bugfix fader GHG emission pricing policy and Sync of NPI and NDC policies to 2025

CHANGELOG.md

@@ -19,6 +19,7 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
 - **modules** update of scaling factors in several modules
 - **scripts** renamed disaggregation_mrdownscale.R -> mrdownscale_LUH2.R
 - **config** default for `c60_bioenergy_subsidy` changed from 369 to 246 USD17MER per ton
+- **scripts** NPI/NDC policies synced to the year 2025 - update of additional data to `additional_data_rev4.59.tgz`
 
 ### added
 - **62_material** added switch to turn off future material demand for bioplastic
@@ -28,6 +29,7 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
 - **32_forestry** added option s32_npi_ndc_reversal
 - **35_natveg** added option s35_npi_ndc_reversal
 - **58_peatland** added option for exogenous peatland rewetting
+- **56_ghg_policy** added new interface pcm_carbon_stock to avoid use of vm_carbon_stock.l in equations
 
 ### removed
 - **31_past** removed `grasslands_apr22` realization
@@ -42,6 +44,8 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
 - **10_land** Simplified land transition matrix for improved feasibility
 - **44_biodiversity** bugfix biorealm_biome.cs3 in input data (shares did not add-up to 1)
 - **config** additional data increased to `additional_data_rev4.58.tgz` which fixes missing gdp2017 conversions in f20_processing_unitcosts
+- **56_ghg_policy** Fader for GHG emission pricing policy
+- **config** settings in default.cfg for some switches in `73_timber` did not work correctly
 
 ## [4.8.2] - 2024-09-24
 
@@ -65,7 +69,7 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
 - **scripts** `.nc` files are no longer created by default after disaggregation
 - **scripts** output/extra/highres.R use default 13_tc realization
 - **scripts** rewrite of merge_report.R based on rds files and rbind, which allows for more flexibility when merging reports. Avoid inconsistent use of "GLO" instead of "World" in report.rds files.
-
+- **modules** renamed `country_dummy` to `country_switch` in all modules
 
 ### added
 - **42_water_demand** added water abstraction type dimension for non-ag uses

DESCRIPTION

@@ -21,14 +21,14 @@ Imports:
     m4fsdp,
     madrat,
     magclass (>= 6.14.0),
-    magpie4 (>= 2.15.6),
+    magpie4 (>= 2.15.8),
     MagpieNCGains,
     magpiesets (>= 0.46.1),
     mip,
     mrcommons,
     patchwork,
     piamenv (>= 0.5.5),
-    piamInterfaces (>= 0.36.9),
+    piamInterfaces (>= 0.37.1),
     piamutils,
     quitte,
     raster,

config/default.cfg

@@ -25,7 +25,7 @@ cfg$model <- "main.gms"   #def = "main.gms"
 cfg$input <- c(regional    = "rev4.116_h12_magpie.tgz",
                cellular    = "rev4.116_h12_fd712c0b_cellularmagpie_c200_MRI-ESM2-0-ssp370_lpjml-8e6c5eb1.tgz",
                validation  = "rev4.116_h12_validation.tgz",
-               additional  = "additional_data_rev4.58.tgz",
+               additional  = "additional_data_rev4.59.tgz",
                calibration = "calibration_H12_27Sep24.tgz")
 
 # NOTE: It is recommended to recalibrate the model when changing cellular input data
@@ -1513,7 +1513,7 @@ cfg$gms$s56_ghgprice_fader <- 0   # def = 0
 # * Pollutants on which the GHG policy fader will applied
 cfg$gms$pollutants_fader <- c("co2_c", "ch4", "n2o_n_direct", "n2o_n_indirect", "nh3_n", "no2_n", "no3_n")   # def = all pollutants
 # * Start year of GHG policy fade-in
-cfg$gms$s56_fader_start <- 2030   # def = 2030
+cfg$gms$s56_fader_start <- 2025   # def = 2025
 # * End year of GHG policy fade-in
 cfg$gms$s56_fader_end <- 2050   # def = 2050
 # * Target value of GHG policy fade-in in end year
@@ -2022,25 +2022,20 @@ cfg$gms$timber <- "default"                  # def = default
 # (implicitly assuming the same rate for plantation harvest and establishment).
 # * 1=on
 # * 0=off
-cfg$gms$s73_timber_demand_switch         <- 1     # def = 1
+cfg$gms$s73_timber_demand_switch <- 1     # def = 1
 
