diff --git a/plots.R b/plots.R
index 1a2eabea..62e5f3a1 100644
--- a/plots.R
+++ b/plots.R
@@ -119,3 +119,364 @@ plot_match_success_rate <- function(data,
   }
   plot
 }
+
+
+generate_individual_outputs <- function(data,
+                                        matched_prioritized,
+                                        output_directory,
+                                        target_type = c("tms", "sda"),
+                                        group_id,
+                                        scenario_source,
+                                        scenario,
+                                        region = "global",
+                                        sector,
+                                        start_year,
+                                        time_horizon) {
+
+  # match input values
+  target_type <- match.arg(target_type)
+
+  target_scenario <- paste0("target_", scenario)
+
+  # validate input values
+  validate_input_args_generate_individual_outputs(
+    output_directory = output_directory,
+    group_id = group_id,
+    scenario_source = scenario_source,
+    target_scenario = target_scenario,
+    region = region,
+    sector = sector,
+    start_year = start_year,
+    time_horizon = time_horizon
+  )
+
+  # validate input data
+  validate_input_data_generate_individual_outputs(
+    data = data,
+    matched_prioritized = matched_prioritized,
+    target_type = target_type
+  )
+
+  # create sub directory for the selected institute
+  dir.create(file.path(output_directory, group_id), showWarnings = FALSE)
+
+  data <- data %>%
+    dplyr::filter(
+      group_id == .env$group_id,
+      scenario_source == .env$scenario_source,
+      region == .env$region,
+      sector %in% .env$sector
+    )
+
+  matched_prioritized <- matched_prioritized %>%
+    dplyr::filter(
+      group_id == .env$group_id,
+      sector %in% .env$sector
+    )
+
+  if (target_type == "tms") {
+    # plot tech mix for given sector
+    data_techmix <- data %>%
+      dplyr::filter(
+        .data$metric %in% c("projected", "corporate_economy", .env$target_scenario),
+        dplyr::between(.data$year, .env$start_year, .env$start_year + .env$time_horizon)
+      ) %>%
+      dplyr::mutate(
+        label = case_when(
+          .data$metric == "projected" ~ "Portfolio",
+          .data$metric == "corporate_economy" ~ "Corporate Economy",
+          .data$metric == .env$target_scenario ~ glue::glue("{r2dii.plot::to_title(toupper(.env$scenario))} Scenario")
+        )
+      ) %>%
+      r2dii.plot::prep_techmix(
+        span_5yr = TRUE
+      )
+
+    plot_techmix <- data_techmix %>%
+      r2dii.plot::plot_techmix()
+
+    # colors in tech mix plot set to make technologies more distinguishable
+    if (sector == "automotive") {
+      plot_techmix <- plot_techmix +
+        ggplot2::scale_fill_manual(
+          values = c("#4a5e54", "#d0d7e1", "#1b324f", "#00c082"),
+          labels = c("ICE", "Hybrid", "Fuelcell","Electric")
+        )
+    } else if (sector == "power") {
+      plot_techmix <- plot_techmix +
+        ggplot2::scale_fill_manual(
+          values = c("#4a5e54", "#d0d7e1", "#a63d57", "#f2e06e", "#1b324f", "#00c082"),
+          labels = paste(c("Coal", "Oil", "Gas", "Nuclear", "Hydro", "Renewables"), "Cap.")
