Mapping climate‑related metrics in the financial sector

Annex A. Methodology for data collection and classification

A.1. OECD Corporate Sustainability dataset

A.1.1. Regional classification

The category “Developed Asia-Pacific excl. US” includes Australia, Canada, Hong Kong (China), Japan, Macau, New Zealand, Singapore, South Korea and Chinese Taipei. “Latin America” includes jurisdictions both in Latin America and in the Caribbean. “Europe” includes all jurisdictions that are fully located in the region, including the United Kingdom and Switzerland but excluding Russia and Türkiye. “Middle East and Africa” includes jurisdictions classified as “Middle East and Central Asia” in IMF’s World Economic Outlook Database. Excluding those already considered in “Developed Asia-Pacific excl. US” and Israel. “Emerging and Developing Asia excl. China” includes all jurisdictions in Asia that are classified as emerging market and developing economies in IMF’s World Economic Outlook Database excluding China. “Others” includes jurisdictions that are not represented in the other categories in the figure (e.g., Türkiye).

Among the 4 922 listed financial institutions considered in this paper, 1 352 financial institutions are listed in Emerging and Developing Asia excl. China in 2024, followed by Europe (921), Developed Asia-Pacific excl. US (758), United States (633), Middle East and Africa (748), Latin America (280), China (115) and other economies (115).

A.1.2. Listed companies

The dataset contains information for 4 922 worldwide listed financial institutions with total owned assets of USD 197 trillion and total assets under management (AUM) of USD 115 trillion. The raw financial dataset contains all security observations listed on each stock exchange. The following cleaning steps are applied:

• Firms listed on an over-the-counter (OTC) market are excluded.

• Firms listed on a multilateral trading facility (MTF) are excluded.

• Security types classified as “units” and “trust” are excluded.

• Security types identified as “REITs” and “investment funds” are excluded.

• Firms identified as delisted are excluded.

• For firms with multiple observations but different countries of domicile, their true country of domicile is manually checked to remove duplicate observations.

• For firms listed on several stock exchanges, only the primary listing is kept.

In the broad category of “financial institutions”, the paper includes the listed companies in the following industries:

• “Retail and Commercial Banks”, which embark commercial banks, consumer finance, and mortgage finance

• “Insurance Companies”

• “Investment Banks”, which refers not only to investing banking but also to brokerage services

• “Asset Managers”, which also comprehends custody activities.

Total assets are computed when analysing retail and commercial banks, insurance companies and investment banks. Assets under management are computed when analysing retail and commercial banks, insurance companies, investment banks and asset managers.

A.1.3. Data management

The OECD Corporate Sustainability dataset integrates market data from multiple providers, with LSEG, Bloomberg, and MSCI being the main sources. This dataset is created by merging various data inputs based on the availability, relevance, and specific attributes of the metrics.

The main data sources were controlled against each other to ensure consistency. Information was retrieved in August 2024, September 2025 and November 2025. No data imputation is performed, and missing data is recorded as unavailable.

The estimated GHG emissions data integrates research work realised thanks to Pladifes COGEM project in the context of the Pladifes EquipEx (ANR-21-ESRE-0036), hosted at the Institut Louis Bachelier.

The SBTi target data is derived from SBTi Target Dashboard (SBTi, 2025[24]).

In order to build a single variable, data may need to be aggregated from various data providers and multiple metrics. In the first instance, data is merged using common company identifiers (ISIN, RIC or company names). In the second instance, different metrics are consolidated into a single variable.

Merging different datasets using company identifiers: Datasets are merged by employing identifiers such as ISIN (International Securities Identification Number), RIC (Refinitiv Identification Code), and company names. When merging Bloomberg data with the LSEG dataset, ISIN is used as the primary identifier, and then any unmatched ISINs in the Bloomberg dataset are checked. For any unmatched, the corresponding RIC is retrieved, and the Bloomberg dataset is merged with the main sample. Subsequently, the company name is retrieved, including any previously used name, and used to identify the remaining unmatched. The same procedures are followed when merging MSCI data with the LSEG dataset. When merging estimates from the Plafides COGEM project with the LSEG dataset, RIC is used.

