What if we told you that nearly a quarter-century of international climate finance—previously scattered, opaque, and difficult to analyze—is now available in one place? This isn’t just another dataset. It’s an insight into how multilateral development institutions have funded the fight against climate change across the globe. The recent release of the International Climate Finance Database (2000–2023) offers researchers, policy makers, and data journalists an excellent approach for examining the flow, purpose, and evolution of climate-related investments across borders. If you are involved with ESG, sustainability, or climate policy, this resource is not merely interesting, it’s essential.
What Is the International Climate Finance Database?
Developed and produced alongside climate finance researchers and machine learning engineers, this dataset is the first global project-level climate-related financial data collection covering multilateral development finance institutions (MDFIs) and multilateral climate funds (MCFs) over a 23-year period (2000–2023).
Developed and produced together with climate finance researchers and machine learning engineers, this is the first time there is a global project-level climate-related financial data collection of the multilateral development finance institutions (MDFIs) and multilateral climate funds (MCFs) for a specified 23-year period (2000-2023).
Available through Figshare, the database includes:
- 295 project records
- Detailed classifications (e.g. mitigation vs. adaptation)
- Subcategories (e.g. solar, wind, hydro, bioenergy, environment)
- Funding modalities (grants, loans, equity, guarantees)
- Institutional breakdowns (e.g. EBRD, IFC, IDB)
- Country-level distributions
The project is documented in a peer-reviewed article published in Nature Scientific Data in June 2025.
Why the International Climate Finance Database Matters: Context and Challenges
Global climate finance is a $600+ billion ecosystem, but transparency is still a big issue. Much of the data publicly available is siloed, inconsistent, or paywalled data. Until now, it has been a nightmare to navigate how macro-scale international lenders have been sup- porting the implementation of climate goals, or goals related to climate action, like ramping solar energy in Morocco, or resilience infrastructure in Bangladesh.
This dataset provides a remedy. It systematically maps who funded what, where, how much, and why.
The Technical Backbone: Machine Learning with MLCF-BERT
The database relies on a machine learning model called MLCF-BERT, trained to classify projects into “adaptation” and “mitigation” categories using natural language processing (NLP). The result is a reproducible, scalable method of climate finance categorization that minimizes human bias and enables large-scale tracking.
Example: A 2015 wind farm development in Egypt funded by EBRD is tagged as Mitigation > Wind Energy, along with its grant and loan structure. This allows climate analysts to easily surface all wind-related investments in North Africa over two decades.
Inside the International Climate Finance Database: How to Navigate the Files
Upon downloading the .zip file from Figshare, you’ll find a Code and data folder. This breaks down into two core parts:
- /Data/Climate_finance_data_2000_2023.xlsx — master dataset with 295 entries
- /Data/Climate_finance_data_by_institution/ — institutional breakdown (e.g., IFC, IDB)
- /Data/Climate_finance_data_by_recipient_country/ — country-specific records
Each record includes:
| Column Name | Description |
|---|---|
| Year | Year of financing |
| Climate_Class | Adaptation or Mitigation |
| Climate_Class_Sub | Project subcategory (e.g. solar, hydro) |
| Financing | Total amount committed |
| Loan, Grant, Equity | Financing structure |
| Institution | Funding body (e.g. World Bank, EBRD) |
| Recipient Country | Country receiving the funds |
This structure allows highly customizable queries: e.g., you can filter for solar energy loans between 2010–2020 in Latin America.
Key Trends Observed (2000–2023)
Using exploratory analysis, several insights emerge:
- Mitigation dominates: Over 70% of projects fall under mitigation (renewables, energy efficiency).
- Top sectors: Solar and hydro energy are the most commonly funded subcategories.
- Diversified funding: While grants are common in lower-income countries, loans and equity dominate in middle-income regions.
- Regional skew: South and Southeast Asia received a disproportionate share of adaptation projects.
These patterns offer critical input for evaluating past decisions and planning future climate financing.
Who Should Use the International Climate Finance Database (and How)
This is not just a research tool. It’s a strategic resource for:
1. Policy makers and NGOs
Understand historic allocations and identify underserved regions or sectors.
2. ESG Analysts
Benchmark climate fund flows and validate reporting metrics in sustainability assessments. Relevant if you’re auditing or rating climate portfolios.
3. Journalists and Investigators
Follow the money: trace financing from major institutions to on-the-ground climate projects.
4. Academic Researchers
Use the raw Excel files in regression models, spatial mapping, or historical policy impact studies.
How It Adds Value to ESG and Open Data Ecosystems
At The Database Search ESG section, we track public datasets that improve transparency in sustainability. The International Climate Finance Database does exactly that. It embodies open access principles and shows how data science can improve institutional accountability.
It also fills a major gap: while private finance gets much media attention, public multilateral climate finance is the bedrock of many national climate strategies. This dataset makes that bedrock visible.
Limitations of the International Climate Finance Database to Keep in Mind
No dataset is perfect. Notably:
- The database contains only 295 entries — it’s a curated subset, not a full census.
- It focuses solely on multilateral finance, excluding bilateral or private flows.
- Project descriptions vary in quality, depending on original source documents.
Still, its methodological rigor and open access format make it a cornerstone for future research.
Final Thoughts: Small Dataset, Big Potential
While 295 rows may seem modest, their depth, consistency, and categorization unlock powerful insights. Whether you’re building dashboards, crafting ESG reports, or advising policy, this resource gives you a clean, trusted dataset to work from.
If transparency, accountability, and impact are more than buzzwords for you, the International Climate Finance Database is worth your time.
For a closer look at how climate disinformation can distort the public understanding of data like the International Climate Finance Database, explore our article on the Hot Air Tool and climate disinformation.
Sources
- Nature Scientific Data: https://www.nature.com/articles/s41597-025-05308-x
- Dataset on Figshare: https://figshare.com/articles/dataset/International_Public_Multilateral_Climate_Finance_Dataset_from_2000_to_2023/28171535/1
