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.

Automated summary

This research examines the direct and indirect effects of steel trade on CO2 emissions using data from 56 nations between 2010 and 2018. The results show that steel exports increase local carbon emissions while decreasing carbon emissions in neighboring countries. Additionally, urbanization and per capita GDP have positive spatial spillover effects, while the effect of R&D intensity is insignificant. Moreover, increased industrialization and renewable energy consumption positively affect carbon emission reduction in local and surrounding nations.