An ecological input-output CGE model for unveiling CO2 emission metabolism under China's dual carbon goals

Abstract

Carbon mitigation is essential for combating climate change and its impacts, safeguarding ecosystems, and protecting human health and livelihoods. This study develops an ecological input-output CGE (EIOC) model that integrates computable general equilibrium (CGE) model, input-output analysis (IOA), and ecological network analysis (ENA) to explore the evolution of the CO2 emission metabolism under long-term carbon tax policies. The EIOC model excels not only in intimately depicting economic relationships and evaluating the economic impacts of policy interventions but also in unveiling the complex internal interactions within the context of emission dynamics through multidimensional network perspectives. The EIOC model is employed to analyze CO2 emission flows under carbon tax policies aimed at fulfilling China's dual carbon goals, thereby validating its applicability. It is found that (i) the implementation of carbon tax policies (P25 and P30 scenarios) would lead to economic decline; however, there would emerge potential for recovery in investment and international trade; (ii) adopting an earlier carbon peak target (P25 scenario) would yield more pronounced emission reductions in the early to mid-term (in 2035, reductions in steel, construction, and manufacturing sectors would exceed those under the P30 scenario by 15.74%, 11.89%, and 11.59% respectively); (iii) long-term policies would significantly impact the dynamics of CO2 emission metabolism; chemical sector would display an increased reliance on the system, indicating a heightened need for inputs from the system; transactions exhibiting increased centrality (between chemical and other mining, and between chemical and manufacturing) reveal their elevated significance as transfer pathways of CO2 flows. Identifying significant changes in CO2 emission metabolism can inform incentivizing or constraining measures aimed at further adjusting emissions.