-# harvesting cost per ton of dry matter produced (USD17MER/tDM)
+# harvesting cost of industrial roundwood per ton of dry matter produced (USD17MER/tDM)
 # * (inflated from default originally in USD05 using USD05 --> USD17 inflation rate: 1.23)
-s73_timber_prod_cost <- 2460            # def = 2000 * 1.23
+cfg$gms$s73_timber_prod_cost_wood <- 148            # def = 120 * 1.23
 
-# harvesting cost per ha of forests (USD17MER/ha)
+# harvesting cost of woodfuel per ton of dry matter produced (USD17MER/tDM)
 # * (inflated from default originally in USD05 using USD05 --> USD17 inflation rate: 1.23)
-s73_timber_harvest_cost <- 2460         # def = 2000 * 1.23
-
-# Cost multiplier for harvesting costs to make natural vegetation harvest expensive
-# than timber plantation harvest. This provides a signal to the model to harvest
-# timber plantations first.
-s73_cost_multiplier <- 1.5              # def = 1.5
+cfg$gms$s73_timber_prod_cost_woodfuel <- 74         # def = 60 * 1.23
 
 # Cost of production without using any land in case the model is running into infeasibilities.
-# This is a last ditch effort for the model and the variable associated with this cost
+# This is a last ditch effort for the model and the slack variable associated with this cost
 # should not be used in a normally feasible model run (USD17MER/tDM)
-s73_free_prod_cost <- 1000000           # def = 1000000
+cfg$gms$s73_free_prod_cost <- 1000000           # def = 1000000
 
 # Switch for modifying woody biomass demand starting in 2035
 # * ("default")   = Default paper demand

config/projects/scenario_config_fsec.csv

@@ -79,6 +79,6 @@ gms$c73_build_demand;;;;;;;;;;;;;;;;;;;;;;;;50pc;;;;;;;;;;;;;;;;;;;;;;;;;;;
 input['cellular'];rev4.116_FSEC_3c888fa5_cellularmagpie_c200_MRI-ESM2-0-ssp460_lpjml-8e6c5eb1.tgz;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;rev4.116_FSEC_0bd54110_cellularmagpie_c200_MRI-ESM2-0-ssp119_lpjml-8e6c5eb1.tgz;rev4.116_FSEC_6819938d_cellularmagpie_c200_MRI-ESM2-0-ssp126_lpjml-8e6c5eb1.tgz;;rev4.116_FSEC_1b5c3817_cellularmagpie_c200_MRI-ESM2-0-ssp245_lpjml-8e6c5eb1.tgz;rev4.116_FSEC_3c888fa5_cellularmagpie_c200_MRI-ESM2-0-ssp460_lpjml-8e6c5eb1.tgz;rev4.116_FSEC_fd712c0b_cellularmagpie_c200_MRI-ESM2-0-ssp370_lpjml-8e6c5eb1.tgz;rev4.116_FSEC_09a63995_cellularmagpie_c200_MRI-ESM2-0-ssp585_lpjml-8e6c5eb1.tgz;;;
 input['regional'];rev4.116_FSEC_magpie.tgz;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
 input['validation'];rev4.116_FSEC_validation.tgz;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
-input['additional'];additional_data_rev4.58.tgz;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
+input['additional'];additional_data_rev4.59.tgz;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
 input['calibration'];calibration_FSEC_19Nov24.tgz;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
 magicc_emis_scen;bjoernAR6_C_SSP2-NDC.mif;;;bjoernAR6_C_SSP2-PkBudg900.mif;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;bjoernAR6_C_SSP1-NDC.mif;;;;;;;;;;;;bjoernAR6_C_RemSDP-900-MagSSP1.mif;;

main.gms

@@ -160,7 +160,7 @@ $title magpie
 * md5sum: NA
 * Repository: https://rse.pik-potsdam.de/data/magpie/public
 * 
-* Used data set: additional_data_rev4.58.tgz
+* Used data set: additional_data_rev4.59.tgz
 * md5sum: NA
 * Repository: https://rse.pik-potsdam.de/data/magpie/public
 * 

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