Understanding the data

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

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. If a sector split is applied to the loan book, any companies that are lost in the process are documented for every loan book.

Match success rate

dplyr::filter(data_dictionary, .data[["dataset"]] == "lbk_match_success_rate")
column
typeof
definition
value
<by_group>
character
Any additional descriptor either at the loan level or at the banking book level. This is used to calculate grouped results by additional dimensions of interest, such as types of FIs or types of loans
Any variable name is permissible, that is not already used otherwise. All entries in the banking book should have a corresponding value. NULL is permissible and implies no grouping
sector
character
The sector of the technology
One of the following: 'power', 'automotive', 'coal', 'oil and gas', 'aviation', 'cement', 'steel'
matched
character
Indicates if the matching values are shown for matched or unmatched loans
Must be one of the following: 'Matched' or 'Not matched'
match_n
integer
Number of loans identified for analysis in a given sector in the given banking book that were successfully matched with companies in the production data
Integer value greater or equal to 0
total_n
integer
Total number of loans in the banking book for a given sector
Integer value greater or equal to 0
match_success_rate_rel
double
Share of matched loans in a secftor relative to total number of loans in the sector in the raw input banking book
Numerical value between 0 and 1
match_outstanding
double
Remaining outstanding loan value in the banking book to the all matched companies in the sector
Numerical value greater or equal to 0
total_outstanding
double
Total outstanding loan value in the banking book in a given sector for matched and unmatched loans
Numerical value greater or equal to 0
match_success_outstanding_rel
double
Share of the matched outstanding loan amount in a sector relative to the total outstanding loan amount in that sector
Numerical value between 0 and 1
match_credit_limit
double
Sum value of the credit limit of all matched loans within a sector
Numerical value greater or equal to 0
total_credit_limit
double
Total value of the credit limit of all loans matched or unmatched within a sector
Numerical value greater or equal to 0
match_success_credit_limit_rel
double
Share of the matched amount of credit limit in a sector relative to the total credit limit in that sector
Numerical value between 0 and 1

Loan book coverage

dplyr::filter(data_dictionary, .data[["dataset"]] == "summary_statistics_loanbook_coverage")
column
typeof
definition
value
<by_group>
character
Any additional descriptor either at the loan level or at the banking book level. This is used to calculate grouped results by additional dimensions of interest, such as types of FIs or types of loans
Any variable name is permissible, that is not already used otherwise. All entries in the banking book should have a corresponding value. NULL is permissible and implies no grouping
region
character
The region for which the analysis has been run. Indicates which production assets have been considered and which scenario region is used
Must be a value available in the input scenario data
sector
character
The sector of the technology
One of the following: 'power', 'automotive', 'coal', 'oil and gas', 'aviation', 'cement', 'steel'
total_exposure
double
Remaining outstanding loan value to the all companies in the sector that have some operations within the given region
Numerical value greater or equal to 0
n_companies_matched
integer
Number of companies identified for analysis in the given region and sector. Any matched company is counted regardless of size of exposure
Integer value greater or equal to 0
n_companies_total
integer
Total number of companies in the reference dataset in the given region and sector
Integer value greater or equal to 0
share_companies_matched
double
Share of companies identified for analysis relative to total number of companies in the reference dataset. This is a proxy for which part of the economy of a region is covered by the analysis
Numerical value between 0 and 1
production_financed
double
Sum of production in a sector and region by all companies identfied for analysis. The amount of production is not weighted by exposure
Numerical value greater or equal to 0
production_total
double
Sum of production in a sector and region by all companies in the reference dataset
Numerical value greater or equal to 0
share_production_financed
double
Share of production of companies identified for analysis relative to production by all companies in the reference dataset. This is a proxy for how much of the output of a region is covered by the analysis
Numerical value between 0 and 1

Companies lost in sector split

dplyr::filter(data_dictionary, .data[["dataset"]] == "lost_companies_sector_split")
column
typeof
definition
value
group_id
character
The name of the loan book as defined by the input file name.
Any name is permissible, that is not already used otherwise.
name_abcd
character
The name of the company
The name of the company

