r2dii.analysis

January 12, 2026 · View on GitHub

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). 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("pak")
pak::pak("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$ \text{is} \text{NA}
#> # \text{A} \text{tibble}: 220  \times  6
#>    \text{sector}  \text{year} \text{region}             \text{scenario\_source} \text{emission\_factor\_metric}
#>    <\text{chr}>  <\text{dbl}> <\text{chr}>              <\text{chr}>           <\text{chr}>                 
#>  1 \text{cement}  2020 \text{advanced} \text{economies} \text{demo\_2020}       \text{projected}             
#>  2 \text{cement}  2020 \text{developing} \text{asia}    \text{demo\_2020}       \text{projected}             
#>  3 \text{cement}  2020 \text{global}             \text{demo\_2020}       \text{projected}             
#>  4 \text{cement}  2021 \text{advanced} \text{economies} \text{demo\_2020}       \text{projected}             
#>  5 \text{cement}  2021 \text{developing} \text{asia}    \text{demo\_2020}       \text{projected}             
#>  6 \text{cement}  2021 \text{global}             \text{demo\_2020}       \text{projected}             
#>  7 \text{cement}  2022 \text{advanced} \text{economies} \text{demo\_2020}       \text{projected}             
#>  8 \text{cement}  2022 \text{developing} \text{asia}    \text{demo\_2020}       \text{projected}             
#>  9 \text{cement}  2022 \text{global}             \text{demo\_2020}       \text{projected}             
#> 10 \text{cement}  2023 \text{advanced} \text{economies} \text{demo\_2020}       \text{projected}             
#> # ℹ 210 \text{more} \text{rows}
#> # ℹ 1 \text{more} \text{variable}: \text{emission\_factor\_value} <\text{dbl}>
$``

- Use `target_market_share` to calculate market-share scenario targets
  at the portfolio level:

``` r
matched %>%
  target_market_share(
    abcd = abcd_demo,
    scenario = scenario_demo_2020,
    region_isos = region_isos_demo
  )
#> # A tibble: 1,210 × 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    148314.
#>  7 automotive electric    2021 global demo_2020       target_sds    161823.
#>  8 automotive electric    2021 global demo_2020       target_sps    149035.
#>  9 automotive electric    2022 global demo_2020       projected     149310.
#> 10 automotive electric    2022 global demo_2020       target_cps    150923.
#> # ℹ 1,200 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$?
#> # \text{A} \text{tibble}: 37{,}349  \times  11
#>    \text{sector}    \text{technology}  \text{year} \text{region} \text{scenario\_source} \text{name\_abcd} \text{metric} \text{production}
#>    <\text{chr}>     <\text{chr}>      <\text{int}> <\text{chr}>  <\text{chr}>           <\text{chr}>     <\text{chr}>       <\text{dbl}>
#>  1 \text{automoti}… \text{electric}    2020 \text{global} \text{demo\_2020}       \text{Bahr}      \text{proje}…          0
#>  2 \text{automoti}… \text{electric}    2020 \text{global} \text{demo\_2020}       \text{Bahr}      \text{targe}…          0
#>  3 \text{automoti}… \text{electric}    2020 \text{global} \text{demo\_2020}       \text{Bahr}      \text{targe}…          0
#>  4 \text{automoti}… \text{electric}    2020 \text{global} \text{demo\_2020}       \text{Bahr}      \text{targe}…          0
#>  5 \text{automoti}… \text{electric}    2020 \text{global} \text{demo\_2020}       \text{Beier}, \text{B}… \text{proje}…          0
#>  6 \text{automoti}… \text{electric}    2020 \text{global} \text{demo\_2020}       \text{Beier}, \text{B}… \text{targe}…          0
#>  7 \text{automoti}… \text{electric}    2020 \text{global} \text{demo\_2020}       \text{Beier}, \text{B}… \text{targe}…          0
#>  8 \text{automoti}… \text{electric}    2020 \text{global} \text{demo\_2020}       \text{Beier}, \text{B}… \text{targe}…          0
#>  9 \text{automoti}… \text{electric}    2020 \text{global} \text{demo\_2020}       \text{Bellini},… \text{proje}…          0
#> 10 \text{automoti}… \text{electric}    2020 \text{global} \text{demo\_2020}       \text{Bellini},… \text{targe}…          0
#> # ℹ 37{,}339 \text{more} \text{rows}
#> # ℹ 3 \text{more} \text{variables}: \text{technology\_share} <\text{dbl}>, \text{scope} <\text{chr}>,
#> #   \text{percentage\_of\_initial\_production\_by\_scope} <\text{dbl}>
$``

[Get
started](https://rmi-pacta.github.io/r2dii.analysis/articles/r2dii-analysis.html).

## Funding

This project has received funding from the [European Union LIFE
program](https://wayback.archive-it.org/12090/20210412123959/https://ec.europa.eu/easme/en/)
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.