renamed _country_dummy to _country_switch in all modules

modules/12_interest_rate/select_apr20/declarations.gms

@@ -8,6 +8,6 @@
 parameters
  pm_interest(t_all,i)               Interest rate in each region and timestep (% per yr)
 * country-specific region scenario switch
- p12_country_dummy(iso)              Dummy parameter indicating whether country is affected by interest rate scenario (1)
+ p12_country_switch(iso)              Switch indicating whether country is affected by interest rate scenario (1)
  p12_reg_shr(t_all,i)                Weighted share of region with regards to interest rate scenario of countries (1)
 ;

modules/12_interest_rate/select_apr20/preloop.gms

@@ -9,12 +9,12 @@
 * shall be applied.
 * In the default case, the interest rate scenario affects all countries when
 * activated.
-p12_country_dummy(iso) = 0;
-p12_country_dummy(select_countries12) = 1;
+p12_country_switch(iso) = 0;
+p12_country_switch(select_countries12) = 1;
 * Because MAgPIE is not run at country-level, but at region level, a region
 * share is calculated that translates the countries' influence to regional level.
 * Countries are weighted by their population size.
-p12_reg_shr(t_all,i) = sum(i_to_iso(i,iso), p12_country_dummy(iso) * im_pop_iso(t_all,iso)) / sum(i_to_iso(i,iso), im_pop_iso(t_all,iso));
+p12_reg_shr(t_all,i) = sum(i_to_iso(i,iso), p12_country_switch(iso) * im_pop_iso(t_all,iso)) / sum(i_to_iso(i,iso), im_pop_iso(t_all,iso));
 
 * Interest rate in countries selected in select_countries12
 $ifthen "%c12_interest_rate%" == "coupling"

modules/15_food/anthro_iso_jun22/declarations.gms

@@ -176,7 +176,7 @@ parameters
  i15_exo_foodscen_fader(t,iso)                Fader that converges per capita food consumption to an exogenous diet scenario (1)
 
 * country-specific scenario switch
- p15_country_dummy(iso)                       Dummy parameter indicating whether country is affected by diet scenarios (1)
+ p15_country_switch(iso)                       Switch indicating whether country is affected by diet scenarios (1)
 
 * calculate diet iteration breakpoint
  p15_income_pc_real_ppp(t,i)                  Regional per capita income after price shock on regional level (USD17PPP per capita)

modules/15_food/anthro_iso_jun22/preloop.gms

@@ -58,8 +58,8 @@ Elseif s15_milk_share_fadeout_india = 1,
 * Switch to determine countries for which exogenous food scenarios (EAT Lancet diet and
 * food waste scenarios), and food substitution scenarios shall be applied.
 * In the default case, the exogenous food scenarios affect all countries.
-p15_country_dummy(iso) = 0;
-p15_country_dummy(scen_countries15) = 1;
+p15_country_switch(iso) = 0;
+p15_country_switch(scen_countries15) = 1;
 
 ** The following lines define scenario faders for substituting different food groups
 * If s15_exo_foodscen_functional_form = 1, the exogenous food scenario is faded in linearly.
@@ -96,13 +96,13 @@ elseif s15_food_subst_functional_form = 2,
 
