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

Abstract

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.

Foreign Direct Investment Performance Drivers at the Country Level: A Robust Compromise Multi-Criteria Decision-Making Approach

“…The literature has also examined the socioeconomic factors and their impact on different industries (Wanke et al, 2023b;Antunes et al, 2023aAntunes et al, , 2023bZhao et al, 2022). More specifically, in the banking context, Wanke et al (2023b) investigate the impact of a series of contextual variables, including inflation, human development index, GDP growth, infant mortality, life expectancy, and energy use on bank efficiency through a generalized linear autoregressive moving average model.…”

“…Similar techniques were also applied by Antunes et al (2023b) to investigate a number of socioeconomic factors, including research and development expenses, FDI, paved road, GDP per capita, population density, innovation index, and second industry share, on road transportation sustainability performance in China. Zhao et al (2022) investigate the impact of socioeconomic factors on the Chinese energy production chain through a neural network analysis. The factors considered include birth rate, consumer price index, passengers using different transport modes, students in different levels of education, and urban population, among others.…”