IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i7p6033-d1112224.html
   My bibliography  Save this article

Features of Industrial Green Technology Innovation in the Yangtze River Economic Belt of China Based on Spatial Correlation Network

Author

Listed:
  • Mengchao Yao

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

  • Ziqi Li

    (School of Economics & Management, Huzhou University, Huzhou 313000, China)

  • Yunfei Wang

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

Abstract

A generic phrase for technical and managerial innovation geared toward environmental conservation is “green-technology innovation.” It is essential to attain ecologically friendly development that promotes economic progress. Promoting the combined growth of the economy, society, and environment is extremely important. The industrial-green-technology innovation efficiency of 110 cities in the Yangtze River Economic Belt is calculated using the Sup-SBM model from 2011 to 2021 while considering undesirable output. The modified gravity model is then used to convert the attribute data of industrial-green-technology innovation efficiency into relational data. The Yangtze River Economic Belt uses the social-network-analysis (SNA) approach to investigate the geographical correlation-network properties of industrial-green-technology innovation efficiency. The findings demonstrate the following: (1) There is a rising trend in the degree of industrial-green-technology innovation efficiency between different cities in the Yangtze River Economic Belt, and this pattern is known as “three plates.” (2) The examination of network characteristics reveals an indigenous core–edge structure in space, with the network density of the Yangtze River Economic Belt displaying an increasing trend over the research period. (3) Individual characteristic analysis reveals that although the innovation-efficiency network tends to be flat, the degree centrality and closeness centrality of industrial-green-technology innovation efficiency in the Yangtze River Economic Belt indicate an upward trend over the research period. In addition, Chengdu in the upstream region, Wuhan in the center, and Shanghai in the downstream area serve as bridge and intermediary nodes in the spatial correlation network. (4) Block-model analysis reveals a close spatial link between blocks. A more complex and durable spatial link is now possible because of the spatial relationship of green-innovation efficiency in cities, which has shattered the boundaries imposed by traditional geographic space. The Yangtze River Economic Belt will be jointly promoted by several of the policy recommendations in this paper, aligning with that.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:7:p:6033-:d:1112224
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/7/6033/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/7/6033/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Ge & Li, Yang & Zuo, Jian & Hu, Wenbo & Nie, Qingwei & Lei, Heqian, 2021. "Who drives green innovations? Characteristics and policy implications for green building collaborative innovation networks in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    2. 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).
    3. Saumyaranjan Sahoo & Anil Kumar & Arvind Upadhyay, 2023. "How do green knowledge management and green technology innovation impact corporate environmental performance? Understanding the role of green knowledge acquisition," Business Strategy and the Environment, Wiley Blackwell, vol. 32(1), pages 551-569, January.
    4. Zhangsheng Liu & Liuqingqing Yang & Liqin Fan, 2021. "Induced Effect of Environmental Regulation on Green Innovation: Evidence from the Increasing-Block Pricing Scheme," IJERPH, MDPI, vol. 18(5), pages 1-15, March.
    5. Lei Wang & Zengrui Qi & Qinghua Pang & Yibo Xiang & Yanli Sun, 2020. "Analysis on the Agricultural Green Production Efficiency and Driving Factors of Urban Agglomerations in the Middle Reaches of the Yangtze River," Sustainability, MDPI, vol. 13(1), pages 1-18, December.
    6. Du, Kerui & Cheng, Yuanyuan & Yao, Xin, 2021. "Environmental regulation, green technology innovation, and industrial structure upgrading: The road to the green transformation of Chinese cities," Energy Economics, Elsevier, vol. 98(C).
    7. 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.
    8. Wenjun Zhou & Xiaorong Huang & Hao Dai & Yuanmeng Xi & Zhansheng Wang & Long Chen, 2022. "Research on the Impact of Economic Policy Uncertainty on Enterprises’ Green Innovation—Based on the Perspective of Corporate Investment and Financing Decisions," Sustainability, MDPI, vol. 14(5), pages 1-24, February.
    9. 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).
    10. de Paulo, Alex Fabianne & Porto, Geciane Silveira, 2017. "Solar energy technologies and open innovation: A study based on bibliometric and social network analysis," Energy Policy, Elsevier, vol. 108(C), pages 228-238.
    11. 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.
    12. Zhang, Jie & Zhang, Ke & Zhao, Feng, 2020. "Research on the regional spatial effects of green development and environmental governance in China based on a spatial autocorrelation model," Structural Change and Economic Dynamics, Elsevier, vol. 55(C), pages 1-11.
    13. Yang Liu & Yanlin Yang & Huihui Li & Kaiyang Zhong, 2022. "Digital Economy Development, Industrial Structure Upgrading and Green Total Factor Productivity: Empirical Evidence from China’s Cities," IJERPH, MDPI, vol. 19(4), pages 1-23, February.
    14. Leng, Zhihui & Sun, Han & Cheng, Jinhua & Wang, Hai & Yao, Zhen, 2021. "China's rare earth industry technological innovation structure and driving factors: A social network analysis based on patents," Resources Policy, Elsevier, vol. 73(C).
    15. Tang, Chang & Xu, Yuanyuan & Hao, Yu & Wu, Haitao & Xue, Yan, 2021. "What is the role of telecommunications infrastructure construction in green technology innovation? A firm-level analysis for China," Energy Economics, Elsevier, vol. 103(C).
    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. Wenke Wang & Jue Wang & Kebei Liu & Yenchun Jim Wu, 2020. "Overcoming Barriers to Agriculture Green Technology Diffusion through Stakeholders in China: A Social Network Analysis," IJERPH, MDPI, vol. 17(19), pages 1-22, September.
    Full references (including those not matched with items on IDEAS)