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")
column
typeof
definition
value
<by_group>
character
Any additional descriptor either at the loan level or at the banking book level. This is used to calculate grouped results by additional dimensions of interest, such as types of FIs or types of loans
Any variable name is permissible, that is not already used otherwise. All entries in the banking book should have a corresponding value. NULL is permissible and implies no grouping
sector
character
The sector of the technology
One of the following: 'power', 'automotive', 'coal', 'oil and gas'
technology
character
The technology
One of the in-scope PACTA technologies that belong to the sector indicated in 'sector'
year
integer
The year of the data
A year greater or equal to the 'start_year' of the analyis
region
character
The region for which the analysis has been run. Indicates which production assets have been considered and which scenario region is used
Must be a value available in the input scenario data
scenario_source
character
The publication the scenario data is based on
Must be available in the input scenario data. Usually, available sources are: 'weo', 'geco', 'isf'. Usually follows the pattern '<source>_<publication_year>'
metric
character
Indicates if the production related values refer to the projected activities of the underlying counterparty, to the economy wide benchmark, or to allocated levels of activity based on the scenarios
Must be one of the following: 'projected', 'corporate_economy', or 'target_<scenario>'
production
double
The production level of the given 'metric'
Numerical value greater or equal to 0
technology_share
double
The share of the 'production' the given 'technology' relative to all technologies of the corresponding 'sector' for the gien combination of 'group_id', 'region', 'year' and 'metric'
Numerical value between 0 and 1
scope
character
Indicates if the targets for the given technology have been calculated based on the TMSR (technology) or the SMSP (sector). High-carbon technologies that need to decrease have their targets calculated on the technology level, whereas low-carbon technologies that need to increase have them calculated on the sector level
Must be one of: 'technology' or 'sector'
percentage_of_initial_production_by_scope
double
Relative change compared to the start value (by scope). Used for displaying the change in activity over time on a common scale
Numerical value. Can be negative or positive

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")
column
typeof
definition
value
<by_group>
character
Any additional descriptor either at the loan level or at the banking book level. This is used to calculate grouped results by additional dimensions of interest, such as types of FIs or types of loans
Any variable name is permissible, that is not already used otherwise. All entries in the banking book should have a corresponding value. NULL is permissible and implies no grouping
sector
character
The sector of the technology
One of the following: 'aviation', 'cement', 'steel'
year
integer
The year of the data
A year greater or equal to the 'start_year' of the analyis
region
character
The region for which the analysis has been run. Indicates which production assets have been considered and which scenario region is used
Must be a value available in the input scenario data
scenario_source
character
The publication the scenario data is based on
Must be available in the input scenario data. Usually, available sources are: 'weo', 'geco', 'isf'. Usually follows the pattern '<source>_<publication_year>'
emission_factor_metric
character
Indicates if the emission intensity related values refer to the projected activities of the underlying counterparty, to the economy wide benchmark, or to allocated levels of activity based on the scenarios
Must be one of the following: 'projected', 'corporate_economy', or 'target_<scenario>'
emission_factor_value
double
The physical emission intensity level of the given 'emission_factor_metric'
Numerical value greater or equal to 0

