Reflexive Evidence-Based Multimodal Learning for Clean Energy Transitions: Causal Insights on Cooking Fuel Access, Urbanization, and Carbon Emissions

Shan, Shan

Reflexive Evidence-Based Multimodal Learning for Clean Energy Transitions: Causal Insights on Cooking Fuel Access, Urbanization, and Carbon Emissions (Papers Track)

Shan Shan (Zhejiang University)

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Generative Modeling Behavioral and Social Science Carbon Capture & Sequestration Climate Science & Modeling Public Policy Societal Adaptation & Resilience Causal & Bayesian Methods Data Mining Interpretable ML Natural Language Processing Unsupervised & Semi-Supervised Learning

Abstract

Achieving Sustainable Development Goal 7 (Affordable and Clean Energy) requires not only technological innovation but also a deeper understanding of the socio-economic factors that influence energy access and carbon emissions. Despite growing attention to these drivers, key questions remain, particularly regarding how to quantify socio-economic impacts, how these impacts interact across domains such as policy, technology, and infrastructure, and how feedback processes shape energy systems. To address these gaps, this study introduces ClimateAgents, an AI-based framework that combines large language models with domain-specialized agents to support hypothesis generation and scenario exploration. Leveraging 20 years of socio-economic and emissions data from 265 economies, countries and regions, and 98 indicators drawn from the World Bank database, the framework applies a machine learning–based causal inference approach to identify key determinants of carbon emissions in an evidence-based, data-driven manner. The analysis highlights three primary drivers: (1) access to clean cooking fuels in rural areas, (2) access to clean cooking fuels in urban areas, and (3) the percentage of population living in urban areas. These findings underscore the critical role of clean cooking technologies and urbanization patterns in shaping emission outcomes. In line with growing calls for evidence-based AI policy, ClimateAgents offers a modular and reflexive learning system that supports the generation of credible and actionable insights for policy. By integrating heterogeneous data modalities, including structured indicators, policy documents, and semantic reasoning, the framework contributes to adaptive policymaking infrastructures that can evolve with complex socio-technical challenges. This approach aims to support a shift from siloed modeling to reflexive, modular systems designed for dynamic, context-aware climate action.