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RETRACTED: 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. "RETRACTED: 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
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