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")
column
typeof
definition
value
<by_group>
character
Any additional descriptor either at the loan level or at the banking book level. This is used to calculate grouped results by additional dimensions of interest, such as types of FIs or types of loans
Any variable name is permissible, that is not already used otherwise. All entries in the banking book should have a corresponding value. NULL is permissible and implies no grouping
sector
character
The sector of the technology
One of the following: 'power', 'automotive', 'coal', 'oil and gas'
technology
character
The technology
One of the in-scope PACTA technologies that belong to the sector indicated in 'sector'
year
integer
The year of the data
A year greater or equal to the 'start_year' of the analyis
region
character
The region for which the analysis has been run. Indicates which production assets have been considered and which scenario region is used
Must be a value available in the input scenario data
scenario_source
character
The publication the scenario data is based on
Must be available in the input scenario data. Usually, available sources are: 'weo', 'geco', 'isf'. Usually follows the pattern '<source>_<publication_year>'
metric
character
Indicates if the production related values refer to the projected activities of the underlying counterparty, to the economy wide benchmark, or to allocated levels of activity based on the scenarios
Must be one of the following: 'projected', 'corporate_economy', or 'target_<scenario>'
production
double
The production level of the given 'metric'
Numerical value greater or equal to 0
technology_share
double
The share of the 'production' the given 'technology' relative to all technologies of the corresponding 'sector' for the gien combination of 'group_id', 'region', 'year' and 'metric'
Numerical value between 0 and 1
scope
character
Indicates if the targets for the given technology have been calculated based on the TMSR (technology) or the SMSP (sector). High-carbon technologies that need to decrease have their targets calculated on the technology level, whereas low-carbon technologies that need to increase have them calculated on the sector level
Must be one of: 'technology' or 'sector'
percentage_of_initial_production_by_scope
double
Relative change compared to the start value (by scope). Used for displaying the change in activity over time on a common scale
Numerical value. Can be negative or positive
label
character
Same as 'metric', formatted for display in plot
Must be one of the following: 'projected', 'corporate_economy', or 'target_<scenario>', but formatted for display
label_tech
character
Same as 'technology', formatted for display in plot
One of the in-scope PACTA technologies that belong to the sector indicated in 'sector'
value
double
Same as 'technology_share', for display in plot
Numerical value between 0 and 1

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")
column
typeof
definition
value
<by_group>
character
Any additional descriptor either at the loan level or at the banking book level. This is used to calculate grouped results by additional dimensions of interest, such as types of FIs or types of loans
Any variable name is permissible, that is not already used otherwise. All entries in the banking book should have a corresponding value. NULL is permissible and implies no grouping
sector
character
The sector of the technology
One of the following: 'power', 'automotive', 'coal', 'oil and gas'
technology
character
The technology
One of the in-scope PACTA technologies that belong to the sector indicated in 'sector'
year
integer
The year of the data
A year greater or equal to the 'start_year' of the analyis
region
character
The region for which the analysis has been run. Indicates which production assets have been considered and which scenario region is used
Must be a value available in the input scenario data
scenario_source
character
The publication the scenario data is based on
Must be available in the input scenario data. Usually, available sources are: 'weo', 'geco', 'isf'. Usually follows the pattern '<source>_<publication_year>'
metric
character
Indicates if the production related values refer to the projected activities of the underlying counterparty, to the economy wide benchmark, or to allocated levels of activity based on the scenarios
Must be one of the following: 'projected', 'corporate_economy', or 'target_<scenario>'
production
double
The production level of the given 'metric'
Numerical value greater or equal to 0
technology_share
double
The share of the 'production' the given 'technology' relative to all technologies of the corresponding 'sector' for the gien combination of 'group_id', 'region', 'year' and 'metric'
Numerical value between 0 and 1
scope
character
Indicates if the targets for the given technology have been calculated based on the TMSR (technology) or the SMSP (sector). High-carbon technologies that need to decrease have their targets calculated on the technology level, whereas low-carbon technologies that need to increase have them calculated on the sector level
Must be one of: 'technology' or 'sector'
percentage_of_initial_production_by_scope
double
Relative change compared to the start value (by scope). Used for displaying the change in activity over time on a common scale
Numerical value. Can be negative or positive
label
character
Same as 'metric', formatted for display in plot
Must be one of the following: 'projected', 'corporate_economy', or 'target_<scenario>', but formatted for display
value
double
Same as 'percentage_of_initial_production_by_scope', for display in plot
Numerical value. Can be negative or positive

