add correct missing low carbon technologies to abcd

R/join_abcd_scenario.R

@@ -160,11 +160,13 @@ add_green_technologies_to_abcd <- function(data, scenario) {
     unique() %>%
     inner_join(increasing_techs, by = c("sector", "technology"))
 
-  increasing_techs_not_in_abcd <- dplyr::filter(
+  increasing_techs_not_in_abcd <- dplyr::anti_join(
     increasing_techs_in_scenario,
-    !(.data[["technology"]] %in% unique(data$technology))
+    data,
+    by = c("sector", "technology")
   )
 
+  # TODO: the summarize should be replaced with a distinct. the left_join should be an inner_join to avoid adding NAs
   green_rows_to_add <- data %>%
     group_by(
       .data$name_company,

tests/testthat/test-target_market_share.R

@@ -1494,3 +1494,75 @@ test_that("with duplicated id_loan throws informative error (#489)", {
     class = "unique_ids"
   )
 })
+
+test_that("target_market_share() calculates target_* values for missing low carbon technologies (#495)", {
+  match_result <- fake_matched(name_abcd = "company a")
+
+  abcd <- fake_abcd(
+    name_company = "company a",
+    sector = c(rep("automotive", 2), rep("hdv", 6)),
+    technology = c(rep("ice", 4), rep("hybrid", 2), rep("electric", 2)),
+    year = rep(c(2020, 2025), 4)
+  )
+
+  scen <- fake_scenario(
+    sector = "automotive",
+    technology = c(rep("ice", 2), rep("hybrid", 2), rep("electric", 2)),
+    year = rep(c(2020, 2025), 3),
+    tmsr = c(1, 0.5, 1, 1.5, 1, 1.5),
+    smsp = c(0, -0.08, 0, 0.1, 0, 0.1)
+  )
+
+  scen_technologies <- scen %>%
+    dplyr::filter(.data$sector == "automotive") %>%
+    dplyr::arrange(.data$technology) %>%
+    dplyr::distinct(.data$technology) %>%
+    dplyr::pull()
+
+  results_tms_comp <- target_market_share(
+    match_result,
+    abcd,
+    scen,
+    region_isos = region_isos_stable,
+    by_company = TRUE,
+    weight_production = FALSE
+  )
+
+  results_tms_comp_targets <- results_tms_comp %>%
+    dplyr::filter(
+      .data$name_abcd == "company a",
+      .data$sector == "automotive",
+      grepl("target_", .data$metric)
+    ) %>%
+    dplyr::arrange(.data$technology) %>%
+    dplyr::distinct(.data$technology) %>%
+    dplyr::pull()
+
+  expect_equal(
+    results_tms_comp_targets,
+    scen_technologies
+  )
+
+  results_tms_lbk <- target_market_share(
+    match_result,
+    abcd,
+    scen,
+    region_isos = region_isos_stable,
+    by_company = FALSE,
+    weight_production = TRUE
+  )
+
+  results_tms_lbk_targets <- results_tms_lbk %>%
+    dplyr::filter(
+      .data$sector == "automotive",
+      grepl("target_", .data$metric)
+    ) %>%
+    dplyr::arrange(.data$technology) %>%
+    dplyr::distinct(.data$technology) %>%
+    dplyr::pull()
+
+  expect_equal(
+    results_tms_lbk_targets,
+    scen_technologies
+  )
+})