Spatio-Temporal Impact of Global Migration on Carbon Transfers Based on Complex Network and Stepwise Regression Analysis

Abstract

Considering the advancement of economic globalization, the reasons for migration together with the lifestyles of migrants will change the use of energy, environment of origin and destination. This study therefore explores the patterns of global trade-induced carbon emission transfers using “center-of-gravity” and complex network analysis. We further investigate the determinants of carbon transfers by integrating the impact of population migration through the STIRPAT framework for 64 countries over the period 2005–2015 using the stepwise regression approach. Our results unveil that higher levels of migration flow induce higher carbon flow. Specifically, every 1% increase in migration, triggers carbon transfers to increase within the range of 0.118%−0.124%. The rising impact of migration cannot be ignored, even though the coefficients were not so high. Besides, for both male and female migrants, their impact on carbon transfers generated by the intermediate products were higher than those generated by the final products. However, the influence is more obvious in male migrants. With the aim of dividing the sample of countries into three income groups, the results generally show that the impacts of migration vary across levels of income. Therefore, the environmental pressure caused by immigration should be considered by destination countries in the formulating of migration policies. On the other hand, origin countries should take some responsibility for carbon emissions according to their development characteristics.

Analysis of the variation tendency and influencing factors of reference evapotranspiration in Shandong Province, China

“…The main idea of stepwise regression analysis is to introduce regression equations one by one from large to small according to the size, significance or contribution of all independent variables introduced (Gao et al, 2022). A selection criterion most commonly used for stepwise regression is the Akaike information criterion (AIC) (Akaike, 1974).…”

Towards Sustainable Development: Investigating the Heterogeneity and Driving Factors of Green Total Factor Productivity in Coal Enterprises

“…We utilize the generalized least squares method to estimate the parameters and employ stepwise regression to assess the robustness of the results [34]. This is done to avoid problems such as serial correlation and heteroscedasticity, which can negatively impact the fairness, validity, and consistency of the parameter estimates.…”

The Spatial Structure and Driving Mechanisms of Multi-Source Networks in the Chengdu–Chongqing Economic Circle of China

“…According to Zhang, the node dissimilarity can first be computed using the node's probability distribution vector [44]. The whole network difference between two networks is then obtained on the basis of differences between all nodes [45].…”

“…where Y i represents the urban contact matrix, i = 1, 2, 3; a 0 is a constant; a 1 − a n are the regression coefficients; and x 1 − x n are the explanatory factors of the correlation matrix. According to Zhu, Cui, Zhang, and Wang [44,45,47,52], the factors that affect network relations are usually divided into four levels (Table 1): economic development difference, the difference in urbanization development, policy differences, and geographical factor. Economic disparities can promote the formation of urban relationships, but beyond a certain limit, they can hinder relationship development.…”

“…To quantify the similarities or differences in characteristics between the two networks, we use the dissimilarity metric to calculate the network structure as per the research method of Gao [45] (Table 3). The dissimilarity metric in network structure is composed of the WBD and the CBD.…”