+        )
+    }
+
+    # export tech mix
+    data_techmix %>%
+      readr::write_csv(
+        file = file.path(
+          output_directory,
+          group_id,
+          glue::glue("data_tech_mix_{sector}_{group_id}.csv")
+        ),
+        na = ""
+      )
+
+    ggplot2::ggsave(
+      filename = glue::glue("plot_tech_mix_{sector}_{group_id}.png"),
+      plot = plot_techmix,
+      device = "png",
+      path = file.path(output_directory, group_id)
+    )
+
+    # plot trajectory charts for all available techs in given sector
+    technologies_in_sector <- r2dii.data::increasing_or_decreasing %>%
+      dplyr::filter(.data$sector == .env$sector) %>%
+      dplyr::pull(.data$technology)
+
+    technologies_to_plot <- data %>%
+      dplyr::filter(
+        .data$metric == .env$target_scenario,
+        .data$technology %in% .env$technologies_in_sector
+      ) %>%
+      dplyr::distinct(.data$technology) %>%
+      dplyr::arrange(.data$technology) %>%
+      dplyr::pull()
+
+    for (i in 1:length(technologies_to_plot)) {
+      data_trajectory <- data %>%
+        dplyr::filter(
+          .data$technology == .env$technologies_to_plot[i],
+          dplyr::between(.data$year, .env$start_year, .env$start_year + .env$time_horizon)
+        ) %>%
+        r2dii.plot::prep_trajectory(
+          convert_label = r2dii.plot::recode_metric_trajectory,
+          span_5yr = TRUE,
+          value_col = "percentage_of_initial_production_by_scope"
+        )
+
+      if (sector == "power") {
+        y_lab_trajectory <- "Normalized Capacity"
+      } else {
+        y_lab_trajectory <- "Normalized Production"
+      }
+
+      plot_trajectory <- data_trajectory %>%
+        r2dii.plot::plot_trajectory(
+          center_y = TRUE,
+          perc_y_scale = TRUE
+        ) +
+        ggplot2::xlab("Year") +
+        ggplot2::ylab(y_lab_trajectory)
+
+      # export trajectory chart
+      data_trajectory %>%
+        readr::write_csv(
+          file = file.path(
+            output_directory,
+            group_id,
+            glue::glue("data_trajectory_{sector}_{technologies_to_plot[i]}_{group_id}.csv")
+          ),
+          na = ""
+        )
+
+      ggplot2::ggsave(
+        filename = glue::glue("plot_trajectory_{sector}_{technologies_to_plot[i]}_{group_id}.png"),
+        plot = plot_trajectory,
+        device = "png",
+        path = file.path(output_directory, group_id)
+      )
+    }
+  } else {
+    # plot convergence chart for given sector
+    adjusted_scenario <- paste0("adjusted_scenario_", scenario)
+
+    data_emission_intensity <- data %>%
+      dplyr::filter(
+        dplyr::between(
+          .data$year,
+          .env$start_year,
+          .env$start_year + .env$time_horizon)
+      ) %>%
+      dplyr::filter(
+        .data$emission_factor_metric %in% c(
+          "projected",
+          "corporate_economy",
+          .env$target_scenario,
+          .env$adjusted_scenario
+        )
+      ) %>%
+      dplyr::mutate(
+        emission_factor_metric = factor(
+          .data$emission_factor_metric,
+          levels = c(
+            "projected",
+            "corporate_economy",
+            .env$target_scenario,
+            .env$adjusted_scenario
+          )
+        )
+      ) %>%
+      r2dii.plot::prep_emission_intensity(
+        span_5yr = TRUE
+      )
+
+    colours_scale <- c(
+      "dark_blue",
+      "green",
+      "orange",
+      "grey"
+    )
+
+    if (sector == "cement") {
+      y_lab_emissions_intensity <- "Tons of CO2 per Ton of Cement Produced"
+    } else if (sector == "steel") {
+      y_lab_emissions_intensity <- "Tons of CO2 per Ton of Steel Produced"
+    } else if (sector == "aviation") {
+      y_lab_emissions_intensity <- "Tons of CO2 per Passenger Kilometer"
+    } else {
+      y_lab_emissions_intensity <- "Emission Factor Value"
+    }
+
+    plot_emission_intensity <- data_emission_intensity  %>%
