Advanced Use Cases
Source:vignettes/articles/cookbook_advanced_use_cases.Rmd
cookbook_advanced_use_cases.RmdPACTA for Banks analyses support a wide range of real-world applications. This section highlights key use cases that are particularly suited to this type of analysis, all of which are either actively used or currently being explored by PACTA users.
Potential Follow-up Actions Based on PACTA for Banks Analysis
These use cases leverage standard PACTA for Banks outputs - derived from either the market share approach or the sectoral decarbonization approach — at the loan book or company level. They vary in intended audience, targeting either internal stakeholders (such as management or other departments) or external parties (such as clients, regulators, or the general public). Additionally, some focus on communication and reporting, while others guide internal decision-making or drive strategic change.
This section provides detailed explanations of well-established use cases, while others are mentioned briefly, particularly those still requiring future exploration. Notably, the examples do not include the standard PACTA plots, as follow-up analyses typically rely on precise numerical outputs rather than visualizations. While plots are valuable for interpreting PACTA results, detailed reporting and decision-making are generally based on quantitative data—except in cases of public-facing analyses, which are not covered in depth here.
Use Case 1: Regulatory Compliance
Description
Reporting or disclosing loan book alignment or targets to a financial supervisor or regulator.
Level of Analysis
Regulators typically focus on the overall loan book. Regulations generally apply to the structure of the loan book, making portfolio-level PACTA analysis the most appropriate depth of analysis for this use case.
Potential Application
Overview
Financial regulators worldwide have introduced climate-related financial risk regulations, varying in scope and requirements. Many regulations mandate reporting on climate-relevant exposures, often including forward-looking metrics such as emissions or production alignment with external benchmarks or financial institutions’ transition targets. Reporting requirements may differ based on:
- Type of climate metrics (e.g. absolute emissions, financed emissions, production-based emission intensity, production volumes or technology mix)
- Scope and granularity (e.g., aggregated loan book exposures across all sectors, aggregate loan book exposures across climate-relevant sector, or sector-specific exposure reporting)
- Value chain coverage (e.g., full value chain reporting or focusing on the most emission-intensive segments)
Example
If a regulation aligns with PACTA’s scope, a financial institution could integrate PACTA analysis into regulatory reporting. For instance, in a materiality assessment of climate-related exposures, a bank may map its loan book to PACTA-defined sectors. A bank lending to power generation and steel manufacturing clients, along with other non-PACTA sector clients, could base its materiality assessment on the following calculation:
materiality <- coverage %>%
mutate(sector = if_else(!sector %in% c("power", "cement"), "not in scope", sector)) %>% # we assume this bank only lends to cement, power, and not-in-scope companies
summarise(
sector_sum_loan_size_outstanding = sum(loan_size_outstanding),
.by = sector
) %>%
mutate(
sector_share_loan_size_outstanding = round(sector_sum_loan_size_outstanding / sum(sector_sum_loan_size_outstanding), 2)
)The bank might report to the supervisor along the lines of:
- 32% of the loan book (27925291 USD) go towards power generation
- 15% of the loan book (13099969 USD) go towards cement production
- 53% of the loan book (45559559 USD) go towards other sectors that are not climate relevant
This can be a direct outcome based on the matched and prioritized data set compared with values in the raw loan book data set.
Beyond exposure and materiality reporting, some regulations may require alignment metrics or sector targets, which PACTA can also provide. Forward-looking metrics often require comparison to an external benchmark, typically derived from climate transition scenarios. If a regulation mandates a specific benchmark data source, financial institutions should adhere to it. Otherwise, they should be prepared to justify their choice, whether based on sectoral granularity, perceived credibility or likelihood of scenario assumptions, or regulatory preference for credibility and comparability. In some cases, regulators may provide explicit benchmark values for alignment assessments.
