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r2dii.match

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These tools implement in R a fundamental part of the software PACTA (Paris Agreement Capital Transition Assessment), which is a free tool that calculates the alignment between financial portfolios and climate scenarios (https://2degrees-investing.org/). Financial institutions use PACTA to study how their capital allocation impacts the climate. This package matches data from financial portfolios to asset level data from market-intelligence databases (e.g. power plant capacities, emission factors, etc.). This is the first step to assess if a financial portfolio aligns with climate goals.

Installation

Install the released version of r2dii.match from CRAN with:

# install.packages("r2dii.match")

Or install the development version of r2dii.match from GitHub with:

# install.packages("devtools")
devtools::install_github("2DegreesInvesting/r2dii.match")

How to raise an issue?

Example

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

Matching is achieved in two main steps:

1. Run fuzzy matching

match_name() will extract all unique counterparty names from the columns: direct_loantaker, ultimate_parent or intermediate_parent* and run fuzzy matching against all company names in the abcd:

match_result <- match_name(loanbook_demo, abcd_demo)
match_result 
#> # A tibble: 410 × 28
#>    id_loan id_direct_loantak… name_direct_loa… id_intermediate… name_intermedia…
#>    <chr>   <chr>              <chr>            <chr>            <chr>           
#>  1 L1      C294               Yuamen Xinneng … <NA>             <NA>            
#>  2 L3      C292               Yuama Ethanol L… IP5              Yuama Inc.      
#>  3 L3      C292               Yuama Ethanol L… IP5              Yuama Inc.      
#>  4 L5      C305               Yukon Energy Co… <NA>             <NA>            
#>  5 L5      C305               Yukon Energy Co… <NA>             <NA>            
#>  6 L6      C304               Yukon Developme… <NA>             <NA>            
#>  7 L6      C304               Yukon Developme… <NA>             <NA>            
#>  8 L8      C303               Yueyang City Co… <NA>             <NA>            
#>  9 L9      C301               Yuedxiu Corp One IP10             Yuedxiu Group   
#> 10 L10     C302               Yuexi County AA… <NA>             <NA>            
#> # … with 400 more rows, and 23 more variables: id_ultimate_parent <chr>,
#> #   name_ultimate_parent <chr>, loan_size_outstanding <dbl>,
#> #   loan_size_outstanding_currency <chr>, loan_size_credit_limit <dbl>,
#> #   loan_size_credit_limit_currency <chr>, sector_classification_system <chr>,
#> #   sector_classification_input_type <chr>,
#> #   sector_classification_direct_loantaker <dbl>, fi_type <chr>,
#> #   flag_project_finance_loan <chr>, name_project <lgl>, …

2. Prioritize validated matches

The user should then manually validate the output of [match_name()], ensuring that the value of the column score is equal to 1 for perfect matches only.

Once validated, the prioritize() function, will choose only the valid matches, prioritizing (by default) direct_loantaker matches over ultimate_parent matches:

prioritize(match_result)
#> # A tibble: 216 × 28
#>    id_loan id_direct_loantak… name_direct_loa… id_intermediate… name_intermedia…
#>    <chr>   <chr>              <chr>            <chr>            <chr>           
#>  1 L6      C304               Yukon Developme… <NA>             <NA>            
#>  2 L13     C297               Yuba City Cogen… <NA>             <NA>            
#>  3 L20     C287               Ytl Powerseraya… <NA>             <NA>            
#>  4 L21     C286               Ytl Power Inter… <NA>             <NA>            
#>  5 L22     C285               Ytl Corp Bhd     <NA>             <NA>            
#>  6 L23     C283               Ypic Internatio… <NA>             <NA>            
#>  7 L24     C282               Ypfb Corporacion <NA>             <NA>            
#>  8 L25     C281               Ypf Sa           <NA>             <NA>            
#>  9 L26     C280               Ypf Energia Ele… <NA>             <NA>            
#> 10 L27     C278               Younicos Ag      <NA>             <NA>            
#> # … with 206 more rows, and 23 more variables: id_ultimate_parent <chr>,
#> #   name_ultimate_parent <chr>, loan_size_outstanding <dbl>,
#> #   loan_size_outstanding_currency <chr>, loan_size_credit_limit <dbl>,
#> #   loan_size_credit_limit_currency <chr>, sector_classification_system <chr>,
#> #   sector_classification_input_type <chr>,
#> #   sector_classification_direct_loantaker <dbl>, fi_type <chr>,
#> #   flag_project_finance_loan <chr>, name_project <lgl>, …

The result is a dataset with identical columns to the input loanbook, and added columns bridging all matched loans to their abcd counterpart.

Get started.

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.