As regulatory pressures and stakeholder expectations rise, financial institutions need accurate, scalable, cost-effective ways to measure, disclose, and reduce financed emissions and improve portfolio resilience.
Climative partnered with a top 10 North American bank to develop a whitepaper that describes a novel approach tested in a project (click here to request a copy). Results show how Climative’s Automated Carbon Model helps financial institutions report portfolio emissions at scale with PCAF Data Quality Score (DQS) of 3 or higher. Climative is now a PCAF Accredited Partner, recognizing our alignment with global standards for reporting and disclosing financed emissions.
Good to Know:
PCAF is the Partnership for Carbon Accounting Financials.
Data Quality Score (DQS) ranges from 1 (highest data quality) to 5 (lowest data quality).
Financial institutions face three challenges in measuring and reporting their financed emissions footprint and setting carbon reduction targets:
The Automated Carbon Model (ACMTM) uses machine learning algorithms, historical energy audit results, property-level building characteristics data, weather data, and region-specific emission factors to offer a robust framework for measuring and tracking financed emissions. This approach addresses the key challenges (data gaps, low data quality, scale and cost) and gives actionable roadmaps for reducing climate risk and carbon emissions.
Key findings from the project between Climative and a top 10 North American bank:
By adopting the ACMTM and partnering with Climative, financial institutions can improve their emissions reporting data quality, set more meaningful carbon reduction targets, and develop effective strategies for managing climate-related risks and opportunities in their mortgage portfolios.
The Climative team would be happy to discuss the methodology and implications of these results. Please request the full whitepaper below, or click here to schedule a call with the author and our Director of Data Science, Tianshu Huang.
Tian is a dedicated and creative statistical modeler who leads the development of Climative’s data strategy and machine learning algorithms. She believes that ethical and thoughtful use of AI will help us achieve climate goals while creating equal opportunity for all.
Climative provides a collaborative AI-assisted data platform for organizations to enable personalized advice and offers to building owners, taking the guesswork out of building upgrades and transforming the low carbon economy.
