IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i8p4717-d794175.html
   My bibliography  Save this article

Influences of Environmental Regulations on Industrial Green Technology Innovation Efficiency in China

Author

Listed:
  • Wanfang Shen

    (Shandong Key Laboratory of Blockchain Finance, Shandong University of Finance and Economics, Jinan 250014, China)

  • Jianing Shi

    (School of Mathematics and Quantitative Economics, Shandong University of Finance and Economics, Jinan 250002, China)

  • Qinggang Meng

    (School of Mathematics and Quantitative Economics, Shandong University of Finance and Economics, Jinan 250002, China)

  • Xiaolan Chen

    (Shandong Technology Innovation Center of Social Governance Intelligence, Shandong University of Finance and Economics, Jinan 250014, China)

  • Yufei Liu

    (School of Mathematics and Quantitative Economics, Shandong University of Finance and Economics, Jinan 250002, China)

  • Ken Cheng

    (Kent Business School, University of Kent, Canterbury CT1 7NZ, UK
    Centre for Evaluation Studies, Beijing Normal University, Zhuhai 519088, China)

  • Wenbin Liu

    (Centre for Evaluation Studies, Beijing Normal University, Zhuhai 519088, China
    Division of Business and Management, Beijing Normal University, Hong Kong Baptist University United International College, Zhuhai 519087, China)

Abstract

The Paris Agreement marks global response to climate change after 2020 and China has proposed the dual carbon goals, carbon peaking and carbon neutrality, in response. This paper analyses the contribution to dual carbon goals by analyzing the impact of environmental regulations (ERs) on green technology innovation (GTI) in China. First, considering variances in energy consumption structure across provinces and industries, industrial CO 2 emission is calculated and set as an undesirable output of industrial GTI. Then, industrial green technology innovation efficiencies (GTIE) of 29 provinces in China between 2005–2017 are calculated using a non-oriented two-stage network SBM-DEA model assuming variable returns to scale. Last, dynamic evolution and regional differences of industrial GTIE during green technology R&D, green technology commercialization, and overall GTI stages are respectively observed, and the influences from different types of ERs, command-based (CER), market-based (MER), and voluntary (VER), on industrial GTIE are analyzed. We identify China is overall experiencing relatively low but gradually increasing industrial GTIE and Industrial GTIE present gradient changes across provinces with increasingly prominent regional difference. It is found that influences of types of ERs on industrial GTIE present dynamic effect, threshold effect, lag effect and regional differences.

