IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i16p6908-d1454599.html
   My bibliography  Save this article

Assessment of Green Innovation Efficiency in Chinese Industrial Enterprises Based on an Improved Relational Two-Stage DEA Approach: Regional Disparities and Convergence Analysis

Author

Listed:
  • Xiaohong Chen

    (School of Business, Central South University, Changsha 410083, China
    Xiangjiang Laboratory, Changsha 410205, China
    School of Advanced Interdisciplinary Studies, School of Management Science and Engineering, Hunan University of Technology and Business, Changsha 410205, China)

  • Ruochen Xu

    (School of Business, Central South University, Changsha 410083, China)

Abstract

Industrial enterprises are characterized by significant energy consumption and high emissions. Therefore, the implementation of green innovation by these enterprises is beneficial for promoting sustainable economic development and safeguarding the ecological environment. In this study, a relational two-stage DEA model containing shared inputs and undesired outputs is constructed to evaluate and decompose the green innovation efficiency (GIE) of Chinese industrial enterprises across 30 provinces from 2012 to 2021. Since the objective function of this model is nonlinear, a heuristic search method is employed for its resolution. On the basis of efficiency evaluation, the Gini coefficient, kernel density estimation, and convergence analysis are further employed to investigate the regional disparities and convergence properties in the two-stage efficiency of green innovation. Our findings are as follows: (1) The average GIE of Chinese industrial enterprises demonstrates a fluctuating upward trajectory, with significant regional disparities observed between provinces. (2) Regional disparities in R&D efficiency (RDE) and achievement conversion efficiency (ACE) have diminished in all regions. Super-variable density and interregional differences serve as the primary sources of regional disparities in RDE and ACE, respectively. (3) The presence of absolute and conditional convergence in RDE and ACE is observed across all regions. To improve the GIE of Chinese industrial enterprises, it is imperative to emphasize the heterogeneous impact of economic levels, industrial structure, and the degree of openness across various regions and stages of green innovation.

