Adjusting the global industrial structure for minimizing global carbon emissions: A network-based multi-objective optimization approach

Although many scholars have focused on industrial structure adjustment to find the optimal balance between carbon emission reduction and economic growth, few studies have considered the comprehensive influence of the supply chain structure on carbon emissions. Based previous studies, we proposed a novel network-based optimization model. The results showed that carbon emissions would decrease by 4.31%, 6.26 and 8.07% with GDP increasing by 5.53%. 4.45% and 2.50% in different scenarios in the proposed network-based model, which performed better than the previous non-network-based model. There were some principal sectors which played special roles in the optimization of global industrial structure. To achieve the goal of global carbon emission reduction, some sectors should significantly reduce their total outputs such as materials and energy in China, while other sectors could continue to increase their total outputs such as machinery and services in China and South Korea. The results also showed that the change rate of carbon emissions was related with the costs of carbon emissions for the GDP growth. Countries with higher costs of carbon emissions, such as China, India and Russia, would burden more responsibilities. Furthermore, we found that the changes of the industrial structures of countries (regions) were different under global carbon emission reduction. Due to the current technology limitation, the production activities of energy and material industries in developing countries, such as China and India, should be reduced. Technology exportation of developed countries in such industries would be beneficial for the global carbon reduction. [Display omitted] •We construct structure intensity to measure the influences of supply chain structure.•We build a network-based input-output optimization model.•We optimize the global industrial structure for reducing carbon emissions.