Intro
In many cases, users of this package will want to use the outputs of the analyses for further processing, such as additional analyses or making visualizations based on the design guide of their own organisation. To facilitate such additional use cases, but also simplify interpretation of the outputs generated with this package, this data dictionary documents each type of output table in detail, focusing on data types and definitions.
This article is structured based on the output tables generated by
pacta.multi.loanbook and follows the standard flow of the
user experience as much as possible, so it can be read in the same
sequence as the analysis is run.
Tables
The main steps that generate output tables are:
- Diagnostics and coverage
- Standard PACTA analysis
- Aggregated PACTA metrics
Diagnostics
The diagnostics section is split into determining the match success rate of the loan books analysed and inspecting the real economy activity related to the financing made by the banks through the matched loan books. The former is influenced by the quality of the input loan book data and the completeness of the reference production data against which the loan books are matched. The latter, while it depends on a solid match success rate, is mainly driven by the financing decisions and the portfolio allocation made by the banks.
Loan book coverage
dplyr::filter(data_dictionary, .data[["dataset"]] == "summary_statistics_loanbook_coverage")Standard PACTA analysis
The standard PACTA analysis is run across all input banking books,
but produces the same output metrics as known from the
r2dii.* packages. Results are given at portfolio level
grouped by banking book. Beyond the standard output format, tables are
provided that can be used as input for visualizations, for each of the
standard sectors and technologies.
Target Market Share results (all groups)
Target market share results at the portfolio level for each included banking book
dplyr::filter(data_dictionary, .data[["dataset"]] == "tms_results")Sectoral Decarbonization Approach results (all groups)
SDA results at the portfolio level for each included banking book
dplyr::filter(data_dictionary, .data[["dataset"]] == "sda_results")Data tech mix
Results for a given portfolio and sector, tailored to be used in the tech mix chart
dplyr::filter(data_dictionary, .data[["dataset"]] == "data_tech_mix")Data trajectory
Results for a given portfolio, sector and technology, tailored to be used in the volume trajectory chart
dplyr::filter(data_dictionary, .data[["dataset"]] == "data_trajectory")Data emission intensity
Results for a given portfolio and sector, tailored to be used in the emission intensity chart
dplyr::filter(data_dictionary, .data[["dataset"]] == "data_emission_intensity")Companies included
Lists all companies including exposures, that were analysed for the given loan book and that are therefore included in the data to be visualized.
dplyr::filter(data_dictionary, .data[["dataset"]] == "companies_included")Aggregated PACTA metrics
The aggregated PACTA metrics are also run across all input banking
books. The calculations produce the net aggregate alignment metric,
which is defined in the vignettes “Calculation of a company alignment
metric” and “Calculation of
exposure-weighted aggregated alignment metric” and allows producing
the corresponding plots. Results are grouped at the level defined by the
by_group parameter.
Company technology deviation
For each company in the analyzed banking books, shows the deviation
of the technology build-out in the final year of the analysis from the
corresponding allocated scenario value. This is an intermediate result
that is further processed in the calculation of the net aggregate
alignment metric. Only available for sectors, which have technology
level calculations using the target market share, namely
automotive, coal, oil and gas, power.
dplyr::filter(data_dictionary, .data[["dataset"]] == "company_technology_deviation_tms")Company net alignment metric for TMS sectors
For each company in the analyzed banking books, shows the net
aggregate alignment metric for sectors, which have technology level
calculations using the target market share, namely
automotive, coal, oil and gas, power. See
vignette("company_alignment_metric") for methodological
documentation.
dplyr::filter(data_dictionary, .data[["dataset"]] == "company_alignment_net_tms")Disaggregated company buildout/phaseout alignment metric for TMS sectors
For each company in the analyzed banking books, shows the aggregate
alignment metric - disaggregated into its buildout and phaseout
components - for sectors, which have technology level calculations using
the target market share, namely
automotive, coal, oil and gas, power. See
vignette("company_alignment_metric") for methodological
documentation.
dplyr::filter(data_dictionary, .data[["dataset"]] == "company_alignment_bo_po_tms")Company net alignment metric for SDA sectors
For each company in the analyzed banking books, shows the net
aggregate alignment metric for sectors, which have sector level
calculations using the sectoral decarbonization approach (SDA), namely
aviation, cement, steel. See
vignette("company_alignment_metric") for methodological
documentation.
dplyr::filter(data_dictionary, .data[["dataset"]] == "company_alignment_net_sda")Company net aggregate alignment metric with financial exposures
For each company in the analyzed banking books, shows the net
aggregate alignment metric for all available sectors. This table
includes the financial exposure to each of the analyzed parts of the
banking books, split as defined in by_group.
dplyr::filter(data_dictionary, .data[["dataset"]] == "company_exposure_net_aggregate_alignment")Disaggregated company buildout/phaseout alignment metric with financial exposures
For each company in the analyzed banking books, shows the net
aggregate alignment metric - disaggregated by its buildout and phaseout
components - for all sectors that use technology level TMS calculations,
namely automotive, coal, oil and gas, power. This table
includes the financial exposure to each of the analyzed parts of the
banking books, split as defined in by_group. Note that the
financial exposure is not disaggregated, the alignment metric is.
dplyr::filter(data_dictionary, .data[["dataset"]] == "company_exposure_bo_po_aggregate_alignment")Loan book net aggregate alignment metric with financial exposures
For each loan book level group (split as defined in
by_group), shows the net aggregate alignment metric for all
available sectors. This table includes the financial exposure to each of
the analyzed parts of the banking books. Company level results are
aggregated to the loan book level, using their relative financial
exposure as weights.
dplyr::filter(data_dictionary, .data[["dataset"]] == "loanbook_exposure_net_aggregate_alignment")Disaggregated loan book buildout/phaseout alignment metric with financial exposures
For each loan book level group (split as defined in
by_group), shows the net aggregate alignment metric -
disaggregated by its buildout and phaseout components - for all sectors
using technology level TMS calculations, namely
automotive, coal, oil and gas, power. Company level results
are aggregated to the loan book level, using their relative financial
exposure as weights.
dplyr::filter(data_dictionary, .data[["dataset"]] == "loanbook_exposure_bo_po_aggregate_alignment")Input data for Sankey plot
Data set meant to be used as input into
plot_sankey().
dplyr::filter(data_dictionary, .data[["dataset"]] == "data_sankey")Input data for alignment-by-exposure scatter plot
Data set meant to be used as input into
plot_scatter_alignment_exposure().
dplyr::filter(data_dictionary, .data[["dataset"]] == "data_scatter_alignment_exposure")Input data for buildout/phaseout scatter plot
Data set meant to be used as input into
plot_scatter().
dplyr::filter(data_dictionary, .data[["dataset"]] == "data_scatter_sector")