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")
column
typeof
definition
value
<by_group>
character
Any additional descriptor either at the loan level or at the banking book level. This is used to calculate grouped results by additional dimensions of interest, such as types of FIs or types of loans
Any variable name is permissible, that is not already used otherwise. All entries in the banking book should have a corresponding value. NULL is permissible and implies no grouping
sector
character
The sector of the technology
One of the following: 'aviation', 'cement', 'steel'
year
integer
The year of the data
A year greater or equal to the 'start_year' of the analyis
region
character
The region for which the analysis has been run. Indicates which production assets have been considered and which scenario region is used
Must be a value available in the input scenario data
scenario_source
character
The publication the scenario data is based on
Must be available in the input scenario data. Usually, available sources are: 'weo', 'geco', 'isf'. Usually follows the pattern '<source>_<publication_year>'
emission_factor_metric
character
Indicates if the emission intensity related values refer to the projected activities of the underlying counterparty, to the economy wide benchmark, or to allocated levels of activity based on the scenarios
Must be one of the following: 'projected', 'corporate_economy', or 'target_<scenario>'
emission_factor_value
double
The physical emission intensity level of the given 'emission_factor_metric'
Numerical value greater or equal to 0
label
character
Same as 'emission_factor_metric', formatted for display in plot
Must be one of the following: 'projected', 'corporate_economy', or 'target_<scenario>', but formatted for display

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")
column
typeof
definition
value
<by_group>
character
Any additional descriptor either at the loan level or at the banking book level. This is used to calculate grouped results by additional dimensions of interest, such as types of FIs or types of loans
Any variable name is permissible, that is not already used otherwise. All entries in the banking book should have a corresponding value. NULL is permissible and implies no grouping
name_abcd
character
The name of the company
The name of the company
sector_abcd
character
The sector of the technology
One of the following: 'power', 'automotive', 'coal', 'oil and gas', 'aviation', 'cement', 'steel'
loan_size_outstanding
double
Remaining outstanding loan value to the given counterparty
Numerical value greater or equal to 0
loan_size_outstanding_currency
character
Denomination of the outstanding loans listed in the given banking book
Three letter currency code following the ISO 4217 standard. Only one currency allowed per banking book
loan_size_credit_limit
double
Maximum value provided to the a counterparty under the given credit line
Numerical value greater or equal to 0
loan_size_credit_limit_currency
character
Denomination of the credit lines of loans listed in the given banking book
Three letter currency code following the ISO 4217 standard. Only one currency allowed per banking book

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")
column
typeof
definition
value
sector
character
The sector of the technology
One of the following: 'power', 'automotive', 'coal', 'oil and gas'
technology
character
The technology
One of the in-scope PACTA technologies that belong to the sector indicated in 'sector'
year
integer
The year of the data
A year between the 'start_year' of the analyis and the 'start_year' plus the 'time_frame'
region
character
The region for which the analysis has been run. Indicates which production assets have been considered and which scenario region is used
Must be a value available in the input scenario data
scenario_source
character
The publication the scenario data is based on
Must be available in the input scenario data. Usually, available sources are: 'weo', 'geco', 'isf'. Usually follows the pattern '<source>_<publication_year>'
name_abcd
character
The name of the company
The name of the company
projected
double
The projected activity level of the technology in the year for the given company
Numerical value greater or equal to 0
target_<scenario>
double
The target activity level of the technology in the year for the given company
Numerical value greater or equal to 0
total_tech_deviation
double
The total difference between 'target_<scenario>' and 'projected'
Numerical value. Can be negative or positive
direction
character
The direction the technology follows long term in the scenario
One of the following: 'buildout', 'phaseout'
activity_unit
character
The unit the activity is measured in for a given sector
The unit corresponding to the sector. For example 'MW' of capacity for power