For example, a regulator may require financial institutions to report sectoral physical emission intensity (for SDA sectors) or the high-carbon share (for market share sectors) of their loan book, compared to both a 2030 scenario benchmark (e.g., scenario X) and the financial institution’s own 2030 target. In such cases, users can extract the portfolio-weighted emission intensity from their PACTA for Banks results and compare it to the corresponding 2030 scenario value. If the financial institution has set sector-specific targets, these should also be included in the reporting.
tech_mix_power_current <- market_share_targets_portfolio %>%
filter(
sector == "power",
region == "global",
metric == "projected",
year == 2020
) %>%
select(sector, technology, metric, year, technology_share) %>%
mutate(technology_share = round(technology_share, 3))
tech_mix_power_target <- market_share_targets_portfolio %>%
filter(
sector == "power",
region == "global",
metric == "target_sds",
year == 2030
) %>%
select(sector, technology, metric, year, technology_share) %>%
mutate(technology_share = round(technology_share, 3))
tech_mix_power <- tech_mix_power_current %>%
bind_rows(tech_mix_power_target)
ei_cement_current <- sda_targets_portfolio %>%
filter(
sector == "cement",
region == "global",
emission_factor_metric == "projected",
year == 2020
) %>%
select(sector, emission_factor_metric, year, emission_factor_value) %>%
mutate(emission_factor_value = round(emission_factor_value, 3))
ei_cement_target <- sda_targets_portfolio %>%
filter(
sector == "cement",
region == "global",
emission_factor_metric == "target_demo",
year == 2030
) %>%
select(sector, emission_factor_metric, year, emission_factor_value) %>%
mutate(emission_factor_value = round(emission_factor_value, 3))
ei_cement_scenario <- sda_targets_portfolio %>%
filter(
sector == "cement",
region == "global",
emission_factor_metric == "adjusted_scenario_demo",
year == 2030
) %>%
select(sector, emission_factor_metric, year, emission_factor_value) %>%
mutate(emission_factor_value = round(emission_factor_value, 3))
ei_cement <- ei_cement_current %>%
bind_rows(ei_cement_target) %>%
bind_rows(ei_cement_scenario)The outcome would look as follows:
And accordingly, the bank might report:
- The current high-carbon share (coal-fired, gas-fired and oil-fired power plants) of the companies the bank lends to in the power sector is: 32.6%
- The target high-carbon share in the power sector that the bank has set itself based in the “target_sda” scenario for 2030 is: 30.7%
- The current emission intensity of the companies the bank lends to in the cement sector is: 0.664 tonnes of CO2/tonne of cement
- The target emission intensity in the cement sector that the bank has set itself based in the “target_demo” scenario for 2030 is: 0.37 tonnes of CO2/tonne of cement
- The scenario value of the emission intensity in the cement sector in the “adjusted_scenario_demo” scenario for 2030 is: 0.373 tonnes of CO2/tonne of cement
This would be an indication that the bank has not reached its 2030 targets in either of the two sectors, as of 2020, the start year of the analysis.
The bank would also report that their target for the cement sector is slightly more ambitious than the scenario value. For sectors using a market share approach, the PACTA calculations do not directly return the technology share of the scenario. Users would have to derive this value based on the input scenario data, if necessary.
Notes and Caveats
Regulatory Fit: PACTA is well-suited for reporting where regulations require sectoral decomposition of exposures and climate metrics based on production-based emission intensity or absolute production volumes. However, PACTA focuses on the highest-emitting parts of sectoral value chains (e.g., power generation but not transmission or services). If regulations require full value chain coverage, PACTA may not be suitable. Engaging with regulatory bodies to clarify expectations is recommended.
Out-of-Scope Sectors: If regulations require disclosure on sectors beyond PACTA’s scope, users must assess unmatched loans to determine if they align with additional required sectors. Similarly, for in-scope sectors, reviewing unmatched loans may be necessary to account for upstream or downstream value chain segments not covered by PACTA.
Jurisdictional Differences: Regulatory requirements vary by country, meaning PACTA cannot provide universal guidance on compliance. Users should assess how PACTA aligns with their specific regulations and consult their supervisory authority if uncertain.
Scenario Compatibility: PACTA requires sectoral pathways that map to its scopes and based on production metrics. Scenarios limited to absolute emissions, financial projections, or lacking sectoral granularity are not compatible. If regulations mandate an incompatible scenario, alternative analysis methods may be needed.
Use Case 2: Sectoral Target Setting
Level of Analysis
Financial institutions aiming to reduce their Scope 3 emissions often focus on decarbonizing client exposures in their loan books. Since emission reduction strategies and timelines vary across sectors, setting sector-specific targets helps account for these differences.
For loan book decarbonization, applying a sectoral approach provides an appropriate level of granularity for institution-wide targets. Company-level targets, which may require additional detail beyond PACTA for Banks, can be developed in collaboration with individual clients.