+      r2dii.plot::plot_emission_intensity() +
+      r2dii.plot::scale_colour_r2dii(
+        colour_labels = colours_scale,
+        labels = c(
+          "Portfolio",
+          "Corporate Economy",
+          glue::glue("Target {r2dii.plot::to_title(toupper(scenario))}"),
+          glue::glue("Adjusted Scenario {r2dii.plot::to_title(toupper(scenario))}")
+        )
+      ) +
+      ggplot2::xlab("Year") +
+      ggplot2::ylab(y_lab_emissions_intensity)
+
+    # export convergence chart
+    data_emission_intensity %>%
+      readr::write_csv(
+        file = file.path(
+          output_directory,
+          group_id,
+          glue::glue("data_emission_intensity_{sector}_{group_id}.csv")
+        ),
+        na = ""
+      )
+
+    ggplot2::ggsave(
+      filename = glue::glue("plot_emission_intensity_{sector}_{group_id}.png"),
+      plot = plot_emission_intensity,
+      device = "png",
+      path = file.path(output_directory, group_id)
+    )
+  }
+  companies_included <- matched_prioritized %>%
+    dplyr::select(
+      "group_id", "name_abcd", "sector_abcd", "loan_size_outstanding",
+      "loan_size_outstanding_currency", "loan_size_credit_limit",
+      "loan_size_credit_limit_currency"
+    )
+
+  companies_included %>%
+    readr::write_csv(
+      file = file.path(
+        output_directory,
+        group_id,
+        glue::glue("companies_included_{sector}_{group_id}.csv")
+      ),
+      na = ""
+    )
+
+}
+
+
+validate_input_args_generate_individual_outputs <- function(output_directory,
+                                                            group_id,
+                                                            scenario_source,
+                                                            target_scenario,
+                                                            region,
+                                                            sector,
+                                                            start_year,
+                                                            time_horizon) {
+  if (!length(output_directory) == 1) {
+    stop("Argument output_directory must be of length 1. Please check your input.")
+  }
+  if (!inherits(output_directory, "character")) {
+    stop("Argument output_directory must be of class character. Please check your input.")
+  }
+  if (!length(group_id) == 1) {
+    stop("Argument group_id must be of length 1. Please check your input.")
+  }
+  if (!length(scenario_source) == 1) {
+    stop("Argument scenario_source must be of length 1. Please check your input.")
+  }
+  if (!inherits(scenario_source, "character")) {
+    stop("Argument scenario_source must be of class character. Please check your input.")
+  }
+  if (!length(target_scenario) == 1) {
+    stop("Argument target_scenario must be of length 1. Please check your input.")
+  }
+  if (!inherits(target_scenario, "character")) {
+    stop("Argument target_scenario must be of class character. Please check your input.")
+  }
+  if (!length(region) == 1) {
+    stop("Argument region must be of length 1. Please check your input.")
+  }
+  if (!inherits(region, "character")) {
+    stop("Argument region must be of class character. Please check your input.")
+  }
+  if (!length(sector) == 1) {
+    stop("Argument sector must be of length 1. Please check your input.")
+  }
+  if (!inherits(sector, "character")) {
+    stop("Argument sector must be of class character. Please check your input.")
+  }
+  if (!length(start_year) == 1) {
+    stop("Argument start_year must be of length 1. Please check your input.")
+  }
+  if (!inherits(start_year, "integer")) {
+    stop("Argument start_year must be of class integer. Please check your input.")
+  }
+  if (!length(time_horizon) == 1) {
+    stop("Argument time_horizon must be of length 1. Please check your input.")
+  }
+  if (!inherits(time_horizon, "integer")) {
+    stop("Argument time_horizon must be of class integer. Please check your input.")
