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Add targets for production, using the market share approach

Source: R/target_market_share.R
target_market_share.Rd

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

Usage

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

Arguments

data

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

abcd

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

increasing_or_decreasing

A data frame like r2dii.data::increasing_or_decreasing.

Value

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

Handling grouped data

This function ignores existing groups and outputs ungrouped data.

See also

Other functions to calculate scenario targets: target_sda()

Examples


library(r2dii.data)
library(r2dii.match)

loanbook <- head(loanbook_demo, 100)
abcd <- head(abcd_demo, 100)

matched <- loanbook %>%
  match_name(abcd) %>%
  prioritize()

# Calculate targets at portfolio level
matched %>%
  target_market_share(
    abcd = abcd,
    scenario = scenario_demo_2020,
    region_isos = region_isos_demo
    )
#> # A tibble: 373 × 10
#>    sector technology  year region scenario_source metric     production
#>    <chr>  <chr>      <int> <chr>  <chr>           <chr>           <dbl>
#>  1 power  hydrocap    2020 global demo_2020       projected      16990.
#>  2 power  hydrocap    2020 global demo_2020       target_cps     16990.
#>  3 power  hydrocap    2020 global demo_2020       target_sds     16990.
#>  4 power  hydrocap    2020 global demo_2020       target_sps     16990.
#>  5 power  hydrocap    2021 global demo_2020       projected      16743.
#>  6 power  hydrocap    2021 global demo_2020       target_cps     17004.
#>  7 power  hydrocap    2021 global demo_2020       target_sds     17012.
#>  8 power  hydrocap    2021 global demo_2020       target_sps     17005.
#>  9 power  hydrocap    2022 global demo_2020       projected      16497.
#> 10 power  hydrocap    2022 global demo_2020       target_cps     17018.
#> # ℹ 363 more rows
#> # ℹ 3 more variables: technology_share <dbl>, scope <chr>,
#> #   percentage_of_initial_production_by_scope <dbl>

# Calculate targets at company level
matched %>%
  target_market_share(
  abcd = abcd,
  scenario = scenario_demo_2020,
  region_isos = region_isos_demo,
  by_company = TRUE
  )
#> 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: 1,408 × 11
#>    sector technology  year region scenario_source name_abcd    metric production
#>    <chr>  <chr>      <int> <chr>  <chr>           <chr>        <chr>       <dbl>
#>  1 power  hydrocap    2020 global demo_2020       Giordano, G… proje…     16990.
#>  2 power  hydrocap    2020 global demo_2020       Giordano, G… targe…     16990.
#>  3 power  hydrocap    2020 global demo_2020       Giordano, G… targe…     16990.
#>  4 power  hydrocap    2020 global demo_2020       Giordano, G… targe…     16990.
#>  5 power  hydrocap    2021 global demo_2020       Giordano, G… proje…     16743.
#>  6 power  hydrocap    2021 global demo_2020       Giordano, G… targe…     17004.
#>  7 power  hydrocap    2021 global demo_2020       Giordano, G… targe…     17012.
#>  8 power  hydrocap    2021 global demo_2020       Giordano, G… targe…     17005.
#>  9 power  hydrocap    2022 global demo_2020       Giordano, G… proje…     16497.
#> 10 power  hydrocap    2022 global demo_2020       Giordano, G… targe…     17018.
#> # ℹ 1,398 more rows
#> # ℹ 3 more variables: technology_share <dbl>, scope <chr>,
#> #   percentage_of_initial_production_by_scope <dbl>

matched %>%
  target_market_share(
    abcd = abcd,
    scenario = scenario_demo_2020,
    region_isos = region_isos_demo,
    # Calculate unweighted targets
    weight_production = FALSE
    )
#> # A tibble: 373 × 10
#>    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    121129.
#>  7 power  hydrocap    2021 global demo_2020       target_sds    121187.
#>  8 power  hydrocap    2021 global demo_2020       target_sps    121139.
#>  9 power  hydrocap    2022 global demo_2020       projected     117515.
#> 10 power  hydrocap    2022 global demo_2020       target_cps    121227.
#> # ℹ 363 more rows
#> # ℹ 3 more variables: technology_share <dbl>, scope <chr>,
#> #   percentage_of_initial_production_by_scope <dbl>