To investigate whether increasing trade openness results in more severe environmental problems, this study investigates the impact of trade openness on carbon dioxide (CO2) emissions using panel data from 64 countries along the Belt and Road from 2001–2019. Fully considering the potential heterogeneity, the panel quantile regression approach is utilized. Moreover, this study explores the three major mediating effects of the process, namely the energy-substitution effect, economic effect, and technology effect. The empirical results indicate that the improvement in trade openness has a significantly positive effect on CO2 emissions, and it also shows that the impact varies with different levels of CO2 emissions. Furthermore, the indirect effect of trade openness on CO2 emissions via the economic effect is positive, while the indirect effect via the energy-substitution and the technology effect is negative. Therefore, it is necessary to improve renewable energy consumption, decrease energy intensity, and formulate related policies to reduce carbon emissions policies in terms of local conditions.

Global trade is based on a group of multifaceted interactions between nations that can be modeled as an incredibly dense network of intertwined agents. On the one hand, this network might favor the trade performance of countries, but on the other, it can also discourage international trade. In this article, we investigate whether and how much the structure of the trade network may explain for the performances of intra-African trade among certain African nations. We calculated the centrality indexes for the nations and applied them to regression analysis. We then employ a negative binomial regression framework with these indicators as target regressors. In doing so, we also compare the effects of different measures of centrality-specifically, the degree centrality measures and the clustering coefficient. Our findings suggest that, albeit boosting the degree centrality index tends to improve the trade flows inside Africa, on average, the intra-African trade flow was shown to be negatively impacted by the clustering coefficient, which is congruent with theory and our predictions.

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