Suggested Citation

  • Wanfang Shen & Jianing Shi & Qinggang Meng & Xiaolan Chen & Yufei Liu & Ken Cheng & Wenbin Liu, 2022. "Influences of Environmental Regulations on Industrial Green Technology Innovation Efficiency in China," Sustainability, MDPI, vol. 14(8), pages 1-25, April.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:8:p:4717-:d:794175
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/8/4717/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/8/4717/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kao, Chiang, 2009. "Efficiency decomposition in network data envelopment analysis: A relational model," European Journal of Operational Research, Elsevier, vol. 192(3), pages 949-962, February.
    2. Rubashkina, Yana & Galeotti, Marzio & Verdolini, Elena, 2015. "Environmental regulation and competitiveness: Empirical evidence on the Porter Hypothesis from European manufacturing sectors," Energy Policy, Elsevier, vol. 83(C), pages 288-300.
    3. Tone, Kaoru & Tsutsui, Miki, 2009. "Network DEA: A slacks-based measure approach," European Journal of Operational Research, Elsevier, vol. 197(1), pages 243-252, August.
    4. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    5. Feng Wu & Xiaopeng Fu & Ting Zhang & Dan Wu & Stavros Sindakis, 2022. "Examining Whether Government Environmental Regulation Promotes Green Innovation Efficiency—Evidence from China’s Yangtze River Economic Belt," Sustainability, MDPI, vol. 14(3), pages 1-14, February.
    6. Raymond W. Goldsmith, 1951. "A Perpetual Inventory of National Wealth," NBER Chapters, in: Studies in Income and Wealth, Volume 14, pages 5-73, National Bureau of Economic Research, Inc.
    7. Martínez-Zarzoso, Inmaculada & Bengochea-Morancho, Aurelia & Morales-Lage, Rafael, 2019. "Does environmental policy stringency foster innovation and productivity in OECD countries?," Energy Policy, Elsevier, vol. 134(C).
    8. Zohal Habibi & Hamed Habibi & Mohammad Aqa Mohammadi, 2022. "The Potential Impact of COVID-19 on the Chinese GDP, Trade, and Economy," Economies, MDPI, vol. 10(4), pages 1-16, March.
    9. Goto, Akira & Suzuki, Kazuyuki, 1989. "R&D Capital, Rate of Return on R&D Investment and Spillover of R&D in Japanese Manufacturing Industries," The Review of Economics and Statistics, MIT Press, vol. 71(4), pages 555-564, November.
    10. Tong Zhao & Haihua Zhou & Jinde Jiang & Wenyan Yan, 2022. "Impact of Green Finance and Environmental Regulations on the Green Innovation Efficiency in China," Sustainability, MDPI, vol. 14(6), pages 1-17, March.
    11. Qiu, Larry D. & Zhou, Mohan & Wei, Xu, 2018. "Regulation, innovation, and firm selection: The porter hypothesis under monopolistic competition," Journal of Environmental Economics and Management, Elsevier, vol. 92(C), pages 638-658.
    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. Qiong Wang & Yihan Wei, 2023. "Research on the Influence of Digital Economy on Technological Innovation: Evidence from Manufacturing Enterprises in China," Sustainability, MDPI, vol. 15(6), pages 1-23, March.
    2. Yaliu Yang & Yuan Wang & Cui Wang & Yingyan Zhang & Cuixia Zhang, 2022. "Temporal and Spatial Evolution of the Science and Technology Innovative Efficiency of Regional Industrial Enterprises: A Data-Driven Perspective," Sustainability, MDPI, vol. 14(17), pages 1-21, August.
    3. Junfang Hao & Wanqiang Xu & Zhuo Chen & Baiyun Yuan & Yuping Wu, 2024. "Impact of Heterogeneous Environmental Regulations on Green Innovation Efficiency in China’s Industry," Sustainability, MDPI, vol. 16(1), pages 1-16, January.
    4. Xiaodi Yang & Di Wang, 2022. "Heterogeneous Environmental Regulation, Foreign Direct Investment, and Regional Carbon Dioxide Emissions: Evidence from China," Sustainability, MDPI, vol. 14(11), pages 1-19, May.
    5. Wanfang Shen & Yufei Liu & Xiaowen Liu & Jianing Shi & Wenbin Liu & Chengye Liu, 2023. "The Effect of Industrial Structure Upgrading and Human Capital Structure Upgrading on Green Development Efficiency—Based on China’s Resource-Based Cities," Sustainability, MDPI, vol. 15(5), pages 1-26, March.
    6. Jingjing Qian & Chao Chen & Yun Zhong, 2022. "Environmental Regulation and Sustainable Growth of Enterprise Value: Mediating Effect Analysis Based on Technological Innovation," Sustainability, MDPI, vol. 14(21), pages 1-16, October.
    7. 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.

