IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i11p6361-d822512.html
   My bibliography  Save this article

Spatial and Temporal Evolution Analysis of Industrial Green Technology Innovation Efficiency in the Yangtze River Economic Belt

Author

Listed:
  • Mengchao Yao

    (DongWu Business School, Soochow University, Suzhou 215006, China)

  • Jinjun Duan

    (DongWu Business School, Soochow University, Suzhou 215006, China)

  • Qingsong Wang

    (DongWu Business School, Soochow University, Suzhou 215006, China)

Abstract

As a fusion point of innovation-driven green development, green technology innovation has become an essential engine for green transformation and high-quality economic development of the Yangtze River Economic Belt. Based on the panel data of 110 cities in the Yangtze River Economic Belt from 2006 to 2020, this paper uses the super-SBM model to measure the efficiency of industrial green technology innovation. Then, the Dagum Gini coefficient and its subgroup decomposition method, kernel density estimation, and the spatial Markov chain will discuss the convergence characteristics and dynamic evolution law of industrial green technology innovation efficiency in the Yangtze River Economic Belt. The results indicate several key points. (1) On the whole, the industrial green innovation efficiency of the Yangtze River Economic Belt shows a trend of the “N” type, which increases slowly at first and then decreases and then increases, and shows a non-equilibrium feature of “east high and west low” in space. (2) The average GML index of industrial green technology innovation efficiency in the Yangtze River Economic Belt is greater than 1, and technological progress is the main driving force in promoting efficiency growth. (3) There are spatial and temporal differences in industrial green technological innovation efficiency in the Yangtze River Economic Belt. Interregional differences and hypervariable density are the primary sources of overall differences. (4) During the study period, the absolute difference in industrial green technology innovation efficiency among regions showed a trend of “expansion-reduction-expansion”, and the innovation efficiency gradually converged to a single equilibrium point. (5) The industrial green technology innovation efficiency transfer in the Yangtze River Economic Belt shows a specific spatial dependence. Accordingly, policy suggestions are put forward to further improve industrial green technological innovation in the Yangtze River Economic Belt.

