Danmarks Nationalbank details climate footprint indicators
Danmarks Nationalbank has published a comprehensive methodology document for its climate-related indicators. These indicators aim to measure the financial sector's financing of greenhouse gas emissions and exposure to emission-intensive companies.
Mapping financial sector's climate exposure
The climate indicators track greenhouse gas emissions from the financial sector's investments and lending.
They cover investments in listed equities, corporate bonds, and Danish mortgage bonds by insurance and pension companies, investment funds, banks, and mortgage credit institutions.
The purpose is to show the sector's financing of emissions and exposure to emission-intensive companies.
Key indicators include Absolute financed emissions, Carbon intensity, Weighted average carbon intensity, and CO2e footprint.
These are experimental statistics, with sources and methods subject to change as data quality improves and global standards evolve.
Deconstructing financed emissions
The methodology distinguishes between Scope 1 (direct emissions), Scope 2 (indirect from energy purchases), and Scope 3 (indirect in the value chain) emissions.
Calculation methods are based on recommendations from the Task Force on Climate-Related Financial Disclosures (TCFD) and the Partnership for Carbon Accounting Financials (PCAF).
Absolute financed emissions quantify total greenhouse gases financed by a portfolio based on ownership share.
Carbon intensity measures emissions relative to revenue, indicating how emission-intensive portfolio companies are.
Weighted average carbon intensity assesses a portfolio's exposure to such companies.
Pioneering, but still evolving
Danmarks Nationalbank's initiative provides crucial transparency in a complex and rapidly developing area.
However, the 'experimental' nature and acknowledged lack of global standards highlight ongoing challenges in climate finance measurement.
This underscores the need for continued methodological development and international harmonization to ensure robust and comparable data.