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Research on Regional Differences and Influencing Factors of Chinese Industrial Green Technology Innovation Efficiency Based on Dagum Gini Coefficient Decomposition

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  • Liyuan Zhang

    (School of Business and Creative Industries, University of the West of Scotland, Paisley PA1 2BE, UK)

  • Xiang Ma

    (School of Mathematics and Information Science, Nanchang Normal University, Nanchang 330032, China
    Graduate School of Management of Technology, Pukyong National University, Busan 48547, Korea)

  • Young-Seok Ock

    (Graduate School of Management of Technology, Pukyong National University, Busan 48547, Korea)

  • Lingli Qing

    (Graduate School of Management of Technology, Pukyong National University, Busan 48547, Korea)

Abstract

Industrial green technology innovation has become an important content in achieving high-quality economic growth and comprehensively practicing the new development concept in the new era. This paper measures the efficiency of industrial green technology innovation and regional differences based on Chinese provincial panel data from 2005 to 2018, using a combination of the super efficiency slacks-based measure (SBM) model for considering undesirable outputs and the Dagum Gini coefficient method, and discusses and analyses the factors influencing industrial green technology innovation efficiency by constructing a spatial econometric model. The results show that: firstly, industrial green technology innovation efficiency in China shows a relatively stable development trend, going through three stages: “stationary period”, “recession period” and “growth period”. However, the efficiency gap between different regions is obvious, specifically in the eastern > central > western regions of China, and the industrial green technology efficiency innovation in the central and western regions is lower than the national average. Secondly, regional differences in the efficiency of industrial green technology innovation in China are evident but tend to narrow overall, with the main reason for the overall difference being regional differences. In terms of intra-regional variation, variation within the eastern region is relatively stable, variation within the central region is relatively low and shows an inverted ‘U’ shaped trend, and variation within the western region is high and shows a fluctuating downward trend. Thirdly, the firm size, government support, openness to the outside world, environmental regulations and education levels contribute to the efficiency of industrial green technology innovation. In addition, the industrial structure hinders the efficiency of industrial green technology innovation, and each influencing factor has different degrees of spatial spillover effects.

Suggested Citation

  • Liyuan Zhang & Xiang Ma & Young-Seok Ock & Lingli Qing, 2022. "Research on Regional Differences and Influencing Factors of Chinese Industrial Green Technology Innovation Efficiency Based on Dagum Gini Coefficient Decomposition," Land, MDPI, vol. 11(1), pages 1-20, January.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:1:p:122-:d:723082
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    References listed on IDEAS

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    1. Marra, Alessandro & Antonelli, Paola & Pozzi, Cesare, 2017. "Emerging green-tech specializations and clusters – A network analysis on technological innovation at the metropolitan level," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 1037-1046.
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    14. Wang, Qian & Ren, Shuming, 2022. "Evaluation of green technology innovation efficiency in a regional context: A dynamic network slacks-based measuring approach," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    15. Zefeng Mi & Zhixin Qiu & Gang Zeng & Can Zhou & Lei Ye, 2023. "The innovation effect of low‐carbon technology transfer from the perspective of carbon emission reduction demand: A case study of the Yangtze River Economic Belt in China," Growth and Change, Wiley Blackwell, vol. 54(2), pages 625-648, June.
    16. Zhang, Jingshen & Zhou, Xinzhu & Bai, Rong & Dong, Haoyang & Tang, Tingting & Wang, Zeyu & Yang, Ya & Huang, Feng, 2024. "Impact of environmental supervision reform on green innovation in mineral enterprises," Resources Policy, Elsevier, vol. 88(C).
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    20. Qiangyi Li & Lan Yang & Fangxin Jiang & Yangqing Liu & Chenyang Guo & Shuya Han, 2022. "Distribution Characteristics, Regional Differences and Spatial Convergence of the Water-Energy-Land-Food Nexus: A Case Study of China," Land, MDPI, vol. 11(9), pages 1-28, September.
    21. Xiaofeng Xu & Dongdong He & Tao Wang & Xiangyu Chen & Yichen Zhou, 2023. "Technological Innovation Efficiency of Listed Carbon Capture Companies in China: Based on the Dual Dimensions of Legal Policy and Technology," Energies, MDPI, vol. 16(3), pages 1-16, January.
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    23. Xiaonan Fan & Sainan Ren & Yang Liu, 2023. "The Driving Factors of Green Technology Innovation Efficiency—A Study Based on the Dynamic QCA Method," Sustainability, MDPI, vol. 15(12), pages 1-25, June.

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