Correction: Wang et al. 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 2025, 17, 6229

Wang, Jie; Xiong, Pingping; Wang, Shanshan; Yuan, Ziheng; Shangguan, Jiawei

doi:10.3390/su17188177

Open AccessCorrection

Correction: Wang et al. 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 2025, 17, 6229

by

Jie Wang

¹,

Pingping Xiong

^2,3,*,

Shanshan Wang

¹,

Ziheng Yuan

⁴ and

Jiawei Shangguan

²

¹

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

²

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

³

Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science and Technology, Nanjing 210044, China

⁴

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

^*

Author to whom correspondence should be addressed.

Sustainability 2025, 17(18), 8177; https://doi.org/10.3390/su17188177

Submission received: 31 July 2025 / Accepted: 3 September 2025 / Published: 11 September 2025

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The authors would like to make the following corrections to the published paper [1]. The changes are as follows.

(1): An error was caused by inadvertently importing an incorrect data file when incorporating the sensitivity analysis into this study during the rushed revision process. The authors would like to change twelve parts of the table content, so they need to replace the original Table 4.

Table 4. Sensitivity analysis results of parameters.

Regions	λ	λ_New	Error (%)	γ	γ_New	Error (%)
Shanghai	0.999	0.984	1.502	0.998	1.012	1.403
Jiangxi	1.000	0.947	5.300	1.000	0.973	2.700
Zhejiang	0.995	0.999	0.402	0.987	1.003	1.621
Jiangsu	1.000	0.986	1.400	1.000	0.942	5.800
Hunan	0.992	0.959	3.327	1.103	1.012	8.250
Chongqing	0.894	0.978	9.396	1.021	0.998	2.253
Sichuan	0.001	0.006	600.000	1.190	1.112	6.555
Yunnan	0.879	0.903	2.730	0.756	0.791	4.630
Anhui	0.012	0.039	225.000	0.940	0.971	3.298
Guizhou	0.658	0.712	8.207	1.215	1.196	1.564
Hubei	0.972	0.896	7.819	1.961	1.798	8.312

The above table has been replaced with the one below.

Table 4. Sensitivity analysis results of parameters.

Regions	λ	λ_New	Error (%)	γ	γ_New	Error (%)
Shanghai	0.999	0.984	1.502	0.998	1.012	1.403
Jiangxi	1.000	0.947	5.300	1.000	0.973	2.700
Zhejiang	0.995	0.999	0.402	0.987	1.003	1.621
Jiangsu	1.000	0.986	1.400	1.000	0.942	5.800
Hunan	1.000	0.959	4.100	1.000	0.978	2.200
Chongqing	0.894	0.978	9.396	1.021	0.998	2.253
Sichuan	0.001	0.006	600.000	1.190	1.112	6.555
Yunnan	0.879	0.903	2.730	0.756	0.791	4.630
Anhui	0.012	0.039	225.000	0.940	0.971	3.298
Guizhou	1.000	0.937	6.300	1.000	1.074	7.400
Hubei	0.972	0.896	7.819	1.961	1.798	8.312

(2): A typographical error made during data entry was unfortunately missed due to the limited time spent proofreading. The authors would like to change one part of the table content, so they need to replace the original Table 8.

Table 8. Actual and fitted values of GTI efficiency in the remaining regions of the YREB.

			GM(1,1)	GM(1,N)	GM(1.N\|λ,γ)
Jiangxi	Simulation	MAPE (%)	0.5189	0.4534	0.4534
Jiangxi	Prediction		5.6712	3.8865	1.9825
Hunan	Simulation		0.9113	0.8001	0.8001
Hunan	Prediction		3.4865	2.7579	2.7579
Sichuan	Simulation		13.0805	6.2878	5.8352
Sichuan	Prediction		21.1672	4.3241	4.0716
Guizhou	Simulation		23.2815	5.7612	5.7612
Guizhou	Prediction		237.5878	59.7805	59.7805
Anhui	Simulation		12.9514	4.2272	3.1393
Anhui	Prediction		17.0766	10.1731	9.2925
Yunnan	Simulation		35.6197	24.619	23.8977
Yunnan	Prediction		14.1961	8.3742	6.6339
Chongqing	Simulation		9.6739	2.5382	2.5233
Chongqing	Prediction		36.7802	14.9648	10.7813
Hubei	Simulation		16.7367	12.6257	11.9624
Hubei	Prediction		24.9774	16.3928	11.8653

The above table has been replaced with the one below.

