diff --git a/example.config.yml b/example.config.yml
index af131bb7..50934b43 100644
--- a/example.config.yml
+++ b/example.config.yml
@@ -52,3 +52,5 @@ default:
     plot_height: 8
     plot_units: "in"
     plot_resolution: 300
+  aggregate_alignment_metric:
+    by_group: "group_id"
diff --git a/expected_columns.R b/expected_columns.R
index 82d1d76b..b8107728 100644
--- a/expected_columns.R
+++ b/expected_columns.R
@@ -90,6 +90,7 @@ col_types_matched_prioritized <- readr::cols_only(
   borderline = "l"
 )
 col_select_matched_prioritized <- names(col_types_matched_prioritized[["cols"]])
+col_standard_matched_prioritized <- c(col_select_matched_prioritized[!col_select_matched_prioritized == "group_id"])
 
 # expected columns matched_all_groups file
 # col_types_matched_manual <- readr::cols_only(
diff --git a/run_aggregate_alignment_metric.R b/run_aggregate_alignment_metric.R
new file mode 100644
index 00000000..4ac92dd0
--- /dev/null
+++ b/run_aggregate_alignment_metric.R
@@ -0,0 +1,273 @@
+# set up project and load packages----
+library(dplyr)
+library(glue)
+library(pacta.multi.loanbook.analysis)
+library(r2dii.analysis)
+library(r2dii.data)
+
+# source helpers----
+source("expected_columns.R")
+
+# load config----
+config_dir <- config::get("directories")
+config_files <- config::get("file_names")
+config_project_parameters <- config::get("project_parameters")
+config_prepare_sector_split <- config::get("sector_split")
+config_aggregate_alignment_metric <- config::get("aggregate_alignment_metric")
+
+dir_abcd <- config_dir$dir_abcd
+dir_matched <- config_dir$dir_matched
+dir_output <- config_dir$dir_output
+dir_output_aggregated <- file.path(dir_output, "aggregated")
+apply_sector_split <- config_prepare_sector_split$apply_sector_split
+
+if (apply_sector_split) {
+  dir_output_aggregated <- file.path(
+    dir_output,
+    config_prepare_sector_split$sector_split_type,
+    "aggregated"
+  )
+}
+
+dir.create(dir_output_aggregated, recursive = TRUE)
+
+path_scenario_tms <- file.path(config_dir$dir_scenario, config_files$filename_scenario_tms)
+path_scenario_sda <- file.path(config_dir$dir_scenario, config_files$filename_scenario_sda)
+
+scenario_source_input <- config_project_parameters$scenario_source
+scenario_select <- config_project_parameters$scenario_select
+region_select <- config_project_parameters$region_select
+start_year <- config_project_parameters$start_year
+time_frame <- config_project_parameters$time_frame
+
+by_group <- config_aggregate_alignment_metric$by_group
+if (by_group == "NULL") {by_group <- NULL}
+if (length(by_group) >= 1) {
+  by_group <- gsub(" ", "", unlist(strsplit(by_group, split = ",")))
+}
+
+# load input data----
+region_isos_select <- r2dii.data::region_isos %>%
+  dplyr::filter(
+    .data$source == .env$scenario_source_input,
+    .data$region %in% .env$region_select
+  )
+
+scenario_input_tms <- readr::read_csv(
+  path_scenario_tms,
+  col_types = col_types_scenario_tms,
+  col_select = dplyr::all_of(col_select_scenario_tms)
+)
+scenario_input_sda <- readr::read_csv(
+  path_scenario_sda,
+  col_types = col_types_scenario_sda,
+  col_select = dplyr::all_of(col_select_scenario_sda)
+)
+
+abcd <- readr::read_csv(
+  file.path(dir_abcd, "abcd_final.csv"),
+  col_types = col_types_abcd_final,
+  col_select = dplyr::all_of(cols_abcd)
+)
+
+# read matched and prioritized loan book----
+list_matched_prio <- list.files(dir_matched)[grepl("matched_prio_", list.files(dir_matched))]
+
+matched_prioritized <- NULL
+
+# combine all matched loan books into one object to loop over
+for (i in list_matched_prio) {
+  matched_prioritized_i <- readr::read_csv(
+    file.path(dir_matched, i),
+    col_types = col_types_matched_prioritized
+  )
+
+  matched_prioritized <- matched_prioritized %>%
+    dplyr::bind_rows(matched_prioritized_i)
+}
+
+# aggregate P4B alignment----
+
+## set specifications----
+
+# for the calculation of the aggregate company alignment metric, we do not force companies
+# to enter a new market to build out hydro power or nuclear power, as this may
+# not be feasible for political and/or geographic reasons.
