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Green Innovation in Regional Logistics: Level Evaluation and Spatial Analysis

Green innovation is imperative for the high-quality and sustainable development of the modern logistics industry. It is also key for achieving the goals of peak emissions and carbon neutrality. This study provides a way of thinking about the evaluation of the green innovation level of the logistics...

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Autores principales: Zhang, Hao, Sun, Xin, Dong, Kailong, Sui, Lianghui, Wang, Min, Hong, Qiong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9819665/
https://www.ncbi.nlm.nih.gov/pubmed/36613056
http://dx.doi.org/10.3390/ijerph20010735
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author Zhang, Hao
Sun, Xin
Dong, Kailong
Sui, Lianghui
Wang, Min
Hong, Qiong
author_facet Zhang, Hao
Sun, Xin
Dong, Kailong
Sui, Lianghui
Wang, Min
Hong, Qiong
author_sort Zhang, Hao
collection PubMed
description Green innovation is imperative for the high-quality and sustainable development of the modern logistics industry. It is also key for achieving the goals of peak emissions and carbon neutrality. This study provides a way of thinking about the evaluation of the green innovation level of the logistics industry. The variance inflation factor-variance coefficient method was employed to construct an evaluation index system of the regional logistics green innovation level (RLGIL) from three dimensions. Empirical data were collected from statistical yearbooks covering 30 provinces in China from 2013 to 2017. Thereafter, the combination weighting-based GRA-TOPSIS method was applied to evaluate the RLGIL, and the spatial distribution differences and spatiotemporal evolution characteristics of inter-provincial green innovation levels were analyzed. The RLGILs in the 30 provinces were found to be generally unbalanced, and the differences between the eastern and western regions were significant. Guangdong, Jiangsu, and Zhejiang had stronger RLGILs, whereas most other provinces did not reach the average level. The RLGIL of the 30 provinces had a high positive spatial correlation and spatial aggregating effect. From a national perspective, the values for the RLGIL were generally higher in the eastern and southern regions and lower in the western and northern regions. Although significant differences were found in the RLGIL of these provinces, the overall development trend was stable.
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spelling pubmed-98196652023-01-07 Green Innovation in Regional Logistics: Level Evaluation and Spatial Analysis Zhang, Hao Sun, Xin Dong, Kailong Sui, Lianghui Wang, Min Hong, Qiong Int J Environ Res Public Health Study Protocol Green innovation is imperative for the high-quality and sustainable development of the modern logistics industry. It is also key for achieving the goals of peak emissions and carbon neutrality. This study provides a way of thinking about the evaluation of the green innovation level of the logistics industry. The variance inflation factor-variance coefficient method was employed to construct an evaluation index system of the regional logistics green innovation level (RLGIL) from three dimensions. Empirical data were collected from statistical yearbooks covering 30 provinces in China from 2013 to 2017. Thereafter, the combination weighting-based GRA-TOPSIS method was applied to evaluate the RLGIL, and the spatial distribution differences and spatiotemporal evolution characteristics of inter-provincial green innovation levels were analyzed. The RLGILs in the 30 provinces were found to be generally unbalanced, and the differences between the eastern and western regions were significant. Guangdong, Jiangsu, and Zhejiang had stronger RLGILs, whereas most other provinces did not reach the average level. The RLGIL of the 30 provinces had a high positive spatial correlation and spatial aggregating effect. From a national perspective, the values for the RLGIL were generally higher in the eastern and southern regions and lower in the western and northern regions. Although significant differences were found in the RLGIL of these provinces, the overall development trend was stable. MDPI 2022-12-30 /pmc/articles/PMC9819665/ /pubmed/36613056 http://dx.doi.org/10.3390/ijerph20010735 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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/).
spellingShingle Study Protocol
Zhang, Hao
Sun, Xin
Dong, Kailong
Sui, Lianghui
Wang, Min
Hong, Qiong
Green Innovation in Regional Logistics: Level Evaluation and Spatial Analysis
title Green Innovation in Regional Logistics: Level Evaluation and Spatial Analysis
title_full Green Innovation in Regional Logistics: Level Evaluation and Spatial Analysis
title_fullStr Green Innovation in Regional Logistics: Level Evaluation and Spatial Analysis
title_full_unstemmed Green Innovation in Regional Logistics: Level Evaluation and Spatial Analysis
title_short Green Innovation in Regional Logistics: Level Evaluation and Spatial Analysis
title_sort green innovation in regional logistics: level evaluation and spatial analysis
topic Study Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9819665/
https://www.ncbi.nlm.nih.gov/pubmed/36613056
http://dx.doi.org/10.3390/ijerph20010735
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