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")
column
typeof
definition
value
name_abcd
character
The name of the company
The name of the company
sector
character
The sector of the technology
One of the following: 'power', 'automotive', 'coal', 'oil and gas'
activity_unit
character
The unit the activity is measured in for a given sector
The unit corresponding to the sector. For example 'MW' of capacity for power
region
character
The region for which the analysis has been run. Indicates which production assets have been considered and which scenario region is used
Must be a value available in the input scenario data
scenario_source
character
The publication the scenario data is based on
Must be available in the input scenario data. Usually, available sources are: 'weo', 'geco', 'isf'. Usually follows the pattern '<source>_<publication_year>'
scenario
character
The name of the scenario against which alignment is measured
Must be available in the input scenario data. Must be a scenario provided in the indicated 'scenario_source'
year
integer
The year of the data
A year between the 'start_year' of the analyis and the 'start_year' plus the 'time_frame'
direction
character
At the sector level, 'direction' indicates if the alignment value is aggregated across buildout or phaseout technologies, or if it is the net aggregate of the sector.
In this case, must be: 'net'
total_deviation
double
Net aggregate deviation of all underlying technologies, accounting for directionality. A more positive number means that the combination of underlying technologies is more aligned, a more negative one implies higher misalignment
Numerical value. Can be negative or positive
alignment_metric
double
Net aggregate alignment value by sector. A ratio that is calculated as the total deviation divided by the net scenario value (accounting for directionality). A positive value shows the company plans are ahead of target. A negative value means they are behind target
Numerical value. Can be negative or positive

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")
column
typeof
definition
value
name_abcd
character
The name of the company
The name of the company
sector
character
The sector of the technology
One of the following: 'power', 'automotive', 'coal', 'oil and gas'
activity_unit
character
The unit the activity is measured in for a given sector
The unit corresponding to the sector. For example 'MW' of capacity for power
region
character
The region for which the analysis has been run. Indicates which production assets have been considered and which scenario region is used
Must be a value available in the input scenario data
scenario_source
character
The publication the scenario data is based on
Must be available in the input scenario data. Usually, available sources are: 'weo', 'geco', 'isf'. Usually follows the pattern '<source>_<publication_year>'
scenario
character
The name of the scenario against which alignment is measured
Must be available in the input scenario data. Must be a scenario provided in the indicated 'scenario_source'
year
integer
The year of the data
A year between the 'start_year' of the analyis and the 'start_year' plus the 'time_frame'
direction
character
At the sector level, 'direction' indicates if the alignment value is aggregated across buildout or phaseout technologies, or if it is the net aggregate of the sector.
In this case, must be: 'buildout' or 'phaseout'
total_deviation
double
Aggregate deviation of all underlying technologies of the same directionality. A more positive number means that the combination of underlying technologies is more aligned, a more negative one implies higher misalignment
Numerical value. Can be negative or positive
alignment_metric
double
Net aggregate alignment value by sector, disaggregated by direction of the underlying technlogies. A ratio that is calculated as the total deviation divided by the net scenario value (accounting for directionality). A positive value shows the company plans are ahead of target. A negative value means they are behind target
Numerical value. Can be negative or positive

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")
column
typeof
definition
value
name_abcd
character
The name of the company
The name of the company
sector
character
The sector of the technology
One of the following: 'aviation', 'cement', 'steel'
activity_unit
character
The unit the physical emission intensity is measured in for a given sector
The unit corresponding to the sector. For example 'tCO2 / t cement'
region
character
The region for which the analysis has been run. Indicates which production assets have been considered and which scenario region is used
Must be a value available in the input scenario data
scenario_source
character
The publication the scenario data is based on
Must be available in the input scenario data. Usually, available sources are: 'weo', 'geco', 'isf'. Usually follows the pattern '<source>_<publication_year>'
scenario
character
The name of the scenario against which alignment is measured
Must be available in the input scenario data. Must be a scenario provided in the indicated 'scenario_source'
year
integer
The year of the data
A year between the 'start_year' of the analyis and the 'start_year' plus the 'time_frame'
direction
character
At the sector level, 'direction' indicates if the alignment value is aggregated across buildout or phaseout technologies, or if it is the net aggregate of the sector.
In this case, must be: 'net'
total_deviation
double
Net aggregate deviation of all underlying technologies, accounting for directionality. A more positive number means that the combination of underlying technologies is more aligned, a more negative one implies higher misalignment
Numerical value. Can be negative or positive
alignment_metric
double
Net aggregate alignment value by sector. A ratio that is calculated as the total deviation divided by the net scenario value (accounting for directionality). A positive value shows the company plans are ahead of target. A negative value means they are behind target
Numerical value. Can be negative or positive

