The Spatial Network Structure of China’s Regional Carbon Emissions and Its Network Effect

Abstract

Under the “new normal”, China is facing more severe carbon emissions reduction targets. This paper estimates the carbon emission data of various provinces in China from 2008 to 2014, constructs a revised gravity model, and analyzes the network structure and effects of carbon emissions in various provinces by using social network analysis (SNA) and quadratic assignment procedure (QAP) analysis methods. The conclusions show that there are obvious spatial correlations between China’s provinces and regions in terms of carbon emissions: Tianjin, Shanghai, Zhejiang, Jiangsu and Guangdong are in the center of the carbon emission network, and play the role of “bridges”. Carbon emissions can be divided into four blocks: “bidirectional spillover block”, “net beneficial block”, “net spillover block” and “broker block”. The differences in the energy consumption, economic level and geographical location of the provinces have a significant impact on the spatial correlation relationship of carbon emissions. Finally, the improvement of the robustness of the overall network structure and the promotion of individual network centrality can significantly reduce the intensity of carbon emissions.

Research on the Structural Features and Influential Factors of the Spatial Network of China’s Regional Ecological Efficiency Spillover

“…On the basis of this, the "relational data" adopted in the paper can explain the relatively high similarity among variables. In order to avoid measuring errors caused by multicollinearity, the of Quadrati Assignment Procedure (QAP) method was adopted in this paper to conduct empirical analysis on the influential factors of the spatial correlation network of China's regional ecological efficiency spillover [58].…”

Research on the Spatial Network Characteristics and Synergetic Abatement Effect of the Carbon Emissions in Beijing–Tianjin–Hebei Urban Agglomeration

“…To compare with the existing research [27][28][29] on China's provincial carbon emission spatial network, our analysis shows that in the process of China's current urban agglomeration development, there also exists a stable and complex carbon emission spatial network structure and correlation relationship among the cities in the urban agglomeration. The difference is that, in the national carbon emission spatial network, the network hierarchical structure is gradually broken and an increasing number of small provinces have changed the subordinate and edge positions in the network.…”

“…Zhang et al used Arc GIS to calculate the carbon emission and absorption rates in China's Beijing-Tianjin-Hebei agglomeration [26]. From the perspective of research methods, some studies used social network analysis (SNA) [27][28][29] to analyze the regional carbon emission spatial network structure. Most of the research on carbon emissions focuses on carbon emission measurement and decomposition, carbon emissions' influencing factors, and carbon emission efficiency [30][31][32].…”

Pattern and Influencing Factors of Foreign Direct Investment Networks between Countries along the “Belt and Road” Regions

“…Because QAP regression analysis is effectively able to solve the autocorrelation problem between network variables data, and can always control the deviation within a certain range and remain stable, many scholars have applied this method to research influencing factors of network patterns [35][36][37]. Using QAP regression analysis to study the factors affecting network patterns, the related network variables and hypotheses must be determined, then a QAP regression model constructed, and finally the Ucinet software can be used for the model and hypothesis test.…”