) 
-      + pm_data(regi,"learnMult_wFC",teLearn)
-        * ( sum(regi2, vm_capCum(t,regi2,teLearn))
-            + pm_capCumForeign(t,regi,teLearn)
-          ) ** pm_data(regi,"learnExp_wFC",teLearn)
-    )$( t.val ge 2020 AND t.val le 2100 )
-$endif.floorscen
-
-$ifthen.floorscen %cm_floorCostScen% == "techtrans"
-  + ( pm_data(regi,"floorcost",teLearn) 
-      + pm_data(regi,"learnMult_wFC",teLearn)
-        * ( sum(regi2, vm_capCum(t,regi2,teLearn))
-            + pm_capCumForeign(t,regi,teLearn)
-          ) ** pm_data(regi,"learnExp_wFC",teLearn)
-    )$( t.val ge 2020 AND t.val le 2100 )
-$endif.floorscen
-
-$ifthen.floorscen %cm_floorCostScen% == "default"
-*** after c_LearnTeConvEndYear: globally harmonized costs
-  + ( fm_dataglob("floorcost",teLearn) 
-      + fm_dataglob("learnMult_wFC",teLearn)
-        * ( sum(regi2, vm_capCum(t,regi2,teLearn)) 
-            + pm_capCumForeign(t,regi,teLearn) 
-            ) **(fm_dataglob("learnExp_wFC",teLearn))
-  )$(t.val gt c_LearnTeConvEndYear)
-$endif.floorscen
 ;
 *' @stop
 

diff --git a/core/loop.gms b/core/loop.gms
@@ -97,7 +97,7 @@ putclose runtime gyear(jnow):0:0 "-" gmonth(jnow):0:0 "-" gday(jnow):0:0 " " gho
 ***     Track of changes between iterations
 ***---------------------------------------------------------
 loop(entyPe$(NOT sameas(entyPe,"peur")),
-  o_negitr_cumulative_peprod(iteration,entyPe) = 0.031536
+  o_negitr_cumulative_peprod(iteration,entyPe) = sm_TWa_2_EJ / 1000
     * sum(regi,
         sum(ttot$( (ttot.val lt 2100) AND (ttot.val gt 2005)), vm_prodPe.l(ttot,regi,entyPe) * pm_ts(ttot)  )
         + sum(ttot$(ttot.val eq 2005), vm_prodPe.l(ttot,regi,entyPe) * pm_ts(ttot) * 0.5  )
@@ -112,20 +112,20 @@ sum(regi,
 );
 o_negitr_cumulative_CO2_emineg_co2luc(iteration) =
 sum(regi,
-    sum(ttot$( (ttot.val lt 2100) AND (ttot.val gt 2005)), 3.6667 * vm_emiMacSector.l(ttot,regi,"co2luc") * pm_ts(ttot) )
-    + sum(ttot$(ttot.val eq 2005), 3.6667 * vm_emiMacSector.l(ttot,regi,"co2luc") * pm_ts(ttot) * 0.5 )
-    + sum(ttot$(ttot.val eq 2100), 3.6667 * vm_emiMacSector.l(ttot,regi,"co2luc") * ( pm_ttot_val(ttot)- pm_ttot_val(ttot-1) ) * 0.5 )
+    sum(ttot$( (ttot.val lt 2100) AND (ttot.val gt 2005)), sm_c_2_co2 * vm_emiMacSector.l(ttot,regi,"co2luc") * pm_ts(ttot) )
+    + sum(ttot$(ttot.val eq 2005), sm_c_2_co2 * vm_emiMacSector.l(ttot,regi,"co2luc") * pm_ts(ttot) * 0.5 )
+    + sum(ttot$(ttot.val eq 2100), sm_c_2_co2 * vm_emiMacSector.l(ttot,regi,"co2luc") * ( pm_ttot_val(ttot)- pm_ttot_val(ttot-1) ) * 0.5 )
 );
 
