Advancing the optimization of urban–rural ecosystem service supply-demand mismatches and trade-offs

生态系统服务 景观生态学 可持续发展 供求关系 自然资源经济学 环境资源管理 业务 生态系统 环境经济学 服务(商务) 环境规划 经济 生态学 环境科学 微观经济学 营销 栖息地 生物
作者
Guangji Fang,Xiao Sun,Ranhao Sun,Qinghua Liu,Yu Tao,Peng Yang,Huajun Tang
出处
期刊:Landscape Ecology [Springer Science+Business Media]
卷期号:39 (2) 被引量:9
标识
DOI:10.1007/s10980-024-01849-5
摘要

Abstract Context Intensified human activities have disrupted landscape patterns, causing a reduction in the supply of ecosystem services (ESs) and an increase in demand, especially in urban agglomerations. This supply-demand imbalance will eventually lead to unsustainable landscapes and needs to be optimized. Objective Based on ES supply-demand mismatch and trade-off relationships across urban–rural landscapes, this study explored which ESs need to be optimized and identified priority restoration regions of ESs that require optimization to promote landscape sustainability in Beijing-Tianjin-Hebei urban agglomeration. Methods A methodological framework for ES supply-demand optimization in urban–rural landscapes was developed. urban–rural landscapes were identified using Iso cluster classification tool. ES supply was quantified using biophysical models and empirical formulas, and demand was quantified through consumption and expectations. Restoration Opportunities Optimization Tool was then adopted to identify priority regions. Results From 2000 to 2020, most of ES supply were lowest in urban areas and highest in rural areas, while demand exhibited the opposite. Although supply was increasing, it did not match demand. ES deficits were dominant in urban areas; both deficits and trade-offs were dominant in urban–rural fringe; and trade-offs were dominant in rural areas. There were 13,175 km 2 of priority regions distributed in urban–rural landscapes, and their spatial heterogeneity was influenced by ES deficits and trade-offs. Conclusion Differences in ESs supply-demand relationships affected the necessity of optimizing ESs zoning in urban–rural landscapes. Assigning weights reasonably according to trade-off curves to determine priority regions could facilitate both efficient use of resources and sustainable ES management for urban–rural regions.
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