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("devtools")
devtools::install_github("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: 166 × 6
#> sector year region scenario_source emission_factor_met…¹ emiss…²
#> <chr> <dbl> <chr> <chr> <chr> <dbl>
#> 1 cement 2013 advanced economies demo_2020 projected 0.0217
#> 2 cement 2013 developing asia demo_2020 projected 0.0606
#> 3 cement 2013 global demo_2020 projected 0.658
#> 4 cement 2014 advanced economies demo_2020 projected 0.0219
#> 5 cement 2014 developing asia demo_2020 projected 0.0604
#> 6 cement 2014 global demo_2020 projected 0.659
#> 7 cement 2015 advanced economies demo_2020 projected 0.0221
#> 8 cement 2015 developing asia demo_2020 projected 0.0603
#> 9 cement 2015 global demo_2020 projected 0.660
#> 10 cement 2016 advanced economies demo_2020 projected 0.0223
#> # … with 156 more rows, and abbreviated variable names ¹emission_factor_metric,
#> # ²emission_factor_valuetarget_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: 1,790 × 10
#> sector techno…¹ year region scena…² metric produ…³ techn…⁴ scope perce…⁵
#> <chr> <chr> <int> <chr> <chr> <chr> <dbl> <dbl> <chr> <dbl>
#> 1 automotive electric 2020 global demo_2… proje… 324592. 0.0759 sect… 0
#> 2 automotive electric 2020 global demo_2… targe… 324592. 0.0759 sect… 0
#> 3 automotive electric 2020 global demo_2… targe… 324592. 0.0759 sect… 0
#> 4 automotive electric 2020 global demo_2… targe… 324592. 0.0759 sect… 0
#> 5 automotive electric 2021 global demo_2… proje… 339656. 0.0786 sect… 0.00352
#> 6 automotive electric 2021 global demo_2… targe… 329191. 0.0744 sect… 0.00108
#> 7 automotive electric 2021 global demo_2… targe… 352505. 0.0809 sect… 0.00653
#> 8 automotive electric 2021 global demo_2… targe… 330435. 0.0747 sect… 0.00137
#> 9 automotive electric 2022 global demo_2… proje… 354720. 0.0813 sect… 0.00705
#> 10 automotive electric 2022 global demo_2… targe… 333693. 0.0730 sect… 0.00213
#> # … with 1,780 more rows, and abbreviated variable names ¹technology,
#> # ²scenario_source, ³production, ⁴technology_share,
#> # ⁵percentage_of_initial_production_by_scopematched %>%
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: 32,402 × 11
#> sector techno…¹ year region scena…² name_…³ metric produ…⁴ techn…⁵ scope
#> <chr> <chr> <int> <chr> <chr> <chr> <chr> <dbl> <dbl> <chr>
#> 1 automotive electric 2020 global demo_2… toyota… proje… 324592. 0.0759 sect…
#> 2 automotive electric 2020 global demo_2… toyota… targe… 324592. 0.0759 sect…
#> 3 automotive electric 2020 global demo_2… toyota… targe… 324592. 0.0759 sect…
#> 4 automotive electric 2020 global demo_2… toyota… targe… 324592. 0.0759 sect…
#> 5 automotive electric 2021 global demo_2… toyota… proje… 339656. 0.0786 sect…
#> 6 automotive electric 2021 global demo_2… toyota… targe… 329191. 0.0744 sect…
#> 7 automotive electric 2021 global demo_2… toyota… targe… 352505. 0.0809 sect…
#> 8 automotive electric 2021 global demo_2… toyota… targe… 330435. 0.0747 sect…
#> 9 automotive electric 2022 global demo_2… toyota… proje… 354720. 0.0813 sect…
#> 10 automotive electric 2022 global demo_2… toyota… targe… 333693. 0.0730 sect…
#> # … with 32,392 more rows, 1 more variable:
#> # percentage_of_initial_production_by_scope <dbl>, and abbreviated variable
#> # names ¹technology, ²scenario_source, ³name_abcd, ⁴production,
#> # ⁵technology_shareThe target_*() functions provide shortcuts for common
operations. They wrap some utility functions that you may also use
directly:
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
)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: 702 × 9
#> sector_abcd technology year scenario tmsr smsp region weighted…¹ weigh…²
#> <chr> <chr> <int> <chr> <dbl> <dbl> <chr> <dbl> <dbl>
#> 1 automotive electric 2020 cps 1 0 global 324592. 0.0380
#> 2 automotive electric 2020 sds 1 0 global 324592. 0.0380
#> 3 automotive electric 2020 sps 1 0 global 324592. 0.0380
#> 4 automotive electric 2021 cps 1.12 0.00108 global 339656. 0.0393
#> 5 automotive electric 2021 sds 1.16 0.00653 global 339656. 0.0393
#> 6 automotive electric 2021 sps 1.14 0.00137 global 339656. 0.0393
#> 7 automotive electric 2022 cps 1.24 0.00213 global 354720. 0.0406
#> 8 automotive electric 2022 sds 1.32 0.0131 global 354720. 0.0406
#> 9 automotive electric 2022 sps 1.29 0.00273 global 354720. 0.0406
#> 10 automotive electric 2023 cps 1.35 0.00316 global 369784. 0.0419
#> # … with 692 more rows, and abbreviated variable names ¹weighted_production,
#> # ²weighted_technology_share
# company level
loanbook_joined_to_abcd_scenario %>%
summarize_weighted_production(scenario, tmsr, smsp, region, name_abcd)
#> # A tibble: 9,036 × 10
#> sector_a…¹ techn…² year scena…³ tmsr smsp region name_…⁴ weigh…⁵ weigh…⁶
#> <chr> <chr> <int> <chr> <dbl> <dbl> <chr> <chr> <dbl> <dbl>
#> 1 automotive electr… 2020 cps 1 0 global toyota… 324592. 0.0380
#> 2 automotive electr… 2020 sds 1 0 global toyota… 324592. 0.0380
#> 3 automotive electr… 2020 sps 1 0 global toyota… 324592. 0.0380
#> 4 automotive electr… 2021 cps 1.12 0.00108 global toyota… 339656. 0.0393
#> 5 automotive electr… 2021 sds 1.16 0.00653 global toyota… 339656. 0.0393
#> 6 automotive electr… 2021 sps 1.14 0.00137 global toyota… 339656. 0.0393
#> 7 automotive electr… 2022 cps 1.24 0.00213 global toyota… 354720. 0.0406
#> 8 automotive electr… 2022 sds 1.32 0.0131 global toyota… 354720. 0.0406
#> 9 automotive electr… 2022 sps 1.29 0.00273 global toyota… 354720. 0.0406
#> 10 automotive electr… 2023 cps 1.35 0.00316 global toyota… 369784. 0.0419
#> # … with 9,026 more rows, and abbreviated variable names ¹sector_abcd,
#> # ²technology, ³scenario, ⁴name_abcd, ⁵weighted_production,
#> # ⁶weighted_technology_shareThis 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.