Financed emissions data will be included in reporting requirements for financial institutions under the draft S2 standard proposed by the International Sustainability Standards Board (ISSB) to be released in June 2023. Other standards for disclosure by financial institutions, including the Partnership for Carbon Accounting Financials (PCAF) also referenced financed emissions.
Despite the importance of financed emissions to the emissions profile and trajectory of financial institutions – CDP estimated that financed emissions exceeded direct emissions of financial institutions by 700 times – direct data is difficult to access. The RFI Foundation has adapted a methodology for estimating bank-level financed emissions data for 11 Islamic markets in an effort to provide data to fill this important gap which is likely to persist despite the requirement for its disclosure.
RFI Insights' analysis is produced using a top-down approach adapted from the ‘P9XCA’ methodology developed by the Finance and Sustainable Development Chair of Paris Dauphine University at the request of Crédit Agricole CIB and published as a Sectorial Guide for the Financial Sector by French environmental and energy agency ADEME (Agence de l’Environnement et de la Maîtrise de l’Energie).
The methodology takes two data sets, on greenhouse gas emissions and financial assets, in a particular country and matches up financing assets enabling non-financial sectors with the emissions generated by those sectors. There is a pro rata split of financial assets (bank loans, bonds/sukuk, and equity) within each economic sector having a proportional allocation of the GHG emissions of that sector.
The resulting data provides an ‘order of magnitude’ estimate for a financial institutions’ financed emissions. We also present an estimate of indirect (customer Scope 3) emissions for a few key sectors – electricity generation, transportation and waste management – since some standards include (or will in the future include) these emissions within the scope of financed emissions required to be disclosed.
For ease of reference, we have organized the data estimates in the financed emission database into the following categories where ‘direct’ emissions refer to the emissions directly emitted by the counterparties of the financial institution. These would include the customer Scope 1 emissions under most definitions of ‘financed emissions’ which makes up a significant share of those emissions included under Scope 3, Category 15 as defined in the GHG Protocol.
“Net Combined” or “Gross” emissions' does not correspond exactly to widely used definitions of ‘financed emissions' but it includes most of the customer Scope 2 emissions that are required under the GHG Protocol’s definition in the Scope 3 Calculation Guidance for Category 15 (April 2013). It also includes an estimate of the embedded emissions related to transportation and waste management, which would present data that is broader than what is required to be reported by the GHG Protocol and under the draft S2 Climate-related Disclosures but narrower than the full value-chain emissions disclosures that are phased in by the PCAF Global GHG Accounting and Reporting Standard.
In our estimate, we reallocate a country’s reported emissions related to electricity generation, transportation and waste management as ‘indirect’ emissions of the counterparties of a financial institution (including counterparties from those three sectors). The reallocation to each sector is assumed to be proportional to the sector’s share of the country’s GDP with an emissions factor adjustment made to account for greater resource use by industrial sectors compared to services sectors.
For ‘net’ emissions, our estimates will be cumulatively consistent with the country’s reported GHG emissions total and avoid any double-counting. Since the ISSB’s draft S2 climate-related disclosure standard also references in §21(a) ‘absolute gross’ emissions disclosures that would make emissions disclosures equivalent regardless of whether the value chain is consolidated or independent, we have added the option to present the data where direct and indirect emissions are shown cumulatively, which includes some double-counting of value-chain emissions.
| Direct Emissions (KgCO2) | Refers to the distribution of the GHG emissions before reallocating the indirect emissions (Electricity, Transportation, & Waste) through to their ultimate demand within other sectors |
| Indirect (Net) Combined Emissions (KgCO2) | Refers to the combination of direct and indirect emissions demands for Electricity, Transportation and Waste Management where relevant emissions are subtracted from the supplying sector and added to the demanding sector to avoid double counting of emissions. |
| Indirect (Gross) Combined Emissions (KgCO2) | Refers to the combination of direct and indirect emissions demands for Electricity, Transportation and Waste Management. Relevant emissions are not subtracted from the supplying sector and added to the demanding sector and total estimates show more value chain emissions but does include some double counting of emissions. |
For further information on definitions and methods please refer to the our methodology & data sources page.
This work is licensed under a Creative Commons Attribution 4.0 International License. If you use the information in this database, please note the disclaimer of warranties and limitation of liabilities contained in Section 5.
Suggested Citation: RFI Foundation. 2023. RFI Insights: Financed Emissions in Islamic Markets. Available at: https://rfi-insights.org/financed-emissions-in-islamic-markets
The data that we have estimated and included in this financed emissions database are designed to provide ‘order of magnitude’ estimates about the financed emissions exposures within the 11 Islamic markets that we cover. By ‘order of magnitude’ we mean that the estimates are not intended to be precise data on the emissions of the countries or financial institutions referenced in the database.
One use of the data for financial sector stakeholders would be to compare the public disclosures about climate mitigation and transition planning of a particular financial institution to see whether this is aligned from a materiality perspective. There may be situations where top-down data such as what we have produced may diverge from a financial institution’s actual concentration of financed emissions risks.
In the absence of other sources of validated or audited data released by the institutions, their regulators, main customers or other sources, an ‘order of magnitude’ estimate can provide a starting point for engagement by that financial institution’s stakeholders including investors, regulators, customers, employees or other NGOs.
For financial institutions that are considering their climate risks, or who have already begun, this data can complement the data they are receiving from customers or from other sources. Most emissions data available from companies is estimated and different data sources can provide significant variability of emissions intensity for the same counterparty. A top-down emissions estimate such as the one in this database can be used as a point of comparison for reported or estimated bottoms-up emissions data.
There are many reasons why the data wouldn’t be precise from either a top-down source or from bottom-up sources. Top-down estimates rely on broad sectoral categories that have substantial heterogeneity in emissions intensity within them. They may not capture every financial asset in a country – for example, there is much more data available on bank financing and capital market assets than there is on privately-held equity.
Similarly, bottoms-up data may be directly reported by the emitting entity but may nonetheless be estimated. Unless it is audited as conforming to a single reporting standard, it may not be verified, comparable and credible and could be misstated whether or not intentionally. Emissions data beyond the emissions directly under the control of the reporting entity (and for emissions even those it appears to be in control of like fugitive emissions) add a level of uncertainty.
All of these sources of uncertainty are important when making use of our data or the data from other sources. Measurement and estimation of emissions is still developing, but the actions that science tells us are required to control climate change cannot be delayed on the basis of lack of data. Therefore, it is important to use the data that is available, supplement it with information from different sources (e.g., top-down and bottom-up), and continually review and revise the sources and uses of data.
We want to know how you are using the data from the RFI Insights Financed Emissions Database. Drop us a line at info@rfi-foundation.org.
This page provides the overall summary of financed emissions across sectors and countries. You can easily see what sectors have the highest concentration of financed direct and indirect emissions, making it a great way to start using this database before diving into more details in the next pages. For best experience, we recommend comparing two countries at a time. If you’re looking for access to more of the underlying data, please contact us.
This page looks at each element of the financial system (Banking, Equity, and Bonds & Sukuk), and their financed direct and indirect emissions across sectors. This highlights the major sources of financing emissions and the differences between these dynamics across countries and regions. For best experience, we recommend comparing two countries at a time. If you are looking for a large dataset, do not hesitate to contact us.
This page breaks emissions down to bank level data. it allows you to look at the estimated direct and indirect emissions of each bank. You can also compare multiple banks at once. For best experience, we recommend comparing two banks at a time. If you’re looking for access to more of the underlying data, please contact us.