 
 * Food substitution scenarios including functional forms, targets and transition periods
-i15_ruminant_fadeout(t,iso) = 1 - p15_country_dummy(iso)*p15_ruminant_subst_fader(t);
-i15_fish_fadeout(t,iso) = 1 - p15_country_dummy(iso)*p15_fish_subst_fader(t);
-i15_alcohol_fadeout(t,iso) = 1 - p15_country_dummy(iso)*p15_alcohol_subst_fader(t);
-i15_livestock_fadeout(t,iso) = 1 - p15_country_dummy(iso)*p15_livestock_subst_fader(t);
-i15_rumdairy_fadeout(t,iso) = 1 - p15_country_dummy(iso)*p15_rumdairy_subst_fader(t);
-i15_rumdairy_scp_fadeout(t,iso) = 1 - p15_country_dummy(iso)*p15_rumdairy_scp_subst_fader(t);
-i15_livestock_fadeout_threshold(t,iso) = 1 - p15_country_dummy(iso)*p15_livestock_threshold_subst_fader(t);
+i15_ruminant_fadeout(t,iso) = 1 - p15_country_switch(iso)*p15_ruminant_subst_fader(t);
+i15_fish_fadeout(t,iso) = 1 - p15_country_switch(iso)*p15_fish_subst_fader(t);
+i15_alcohol_fadeout(t,iso) = 1 - p15_country_switch(iso)*p15_alcohol_subst_fader(t);
+i15_livestock_fadeout(t,iso) = 1 - p15_country_switch(iso)*p15_livestock_subst_fader(t);
+i15_rumdairy_fadeout(t,iso) = 1 - p15_country_switch(iso)*p15_rumdairy_subst_fader(t);
+i15_rumdairy_scp_fadeout(t,iso) = 1 - p15_country_switch(iso)*p15_rumdairy_scp_subst_fader(t);
+i15_livestock_fadeout_threshold(t,iso) = 1 - p15_country_switch(iso)*p15_livestock_threshold_subst_fader(t);
 
 
 ** The following lines define the scenario fader for the exogeneous food scenario
@@ -117,7 +117,7 @@ elseif s15_exo_foodscen_functional_form = 2,
 );
 
 * Fade in scenarios at country level
-i15_exo_foodscen_fader(t,iso) = p15_exo_food_scenario_fader(t) * p15_country_dummy(iso);
+i15_exo_foodscen_fader(t,iso) = p15_exo_food_scenario_fader(t) * p15_country_switch(iso);
 
 * Select from the data set of EAT Lancet scenarios the target years that are
 * consistent with the target year of the fader:

modules/15_food/anthro_iso_jun22/presolve.gms

@@ -13,14 +13,14 @@ if(m_year(t) <= sm_fix_SSP2,
  i15_dem_halfsat(iso,regr15)     = f15_demand_paras(regr15,"SSP2","halfsaturation");
  i15_dem_nonsat(iso,regr15)      = f15_demand_paras(regr15,"SSP2","non_saturation");
 else
- i15_dem_intercept(iso,regr15)   = f15_demand_paras(regr15,"%c15_food_scenario%","intercept")*p15_country_dummy(iso)
-                                 + f15_demand_paras(regr15,"%c15_food_scenario_noselect%","intercept")*(1-p15_country_dummy(iso));
- i15_dem_saturation(iso,regr15)  = f15_demand_paras(regr15,"%c15_food_scenario%","saturation")*p15_country_dummy(iso)
-                                 + f15_demand_paras(regr15,"%c15_food_scenario_noselect%","saturation")*(1-p15_country_dummy(iso));
- i15_dem_halfsat(iso,regr15)     = f15_demand_paras(regr15,"%c15_food_scenario%","halfsaturation")*p15_country_dummy(iso)
-                                 + f15_demand_paras(regr15,"%c15_food_scenario_noselect%","halfsaturation")*(1-p15_country_dummy(iso));
- i15_dem_nonsat(iso,regr15)      = f15_demand_paras(regr15,"%c15_food_scenario%","non_saturation")*p15_country_dummy(iso)
-                                 + f15_demand_paras(regr15,"%c15_food_scenario_noselect%","non_saturation")*(1-p15_country_dummy(iso));
+ i15_dem_intercept(iso,regr15)   = f15_demand_paras(regr15,"%c15_food_scenario%","intercept")*p15_country_switch(iso)
+                                 + f15_demand_paras(regr15,"%c15_food_scenario_noselect%","intercept")*(1-p15_country_switch(iso));
+ i15_dem_saturation(iso,regr15)  = f15_demand_paras(regr15,"%c15_food_scenario%","saturation")*p15_country_switch(iso)
+                                 + f15_demand_paras(regr15,"%c15_food_scenario_noselect%","saturation")*(1-p15_country_switch(iso));
+ i15_dem_halfsat(iso,regr15)     = f15_demand_paras(regr15,"%c15_food_scenario%","halfsaturation")*p15_country_switch(iso)
+                                 + f15_demand_paras(regr15,"%c15_food_scenario_noselect%","halfsaturation")*(1-p15_country_switch(iso));
+ i15_dem_nonsat(iso,regr15)      = f15_demand_paras(regr15,"%c15_food_scenario%","non_saturation")*p15_country_switch(iso)
+                                 + f15_demand_paras(regr15,"%c15_food_scenario_noselect%","non_saturation")*(1-p15_country_switch(iso));
 );
 
