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The measure of technical efficiency of China’s provinces with carbon emission factor and the analysis of the influence of structural variables

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  • Gao, Yuning
  • Zhang, Meichen

Abstract

This paper applies the directional distance function to measure the technical efficiency incorporates carbon dioxide emissions as an undesirable output of 29 provinces in China from 1978 to 2015. In addition, we presented the best-practice provinces during the representative years and analyzed the structural variables of technical efficiency based on the Tobit model. This study finds that regional economic development model directly impacts its level of technical efficiency, and when the level of economic development is lower, the structural variables, such as energy, industrial and employment structure, have a greater impact on the technical efficiency of the region. Therefore, improving the economic development trajectory is the key to raising the level of regional technical efficiency. By optimizing and upgrading regional energy and industrial structures, ensuring the quality of economic development, the goal ofeconomic sustainable development can be attained.

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  • Gao, Yuning & Zhang, Meichen, 2019. "The measure of technical efficiency of China’s provinces with carbon emission factor and the analysis of the influence of structural variables," Structural Change and Economic Dynamics, Elsevier, vol. 49(C), pages 120-129.
    • Handle: RePEc:eee:streco:v:49:y:2019:i:c:p:120-129
    DOI: 10.1016/j.strueco.2018.11.005
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    Cited by:

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    2. Shichun Xu & Yongmei Miao & Yiwen Li & Yifeng Zhou & Xiaoxue Ma & Zhengxia He & Bin Zhao & Shuxiao Wang, 2019. "What Factors Drive Air Pollutants in China? An Analysis from the Perspective of Regional Difference Using a Combined Method of Production Decomposition Analysis and Logarithmic Mean Divisia Index," Sustainability, MDPI, vol. 11(17), pages 1-19, August.
    3. Da Gao & Xinlin Mo & Ruochan Xiong & Zhiliang Huang, 2022. "Tax Policy and Total Factor Carbon Emission Efficiency: Evidence from China’s VAT Reform," IJERPH, MDPI, vol. 19(15), pages 1-17, July.
    4. Feng, Gen-Fu & Niu, Peng & Wang, Jun-Zhuo & Liu, Jian, 2022. "Capital market liberalization and green innovation for sustainability: Evidence from China," Economic Analysis and Policy, Elsevier, vol. 75(C), pages 610-623.
    5. Haochang Yang & Faming Zhang & Yixin He, 2021. "Exploring the effect of producer services and manufacturing industrial co-agglomeration on the ecological environment pollution control in China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(11), pages 16119-16144, November.
    6. Yang, Haochang & Li, Lianshui & Liu, Yaobin, 2022. "The effect of manufacturing intelligence on green innovation performance in China," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    7. Xufeng Su & Xiaodong Yang & Jinning Zhang & Jinling Yan & Junfeng Zhao & Jianliang Shen & Qiying Ran, 2021. "Analysis of the Impacts of Economic Growth Targets and Marketization on Energy Efficiency: Evidence from China," Sustainability, MDPI, vol. 13(8), pages 1-17, April.
    8. Wang, Feng & Wu, Min & Wang, Jingcao, 2023. "Can increasing economic complexity improve China's green development efficiency?," Energy Economics, Elsevier, vol. 117(C).
    9. Gao, Yuning & Zhang, Meichen & Zheng, Jinghai, 2021. "Accounting and determinants analysis of China's provincial total factor productivity considering carbon emissions," China Economic Review, Elsevier, vol. 65(C).
    10. Niu, Peng & Yang, Yujia & Sun, Lirong, 2023. "High quality imports and green innovation," Innovation and Green Development, Elsevier, vol. 2(2).

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