 o_negitr_cumulative_CO2_emineg_cement(iteration) =
 sum(regi,
-    sum(ttot$( (ttot.val lt 2100) AND (ttot.val gt 2005)), 3.6667 * vm_emiMacSector.l(ttot,regi,"co2cement_process") * pm_ts(ttot) )
-    + sum(ttot$(ttot.val eq 2005), 3.6667 * vm_emiMacSector.l(ttot,regi,"co2cement_process") * pm_ts(ttot) * 0.5 )
-    + sum(ttot$(ttot.val eq 2100), 3.6667 * vm_emiMacSector.l(ttot,regi,"co2cement_process") * ( pm_ttot_val(ttot)- pm_ttot_val(ttot-1) ) * 0.5 )
+    sum(ttot$( (ttot.val lt 2100) AND (ttot.val gt 2005)), sm_c_2_co2 * vm_emiMacSector.l(ttot,regi,"co2cement_process") * pm_ts(ttot) )
+    + sum(ttot$(ttot.val eq 2005), sm_c_2_co2 * vm_emiMacSector.l(ttot,regi,"co2cement_process") * pm_ts(ttot) * 0.5 )
+    + sum(ttot$(ttot.val eq 2100), sm_c_2_co2 * vm_emiMacSector.l(ttot,regi,"co2cement_process") * ( pm_ttot_val(ttot)- pm_ttot_val(ttot-1) ) * 0.5 )
 );
 o_negitr_cumulative_CO2_emieng_seq(iteration)
   =
-    3.6667
+    sm_c_2_co2
   * sum(regi,
       sum((ttot,emi2te(enty,enty2,te,"cco2"))$( ttot.val gt 2005 AND ttot.val lt 2100 ),
         vm_emiTeDetail.l(ttot,regi,enty,enty2,te,"cco2")

diff --git a/main.gms b/main.gms
@@ -1195,19 +1195,19 @@ parameter
   cm_H2InBuildOnlyAfter = 2150;   !! def = 2150 (rule out H2 in buildings)
 *' For all years until the given year, FE demand for H2 in buildings is set to zero
 parameter
-  c_teNoLearngConvEndYr      "Year at which regional costs of non-learning technologies converge"
+  c_teNoLearnConvEndYr      "Year at which regional costs of non-learning technologies converge"
 ;
-  c_teNoLearngConvEndYr  = 2070;   !! def = 2070
+  c_teNoLearnConvEndYr  = 2070;   !! def = 2070
 *'
 parameter
-  c_LearnTeConvStartYear  "start year of cost convergence of learning technologies"
+  c_teLearnConvStartYr  "start year of cost convergence of learning technologies"
 ;
-c_LearnTeConvStartYear = 2025; !! def = 2025
+c_teLearnConvStartYr = 2025; !! def = 2025
 *'
 parameter
-  c_LearnTeConvEndYear "end year of cost convergence of learning technologies"
+  c_teLearnConvEndYr "end year of cost convergence of learning technologies"
 ;
-c_LearnTeConvEndYear = 2080;   !! def = 2080
+c_teLearnConvEndYr = 2080;   !! def = 2080
 *'
 parameter
   c_earlyRetiValidYr         "Year before which the early retirement rate designated by c_tech_earlyreti_rate holds"
@@ -1691,15 +1691,15 @@ $setglobal cm_in_limit_price_change "ue_steel_primary, kap_steel_primary"   !! d
 *** cm_calibration_string "def = off, else = additional string to include in the calibration name to be used" label for your calibration run to keep calibration files with different setups apart (e.g. with low elasticities, high elasticities)
 $setglobal cm_calibration_string  off    !!  def  =  off
 *** cm_techcosts -     use regionalized or globally homogenous technology costs for certain technologies
-*** (REG) regionalized technology costs with linear convergence between 2020 and year c_teNoLearngConvEndYr
+*** (REG) regionalized technology costs with linear convergence between 2020 and year c_teNoLearnConvEndYr
 *** (REG2040) regionalized technology costs given by p_inco0 until 2040, then stable without convergence
 *** (GLO) globally homogenous technology costs
 $setglobal cm_techcosts  REG       !! def = REG  !! regexp = REG|REG2040|GLO
 *** cm_floorCostScen regionally differentiated floor cost scenarios
 *** (default) uniform floor cost (almost no regional differentiation)
 *** (pricestruc) regionally differentiated floor costs, the differentiated costs have the same ratio between regions as the ratio between 2020 tech cost values
-*** (techtrans) regionally differentiated floor costs, which are the universal global floor costs in the default case time the MER PPP price ratios. new floor cost = MER/PPP * old floor cost
-$setglobal cm_floorCostScen default       !! def = default
+*** (gdpBased) regionally differentiated floor costs based on GDP per capita in 2050: regions with above-average GDP get higher floor costs (up to 1.5x), regions with below-average GDP get lower floor costs (down to 0.5x)
+$setglobal cm_floorCostScen default       !! def = default  !! regexp = default|pricestruc|gdpBased
 *** cfg$gms$cm_EDGEtr_scen  "the EDGE-T scenario"  # def <- "Mix1". For calibration runs: Mix1. Mix2, Mix3, Mix4 also available - numbers after the "mix" denote policy strength, with 1 corresponding roughly to Baseline/NPI, 2= NDC, 3= Budg1500, 4 = Budg800
 ***  The following descriptions are based on scenario results for EUR in 2050 unless specified otherwise.
 ***  Whenever we give numbers, please be aware that they are just there to estimate the ballpark.