Energy-system characteristic shifts and their quantitative impacts on China's CO2 trajectory: Evidence from a high-resolution energy allocation analysis–LMDI sectoral decomposition

Understanding how policy, technology and structural change interact along China's energy chain is essential for meeting its 2030 carbon peak and 2060 carbon neutrality goals. This study constructed a carbon-tracking framework integrating energy allocation analysis with the logarithmic mean Divisia index (EAA-LMDI). Using energy input-output tables, the authors derived the primary energy consumption responsibility conversion factor (KPECR), fossil fuel component factor (Kfossil), and primary CO2 emission factor (KC), and visualized carbon flows with Sankey diagrams. The approach captured hidden conversion losses across extraction, conversion, and end-use, and decomposed influencing factors of CO2 emissions for 27 economic and residential sectors during 2005–2020. Total CO2 responsibility increased from 5534 Mt in 2005 to 10,177 Mt in 2020, with 88 %–92 % from the economic sector. GDP per capita was the main driver (7788 Mt), while lower energy intensity (−2731 Mt) and declining Kfossil (−501 Mt) helped restrain growth. Between 2015 and 2020, the rebound of energy-intensive industries and rapid electrification offset the emissions reductions from economic restructuring. Although electrification is clean at the end-use stage, it increases upstream emissions, as over 60 % of electricity still comes from coal. Improved coal-fired unit efficiency reduced the electricity's KPECR, avoiding 88 Mt of emissions. Policy priorities include: (1) flexibility retrofits of coal-fired power plants with industrial waste-heat recovery; (2) tighter controls to avert cyclical overcapacity in heavy industry; and (3) cross-sector digital platforms for energy-carbon coordination. The EAA-LMDI framework links sectoral behaviour and technical change, providing a robust analytical tool for multi-stage decarbonization pathways in coal-dependent economies.