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")
column
typeof
definition
value
<by_group>
character
Any additional descriptor either at the loan level or at the banking book level. This is used to calculate grouped results by additional dimensions of interest, such as types of FIs or types of loans
Any variable name is permissible, that is not already used otherwise. All entries in the banking book should have a corresponding value. NULL is permissible and implies no grouping
name_abcd
character
The name of the company
The name of the company
sector
character
The sector of the technology
One of the following: 'power', 'automotive', 'coal', 'oil and gas', 'aviation', 'cement', 'steel'
activity_unit
character
The unit the activity is measured in for a given sector
The unit corresponding to the sector. For example 'MW' of capacity for power
region
character
The region for which the analysis has been run. Indicates which production assets have been considered and which scenario region is used
Must be a value available in the input scenario data
scenario_source
character
The publication the scenario data is based on
Must be available in the input scenario data. Usually, available sources are: 'weo', 'geco', 'isf'. Usually follows the pattern '<source>_<publication_year>'
scenario
character
The name of the scenario against which alignment is measured
Must be available in the input scenario data. Must be a scenario provided in the indicated 'scenario_source'
year
integer
The year of the data
A year between the 'start_year' of the analyis and the 'start_year' plus the 'time_frame'
direction
character
At the sector level, 'direction' indicates if the alignment value is aggregated across buildout or phaseout technologies, or if it is the net aggregate of the sector.
In this case, must be: 'net'
total_deviation
double
Aggregate deviation of all underlying technologies of the same directionality. A more positive number means that the combination of underlying technologies is more aligned, a more negative one implies higher misalignment
Numerical value. Can be negative or positive
alignment_metric
double
Net aggregate alignment value by sector. A ratio that is calculated as the total deviation divided by the net scenario value (accounting for directionality). A positive value shows the company plans are ahead of target. A negative value means they are behind target
Numerical value. Can be negative or positive
loan_size_outstanding_currency
character
Denomination of the loans listed in the given banking book
Three letter currency code following the ISO 4217 standard. Only one currency allowed per banking book
loan_size_outstanding
double
Remaining outstanding loan value to the given counterparty
Numerical value greater or equal to 0
exposure_weight
double
Relative size of the loan compared to the overall size of the analysed banking book
Numerical value greater or equal to 0

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")
column
typeof
definition
value
<by_group>
character
Any additional descriptor either at the loan level or at the banking book level. This is used to calculate grouped results by additional dimensions of interest, such as types of FIs or types of loans
Any variable name is permissible, that is not already used otherwise. All entries in the banking book should have a corresponding value. NULL is permissible and implies no grouping
name_abcd
character
The name of the company
The name of the company
sector
character
The sector of the technology
One of the following: 'power', 'automotive', 'coal', 'oil and gas'
activity_unit
character
The unit the activity is measured in for a given sector
The unit corresponding to the sector. For example 'MW' of capacity for power
region
character
The region for which the analysis has been run. Indicates which production assets have been considered and which scenario region is used
Must be a value available in the input scenario data
scenario_source
character
The publication the scenario data is based on
Must be available in the input scenario data. Usually, available sources are: 'weo', 'geco', 'isf'. Usually follows the pattern '<source>_<publication_year>'
scenario
character
The name of the scenario against which alignment is measured
Must be available in the input scenario data. Must be a scenario provided in the indicated 'scenario_source'
year
integer
The year of the data
A year between the 'start_year' of the analyis and the 'start_year' plus the 'time_frame'
direction
character
At the sector level, 'direction' indicates if the alignment value is aggregated across buildout or phaseout technologies, or if it is the net aggregate of the sector.
In this case, must be: 'buildout' or 'phaseout'
total_deviation
double
Aggregate deviation of all underlying technologies of the same directionality. A more positive number means that the combination of underlying technologies is more aligned, a more negative one implies higher misalignment
Numerical value. Can be negative or positive
alignment_metric
double
Net aggregate alignment value by sector, disaggregated by direction of the underlying technlogies. A ratio that is calculated as the total deviation divided by the net scenario value (accounting for directionality). A positive value shows the company plans are ahead of target. A negative value means they are behind target
Numerical value. Can be negative or positive
loan_size_outstanding_currency
character
Denomination of the loans listed in the given banking book
Three letter currency code following the ISO 4217 standard. Only one currency allowed per banking book
loan_size_outstanding
double
Remaining outstanding loan value to the given counterparty
Numerical value greater or equal to 0
exposure_weight
double
Relative size of the loan compared to the overall size of the analysed banking book
Numerical value greater or equal to 0

