IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i13p6229-d1696636.html
   My bibliography  Save this article

Evaluating and Predicting Green Technology Innovation Efficiency in the Yangtze River Economic Belt: Based on the Joint SBM Model and GM(1,N|λ,γ) Model

Author

Listed:
  • Jie Wang

    (School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Pingping Xiong

    (School of Management Science and Engineering, Research Institute for Risk Governance and Emergency Decision-Making, Nanjing University of Information Science and Technology, Nanjing 210044, China
    Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Shanshan Wang

    (School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Ziheng Yuan

    (School of Business, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Jiawei Shangguan

    (School of Management Science and Engineering, Research Institute for Risk Governance and Emergency Decision-Making, Nanjing University of Information Science and Technology, Nanjing 210044, China)

Abstract

Green technology innovation (GTI) is pivotal for driving energy transition and low-carbon development in manufacturing. This study evaluates the spatiotemporal efficiency and predicts trends of GTI in China’s Yangtze River Economic Belt (YREB, 2010–2022) using a combined “input-desirable output-undesirable output” framework. Combining the SBM and super-efficiency SBM models, we evaluate regional GTI efficiency (2010–2022) and reveal its spatiotemporal patterns. An improved GM(1,N|λ,γ) model with a new information adjustment parameter (λ) and nonlinear parameter (γ) is applied for prediction. Key findings include: (1) The GTI efficiency remains generally low during the study period (provincial average: 0.7049–1.4526), showing an “east-high, west-low” spatial heterogeneity. Temporally, provincial efficiency peaked in 2016, with intensified fluctuations around 2020 due to policy iterations and external shocks. (2) Regional efficiency displays a stepwise decline pattern from downstream to middle-upstream areas. Middle-upstream regions face efficiency constraints from insufficient inputs and undesirable output redundancy, yet exhibit significant optimization potential. (3) Parameter analysis highlights that downstream provinces (γ ≈ 1) exhibit mature green adoption, while mid-upstream regions (e.g., Hubei) face severe technological lock-in and reliance on traditional energy. Additionally, middle and downstream provinces (e.g., Sichuan, Anhui) with low λ values show rapid policy responsiveness, but face efficiency volatility from frequent shifts. (4) The improved GM(1,N|λ,γ) model shows markedly enhanced prediction accuracy compared to traditional grey models, effectively addressing the “poor-information, grey-characteristic” data trend extraction challenges in GTI research. Based on these findings, targeted policy recommendations are proposed to advance GTI development.

