Merge branch 'master' of https://github.com/pik-piam/remind2 into sma…

…llfix

CITATION.cff

@@ -3,7 +3,7 @@ message: If you use this software, please cite it using the metadata from this f
 type: software
 title: 'remind2: The REMIND R package (2nd generation)'
 version: 1.111.2
-date-released: '2023-05-31'
+date-released: '2023-06-03'
 abstract: Contains the REMIND-specific routines for data and model output manipulation.
 authors:
 - family-names: Rodrigues

DESCRIPTION

@@ -2,7 +2,7 @@ Type: Package
 Package: remind2
 Title: The REMIND R package (2nd generation)
 Version: 1.111.2
-Date: 2023-05-31
+Date: 2023-06-03
 Authors@R: c(
     person("Renato", "Rodrigues", , "renato.rodrigues@pik-potsdam.de", role = c("aut", "cre")),
     person("Lavinia", "Baumstark", role = "aut"),

R/reportFE.R

@@ -1358,16 +1358,20 @@ reportFE <- function(gdx, regionSubsetList = NULL,
       # some initializations required for building library with dplyr operations below
       encar <- data <- value <- value_subsectors <- SSP <- Value_NonEn <- encar <- region <- period <- NULL
 
-
-
       # read in FE industry non-energy use trajectories from industry subsectors run
       df.fe_nechem <- read.csv(system.file("extdata","pm_fe_nechem.cs4r",package = "remind2"),
                                sep = ",", skip = 4, header = F)
-
-      # df.fe_nechem <- read.csv("./inst/extdata/pm_fe_nechem.cs4r",
-      #                          sep = ",", skip = 4, header = F)
-
       colnames(df.fe_nechem) <- c("period", "region","SSP","encar","value_subsectors")
+
+      # rescaling non-energy use to match 2020 EU27 values for total non-energy use
+      df.fe_nechem <- df.fe_nechem %>%
+        mutate(value_subsectors = ifelse(region %in% c("DEU", "FRA", "ECE", "ECS", "ENC", "ESC", "ESW", "EWN"), 
+          value_subsectors *
+           3.835 / # average between 2018-2021 = 3.835 EJ (https://ec.europa.eu/eurostat/databrowser/view/NRG_BAL_C__custom_6407922/bookmark/table?lang=en&bookmarkId=f7c8aa0e-3cf6-45d6-b85c-f2e76e90b4aa)
+           df.fe_nechem %>% filter(region %in% c("DEU", "FRA", "ECE", "ECS", "ENC", "ESC", "ESW", "EWN"), period == 2020, SSP == "SSP2") %>% summarize(value_subsectors = sum(value_subsectors)) %>% pull(value_subsectors), # original 2020 df.fe_nechem total non-energy use
+          value_subsectors)
+          )
+
       vars.nechem <- c("FE|Industry|+|Liquids (EJ/yr)",
                        "FE|Industry|+|Gases (EJ/yr)",
                        "FE|Industry|+|Solids (EJ/yr)")