Xiaodan Li

The association effect between provincial transportation carbon emissions has become an important issue in regional carbon emission management. This study explored the relationship and development trends associated with regional transportation carbon emissions. A social network method was used to analyze the structural characteristics of the spatial association of transportation carbon emissions. Indicators for each of the structural characteristics were selected from three dimensions: The integral network, node network, and spatial clustering. Then, this study established an association network for transportation carbon emissions (ANTCE) using a gravity model with China’s provincial data during the period of 2007 to 2016. Further, a block model (a method of partitioning provinces based on the information of transportation carbon emission) was used to group the ANTCE network of inter-provincial transportation carbon emissions to examine the overall association structure. There were three key findings. First, the tightness of China’s ANTCE network is growing, and its complexity and robustness are gradually increasing. Second, China’s ANTCE network shows a structural characteristic of “dense east and thin west.” That is, the transportation carbon emissions of eastern provinces in China are highly correlated, while those of central and western provinces are less correlated. Third, the eastern provinces belong to the two-way spillover or net benefit block, the central regions belong to the broker block, and the western provinces belong to the net spillover block. This indicates that the transportation carbon emissions in the western regions are flowing to the eastern and central regions. Finally, a regression analysis using a quadratic assignment procedure (QAP) was used to explore the spatial association between provinces. We found that per capita gross domestic product (GDP) and fixed transportation investments significantly influence the association and spillover effects of the ANTCE network. The research findings provide a theoretical foundation for the development of policies that may better coordinate carbon emission mitigation in regional transportation.

In recent years, the frequent occurrence of rainstorms has seriously affected urban–public transport systems. In this study, we examined the impact of rainstorms on the vulnerability of urban–public transport systems consisting of both ground bus and metro systems, which was abstracted into an undirected weighted Bus–Metro complex bilayer network (Bus–Metro CBN) and the passenger volume was regarded as its weight. Through the changes in the node scale, network efficiency, and passenger volume in the maximal connected component of the Bus–Metro CBN, we constructed a vulnerability operator to quantitatively calculate the vulnerability of the Bus–Metro CBN. Then, the flow-based couple map lattices (CMLs) model was proposed to simulate cascading failure scenarios of the Bus–Metro CBN under rainstorm conditions, in which the rainstorm is introduced through a perturbation variable. The simulation results show that under the condition of passenger flow overload, the network may have a two-stage cascading failure process. The impact analysis shows that there is a rainstorm intensity threshold that causes the Bus–Metro CBN to collapse. Meanwhile, we obtained the optimal node and edge capacity through capacity analysis. In addition, our analysis implies that the vulnerability of the Bus–Metro CBN network in most scenarios is mainly caused by the degradation of network structure rather than the loss of passenger flow. The network coupling strength analysis results show that the node coupling strength has greater potential to reduce the vulnerability than edge coupling strength. This indicates that traffic managers should prioritize controlling the mutual influence between bus stops (or metro stations) to reduce the vulnerability of the Bus–Metro CBN more effectively.

Undesirable outputs, such as carbon emissions and loss of property due to traffic accidents, hold great significance for the sustainable development of the transport industry. In this study, we applied a super-efficiency data envelopment analysis model with a slack-based measure (Super-SBM DEA) considering undesirable outputs to measure the integrated transport efficiency (ITE) of 31 provinces in China during the period of 2009–2016. Following this, we used a spatial autocorrelation model to test and verify the spatial autocorrelation of the ITEs at the level of the 31 provinces, and further to explore the aggregating features. Finally, considering the spatial effects that emerged, we constructed a β-convergence model to analyze the convergence characteristics of China’s ITEs and investigate its conditional factors. The research results show that the average ITE demonstrated a linear growth trend; the effective decision-making units (the ITE value was greater than 1) are only 11 provinces, accounting for about 35% by 2016. The mean of ITEs was also found to present a law of decreasing order of Eastern, Central and Western Zones. However, the Central Zone and Western Zone have a better efficiency improvement trend compared to the Eastern Zone. The Moran’s I index was bigger than zero, indicating that the ITEs formed a spatial autocorrelation phenomenon. The Moran scatter plots further showed that the provincial ITEs mainly followed the patterns of high–high, high–low and low–low aggregation. The ITE of the 31 provinces was found to have a clear absolute β-convergence and conditional β-convergence characteristics. Moreover, the level of economic development, household per capita traffic consumption, transport industry scale, technology advancement and transport intensity were all seen to have an important impact on the convergence of integrated transport efficiency. It is hoped that the findings of this study may contribute further insights and practical knowledge to effectively measuring the development level of China’s integrated transport efficiency, and to understanding future changes in the ITE gap among Chinese provinces.

