Title:	Measure Climate Scenario Alignment of Corporate Loans
Version:	0.5.2
Description:	These tools help you to assess if a corporate lending portfolio aligns with climate goals. They summarize key climate indicators attributed to the portfolio (e.g. production, emission factors), and calculate alignment 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 decisions align with climate change mitigation goals.
License:	MIT + file LICENSE
URL:	https://rmi-pacta.github.io/r2dii.analysis/, https://github.com/RMI-PACTA/r2dii.analysis
BugReports:	https://github.com/RMI-PACTA/r2dii.analysis/issues
Depends:	R (≥ 3.5)
Imports:	dplyr (≥ 0.8.5), glue, lifecycle, magrittr, r2dii.data (≥ 0.4.0), rlang (≥ 0.1.2), tidyr, tidyselect, zoo
Suggests:	cli, covr, r2dii.match, readr, rmarkdown, roxygen2, spelling, testthat (≥ 3.0.0), waldo, withr
Config/testthat/edition:	3
Config/Needs/website:	rmi-pacta/pacta.pkgdown.rmitemplate
Encoding:	UTF-8
Language:	en-US
RoxygenNote:	7.3.2
LazyData:	true
NeedsCompilation:	no
Packaged:	2025-06-17 13:29:41 UTC; cjrmi
Author:	Jacob Kastl [aut, cre, ctr], Alex Axthelm [aut, ctr], Jackson Hoffart [aut, ctr], Mauro Lepore [aut, ctr], Klaus Hogedorn [aut], Nicky Halterman [aut], RMI [cph, fnd]
Maintainer:	Jacob Kastl <jacob.kastl@gmail.com>
Repository:	CRAN
Date/Publication:	2025-06-17 23:10:07 UTC

r2dii.analysis: Measure Climate Scenario Alignment of Corporate Loans

Description

These tools help you to assess if a corporate lending portfolio aligns with climate goals. They summarize key climate indicators attributed to the portfolio (e.g. production, emission factors), and calculate alignment 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 decisions align with climate change mitigation goals.

Author(s)

Maintainer: Jacob Kastl jacob.kastl@gmail.com (ORCID) [contractor]

Authors:

• Alex Axthelm aaxthelm@rmi.org (ORCID) [contractor]

• Jackson Hoffart jackson.hoffart@gmail.com (ORCID) [contractor]

• Mauro Lepore maurolepore@gmail.com (ORCID) [contractor]

• Klaus Hogedorn klaus@2degrees-investing.org

• Nicky Halterman nicholas.i.halterman@gmail.com

Other contributors:

• RMI PACTA4banks@rmi.org [copyright holder, funder]

See Also

Useful links:

• https://rmi-pacta.github.io/r2dii.analysis/

• https://github.com/RMI-PACTA/r2dii.analysis

• Report bugs at https://github.com/RMI-PACTA/r2dii.analysis/issues

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

Value

The result of calling rhs(lhs).

Data Dictionary

Description

A table of column names and descriptions of data frames used or exported by the functions in this package.

Usage

data_dictionary

Format

data_dictionary

dataset

Name of the dataset

column

Name of the column

typeof

Type of the column

definition

Definition of the column

Examples

data_dictionary

Join a data-loanbook object to the abcd and scenario

Description

This function was deprecated because it is not required as a user-facing function for PACTA for Banks. It is still used internally though. All relevant outputs of the PACTA for Banks analysis can be obtained using the target_market_share() and target_sda() functions.

join_abcd_scenario() is a simple wrapper of several calls to ⁠dplyr::join_*()⁠, forming the master dataset to be used in later steps of the analysis.

Usage

join_abcd_scenario(
  data,
  abcd,
  scenario,
  region_isos = r2dii.data::region_isos,
  add_green_technologies = FALSE
)

Arguments

Value

Returns a fully joined data frame, linking portfolio, abcd and scenario.

See Also

Other utility functions: summarize_weighted_production()

Examples

library(r2dii.data)
library(r2dii.match)

valid_matches <- match_name(loanbook_demo, abcd_demo) %>%
  # WARNING: Remember to validate matches (see `?prioritize`)
  prioritize()

valid_matches %>%
  join_abcd_scenario(
    abcd = abcd_demo,
    scenario = scenario_demo_2020,
    region_isos = region_isos_demo
  )

Summaries based on the weight of each loan per sector per year

Description

These functions (summarize_weighted_production() and summarize_weighted_percent_change()) were deprecated because they are not required as a user-facing function for PACTA for Banks. They are still used internally though. All relevant outputs of the PACTA for Banks analysis can be obtained using the target_market_share() and target_sda() functions.

Based on on the weight of each loan per sector per year, summarize_weighted_production() and summarize_weighted_percent_change() summarize the production and percent-change, respectively.

Usage

summarize_weighted_production(data, ..., use_credit_limit = FALSE)

summarize_weighted_percent_change(data, ..., use_credit_limit = FALSE)

Arguments

Value

A tibble with the same groups as the input (if any) and columns: sector, technology, and year; and weighted_production or weighted_production for summarize_weighted_production() and summarize_weighted_percent_change(), respectively.