Suggested Citation

  • Xiaohong Chen & Ruochen Xu, 2024. "Assessment of Green Innovation Efficiency in Chinese Industrial Enterprises Based on an Improved Relational Two-Stage DEA Approach: Regional Disparities and Convergence Analysis," Sustainability, MDPI, vol. 16(16), pages 1-29, August.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:16:p:6908-:d:1454599
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/16/6908/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/16/6908/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chen, Yao & Liang, Liang & Zhu, Joe, 2009. "Equivalence in two-stage DEA approaches," European Journal of Operational Research, Elsevier, vol. 193(2), pages 600-604, March.
    2. Zhu, Lin & Luo, Jian & Dong, Qingli & Zhao, Yang & Wang, Yunyue & Wang, Yong, 2021. "Green technology innovation efficiency of energy-intensive industries in China from the perspective of shared resources: Dynamic change and improvement path," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    3. Namazi, Mehdi & Mohammadi, Emran, 2018. "Natural resource dependence and economic growth: A TOPSIS/DEA analysis of innovation efficiency," Resources Policy, Elsevier, vol. 59(C), pages 544-552.
    4. 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.
    5. Wang, Qunwei & Hang, Ye & Sun, Licheng & Zhao, Zengyao, 2016. "Two-stage innovation efficiency of new energy enterprises in China: A non-radial DEA approach," Technological Forecasting and Social Change, Elsevier, vol. 112(C), pages 254-261.
    6. Gen Li & Jingwen Wang & Fan Liu & Tao Wang & Ying Zhou & Airui Tian, 2023. "Regional Differences and Convergence of Technical Efficiency in China’s Marine Economy under Carbon Emission Constraints," Sustainability, MDPI, vol. 15(9), pages 1-17, May.
    7. Fei Liu & Ting Nan & Xinliang Wang, 2022. "Causes and Evolution Characteristics of Green Innovation Efficiency Loss: The Perspective of Factor Mismatch under Local Government Competition," Sustainability, MDPI, vol. 14(14), pages 1-29, July.
    8. 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.
    9. Seiford, Lawrence M. & Zhu, Joe, 2002. "Modeling undesirable factors in efficiency evaluation," European Journal of Operational Research, Elsevier, vol. 142(1), pages 16-20, October.
    10. Kao, Chiang & Hwang, Shiuh-Nan, 2008. "Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan," European Journal of Operational Research, Elsevier, vol. 185(1), pages 418-429, February.
    11. Gao, Kang & Yuan, Yijun, 2022. "Spatiotemporal pattern assessment of China’s industrial green productivity and its spatial drivers: Evidence from city-level data over 2000–2017," Applied Energy, Elsevier, vol. 307(C).
    12. Bi, Kexin & Huang, Ping & Wang, Xiangxiang, 2016. "Innovation performance and influencing factors of low-carbon technological innovation under the global value chain: A case of Chinese manufacturing industry," Technological Forecasting and Social Change, Elsevier, vol. 111(C), pages 275-284.
    13. Fujii, Hidemichi & Managi, Shunsuke, 2019. "Decomposition analysis of sustainable green technology inventions in China," Technological Forecasting and Social Change, Elsevier, vol. 139(C), pages 10-16.
    14. Chen, Xiafei & Liu, Zhiying & Zhu, Qingyuan, 2020. "Reprint of "Performance evaluation of China's high-tech innovation process :Analysis based on the innovation value chain"," Technovation, Elsevier, vol. 94.
    15. Min, Sujin & Kim, Juseong & Sawng, Yeong-Wha, 2020. "The effect of innovation network size and public R&D investment on regional innovation efficiency," Technological Forecasting and Social Change, Elsevier, vol. 155(C).
    16. 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.
    17. Aytekin, Ahmet & Ecer, Fatih & Korucuk, Selçuk & Karamaşa, Çağlar, 2022. "Global innovation efficiency assessment of EU member and candidate countries via DEA-EATWIOS multi-criteria methodology," Technology in Society, Elsevier, vol. 68(C).
    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. 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.
    2. 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.
    3. Li, Feng & Zhu, Qingyuan & Chen, Zhi, 2019. "Allocating a fixed cost across the decision making units with two-stage network structures," Omega, Elsevier, vol. 83(C), pages 139-154.
    4. 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).
    5. Bresciani, Stefano & Puertas, Rosa & Ferraris, Alberto & Santoro, Gabriele, 2021. "Innovation, environmental sustainability and economic development: DEA-Bootstrap and multilevel analysis to compare two regions," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    6. 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.
    7. Feng Li & Qingyuan Zhu & Jun Zhuang, 2018. "Analysis of fire protection efficiency in the United States: a two-stage DEA-based approach," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 40(1), pages 23-68, January.
    8. Zhong, Meirui & Huang, Gangli & He, Ruifang, 2022. "The technological innovation efficiency of China's lithium-ion battery listed enterprises: Evidence from a three-stage DEA model and micro-data," Energy, Elsevier, vol. 246(C).
    9. Kaffash, Sepideh & Azizi, Roza & Huang, Ying & Zhu, Joe, 2020. "A survey of data envelopment analysis applications in the insurance industry 1993–2018," European Journal of Operational Research, Elsevier, vol. 284(3), pages 801-813.
    10. Antunes, Jorge Junio Moreira & Neves, Juliana Campos & Elmor, Larissa Rosa Carneiro & Araujo, Michel Fontaine Reis De & Wanke, Peter Fernandes & Tan, Yong, 2023. "A new perspective on the U.S. energy efficiency: The political context," Technological Forecasting and Social Change, Elsevier, vol. 186(PA).
    11. Kao, Chiang, 2018. "Multiplicative aggregation of division efficiencies in network data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 270(1), pages 328-336.
    12. Kao, Chiang, 2020. "Measuring efficiency in a general production possibility set allowing for negative data," European Journal of Operational Research, Elsevier, vol. 282(3), pages 980-988.
    13. Fukuyama, Hirofumi & Weber, William L., 2010. "A slacks-based inefficiency measure for a two-stage system with bad outputs," Omega, Elsevier, vol. 38(5), pages 398-409, October.
    14. 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.
    15. Ibrahim Alnafrah, 2021. "Efficiency evaluation of BRICS’s national innovation systems based on bias-corrected network data envelopment analysis," Journal of Innovation and Entrepreneurship, Springer, vol. 10(1), pages 1-28, December.
    16. 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.
    17. Xiang Ji & Jie Wu & Qingyuan Zhu & Jiasen Sun, 2019. "Using a hybrid heterogeneous DEA method to benchmark China’s sustainable urbanization: an empirical study," Annals of Operations Research, Springer, vol. 278(1), pages 281-335, July.
    18. Simona Cohen-Kadosh & Zilla Sinuany-Stern, 2020. "Hip fracture surgery efficiency in Israeli hospitals via a network data envelopment analysis," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 28(1), pages 251-277, March.
    19. 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.
    20. Kao, Chiang, 2016. "Efficiency decomposition and aggregation in network data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 255(3), pages 778-786.

    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:16:y:2024:i:16:p:6908-:d:1454599. 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.