 option nlp = conopt4;

modules/15_food/anthropometrics_jan18/declarations.gms

@@ -172,7 +172,7 @@ parameters
  i15_exo_foodscen_fader(t,i)                  Fader that converges per capita food consumption to an exogenous diet scenario (1)
 
 * country-specific scenario switch
- p15_country_dummy(iso)                       Dummy parameter indicating whether country is affected by diet scenarios (1)
+ p15_country_switch(iso)                       Switch indicating whether country is affected by diet scenarios (1)
  p15_foodscen_region_shr(t_all,i)             Weighted share of region with regards to diet scenario of countries (1)
 
 * calculate diet iteration breakpoint

modules/15_food/anthropometrics_jan18/preloop.gms

@@ -64,13 +64,13 @@ p15_demand2intake_ratio_ref(i) = 0;
 * Switch to determine countries for which  exogenous food scenarios (EAT Lancet diet and
 * food waste scenarios), and food substitution scenarios shall be applied.
 * In the default case, the food scenario affects all countries when activated.
-p15_country_dummy(iso) = 0;
-p15_country_dummy(scen_countries15) = 1;
+p15_country_switch(iso) = 0;
+p15_country_switch(scen_countries15) = 1;
 
 * Because MAgPIE is not run at country-level, but at region level, a region
 * share is calculated that translates the countries' influence to regional level.
 * Countries are weighted by their population size.
-p15_foodscen_region_shr(t_all,i) = sum(i_to_iso(i,iso), p15_country_dummy(iso) * im_pop_iso(t_all,iso)) / sum(i_to_iso(i,iso), im_pop_iso(t_all,iso));
+p15_foodscen_region_shr(t_all,i) = sum(i_to_iso(i,iso), p15_country_switch(iso) * im_pop_iso(t_all,iso)) / sum(i_to_iso(i,iso), im_pop_iso(t_all,iso));
 