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")
column
typeof
definition
value
<by_group>
character
Any additional descriptor either at the loan level or at the banking book level. This is used to calculate grouped results by additional dimensions of interest, such as types of FIs or types of loans
Any variable name is permissible, that is not already used otherwise. All entries in the banking book should have a corresponding value. NULL is permissible and implies no grouping
scenario
character
The name of the scenario against which alignment is measured
Must be available in the input scenario data. Must be a scenario provided in the indicated 'scenario_source'
region
character
The region for which the analysis has been run. Indicates which production assets have been considered and which scenario region is used
Must be a value available in the input scenario data
sector
character
The sector of the technology
One of the following: 'power', 'automotive', 'coal', 'oil and gas', 'aviation', 'cement', 'steel'
year
integer
The year of the data
A year between the 'start_year' of the analyis and the 'start_year' plus the 'time_frame'
direction
character
At the sector level, 'direction' indicates if the alignment value is aggregated across buildout or phaseout technologies, or if it is the net aggregate of the sector.
In this case, must be: 'net'
n_companies
double
Number of individual company-by-sector combinations in the given sector analysed within this (group of) banking book(s)
Numerical value greater or equal to 0
n_companies_aligned
double
Number of individual company-by-sector combinations within the given sector that have an alignment metric greater or equal to 0
Numerical value greater or equal to 0 and smaller or equal to 'n_companies'
share_companies_aligned
double
Share of 'n_companies_aligned' relative to 'n_companies'
Numerical value. Must be between 0 and 1
exposure_weighted_net_alignment
double
Net aggregate alignment value aggregated to the banking book-by-sector level. Individual company alignment metrics are allocated based on financial exposure, using the 'exposure_weight'
Numerical value. Can be negative or positive
sum_loan_size_outstanding
double
Sum of outstanding loan size at the banking book-by-sector level of all loans analysed within this (group of) banking book(s)
Numerical value greater or equal to 0
sum_exposure_companies_aligned
double
Sum of outstanding loan size at the banking book-by-sector level of all loans to aligned companies within this (group of) banking book(s)
Numerical value greater or equal to 0 and smaller or equal to 'sum_loan_size_outstanding'
share_exposure_aligned
double
Share of 'sum_exposure_companies_aligned' relative to 'sum_loan_size_outstanding'
Numerical value. Must be between 0 and 1

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")
column
typeof
definition
value
<by_group>
character
Any additional descriptor either at the loan level or at the banking book level. This is used to calculate grouped results by additional dimensions of interest, such as types of FIs or types of loans
Any variable name is permissible, that is not already used otherwise. All entries in the banking book should have a corresponding value. NULL is permissible and implies no grouping
scenario
character
The name of the scenario against which alignment is measured
Must be available in the input scenario data. Must be a scenario provided in the indicated 'scenario_source'
region
character
The region for which the analysis has been run. Indicates which production assets have been considered and which scenario region is used
Must be a value available in the input scenario data
sector
character
The sector of the technology
One of the following: 'power', 'automotive', 'coal', 'oil and gas'
year
integer
The year of the data
A year between the 'start_year' of the analyis and the 'start_year' plus the 'time_frame'
direction
character
At the sector level, 'direction' indicates if the alignment value is aggregated across buildout or phaseout technologies, or if it is the net aggregate of the sector.
In this case, must be: 'buildout' or 'phaseout'
n_companies
double
Number of individual company-by-sector combinations in the given sector analysed within this (group of) banking book(s)
Numerical value greater or equal to 0
n_companies_aligned
double
Number of individual company-by-sector combinations within the given sector that have an alignment metric greater or equal to 0
Numerical value greater or equal to 0 and smaller or equal to 'n_companies'
share_companies_aligned
double
Share of 'n_companies_aligned' relative to 'n_companies'
Numerical value. Must be between 0 and 1
exposure_weighted_net_alignment
double
Net aggregate alignment value aggregated to the banking book-by-sector level, disaggregated into 'buildout' and 'phaseout' components. Individual company alignment metrics are allocated based on financial exposure, using the 'exposure_weight'
Numerical value. Can be negative or positive

