Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
The study of industrial land transformation effect is of great significance for promoting the sustainable and healthy development of the industrial economy. This paper adopts panel data of 10 provinces in eastern China from 2008 to 2020, constructs an indicator system including five dimensions on the premise of clarifying industrial land transformation and applies the comprehensive evaluation method of gray relational theory to measure its effect. The results show that: 1) overall, industrial land transformation effect in the eastern region shows a good development trend but there are gaps between different provinces, which have been expanding over time in the extreme values. 2) At the provincial level, in terms of industrial land transformation effect, Beijing, Shanghai, Guangdong, and Jiangsu are in the leading positions; Zhejiang, Fujian, and Hebei are in the middle positions; and Tianjin, Hainan and Shandong are slightly behind. 3) At the dimensional level, Industrial land development dimension and industrial land employment dimension generally show a good trend; the spatial pattern of industrial land optimization dimension and environmental pollution control dimension does not change significantly with most provinces at a low level; development conditions support dimension shows a positive spatial trend, indicating that each province attach importance to infrastructure construction and scientific technological progress, creating positive conditions for industrial land transformation. Overall, the results identify whether industrial land in eastern China is being used rationally, which has practical implications for promoting industrial structure upgrading, scientific and technological progress and ecological environment improvement.
The study of industrial land transformation effect is of great significance for promoting the sustainable and healthy development of the industrial economy. This paper adopts panel data of 10 provinces in eastern China from 2008 to 2020, constructs an indicator system including five dimensions on the premise of clarifying industrial land transformation and applies the comprehensive evaluation method of gray relational theory to measure its effect. The results show that: 1) overall, industrial land transformation effect in the eastern region shows a good development trend but there are gaps between different provinces, which have been expanding over time in the extreme values. 2) At the provincial level, in terms of industrial land transformation effect, Beijing, Shanghai, Guangdong, and Jiangsu are in the leading positions; Zhejiang, Fujian, and Hebei are in the middle positions; and Tianjin, Hainan and Shandong are slightly behind. 3) At the dimensional level, Industrial land development dimension and industrial land employment dimension generally show a good trend; the spatial pattern of industrial land optimization dimension and environmental pollution control dimension does not change significantly with most provinces at a low level; development conditions support dimension shows a positive spatial trend, indicating that each province attach importance to infrastructure construction and scientific technological progress, creating positive conditions for industrial land transformation. Overall, the results identify whether industrial land in eastern China is being used rationally, which has practical implications for promoting industrial structure upgrading, scientific and technological progress and ecological environment improvement.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.