Climate change and rapid urbanization pose significant challenges to the conservation and management of agricultural heritage systems,including decline in agricultural land,loss of labor,and ecosystem degradation.Alth...Climate change and rapid urbanization pose significant challenges to the conservation and management of agricultural heritage systems,including decline in agricultural land,loss of labor,and ecosystem degradation.Although existing studies have proposed general strategies with theoretical guidance and specific strategies for particular systems to promote the conservation of agricultural heritage systems,there remains a large knowledge gap in effective and differentiated management strategies at the regional level.This is especially so in China because of the clear regional differences in the natural and socioeconomic conditions of the widely distributed China Nationally Important Agricultural Heritage Systems(China-NIAHS).In this study,we integrated multi-source data and spatial analysis to reveal the distribution characteristics of existing China-NIAHS and proposed differentiated management strategies.Results show that there are four clustering distribution zones of China-NIAHS,i.e.,the northwest clustering zone west of the Heihe-Tengchong Line(ZoneⅠ),the clustering belt with‘Northeast-Hebei-Shandong'as core(ZoneⅡ),the Yangtze River Delta clustering zone(ZoneⅢ),and the Hunan-Chongqing-Yunnan-Guizhou clustering zone(ZoneⅣ).Different management strategies are proposed for the China-NIAHS in each clustering zone.Specifically,ZoneⅠshould focus on maintaining their ecological functions and services,while ZoneⅡshould aim for livelihood supply,sustainable resource use,and ecological protection.For ZoneⅢ,rapid urbanization could become a positive driving force for China-NIAHS conservation through sustainable tourism and reasonable urban zoning.ZoneⅣshould emphasize the mutual support between characteristic product development and the brand effect of the China-NIAHS.These findings will help establish regional and targeted management strategies for China-NIAHS and provide a reference for the conservation of agricultural heritage systems in other countries.展开更多
The systemic importance of a bank is usually measured by its effect on the banking system,conditional on the insolvency of the bank and solvency of other banks.However,banks encounter different kinds of shocks simulta...The systemic importance of a bank is usually measured by its effect on the banking system,conditional on the insolvency of the bank and solvency of other banks.However,banks encounter different kinds of shocks simultaneously in reality.So that,the conditional re-sults give biased estimates of banks'systemic importance when potential risks are ignored.Researchers like Tarashev et al.proposed the Shapley value method to deal with risk in-teractions,but it suffers heavy computational costs.This paper proposes an ANOVA-like decomposition method to measure the systemic importance of banks in more compli-cated and realistic environments,which considers both interactions and individual effects of multiple shocks and provides a more exact estimation of systemic importance.It is found that the method proposed in this paper fits well in the network models.And meanwhile,a discussion between the method proposed in this paper and the Shapley value method is made based on the numerical example,which aims to demonstrate it's the advantages.The Shapley value method requires 2n subsystems,while the ANOVA-like decomposition method requires only n+1 model runs.In the application part,the pro-posed method is adopted to measure the systemic importance of 16 Chinese listed banks.With low computational costs,the model outputs the individual effect,interaction,and total effect of each bank.The results confirm that interactions of different shocks play a significant role in the systemic importance of a bank;thus,the total effect considering interactions should be adopted.展开更多
基金Strategic Priority Research Program of the Chinese Academy of Sciences,No.XDA23100203。
文摘Climate change and rapid urbanization pose significant challenges to the conservation and management of agricultural heritage systems,including decline in agricultural land,loss of labor,and ecosystem degradation.Although existing studies have proposed general strategies with theoretical guidance and specific strategies for particular systems to promote the conservation of agricultural heritage systems,there remains a large knowledge gap in effective and differentiated management strategies at the regional level.This is especially so in China because of the clear regional differences in the natural and socioeconomic conditions of the widely distributed China Nationally Important Agricultural Heritage Systems(China-NIAHS).In this study,we integrated multi-source data and spatial analysis to reveal the distribution characteristics of existing China-NIAHS and proposed differentiated management strategies.Results show that there are four clustering distribution zones of China-NIAHS,i.e.,the northwest clustering zone west of the Heihe-Tengchong Line(ZoneⅠ),the clustering belt with‘Northeast-Hebei-Shandong'as core(ZoneⅡ),the Yangtze River Delta clustering zone(ZoneⅢ),and the Hunan-Chongqing-Yunnan-Guizhou clustering zone(ZoneⅣ).Different management strategies are proposed for the China-NIAHS in each clustering zone.Specifically,ZoneⅠshould focus on maintaining their ecological functions and services,while ZoneⅡshould aim for livelihood supply,sustainable resource use,and ecological protection.For ZoneⅢ,rapid urbanization could become a positive driving force for China-NIAHS conservation through sustainable tourism and reasonable urban zoning.ZoneⅣshould emphasize the mutual support between characteristic product development and the brand effect of the China-NIAHS.These findings will help establish regional and targeted management strategies for China-NIAHS and provide a reference for the conservation of agricultural heritage systems in other countries.
基金This research was supported by the National Natural Science Foundation of China under Grants 71425002,71571179
文摘The systemic importance of a bank is usually measured by its effect on the banking system,conditional on the insolvency of the bank and solvency of other banks.However,banks encounter different kinds of shocks simultaneously in reality.So that,the conditional re-sults give biased estimates of banks'systemic importance when potential risks are ignored.Researchers like Tarashev et al.proposed the Shapley value method to deal with risk in-teractions,but it suffers heavy computational costs.This paper proposes an ANOVA-like decomposition method to measure the systemic importance of banks in more compli-cated and realistic environments,which considers both interactions and individual effects of multiple shocks and provides a more exact estimation of systemic importance.It is found that the method proposed in this paper fits well in the network models.And meanwhile,a discussion between the method proposed in this paper and the Shapley value method is made based on the numerical example,which aims to demonstrate it's the advantages.The Shapley value method requires 2n subsystems,while the ANOVA-like decomposition method requires only n+1 model runs.In the application part,the pro-posed method is adopted to measure the systemic importance of 16 Chinese listed banks.With low computational costs,the model outputs the individual effect,interaction,and total effect of each bank.The results confirm that interactions of different shocks play a significant role in the systemic importance of a bank;thus,the total effect considering interactions should be adopted.