Preparatory Steps

This section provides an overview of the preparatory steps that need to be taken before running the PACTA for Banks analysis. It includes information on the required input data sets and the required software.

Required Input Data Sets

The PACTA for Banks analysis requires a number of input data sets to run. Some of these can be obtained from external sources, while others need to be prepared by the user. Furthermore, some of the input data sets are optional.

The main input data sets required for the analysis are the following:

Asset-based Company Data (ABCD)

required input
external source
XLSX file

This data set provides information on the production profiles and emission intensities of companies active in the following real economy sectors: Automotive (light-duty vehicles) manufacturing, aviation, cement production, coal mining, upstream oil & gas extraction, power generation, and steel production. The ABCD is typically obtained from third party data providers. However, it is possible to prepare the ABCD yourself or complement an external data set with entries that may not be covered out of the box.

The ABCD data set must be an XLSX file and contains the following columns:

company_id
emission_factor
emission_factor_unit
is_ultimate_owner
lei
name_company
plant_location
production
production_unit
sector
technology
year

While PACTA is data agnostic and allows using data from any provider that offers the appropriate format, one option to obtain ABCD for this analysis is to buy the data from the data provider Asset Impact. Further information on how to obtain ABCD for PACTA and documentation of the individual sectors and data points can be found on the Asset Impact website.

Scenario Data

required input(s)
external source
CSV file(s)

The scenario data set provides information on the trajectories of technologies/fuel types and of emission intensity pathways for each of (or a subset of) the sectors covered in PACTA.

For sectors with technology level trajectories, the data set provides the TMSR and SMSP pathways based on the Market Share Approach, an allocation rule that implies all companies active in a sector have to adjust their production in a way that keeps market shares constant and solves for the aggregate climate transition scenario. For more information on how to calculate the TMSR and the SMSP, see the PACTA for Banks documentation.

The target market share scenario data set must be a CSV file and contains the following columns:

region
scenario
scenario_source
sector
smsp
technology
tmsr
year

For sectors that do not have technology level pathways, PACTA uses the Sectoral Decarbonization Approach (SDA), an allocation rule that implies that all companies in a sector have to converge their physical emission intensity at a future scenario value - e.g. in the year 2050. This implies that more polluting companies have to reduce their physical emissions intensity more drastically than companies using cleaner technology. It does not have any direct implications on the amount of units produced by any company. For further information on calculating the SDA in PACTA, please see the PACTA for Banks documentation.

The SDA scenario data set must be a CSV file and contains the following columns:

region
scenario
scenario_source
sector
smsp
technology
tmsr
year

While the raw input values of the scenarios are based on models from external third party organisations - such as the World Energy Outlook by the International Energy Agency (IEA), the Global Energy and Climate Outlook by the Joint Research Center of the European Commission (JRC), or the One Earth Climate Model by the Institute for Sustainable Futures (ISF) - the input data set for PACTA must be prepared using additional steps, which are documented publicly on the following GitHub repositories:

Since RMI has taken over stewardship of PACTA, the prepared scenario files can also be accessed as CSV downloads in the “Methodology and Supporting Documents” section of the PACTA website. The files are usually updated annually based on the latest scenario publications and as a general rule, the year of the publication defines the initial year of the scenario data set. This is commonly also used as the start year of the analysis.

Raw Loan Book

required input
self-prepared
CSV file

The raw loan book is the financial data set that you would like to analyze. It contains information on the loans that you have provided to companies. As a bank, the data required will be available in your internal systems.

The raw loan book must be prepared as CSV files and contain at a minimum the following columns:

id_direct_loantaker
id_loan
id_ultimate_parent
isin_direct_loantaker
lei_direct_loantaker
loan_size_credit_limit
loan_size_credit_limit_currency
loan_size_outstanding
loan_size_outstanding_currency
name_direct_loantaker
name_ultimate_parent
sector_classification_direct_loantaker
sector_classification_system

For detailed descriptions of how to prepare raw loan books, see the “Training Materials” section of the PACTA for Banks documentation. The “User Guide 2”, the “Data Dictionary”, and the “Loan Book Template” files can all be helpful in preparing your data.

