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The spatial-temporal variation and convergence of green innovation efficiency in the Yangtze River Economic Belt in China

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Abstract

The improvement of green innovation efficiency (GIE) in the Yangtze river economic belt (YREB) is beneficial to China’s green transformation and upgrading because of its economic and ecological position. Therefore, based on the slacks-based measure of super-efficiency (Super-SBM) model, the paper studies the GIE and its spatial-temporal variation characteristics in the YREB during the period 2003–2015, and analyzes the spatial correlation and spatial-temporal convergence of GIE with the exploratory spatial data analysis (ESDA) method and convergence analysis method. The results show that the GIE in the YREB shows an “U-shaped” change pattern in time and an extremely unbalanced development pattern in space. The areas with high GIE contribute to the improvement of overall GIE, whereas they do not exert a radiation and driving effect on areas with low GIE. Accordingly, because of the short board effect, the convergent speed of the GIE is decreasing. To be specific, the GIE keeps converging in the upper and lower reaches, except for the year 2010 when GIE in the middle reaches changed from being convergent to being non-convergent. Even though environmental policy exerts great impacts on the improvement of GIE, the lack of collaborative environmental governance leads to the non-convergent and unbalanced development of the GIE. Therefore, green coordinated development of the YREB is necessary.

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Notes

  1. Data resource from China Statistical Yearbook (2017).

  2. Nine provinces are Zhejiang, Jiangsu, Anhui, Hubei, Jiangxi, Hunan, Sichuan, Yunnan, and Guizhou and two municipalities are Shanghai and Chongqing.

  3. Data resource: http://english.www.gov.cn/statecouncil/hanzheng/201911/12/content_WS5dcaa865c6d0bcf8c4c16f61.html.

  4. The Yangtze River Basin can be divided into areas of upper reaches (Chongqing, Sichuan, Guizhou, Yunnan), middle reaches (Jiangxi, Hubei, Hunan), and lower reaches (Shanghai, Jiangsu, Zhejiang, Anhui).

  5. Data resource from local government websites of the YREB.

  6. GIE mean value (A), GIE mean value plus half A standard deviation (B), and GIE mean value half A standard deviation (C), four categories are classified according to GIEI≥B, A ≤ GIE < B, C ≤ GIE < A and GIE < C.

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Funding

This research was jointly supported by the Humanities and Social Sciences Planned Foundation from Ministry of Education of China (Grant No.19YJA630103) and the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (Grant No.CUGQY1942) and Hubei province regional innovation ability monitoring and analysis soft science research base open fund project (Grant No.HBQY2018z11).

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  1. School of Economics and Management, China University of Geosciences, Wuhan, 430074, Hubei, China

    Shuoran Xu, Ting Wu & Yao Zhang

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  1. Shuoran Xu
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  2. Ting Wu
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  3. Yao Zhang
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Correspondence to Ting Wu.

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Xu, S., Wu, T. & Zhang, Y. The spatial-temporal variation and convergence of green innovation efficiency in the Yangtze River Economic Belt in China. Environ Sci Pollut Res 27, 26868–26881 (2020). https://doi.org/10.1007/s11356-020-08865-3

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