Machine Learning Applications in Climate Finance, Energy Systems and Sustainable Economic Policy

Abstract

Background: The convergence of climate urgency and digital innovation has positioned machine learning (ML) as a transformative force in sustainable development. Climate finance, energy systems and economic policy now generate large, noisy and heterogeneous datasets that exceed the limits of many traditional analytical tools. Objective: This manuscript examines ML applications across three interconnected domains: climate finance, energy systems and sustainable economic policy. Methods: A systematic literature review following PRISMA principles was combined with bibliometric mapping, meta-analysis of model performance, comparative assessment of ML techniques, explainable-AI synthesis and case-study evidence from climate-related financial policies across 87 countries. Results: ML models demonstrate superior predictive accuracy in carbon-price forecasting, renewable-energy forecasting, ESG scoring and policy evaluation. Ensemble models improve carbon-price forecasting by approximately 15-25% compared with traditional econometric approaches; deep-learning models for solar-PV forecasting commonly achieve MAPE values of 2-5%; and SHAP-based policy analysis shows that advanced economies gain stronger policy impact than emerging markets because of better institutions, data infrastructure and enforcement capacity. Conclusion: ML can strengthen sustainability decision-making by improving prediction, reducing uncertainty, revealing hidden patterns and supporting integrated modelling. However, data quality, interpretability, algorithmic bias, regulatory fragmentation and uneven institutional capacity remain decisive barriers. The study recommends standardized data systems, AI governance rules, explainable models, regional capacity building and integrated decision-support frameworks.

Keywords

Machine Learning; Climate Finance; Energy Systems; Sustainable Policy; Carbon Markets; Esg; Renewable Energy; Explainable Ai; Ensemble Learning; Green Taxation.