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README.md
In r2dii.analysis: Measure Climate Scenario Alignment of Corporate Loans
r2dii.analysis 
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.
Installation
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")
Example
- 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 – with
install.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()
Add Scenario Targets
- 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: 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_value
- Use
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: 1,790 × 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_scope
- 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: 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_share
Utility Functions
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: 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_share
Funding
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.
Try the r2dii.analysis package in your browser
Any scripts or data that you put into this service are public.
r2dii.analysis documentation built on Nov. 3, 2022, 5:06 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
r2dii.analysis
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.
Installation
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")
Example
- 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()
Add Scenario Targets
- 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: 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_value
- 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: 1,790 × 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_scope
- 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: 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_share
Utility Functions
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: 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_share
Funding
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.
Try the r2dii.analysis package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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