 
 ** The following lines define scenario faders for substituting different food groups

modules/15_food/anthropometrics_jan18/presolve.gms

@@ -13,14 +13,14 @@ if(m_year(t) <= sm_fix_SSP2,
  i15_dem_halfsat(iso,regr15)     = f15_demand_paras(regr15,"SSP2","halfsaturation");
  i15_dem_nonsat(iso,regr15)      = f15_demand_paras(regr15,"SSP2","non_saturation");
 else
- i15_dem_intercept(iso,regr15)   = f15_demand_paras(regr15,"%c15_food_scenario%","intercept")*p15_country_dummy(iso)
-                                 + f15_demand_paras(regr15,"%c15_food_scenario_noselect%","intercept")*(1-p15_country_dummy(iso));
- i15_dem_saturation(iso,regr15)  = f15_demand_paras(regr15,"%c15_food_scenario%","saturation")*p15_country_dummy(iso)
-                                 + f15_demand_paras(regr15,"%c15_food_scenario_noselect%","saturation")*(1-p15_country_dummy(iso));
- i15_dem_halfsat(iso,regr15)     = f15_demand_paras(regr15,"%c15_food_scenario%","halfsaturation")*p15_country_dummy(iso)
-                                 + f15_demand_paras(regr15,"%c15_food_scenario_noselect%","halfsaturation")*(1-p15_country_dummy(iso));
- i15_dem_nonsat(iso,regr15)      = f15_demand_paras(regr15,"%c15_food_scenario%","non_saturation")*p15_country_dummy(iso)
-                                 + f15_demand_paras(regr15,"%c15_food_scenario_noselect%","non_saturation")*(1-p15_country_dummy(iso));
+ i15_dem_intercept(iso,regr15)   = f15_demand_paras(regr15,"%c15_food_scenario%","intercept")*p15_country_switch(iso)
+                                 + f15_demand_paras(regr15,"%c15_food_scenario_noselect%","intercept")*(1-p15_country_switch(iso));
+ i15_dem_saturation(iso,regr15)  = f15_demand_paras(regr15,"%c15_food_scenario%","saturation")*p15_country_switch(iso)
+                                 + f15_demand_paras(regr15,"%c15_food_scenario_noselect%","saturation")*(1-p15_country_switch(iso));
+ i15_dem_halfsat(iso,regr15)     = f15_demand_paras(regr15,"%c15_food_scenario%","halfsaturation")*p15_country_switch(iso)
+                                 + f15_demand_paras(regr15,"%c15_food_scenario_noselect%","halfsaturation")*(1-p15_country_switch(iso));
+ i15_dem_nonsat(iso,regr15)      = f15_demand_paras(regr15,"%c15_food_scenario%","non_saturation")*p15_country_switch(iso)
+                                 + f15_demand_paras(regr15,"%c15_food_scenario_noselect%","non_saturation")*(1-p15_country_switch(iso));
 );
 
 option nlp = conopt4;

modules/22_land_conservation/area_based_apr22/declarations.gms

@@ -19,7 +19,7 @@ parameters
  p22_past_restore_pot(t,j)                          Potential pasture restoration area (mio. ha)
  p22_other_restore_pot(t,j)                         Potential other land restoration area (mio. ha)
  p22_country_weight(i)                              Land conservation country weight per region (1)
- p22_country_dummy(iso)                             Dummy parameter indicating whether country is affected by selected land conservation policy (1)
+ p22_country_switch(iso)                             Switch indicating whether country is affected by selected land conservation policy (1)
  i22_land_iso(iso)                                  Total land area at ISO level (mio. ha)
 ;
 

modules/22_land_conservation/area_based_apr22/preloop.gms

@@ -12,13 +12,13 @@
 * Regional share of land conservation policies in selective countries:
 * Country switch to determine countries for which land conservation shall be applied.
 * In the default case, the land conservation affects all countries when activated.
-p22_country_dummy(iso) = 0;
-p22_country_dummy(policy_countries22) = 1;
+p22_country_switch(iso) = 0;
+p22_country_switch(policy_countries22) = 1;
 * Because MAgPIE is not run at country-level, but at region level, a region
 * share is calculated that translates the countries' influence to regional level.
 * Countries are weighted by total land area.
 i22_land_iso(iso) = sum(land, fm_land_iso("y1995",iso,land));
-p22_country_weight(i) = sum(i_to_iso(i,iso), p22_country_dummy(iso) * i22_land_iso(iso)) / sum(i_to_iso(i,iso), i22_land_iso(iso));
+p22_country_weight(i) = sum(i_to_iso(i,iso), p22_country_switch(iso) * i22_land_iso(iso)) / sum(i_to_iso(i,iso), i22_land_iso(iso));
 
 * ---------------------------------------------------------------------
 * Initialise baseline protection and conservation priority areas

modules/29_cropland/detail_apr24/declarations.gms

@@ -16,7 +16,7 @@ parameters
  i29_snv_relocation_target(j)       Overall cropland area that requires relocation due to SNV policy (mio. ha)
  p29_snv_relocation(t,j)            Cropland area that is actually relocated during time step (mio. ha)
  p29_max_snv_relocation(t,j)        Maximum cropland relocation during time step (mio. ha)
- p29_country_dummy(iso)             Dummy parameter indicating whether country is affected by selected cropland policy (1)
+ p29_country_switch(iso)             Switch indicating whether country is affected by selected cropland policy (1)
  pm_avl_cropland_iso(iso)           Available land area for cropland at ISO level (mio. ha)
  i29_snv_scenario_fader(t_all)      SNV scenario fader (1)
 

