With the increasing number of quantitative models available to forecast the volatility of crude oil prices, the assessment of the relative performance of competing models becomes a critical task. Our survey of the lit...With the increasing number of quantitative models available to forecast the volatility of crude oil prices, the assessment of the relative performance of competing models becomes a critical task. Our survey of the literature revealed that most studies tend to use several performance criteria to evaluate the performance of competing forecasting models;however, models are compared to each other using a single criterion at a time, which often leads to different rankings for different criteria—A situation where one cannot make an informed decision as to which model performs best when taking all criteria into account. In order to overcome this methodological problem, Xu and Ouenniche [1] proposed a multidimensional framework based on an input-oriented radial super-efficiency Data Envelopment Analysis (DEA) model to rank order competing forecasting models of crude oil prices’ volatility. However, their approach suffers from a number of issues. In this paper, we overcome such issues by proposing an alternative framework.展开更多
As a traditional agricultural country,China has always prioritized agricultural development,and has increasingly focused on green and sustainable agricultural development.Based on the inter-provincial panel data for C...As a traditional agricultural country,China has always prioritized agricultural development,and has increasingly focused on green and sustainable agricultural development.Based on the inter-provincial panel data for China from 1997 to 2019,this study divided these data into five periods according to the Five-Year Plan(FYP)of China,measured the agricultural eco-efficiency(AEE)values using the Super-SBM model,and then determined the spatial association network of the inter-provincial AEE of China using the improved gravity model.Finally,social network analysis(SNA)was used to further analyze the evolution process of AEE,and we de-veloped a framework of how multidimensional proximity,which includes geographical,economic,technological,cognitive,and institutional proximity,made an influence on the formation of AEE spatial relation network.The findings indicated that:1)in 1997−2019,the AEE in China was present in some spatial and temporal differences characteristics at the provincial scale,and we specifically found that national macro-regulation and policy incentives played a positive role in the long-term development of AEE.2)The spatial correlation of AEE development among provincial regions were becoming closer and exhibits obvious spatial correlation and spillover effects.The evolution of the AEE network has clearly observable trends of hierarchization and aggregation,and the complexity of the correlation network continues to increase and exhibits spatial clustering characteristics that are dense in the east and sparse in the west.The network structure has changed from monocentric radiation to a multicentric network,and network nodes select the more advantageous nodes with which to connect.3)Finally,the geographical proximity had a significant negative effect;the economic,technological,and institutional proximities were all observed to contribute to the AEE network formation,and cognitive proximity did not significantly influence this network formation.展开更多
电力系统的环境效率变化直接影响中国节能减排目标的实现,文章采用基于松弛变量的网络数据包络分析模型(slack based measure-network data envelopment analysis:SBM-NDEA)研究30个省级行政区域(未包括港澳台和西藏)电力系统环境效率...电力系统的环境效率变化直接影响中国节能减排目标的实现,文章采用基于松弛变量的网络数据包络分析模型(slack based measure-network data envelopment analysis:SBM-NDEA)研究30个省级行政区域(未包括港澳台和西藏)电力系统环境效率变动情况。通过对运算结果的分析发现:由于考虑了发电部门和输配部门间的竞争合作关系,SBM-NDEA模型结果的区分度更高,比传统模型更适用于电力系统效率评价;是否将上网电量作为输入对评价结果影响很大;加强对中西部电力输出地区的转移支付,有利于实现电力系统的调度优化。展开更多
文摘With the increasing number of quantitative models available to forecast the volatility of crude oil prices, the assessment of the relative performance of competing models becomes a critical task. Our survey of the literature revealed that most studies tend to use several performance criteria to evaluate the performance of competing forecasting models;however, models are compared to each other using a single criterion at a time, which often leads to different rankings for different criteria—A situation where one cannot make an informed decision as to which model performs best when taking all criteria into account. In order to overcome this methodological problem, Xu and Ouenniche [1] proposed a multidimensional framework based on an input-oriented radial super-efficiency Data Envelopment Analysis (DEA) model to rank order competing forecasting models of crude oil prices’ volatility. However, their approach suffers from a number of issues. In this paper, we overcome such issues by proposing an alternative framework.
基金Under the auspices of National Key R&D Program of China(No.2018YFD 1100104)Natural Science Foundation of Anhui Province(No.2108085-MD29)National Natural Science Foundation of China(No.41571400)。
文摘As a traditional agricultural country,China has always prioritized agricultural development,and has increasingly focused on green and sustainable agricultural development.Based on the inter-provincial panel data for China from 1997 to 2019,this study divided these data into five periods according to the Five-Year Plan(FYP)of China,measured the agricultural eco-efficiency(AEE)values using the Super-SBM model,and then determined the spatial association network of the inter-provincial AEE of China using the improved gravity model.Finally,social network analysis(SNA)was used to further analyze the evolution process of AEE,and we de-veloped a framework of how multidimensional proximity,which includes geographical,economic,technological,cognitive,and institutional proximity,made an influence on the formation of AEE spatial relation network.The findings indicated that:1)in 1997−2019,the AEE in China was present in some spatial and temporal differences characteristics at the provincial scale,and we specifically found that national macro-regulation and policy incentives played a positive role in the long-term development of AEE.2)The spatial correlation of AEE development among provincial regions were becoming closer and exhibits obvious spatial correlation and spillover effects.The evolution of the AEE network has clearly observable trends of hierarchization and aggregation,and the complexity of the correlation network continues to increase and exhibits spatial clustering characteristics that are dense in the east and sparse in the west.The network structure has changed from monocentric radiation to a multicentric network,and network nodes select the more advantageous nodes with which to connect.3)Finally,the geographical proximity had a significant negative effect;the economic,technological,and institutional proximities were all observed to contribute to the AEE network formation,and cognitive proximity did not significantly influence this network formation.
文摘电力系统的环境效率变化直接影响中国节能减排目标的实现,文章采用基于松弛变量的网络数据包络分析模型(slack based measure-network data envelopment analysis:SBM-NDEA)研究30个省级行政区域(未包括港澳台和西藏)电力系统环境效率变动情况。通过对运算结果的分析发现:由于考虑了发电部门和输配部门间的竞争合作关系,SBM-NDEA模型结果的区分度更高,比传统模型更适用于电力系统效率评价;是否将上网电量作为输入对评价结果影响很大;加强对中西部电力输出地区的转移支付,有利于实现电力系统的调度优化。