+# in the power sector, only renewables continues to follow the SMSP logic
+increasing_or_decreasing_aggregate_alignment <- r2dii.data::increasing_or_decreasing %>%
+  dplyr::mutate(
+    increasing_or_decreasing = dplyr::if_else(
+      .data$technology %in% c("hydrocap", "nuclearcap"),
+      "decreasing",
+      .data$increasing_or_decreasing
+    )
+  )
+
+# define if technologies should be treated as build out or phase down in the
+# aggregation
+technology_direction <- scenario_input_tms %>%
+  dplyr::filter(.data$year %in% c(.env$start_year, .env$start_year + .env$time_frame)) %>%
+  dplyr::distinct(
+    .data$scenario_source,
+    .data$scenario,
+    .data$sector,
+    .data$technology,
+    .data$region
+  ) %>%
+  dplyr::inner_join(r2dii.data::increasing_or_decreasing, by = c("sector", "technology")) %>%
+  dplyr::mutate(
+    directional_dummy = dplyr::if_else(
+      .data$increasing_or_decreasing == "increasing",
+      1,
+      -1
+    )
+  ) %>%
+  dplyr::select(-"increasing_or_decreasing")
+
+# remove non standard columns from matched_prioritzed when calling r2dii.analysis
+# TODO: check if this needs to be adjusted to remove other by_group columns
+matched_prio_non_standard_cols <- names(matched_prioritized)[!names(matched_prioritized) %in% col_standard_matched_prioritized]
+
+## prepare TMS company level P4B results for aggregation----
+tms_result_for_aggregation <- r2dii.analysis::target_market_share(
+  data = matched_prioritized %>%
+    dplyr::select(-dplyr::all_of(matched_prio_non_standard_cols)),
+  abcd = abcd,
+  scenario = scenario_input_tms,
+  region_isos = region_isos_select,
+  by_company = TRUE,
+  weight_production = FALSE,
+  increasing_or_decreasing = increasing_or_decreasing_aggregate_alignment
+)
+
+## aggregate TMS P4B results to company level alignment metric----
+# calculate aggregation for the loan book
+
+company_technology_deviation_tms <- tms_result_for_aggregation %>%
+  pacta.multi.loanbook.analysis::calculate_company_tech_deviation(
+    technology_direction = technology_direction,
+    scenario_source = scenario_source_input,
+    scenario = scenario_select,
+    # bridge_tech = "gascap",
+    time_frame = time_frame
+  )
+
+company_technology_deviation_tms %>%
+  readr::write_csv(
+    file.path(dir_output_aggregated, "company_technology_deviation_tms.csv"),
+    na = ""
+  )
+
+company_alignment_net_tms <- company_technology_deviation_tms %>%
+  pacta.multi.loanbook.analysis::calculate_company_aggregate_alignment_tms(
+    scenario_source = scenario_source_input,
+    scenario = scenario_select,
+    level = "net"
+  )
+
+company_alignment_net_tms %>%
+  readr::write_csv(
+    file.path(dir_output_aggregated, "company_alignment_net_tms.csv"),
+    na = ""
+  )
+
+company_alignment_bo_po_tms <- company_technology_deviation_tms %>%
+  pacta.multi.loanbook.analysis::calculate_company_aggregate_alignment_tms(
+    scenario_source = scenario_source_input,
+    scenario = scenario_select,
+    level = "bo_po"
+  )
+
+company_alignment_bo_po_tms %>%
+  readr::write_csv(
+    file.path(dir_output_aggregated, "company_alignment_bo_po_tms.csv"),
+    na = ""
+  )
+
+## prepare SDA company level P4B results for aggregation----
+sda_result_for_aggregation <- r2dii.analysis::target_sda(