Merging different metrics into a single variable: In some cases, it is necessary to merge multiple metrics to enhance coverage. The criteria for merging metrics are relevance, quality, and coverage.

Relevance: metrics that are most relevant to the analysis are prioritised.

Quality: For relevant metrics, priority has been given to data from high-quality providers. Quality is assessed by manually comparing data for large firms with available public sources.

Coverage: When relevance and quality are similar, the metric with the highest data coverage is prioritised.

Only Bloomberg data is used for reported GHG emissions. Estimated GHG emissions from Bloomberg, MSCI and COGEM are merged. Whenever a company lacks estimated data from one or more providers, Bloomberg data is prioritised, followed by COGEM and MSCI data.

A.1.4. Corporate sustainability

This firm-level dataset (OECD Corporate Sustainability dataset) presents information on whether companies disclose sustainability information and the used accounting standards, the external assurance of sustainability information, GHG emission reduction targets, climate-related risks management, the presence of a sustainability committee reporting directly to the board, self-reported board level oversight of climate-related issues and executive remuneration linked to sustainability factors.

Sustainability disclosure by trusts, funds or special purpose acquisition companies is excluded from the universe under analysis. Sustainability disclosure for years prior to 2023 is also excluded.

Figure 2.1 displays the reported GHG emissions (scope 1, scope 2, and scope 3) and data coverage among listed financial institutions globally over the past decade (2016 to 2024). For both data coverage and GHG emission disclosure analyses, only Bloomberg data is used for all years. Companies that reported zero emissions are excluded from the analysis.

Figure 2.2 displays the shares of listed financial institutions with reported or estimated scope 1 and scope 2 GHG emissions available (by no. of companies, by total assets, by AUM and by tons of emissions) within each region. Only financial institutions reporting either scope 1 or scope 2 GHG emissions are considered when measuring the share among all listed financial institutions. The same approach is used for estimated emissions. GHG emission dataset is prepared according to the methodology described in Section A.1.3.

Figure 2.3 displays the percentage of listed financial institutions with reported or estimated scope 3 GHG emissions available (by no. of companies, by total assets, by AUM and by tons of emissions), globally and within each region. Only financial institutions reporting scope 3 GHG emissions are considered when measuring the share among all listed financial institutions. The same approach is used for estimated emissions. GHG emission dataset is prepared according to the methodology described in Annex A.1.3

Figure 2.4 displays the changes in scope 3 emissions and category 15 scope 3 emissions for 2024-2023 and 2023-2022 (by volume of GHG emissions), globally and within each region. Only companies with reported emissions for 2024, 2023 and 2022 are included. GHG emission dataset is prepared according to the methodology described in Annex A.1.3.

Figure 2.5 displayed the scope 3 category 15 GHG emissions as a share of scope 3 emissions by listed financial institutions in 2024 by category of financial institution, globally and within each region. Reported GHG emissions are first considered. If not available, estimated GHG emissions are taken into account.

Figure 2.6 displays the share of listed financial institutions disclosing the methodologies that were used in reporting GHG emissions (by no. of companies and by total assets and AUM) in 2024 among all listed financial institutions that reported GHG emissions, globally and within each region. The figure includes listed financial institutions that reported either scope 1 or scope 2 and listed financial institutions that reported scope 3 GHG emissions in 2024. GHG emission dataset is prepared according to the methodology described in Section A.1.3.

Figure 2.7 displays the reported and estimated GHG emissions (by no. of companies and by total assets and AUM) globally and within each region. Only listed financial institutions with available data for both reported and estimated emission data are included in the analysis. GHG emission dataset is prepared according to the methodology described in Section A.1.3

Figure 2.8 displays the GHG emission intensity and the share of financial institutions for which it is measurable, globally and within each region. The GHG emission intensity is calculated by dividing tons of CO₂e emissions by total assets in millions of US dollars. Reported GHG emissions are first considered. If unavailable, estimated GHG emissions are used. Only companies for which either reported or estimated emissions for scope 1 and 2 are considered when assessing scope 1 and 2 emission intensity. Only companies for which either reported or estimated scope 3 emissions are considered when assessing scope 3 emission intensity.