Suggested Citation

  • Jie Wang & Pingping Xiong & Shanshan Wang & Ziheng Yuan & Jiawei Shangguan, 2025. "Evaluating and Predicting Green Technology Innovation Efficiency in the Yangtze River Economic Belt: Based on the Joint SBM Model and GM(1,N|λ,γ) Model," Sustainability, MDPI, vol. 17(13), pages 1-28, July.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:13:p:6229-:d:1696636
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/13/6229/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/13/6229/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sen, Suphi, 2015. "Corporate governance, environmental regulations, and technological change," European Economic Review, Elsevier, vol. 80(C), pages 36-61.
    2. Zhang, Yue-Jun & Peng, Yu-Lu & Ma, Chao-Qun & Shen, Bo, 2017. "Can environmental innovation facilitate carbon emissions reduction? Evidence from China," Energy Policy, Elsevier, vol. 100(C), pages 18-28.
    3. Khatib Ahmad Khan & Waheed Ahmad & Azeem Oluwaseyi Zubair & Mohammad Subhan & Muhammad Ibrahim Shah, 2025. "From invention to progress: Energy technology innovation and sustainable development in OECD economies," PLOS ONE, Public Library of Science, vol. 20(2), pages 1-23, February.
    4. Xu Dong & Wensi Fu & Yali Yang & Chenguang Liu & Guizhi Xue, 2022. "Study on the Evaluation of Green Technology Innovation Efficiency and Its Influencing Factors in the Central Plains City Cluster of China," Sustainability, MDPI, vol. 14(17), pages 1-24, September.
    5. Barbieri, Nicolò & Marzucchi, Alberto & Rizzo, Ugo, 2020. "Knowledge sources and impacts on subsequent inventions: Do green technologies differ from non-green ones?," Research Policy, Elsevier, vol. 49(2).
    6. Gray, Wayne B. & Shadbegian, Ronald J., 2003. "Plant vintage, technology, and environmental regulation," Journal of Environmental Economics and Management, Elsevier, vol. 46(3), pages 384-402, November.
    7. David J. Teece & Gary Pisano & Amy Shuen, 1997. "Dynamic capabilities and strategic management," Strategic Management Journal, Wiley Blackwell, vol. 18(7), pages 509-533, August.
    8. 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).
    9. Wang, Qiang & Song, Xiaoxing & Li, Rongrong, 2018. "A novel hybridization of nonlinear grey model and linear ARIMA residual correction for forecasting U.S. shale oil production," Energy, Elsevier, vol. 165(PB), pages 1320-1331.
    10. 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.
    11. Arthur, W Brian, 1989. "Competing Technologies, Increasing Returns, and Lock-In by Historical Events," Economic Journal, Royal Economic Society, vol. 99(394), pages 116-131, March.
    12. Yuanyuan Lin & Nianqi Deng & Hailian Gao, 2018. "Research on Technological Innovation Efficiency of Tourist Equipment Manufacturing Enterprises," Sustainability, MDPI, vol. 10(12), pages 1-17, December.
    13. Zhao, Huiru & Guo, Sen, 2016. "An optimized grey model for annual power load forecasting," Energy, Elsevier, vol. 107(C), pages 272-286.
    14. Wagner, Marcus, 2007. "On the relationship between environmental management, environmental innovation and patenting: Evidence from German manufacturing firms," Research Policy, Elsevier, vol. 36(10), pages 1587-1602, December.
    15. Cook, Wade D. & Seiford, Larry M., 2009. "Data envelopment analysis (DEA) - Thirty years on," European Journal of Operational Research, Elsevier, vol. 192(1), pages 1-17, January.
    16. Joseph E. Stiglitz, 2002. "Information and the Change in the Paradigm in Economics," American Economic Review, American Economic Association, vol. 92(3), pages 460-501, June.
    17. Li, Ke & Zou, Danyu & Li, Hailing, 2023. "Environmental regulation and green technical efficiency: A process-level data envelopment analysis from Chinese iron and steel enterprises," Energy, Elsevier, vol. 277(C).
    18. 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.
    19. Chintrakarn, Pandej, 2008. "Environmental regulation and U.S. states' technical inefficiency," Economics Letters, Elsevier, vol. 100(3), pages 363-365, September.
    20. 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.
    21. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    22. Cong Li & Xihua Liu & Xue Bai & Muhammad Umar, 2020. "Financial Development and Environmental Regulations: The Two Pillars of Green Transformation in China," IJERPH, MDPI, vol. 17(24), pages 1-17, December.
    23. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    24. Feng, Suling & Zhang, Rong & Li, Guoxiang, 2022. "Environmental decentralization, digital finance and green technology innovation," Structural Change and Economic Dynamics, Elsevier, vol. 61(C), pages 70-83.