    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. Yongqi Feng & Haolin Zhang & Yung-ho Chiu & Tzu-Han Chang, 2021. "Innovation efficiency and the impact of the institutional quality: a cross-country analysis using the two-stage meta-frontier dynamic network DEA model," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3091-3129, April.
    2. Li, Yongjun & Chen, Yao & Liang, Liang & Xie, Jianhui, 2012. "DEA models for extended two-stage network structures," Omega, Elsevier, vol. 40(5), pages 611-618.
    3. Hsiao-Yen Mao & Wen-Min Lu & Hsin-Yen Shieh, 2023. "Exploring the Influence of Environmental Investment on Multinational Enterprises’ Performance from the Sustainability and Marketability Efficiency Perspectives," Sustainability, MDPI, vol. 15(10), pages 1-23, May.
    4. Antonio Peyrache & Maria C. A. Silva, 2022. "Efficiency and Productivity Analysis from a System Perspective: Historical Overview," Springer Books, in: Duangkamon Chotikapanich & Alicia N. Rambaldi & Nicholas Rohde (ed.), Advances in Economic Measurement, chapter 0, pages 173-230, Springer.
    5. Huang, Tai-Hsin & Chen, Kuan-Chen & Lin, Chung-I, 2018. "An extension from network DEA to copula-based network SFA: Evidence from the U.S. commercial banks in 2009," The Quarterly Review of Economics and Finance, Elsevier, vol. 67(C), pages 51-62.
    6. Kao, Chiang, 2017. "Efficiency measurement and frontier projection identification for general two-stage systems in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 261(2), pages 679-689.
    7. Mohsen Khodakarami & Amir Shabani & Reza Farzipoor Saen, 2016. "Concurrent estimation of efficiency, effectiveness and returns to scale," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(5), pages 1202-1220, April.
    8. Kao, Chiang, 2016. "Efficiency decomposition and aggregation in network data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 255(3), pages 778-786.
    9. Zhang, Linyan & Chen, Kun, 2019. "Hierarchical network systems: An application to high-technology industry in China," Omega, Elsevier, vol. 82(C), pages 118-131.
    10. Yi Li & Lili Ding & Yongliang Yang, 2020. "Can the Introduction of an Environmental Target Assessment Policy Improve the TFP of Textile Enterprises? A Quasi-Natural Experiment Based on the Huai River Basin in China," Sustainability, MDPI, vol. 12(4), pages 1-19, February.
    11. Jin, Baoling & Han, Ying & Kou, Po, 2023. "Dynamically evaluating the comprehensive efficiency of technological innovation and low-carbon economy in China's industrial sectors," Socio-Economic Planning Sciences, Elsevier, vol. 86(C).
    12. Wanke, Peter & Tsionas, Mike G. & Chen, Zhongfei & Moreira Antunes, Jorge Junio, 2020. "Dynamic network DEA and SFA models for accounting and financial indicators with an analysis of super-efficiency in stochastic frontiers: An efficiency comparison in OECD banking," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 456-468.
    13. Nafiseh Javaherian & Ali Hamzehee & Hossein Sayyadi Tooranloo, 2021. "A compositional approach to two-stage Data Envelopment Analysis in intuitionistic fuzzy environment," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 31(1), pages 21-39.
    14. Chen, Ci & Yan, Hong, 2011. "Network DEA model for supply chain performance evaluation," European Journal of Operational Research, Elsevier, vol. 213(1), pages 147-155, August.
    15. Kao, Chiang, 2020. "Decomposition of slacks-based efficiency measures in network data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 283(2), pages 588-600.
    16. Tatiana Bencova & Andrea Bohacikova, 2022. "DEA in Performance Measurement of Two-Stage Processes: Comparative Overview of the Literature," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 5, pages 111-129.
    17. Reza Feizabadi & Mehri Bagherian, 2023. "Identifying the Influential Factors in Increasing the Efficiency of Network Systems: A Mixed Binary Linear Programming," SN Operations Research Forum, Springer, vol. 4(4), pages 1-14, December.
    18. Abdulla, Eman & Lim, King Yoong & Morris, Diego & Saliba, Faten, 2022. "Climate Change, Gender Equality, and Firm-Level Innovation : Cross-Country Evidence," The Warwick Economics Research Paper Series (TWERPS) 1429, University of Warwick, Department of Economics.
    19. Victor John M. Cantor & Kim Leng Poh, 2020. "Efficiency measurement for general network systems: a slacks-based measure model," Journal of Productivity Analysis, Springer, vol. 54(1), pages 43-57, August.
    20. Avkiran, Necmi Kemal, 2015. "An illustration of dynamic network DEA in commercial banking including robustness tests," Omega, Elsevier, vol. 55(C), pages 141-150.

    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:14:y:2022:i:8:p:4717-:d:794175. 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.