Analysis of Factors Influencing Energy Efficiency Based on Spatial Quantile Autoregression: Evidence from the Panel Data in China
This research mainly studies the factors influencing the efficiency of energy utilization. Firstly, by calculating <inline-formula><math display="inline"><semantics><mrow><mi>M</mi><mi>o</mi><mi>r</mi><mi>a</mi><msu...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/2/504 |
| _version_ | 1827599628869042176 |
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| author | Jinping Zhang Qiuru Lu Li Guan Xiaoying Wang |
| author_facet | Jinping Zhang Qiuru Lu Li Guan Xiaoying Wang |
| author_sort | Jinping Zhang |
| collection | DOAJ |
| description | This research mainly studies the factors influencing the efficiency of energy utilization. Firstly, by calculating <inline-formula><math display="inline"><semantics><mrow><mi>M</mi><mi>o</mi><mi>r</mi><mi>a</mi><msup><mi>n</mi><mo>’</mo></msup><mi>s</mi><mspace width="4pt"></mspace><mi>I</mi></mrow></semantics></math></inline-formula> 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. |
| first_indexed | 2024-03-09T04:17:44Z |
| format | Article |
| id | doaj.art-2aacf1ac004d4d7a943f42f32b88919c |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T04:17:44Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-2aacf1ac004d4d7a943f42f32b88919c2023-12-03T13:50:21ZengMDPI AGEnergies1996-10732021-01-0114250410.3390/en14020504Analysis of Factors Influencing Energy Efficiency Based on Spatial Quantile Autoregression: Evidence from the Panel Data in ChinaJinping Zhang0Qiuru Lu1Li Guan2Xiaoying Wang3School of Mathematics and Physics, North China Electric Power University, Beijing 102206, ChinaSchool of Mathematics and Physics, North China Electric Power University, Beijing 102206, ChinaCollege of Applied Sciences, Beijing University of Technology, Beijing 100124, ChinaSchool of Mathematics and Physics, North China Electric Power University, Beijing 102206, ChinaThis research mainly studies the factors influencing the efficiency of energy utilization. Firstly, by calculating <inline-formula><math display="inline"><semantics><mrow><mi>M</mi><mi>o</mi><mi>r</mi><mi>a</mi><msup><mi>n</mi><mo>’</mo></msup><mi>s</mi><mspace width="4pt"></mspace><mi>I</mi></mrow></semantics></math></inline-formula> 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.https://www.mdpi.com/1996-1073/14/2/504<i>Moran’s I</i>energy efficiencyspatial quantile autoregression (SQAR)instrumental variable |
| spellingShingle | Jinping Zhang Qiuru Lu Li Guan Xiaoying Wang Analysis of Factors Influencing Energy Efficiency Based on Spatial Quantile Autoregression: Evidence from the Panel Data in China Energies <i>Moran’s I</i> energy efficiency spatial quantile autoregression (SQAR) instrumental variable |
| title | Analysis of Factors Influencing Energy Efficiency Based on Spatial Quantile Autoregression: Evidence from the Panel Data in China |
| title_full | Analysis of Factors Influencing Energy Efficiency Based on Spatial Quantile Autoregression: Evidence from the Panel Data in China |
| title_fullStr | Analysis of Factors Influencing Energy Efficiency Based on Spatial Quantile Autoregression: Evidence from the Panel Data in China |
| title_full_unstemmed | Analysis of Factors Influencing Energy Efficiency Based on Spatial Quantile Autoregression: Evidence from the Panel Data in China |
| title_short | Analysis of Factors Influencing Energy Efficiency Based on Spatial Quantile Autoregression: Evidence from the Panel Data in China |
| title_sort | analysis of factors influencing energy efficiency based on spatial quantile autoregression evidence from the panel data in china |
| topic | <i>Moran’s I</i> energy efficiency spatial quantile autoregression (SQAR) instrumental variable |
| url | https://www.mdpi.com/1996-1073/14/2/504 |
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