以蓝田县作为研究区,选取高程、坡度、坡向、曲率、降雨量、距水系距离、地层岩性、距断层距离、距道路距离、归一化植被指数(normalized difference vegetation index,NDVI)共10类评价因子,分别采用皮尔森相关系数、方差膨胀因子、容忍...以蓝田县作为研究区,选取高程、坡度、坡向、曲率、降雨量、距水系距离、地层岩性、距断层距离、距道路距离、归一化植被指数(normalized difference vegetation index,NDVI)共10类评价因子,分别采用皮尔森相关系数、方差膨胀因子、容忍度3种指标对评价因子之间的多重共线性问题进行分析。结果表明,各选取因子之间多重共线性较低,可以认为各因子相对独立。随后,采用频率比法分析各评价因子与滑坡灾害点之间的空间分布关系。分别利用熵指数(index of entropy,IOE)模型、逻辑回归(logistic regression,LR)模型以及两种模型耦合作用下的逻辑回归与熵指数耦合(integration of logistic regression and index of entropy,IOE-LR)模型对研究区滑坡易发性进行评价,得到各模型下的研究区滑坡灾害易发性区划图。最终采用接受者操作特性(receiver operating characterstic,ROC)曲线验证并比较了3种模型的性能。成功率曲线表明,IOE模型、LR模型、IOE-LR模型的ROC曲线下的面积(area under curve,AUC)分别为0.735、0.742和0.805;预测率曲线表明,IOE模型、LR模型、IOE-LR模型的ROC曲线下的面积AUC分别为0.732、0.785和0.830,其中IOE-LR模型均具有最高的准确率。生成的滑坡易发性区划图可以为蓝田县政府合理解决土地利用规划问题以及减轻滑坡风险提供有效参考。展开更多
An intuitive portrayal of the correlation between the carbon and energy markets is essential for risk control and green financial investment management.In this paper,we investigate the asymmetric spillovers between th...An intuitive portrayal of the correlation between the carbon and energy markets is essential for risk control and green financial investment management.In this paper,we investigate the asymmetric spillovers between the carbon mar-ket and energy market returns.To achieve that,we improve the Diebold-Yilmaz index model by a time-varying vector autoregressive(TVP-VAR)model.In a unified network,our daily dataset includes the closing prices of the Hubei carbon market,Shenzhen carbon market,coal futures,and energy stock index.The findings reveal that both the Hubei and Shen-zhen pilots typically generate net information spillovers on energy futures.In connection with energy stocks,the Hubei carbon market acts as a net receiver,while the Shenzhen carbon market is a net transmitter.Compared with the Hubei pi-lot,the Shenzhen pilot is more tightly connected to the energy markets.Furthermore,the spillovers of the carbon markets exhibit significant asymmetry.In most cases,they have more substantial impacts on the energy markets when the prices of emission allowances rise.The direction and magnitude of asymmetric spillovers across markets vary over time and can be influenced by certain economic or political events.展开更多
文摘以蓝田县作为研究区,选取高程、坡度、坡向、曲率、降雨量、距水系距离、地层岩性、距断层距离、距道路距离、归一化植被指数(normalized difference vegetation index,NDVI)共10类评价因子,分别采用皮尔森相关系数、方差膨胀因子、容忍度3种指标对评价因子之间的多重共线性问题进行分析。结果表明,各选取因子之间多重共线性较低,可以认为各因子相对独立。随后,采用频率比法分析各评价因子与滑坡灾害点之间的空间分布关系。分别利用熵指数(index of entropy,IOE)模型、逻辑回归(logistic regression,LR)模型以及两种模型耦合作用下的逻辑回归与熵指数耦合(integration of logistic regression and index of entropy,IOE-LR)模型对研究区滑坡易发性进行评价,得到各模型下的研究区滑坡灾害易发性区划图。最终采用接受者操作特性(receiver operating characterstic,ROC)曲线验证并比较了3种模型的性能。成功率曲线表明,IOE模型、LR模型、IOE-LR模型的ROC曲线下的面积(area under curve,AUC)分别为0.735、0.742和0.805;预测率曲线表明,IOE模型、LR模型、IOE-LR模型的ROC曲线下的面积AUC分别为0.732、0.785和0.830,其中IOE-LR模型均具有最高的准确率。生成的滑坡易发性区划图可以为蓝田县政府合理解决土地利用规划问题以及减轻滑坡风险提供有效参考。
基金supported by the National Natural Science Foundation of China(71973001).
文摘An intuitive portrayal of the correlation between the carbon and energy markets is essential for risk control and green financial investment management.In this paper,we investigate the asymmetric spillovers between the carbon mar-ket and energy market returns.To achieve that,we improve the Diebold-Yilmaz index model by a time-varying vector autoregressive(TVP-VAR)model.In a unified network,our daily dataset includes the closing prices of the Hubei carbon market,Shenzhen carbon market,coal futures,and energy stock index.The findings reveal that both the Hubei and Shen-zhen pilots typically generate net information spillovers on energy futures.In connection with energy stocks,the Hubei carbon market acts as a net receiver,while the Shenzhen carbon market is a net transmitter.Compared with the Hubei pi-lot,the Shenzhen pilot is more tightly connected to the energy markets.Furthermore,the spillovers of the carbon markets exhibit significant asymmetry.In most cases,they have more substantial impacts on the energy markets when the prices of emission allowances rise.The direction and magnitude of asymmetric spillovers across markets vary over time and can be influenced by certain economic or political events.
