Abstract
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.
Highlights
Global attention is currently focused on carbon emissions and climate change
Based on the spatial econometric analysis results of the factors affecting China’s transportation-driven carbon emissions [38], Equation (11) shows that we assumed that the influencing factors included: The spatial adjacency relationship (SAM), per capita gross domestic product (GDP) (PAG), fixed transportation investment (TFI), passenger turnover (PAT), freight turnover (FRT), urbanization rate (UBR), and energy utilization rate (EUR): T = f (SAM, PAG, transportation fixed investment (TFI), PAT, FRT, UBR, EUR)
We established the spatial relationship matrix of provincial transportation carbon emissions based on the modified gravity model
Summary
Global attention is currently focused on carbon emissions and climate change. According to the International Energy Agency (IEA), carbon dioxide levels rose by 1.4% in 2017, reaching a record high of 32.5 billion tons. In the past two years, the State Council of China has implemented some policies for developing low-carbon transportation systems This background highlights the importance of further studying the characteristics of China’s provincial transportation carbon emissions. The interaction of economic development, energy structure, location, and transportation infrastructure between the provinces can strengthen the spatial heterogeneity of China’s transportation carbon emissions. This highlights the complexity in spatial association structure and the potential synergies of reducing regional transportation carbon emissions [3]. The last section summarizes the full study and proposes corresponding policy recommendations
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