Abstract
Global ecosystem services (ESs) are experiencing a significant decline, necessitating the development of robust environmental governance policies. To address the lack of integrated planning with heavy industry as the research object and a lack of knowledge of ES trade-offs and synergies in China’s ecological and environmental governance. In this study, the spatial and temporal variations of four ESs (water yield (WY), soil conservation (SC), carbon storage (CS), and habitat quality (HQ)) were determined in the study area of Liaoning Province. Explore the mechanisms that shape ecosystem service trade-offs and synergies and the factors that influence them. Spearman’s correlation and difference analyses were proposed to determine the spatial and temporal distributions of trade-offs and synergistic relationships among ESs. In addition, we constructed a multiscale geo-weighted regression (MGWR) model to investigate driver spatial heterogeneity affecting trade-offs and synergies. The results revealed that (1) In the study area, ESs were on the rise in Liaoning Province. (2) Temporally, ESs were overwhelmingly dominated by synergies; at the spatial scale, ESs were dominated by trade-offs of varying degrees, with the area of synergy between WY and SC being the highest. (3) ESs demonstrated spatial heterogeneity in intensity and were more impacted by natural factors such as vegetation cover, elevation, and precipitation than by characteristics related to human activity. This study helps improve understanding of the interactions and dependencies among ESs and can provide a reference for ecological governance and improvements in Liaoning Province.
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A-YQ: conceptualization, methodology, software, data processing, writing—original draft, validation. D-FR: supervision, project administration. A-HC: investigation, software, writing—review and editing. W-ZZ and M-WX: data analysis, visualization, writing—review and editing.
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Ren, DF., Qiu, AY., Cao, AH. et al. Spatial Responses of Ecosystem Service Trade-offs and Synergies to Impact Factors in Liaoning Province. Environmental Management (2023). https://doi.org/10.1007/s00267-023-01919-5
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DOI: https://doi.org/10.1007/s00267-023-01919-5