Leveraging Explainable AI to Decode Energy Poverty in China: Implications for SDGs and National Policy

The precise identification of energy poor households is a critical step towards achieving the United Nations Sustainable Development Goals (SDGs), particularly SDG 7 (Affordable and Clean Energy) and SDG 1 (No Poverty), while also intersecting with climate action (SDG 13). As the world’s largest developing country, China faces unique energy poverty challenges characterized by significant regional disparities and uneven access to modern energy services. To support targeted interventions and equitable policy-making, this study proposes an explainable artificial intelligence (XAI) framework for predicting and interpreting energy poverty. Utilizing nationally representative data from the China Family Panel Studies (CFPS) from 2014 to 2020, we developed a predictive model that integrates a Convolutional Neural Network with SHapley Additive exPlanations (SHAP). Our model, EPPE-FCS, demonstrated exceptional predictive performance, achieving an average accuracy of 98.23%, outperforming several mainstream benchmarks. Crucially, the SHAP interpretability analysis revealed that annual per capita household expenditure is the most influential driver, while the contribution of energy burden indicators (electricity and gas expenses) exhibited a significant decreasing trend. This trend likely reflects the positive impact of China’s national policies, such as the “Clean Heating Initiative” and “Targeted Poverty Alleviation,” on improving energy infrastructure and affordability. The findings underscore the necessity of a dual-track policy that combines immediate energy cost subsidies with long-term strategies for income enhancement and clean energy transition. This research provides policymakers with a robust tool to alleviate energy poverty, thereby advancing a just, sustainable, and climate-resilient energy future in China and other developing regions.