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://2degrees-investing.org/). Financial institutions use ‘PACTA’ to study how their capital allocation impacts the climate.
Before you install r2dii.analysis you may want to:
- Try an rstudio.cloud project with this package already installed.
- Learn how to minimize installation errors.
When you are ready, 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("devtools")
devtools::install_github("2DegreesInvesting/r2dii.analysis")- Use
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 – withinstall.packages(c("r2dii.data", "r2dii.match"))– so you can reproduce our examples.
library(r2dii.data)
library(r2dii.match)
library(r2dii.analysis)- Use
r2dii.match::match_name()to identify matches between your loanbook and the asset level data.
matched <- match_name(loanbook_demo, ald_demo) %>%
prioritize()- Use
target_sda()to calculate SDA targets of CO2 emissions.
matched %>%
target_sda(
ald = ald_demo,
co2_intensity_scenario = co2_intensity_scenario_demo
)
#> Warning: Removing ald rows where `emission_factor` is NA
#> # A tibble: 508 x 4
#> sector year emission_factor_metric emission_factor_value
#> <chr> <dbl> <chr> <dbl>
#> 1 automotive 2002 projected 0.397
#> 2 automotive 2003 projected 0.389
#> 3 automotive 2004 projected 0.381
#> 4 automotive 2005 projected 0.373
#> 5 automotive 2006 projected 0.365
#> 6 automotive 2007 projected 0.358
#> 7 automotive 2008 projected 0.350
#> 8 automotive 2009 projected 0.342
#> 9 automotive 2010 projected 0.334
#> 10 automotive 2011 projected 0.326
#> # … with 498 more rows- Use
target_market_shareto calculate market-share scenario targets at the portfolio level:
matched %>%
target_market_share(
ald = ald_demo,
scenario = scenario_demo_2020,
region_isos = region_isos_demo
)
#> # A tibble: 3,642 x 8
#> sector technology year region scenario_source metric production
#> <chr> <chr> <int> <chr> <chr> <chr> <dbl>
#> 1 automotive electric 2020 global demo_2020 projected 324592.
#> 2 automotive electric 2020 global demo_2020 target_cps 324592.
#> 3 automotive electric 2020 global demo_2020 target_sds 324592.
#> 4 automotive electric 2020 global demo_2020 target_sps 324592.
#> 5 automotive electric 2021 global demo_2020 projected 339656.
#> 6 automotive electric 2021 global demo_2020 target_cps 329191.
#> 7 automotive electric 2021 global demo_2020 target_sds 352505.
#> 8 automotive electric 2021 global demo_2020 target_sps 330435.
#> 9 automotive electric 2022 global demo_2020 projected 354720.
#> 10 automotive electric 2022 global demo_2020 target_cps 333693.
#> # … with 3,632 more rows, and 1 more variable: technology_share <dbl>- Or at the company level:
matched %>%
target_market_share(
ald = ald_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: 14,754 x 9
#> sector technology year region scenario_source name_ald metric production
#> <chr> <chr> <int> <chr> <chr> <chr> <chr> <dbl>
#> 1 automo… electric 2020 global demo_2020 toyota mot… proje… 324592.
#> 2 automo… electric 2020 global demo_2020 toyota mot… targe… 324592.
#> 3 automo… electric 2020 global demo_2020 toyota mot… targe… 324592.
#> 4 automo… electric 2020 global demo_2020 toyota mot… targe… 324592.
#> 5 automo… electric 2021 global demo_2020 toyota mot… proje… 339656.
#> 6 automo… electric 2021 global demo_2020 toyota mot… targe… 329191.
#> 7 automo… electric 2021 global demo_2020 toyota mot… targe… 352505.
#> 8 automo… electric 2021 global demo_2020 toyota mot… targe… 330435.
#> 9 automo… electric 2022 global demo_2020 toyota mot… proje… 354720.
#> 10 automo… electric 2022 global demo_2020 toyota mot… targe… 333693.
#> # … with 14,744 more rows, and 1 more variable: technology_share <dbl>The target_*() functions provide shortcuts for common operations. They
wrap some utility functions that you may also use directly:
- Use
join_ald_scenario()to join a matched dataset to the relevant scenario data, and to pick assets in the relevant regions.
loanbook_joined_to_ald_scenario <- matched %>%
join_ald_scenario(
ald = ald_demo,
scenario = scenario_demo_2020,
region_isos = region_isos_demo
)- Use
summarize_weighted_production()with different grouping arguments to calculate scenario-targets:
# portfolio level
loanbook_joined_to_ald_scenario %>%
summarize_weighted_production(scenario, tmsr, smsp, region)
#> # A tibble: 702 x 9
#> sector_ald technology year scenario tmsr smsp region weighted_production
#> <chr> <chr> <int> <chr> <dbl> <dbl> <chr> <dbl>
#> 1 automotive electric 2020 cps 1 0 global 324592.
#> 2 automotive electric 2020 sds 1 0 global 324592.
#> 3 automotive electric 2020 sps 1 0 global 324592.
#> 4 automotive electric 2021 cps 1.12 0.00108 global 339656.
#> 5 automotive electric 2021 sds 1.16 0.00653 global 339656.
#> 6 automotive electric 2021 sps 1.14 0.00137 global 339656.
#> 7 automotive electric 2022 cps 1.24 0.00213 global 354720.
#> 8 automotive electric 2022 sds 1.32 0.0131 global 354720.
#> 9 automotive electric 2022 sps 1.29 0.00273 global 354720.
#> 10 automotive electric 2023 cps 1.35 0.00316 global 369784.
#> # … with 692 more rows, and 1 more variable: weighted_technology_share <dbl>
# company level
loanbook_joined_to_ald_scenario %>%
summarize_weighted_production(scenario, tmsr, smsp, region, name_ald)
#> # A tibble: 9,036 x 10
#> sector_ald technology year scenario tmsr smsp region name_ald
#> <chr> <chr> <int> <chr> <dbl> <dbl> <chr> <chr>
#> 1 automotive electric 2020 cps 1 0 global toyota motor corp
#> 2 automotive electric 2020 sds 1 0 global toyota motor corp
#> 3 automotive electric 2020 sps 1 0 global toyota motor corp
#> 4 automotive electric 2021 cps 1.12 0.00108 global toyota motor corp
#> 5 automotive electric 2021 sds 1.16 0.00653 global toyota motor corp
#> 6 automotive electric 2021 sps 1.14 0.00137 global toyota motor corp
#> 7 automotive electric 2022 cps 1.24 0.00213 global toyota motor corp
#> 8 automotive electric 2022 sds 1.32 0.0131 global toyota motor corp
#> 9 automotive electric 2022 sps 1.29 0.00273 global toyota motor corp
#> 10 automotive electric 2023 cps 1.35 0.00316 global toyota motor corp
#> # … with 9,026 more rows, and 2 more variables: weighted_production <dbl>,
#> # weighted_technology_share <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.