Demographic efficiency drivers in the Chinese energy production chain: A hybrid neural multi‐activity network data envelopment analysis

For meeting the external requirements of the Paris Agreement and reducing energy consumption per gross domestic product, China needs to improve its energy efficiency. Although the existing studies have attempted to investigate energy efficiency from different perspectives, little effort has yet been made to consider the collaboration among different stages in the production chain to produce energy outputs. In addition, various studies have also examined the determinants of energy efficiency, however, they mainly focused on technology and economic factors, no study has yet proposed and considered the influence of geographical factors on energy efficiency. In this article, we fill in the gap and make theoretical and empirical contributions to the literature. In this study, a two‐stage analysis method is used to analyse energy efficiency and the influencing factors in China between 2009 and 2021. More specifically, from the theoretical/methodological perspective, a multi‐activity network data envelopment analysis model is used to measure energy efficiency of different processes in the energy production chain. From the empirical perspective, we attempt to investigate the influence of geographical factors on energy efficiency through a neural network analysis. Meanwhile, the comparisons among different provinces are made. The result shows that the overall energy efficiency is low in China, and China relies more on the traditional energy industry than the clean energy industry. The efficiency level experiences a level of volatility over the examined period. Finally, we find that raw fuel pre‐process and industry have a significant and positive impact on energy efficiency in China.