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Spatiotemporal Evolution of Ecosystem Health of China’s Provinces Based on SDGs

Author

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  • Run Zhao

    (College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China)

  • Chaofeng Shao

    (College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China)

  • Rong He

    (Sichuan Academy of Environmental Policy and Planning, Chengdu 610041, China)

Abstract

In the context of increasing ecological scarcity, maintaining the balance between natural and artificial capital has become a popular research topic in the field of ecosystem health. From the perspective of coordinating natural and artificial capital and maintaining the balance between human systems and the Earth’s ecosystem, the Ecosystem Health Index (EHI) was developed on the basis of the Sustainable Development Goals (SDGs). The EHI consists of the Social Progress Index (SPI), Economic Development Index (EDI), Natural Environment Index (NEI), and a pressure adjustment coefficient. Comprehensive indicator assessment models were used to analyze the spatial and temporal evolution of the EHIs in 30 of China’s provinces from 2013 to 2019. A three-dimensional judgment matrix was used to classify the 30 provinces into four basic types. The results show the following: (1) From 2013 to 2019, the EHIs of all provinces improved to different degrees, with 19 provinces achieving a healthy state. (2) Spatially, the EHI showed some regional aggregation in 2013. Provinces with high EHIs were concentrated in the west, followed by those in the east, and those in the central provinces had the lowest EHIs. However, the differences between regions had narrowed by 2019. (3) The spatial distribution patterns of the NEI and the EDI varied widely, and most provinces did not reach a high level of coordination between natural and artificial capital. (4) The environmental pressure in all provinces, except Liaoning, decreased over time. In some cases, excessive pressure decreased the pressure-adjusted EHI, regardless of the EHI value. (5) According to the results of the ecosystem health classification in each province, the factors that hinder ecosystem health vary from place to place.

Suggested Citation

  • Run Zhao & Chaofeng Shao & Rong He, 2021. "Spatiotemporal Evolution of Ecosystem Health of China’s Provinces Based on SDGs," IJERPH, MDPI, vol. 18(20), pages 1-19, October.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:20:p:10569-:d:652394
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    References listed on IDEAS

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    1. Meijuan Hu & Suleman Sarwar & Zaijun Li, 2021. "Spatio-Temporal Differentiation Mode and Threshold Effect of Yangtze River Delta Urban Ecological Well-Being Performance Based on Network DEA," Sustainability, MDPI, vol. 13(8), pages 1-19, April.
    2. Arrow, Kenneth & Bolin, Bert & Costanza, Robert & Dasgupta, Partha & Folke, Carl & Holling, C.S. & Jansson, Bengt-Owe & Levin, Simon & Mäler, Karl-Göran & Perrings, Charles & Pimentel, David, 1996. "Economic growth, carrying capacity, and the environment," Environment and Development Economics, Cambridge University Press, vol. 1(1), pages 104-110, February.
    3. Wei Shen & Zhicheng Zheng & Yaochen Qin & Yang Li, 2020. "Spatiotemporal Characteristics and Driving Force of Ecosystem Health in an Important Ecological Function Region in China," IJERPH, MDPI, vol. 17(14), pages 1-19, July.
    4. Delin Liu & Shilong Hao, 2016. "Ecosystem Health Assessment at County-Scale Using the Pressure-State-Response Framework on the Loess Plateau, China," IJERPH, MDPI, vol. 14(1), pages 1-11, December.
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    Cited by:

    1. Chenxi Li & Zhihong Zong & Haichao Qie & Yingying Fang & Qiao Liu, 2023. "CiteSpace and Bibliometric Analysis of Published Research on Forest Ecosystem Services for the Period 2018–2022," Land, MDPI, vol. 12(4), pages 1-16, April.

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