modules/29_cropland/detail_apr24/preloop.gms

@@ -59,13 +59,13 @@ elseif s29_treecover_bii_coeff = 1,
 
 * Country switch to determine countries for which certain policies shall be applied.
 * In the default case, the policy affects all countries when activated.
-p29_country_dummy(iso) = 0;
-p29_country_dummy(policy_countries29) = 1;
+p29_country_switch(iso) = 0;
+p29_country_switch(policy_countries29) = 1;
 * Because MAgPIE is not run at country-level, but at region level, a region
 * share is calculated that translates the countries' influence to regional level.
 * Countries are weighted by available cropland area.
 pm_avl_cropland_iso(iso) = f29_avl_cropland_iso(iso,"%c29_marginal_land%");
-p29_country_weight(i) = sum(i_to_iso(i,iso), p29_country_dummy(iso) * pm_avl_cropland_iso(iso)) / sum(i_to_iso(i,iso), pm_avl_cropland_iso(iso));
+p29_country_weight(i) = sum(i_to_iso(i,iso), p29_country_switch(iso) * pm_avl_cropland_iso(iso)) / sum(i_to_iso(i,iso), pm_avl_cropland_iso(iso));
 
 * Initialize biodiversity value
 vm_fallow.l(j) = 0;

modules/29_cropland/simple_apr24/declarations.gms

@@ -12,7 +12,7 @@ parameters
  i29_snv_relocation_target(j)       Overall cropland area that requires relocation due to SNV policy (mio. ha)
  p29_snv_relocation(t,j)            Cropland area that is actually relocated during time step (mio. ha)
  p29_max_snv_relocation(t,j)        Maximum cropland relocation during time step (mio. ha)
- p29_country_dummy(iso)             Dummy parameter indicating whether country is affected by selected cropland policy (1)
+ p29_country_switch(iso)             Switch indicating whether country is affected by selected cropland policy (1)
  pm_avl_cropland_iso(iso)           Available land area for cropland at ISO level (mio. ha)
  i29_snv_scenario_fader(t_all)      SNV scenario fader (1)
 ;

modules/29_cropland/simple_apr24/preloop.gms

@@ -27,10 +27,10 @@ elseif s29_snv_shr > s29_snv_relocation_data_x1,
 
 * Country switch to determine countries for which certain policies shall be applied.
 * In the default case, the policy affects all countries when activated.
-p29_country_dummy(iso) = 0;
-p29_country_dummy(policy_countries29) = 1;
+p29_country_switch(iso) = 0;
+p29_country_switch(policy_countries29) = 1;
 * Because MAgPIE is not run at country-level, but at region level, a region
 * share is calculated that translates the countries' influence to regional level.
 * Countries are weighted by available cropland area.
 pm_avl_cropland_iso(iso) = f29_avl_cropland_iso(iso,"%c29_marginal_land%");
-p29_country_weight(i) = sum(i_to_iso(i,iso), p29_country_dummy(iso) * pm_avl_cropland_iso(iso)) / sum(i_to_iso(i,iso), pm_avl_cropland_iso(iso));
+p29_country_weight(i) = sum(i_to_iso(i,iso), p29_country_switch(iso) * pm_avl_cropland_iso(iso)) / sum(i_to_iso(i,iso), pm_avl_cropland_iso(iso));

modules/30_croparea/detail_apr24/declarations.gms

@@ -14,7 +14,7 @@ parameters
  i30_betr_target(t,j)                   Target share for bioenergy land on total cropland (1)
  i30_betr_penalty(t)                    Penalty for violation of betr target (USD17MER per ha)
  p30_country_weight(i)                  Policy country weight per region (1)
- p30_country_dummy(iso)                 Dummy parameter indicating whether country is affected by selected policy (1)
+ p30_country_switch(iso)                 Switch indicating whether country is affected by selected policy (1)
 ;
 
