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Based on the gross trade accounting method and WIOD data, this paper quantifies the global carbon transfer from 2000 to 2014. The social network analysis and temporal exponential random graph model (TERGM) are employed to investigate the structural change and influencing factors of global carbon transfer network from a global value chains (GVCs) perspective. Results show that the deepening of the GVC division connection has enhanced the relations in global carbon transfer network, and the low reciprocity and high disassortativity lie in the difference in GVC division positions among economies. Block model analysis manifests that Plate Ⅱ acts as the engine plate in the network which consists of China and the other four economies. The GVC division connection and relative GVC division position significantly facilitate the formation of carbon emission transfer relations. Moreover, economies' attribute effects and endogenous network structure effects are non-negligible important determinants of network formation. [Display omitted] • Structural change of global carbon transfer network derives from the GVC division. • Global carbon transfer network has low reciprocity and high disassortativity. • Plate Ⅱ composed of China and the other four economies is the engine plate. • Temporal exponential random graph model is used to analyze the influencing factors. • GVC division connection and relative division position boot the network formation. [ABSTRACT FROM AUTHOR]

Using the social network analysis method, this paper explores the characteristics of the spatial association network structure of China's provincial transportation carbon emissions from 2005 to 2015 and its driving factors. The results are as follows: (1) The spatial association of China's provincial transportation carbon emissions exhibits an intuitive network structure. The degree of closeness which were 0.2253 and 0.2333 in 2005 and 2015 respectively shows an increasing trend on the whole. However, it hit the bottom in 2011, reaching a minimum of 0.2230, which is the "low closeness degree inflection point". (2) The spatial association network presents a significant "core-edge" distribution pattern. Specifically, in the central and eastern regions of China, Henan and Jiangsu have degrees of up to 58.621 and 44.828, which are at the network center. However, some remote regions, like Jilin and Hainan, have degrees of less than 20.000, which are marginalized. (3) The geographical adjacency, the expansion of the difference in R&D investment, and the narrowing of the difference in economic development promote the formation of the interprovincial spatial association. Therefore, in the process of controlling carbon emissions from transportation, the government should focus on the provinces at the network center and pay attention to the impact of the R&D investment on the spatial association of transportation carbon emissions. • The evolution trend of the spatial association network structure is analyzed. • The "core-edge" distribution of the spatial association network is described. • "Henan is a key governance province" in this study. • "Reducing the R&D investment difference between provinces" is proposed. [ABSTRACT FROM AUTHOR]