Spatial correlation network characteristics of embodied carbon transfer in global agricultural trade

The spatial spillover effect of international steel trade on carbon dioxide emissions

The urgent global climate change situation forces the steel industry to confront enormous challenges and complex tasks. This research focuses on the steel sector and incorporates data from 56 nations between 2010 and 2018. We analyze the direct and indirect effects of the steel trade on CO₂ emissions using the spatial Durbin model. The results show that Moran's I ranges from 0.414 to 0.521, suggesting that carbon emissions are very spatially dependent. Developed countries such as Japan, Germany, and the USA form high-high agglomeration areas. In contrast, Brazil, Thailand, and India constitute low-low agglomeration areas. Under the three spatial weight matrices, the direct effect coefficients of steel exports are 0.045, 0.038, and 0.057, and the indirect effect coefficients are - 0.006, - 0.076, and - 0.015, indicating that steel exports increase local CO₂ emissions while decreasing carbon emissions in neighboring countries. Urbanization and per capita GDP have positive spatial spillover effects, while the spatial effect of R&D intensity is insignificant. Increased industrialization and renewable energy consumption positively affect carbon emission reduction in local and surrounding nations. The study provides empirical evidence for the steel industry to develop emission reduction strategies.

Spatial and Temporal Characteristics and Drivers of Agricultural Carbon Emissions in Jiangsu Province, China

Scientific measurement and analysis of the spatial and temporal distribution characteristics of agricultural carbon emissions (ACEs) and the influencing factors are important prerequisites for the formulation of reasonable ACEs reduction policies. Compared with previous studies, this paper fully considers the heterogeneity of rice carbon emission coefficients, measures and analyzes the spatial and temporal characteristics of ACEs in Jiangsu Province from three carbon sources, including agricultural land use, rice cultivation, and livestock and poultry breeding, and explores spatial clustering patterns and driving factors, which can provide a reference for agricultural low-carbon production. The results indicate that from 2005 to 2020, Jiangsu's ACEs showed a decreasing trend, with an average annual decrease of 0.32%, while agricultural carbon emission density (ACED) showed an increasing trend, with an average annual increase of 0.16%. The area with the highest values for ACEs is concentrated in the northern region of Jiangsu, while the areas with the highest values for ACED are distributed in the southern region. The spatial clustering characteristics of ACEs have been strengthening. The "H-H" agglomeration is mainly concentrated in Lianyungang and Suqian, while the "L-L" agglomeration is concentrated in Zhenjiang, Changzhou, and Wuxi. Each 1% change in rural population, economic development level, agricultural technology factors, agricultural industry structure, urbanization level, rural investment, and per capita disposable income of farmers causes changes of 0.112%, -0.127%, -0.116%, 0.192%, -0.110%, -0.114%, and -0.123% in Jiangsu's ACEs, respectively. To promote carbon emission reduction in agriculture in Jiangsu Province, we should actively promote the development of regional synergistic carbon reduction, accelerate the construction of new urbanization, and guide the coordinated development of agriculture, forestry, animal husbandry, and fisheries industries.