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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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    5. 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).
    6. Mengchao Yao & Ziqi Li & Yunfei Wang, 2023. "Features of Industrial Green Technology Innovation in the Yangtze River Economic Belt of China Based on Spatial Correlation Network," Sustainability, MDPI, vol. 15(7), pages 1-21, March.
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    10. Li, Huidi & Liu, Jiali & Wang, Huiru, 2024. "Impact of green technology innovation on the quality of regional economic development," International Review of Economics & Finance, Elsevier, vol. 93(PB), pages 463-476.
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    12. 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).
    13. Juan Zhang & Qin Chen, 2022. "The Impact of Farmland Tenure Security on China’s Agricultural Production Efficiency: A Perspective of Agricultural Production Factors," Sustainability, MDPI, vol. 14(23), pages 1-22, December.
    14. Xiang-Fei Ma & Ru Zhang & Yi-Fan Ruan, 2023. "How to Evaluate the Level of Green Development Based on Entropy Weight TOPSIS: Evidence from China," IJERPH, MDPI, vol. 20(3), pages 1-15, January.
    15. Yang Liu & Yanlin Yang & Shuang Zheng & Lei Xiao & Hongjie Gao & Hechen Lu, 2022. "Dynamic Impact of Technology and Finance on Green Technology Innovation Efficiency: Empirical Evidence from China’s Provinces," IJERPH, MDPI, vol. 19(8), pages 1-17, April.
    16. Haoran Bai & Nan Li, 2024. "Exploring Green Economic Efficiency Trends in Dominant Chinese Urban Agglomerations: A Super-Efficient SBM Model Approach," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(3), pages 10325-10348, September.
    17. Lin, Renzao & Wang, Zhe & Gao, Chunjiao, 2023. "Re-examining resources taxes and sustainable financial expansion: An empirical evidence of novel panel methods for China's provincial data," Resources Policy, Elsevier, vol. 80(C).
    18. 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.
    19. 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.
    20. Fang Song & Xuerong Xu, 2023. "How Operation Scale Improve the Production Technical Efficiency of Grape Growers? An Empirical Evidence of Novel Panel Methods for China’s Survey Data," Sustainability, MDPI, vol. 15(4), pages 1-19, February.
    21. Long Qian & Yunjie Zhou & Ying Sun, 2023. "Regional Differences, Distribution Dynamics, and Convergence of the Green Total Factor Productivity of China’s Cities under the Dual Carbon Targets," Sustainability, MDPI, vol. 15(17), pages 1-26, August.
    22. Wang, Zhe & Chen, Huangxin & Teng, Yin-Pei, 2023. "Role of greener energies, high tech-industries and financial expansion for ecological footprints: Implications from sustainable development perspective," Renewable Energy, Elsevier, vol. 202(C), pages 1424-1435.
    23. Jun Liu & Yu Qian & Huihong Chang & Jeffrey Yi-Lin Forrest, 2022. "The Impact of Technology Innovation on Enterprise Capacity Utilization—Evidence from China’s Yangtze River Economic Belt," Sustainability, MDPI, vol. 14(18), pages 1-17, September.
    24. Fanchao Kong & Kaixiao Zhang & Hengshu Fu & Lina Cui & Yang Li & Tengteng Wang, 2023. "Temporal–Spatial Variations and Convergence Analysis of Land Use Eco-Efficiency in the Urban Agglomerations of the Yellow River Basin in China," Sustainability, MDPI, vol. 15(16), pages 1-20, August.
    25. 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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