Tracking and Mobilizing Private Sector Climate Adaptation Finance

This work has two central objectives: 

1. To develop a taxonomy for private adaptation finance tracking that is comprehensive, descriptive, and in harmony with wider efforts in the community, building upon existing attempts to define and classify adaptation finance in both a public and private context. The objective of this work is to structure the taxonomy so that it can be increasingly incorporated into CPI’s existing tracking of climate finance, including the GLCF. 
2. To build a repeatable and flexible computational framework for expanding the scope of trackable private adaptation finance data. The method (which is ultimately intended to be open source) will allow CPI—and ideally the broader community, including policymakers and capital allocators in the private sector—to more readily assess the adaptation relevance of investments. The dataset builds upon prior adaptation finance tracking approaches, with the inclusion of additional data sources. The application of a suite of machine learning tools and algorithms that leverage large language models allows efficient and accurate categorization of finance flows according to the taxonomy developed and can accommodate evolution of that taxonomy.  

In pursuit of the above objectives, CPI, in collaboration with Vibrant Data Labs, has developed a computational approach to improve tracking of private sector adaptation finance data. These efforts have increased private adaptation finance tracked by more than four times from the approximately USD 1 billion previously tracked in CPI’s GLCF on average annually for the period 2019 to 2022 for the same set of private sector institutions. By applying the new methodology – including the bespoke taxonomy for identifying adaptation-relevant private finance – the tracking in this report now captures annual average flows of USD 4.7 billion from the private sector to adaptation-relevant activities.