Low-carbon and green development is important to promote the sustainable economic and social development of countries along the One Belt and One Road. These countries have distinct differences in their ability to withstand carbon emission pressures and their driving factors, due to different stages of development and economic characteristics. This paper presents a model of ecological pressure of the carbon footprint in energy consumption (EP_cfec), defined by three states: light, balanced, and heavy pressure. The EP_cfec was calculated from data on 56 countries along the One Belt and One Road from 1994–2014, and analysis of the data’s temporal and spatial evolutionary rules was conducted. Furthermore, we used the LMDI method to extract the driving forces of EP_cfec and evaluated the contribution of these factors to the overall region and seven sub-regions. The results showed that EP_cfec growth slowed over time, with the value of EP_cfec reaching 3190.51 in 2014. Resource-rich countries have a greater value of EP_cfec and are mainly distributed in parts of West Asia, North Africa, and Southeast Asia. The per capita export of goods and services, and the population density on productive land contribute to ecological pressure on the carbon footprint. Energy structure, the influence of international trade on GDP, and energy intensity exerted an inhibitory effect on the ecological pressure of the carbon footprint. This paper proposes mitigation measures for optimizing energy structure, improving energy efficiency, developing low energy consumption, and promoting green international trade. Our results provide support for countries along the One Belt and One Road to mitigate ecological pressures resulting from their carbon footprint.

The coordinated development of regional economies is a major economic goal of many countries around the world. To that end, China has actively carried out a series of strategies to expedite the development of its late-developing regions. This study explores the issues raised by this coordinated development from the perspective of late-development advantages, which refer to a region’s late-development advantages compared with the early-developing regions in the country. The 15 indicators applied for evaluating the late-development advantages fall into five categories including capital, technology, industrial structure, institutions and human resources. Then, the model of entropy-weighted technique for order preference by similarity to an ideal solution (EW-TOPSIS) is applied to evaluate the late-development advantages of China’s provinces. Following this, ArcGIS and GeoDa are used to analyze the spatio-temporal evolution pattern of the late-development advantages of China’s provinces, and to compare the spatio-temporal effect of these advantages between the provinces. The results show that the overall late-development advantages of China’s provinces had a downward trend from 2006 to 2015, with the Eastern Region falling by 8.07%, the Central Region falling by 14.37% and the Western Region falling by 8.05%, indicating that the development gap between China’s Eastern and Western Regions is still large. The temporal effect analysis shows the temporal autocorrelation changes from positive status to negative status with the increase of lagging order, which means the trend of late-development advantage will reverse over time. The spatial effect analysis shows there were only significant Low-Low and Low-High aggregation in 2006 and 2010, but significant High-High and High-Low aggregations emerge in 2012 and 2015, implying that the development environment has effectively promoted the use of the provincial late-development advantage. The research results could provide theoretical basis for the policy making of the accelerating development of late-developing regions in 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.

Passenger transport has become a significant producer of carbon emissions in China, thus strongly contributing to climate change. In this paper, we first propose a model of ecological pressure of the carbon footprint in passenger transport (EP_cfpt). In the model, the EP_cfpt values of all the provinces and autonomous regions of China are calculated and analyzed during the period of 2006–2015. For the outlier EP_cfpt values of Beijing, Shanghai and Tianjin, the research areas are classified into two scenarios: the first scenario (all the provinces and autonomous regions) and the second scenario (not including Beijing, Shanghai and Tianjin). The global spatial autocorrelation analysis of the first scenario shows that the EP_cfpt might be randomly distributed, while it shows positive spatial autocorrelation in the second scenario. Furthermore, we carry out the local spatial autocorrelation analysis of the second scenario, and find that the low aggregation areas are the most common type and are mainly located in the west of China. Then the disparities in EP_cfpt between China’s Eight Comprehensive Economic Zones are further analyzed. Finally, we put forward a number of policy recommendations in relation to the spatio-temporal changes and the regional disparities of EP_cfpt in China. This study provides related references for proposing effective policy measures to reduce the ecological pressure of carbon emissions from the passenger transport sector.