Tracking and Mobilizing Private Sector Climate Adaptation Finance

The report introduces a machine learning-based approach and a detailed taxonomy to better capture private sector adaptation finance, which is historically under-reported. By applying this new methodology, the Climate Policy Initiative increased its tracked annual average private adaptation finance for 2019-2022 from USD 1.0 billion to USD 4.7 billion.

• Adaptation finance tracked in the Global Landscape of Climate Finance (GLCF) reached an annual average of USD 63 billion for 2021/22, representing a 28% increase from the 2019/20 average. However, this remains significantly below the estimated USD 212 billion per year needed by 2030 for developing economies.
• Private sector adaptation finance is severely under-reported, with previous CPI tracking showing an annual average of approximately USD 1.5 billion in 2021/22. Specific gaps in tracking include finance from private equity and venture capital for early-stage companies, adaptation-focused SMEs, insurance premiums for resilient construction, consumer spending, and corporate balance sheet finance.
• The implementation of a new computational framework and bespoke taxonomy increased the annual average tracked private adaptation finance for the 2019-2022 period from USD 1.0 billion to USD 4.7 billion. The most significant improvements in data capture were seen for corporations, individuals/households, commercial financial institutions, and asset managers.
• The new tracking methodology categorizes finance by the likelihood of adaptation relevance: 5% is classified as high likelihood, 79% as medium likelihood, and 16% as low likelihood. Additionally, 27% of the tracked finance is identified as having a potential risk for maladaptation or significant harm.
• The developed taxonomy classifies adaptation activities across seven themes and 165 specific activities (as of September 2024). It tags activities based on criteria including gender relevance, cities relevance, mitigation co-benefits, and the specific climate hazards addressed.
• The data process utilizes a six-step approach involving data scoping, collection, tagging, the development of a supervised large language model (LLM) classifier to categorize entities as adaptation, mitigation, or dual-purpose, taxonomy mapping, and final analysis.

APA

Climate Policy Initiative (2024). Tracking and Mobilizing Private Sector Climate Adaptation Finance. https://www.climatepolicyinitiative.org/publication/tracking-and-mobilizing-private-sector-climate-adaptation-finance/

Chicago

Climate Policy Initiative. Tracking and Mobilizing Private Sector Climate Adaptation Finance. 2024. https://www.climatepolicyinitiative.org/publication/tracking-and-mobilizing-private-sector-climate-adaptation-finance/.

Wikipedia

{{cite report |author=Climate Policy Initiative |title=Tracking and Mobilizing Private Sector Climate Adaptation Finance |date=25 September 2024 |url=https://www.climatepolicyinitiative.org/publication/tracking-and-mobilizing-private-sector-climate-adaptation-finance/ |access-date=23 October 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{climatepolicyinitiative2024tracking, author = {{Climate Policy Initiative}}, title = {{Tracking and Mobilizing Private Sector Climate Adaptation Finance}}, institution = {Climate Policy Initiative}, year = {2024}, month = sep, url = {https://www.climatepolicyinitiative.org/publication/tracking-and-mobilizing-private-sector-climate-adaptation-finance/}, urldate = {2026-10-23}, note = {Indexed by Climate Insights Directory} }