Integrating ecosystem service supply and demand into ecological risk assessment: a comprehensive framework and case study

生态系统服务 环境资源管理 供求关系 风险评估 风险管理 情景分析 供水 构造(python库) 服务(商务) 环境经济学 水土保持 业务 生态系统 环境规划 环境科学 地理 计算机科学 生态学 环境工程 经济 农业 财务 程序设计语言 微观经济学 考古 营销 计算机安全 生物
作者
Zhuangzhuang Wang,Liwei Zhang,Xupu Li,Yingjie Li,Bojie Fu
出处
期刊:Landscape Ecology [Springer Science+Business Media]
卷期号:36 (10): 2977-2995 被引量:45
标识
DOI:10.1007/s10980-021-01285-9
摘要

Human beings face a growing supply–demand risk in ecosystem services (SDRES) due to anthropogenic environmental change and human activity. It is urgent to construct an integrated framework that can identify where and the extent to which SDRES threatens human wellbeing. This study attempts to construct a framework to characterize SDRES. This framework could help optimize ecosystem management and protect priority areas at high risk. We used the overlay analysis method to construct a framework that characterizes eight risk levels using four comprehensive indicators: supply–demand ratio, the trend of supply–demand ratio, ES supply trend, and trade-off and synergy. We assessed the supply–demand risk of freshwater, grain, and soil conservation services in the Qinling Mountains Region of China, as a case study to illustrate the feasibility of the framework. Our results showed the different supply–demand risk levels of freshwater, grain, and soil conservation services in the Qinling Mountains Region. Supply–demand risk for freshwater and grain were similar, with high-risk areas distributed mainly in urban regions. High-risk areas for soil conservation service were patchy and scattered throughout the region. The framework and indicators proposed in this study are applicable for SDRES assessment at regional scales. This spatially-explicit model can help inform decision-makers with priority areas setting and develop effective risk management strategies. Future studies could integrate scenario simulation and multi-scale assessments into the SDRES framework to further reduce the uncertainty in risk assessment models and improve risk mitigation capability.
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