Gray Forecast of Ecosystem Services Value and Its Driving Forces in Karst Areas of China: A Case Study in Guizhou Province, China

Overview

Journal Int J Environ Res Public Health

Publisher MDPI

Specialties Environmental Health
Public Health

Date 2021 Dec 10

PMID 34886131

Citations 1

Authors

Sipei Pan

Jiale Liang

Wanxu Chen

Jiangfeng Li

Ziqi Liu

Affiliations

Soon will be listed here.

Abstract

A sound ecosystem is the prerequisite for the sustainable development of human society, and the karst ecosystem is a key component of the global ecosystem, which is essential to human welfare and livelihood. However, there remains a gap in the literature on the changing trend and driving factors of ecosystem services value () in karst areas. In this study, Guizhou Province, a representative region of karst mountainous areas, was taken as a case to bridge the gap. in the karst areas was predicted, based on the land use change data in 2009-2018, and the driving mechanisms were explored through the gray correlation analysis method. Results show that a total loss of CNY 21.47 billion from 2009 to 2018 is due to the conversion of a total of 22.566% of the land in Guizhou, with forest land as the main cause of change. By 2025 and 2030, the areas of garden land, water area, and construction land in Guizhou Province will continue to increase, whereas the areas of cultivated land, forest land, and garden land will decline. The total shows a downward trend and will decrease to CNY 218.71 billion by 2030. Gray correlation analysis results illuminate that the total population and tertiary industry proportion are the uppermost, among all the driving factors that affect change. The findings in this study have important implications for optimizing and adjusting the land use structure ecological protection and will enrich the literature on in ecologically fragile areas.

Citing Articles

Spatio-temporal evolution and trend prediction of urban ecosystem service value based on CLUE-S and GM (1,1) compound model.

Feng H, Lei X, Yu G, Changchun Z Environ Monit Assess. 2023; 195(11):1282.

PMID: 37812253 PMC: 10562314. DOI: 10.1007/s10661-023-11853-y.

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