Potential application
Overview
Financial institutions set sectoral decarbonization targets for various reasons, including regulatory requirements, reputational goals, and strategic business considerations. Targets can be based on absolute or financed emissions, though these are outside PACTA’s scope. Instead, PACTA supports targets based on production pathways and production-based emission intensities, which directly relate to major sources of GHG emissions.
Sectoral targets in PACTA are calculated using either the market share approach (for sectors with technology-level production pathways) or the sectoral decarbonization approach (for sectors with production-based emission intensities). Both approaches distribute the decarbonization burden equally across all actors in a sector, based on the loan book’s initial composition.
Financial institutions may choose to set targets for all sectors or focus on climate-relevant ones, applying a uniform approach or tailoring methods to different sectors. PACTA for Banks is particularly effective for key emissions-intensive sectors such as oil and gas extraction, coal mining, automotive manufacturing, power generation, cement production, steel manufacturing, and aviation. It focuses on the most impactful parts of each sector’s value chain, ensuring targets that drive meaningful emissions reductions.
Example
To calculate sector-level targets for a loan book, users can filter portfolio-level PACTA for Banks results by target year, scenario benchmark, and relevant geographic region. The baseline year is determined by selecting a scenario file where the first year aligns with the intended baseline. Ensuring consistency across the ABCD dataset, loan book composition, and baseline year is critical for accurate target-setting.
tech_mix_sector_targets <- market_share_targets_portfolio %>%
filter(
region == "global",
metric == "target_sds",
year == 2030
) %>%
select(sector, technology, metric, year, technology_share) %>%
mutate(technology_share = round(technology_share, 3))
high_carbon_share_sector_targets <- tech_mix_sector_targets %>%
filter(technology %in% c("coalcap", "gascap", "oilcap", "ice")) %>%
summarise(
high_carbon_share = sum(technology_share, na.rm = TRUE),
.by = c("sector", "metric", "year")
)
ei_sector_targets <- sda_targets_portfolio %>%
filter(
region == "global",
emission_factor_metric == "target_demo",
year == 2030
) %>%
select(sector, emission_factor_metric, year, emission_factor_value) %>%
mutate(emission_factor_value = round(emission_factor_value, 3))
available_sectors <- unique(pull(filter(matched, sector %in% c(unique(scenario$sector), unique(co2$sector))), sector))In this example, we set targets for a bank, based on its loan book composition in 2020. The loan book contains exposures to the following PACTA sectors, for which trajectories are available in the scenarios: power, automotive, cement, steel. The bank wants to set targets for the year 2030. The bank has decided to use the “target_sda” scenario as a benchmark for setting targets based on the market share approach and “target_demo” for setting targets based on the sectoral decarbonization approach. Targets are set based on global scenario benchmarks. The sector targets for 2030 based on this loan book are as follows:
For sectors with technology pathways, the banks could set targets based on the high-carbon share of technologies in the sector. Alternatively it could break down the targets by individual technologies.
In sectors without technology pathways, the bank could set targets based on the emission intensity of the sector.
Notes and Caveats
Sectoral Coverage: PACTA is suitable for setting production-based targets focused on high-impact segments of sectoral value chains. It is not designed for targets based on absolute or financed emissions. If an institution seeks full-sector coverage across all client industries or a uniform approach for all sectors, PACTA alone will not suffice and may need to be supplemented with other tools.
Value Chain Focus: PACTA focuses on value chain segments most critical to GHG reductions, requiring climate transition scenarios with either sector-level production-based emission intensities or technology-level production pathways. Depending on sector characteristics, targets are derived using either the market share approach or the sectoral decarbonization approach.
Regional Benchmarks: Institutions may prefer regional benchmarks, particularly in emerging markets and developing countries (EMDCs), where transitions may lag due to financial constraints or economic development needs. However, not all climate scenarios provide granular regional breakdowns for every sector.
Rebaselining Targets: Sector targets are initially based on the loan book composition in the baseline year. If the loan book structure changes significantly, financial institutions may consider rebaselining targets by recalculating them using the same baseline and target years but with an updated loan book composition. This ensures targets reflect current exposures while maintaining consistency with the original methodology.
