Identification of ecosystem service bundles and driving factors in Beijing and its surrounding areas

Tianqian Chen; Zhe Feng; Huafu Zhao; Kening Wu

doi:10.1016/j.scitotenv.2019.134687

Identification of ecosystem service bundles and driving factors in Beijing and its surrounding areas

Sci Total Environ. 2020 Apr 1:711:134687. doi: 10.1016/j.scitotenv.2019.134687. Epub 2019 Nov 22.

Authors

Tianqian Chen¹, Zhe Feng², Huafu Zhao³, Kening Wu³

Affiliations

¹ School of Land Science and Technology, China University of Geosciences, Beijing 100083, China.
² School of Land Science and Technology, China University of Geosciences, Beijing 100083, China; Key Laboratory of Land Consolidation and Rehabilitation, Ministry of Land and Resources, Beijing 100035, China. Electronic address: zhefeng@cugb.edu.cn.
³ School of Land Science and Technology, China University of Geosciences, Beijing 100083, China; Key Laboratory of Land Consolidation and Rehabilitation, Ministry of Land and Resources, Beijing 100035, China.

PMID: 31812415
DOI: 10.1016/j.scitotenv.2019.134687

Abstract

In high-intensity human activity areas, such as metropolises, rapid changes in land use, agricultural intensification, and population urbanization have resulted in profound and complex transformations in socio-economic ecosystems. The study of ecosystem service (ES) bundle is conducive to various aspects, such as determination of the variation characteristics of ES; identification of the mechanism of interdependence within ES; and driving mechanism of socio-economic-ecological factors to ES to maintain the sustainable development of the region. The research areas include Beijing and its surrounding areas. Ten ES, including grain providing (GP), water yield (WY), carbon sequestration (CS), soil retention (SEC), purified water service, cultural services, and habitat quality (HQ) were selected for valuing and mapping. The ES paired trade-offs and synergetic relationship, bundle was determined, and the bundles' service types and spatial distribution characteristics were analyzed. Subsequently, GeoDetector was used for detecting the factors affecting the bundles' distribution. Results showed that WY, CS, SEC, and HQ were bounded by Tai-hang and Yanshan Mountains. Among the 45 pairs of ES, 38 pairs bore significant correlation. Multiple services had different degrees of positive and negative correlations with other services. For example, GP had a high positive correlation with WY while bearing a high negative correlation with HQ. Seven bundles include SEC, culture, urban, HQ, agriculture, water supply and purification, and water purification. Various factors played decisive roles in the bundles' spatial distribution. Among them, the investment capacity and demand for ecological protection depend on the level of GDP and POP. The formulation of agricultural planting plans is inseparable from TADEM. ASL is directly related to species richness. Results indicate that bundle research can identify the areas of the formation of co-occurrence of trade-offs and synergies and support the formulation of ES optimal management plans for different regions through further research of the driving mechanism.

Keywords: Ecosystem service bundle; GeoDetector; Principal component analysis; Trade-offs and synergies.