This function calculates the portfolio-level production targets, as calculated using the market share approach applied to each relevant climate production forecast.

target_market_share(
  data,
  ald,
  scenario,
  region_isos = r2dii.data::region_isos,
  use_credit_limit = FALSE,
  by_company = FALSE,
  weight_production = TRUE
)

Arguments

data

A "data.frame" like the output of r2dii.match::prioritize.

ald

An asset level data frame like r2dii.data::ald_demo.

scenario

A scenario data frame like r2dii.data::scenario_demo_2020.

region_isos

A data frame like r2dii.data::region_isos (default).

use_credit_limit

Logical vector of length 1. FALSE defaults to using the column loan_size_outstanding. Set to TRUE to use the column loan_size_credit_limit instead.

by_company

Logical vector of length 1. FALSE defaults to outputting production_value at the portfolio-level. Set to TRUE to output production_value at the company-level.

weight_production

Logical vector of length 1. TRUE defaults to outputting production, weighted by relative loan-size. Set to FALSE to output the unweighted production values.

Value

A tibble including the summarized columns metric, production and technology_share. If by_company = TRUE, the output will also have the column name_ald.

Handling grouped data

This function ignores existing groups and outputs ungrouped data.

See also

Other functions to calculate scenario targets: target_sda()

Examples

installed <- requireNamespace("r2dii.data", quietly = TRUE) && requireNamespace("r2dii.match", quietly = TRUE) if (installed) { library(r2dii.data) library(r2dii.match) loanbook <- head(loanbook_demo, 100) ald <- head(ald_demo, 100) matched <- loanbook %>% match_name(ald) %>% prioritize() # Calculate targets at portfolio level matched %>% target_market_share( ald = ald, scenario = scenario_demo_2020, region_isos = region_isos_demo ) # Calculate targets at company level matched %>% target_market_share( ald = ald, scenario = scenario_demo_2020, region_isos = region_isos_demo, by_company = TRUE ) matched %>% target_market_share( ald = ald, scenario = scenario_demo_2020, region_isos = region_isos_demo, # Calculate unweighted targets weight_production = FALSE ) }
#> Warning: You've supplied `by_company = TRUE` and `weight_production = TRUE`. #> This will result in company-level results, weighted by the portfolio #> loan size, which is rarely useful. Did you mean to set one of these #> arguments to `FALSE`?
#> # A tibble: 336 × 8 #> sector technology year region scenario_source metric production #> <chr> <chr> <int> <chr> <chr> <chr> <dbl> #> 1 power hydrocap 2020 global demo_2020 projected 121032. #> 2 power hydrocap 2020 global demo_2020 target_cps 121032. #> 3 power hydrocap 2020 global demo_2020 target_sds 121032. #> 4 power hydrocap 2020 global demo_2020 target_sps 121032. #> 5 power hydrocap 2021 global demo_2020 projected 119274. #> 6 power hydrocap 2021 global demo_2020 target_cps 121141. #> 7 power hydrocap 2021 global demo_2020 target_sds 121205. #> 8 power hydrocap 2021 global demo_2020 target_sps 121151. #> 9 power hydrocap 2022 global demo_2020 projected 117515. #> 10 power hydrocap 2022 global demo_2020 target_cps 121250. #> # … with 326 more rows, and 1 more variable: technology_share <dbl>