Input data for Sankey plot

Data set meant to be used as input into plot_sankey().

dplyr::filter(data_dictionary, .data[["dataset"]] == "data_sankey")
column
typeof
definition
value
loan_size_outstanding
double
Remaining outstanding loan value to the underlying counterparty. Determines the vertical size of each ribbon in the sankey plot
Numerical value greater or equal to 0
initial_node
character
Leftmost node in the sankey plot. Defines the split of the banking books into groups. Any additional descriptor either at the loan level or at the banking book level
Any variable name is permissible, that is not already used otherwise. All entries in the banking book should have a corresponding value. NULL is permissible and implies no grouping
middle_node
character
Middle node in the sankey plot. Indicates the sectors that the underlying counterparties operate in, which implies this node splits the financial exposures according to sectoral exposure
Variable `sector` must be available in the exposure alignment results used as input for the graph.
end_node
character
End node and color indicator. A dummy variable that indicates if the underlying counterparty related to the exposure is aligned based on the net alignment metric or not
Value is one of 'Aligned', 'Not aligned', or 'Unknown'
is_aligned
character
Indicates which alignment category any given flow in the sankey plot correponds to. Coincides with end_note and determines the fill of the flows
Value is one of 'Aligned', 'Not aligned', or 'Unknown'
currency
character
Shows the currency the financial exposure is denominated in
Derived from the currency of the analyzed loan books

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")
column
typeof
definition
value
<by_group>
character
Any additional descriptor either at the loan level or at the banking book level. This is used to calculate grouped results by additional dimensions of interest, such as types of FIs or types of loans
Any variable name is permissible, that is not already used otherwise. All entries in the banking book should have a corresponding value. NULL is permissible and implies no grouping
scenario
character
The name of the scenario against which alignment is measured
Must be available in the input scenario data. Must be a scenario provided in the indicated 'scenario_source'
region
character
The region for which the analysis has been run. Indicates which production assets have been considered and which scenario region is used
Must be a value available in the input scenario data
sector
character
The sector of the technology
One of the following: 'power', 'automotive', 'coal', 'oil and gas', 'aviation', 'cement', 'steel'
year
integer
The year of the data
A year between the 'start_year' of the analyis and the 'start_year' plus the 'time_frame'
exposure_weighted_net_alignment
double
Net aggregate alignment value aggregated to the banking book-by-sector level. Individual company alignment metrics are allocated based on financial exposure, using the 'exposure_weight'
Numerical value. Can be negative or positive
sum_loan_size_outstanding
double
Sum of outstanding loan size at the banking book-by-sector level of all loans analysed within this (group of) banking book(s)
Numerical value greater or equal to 0

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")
column
typeof
definition
value
name
character
Name of the entity to analyse. If analysed at group level, this variable contains the values of <by_group>. If analysed at company level, it contains the values of 'name_abcd'
An identifying name of the entity
buildout
character
Net aggregate alignment value by sector, disaggregated to buildout technologies only
Numerical value. Can be negative or positive
phaseout
character
Net aggregate alignment value by sector, disaggregated to phaseout technologies only
Numerical value. Can be negative or positive
net
character
Net aggregate alignment value by sector of the entity analysed
Numerical value. Can be negative or positive. Sum of the disaggregated components of 'buildout' and 'phaseout'
datapoint
character
The level of the analysis, either group level or company level
Must be one of: 'Group' or 'company'