Energy transition affordability in China: Disparities and determinants

Abstract

This paper develops a parametric global-clustering metafrontier approach integrating input-output information to evaluate energy transition decarbonization costs. We then propose a novel and intuitive index for measuring energy transition affordability (ETA) that couples energy transition decarbonization costs with economic development level. Spatial Gini coefficient decomposition and spatial Markov chain are used to investigate the spatiotemporal disparities and long-term trend of ETA. Furthermore, we design a combined lasso, geographically and temporally weighted regression framework to identify the key determinants of ETA and capture their spatiotemporal heterogeneous effects. We apply the methodology to study the disparaities and determinants of energy transition affordability across 283 cities in China. The results indicate a favorable increasing trend, but a significant inequality of ETA across urban groups. Unique impacts of key determinants on ETA are observed for each city and each year. Our findings underscore the necessity of targeted policies and strategies towards more equitable energy transition in developing countries.