Machine Learning Methods in Climate Finance: A Systematic Review

Andres Alonso-Robisco (Banco de España), Jose Manuel Carbo (Banco de España) and Jose Manuel Marques (Banco de España)

Climate Finance & Economics


Preventing the materialization of climate change is one of the main challenges of our time. The involvement of the financial sector is a fundamental pillar in this task, which has led to the emergence of a new field in the literature, climate finance. In turn, the use of Machine Learning (ML) as a tool to analyze climate finance is on the rise, due to the need to use big data to collect new climate-related information and model complex non-linear relationships. Considering the potential for the use of ML in climate finance and the proliferation of articles in this field, we propose a survey of the academic literature to assess how ML is enabling climate finance to scale up. The contribution of this paper is threefold. First, we do a systematic search in three scientific databases to assemble a corpus of relevant studies. Using topic modeling (Latent Dirichlet Allocation) we uncover representative thematic clusters. This allows us to statistically identify seven granular application domains where ML is playing a significant role in climate finance literature: natural hazards, biodiversity, agricultural risk, carbon markets, energy economics, ESG factors & investing, and climate data. Secondly, we do an analysis highlighting publication trends; and thirdly, we show a breakdown of ML methods applied by research area, aiming to spur further innovative work from ML experts.