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")- 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, abcd_demo) %>%
prioritize()- Use
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>- Use
target_market_shareto 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>- Or at the company level:
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>The target_*() functions provide shortcuts for common operations. They
wrap some utility functions that you may also use directly:
- Use
join_abcd_scenario()to join a matched dataset to the relevant scenario data, and to pick assets in the relevant regions.
loanbook_joined_to_abcd_scenario <- matched %>%
join_abcd_scenario(
abcd = abcd_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_abcd_scenario %>%
summarize_weighted_production(scenario, tmsr, smsp, region)
#> # A tibble: 756 × 9
#> sector_abcd technology year scenario tmsr smsp region
#> <chr> <chr> <int> <chr> <dbl> <dbl> <chr>
#> 1 automotive electric 2020 cps 1 0 global
#> 2 automotive electric 2020 sds 1 0 global
#> 3 automotive electric 2020 sps 1 0 global
#> 4 automotive electric 2021 cps 1.12 0.00108 global
#> 5 automotive electric 2021 sds 1.16 0.00653 global
#> 6 automotive electric 2021 sps 1.14 0.00137 global
#> 7 automotive electric 2022 cps 1.24 0.00213 global
#> 8 automotive electric 2022 sds 1.32 0.0131 global
#> 9 automotive electric 2022 sps 1.29 0.00273 global
#> 10 automotive electric 2023 cps 1.35 0.00316 global
#> # ℹ 746 more rows
#> # ℹ 2 more variables: weighted_production <dbl>,
#> # weighted_technology_share <dbl>
# company level
loanbook_joined_to_abcd_scenario %>%
summarize_weighted_production(scenario, tmsr, smsp, region, name_abcd)
#> # A tibble: 13,023 × 10
#> sector_abcd technology year scenario tmsr smsp region name_abcd
#> <chr> <chr> <int> <chr> <dbl> <dbl> <chr> <chr>
#> 1 automotive electric 2020 cps 1 0 global Bernardi, Bernardi …
#> 2 automotive electric 2020 cps 1 0 global Christiansen PLC
#> 3 automotive electric 2020 cps 1 0 global Donati, Donati e Do…
#> 4 automotive electric 2020 cps 1 0 global DuBuque-DuBuque
#> 5 automotive electric 2020 cps 1 0 global Ferrari-Ferrari SPA
#> 6 automotive electric 2020 cps 1 0 global Ferry and Sons
#> 7 automotive electric 2020 cps 1 0 global Goyette-Goyette
#> 8 automotive electric 2020 cps 1 0 global Guerra, Guerra e Gu…
#> 9 automotive electric 2020 cps 1 0 global Gutkowski, Gutkowsk…
#> 10 automotive electric 2020 cps 1 0 global Hilpert, Hilpert an…
#> # ℹ 13,013 more rows
#> # ℹ 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.