Qinyang Guo

Global trade drives the world’s economic development, while a large amount of embodied carbon is transferred among different countries and regions. Based on a multi-regional input–output model, the trade embodied carbon transfers of bilateral trade between 185 countries/regions around the world were calculated. On the basis, regional trade embodied carbon transfer patterns and major national trade patterns in six continents, eight major economic cooperation organizations, and six representative countries/regions were further analyzed. The results showed that Europe was the continent with the largest embodied carbon inflows from trade and Africa was the continent with the largest embodied carbon outflows from trade. China was the country which had the largest embodied carbon outflows from trade, while the United States, France, Japan, and Germany were countries which had embodied carbon inflows from trade. OECD, EU, and NAFTA were the economic cooperation organizations with embodied carbon inflows from trade, while BRICS, SCO, RCEP, OPEC, and ASEAN were economic cooperation organizations with embodied carbon outflows from trade. Developed countries such as the United States, France, and the United Kingdom protected their environment by exporting high-value products and importing low-value and carbon-intensive products. Developing countries such as China and Russia earned foreign exchange by exporting carbon-intensive and commodity products at a huge environmental cost. In contrast, Germany, China, and Russia played different roles in the global industrial chain, while Germany exchanged more trade surpluses at lower environmental costs. Therefore, for different countries and regions, their own industries should be actively upgraded to adjust the import and export structure, the cooperation and coordination in all regions of the world should be strengthened, and the transfers of embodied carbon needs to be reduced to make the trade model sustainable.

Agricultural ecological efficiency is not only the key link between green development and high-quality development of agriculture, but also an important regulatory indicator for China’s rural revitalization. Based on provincial panel data of China from 2000 to 2019, using land, mechanical, labor, fertilizer, pesticide, and agricultural film as input variables and economic output and agricultural carbon emissions as output variables, the inter-provincial agricultural ecological efficiency is calculated by a super-efficient SBM model, and the traditional spatial Markov probability transfer matrices are constructed based on time series and spatial correlation analyses. By exploring the spatial and temporal dynamic evolution characteristics of agricultural ecological efficiency, it is found that the agricultural ecological efficiency of China increased steadily with fluctuations. In addition, the provincial gap has been narrowing, but the overall level is still at a low level; thus, there is still a large space for improvement in agricultural ecological efficiency. The overall trend of agricultural ecological efficiency shifting to a high level in China is significant, but its evolution has the stability to maintain the original state, and achieving leapfrog transfer is relatively hard. The geospatial pattern plays an important role in the spatial-temporal evolution of agricultural ecological efficiency, with significant spatial agglomeration characteristics. Provinces with high agricultural ecological efficiency enjoy positive spillover effects, while provinces with low agricultural ecological efficiency have negative spillover effects; thus, gradually forming a “club convergence” phenomenon of “high agglomeration, low agglomeration, high radiation, and low suppression” in the spatial pattern. In addition, support for the improvement of agricultural ecological efficiency will be provided in this study.