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Spatiotemporal Patterns and Influencing Factors of Industrial Ecological Efficiency in Northeast China

Author

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  • Wai Li

    (College of Geographical Science, Harbin Normal University, Harbin 150025, China)

  • Xiaohong Chen

    (College of Geographical Science, Harbin Normal University, Harbin 150025, China)

  • Ying Wang

    (College of Geographical Science, Harbin Normal University, Harbin 150025, China)

Abstract

Scientific measurement of regional industrial ecological efficiency and discussion of the development and changes of its spatiotemporal pattern are of great significance to accelerate the high-quality development of regional economy and coordinate the development of industrial economy and ecological environment. Taking the old industrial bases in Northeast China as the research case and 2004–2019 as the research period, a super-slack-based model was used to measure the industrial ecological efficiency of 34 prefecture-level cities in the region. Meanwhile, the spatial autocorrelation model and the geographically and temporally weighted regression (GTWR) model were used to analyze the spatiotemporal pattern characteristics and the spatiotemporal heterogeneity of influencing factors. The results showed that: (1) From a time change perspective, the overall industrial ecological efficiency of Northeast China declined, the mean of the 34 cities decreased from 0.675 to 0.612, the number of cities with a high level of industrial ecological efficiency decreased significantly, the number of cities with a low level of industrial ecological efficiency increased significantly, and the development gap between cities within the region widened. (2) In terms of spatial pattern, the difference in the spatial pattern in the east–west direction decreased, and the spatial pattern in the south–north direction was enhanced. The industrial ecological efficiency of the central part of Northeast China gradually became the highest in the whole region. (3) From 2017, the industrial ecological efficiency had stable spatial autocorrelation characteristics. The local spatial autocorrelation was dominated by L-H-type cluster areas in the mountainous regions and L-L-type cluster areas in central and southern Liaoning province. H-H and H-L types had small numbers. In addition, the trend of H-H cities transforming into H-L cities was obvious, and the high level of urban space spillover effect showed good development. (4) The science and technology input, industrial agglomeration intensity, and environmental regulation of the government generally had a promoting effect on the improvement in industrial ecological efficiency, while the economic extroverted degree had a negative impact. The high-value area of science and technology investment and industrial agglomeration intensity concentrated significantly in the central part. The government focused on ecological protection areas and ecologically sensitive areas, and the economic extroverted degree had a significant positive impact on the two major urban agglomerations in central Northeast China. Therefore, differentiating measures should be taken according to the actual situation of each city to improve the industrial ecological efficiency level in Northeast China.

Suggested Citation

  • Wai Li & Xiaohong Chen & Ying Wang, 2022. "Spatiotemporal Patterns and Influencing Factors of Industrial Ecological Efficiency in Northeast China," Sustainability, MDPI, vol. 14(15), pages 1-18, August.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:15:p:9691-:d:881868
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    1. Quariguasi Frota Neto, J. & Walther, G. & Bloemhof, J. & van Nunen, J.A.E.E. & Spengler, T., 2009. "A methodology for assessing eco-efficiency in logistics networks," European Journal of Operational Research, Elsevier, vol. 193(3), pages 670-682, March.
    2. Picazo-Tadeo, Andrés J. & Beltrán-Esteve, Mercedes & Gómez-Limón, José A., 2012. "Assessing eco-efficiency with directional distance functions," European Journal of Operational Research, Elsevier, vol. 220(3), pages 798-809.
    3. Panayotou, Theodore, 1997. "Demystifying the environmental Kuznets curve: turning a black box into a policy tool," Environment and Development Economics, Cambridge University Press, vol. 2(4), pages 465-484, November.
    4. Xiaoying Ma, 2021. "The Chinese Economy," Springer Books, in: The Economic Impact of Government Policy on China’s Private Higher Education Sector, chapter 0, pages 31-57, Springer.
    5. Timo Kuosmanen & Mika Kortelainen, 2005. "Measuring Eco‐efficiency of Production with Data Envelopment Analysis," Journal of Industrial Ecology, Yale University, vol. 9(4), pages 59-72, October.
    6. Lu, Chengpeng & Ji, Wei & Hou, Muchen & Ma, Tianyang & Mao, Jinhuang, 2022. "Evaluation of efficiency and resilience of agricultural water resources system in the Yellow River Basin, China," Agricultural Water Management, Elsevier, vol. 266(C).
    7. J. Elhorst, 2010. "Applied Spatial Econometrics: Raising the Bar," Spatial Economic Analysis, Taylor & Francis Journals, vol. 5(1), pages 9-28.
    8. Zhimin Dai & Lu Guo & Zhengyi Jiang, 2016. "Study on the industrial Eco-Efficiency in East China based on the Super Efficiency DEA Model: an example of the 2003–2013 panel data," Applied Economics, Taylor & Francis Journals, vol. 48(59), pages 5779-5785, December.
    9. Zhonglin Tang & Geng Sun & Min Fu & Chuanhao Wen & Anđelka Plenković-Moraj, 2019. "Research on the Industrial Energy Eco-Efficiency Evolution Characteristics of the Yangtze River Economic Belt in the Temporal and Spatial Dimension, China," IJERPH, MDPI, vol. 17(1), pages 1-17, December.
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    Cited by:

    1. Mengtian Zhang & Huiling Wang, 2023. "Evolution of Industrial Ecology and Analysis of Influencing Factors: The Yellow River Basin in China," Land, MDPI, vol. 12(7), pages 1-21, June.

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