Use Case 3: Risk Management (Client-Side)
Level of Analysis
Managing transition risk at the client level requires company-specific insights, which can be obtained by running PACTA for Banks at the company level. While this analysis is unlikely to be the sole input for risk management, it serves as a valuable tool in assessing client exposure and alignment with transition pathways.
Potential Application
Overview
A company-level PACTA for Banks analysis provides insights into how a company’s production (or emission intensity) is expected to evolve in the medium term compared to external benchmarks. These benchmarks, derived from climate transition scenarios, are scaled to the company level using either the market share approach or the sectoral decarbonization approach. Deviations from these benchmarks can serve as indicators of transition risk, informing financial institutions’ risk management strategies.
PACTA can complement other company-level risk assessments, including financial models such as net present value or probability of default calculations. However, since these applications fall outside PACTA’s scope, this section focuses on production-based indicators.
When managing transition risk, financial institutions can consider two primary approaches:
Client Engagement: Helping clients mitigate their transition risk by providing insights into how their production plans compare to benchmarks and guiding them towards implementing a transition plan. This may involve shifting away from high-carbon activities, investing in low-carbon technologies, or securing transition finance. More details on client transition assessments and transition finance can be found in RMI’s Transition Finance Resource Hub.
Exposure Management: If the client is unwilling or unable to address transition risk, the financial institution may choose to reduce its exposure, including potentially exiting the client relationship at maturity.
Example
Suppose a bank aims to reduce transition risk in its loan book to align with its defined risk appetite. After assessing its portfolio, say it identifies power and cement as the primary risk-intensive sectors. In the power sector, the key transition risk stems from coal-fired power generation.
The bank can use company-level PACTA results to compare counterparties’ production plans against company-specific benchmarks, based on the same transition scenarios used for sector-level targets. By conducting the analysis in 2020 and assessing deviations over a five-year horizon, the bank can identify which companies are proactively transitioning and which are not, informing its engagement or exposure management strategy.
power_company_future_risk <- market_share_targets_company %>%
filter(
region == "global",
sector == "power",
metric %in% c("projected", "target_sds"),
year == 2025,
technology == "coalcap"
) %>%
select(name_abcd, sector, technology, metric, year, technology_share) %>%
mutate(technology_share = round(technology_share, 3)) %>%
pivot_wider(names_from = "metric", values_from = "technology_share") %>%
mutate(deviation = round((projected - target_sds) / target_sds, 3))
misaligned_coal <- power_company_future_risk %>%
arrange(desc(deviation)) %>%
head(3)
cement_company_future_risk <- sda_targets_company %>%
filter(
region == "global",
sector == "cement",
emission_factor_metric %in% c("projected", "target_demo"),
year == 2025
) %>%
select(name_abcd, sector, emission_factor_metric, year, emission_factor_value) %>%
mutate(emission_factor_value = round(emission_factor_value, 3)) %>%
pivot_wider(names_from = "emission_factor_metric", values_from = "emission_factor_value") %>%
mutate(deviation = round((projected - target_demo) / target_demo, 3))
misaligned_cement <- cement_company_future_risk %>%
arrange(desc(deviation)) %>%
head(3)The bank determines the three most misaligned power and cement companies are the following, each of which it intends to engage with directly.
The bank can now engage with these companies to understand the reasons for the deviation and to help them develop a transition plan to reduce their exposure to high-carbon activities. In the power sector, their highest priority is to engage with Mariani-Mariani s.r.l. and in the cement sector, their top priority is Vitali, Vitali e Vitali s.r.l.. If either of these companies turns out to be unwilling to or incapable to manage their transition risk, the bank can also use this information to assess the relationship with these companies and potentially reduce the financial exposure.
Notes and Caveats
PACTA for Banks should not be the sole factor in assessing client transition risk but serves as a valuable tool for evaluating alignment with the bank’s climate risk tolerance.
Comparing clients to the same scenario benchmark used for sector-level target setting can help ensure alignment with expected market trends.
Banks should incorporate multiple data sources when evaluating client risk, as PACTA alone does not provide a comprehensive risk profile.
Use Case 4: Transition Planning (Client-Side)
Level of Analysis
Company-level PACTA for Banks analysis provides insights into client production plans over the medium term. These projections can be used to evaluate the credibility of a client’s transition plan and its alignment with climate transition benchmarks.
