These tools help you to assess if a financial portfolio aligns with climate goals. They summarize key metrics attributed to the portfolio (e.g. production, emission factors), and calculate targets based on climate scenarios. They implement in R the last step of the free software ‘PACTA’ (Paris Agreement Capital Transition Assessment; https://www.transitionmonitor.com/). Financial institutions use ‘PACTA’ to study how their capital allocation impacts the climate.
Install the released version of r2dii.analysis from CRAN with:
install.packages("r2dii.analysis")
Or install the development version of r2dii.analysis from GitHub with:
# install.packages("pak")
pak::pak("RMI-PACTA/r2dii.analysis")
library()
to attach the packages you need. r2dii.analysis does
not depend on the packages r2dii.data and r2dii.match; but we suggest
you install them – with
install.packages(c("r2dii.data", "r2dii.match"))
– so you can
reproduce our examples.library(r2dii.data)
library(r2dii.match)
library(r2dii.analysis)
r2dii.match::match_name()
to identify matches between your
loanbook and the asset level data.matched <- match_name(loanbook_demo, abcd_demo) %>%
prioritize()
target_sda()
to calculate SDA targets of CO2 emissions.matched %>%
target_sda(
abcd = abcd_demo,
co2_intensity_scenario = co2_intensity_scenario_demo,
region_isos = region_isos_demo
)
#> Warning: Removing rows in abcd where `emission_factor` is NA
#> # A tibble: 220 × 6
#> sector year region scenario_source emission_factor_metric
#> <chr> <dbl> <chr> <chr> <chr>
#> 1 cement 2020 advanced economies demo_2020 projected
#> 2 cement 2020 developing asia demo_2020 projected
#> 3 cement 2020 global demo_2020 projected
#> 4 cement 2021 advanced economies demo_2020 projected
#> 5 cement 2021 developing asia demo_2020 projected
#> 6 cement 2021 global demo_2020 projected
#> 7 cement 2022 advanced economies demo_2020 projected
#> 8 cement 2022 developing asia demo_2020 projected
#> 9 cement 2022 global demo_2020 projected
#> 10 cement 2023 advanced economies demo_2020 projected
#> # ℹ 210 more rows
#> # ℹ 1 more variable: emission_factor_value <dbl>
target_market_share
to calculate market-share scenario targets
at the portfolio level:matched %>%
target_market_share(
abcd = abcd_demo,
scenario = scenario_demo_2020,
region_isos = region_isos_demo
)
#> # A tibble: 1,076 × 10
#> sector technology year region scenario_source metric production
#> <chr> <chr> <int> <chr> <chr> <chr> <dbl>
#> 1 automotive electric 2020 global demo_2020 projected 145649.
#> 2 automotive electric 2020 global demo_2020 target_cps 145649.
#> 3 automotive electric 2020 global demo_2020 target_sds 145649.
#> 4 automotive electric 2020 global demo_2020 target_sps 145649.
#> 5 automotive electric 2021 global demo_2020 projected 147480.
#> 6 automotive electric 2021 global demo_2020 target_cps 146915.
#> 7 automotive electric 2021 global demo_2020 target_sds 153332.
#> 8 automotive electric 2021 global demo_2020 target_sps 147258.
#> 9 automotive electric 2022 global demo_2020 projected 149310.
#> 10 automotive electric 2022 global demo_2020 target_cps 148155.
#> # ℹ 1,066 more rows
#> # ℹ 3 more variables: technology_share <dbl>, scope <chr>,
#> # percentage_of_initial_production_by_scope <dbl>
matched %>%
target_market_share(
abcd = abcd_demo,
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: 14,505 × 11
#> sector technology year region scenario_source name_abcd metric production
#> <chr> <chr> <int> <chr> <chr> <chr> <chr> <dbl>
#> 1 automoti… electric 2020 global demo_2020 Bernardi… proje… 17951.
#> 2 automoti… electric 2020 global demo_2020 Bernardi… targe… 17951.
#> 3 automoti… electric 2020 global demo_2020 Bernardi… targe… 17951.
#> 4 automoti… electric 2020 global demo_2020 Bernardi… targe… 17951.
#> 5 automoti… electric 2020 global demo_2020 Christia… proje… 11471.
#> 6 automoti… electric 2020 global demo_2020 Christia… targe… 11471.
#> 7 automoti… electric 2020 global demo_2020 Christia… targe… 11471.
#> 8 automoti… electric 2020 global demo_2020 Christia… targe… 11471.
#> 9 automoti… electric 2020 global demo_2020 Donati, … proje… 5611.
#> 10 automoti… electric 2020 global demo_2020 Donati, … targe… 5611.
#> # ℹ 14,495 more rows
#> # ℹ 3 more variables: technology_share <dbl>, scope <chr>,
#> # percentage_of_initial_production_by_scope <dbl>
This project has received funding from the European Union LIFE program and the International Climate Initiative (IKI). The Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU) supports this initiative on the basis of a decision adopted by the German Bundestag. The views expressed are the sole responsibility of the authors and do not necessarily reflect the views of the funders. The funders are not responsible for any use that may be made of the information it contains.
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