Misclassified Loans (optional)

optional input
self-prepared
CSV file

The user can provide a list of loans that have been misclassified in the raw loan book. The aim here is specifically to remove false positives, that is, loans that are classified in scope of one of the PACTA sectors, but where manual research shows that the companies do not actually operate within the PACTA scope. Such a false positive may be due to erroneous data entry in the raw loan book, for example. Removing these loans from the falsely indicated sector in the calculation of the match success rate will give a more accurate picture of what match success rate can really be reached.

Manual Sector Classification (optional)

optional input
self-prepared
CSV file

In case the user cannot obtain sector classification codes of any of the classification systems featured in r2dii.data::sector_classifications (currently the following classification systems are featured: GICS, ISIC, NACE, NAICS, PSIC, SIC), the user can provide a manually created sector classification file for matching the loan book to the ABCD instead. Generally, any such manually prepared sector classification file must follow the format of r2dii.data::sector_classifications. It is recommended to use the built in sector classifications if possible, as mapping your own sector classification to the PACTA sectors can be complex and time consuming.

Required Software

Using the r2dii.* suite of packages for the PACTA for Banks analysis requires the following software to be installed on your system:

R (version 4.1.0 or higher)

R is the programming language that the r2dii.* suite of packages is written in. You can download R from the Comprehensive R Archive Network (CRAN).

RStudio (optional)

RStudio is an integrated development environment (IDE) for R developed by Posit. It is not strictly required to run the analysis, but it can be helpful for managing your project and running the analysis. Generally, RStudio is very widely used among the R community and probably the easiest way to interact with most R tools, such as the r2dii.* suite of packages. RStudio Desktop is an open source tool and free of charge. You can download RStudio from the Posit RStudio website.

`r2dii.*` suite of R packages

The r2dii.* suite of R packages is the main software tool that you will use to run the PACTA for Banks analysis.

You can install the r2dii.* suite of R packages from any CRAN mirror by running the following command in R:

install.packages(c("r2dii.data", "r2dii.match", "r2dii.analysis", "r2dii.plot"))

You can install the development versions of the r2dii.* suite of R packages from GitHub with:

# install.packages("pak")
pak::pak(c("RMI-PACTA/r2dii.data", "RMI-PACTA/r2dii.match", "RMI-PACTA/r2dii.analysis", "RMI-PACTA/r2dii.plot"))

We use the pak package as a simple tool to install packages from GitHub.

Connecting to GitHub from RStudio

Note that if you choose to install the r2dii.* suite of R packages from GitHub, you will need to have:

registered a GitHub account,
git installed locally,
set up credentials so that RStudio can communicate with GitHub.

You can find more information on how to do this using the following resources:

Happy Git and GitHub for the useR is a great and comprehensive resource that takes you through the process of setting up git and GitHub with RStudio, including registering a GitHub account, installing git, and connecting RStudio to GitHub.
Additional information on managing your GitHub connection from within RStudio can be found in the usethis package documentation, for example on managing git credentials.

If you only plan to use GitHub to install this package or other packages as shown above, you will not have to have a deep understanding of all the git commands, so there is no need to be overwhelmed by the complexity of git.

Required R packages

The r2dii.* suite of R packages depends on a number of other R packages. These dependencies will be installed automatically when you install the r2dii.* suite of R packages. The required packages are:

data.table, dplyr, ggplot2, ggrepel, glue, lifecycle, magrittr, purrr, r2dii.data, rlang, scales, stringdist, stringi, stringr, tibble, tidyr, tidyselect, zoo

FAQ

How do I install the `r2dii.*` suite of R packages?

The most common ways to install R packages are via CRAN or GitHub. Public institutions often have restrictions on the installation of packages from GitHub, so you may need to install the package from CRAN. In some cases, your institution may mirror CRAN in their internal application registry, so you may need to install the package from there. Should you have any issues with the installation from the internal application registry, it is best to reach out to your IT department. If you cannot obtain the package in any of these ways, please reach out to the package maintainers directly for exploring other options.

How do I install the required R packages?

In principle, all dependencies required to run the r2dii.* suite of R packages will be installed automatically when you install the packages. However, if you encounter any issues with the installation of the required packages, you can install them manually by running the following command in R, where ... should be replaced with the package names from the list above, separated by commas:

install.packages(c(...))

Preparatory Steps