A cross-scale modelling and decarbonisation quantification approach for navigating Carbon Neutrality Pathways in China

Carbon Neutrality Targets and Sustainable Development Goals call for continuous and joint efforts in technical innovations, multidisciplinary platforms, cross-scale modelling, and advanced energy policies, while various decarbonization strategies are challenging for local governments in investing, promoting and implementing for carbon neutrality transition. Furthermore, the lack of qualitative decarbonization potential analysis on various technologies may lead to local optimal instead of global optimum solutions, imposing challenges to local governments on when and how to invest, develop, promote and implement low-carbon technologies and associated policies. In this study, an innovative top-down and bottom-up approach on technical pathways has been proposed for carbon neutrality transition, assisting global optimal layout and frontier technology decision-making from local governments, so as to avoid the dominance or misleading from problematic focuses. By simulating various decarbonization strategies for comparative analysis, policy-makers can clearly know decarbonization potentials and take decision-making on corresponding decarbonization strategies. Results indicate that the most promising technology is clean electricity supply, followed by energy efficiency in buildings and then carbon capture, utilization and storage (CCUS) in HKSAR (with 26.8, 20.2, 18.8 hundred million ton for each sector, respectively), while it will be industry, buildings and appliances, and then transportation in mainland China (total decarbonization amount at 68.6 hundred million ton with 37.5, 21.2 and 9.9 hundred million ton for each sector, respectively). Parametrical analysis can identify critical areas in carbon neutrality transitions. Case study in mainland China and HKSAR verifies the feasibility, generality and effectiveness of the proposed approach, which can be applied to other countries, continents or scaled to support decision-making in specific regions. © 2023 Elsevier Ltd