Interactions among ecosystem services(ESs)are spatially heterogeneous,shaped by both stable(unidirectional)and unstable(context-dependent)influences of socio-economic development and climate change.These complexities ...Interactions among ecosystem services(ESs)are spatially heterogeneous,shaped by both stable(unidirectional)and unstable(context-dependent)influences of socio-economic development and climate change.These complexities pose significant challenges for spatially adaptive ecosystem management.Ecosystem service bundles(ESBs),as recurring combinations of ESs,offer a valuable framework to capture such interactions.Here,we proposed an integrated analytical framework that combines K-means clustering,the geographical detector(GD)model,and geographically and temporally weighted regression(GTWR)models to evaluate how socio-economic and climatic drivers influence ESBs across space and time.Using Hanshui River Basin(HRB)in central China as a case study,we quantified ES dynamics from 2000 to 2020,identified ESBs and stable and unstable effects of multiple drivers.The results showed that carbon sequestration(CS),water yield(WY),and aesthetic landscape(AL)increased by 32.80%,9.00%,and 7.34%,respectively,while soil retention(SR)and food supply(FS)declined by 8.38%and 5.36%.Five distinct ESBs were identified,namely ecologically fragile bundle(EFB),agricultural production bundle(APB),water supply bundle(WSB),forest ecological bundle(FEB),and ecological conservation bundle(ECB).Among these,FEB expanded to more than 35.75%of the HRB,and APB exhibited the sharpest decline(-28.64%).Land use intensity(LUI)was the primary driver of the spatial heterogeneity of ESBs,while synergistic and nonlinear interactions among multiple factors increasingly amplified their effects over time.Notably,annual precipitation(AP)emerged as the only stable basin-scale driver,consistently enhancing ES performance,while gross domestic product(GDP)and normalized difference vegetation index(NDVI)had stable yet spatially differentiated effects across bundles.Our findings highlight the significance of distinguishing stable/unstable driver effects on ES dynamics to inform regionally adaptive ecosystem governance.The proposed framework provides valuable insights into ES interactions,identify spatial priorities,and support policy interventions that balance ecological conservation with socio-economic development.展开更多
以接入配电网的风光-储能系统(Wind-PV energy storage system, WP-ESS)为研究对象,探讨了新能源在储能中的参与策略,旨在应对新能源成为电力市场主力的未来趋势,并为配电市场化运营提供决策参考。首先建立了WP-ESS在日前市场和本地平...以接入配电网的风光-储能系统(Wind-PV energy storage system, WP-ESS)为研究对象,探讨了新能源在储能中的参与策略,旨在应对新能源成为电力市场主力的未来趋势,并为配电市场化运营提供决策参考。首先建立了WP-ESS在日前市场和本地平衡储能负荷中的数学模型,然后采用随机双层优化算法,分析了WP-ESS在不同市场中的战略决策,并比较了其在单一日前能源市场和多个市场中的差异。结果表明,WP-ESS在多个市场中的利润与单一日前能源市场有显著不同,且其战略决策与所参与的市场类型有关。最后,对影响WP-ESS利润的因素进行了灵敏度分析,结果显示,储能容量的增加对利润有正面影响,而系统不平衡电价的变化对利润的影响取决于参与的市场类型。为WP-ESS系统在双碳目标下实现利益最大化提供了理论支持和方法指导。展开更多
基金Under the auspices of National Natural Science Foundation of China(No.42171414)。
文摘Interactions among ecosystem services(ESs)are spatially heterogeneous,shaped by both stable(unidirectional)and unstable(context-dependent)influences of socio-economic development and climate change.These complexities pose significant challenges for spatially adaptive ecosystem management.Ecosystem service bundles(ESBs),as recurring combinations of ESs,offer a valuable framework to capture such interactions.Here,we proposed an integrated analytical framework that combines K-means clustering,the geographical detector(GD)model,and geographically and temporally weighted regression(GTWR)models to evaluate how socio-economic and climatic drivers influence ESBs across space and time.Using Hanshui River Basin(HRB)in central China as a case study,we quantified ES dynamics from 2000 to 2020,identified ESBs and stable and unstable effects of multiple drivers.The results showed that carbon sequestration(CS),water yield(WY),and aesthetic landscape(AL)increased by 32.80%,9.00%,and 7.34%,respectively,while soil retention(SR)and food supply(FS)declined by 8.38%and 5.36%.Five distinct ESBs were identified,namely ecologically fragile bundle(EFB),agricultural production bundle(APB),water supply bundle(WSB),forest ecological bundle(FEB),and ecological conservation bundle(ECB).Among these,FEB expanded to more than 35.75%of the HRB,and APB exhibited the sharpest decline(-28.64%).Land use intensity(LUI)was the primary driver of the spatial heterogeneity of ESBs,while synergistic and nonlinear interactions among multiple factors increasingly amplified their effects over time.Notably,annual precipitation(AP)emerged as the only stable basin-scale driver,consistently enhancing ES performance,while gross domestic product(GDP)and normalized difference vegetation index(NDVI)had stable yet spatially differentiated effects across bundles.Our findings highlight the significance of distinguishing stable/unstable driver effects on ES dynamics to inform regionally adaptive ecosystem governance.The proposed framework provides valuable insights into ES interactions,identify spatial priorities,and support policy interventions that balance ecological conservation with socio-economic development.
文摘以接入配电网的风光-储能系统(Wind-PV energy storage system, WP-ESS)为研究对象,探讨了新能源在储能中的参与策略,旨在应对新能源成为电力市场主力的未来趋势,并为配电市场化运营提供决策参考。首先建立了WP-ESS在日前市场和本地平衡储能负荷中的数学模型,然后采用随机双层优化算法,分析了WP-ESS在不同市场中的战略决策,并比较了其在单一日前能源市场和多个市场中的差异。结果表明,WP-ESS在多个市场中的利润与单一日前能源市场有显著不同,且其战略决策与所参与的市场类型有关。最后,对影响WP-ESS利润的因素进行了灵敏度分析,结果显示,储能容量的增加对利润有正面影响,而系统不平衡电价的变化对利润的影响取决于参与的市场类型。为WP-ESS系统在双碳目标下实现利益最大化提供了理论支持和方法指导。