    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. Hu, Hui & Qi, Shaozhou & Chen, Yuanzhi, 2023. "Using green technology for a better tomorrow: How enterprises and government utilize the carbon trading system and incentive policies," China Economic Review, Elsevier, vol. 78(C).
    2. Lee, Chien-Chiang & Qin, Shuai & Li, Yaya, 2022. "Does industrial robot application promote green technology innovation in the manufacturing industry?," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    3. Qi, Xiulin & Wu, Zhifang & Xu, Jinqing & Shan, Biaoan, 2023. "Environmental justice and green innovation: A quasi-natural experiment based on the establishment of environmental courts in China," Ecological Economics, Elsevier, vol. 205(C).
    4. Wang, Ke-Liang & Sun, Ting-Ting & Xu, Ru-Yu & Miao, Zhuang & Cheng, Yun-He, 2022. "How does internet development promote urban green innovation efficiency? Evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    5. 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.
    6. Bai, Dongbei & Hu, Jin & Irfan, Muhammad & Hu, Mingjun, 2023. "Unleashing the impact of ecological civilization pilot policies on green technology innovation: Evidence from a novel SC-DID model," Energy Economics, Elsevier, vol. 125(C).
    7. Feng, Yuan & Chen, Zhi & Nie, Changfei, 2023. "The effect of broadband infrastructure construction on urban green innovation: Evidence from a quasi-natural experiment in China," Economic Analysis and Policy, Elsevier, vol. 77(C), pages 581-598.
    8. Dian, Jie & Song, Tian & Li, Shenglan, 2024. "Facilitating or inhibiting? Spatial effects of the digital economy affecting urban green technology innovation," Energy Economics, Elsevier, vol. 129(C).
    9. Long Xue & Qianyu Zhang & Xuemang Zhang & Chengyu Li, 2022. "Can Digital Transformation Promote Green Technology Innovation?," Sustainability, MDPI, vol. 14(12), pages 1-20, June.
    10. Yunyao Li & Yanji Ma, 2022. "Research on Industrial Innovation Efficiency and the Influencing Factors of the Old Industrial Base Based on the Lock-In Effect, a Case Study of Jilin Province, China," Sustainability, MDPI, vol. 14(19), pages 1-23, October.
    11. Yu Cao & Cong Xu & Syahrul Nizam Kamaruzzaman & Nur Mardhiyah Aziz, 2022. "A Systematic Review of Green Building Development in China: Advantages, Challenges and Future Directions," Sustainability, MDPI, vol. 14(19), pages 1-29, September.
    12. Xiaoqi Li & Dingfei Guo & Chao Feng, 2022. "The Carbon Emissions Trading Policy of China: Does It Really Promote the Enterprises’ Green Technology Innovations?," IJERPH, MDPI, vol. 19(21), pages 1-15, November.
    13. Biao Hu & Kai Yuan & Tingyun Niu & Liang Zhang & Yuqiong Guan, 2022. "Study on the Spatial and Temporal Evolution Patterns of Green Innovation Efficiency and Driving Factors in Three Major Urban Agglomerations in China—Based on the Perspective of Economic Geography," Sustainability, MDPI, vol. 14(15), pages 1-28, July.
    14. Du, Lixia & Geng, Baiyang, 2024. "Financial technology and financing constraints," Finance Research Letters, Elsevier, vol. 60(C).
    15. Bai, Rui & Lin, Boqiang, 2024. "An in-depth analysis of green innovation efficiency: New evidence based on club convergence and spatial correlation network," Energy Economics, Elsevier, vol. 132(C).
    16. Song Wang & Yuyao Cao & Yifan Wang & Chaoquan Wang, 2024. "The Impact of Innovative and Low-Carbon Pilot Cities on Green Innovation," Sustainability, MDPI, vol. 16(16), pages 1-29, August.
    17. Jiekuan Zhang & Yan Zhang, 2023. "Examining the effects of economic growth pressure on green total factor productivity: evidence from China," Economic Change and Restructuring, Springer, vol. 56(6), pages 4309-4337, December.
    18. Bai, Rui & Lin, Boqiang, 2023. "Nexus between green finance development and green technological innovation: A potential way to achieve the renewable energy transition," Renewable Energy, Elsevier, vol. 218(C).
    19. 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).
    20. Qin, Quande & Yu, Ying & Liu, Yuan & Zhou, Jianqing & Chen, Xiude, 2023. "Industrial agglomeration and energy efficiency: A new perspective from market integration," Energy Policy, Elsevier, vol. 183(C).

    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:jsusta:v:15:y:2023:i:7:p:6033-:d:1112224. 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.