+  }
+
+  invisible()
+}
+
+
+validate_input_data_generate_individual_outputs <- function(data,
+                                                            matched_prioritized,
+                                                            target_type) {
+  if (target_type == "sda") {
+    validate_data_has_expected_cols(
+      data = data,
+      expected_columns = c(
+        "sector", "year", "region", "scenario_source", "emission_factor_metric",
+        "emission_factor_value", "group_id"
+      )
+    )
+  } else if (target_type == "tms") {
+    validate_data_has_expected_cols(
+      data = data,
+      expected_columns = c(
+        "sector", "technology", "year", "region", "scenario_source", "metric",
+        "production", "technology_share", "scope",
+        "percentage_of_initial_production_by_scope", "group_id"
+      )
+    )
+  }
+
+  validate_data_has_expected_cols(
+    data = matched_prioritized,
+    expected_columns = c(
+      "group_id", "name_abcd", "sector", "sector_abcd", "loan_size_outstanding",
+      "loan_size_outstanding_currency", "loan_size_credit_limit",
+      "loan_size_credit_limit_currency"
+    )
+  )
+
+  invisible()
+}
diff --git a/run_pacta.R b/run_pacta.R
new file mode 100644
index 00000000..042a2c32
--- /dev/null
+++ b/run_pacta.R
@@ -0,0 +1,381 @@
+# load packages----
+library(dplyr)
+library(pacta.multi.loanbook.analysis)
+library(r2dii.analysis)
+library(r2dii.data)
+library(r2dii.plot)
+
+
+# source helpers----
+source("expected_columns.R")
+source("plots.R")
+
+# load config----
+config <- config::get()
+
+input_path_scenario <- config$directories$dir_scenario
+input_dir_abcd <- config$directories$dir_abcd
+input_path_matched <- config$directories$dir_matched
+
+input_path_scenario_tms <- file.path(input_path_scenario, config$file_names$filename_scenario_tms)
+input_path_scenario_sda <- file.path(input_path_scenario, config$file_names$filename_scenario_sda)
+
+input_path_abcd <- file.path(input_dir_abcd, "abcd_final.csv")
+
+output_path <- config$directories$dir_output
+output_path_standard <- file.path(output_path, "standard")
+
+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_select <- config$project_parameters$time_frame
+apply_sector_split <- config$sector_split$apply_sector_split
+if (is.null(apply_sector_split)) { apply_sector_split <- FALSE }
+sector_split_type_select <- config$sector_split$sector_split_type
+remove_inactive_companies <- config$prepare_abcd$remove_inactive_companies
+if (is.null(remove_inactive_companies)) { remove_inactive_companies <- FALSE }
+
+# if a sector split is applied, write results into a directory that states the type
+if (apply_sector_split) {
+  output_path_standard <- file.path(output_path, sector_split_type_select, "standard")
+}
+
+dir.create(output_path_standard, recursive = TRUE)
+
+# TODO: add check if all files exist, resort to test files if not
+
+# 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(
+  input_path_scenario_tms,
+  col_types = col_types_scenario_tms,
+  col_select = dplyr::all_of(col_select_scenario_tms)
+)
+
+scenario_input_sda <- readr::read_csv(
+  input_path_scenario_sda,
+  col_types = col_types_scenario_sda,
+  col_select = dplyr::all_of(col_select_scenario_sda)
+)
+
+abcd <- readr::read_csv(
+  input_path_abcd,
+  col_select = dplyr::all_of(cols_abcd),
+  col_types = col_types_abcd_final
+)
+
+# read matched and prioritized loan book----
+list_matched_prio <- list.files(input_path_matched)[grepl("matched_prio_", list.files(input_path_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(input_path_matched, i),
+    col_types = col_types_matched_prioritized,
+    col_select = dplyr::all_of(col_select_matched_prioritized)
+  )
+
+  matched_prioritized <- matched_prioritized %>%
+    dplyr::bind_rows(matched_prioritized_i)
+}
+
+# meta loan book----
+# aggregate all individual loan books into one meta loan book and add that to
+# the full list of loan books
+matched_prioritized_meta <- matched_prioritized %>%
+  dplyr::mutate(
