The trade-environment nexus in global cereal trade: Combing social network and spatial panel econometrics analysis.

The global trade networks of maize, wheat, and rice and their carbon emission networks are established from 2000 to 2020. Social network analysis (SNA) is used to quantify countries' roles in the networks. Spatial econometric model is used to analyze the impacts of socio-economic indicators and the countries' roles according to Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model. The results show that the global cereal trade volume and their carbon emissions continues to rise. The overall cereal trade networks tend to be complicated and tight. Global cereal trade networks are highly heterogeneous networks with numerous peripheral economies and a few hub countries. However, with the rise of developing economies in cereal trade, the dominance of hubs decrease. The panel regression shows that the network characteristics of three cereal trade have different effects on carbon emissions as follows: (1) In rice trade network closeness centrality increases carbon emissions, meaning countries or regions with more trade connectivity emit more carbon emission. However, in wheat trade network it has reducing effects in whole and low-income countries, while increasing effects in high-income countries. (2) Among the three cereal trade, betweenness centrality reflecting mediating role has significantly negative effects on carbon emissions only in rice trade network. (3) Regarding the number of importing partners, it has positive effects on carbon emissions for wheat and maize trade, while negative for rice trade. The number of exporting partners has positive effects in all the three cereal trade. This study confirms carbon mitigation potentials from trade relationship. This study also discusses some implications for agriculture and policymakers in different countries or regions involved in the global cereal trade. [Display omitted] • Global cereal trade and emission networks (maize, wheat, rice) in 2000–2020 are built. • SNA method and SDM model are combined to reveal trade effects on carbon emission. • Rice trade network: more connectivity, higher emissions in all countries. • Wheat trade network: more connectivity, less emissions in low-income countries. • Maize trade network: more importers, more emissions in low-income countries but less emissions in high-income countries. [ABSTRACT FROM AUTHOR]