Potential Application
Overview
Clients may develop transition plans to demonstrate their commitment to transitioning to a low-carbon economy or to seek transition financing. If a transition plan includes projected production levels and/or production-based emission intensities, banks can assess its credibility by comparing these projections with PACTA for Banks results. A credible plan should align with realistic production and emission intensity trajectories.
If there are significant discrepancies between a company’s projections and PACTA results, banks and clients should investigate the cause. Differences may stem from data gaps, asset modeling inconsistencies, or variations in consolidation methods. For instance, PACTA assigns production based on an equity ownership approach, while a client may use a financial control method. Resolving such differences ensures accurate attribution of production values before assessing the validity of the transition plan.
Example
Suppose a bank has a power sector client that plans to reduce its share of coal-fired power generation. The client provides projections for its total coal capacity and power technology mix through 2025. The bank compares these projections with PACTA for Banks results over the same time-frame to evaluate alignment and credibility.
power_company_coal_plan <- market_share_targets_company %>%
filter(
region == "global",
sector == "power",
metric == "projected",
technology == "coalcap"
)
company_select <- power_company_coal_plan$name_abcd[1]
pacta_power_company_coal_plan <- power_company_coal_plan %>%
filter(name_abcd == company_select) %>%
select(name_abcd, sector, technology, metric, year, production, technology_share) %>%
mutate(
production = round(production),
technology_share = round(technology_share, 3)
)The comparison between the two datasets reveals a significantly lower reported coal power share and level of capacity starting in 2023, compared to the PACTA for Banks projections.
Upon engaging with the client Bernardi, Bernardi e Bernardi SPA, it is determined that the client had already deducted the capacity of a coal-fired power plant from the total capacity in their current projections for which initial plans of an early retirement in 2023 were discussed. However, no final deal had been signed at this stage, so the bank and the client agree that the banks projections should be used as the baseline and the client’s projections more accurately describe an aspirational step in the transition plan. With these clarifications, the bank and the client can now assess if their is a role to play for the bank in the early retirement of the coal plant or in other yet-to-be-determined transition activities.
Notes and Caveats
- Companies may use different consolidation methods than PACTA, so production values provided by clients should be carefully reviewed.
- If discrepancies cannot be resolved, users may need to consult the provider of the ABCD data used in the PACTA analysis. Data providers can offer methodological documentation and help identify asset-level issues.
- Since RMI is not a data provider, users must contact the data provider directly. If using Asset Impact, contact details are available on the Asset Impact website.
Other Use Cases
PACTA for Banks can support additional applications beyond those covered in detail. Potential use cases include:
- Risk Management Enhancements: Adjusting internal risk positions based on PACTA analyses.
- Identifying Growth Opportunities: Using PACTA insights to expand into sectors or identify clients well-positioned for transition.
- Developing Transition Finance Solutions: Informing the creation of new products or services in transition finance. More details can be found in the Transition Finance Resource Hub.
With further research, these use cases may be explored in more detail, and incorporated in future editions of this cookbook.
Supplementing ABCD data
When running a PACTA for Banks analysis, some in-scope companies from the loan book may not be found in the ABCD dataset. Since ABCD coverage is not always comprehensive, key exposures may occasionally be missing. If a company is confirmed to be in scope and the matching process does not yield a result, manually supplementing the ABCD dataset is an option.
However, this process can be complex and time-consuming, so it should not be the default approach. Before resorting to manual supplementation, consider refining the matching process by adjusting parameters and rerunning the analysis.
Example
Suppose you have a loanbook containing a loan to the power company “Acme Co.”
library(pacta.loanbook)
loanbook <-
data.frame(
id_loan = "L1",
id_direct_loantaker = "C123",
name_direct_loantaker = "Acme Co.",
id_ultimate_parent = "C45",
name_ultimate_parent = "Acme Holdings",
loan_size_outstanding = 1e7,
loan_size_outstanding_currency = "USD",
loan_size_credit_limit = 1e8,
loan_size_credit_limit_currency = "USD",
sector_classification_system = "NACE",
sector_classification_direct_loantaker = "D35.12"
)
loanbook
#> id_loan id_direct_loantaker name_direct_loantaker id_ultimate_parent name_ultimate_parent loan_size_outstanding loan_size_outstanding_currency loan_size_credit_limit loan_size_credit_limit_currency sector_classification_system sector_classification_direct_loantaker
#> 1 L1 C123 Acme Co. C45 Acme Holdings 1e+07 USD 1e+08 USD NACE D35.12You have not been able to find a match in the ABCD after running multiple iterations of the matching algorithm.
match_name(loanbook = loanbook, abcd = abcd_demo)
#> Warning: Found no match.