Table 8. Actual and fitted values of GTI efficiency in the remaining regions of the YREB.

			GM(1,1)	GM(1,N)	GM(1.N\|λ,γ)
Jiangxi	Simulation	MAPE (%)	0.5189	0.4534	0.4534
Jiangxi	Prediction		5.6712	3.8865	3.8865
Hunan	Simulation		0.9113	0.8001	0.8001
Hunan	Prediction		3.4865	2.7579	2.7579
Sichuan	Simulation		13.0805	6.2878	5.8352
Sichuan	Prediction		21.1672	4.3241	4.0716
Guizhou	Simulation		23.2815	5.7612	5.7612
Guizhou	Prediction		237.5878	59.7805	59.7805
Anhui	Simulation		12.9514	4.2272	3.1393
Anhui	Prediction		17.0766	10.1731	9.2925
Yunnan	Simulation		35.6197	24.619	23.8977
Yunnan	Prediction		14.1961	8.3742	6.6339
Chongqing	Simulation		9.6739	2.5382	2.5233
Chongqing	Prediction		36.7802	14.9648	10.7813
Hubei	Simulation		16.7367	12.6257	11.9624
Hubei	Prediction		24.9774	16.3928	11.8653

(3): To ensure factual accuracy, the imprecise term “lowest efficiency” has been corrected to the more accurate description “low efficiency”. One sentence in “Section 5.2. Policy Implications” on page 25 has been replaced.

The original version is as follows:

For instance, targeting Guizhou province, which exhibits the lowest efficiency, a priority could be placed on establishing green technology transfer platforms connecting universities, research institutes, and local manufacturing enterprises.

The above has been replaced with the sentence below:

For instance, targeting Guizhou province, which exhibits low efficiency, a priority could be placed on establishing green technology transfer platforms connecting universities, research institutes, and local manufacturing enterprises.

(4): An inappropriate regional example has been removed to improve the accuracy of the conclusion’s supporting evidence. One sentence in “Section 5.1. Conclusions” on page 23 has been replaced.

The original version is as follows:

In contrast, midstream and upstream regions (e.g., Hubei, Sichuan, Guizhou) show γ values significantly deviating from 1, reflecting severe technology lock-in and dependence on traditional energy sources.

The above has been replaced with the sentence below:

In contrast, midstream and upstream regions (e.g., Hubei, Sichuan) show γ values significantly deviating from 1, reflecting severe technology lock-in and dependence on traditional energy sources.

(5): Another error resulted from a misplaced decimal point for a specific data point during the manual data entry process used to plot a figure. The authors would like to replace Subfigure 9 in Figure 3, so they need to replace the original Figure 3.

Figure 3. Trends in data on indicators of GTI.

The above figure has been replaced with the one below.

Figure 3. Trends in data on indicators of GTI.

The authors state that the scientific conclusions are unaffected. This correction was approved by the Academic Editor. The original publication has also been updated.

Reference

Wang, J.; Xiong, P.; Wang, S.; Yuan, Z.; Shangguan, J. 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 2025, 17, 6229. [Google Scholar] [CrossRef]

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© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

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Wang, J.; Xiong, P.; Wang, S.; Yuan, Z.; Shangguan, J. Correction: Wang et al. 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 2025, 17, 6229. Sustainability 2025, 17, 8177. https://doi.org/10.3390/su17188177

AMA Style

Wang J, Xiong P, Wang S, Yuan Z, Shangguan J. Correction: Wang et al. 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 2025, 17, 6229. Sustainability. 2025; 17(18):8177. https://doi.org/10.3390/su17188177

Chicago/Turabian Style

Wang, Jie, Pingping Xiong, Shanshan Wang, Ziheng Yuan, and Jiawei Shangguan. 2025. "Correction: Wang et al. 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 2025, 17, 6229" Sustainability 17, no. 18: 8177. https://doi.org/10.3390/su17188177

APA Style

Wang, J., Xiong, P., Wang, S., Yuan, Z., & Shangguan, J. (2025). Correction: Wang et al. 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 2025, 17, 6229. Sustainability, 17(18), 8177. https://doi.org/10.3390/su17188177

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Correction: Wang et al. 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 2025, 17, 6229

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