Network Evolution and Influencing Factors of Global Trade Embodied Carbon Emission

Abstract

As a global issue, climate warming has received extensive attention in recent years. Scientific identification of the network evolution, transfer path, and influencing factors of embodied carbon in trade is of great significance for the global joint response to climate change challenges. In this paper, based on the latest World Input-Output Database and carbon emissions data of 42 countries (the total carbon emissions of 42 countries account for about 80% of the world’s total carbon emissions), we use social network analysis to describe the global trade embodied carbon emission from 2000 to 2014, and explore the relevant factors that influence global embodied carbon emissions. From the overall network, a small number of countries produce more carbon emissions, of which China is gradually in the center of the global carbon emissions network, while the United States and the European Union are always in the center. The point entry degree of developed economies is larger, while the point out degree of developing countries is higher, indicating that part of carbon emissions from developed countries are transferred to developing countries through economic links. Through QAP analysis, it is found that factors such as population, energy structure, geographic distance, final consumption and trade agreements are the main factors of the evolution of the embodied carbon network, and these factors can explain 42.3% of the pattern of the implied carbon network in trade. Moreover, the decrease of the degree of proximity indicates that the path of carbon emission correlation between countries is shortening and it is necessary for the world to join hands to respond to climate change.

Evolution Characteristics and Influencing Factors of Global Dairy Trade

“…Meanwhile, there are correlations between various independent variables. If the traditional test method is used, the problem of multicollinearity will occur, increasing the variance of the parameter estimates, and the suggestion of significance of the variables will lose significance [47]. Compared with traditional OLS regression, QAP regression has advantages in dealing with multicollinearity and structural autocorrelation, and it is widely used in empirical models based on relational data regression [48][49][50].…”

Evolution of China's Role in the Structure of Global Carbon Emission Transfers: An Empirical Analysis Based on Network Governance

“…Few studies have combined SNA with the extended gravity model and conducted in-depth research on the governance mechanism of the global carbon transfer network by constructing NSI indicators and using cross-border panel data models. In addition, in empirical research on trade-embodied carbon, although some studies use the Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT), Quadratic Assignment Procedure (QAP), or Structural Decomposition Analysis (IDA) models to start the quantitative analysis (Di et al, 2022), these studies generally ignore the endogenous problems that may be caused by missing variables, measurement errors, and other factors. At the same time, these research methods fail to consider the specifi c reasons for the evolution of the network structure over time when studying the factors influencing the network at each time point and lack the mining of time-dependent effects.…”