Figure 2.9 shows the share of financial institutions that require an assurance by an external third party on either scope 1 and 2 or scope 3 reported emissions (by no. of companies, by total assets, by AUM and by tons of emissions), globally and within each region.

Figure 2.10 displays the shares of companies that disclosed GHG emission reduction targets (by no. of companies and by total assets and AUM) among all listed financial institutions, globally and within each region.

Figure 2.11 displays the shares of listed financial institutions that disclosed GHG scope 3 emission targets and scope 3 category 15 targets (by no. of companies and by total assets and AUM) among all listed financial institutions that disclosed GHG emission reduction targets, globally and within each region.

Figure 2.12 displays the shares of companies that disclosed a baseline year and target year (by no. of companies and by total assets and AUM) among all listed financial institutions that disclosed GHG emission reduction targets, globally and within each region.

Figure 2.13 displays the shares of companies that disclosed baseline emissions (by no. of companies and by total assets and AUM) among all listed financial institutions that disclosed GHG emission reduction targets, globally and within each region.

Figure 2.14 displays the shares of companies that disclosed scienced-based and net-zero reduction targets (by no. of companies and by total assets and AUM) among all listed companies globally.

Figure 2.15 displays the shares of listed financial institutions committing to setting SBTi science-based targets (by no. of companies and by total assets and AUM) among all listed companies that disclosed GHG emission reduction targets, globally and within each region.

Figure 2.16 displays the share of financial institutions having conducted transition planning (by no. of companies and by total assets and AUM), globally and within each region.

Figure 2.17 displays the shares of companies identifying climate-related risks as material (by no. of companies and by total assets and AUM) among all listed financial institutions, globally and within each region.

Figure 2.18 displays the share of companies having a board level oversight of climate-related issues (by no. of companies and by total assets and AUM) among all listed financial institutions, globally and within each region.

Figure 2.19 displays the share of companies that disclosed having a board committee responsible for sustainability (by no. of companies and by total assets and AUM) among all listed financial institutions, globally and within each region. A company is considered to have such a committee if its responsibilities explicitly include oversight of Corporate Social Responsibility, sustainability, and energy efficiency activities, regardless of the name of the committee. For example, a company with a “risk management committee” would be included in the categorisation if it mentioned that the committee is responsible for managing sustainability risks.

Figure 2.20 displays the share of companies that indicated establishing their executive or board compensation linked to sustainability matters (by no. of companies and by total assets and AUM) among all listed financial institutions, globally and within each region. The compensation policy includes remuneration for executive directors, non-board executives, and other management bodies based on “ESG or sustainability factors”.

Figure 3.1 displays the average annual reduction percentage of GHG emission inventory, globally and within each region. The analysis is based on the OECD Corporate Sustainability dataset, which consolidates data from multiple providers (LSEG, MSCI and Bloomberg). When both baseline and target emission values are available, the percentage reduction is calculated directly from them. In cases where these data are missing or incomplete, the reported reduction percentage disclosed by the company is used. To ensure the consistency and integrity of the data used in the analysis, the following validation checks are performed:

Consistency between target and baseline emission values: targets where target emissions exceed or equal baseline emissions are not considered in the analysis.

Temporal consistency between target and baseline years: targets where the target year equals or precedes the baseline year are not excluded from the analysis.

Plausibility of the time horizon: targets set before 2024 were excluded from the analysis.

Data completeness: where key target components are missing (e.g. baseline or target values), the reduction rate could not be computed, and those records are excluded from quantitative aggregation.

The annual reduction percentage is calculated by dividing the total reduction percentage by the number of years between the base year and the target year, where the total reduction percentage is the targeted emissions as of the base year. When a financial institution does not disclose a standalone scope 3 GHG emission target but scope 3 emissions represent at least 80% of its total emissions, the company’s total GHG emission reduction target is considered. When a single financial institution discloses multiple target years, the earliest year is used.

