Regional Land Eco-Security Evaluation for the Mining City of Daye in China Using the GIS-Based Grey TOPSIS Method

Xinchang Zhang, Min Chen, Kai Guo, Yang Liu, Yi Liu, Weinan Cai, Hua Wu, Zeyi Chen, Yiyun Chen and Jianguo Zhang
Additional contact information
Xinchang Zhang: School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China
Min Chen: Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou 510060, China
Kai Guo: School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China
Yang Liu: Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou 510060, China
Yi Liu: School of Public Administration, Guangdong University of Finance & Economics, Guangzhou 510320, China
Weinan Cai: School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China
Hua Wu: School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China
Zeyi Chen: School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
Yiyun Chen: School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
Jianguo Zhang: Hunan Botong Information Co., Ltd, Changsha 410021, China

Land, 2021, vol. 10, issue 2, 1-18

Abstract: Regional ecological security assessment is a significant methodology for environmental protection, land utilisation, and human development. This study aims to reveal the regional constraints of ecological resources to overcome the difficulties and complexities in quantification of current models used in land ecosystems. For this purpose, the technique for order preference by similarity to an ideal solution (TOPSIS) was linked to a grey relational analysis and integrated with a geographic information system. The obtained method was used to construct a land eco-security evaluation on a regional scale for application in a traditional mining city, Daye, in central China. Parameter analysis was introduced to the method to produce a more realistic spatial distribution of eco-security. Subsequently, based on the pressure–state–response framework, the eco-security index was calculated, and the carrying capacity of land resources and population for each sub-region were analysed. The results showed that: (i) very insecure and insecure classes comprised 5.65% and 18.2% of the total area, respectively, highlighting the vulnerable eco-environmental situation; (ii) moderate secure classes areas comprised a large amount of arable land, spanning an area of 494.5 km 2 ; (iii) secure areas were distributed in the northwest, containing mostly water and wetland areas and accounting for 426.3 km 2 ; and (iv) very secure areas were located on the southeastern region, involving traditional woodland with a better vegetation cover and an overall higher eco-environmental quality. In addition, for each sub-region, the extremely low and low ecological security areas were mainly arable and urban lands, which amounted to 305 and 190 km 2 , respectively. Under the current ecological constraints, sub-region 1 cannot continue supporting the population size in Daye City. The present results demonstrate the accuracy of our methodology, and our method may be used by local managers to make effective decisions for regional environment protection and sustainable use of land resources.

Keywords: regional land eco-security; TOPSIS; grey relational analysis; land ecosystem (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/2073-445X/10/2/118/pdf (application/pdf)
https://www.mdpi.com/2073-445X/10/2/118/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:10:y:2021:i:2:p:118-:d:487322

Access Statistics for this article

Land is currently edited by Ms. Carol Ma

More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().