Abstract
Global climate change caused by greenhouse gas emissions (GHGs) from anthropogenic activities have already become the focus of the world. A more systematic and comprehensive analysis on the factors influencing the changes of global GHGs transferring via trade have not been fully discussed. To this end, employing spatial econometric regression models and multi-regional input-output models, this paper reveals factors influencing the GHGs transferring via trade changes in 39 major economies, so as to develop the relevant GHGs reduction policies. The results indicate that regions with the highest net outflow of GHGs transferring via trade are primarily Russia and Canada, and the adverse effects of promoting GHGs reduction on the national economy could be avoided by these regions owing to trade relations. Additionally, factors influencing the changes in GHGs transferring via trade have significant spatial autocorrelation, and population size and energy structure exert significant spatial spillover effects on the changes in the GHGs transferring via trade. On this basis, this paper suggests that one more effective way to prevent trade from the rigorous demands of environmental governance measures while preserving the economic benefits of international trade may be to facilitate cooperation between countries on GHGs mitigation. Further, we articulate more balanced environment governance policies, including conducting the sharing of advanced energy technologies and developing clearer production technologies.
Highlights
A mass of greenhouse gas emissions (GHGs), including carbon oxides, nitric oxides, and sulfur oxides, from anthropogenic economic activities due to the combustion of fossil fuels have become the leading cause of global environmental issues, such as extreme weather, drought, and rising sea levels [1,2,3]
The discussions on the impacts of influencing factors’ spatial correlation between regions on the changes in GHGs embodied in trade have not been fully discussed. In this context, considering that compared to factor decomposition models, such as structural decomposition analysis, index decomposition analysis, and traditional econometric models, including least square methods, spatial econometric regression models are superior to estimating the spatial spillover effects from a set of influencing factors and investigating their quantitative influences on the changes in the observed explanatory variable [89,90], so these models were applied here to explore the GHGs transferring via trade and the relevant issues on the global scale over a period of time
To spatial and temporal effects, spatial econometric regression models specialized in investigating address this issue, by nesting spatial and temporal effects, spatial econometric regression explicitly models the spatial effects of explanatory variables on the explained variables were used
Summary
A mass of greenhouse gas emissions (GHGs), including carbon oxides, nitric oxides, and sulfur oxides, from anthropogenic economic activities due to the combustion of fossil fuels have become the leading cause of global environmental issues, such as extreme weather, drought, and rising sea levels [1,2,3]. A part of greenhouse gas, such as sulfur oxides, exerts serious adverse effects on our eyes and other issues with the respiratory system [4,5], but it has been recognized as an important source of threats to human survival [6,7]. GHGs reduction has become the focus of world attention. Public Health 2020, 17, 5065; doi:10.3390/ijerph17145065 www.mdpi.com/journal/ijerph
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