Suggested Citation

  • Mengchao Yao & Jinjun Duan & Qingsong Wang, 2022. "Spatial and Temporal Evolution Analysis of Industrial Green Technology Innovation Efficiency in the Yangtze River Economic Belt," IJERPH, MDPI, vol. 19(11), pages 1-20, May.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:11:p:6361-:d:822512
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/11/6361/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/19/11/6361/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Satya Paul & Sriram Shankar, 2020. "Estimating efficiency effects in a panel data stochastic frontier model," Journal of Productivity Analysis, Springer, vol. 53(2), pages 163-180, April.
    2. Dong-hyun Oh, 2010. "A global Malmquist-Luenberger productivity index," Journal of Productivity Analysis, Springer, vol. 34(3), pages 183-197, December.
    3. Xie, Xuemei & Huo, Jiage & Zou, Hailiang, 2019. "Green process innovation, green product innovation, and corporate financial performance: A content analysis method," Journal of Business Research, Elsevier, vol. 101(C), pages 697-706.
    4. Ye Tian & Peng Huang & Xu Zhao, 2020. "Spatial analysis, coupling coordination, and efficiency evaluation of green innovation: A case study of the Yangtze River Economic Belt," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-29, December.
    5. Ming Yi & Yiqian Wang & Modan Yan & Lina Fu & Yao Zhang, 2020. "Government R&D Subsidies, Environmental Regulations, and Their Effect on Green Innovation Efficiency of Manufacturing Industry: Evidence from the Yangtze River Economic Belt of China," IJERPH, MDPI, vol. 17(4), pages 1-17, February.
    6. Xia, Li & Gao, Shuo & Wei, Jiuchang & Ding, Qiying, 2022. "Government subsidy and corporate green innovation - Does board governance play a role?," Energy Policy, Elsevier, vol. 161(C).
    7. Zhu, Lin & Wang, Yong & Shang, Peipei & Qi, Lin & Yang, Guangchun & Wang, Ying, 2019. "Improvement path, the improvement potential and the dynamic evolution of regional energy efficiency in China: Based on an improved nonradial multidirectional efficiency analysis," Energy Policy, Elsevier, vol. 133(C).
    8. Tobias Wendler, 2019. "About the Relationship Between Green Technology and Material Usage," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 74(3), pages 1383-1423, November.
    9. Tifang Ye & Hao Zheng & Xiangyu Ge & Keling Yang, 2021. "Pathway of Green Development of Yangtze River Economics Belt from the Perspective of Green Technological Innovation and Environmental Regulation," IJERPH, MDPI, vol. 18(19), pages 1-26, October.
    10. Pastor, Jesus T. & Lovell, C.A. Knox, 2005. "A global Malmquist productivity index," Economics Letters, Elsevier, vol. 88(2), pages 266-271, August.
    11. Zeng, Juying & Škare, Marinko & Lafont, Juan, 2021. "The co-integration identification of green innovation efficiency in Yangtze River Delta region," Journal of Business Research, Elsevier, vol. 134(C), pages 252-262.
    12. Jun-liang Du & Yong Liu & Wei-xue Diao, 2019. "Assessing Regional Differences in Green Innovation Efficiency of Industrial Enterprises in China," IJERPH, MDPI, vol. 16(6), pages 1-23, March.
    13. Miao, Cheng-lin & Duan, Meng-meng & Zuo, Yang & Wu, Xin-yu, 2021. "Spatial heterogeneity and evolution trend of regional green innovation efficiency--an empirical study based on panel data of industrial enterprises in China's provinces," Energy Policy, Elsevier, vol. 156(C).
    14. Tan, Shukui & Hu, Bixia & Kuang, Bing & Zhou, Min, 2021. "Regional differences and dynamic evolution of urban land green use efficiency within the Yangtze River Delta, China," Land Use Policy, Elsevier, vol. 106(C).
    15. Yongfang Peng & Yingying Fan & Yi Liang, 2021. "A Green Technological Innovation Efficiency Evaluation of Technology-Based SMEs Based on the Undesirable SBM and the Malmquist Index: A Case of Hebei Province in China," Sustainability, MDPI, vol. 13(19), pages 1-19, October.
    16. Samuel Wicki & Erik G. Hansen, 2019. "Green technology innovation: Anatomy of exploration processes from a learning perspective," Business Strategy and the Environment, Wiley Blackwell, vol. 28(6), pages 970-988, September.
    17. Lv, Chengchao & Shao, Changhua & Lee, Chien-Chiang, 2021. "Green technology innovation and financial development: Do environmental regulation and innovation output matter?," Energy Economics, Elsevier, vol. 98(C).
    18. Dagum, Camilo, 1997. "A New Approach to the Decomposition of the Gini Income Inequality Ratio," Empirical Economics, Springer, vol. 22(4), pages 515-531.
    19. Tone, Kaoru, 2002. "A slacks-based measure of super-efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 143(1), pages 32-41, November.
    20. Wei Lisi & Rui Zhu & Chunlin Yuan, 2020. "Embracing green innovation via green supply chain learning: The moderating role of green technology turbulence," Sustainable Development, John Wiley & Sons, Ltd., vol. 28(1), pages 155-168, January.
    21. Li, Hai-ling & Zhu, Xue-hong & Chen, Jin-yu & Jiang, Fei-tao, 2019. "Environmental regulations, environmental governance efficiency and the green transformation of China's iron and steel enterprises," Ecological Economics, Elsevier, vol. 165(C), pages 1-1.
    22. Charoenrat, Teerawat & Harvie, Charles, 2014. "The efficiency of SMEs in Thai manufacturing: A stochastic frontier analysis," Economic Modelling, Elsevier, vol. 43(C), pages 372-393.
