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Keywords = spatial quantile autoregression (SQAR)

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17 pages, 1326 KiB  
Article
Analysis of Factors Influencing Energy Efficiency Based on Spatial Quantile Autoregression: Evidence from the Panel Data in China
by Jinping Zhang, Qiuru Lu, Li Guan and Xiaoying Wang
Energies 2021, 14(2), 504; https://doi.org/10.3390/en14020504 - 19 Jan 2021
Cited by 15 | Viewed by 2583
Abstract
This research mainly studies the factors influencing the efficiency of energy utilization. Firstly, by calculating MoransI and local indicators of spatial association (LISA) of energy efficiency of regions in mainland China, we found that energy efficiency [...] Read more.
This research mainly studies the factors influencing the efficiency of energy utilization. Firstly, by calculating MoransI 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. Full article
(This article belongs to the Section C: Energy Economics and Policy)
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