+    id_loan = paste0(.data$id_loan, "_", .data$group_id),
+    group_id = "meta_loanbook"
+  )
+
+matched_prioritized <- matched_prioritized %>%
+  dplyr::bind_rows(matched_prioritized_meta)
+
+# generate all P4B outputs----
+unique_loanbooks_matched <- unique(matched_prioritized$group_id)
+
+## generate SDA outputs----
+results_sda_total <- NULL
+
+# generate SDA results for each individual loan book, including the meta loan book
+for (i in unique_loanbooks_matched) {
+  matched_i <- matched_prioritized %>%
+    dplyr::filter(.data$group_id == i) %>%
+    dplyr::select(-"group_id")
+
+  results_sda_i <- matched_i %>%
+    r2dii.analysis::target_sda(
+      abcd = abcd,
+      co2_intensity_scenario = scenario_input_sda,
+      region_isos = region_isos_select
+    ) %>%
+    dplyr::mutate(group_id = .env$i)
+
+  results_sda_total <- results_sda_total %>%
+    dplyr::bind_rows(results_sda_i)
+}
+
+# write SDA results to csv
+results_sda_total %>%
+  readr::write_csv(
+    file.path(output_path_standard, "sda_results_all_groups.csv"),
+    na = ""
+  )
+
+
+## generate TMS outputs----
+
+results_tms_total <- NULL
+
+# generate TMS results for each individual loan book, including the meta loan book
+for (i in unique_loanbooks_matched) {
+  matched_i <- matched_prioritized %>%
+    dplyr::filter(.data$group_id == i) %>%
+    dplyr::select(-"group_id")
+
+  results_tms_i <- matched_i %>%
+    r2dii.analysis::target_market_share(
+      abcd = abcd,
+      scenario = scenario_input_tms,
+      region_isos = region_isos_select
+    ) %>%
+    dplyr::mutate(group_id = .env$i)
+
+  results_tms_total <- results_tms_total %>%
+    dplyr::bind_rows(results_tms_i)
+}
+
+# write TMS results to csv
+results_tms_total %>%
+  readr::write_csv(
+    file.path(output_path_standard, "tms_results_all_groups.csv"),
+    na = ""
+  )
+
+# generate P4B plots----
+
+## retrieve set of unique groups to loop over----
+unique_groups_tms <- unique(results_tms_total$group_id)
+unique_groups_sda <- unique(results_sda_total$group_id)
+
+## run automatic result generation ----------
+
+### automotive----
+sector_select <- "automotive"
+for (tms_i in unique_groups_tms) {
+  available_rows <- results_tms_total %>%
+    dplyr::filter(
+      group_id == tms_i,
+      scenario_source == scenario_source_input,
+      grepl(scenario_select, .data$metric),
+      region == region_select,
+      sector == sector_select
+    ) %>%
+    nrow()
+  if (available_rows > 0) {
+    generate_individual_outputs(
+      data = results_tms_total,
+      matched_prioritized = matched_prioritized,
+      output_directory = output_path_standard,
+      target_type = "tms",
+      group_id = tms_i,
+      scenario_source = scenario_source_input,
+      scenario = scenario_select,
+      region = region_select,
+      sector = sector_select,
+      start_year = start_year,
+      time_horizon = time_frame_select
+    )
+  } else {
+    next()
+  }
+}
+### coal----
+sector_select <- "coal"
+for (tms_i in unique_groups_tms) {
+  available_rows <- results_tms_total %>%
+    dplyr::filter(
+      group_id == tms_i,
+      scenario_source == scenario_source_input,
+      grepl(scenario_select, .data$metric),
+      region == region_select,
+      sector == sector_select
+    ) %>%
+    nrow()
+  if (available_rows > 0) {
+    generate_individual_outputs(
+      data = results_tms_total,
+      matched_prioritized = matched_prioritized,
+      output_directory = output_path_standard,
+      target_type = "tms",
+      group_id = tms_i,
+      scenario_source = scenario_source_input,
+      scenario = scenario_select,
+      region = region_select,
+      sector = sector_select,
+      start_year = start_year,
+      time_horizon = time_frame_select
+    )
+  } else {
+    next()
+  }
+}
+### oil and gas----
+sector_select <- "oil and gas"
+for (tms_i in unique_groups_tms) {
+  available_rows <- results_tms_total %>%
+    dplyr::filter(
+      group_id == tms_i,
+      scenario_source == scenario_source_input,
+      grepl(scenario_select, .data$metric),