#> # A tibble: 0 × 20
#> # ℹ 20 variables: id_loan <chr>, id_direct_loantaker <chr>, name_direct_loantaker <chr>, 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_direct_loantaker <chr>, id_2dii <lgl>, level <lgl>,
#> # sector <lgl>, sector_abcd <lgl>, name <lgl>, name_abcd <lgl>, score <lgl>, source <lgl>, borderline <lgl>
match_name(loanbook = loanbook, abcd = abcd_demo, min_score = 0.7)
#> Warning: Found no match.
#> # A tibble: 0 × 20
#> # ℹ 20 variables: id_loan <chr>, id_direct_loantaker <chr>, name_direct_loantaker <chr>, 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_direct_loantaker <chr>, id_2dii <lgl>, level <lgl>,
#> # sector <lgl>, sector_abcd <lgl>, name <lgl>, name_abcd <lgl>, score <lgl>, source <lgl>, borderline <lgl>
match_name(loanbook = loanbook, abcd = abcd_demo, method = "cosine")
#> Warning: Found no match.
#> # A tibble: 0 × 20
#> # ℹ 20 variables: id_loan <chr>, id_direct_loantaker <chr>, name_direct_loantaker <chr>, 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_direct_loantaker <chr>, id_2dii <lgl>, level <lgl>,
#> # sector <lgl>, sector_abcd <lgl>, name <lgl>, name_abcd <lgl>, score <lgl>, source <lgl>, borderline <lgl>Next, you have validated that “Acme Co.” is indeed in scope. Given the financial exposure to this company is highly relevant for running a meaningful PACTA analysis of your loanbook, you have now decided to supplement the ABCD data set with additional data from a different source.
Suppose you manage to compile the relevant data for Acme Co. from a
different source in a my_custom_abcd.xlsx file (following
precisely the same format of ABCD data),
e.g.
You could now read in this XLSX file, merge the imported data with your existing ABCD data by adding the rows to the bottom of the ABCD table, and with the combined ABCD, you can now get a matched loan, when re-running the matching process.
abcd_addition <- readxl::read_excel(path = file.path(tempdir(), "my_custom_abcd.xlsx"))
abcd_combined <- rbind(abcd_demo, abcd_addition)
match_name(loanbook = loanbook, abcd = abcd_combined)
#> # A tibble: 1 × 20
#> id_loan id_direct_loantaker name_direct_loantaker id_ultimate_parent name_ultimate_parent loan_size_outstanding loan_size_outstanding_currency loan_size_credit_limit loan_size_credit_limit_currency sector_classification_system sector_classification_direct_loantaker id_2dii level sector sector_abcd name name_abcd score source borderline
#> <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <lgl>
#> 1 L1 C123 Acme Co. C45 Acme Holdings 10000000 USD 100000000 USD NACE D35.12 DL1 direct_loantaker power power Acme Co. Acme Co. 1 loanbook FALSENote on Adding Manually Researched Data to the ABCD
While technically straightforward, manually supplementing ABCD data requires careful validation:
Scope Consistency: Ensure that production or emission intensity data aligns with ABCD’s sector definitions and value chain scope. Technology classifications should match existing ABCD conventions (e.g., hydro power is separate from renewable power, and plug-in hybrids are distinct from mild hybrids in the automotive sector).
Consolidation Method: If a company does not fully own all production assets, its attributed production should follow the same consolidation method used in the ABCD. For instance, under an equity ownership approach, a company owning 50% of a power plant should only be assigned 50% of its capacity. Similarly, subsidiary production should be proportionally rolled up based on ownership stakes, while assets operated without ownership should be excluded. If the ABCD uses a different consolidation method (e.g., financial control), refer to the data provider’s documentation for alignment. For more on these consolidation methods, see the PACTA for Banks Methodology Document, specifically sections “1.7.2.1 Financial Control (FC) Consolidation Methodology” and “1.7.2.2 Equity Ownership (EO) Consolidation Methodology”.
Data Formatting: Ensure all necessary columns are included in the supplemented data for seamless integration. For details, see the ABCD data dictionary.
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