    25. Kung, Ling-Ming & Yu, Shang-Wu, 2008. "Prediction of index futures returns and the analysis of financial spillovers--A comparison between GARCH and the grey theorem," European Journal of Operational Research, Elsevier, vol. 186(3), pages 1184-1200, May.
    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. Fan Wang & Lili Feng & Jin Li & Lin Wang, 2020. "Environmental Regulation, Tenure Length of Officials, and Green Innovation of Enterprises," IJERPH, MDPI, vol. 17(7), pages 1-16, March.
    2. Sun, Yu & Yang, Feng & Wang, Dawei & Ang, Sheng, 2023. "Efficiency evaluation for higher education institutions in China considering unbalanced regional development: A meta-frontier Super-SBM model," Socio-Economic Planning Sciences, Elsevier, vol. 88(C).
    3. 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).
    4. Rácz, Viktor J. & Vestergaard, Niels, 2016. "Productivity and efficiency measurement of the Danish centralized biogas power sector," Renewable Energy, Elsevier, vol. 92(C), pages 397-404.
    5. Mai, Nhat Chi, 2015. "Efficiency of the banking system in Vietnam under financial liberalization," OSF Preprints qsf6d, Center for Open Science.
    6. Zhao Yang & Hong Fang, 2024. "How Do Environmental Regulation and Decentralization Interactively Affect the Green Productivity of the Construction Industry? Evidence from China," Sustainability, MDPI, vol. 16(14), pages 1-22, July.
    7. Wu, Haitao & Hao, Yu & Ren, Siyu, 2020. "How do environmental regulation and environmental decentralization affect green total factor energy efficiency: Evidence from China," Energy Economics, Elsevier, vol. 91(C).
    8. Ben Lahouel, Béchir & Taleb, Lotfi & Ben Zaied, Younes & Managi, Shunsuke, 2022. "Does primary stakeholder management improve competitiveness? A dynamic network non-parametric frontier approach," Economic Modelling, Elsevier, vol. 116(C).
    9. Lampe, Hannes W. & Hilgers, Dennis, 2015. "Trajectories of efficiency measurement: A bibliometric analysis of DEA and SFA," European Journal of Operational Research, Elsevier, vol. 240(1), pages 1-21.
    10. Alperovych, Yan & Hübner, Georges & Lobet, Fabrice, 2015. "How does governmental versus private venture capital backing affect a firm's efficiency? Evidence from Belgium," Journal of Business Venturing, Elsevier, vol. 30(4), pages 508-525.
    11. Nan Wang & Yunning Ma & Yongrok Choi & Seungho Kang, 2025. "Efficiency Evaluation of the World’s Top Ten Seed Companies: Static and Dynamic Analysis in the Context of Global Consolidation and Sustainability Challenges," Sustainability, MDPI, vol. 17(8), pages 1-25, April.
    12. Michaelides, Panayotis G. & Vouldis, Angelos T. & Tsionas, Efthymios G., 2010. "Globally flexible functional forms: The neural distance function," European Journal of Operational Research, Elsevier, vol. 206(2), pages 456-469, October.
    13. Jin XU & Panagiotis ZERVOPOULOS & Zhenhua QIAN & Gang CHENG, 2012. "A Universal Solution For Units - Invariance In Data Envelopment Analysis," Theoretical and Practical Research in the Economic Fields, ASERS Publishing, vol. 3(2), pages 121-128.
    14. Yulin Lu & Chengyu Li & Min-Jae Lee, 2023. "A Study on the Measurement and Influences of Energy Green Efficiency: Based on Panel Data from 30 Provinces in China," Sustainability, MDPI, vol. 15(21), pages 1-17, October.
    15. Branda, Martin, 2013. "Diversification-consistent data envelopment analysis with general deviation measures," European Journal of Operational Research, Elsevier, vol. 226(3), pages 626-635.
    16. 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.
    17. He, Yiqing & Ding, Xin & Yang, Chuchu, 2021. "Do environmental regulations and financial constraints stimulate corporate technological innovation? Evidence from China," Journal of Asian Economics, Elsevier, vol. 72(C).
    18. Imanirad, Raha & Cook, Wade D. & Aviles-Sacoto, Sonia Valeria & Zhu, Joe, 2015. "Partial input to output impacts in DEA: The case of DMU-specific impacts," European Journal of Operational Research, Elsevier, vol. 244(3), pages 837-844.
    19. Avkiran, Necmi K. & Rowlands, Terry, 2008. "How to better identify the true managerial performance: State of the art using DEA," Omega, Elsevier, vol. 36(2), pages 317-324, April.
    20. Andor, Mark A. & Parmeter, Christopher & Sommer, Stephan, 2019. "Combining uncertainty with uncertainty to get certainty? Efficiency analysis for regulation purposes," European Journal of Operational Research, Elsevier, vol. 274(1), pages 240-252.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:17:y:2025:i:13:p:6229-:d:1696636. 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.