Warning

The percent-change analysis excludes companies with 0 production. percent-change is undefined for companies that have no initial production; including such companies would cause percent-change percentage to be infinite, which is wrong.

See Also

join_abcd_scenario().

Other utility functions: join_abcd_scenario()

Examples

library(r2dii.data)
library(r2dii.match)

loanbook <- head(loanbook_demo, 150)
abcd <- head(abcd_demo, 100)
master <- loanbook %>%
  match_name(abcd) %>%
  prioritize() %>%
  join_abcd_scenario(
    abcd = abcd,
    scenario = scenario_demo_2020,
    region_isos = region_isos_demo
    ) %>%
  dplyr::filter(production != 0)

summarize_weighted_production(master)

summarize_weighted_production(master, use_credit_limit = TRUE)

summarize_weighted_percent_change(master)

summarize_weighted_percent_change(master, use_credit_limit = TRUE)

Add targets for production, using the market share approach

Description

This function calculates the portfolio-level production targets, as calculated using the market share approach applied to each relevant climate production forecast.

Usage

target_market_share(
  data,
  abcd,
  scenario,
  region_isos = r2dii.data::region_isos,
  use_credit_limit = FALSE,
  by_company = FALSE,
  weight_production = TRUE,
  increasing_or_decreasing = r2dii.data::increasing_or_decreasing
)

Arguments

Value

A tibble including the summarized columns metric, production, technology_share, percentage_of_initial_production_by_scope and scope. If by_company = TRUE, the output will also have the column name_abcd.

Handling grouped data

This function ignores existing groups and outputs ungrouped data.

See Also

Other functions to calculate scenario targets: target_sda()

Examples

library(r2dii.data)
library(r2dii.match)

loanbook <- head(loanbook_demo, 100)
abcd <- head(abcd_demo, 100)

matched <- loanbook %>%
  match_name(abcd) %>%
  prioritize()

# Calculate targets at portfolio level
matched %>%
  target_market_share(
    abcd = abcd,
    scenario = scenario_demo_2020,
    region_isos = region_isos_demo
    )

# Calculate targets at company level
matched %>%
  target_market_share(
  abcd = abcd,
  scenario = scenario_demo_2020,
  region_isos = region_isos_demo,
  by_company = TRUE
  )

matched %>%
  target_market_share(
    abcd = abcd,
    scenario = scenario_demo_2020,
    region_isos = region_isos_demo,
    # Calculate unweighted targets
    weight_production = FALSE
    )

Add targets for CO₂ emissions per unit production at the portfolio level, using the SDA approach

Description

This function calculates targets of CO₂ emissions per unit production at the portfolio-level, otherwise referred to as "emissions factors". It uses the sectoral-decarbonization approach (SDA) to calculate these targets.

Usage

target_sda(
  data,
  abcd,
  co2_intensity_scenario,
  use_credit_limit = FALSE,
  by_company = FALSE,
  region_isos = r2dii.data::region_isos
)

Arguments

Value

A tibble including the summarized columns emission_factor_metric and emission_factor_value. If by_company = TRUE, the output will also have the column name_abcd.

Handling grouped data

This function ignores existing groups and outputs ungrouped data.

See Also

Other functions to calculate scenario targets: target_market_share()

Examples

library(r2dii.match)
library(r2dii.data)

loanbook <- head(loanbook_demo, 150)
abcd <- head(abcd_demo, 100)

matched <- loanbook %>%
  match_name(abcd) %>%
  prioritize()

# Calculate targets at portfolio level
matched %>%
  target_sda(
   abcd = abcd,
   co2_intensity_scenario = co2_intensity_scenario_demo,
   region_isos = region_isos_demo
   )

# Calculate targets at company level
matched %>%
  target_sda(
   abcd = abcd,
   co2_intensity_scenario = co2_intensity_scenario_demo,
   region_isos = region_isos_demo,
   by_company = TRUE
   )

Tidy eval helpers

Description

To learn more about tidy eval and how to use these tools, check out the Metaprogramming section of Advanced R.

Value

• sym() creates a symbol from a string and syms() creates a list of symbols from a character vector.

• enquo() and enquos() delay the execution of one or several function arguments. enquo() returns a single quoted expression, which is like a blueprint for the delayed computation. enquos() returns a list of such quoted expressions.

• expr() quotes a new expression locally. It is mostly useful to build new expressions around arguments captured with enquo() or enquos(): expr(mean(!!enquo(arg), na.rm = TRUE)).

• as_name() transforms a quoted variable name into a string. Supplying something else than a quoted variable name is an error.

  That's unlike as_label() which also returns a single string but supports any kind of R object as input, including quoted function calls and vectors. Its purpose is to summarise that object into a single label. That label is often suitable as a default name.

  If you don't know what a quoted expression contains (for instance expressions captured with enquo() could be a variable name, a call to a function, or an unquoted constant), then use as_label(). If you know you have quoted a simple variable name, or would like to enforce this, use as_name().