Figure 3.2Figure 3 displays the shares of companies that set GHG emission reduction targets before 2030, by 2030, between 2030 and 2050, by 2050 or after 2050 (by no. of companies and by total assets and AUM) among all listed financial institutions that disclosed GHG emission reduction target year, globally and within each region. When a company sets multiple targets, the most recent target is considered.

Figure 3.3 displays the average coverage rate of GHG emission targets within each region. The coverage rate for each company represents the ratio between the baseline emissions indicated by the target and the disclosed GHG emissions for the same year. This approach allows the assessment of the share of the GHG emissions covered by the target, that is the share of emission reduction from the baseline year. The analysis draws on the OECD Corporate Sustainability dataset, which consolidates data from multiple providers (LSEG, MSCI and Bloomberg). To ensure the consistency and integrity of the data used in the analysis, the following validation checks are performed:

Consistency between target and baseline emission values: targets where target emissions exceed or equal baseline emissions are not considered in the analysis.

Temporal consistency between target and baseline years: targets where the target year equals or precedes the baseline year are not excluded from the analysis.

Data completeness: where key target components are missing (e.g. baseline or target values), the reduction rate could not be computed, and those records are excluded from quantitative aggregation.

When a financial institution does not disclose a standalone scope 3 GHG emission target but scope 3 emissions represent at least 80% of its total emissions, the company’s total GHG emission reduction target is considered. When a single financial institution discloses multiple target years, the most recent year is used.

Figure 3.4 displays the progress of the targeted total GHG emissions as of 2024, globally and within each region. To measure this progress, the company’s GHG emissions in the current reporting year of the target are compared with the hypothetical value that would be emitted if emissions followed a linear pathway between the base year emissions and target year emissions. When a financial institution does not disclose a standalone scope 3 GHG emission target but scope 3 emissions represent at least 80% of its total emissions, the company’s total GHG emission reduction target is considered. When a single financial institution discloses multiple target years, the most recent year is used.

Figure 3.5 displays the projected GHG emissions (scope1, scope 2 and scope 3) with target and without target from 2023 to 2050 at global level. The projection is the aggregated GHG emissions for those institutions that disclosed a clear reduction target and reported GHG emissions in 2024.

Figure 3.6 displays the shares of companies disclosing the green revenues as a percentage of the total revenues. When disclosed, average green revenues are presented as the mean percentage of green revenues relative to total revenues. The analysis relies on LSEG classification of “green revenues”.

A.1.5. Sustainable bonds

Sustainable bonds are mainly collected from LSEG. This dataset contains deal-level information of nearly 19 000 bonds issued by both the corporate and official sectors from 109 jurisdictions since 2013. This dataset provides a detailed set of information for each sustainable bond issue, including the identity, nationality, and industry of the issuer; the type, interest rate structure, maturity date and rating category of the bond, the amount of and “use of proceeds” obtained from the issue. The issuance amounts were adjusted by 2024 US Consumer Price Index (CPI).

For sustainable bonds, values for corporations correspond to the “gross proceeds” (i.e. the amount paid by investors to acquire the bonds) in most cases. Where the information on the gross proceeds in the original dataset could not be retrieved, the “original amount issued” (i.e. the face value of the bonds in their legal documentation) has been used as follows: 38% of the amount issued from 2013 to 2024 corresponds to the original amount issued, whereas the remaining 62% corresponds to the gross proceeds. For that 62% in which the gross proceeds are used, the gross proceeds are 7% higher than original amount issued. However, the amount issued in “all corporate bonds”, which includes conventional bonds, corresponds to the gross proceeds amounts in all cases.

LSEG data contains both Regulation S and Rule 144A sustainable bonds. Rule 144A presents a safe harbour from the registration requirements of the Securities Act for resales of securities not fungible with securities listed on a US securities exchange to qualified institutional buyers. Regulation S provides a safe harbour from the registration requirements of the Securities Act for offerings made outside the United States (Bruckhaus, 2017[25])). The calculations presented take account of this factor, and an exercise to eliminate the duplication when a single bond was issued both under Regulation S and Rule 144A was performed.