+      region == region_select,
+      sector == sector_select
+    ) %>%
+    nrow()
+  if (available_rows > 0) {
+    generate_individual_outputs(
+      data = results_tms_total,
+      matched_prioritized = matched_prioritized,
+      output_directory = output_path_standard,
+      target_type = "tms",
+      group_id = tms_i,
+      scenario_source = scenario_source_input,
+      scenario = scenario_select,
+      region = region_select,
+      sector = sector_select,
+      start_year = start_year,
+      time_horizon = time_frame_select
+    )
+  } else {
+    next()
+  }
+}
+### power----
+sector_select <- "power"
+for (tms_i in unique_groups_tms) {
+  available_rows <- results_tms_total %>%
+    dplyr::filter(
+      group_id == tms_i,
+      scenario_source == scenario_source_input,
+      grepl(scenario_select, .data$metric),
+      region == region_select,
+      sector == sector_select
+    ) %>%
+    nrow()
+  if (available_rows > 0) {
+    generate_individual_outputs(
+      data = results_tms_total,
+      matched_prioritized = matched_prioritized,
+      output_directory = output_path_standard,
+      target_type = "tms",
+      group_id = tms_i,
+      scenario_source = scenario_source_input,
+      scenario = scenario_select,
+      region = region_select,
+      sector = sector_select,
+      start_year = start_year,
+      time_horizon = time_frame_select
+    )
+  } else {
+    next()
+  }
+}
+
+### aviation----
+sector_select <- "aviation"
+for (sda_i in unique_groups_sda) {
+  available_rows <- results_sda_total %>%
+    dplyr::filter(
+      group_id == sda_i,
+      scenario_source == scenario_source_input,
+      grepl(scenario_select, .data$emission_factor_metric),
+      region == region_select,
+      sector == sector_select
+    ) %>%
+    nrow()
+  if (available_rows > 0) {
+    generate_individual_outputs(
+      data = results_sda_total,
+      matched_prioritized = matched_prioritized,
+      output_directory = output_path_standard,
+      target_type = "sda",
+      group_id = sda_i,
+      scenario_source = scenario_source_input,
+      scenario = scenario_select,
+      region = region_select,
+      sector = sector_select,
+      start_year = start_year,
+      time_horizon = time_frame_select
+    )
+  } else {
+    next()
+  }
+}
+### cement----
+sector_select <- "cement"
+for (sda_i in unique_groups_sda) {
+  available_rows <- results_sda_total %>%
+    dplyr::filter(
+      group_id == sda_i,
+      scenario_source == scenario_source_input,
+      grepl(scenario_select, .data$emission_factor_metric),
+      region == region_select,
+      sector == sector_select
+    ) %>%
+    nrow()
+  if (available_rows > 0) {
+    generate_individual_outputs(
+      data = results_sda_total,
+      matched_prioritized = matched_prioritized,
+      output_directory = output_path_standard,
+      target_type = "sda",
+      group_id = sda_i,
+      scenario_source = scenario_source_input,
+      scenario = scenario_select,
+      region = region_select,
+      sector = sector_select,
+      start_year = start_year,
+      time_horizon = time_frame_select
+    )
+  } else {
+    next()
+  }
+}
+### steel----
+sector_select <- "steel"
+for (sda_i in unique_groups_sda) {
+  available_rows <- results_sda_total %>%
+    dplyr::filter(
+      group_id == sda_i,
+      scenario_source == scenario_source_input,
+      grepl(scenario_select, .data$emission_factor_metric),
+      region == region_select,
+      sector == sector_select
+    ) %>%
+    nrow()
+  if (available_rows > 0) {
+    generate_individual_outputs(
+      data = results_sda_total,
+      matched_prioritized = matched_prioritized,
+      output_directory = output_path_standard,
+      target_type = "sda",
+      group_id = sda_i,
+      scenario_source = scenario_source_input,
+      scenario = scenario_select,
+      region = region_select,
+      sector = sector_select,
+      start_year = start_year,
+      time_horizon = time_frame_select
+    )
+  } else {
+    next()
+  }
+}