+  data = matched_prioritized %>%
+    dplyr::select(-dplyr::all_of(matched_prio_non_standard_cols)),
+  abcd = abcd,
+  co2_intensity_scenario = scenario_input_sda,
+  by_company = TRUE,
+  region_isos = region_isos_select
+)
+
+sda_result_for_aggregation <- sda_result_for_aggregation %>%
+  dplyr::filter(.data$year >= .env$start_year)
+
+## aggregate SDA P4B results to company level alignment metric----
+company_alignment_net_sda <- sda_result_for_aggregation %>%
+  pacta.multi.loanbook.analysis::calculate_company_aggregate_alignment_sda(
+    scenario_source = scenario_source_input,
+    scenario = scenario_select,
+    time_frame = time_frame
+  )
+
+company_alignment_net_sda %>%
+  readr::write_csv(
+    file.path(dir_output_aggregated, "company_alignment_net_sda.csv"),
+    na = ""
+  )
+
+## calculate sector and loan book level aggregate alignment based on company exposures in loan book----
+
+# the company level aggregate alignment metrics are then joined with the matched
+# loan book to derive some high level summary statistics on the loan book level
+company_alignment_net <- dplyr::bind_rows(company_alignment_net_tms, company_alignment_net_sda)
+
+# show exposures (n companies and loan size) by alignment with given scenario
+write_alignment_metric_to_csv <- function(data,
+                                          output_dir,
+                                          level,
+                                          .by = NULL) {
+  if (is.null(.by)) {
+    by_group <- ""
+  } else {
+    by_group <- glue::glue("_by_{paste(.by, collapse = \"_\")}")
+  }
+
+  data$company %>%
+    readr::write_csv(
+      file = file.path(output_dir, glue::glue("company_exposure_{level}_aggregate_alignment{by_group}.csv")),
+      na = ""
+    )
+
+  data$aggregate %>%
+    readr::write_csv(
+      file = file.path(output_dir, glue::glue("loanbook_exposure_{level}_aggregate_alignment{by_group}.csv")),
+      na = ""
+    )
+
+}
+
+# net
+aggregated_alignment_net <- company_alignment_net %>%
+  pacta.multi.loanbook.analysis::aggregate_alignment_loanbook_exposure(
+    matched = matched_prioritized,
+    level = "net",
+    .by = by_group
+  )
+
+write_alignment_metric_to_csv(
+  data = aggregated_alignment_net,
+  output_dir = dir_output_aggregated,
+  level = "net",
+  .by = by_group
+)
+
+# buildout / phaseout
+aggregated_alignment_bo_po <- company_alignment_bo_po_tms %>%
+  pacta.multi.loanbook.analysis::aggregate_alignment_loanbook_exposure(
+    matched = matched_prioritized,
+    level = "bo_po",
+    .by = by_group
+  )
+
+write_alignment_metric_to_csv(
+  data = aggregated_alignment_bo_po,
+  output_dir = dir_output_aggregated,
+  level = "bo_po",
+  .by = by_group
+)
diff --git a/run_match_prioritize.R b/run_match_prioritize.R
index a0745804..6adaca0a 100644
--- a/run_match_prioritize.R
+++ b/run_match_prioritize.R
@@ -111,6 +111,12 @@ for (i in seq_along(matched_lbk_manual)) {
       )
   }
 
+  ## ensure that id_loan is unique across all loan books----
+  matched_prio_i <- matched_prio_i %>%
+    dplyr::mutate(
+      id_loan = paste(.data$id_loan, i, sep = "_")
+    )
+
   ## write matched prioritized loan book to file----
   matched_prio_i %>%
     readr::write_csv(