When calculating the outstanding amount of bonds in a given year, issues that are no longer outstanding due to being redeemed earlier than their maturity should also be deducted. Outstanding values refer to the “principal amount” or otherwise to the “original amount issued” (i.e. the face value of the bonds in their legal documentation) when the “principal amount” could not be retrieved. The early redemption data are obtained from LSEG and cover bonds that have been redeemed early due to being repaid via final default distribution, called, liquidated, put or repurchased. The early redemption data are merged with the primary bond market data via international securities identification numbers (i.e. International Securities Identification Numbering).

A.1.6. Sustainable loans

Syndicated loans are collected from LSEG. A loan is considered syndicated when the issue is syndicated, that is a group of investment banks purchases shares from the issuer for resale to investors (also called an underwriting group). The dataset contains deal-level information of nearly 130 000 syndicated loans, with the announcement data between 2015 and 2024. The total amount corresponds to full loan package amount for all tranches.

Sustainable loans are those syndicated loans for which either the proceeds of the loan are used for green purposes (“green loans”), or the pricing of a loan is tied to the borrower’s performance against pre-determined sustainability criteria (“sustainability-linked loans”) or borrower’s industry is one of the following TRBC – Refinitiv business classification codes (“other sustainable loans”): Renewable Energy Equipment & Services, Wind Systems & Equipment, Stationary Fuel Cells, Photovoltaic Solar Systems & Equipment, Thermal Solar Systems & Equipment, Biomass Power Energy Equipment, Waste to Energy Systems & Equipment, Hydropower Equipment, Wave Power Energy Equipment, Renewable Energy Services, Geothermal Equipment, Renewable Fuels, Biodiesel, Ethanol Fuels, Pyrolytic & Synthetic Fuels, Biomass & Biogas Fuels, Hydrogen Fuel, Carbon Capture & Storage, Electrical (Alternative) Vehicles, Sustainable & Energy Efficient Home Builders, Organic Farming, Power Charging Stations, Alternative Electric Utilities, Hydroelectric & Tidal Utilities, Solar Electric Utilities, Wind Electric Utilities, Biomass & Waste to Energy Electric Utilities, Geothermal Electric Utilities, Independent Power Producers, Renewable Independent Power Producers.

A.1.7. OECD approach: assessing corporate GHG emission reduction targets

This paper evaluates the GHG emission reduction targets that are committed by corporates in their sustainability-related reports (hereafter referred to as corporate targets) using three key indicators: (1) the annual reduction rate, to measure the ambition of the corporate targets; (2) the GHG emission target coverage ratio, to assess their comprehensiveness, and (3) the “on track” status of targets, to assess the feasibility of corporate targets.

The ambition of corporate targets is assessed using the annual reduction rate, calculated with the following formula:

${Ambition}_i=\left(\frac{{target\mathrm{ }value}_i-{base\mathrm{ }value}_i}{{target\mathrm{ }value}_i}\right)/({target\mathrm{ }year}_i\mathrm{ }-{base\mathrm{ }year}_i)$

The comprehensiveness of corporate targets is assessed using the coverage rate, calculated with the following formula:

${\text{Comprehensiveness}}_i={max}_t(\mathrm{ }\mathrm{ }{base\mathrm{ }value}_{i,t}\mathrm{ }/\mathrm{ }{GHG\mathrm{ }emission}_{i,t}$)

The feasibility of corporate targets is calculated with the following formula:

${Feasibility}_i=\begin{array}{c}n track, if sum\left({GHG emission}_{i,n,2024}<{path}_{i,n,2024}\right)=N\\ not on track, if sum\left({GHG emission}_{i,n,2024}>{path}_{i,n,2024}\right)=N\end{array}$

${path}_{i,n,2023}={linear\mathrm{ }model(bas{e}_{value},\mathrm{ }targe{t}_{vale})}_{2024}$

where N stands for the number of targets.

Figure 3.1, Figure 3.2 and Figure 3.4 focus on the scope 3 GHG emissions targets, integrating total GHG emission targets when a firm’s scope 3 GHG emissions account for 80% or higher percentage of the total GHG emissions.