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

Jie Wang, Pingping Xiong (), Shanshan Wang, Ziheng Yuan and Jiawei Shangguan
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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
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

Sustainability, 2025, vol. 17, issue 13, 1-28

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.

Keywords: green technology innovation; SBM model; super-efficiency SBM model; GM(1,N|λ,γ) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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