This study develops an integrated analysis framework, called scenario-based extraction method (SEM) using four different input-output methods—unit structure analysis, cluster analysis, extended global extraction analysis and structural decomposition analysis. For the empirical analysis, we used the latest 2014 World Input–Output Database and modeled the global supply chain (GSC) CO2 network structure induced by the final demand for one relevant industry in one relevant country (the Japanese automobile industry in this study). The cluster analysis based on the GSC network data revealed CO2 emission-intensive clusters existed in this network with overconcentrated CO2 emissions outside of Japan. From the SEM analysis, we also found that the restructuring of the Japanese automotive supply chain based on extracting the largest CO2 emission cluster (i.e., CO2 emission hotspot) reduces its global carbon footprint by 6.5%. Simultaneously, the restructuring increases CO2 emissions in all countries other than a hotspot country, particularly in some important locations for the substitute production. We conclude that Japan's current automotive supply chain can significantly reduce CO2 emissions through structural reforms. Our framework can help in designing appropriate policies for restructuring green supply chains.
All Science Journal Classification (ASJC) codes
- Economics and Econometrics