    23. Hu, Guoqiang & Wang, Xiaoqi & Wang, Yu, 2021. "Can the green credit policy stimulate green innovation in heavily polluting enterprises? Evidence from a quasi-natural experiment in China," Energy Economics, Elsevier, vol. 98(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yingshi Shang & Yanmin Niu & Peng Song, 2023. "Regional Differences and Influencing Factors of Green Innovation Efficiency in China’s 285 Cities," Sustainability, MDPI, vol. 16(1), pages 1-19, December.
    2. 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.
    3. 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.
    4. Xiaochi Qu & Haozhe Zhang & Guohua Bi & Kangchuan Su & Zhongxun Zhang & Yao Qian & Qingyuan Yang, 2022. "Spatial Effects of the Land Supply Scale of Different Industrial Sectors on High-Quality Development in the Yangtze River Economic Belt," Land, MDPI, vol. 11(11), pages 1-23, October.
    5. Xin Zhang & Feng Xu, 2023. "Environmental Regulation and Spatial Spillover Effect of Green Technology Innovation: An Empirical Study on the Spatial Durbin Model," Sustainability, MDPI, vol. 15(19), pages 1-19, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Shuangmei & Zhu, Xuehong & Zhang, Tao, 2023. "Optimum combination of heterogeneous environmental policy instruments and market for green transformation: Empirical evidence from China's metal sector," Energy Economics, Elsevier, vol. 123(C).
    2. Shihong Zeng & Gen Li & Shaomin Wu & Zhanfeng Dong, 2022. "The Impact of Green Technology Innovation on Carbon Emissions in the Context of Carbon Neutrality in China: Evidence from Spatial Spillover and Nonlinear Effect Analysis," IJERPH, MDPI, vol. 19(2), pages 1-25, January.
    3. Ma, Yechi & Sha, Yezhou & Wang, Zilong & Zhang, Wenjing, 2023. "The effect of the policy mix of green credit and government subsidy on environmental innovation," Energy Economics, Elsevier, vol. 118(C).
    4. Weixiang Zhao & Yankun Xu, 2022. "Public Expenditure and Green Total Factor Productivity: Evidence from Chinese Prefecture-Level Cities," IJERPH, MDPI, vol. 19(9), pages 1-27, May.
    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. Xiaosan Zhang & Xiaojie Hu & Fang Wu, 2022. "Fiscal Decentralization, Taxation Efforts and Corporate Green Technology Innovation in China Based on Moderating and Heterogeneity Effects," Sustainability, MDPI, vol. 14(22), pages 1-21, November.
    7. Ruomeng Zhou & Yunsheng Zhang, 2023. "Measurement of Urban Green Total Factor Productivity and Analysis of Its Temporal and Spatial Evolution in China," Sustainability, MDPI, vol. 15(12), pages 1-32, June.
    8. Liwen Sun & Ying Han, 2022. "Spatial Correlation Network Structure and Influencing Factors of Two-Stage Green Innovation Efficiency: Evidence from China," Sustainability, MDPI, vol. 14(18), pages 1-22, September.
    9. Fenfen Li & Bo Dai & Qifan Wu, 2021. "Dynamic Green Growth Assessment of China’s Industrial System with an Improved SBM Model and Global Malmquist Index," Mathematics, MDPI, vol. 9(20), pages 1-26, October.
    10. He, Haonan & Li, Shiqiang & Wang, Shanyong & Zhang, Chaojia & Ma, Fei, 2023. "Value of dual-credit policy: Evidence from green technology innovation efficiency," Transport Policy, Elsevier, vol. 139(C), pages 182-198.
    11. Siying Hu & Shangkun Lu & Huiqiu Zhou, 2023. "Public Investment, Environmental Regulation, and the Sustainable Development of Agriculture in China Based on the Decomposition of Green Total Factor Productivity," Sustainability, MDPI, vol. 15(2), pages 1-21, January.
    12. Yongyi Cheng & Liheng Lu & Tianyuan Shao & Manhong Shen & Laiqun Jin, 2018. "Decomposition Analysis of Factors Affecting Changes in Industrial Wastewater Emission Intensity in China: Based on a SSBM-GMI Approach," IJERPH, MDPI, vol. 15(12), pages 1-23, December.
    13. Yongyi Cheng & Tianyuan Shao & Huilin Lai & Manhong Shen & Yi Li, 2019. "Total-Factor Eco-Efficiency and Its Influencing Factors in the Yangtze River Delta Urban Agglomeration, China," IJERPH, MDPI, vol. 16(20), pages 1-14, October.
    14. Suyang Xiao & Susu Wang & Fanhua Zeng & Wei-Chiao Huang, 2022. "Spatial Differences and Influencing Factors of Industrial Green Total Factor Productivity in Chinese Industries," Sustainability, MDPI, vol. 14(15), pages 1-24, July.
    15. Chunbin Zhang & Rong Zhou & Jundong Hou & Mengtong Feng, 2022. "Spatial-Temporal Evolution and Convergence Characteristics of Agricultural Eco-Efficiency in China from a Low-Carbon Perspective," Sustainability, MDPI, vol. 14(24), pages 1-24, December.
    16. Sheng Xu & Wenran Pan & Demei Wen, 2023. "Do Carbon Emission Trading Schemes Promote the Green Transition of Enterprises? Evidence from China," Sustainability, MDPI, vol. 15(8), pages 1-28, April.
    17. Dan Xue & Xianzong Li & Fayyaz Ahmad & Nabila Abid & Zulqarnain Mushtaq, 2022. "Exploring Tourism Efficiency and Its Drivers to Understand the Backwardness of the Tourism Industry in Gansu, China," IJERPH, MDPI, vol. 19(18), pages 1-15, September.
    18. Zeng, Juying & Škare, Marinko & Lafont, Juan, 2021. "The co-integration identification of green innovation efficiency in Yangtze River Delta region," Journal of Business Research, Elsevier, vol. 134(C), pages 252-262.
    19. Chenxi Zhang & Shanyue Jin, 2022. "How Does an Environmental Information Disclosure of a Buyer’s Enterprise Affect Green Technological Innovations of Sellers’ Enterprise?," IJERPH, MDPI, vol. 19(22), pages 1-25, November.
    20. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:11:p:6361-:d:822512. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.