Using Machine Learning to Track International Climate Finance

Researchers used natural language processing to track international climate finance based on textual descriptions of development projects.

Photo by William Gibson on Unsplash

International climate finance is a key ingredient for global action to curb global temperature rises and prepare societies for the already irreversible effects of climate change. At the 2009 Copenhagen summit, high-income countries committed to mobilize US$ 100 billion annually from 2020 onwards to support mitigation and adaptation in developing countries. This promise was a crucial step to get lower and middle income countries to define national emissions reduction targets and led to the Paris Agreement where, for the first time, all countries committed to climate action.

The latest report by the OECD shows that, in 2020, contributor countries failed to meet the US$ 100 billion climate finance target. This weakens trust of recipient countries and complicates the current climate negotiations in Egypt (COP27), where countries are starting to define a new climate finance target for the period after 2025—the “new collective quantified goal.”

Tracking global flows of climate finance is difficult. Countries and institutions self-report the projects and corresponding finances which contribute to the US$ 100 billion target. However, these contributors have different methods for assessing what counts as climate finance, and the reporting process is not transparent. Moreover, researchers and NGOs have found that many projects are misreported and should not be counted as climate finance.

Analyzing project descriptions with natural language processing

In our new study, Florian Egli, Anna Stünzi, and I developed a natural language processing model (ClimateFinanceBERT) that identifies climate finance projects based on their textual descriptions and classifies them into granular climate finance categories (e.g., solar energy or energy efficiency). Using ClimateFinanceBERT, we analyzed 2.7 million descriptions of bilateral development projects from 2000 to 2019. The model classified 80,023 of these projects as climate finance (52% adaptation and 48% mitigation projects), totalling US$ 80 billion.

For the period after the Paris Agreement (2016–2019), our estimates are about 64% lower than the officially reported project assessments. These findings show a great disparity between promised and delivered climate finance and support claims that contributor-reported numbers may be inflated.

The study also provides important take-aways on how machine learning could help address major challenges in climate finance accounting. The machine learning based approach offers three benefits: flexibility regarding the scope of climate finance, consistency in the evaluations of textual descriptions, and scalability that enables cost- and time-effective analysis regardless of the number of documents.

What role can machine learning play in climate finance accounting?

Despite its benefits, a machine learning-based approach cannot substitute entirely for human evaluations.

Climate finance is complex, and so is the task of classifying it. Projects related to climate change adaptation are highly context-specific. They may require expert knowledge of other external factors, which are not provided in the project descriptions, to classify them correctly. For example, water and sanitation projects are more relevant for climate adaptation in regions affected by droughts.

Furthermore, the complexity of climate finance means machine learning classifiers may misclassify some infrequent and underrepresented types of projects. This risk is intrinsic to supervised classification: a classifier trained on one set of project descriptions must generalize to new descriptions it hasn’t seen during training.

In our study, we trained our classifier on 1,500 climate finance projects and then classified 2.7 million project descriptions. To mitigate blind spots, we:

While machine learning will not be able to replace human evaluators, it is a helpful tool that can complement human assessment by double-checking reported projects, simulating outcomes for different climate finance scopes, and analyzing how climate finance is distributed over different sub-categories.

While machine learning is not a silver bullet, it allows parties such as contributors, recipients, and NGOs to review climate finance contributions based on consistent criteria and a flexible scope. This would enable all parties to discuss targets for climate finance and climate action at eye level. Ultimately, tracking international climate finance with machine learning could help restore trust in the global community, which is essential for agreeing on credible climate finance targets and translating financial support into effective climate action.

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This post represents the views of its authors, and does not necessarily represent the views of Climate Change AI.