 positive variables

modules/30_croparea/detail_apr24/preloop.gms

@@ -33,12 +33,12 @@ if(s30_implementation = 1,
 
 * Country switch to determine countries for which certain policies shall be applied.
 * In the default case, the policy affects all countries when activated.
-p30_country_dummy(iso) = 0;
-p30_country_dummy(policy_countries30) = 1;
+p30_country_switch(iso) = 0;
+p30_country_switch(policy_countries30) = 1;
 * Because MAgPIE is not run at country-level, but at region level, a region
 * share is calculated that translates the countries' influence to regional level.
 * Countries are weighted by available cropland area.
-p30_country_weight(i) = sum(i_to_iso(i,iso), p30_country_dummy(iso) * pm_avl_cropland_iso(iso)) / sum(i_to_iso(i,iso), pm_avl_cropland_iso(iso));
+p30_country_weight(i) = sum(i_to_iso(i,iso), p30_country_switch(iso) * pm_avl_cropland_iso(iso)) / sum(i_to_iso(i,iso), pm_avl_cropland_iso(iso));
 
 * Initialize biodiversity value
 vm_bv.l(j,"crop_ann",potnatveg) =

modules/30_croparea/simple_apr24/declarations.gms

@@ -11,7 +11,7 @@ parameters
  i30_betr_target(t,j)                   Target share for bioenergy land on total cropland (1)
  i30_betr_penalty(t)                    Penalty for violation of betr target (USD17MER per ha)
  p30_country_weight(i)                  Policy country weight per region (1)
- p30_country_dummy(iso)                 Dummy parameter indicating whether country is affected by selected policy (1)
+ p30_country_switch(iso)                 Switch indicating whether country is affected by selected policy (1)
 ;
 
 positive variables

modules/30_croparea/simple_apr24/preloop.gms

@@ -17,12 +17,12 @@ fm_croparea(t_past,j,w,kcr)$(fm_croparea(t_past,j,w,kcr)<0) = 0;
 
 * Country switch to determine countries for which certain policies shall be applied.
 * In the default case, the policy affects all countries when activated.
-p30_country_dummy(iso) = 0;
-p30_country_dummy(policy_countries30) = 1;
+p30_country_switch(iso) = 0;
+p30_country_switch(policy_countries30) = 1;
 * Because MAgPIE is not run at country-level, but at region level, a region
 * share is calculated that translates the countries' influence to regional level.
 * Countries are weighted by available cropland area.
-p30_country_weight(i) = sum(i_to_iso(i,iso), p30_country_dummy(iso) * pm_avl_cropland_iso(iso)) / sum(i_to_iso(i,iso), pm_avl_cropland_iso(iso));
+p30_country_weight(i) = sum(i_to_iso(i,iso), p30_country_switch(iso) * pm_avl_cropland_iso(iso)) / sum(i_to_iso(i,iso), pm_avl_cropland_iso(iso));
 
 * Initialize biodiversity value
 vm_bv.l(j,"crop_ann",potnatveg) =

modules/42_water_demand/agr_sector_aug13/declarations.gms

@@ -14,7 +14,7 @@ parameters
  i42_env_flow_policy(t,i)                  Determines whether environmental flow protection is enforced (1)
  p42_efp(t_all,scen42)                     Determines whether environmental flow protection is enforced and its fading in of environmental flow policy (1)
  p42_efp_fader(t_all)                      Determines the fading in of environmental flow policy (1)
- p42_country_dummy(iso)                    Dummy parameter indicating whether country is affected by EFP (1)
+ p42_country_switch(iso)                    Switch indicating whether country is affected by EFP (1)
  p42_EFP_region_shr(t_all,i)               Weighted share of region with regards to EFP (1)
  ic42_pumping_cost(i)                      Parameter to capture values for pumping costs in a particular time step (USD17MER per m^3)
  i42_watdem_total(t,j,watdem_ineldo,wtype) Non-agricultural water demand for entire year used in post-processing (mio. m^3 per yr)