Spatial and Temporal Evolution of Regional Energy Efficiency in China and Its Influencing Factors
Finding ways to improve regional energy efficiency is important for the Chinese government to achieve its dual carbon target. This paper aims to explore ways to improve regional energy efficiency by studying the spatial–temporal dynamic evolution of energy efficiency. To scientifically study the evo...
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2024-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/23/6168 |
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| author | Jinqiu Li Yufeng Hu Hui Zhang |
| author_facet | Jinqiu Li Yufeng Hu Hui Zhang |
| author_sort | Jinqiu Li |
| collection | DOAJ |
| description | Finding ways to improve regional energy efficiency is important for the Chinese government to achieve its dual carbon target. This paper aims to explore ways to improve regional energy efficiency by studying the spatial–temporal dynamic evolution of energy efficiency. To scientifically study the evolution trend in regional energy efficiency in China, this study uses convergence analysis, a spatial Gini coefficient decomposition model (no spatial consideration), and a spatial Markov chain model and spatial measurement model (spatial consideration). The results show the following: from 2008 to 2019, the mean value of regional single-factor energy efficiency (RS) showed an obvious trend of continuous increase, while the mean value of regional green total-factor energy efficiency (RT) changed from a trend of continuous decline to a relatively stable trend. The overall Gini coefficient of RS showed a trend of “steady–rising–steady”, and the overall Gini coefficient of RT showed a trend of “steady–small increase–sharp increase–fall”. There was club convergence in the two types of regional energy efficiency, and both of them achieved certain “leapfrog” changes. The factors that had a significant impact on RS include human capital, industrialization, openness, urbanization, financial development, and innovation environment. The significant factors for RT included governance structure, industrialization, openness, policy support, and financial development. The limitation of this paper is that only provincial data were used. In the future, city-level data can be mined and more detailed policy suggestions can be put forward for city-level differences. The research method used in this paper to study regional energy efficiency evolution trends is also applicable to other countries. |
| format | Article |
| id | doaj-art-bf80d3ac76d54d6b8ba36a02b07c38c1 |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-bf80d3ac76d54d6b8ba36a02b07c38c12024-12-13T16:26:15ZengMDPI AGEnergies1996-10732024-12-011723616810.3390/en17236168Spatial and Temporal Evolution of Regional Energy Efficiency in China and Its Influencing FactorsJinqiu Li0Yufeng Hu1Hui Zhang2Finance School, Tongling University, Tongling 244061, ChinaFinance School, Tongling University, Tongling 244061, ChinaFinance School, Tongling University, Tongling 244061, ChinaFinding ways to improve regional energy efficiency is important for the Chinese government to achieve its dual carbon target. This paper aims to explore ways to improve regional energy efficiency by studying the spatial–temporal dynamic evolution of energy efficiency. To scientifically study the evolution trend in regional energy efficiency in China, this study uses convergence analysis, a spatial Gini coefficient decomposition model (no spatial consideration), and a spatial Markov chain model and spatial measurement model (spatial consideration). The results show the following: from 2008 to 2019, the mean value of regional single-factor energy efficiency (RS) showed an obvious trend of continuous increase, while the mean value of regional green total-factor energy efficiency (RT) changed from a trend of continuous decline to a relatively stable trend. The overall Gini coefficient of RS showed a trend of “steady–rising–steady”, and the overall Gini coefficient of RT showed a trend of “steady–small increase–sharp increase–fall”. There was club convergence in the two types of regional energy efficiency, and both of them achieved certain “leapfrog” changes. The factors that had a significant impact on RS include human capital, industrialization, openness, urbanization, financial development, and innovation environment. The significant factors for RT included governance structure, industrialization, openness, policy support, and financial development. The limitation of this paper is that only provincial data were used. In the future, city-level data can be mined and more detailed policy suggestions can be put forward for city-level differences. The research method used in this paper to study regional energy efficiency evolution trends is also applicable to other countries.https://www.mdpi.com/1996-1073/17/23/6168regional energy efficiencyspace–time evolutionspatial Gini coefficient decomposition modelspatial Markov chain modelspatial econometric model |
| spellingShingle | Jinqiu Li Yufeng Hu Hui Zhang Spatial and Temporal Evolution of Regional Energy Efficiency in China and Its Influencing Factors Energies regional energy efficiency space–time evolution spatial Gini coefficient decomposition model spatial Markov chain model spatial econometric model |
| title | Spatial and Temporal Evolution of Regional Energy Efficiency in China and Its Influencing Factors |
| title_full | Spatial and Temporal Evolution of Regional Energy Efficiency in China and Its Influencing Factors |
| title_fullStr | Spatial and Temporal Evolution of Regional Energy Efficiency in China and Its Influencing Factors |
| title_full_unstemmed | Spatial and Temporal Evolution of Regional Energy Efficiency in China and Its Influencing Factors |
| title_short | Spatial and Temporal Evolution of Regional Energy Efficiency in China and Its Influencing Factors |
| title_sort | spatial and temporal evolution of regional energy efficiency in china and its influencing factors |
| topic | regional energy efficiency space–time evolution spatial Gini coefficient decomposition model spatial Markov chain model spatial econometric model |
| url | https://www.mdpi.com/1996-1073/17/23/6168 |
| work_keys_str_mv | AT jinqiuli spatialandtemporalevolutionofregionalenergyefficiencyinchinaanditsinfluencingfactors AT yufenghu spatialandtemporalevolutionofregionalenergyefficiencyinchinaanditsinfluencingfactors AT huizhang spatialandtemporalevolutionofregionalenergyefficiencyinchinaanditsinfluencingfactors |