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.
Before you install r2dii.match you may want to:
When you are ready, install the released version of r2dii.match from CRAN with:
Or install the development version of r2dii.match from GitHub with:
Matching is achieved in two main steps:
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 ald:
match_result <- match_name(loanbook_demo, ald_demo)
match_result
#> # A tibble: 502 x 27
#> id_loan id_direct_loant… name_direct_loa… id_intermediate… name_intermedia…
#> <chr> <chr> <chr> <chr> <chr>
#> 1 L170 C203 Tesla Inc <NA> <NA>
#> 2 L180 C217 Weichai Power C… <NA> <NA>
#> 3 L181 C218 Wheego <NA> <NA>
#> 4 L195 C313 Zhengzhou Yuton… <NA> <NA>
#> 5 L174 C211 Tvr <NA> <NA>
#> 6 L198 C317 Ziyang Nanjun <NA> <NA>
#> 7 L193 C310 Zamyad <NA> <NA>
#> 8 L165 C195 Sunwin Bus <NA> <NA>
#> 9 L154 C171 Shandong Tangju… <NA> <NA>
#> 10 L164 C193 Subaru Corp <NA> <NA>
#> # … with 492 more rows, and 22 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>,
#> # lei_direct_loantaker <lgl>, isin_direct_loantaker <lgl>, id_2dii <chr>,
#> # level <chr>, sector <chr>, sector_ald <chr>, name <chr>, name_ald <chr>,
#> # score <dbl>, source <chr>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: 267 x 27
#> id_loan id_direct_loant… name_direct_loa… id_intermediate… name_intermedia…
#> <chr> <chr> <chr> <chr> <chr>
#> 1 L151 C168 Shaanxi Auto <NA> <NA>
#> 2 L152 C169 Shandong Auto <NA> <NA>
#> 3 L153 C170 Shandong Kama <NA> <NA>
#> 4 L154 C171 Shandong Tangju… <NA> <NA>
#> 5 L155 C173 Shanghai Automo… <NA> <NA>
#> 6 L156 C176 Shanxi Dayun <NA> <NA>
#> 7 L157 C178 Shenyang Polars… <NA> <NA>
#> 8 L158 C180 Shuanghuan Auto <NA> <NA>
#> 9 L159 C182 Sichuan Auto <NA> <NA>
#> 10 L160 C184 Singulato <NA> <NA>
#> # … with 257 more rows, and 22 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>,
#> # lei_direct_loantaker <lgl>, isin_direct_loantaker <lgl>, id_2dii <chr>,
#> # level <chr>, sector <chr>, sector_ald <chr>, name <chr>, name_ald <chr>,
#> # score <dbl>, source <chr>The result is a dataset with identical columns to the input loanbook, and added columns bridging all matched loans to their ald counterpart.