Analysis of Factors Influencing Energy Efficiency Based on Spatial Quantile Autoregression: Evidence from the Panel Data in China

• Published in: Energies (Basel) Vol. 14; no. 2; p. 504
• Main Authors: Zhang, Jinping, Lu, Qiuru, Guan, Li, Wang, Xiaoying
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2021
• Subjects: Clustering; Data envelopment analysis; Economic development; Energy efficiency; Energy management; Energy utilization; Hypotheses; instrumental variable; Longitudinal studies; Moran’s I; Parameter estimation; Random variables; Regression analysis; spatial quantile autoregression (SQAR); Statistics; China
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en14020504

This research mainly studies the factors influencing the efficiency of energy utilization. Firstly, by calculating Moran’sI and local indicators of spatial association (LISA) of energy efficiency of regions in mainland China, we found that energy efficiency shows obvious spatial autocorrelation and spatial clustering phenomena. Secondly, we established the spatial quantile autoregression (SQAR) model, in which the energy efficiency is the response variable with seven influence factors. The seven factors include industrial structure, resource endowment, level of economic development etc. Based on the provincial panel data (1998–2016) of mainland China (data source: China Statistical Yearbook, Statistical Yearbook of provinces), the findings indicate that level of economic development and industrial structure have a significant role in promoting energy efficient. Resource endowment, government intervention and energy efficiency show a negative correlation. However, the negative effect of government intervention is weakened with the increase of energy efficiency. Lastly, we compare the results of SQAR with that of ordinary spatial autoregression (SAR). The empirical result shows that the SQAR model is superior to SAR model in influencing factors analysis of energy efficiency.