Abstract
Carbon emission efficiency directly determines the level of green economic development. Based on the panel data of China’s Yangtze River Economic Belt (YEB) from 2008 to 2017, this paper uses the stochastic frontier analysis (SFA) model to analyze the overall carbon emission efficiency level, influencing factors, and changing trends, with a view to discussing the relationship between economic development and carbon emission efficiency. The results suggest, first, the overall carbon emission efficiency of the YEB is on an upward trend, but there is still much room for improvement. Second, the impact of industrialization and urbanization on carbon emission efficiency follows a U-shaped. As industrialization and urbanization progress, the impact on carbon emission efficiency shows a downward and then upward trend. Third, due to the rebound effect, technological progress has a slight negative impact on carbon emission efficiency. Energy consumption structure, government intervention, and foreign trade are all negative incentive factors. Therefore, efforts to improve carbon emission efficiency in the YEB should focus on transforming the economic growth model, adjusting the industrial structure, improving the energy consumption structure, and innovating green technology. The research results can provide a reference for the government policymakers to develop a green economy.
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Data availability
All data are from the China statistical yearbook.
Notes
In China, provinces and municipalities are all provincial-level administrative regions, so all use “province” in this paper.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (No. 71471061). Thanks to Ms. Qianqian Xu for her help on the data.
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Caiqing Zhang: methodology, resources, writing—review & editing. Panyu Chen: conceptualization, software, validation, investigation, data curation, writing—original draft.
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Zhang, C., Chen, P. Industrialization, urbanization, and carbon emission efficiency of Yangtze River Economic Belt—empirical analysis based on stochastic frontier model. Environ Sci Pollut Res 28, 66914–66929 (2021). https://doi.org/10.1007/s11356-021-15309-z
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DOI: https://doi.org/10.1007/s11356-021-15309-z