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A platform for research: civil engineering, architecture and urbanism
Regional Differences and Spatial Aggregation of Sustainable Transport Efficiency: A Case Study of China
In the past few decades, traffic congestion, traffic accidents, and air pollution caused by transport become increasingly serious in China, so the issue of sustainable development of transport has attracted much attention. This study explores the development level of provincial sustainable transport in China, measures the level of sustainable development of China’s transport from the perspective of transport efficiency, and analyzes the differences and spatial effects of sustainable transport development. The undesired output of the super slacks-based-measurement data envelopment analysis (US-SBM-DEA) model is used to measure the sustainable transport efficiency (STE) in different provinces of China, and then the coefficient of variation (CV) and Gini coefficient (GC) are used to explore the regional differences of STE. Finally, we analyze the spatial aggregation of STE by the index of Moran’s I. The results show that the regional mean of STE presents a distribution in the order of eastern > western > central > northeastern regions. The CV reveals that there is a local σ-convergence in the STE differences among the four regions during the study period. The Moran scatter plot of STE shows that the provincial STEs in China are mainly the aggregations of high–low and low–low with the latter being more obvious. The GC basically remained at a relatively stable level during 2007–2015 while quickly decreased in 2016. i.e., the equity of sustainable transport increased dramatically in 2016. These results meet the actual development of the sustainable transport in China’s provinces and reflect the level of sustainable transport efficiency more objectively. The results of this study provides theoretical support for the provincial governments to formulate efficient transport policies.
Regional Differences and Spatial Aggregation of Sustainable Transport Efficiency: A Case Study of China
In the past few decades, traffic congestion, traffic accidents, and air pollution caused by transport become increasingly serious in China, so the issue of sustainable development of transport has attracted much attention. This study explores the development level of provincial sustainable transport in China, measures the level of sustainable development of China’s transport from the perspective of transport efficiency, and analyzes the differences and spatial effects of sustainable transport development. The undesired output of the super slacks-based-measurement data envelopment analysis (US-SBM-DEA) model is used to measure the sustainable transport efficiency (STE) in different provinces of China, and then the coefficient of variation (CV) and Gini coefficient (GC) are used to explore the regional differences of STE. Finally, we analyze the spatial aggregation of STE by the index of Moran’s I. The results show that the regional mean of STE presents a distribution in the order of eastern > western > central > northeastern regions. The CV reveals that there is a local σ-convergence in the STE differences among the four regions during the study period. The Moran scatter plot of STE shows that the provincial STEs in China are mainly the aggregations of high–low and low–low with the latter being more obvious. The GC basically remained at a relatively stable level during 2007–2015 while quickly decreased in 2016. i.e., the equity of sustainable transport increased dramatically in 2016. These results meet the actual development of the sustainable transport in China’s provinces and reflect the level of sustainable transport efficiency more objectively. The results of this study provides theoretical support for the provincial governments to formulate efficient transport policies.
Regional Differences and Spatial Aggregation of Sustainable Transport Efficiency: A Case Study of China
Fei Ma (author) / Xiaodan Li (author) / Qipeng Sun (author) / Fei Liu (author) / Wenlin Wang (author) / Libiao Bai (author)
2018
Article (Journal)
Electronic Resource
Unknown
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