LAPSE:2023.29185
Published Article
LAPSE:2023.29185
Analysis of Factors Influencing Energy Efficiency Based on Spatial Quantile Autoregression: Evidence from the Panel Data in China
Jinping Zhang, Qiuru Lu, Li Guan, Xiaoying Wang
April 13, 2023
Abstract
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.
Keywords
Energy Efficiency, instrumental variable, Moran’s I, spatial quantile autoregression (SQAR)
Suggested Citation
Zhang J, Lu Q, Guan L, Wang X. Analysis of Factors Influencing Energy Efficiency Based on Spatial Quantile Autoregression: Evidence from the Panel Data in China. (2023). LAPSE:2023.29185
Author Affiliations
Zhang J: School of Mathematics and Physics, North China Electric Power University, Beijing 102206, China [ORCID]
Lu Q: School of Mathematics and Physics, North China Electric Power University, Beijing 102206, China
Guan L: College of Applied Sciences, Beijing University of Technology, Beijing 100124, China
Wang X: School of Mathematics and Physics, North China Electric Power University, Beijing 102206, China
Journal Name
Energies
Volume
14
Issue
2
Article Number
en14020504
Year
2021
Publication Date
2021-01-19
ISSN
1996-1073
Version Comments
Original Submission
Other Meta
PII: en14020504, Publication Type: Journal Article
Record Map
Published Article

LAPSE:2023.29185
This Record
External Link

https://doi.org/10.3390/en14020504
Publisher Version
Download
Files
Apr 13, 2023
Main Article
License
CC BY 4.0
Meta
Record Statistics
Record Views
290
Version History
[v1] (Original Submission)
Apr 13, 2023
 
Verified by curator on
Apr 13, 2023
This Version Number
v1
Citations
Most Recent
This Version
URL Here
http://psecommunity.org/LAPSE:2023.29185
 
Record Owner
Auto Uploader for LAPSE
Links to Related Works
Directly Related to This Work
Publisher Version