Patterns, drivers and projections of carbon footprints in urban Chinese households: Based on interdisciplinary approaches

Climate change, driven by greenhouse gas emissions from human activities, poses a significant challenge to global sustainability. The urban household sector is a major contributor to carbon emissions, and the measurement and control of urban household carbon footprints is a global challenge. We compiled a novel and extensive dataset comprising 283,529 household-level emission records from 208 cities across China, spanning the period from 2002 to 2020. Employing interdisciplinary methods, including environmental input-output models, the Logarithmic Mean Divisia Index (LMDI), and Random Forest with SHapley Additive Explanations (SHAP) algorithms, we analyze the patterns, drivers, and projections of urban household carbon emissions. Our findings indicate that indirect emissions consistently exceeded direct emissions, with a peak around 2016, while direct emissions exhibited a steady increase. Spatial disparities are evident, with higher emissions observed in eastern coastal regions, municipalities, and provincial capitals. Key drivers of emissions include per capita GDP, population density, and extreme weather, while technological advancements have a strong suppressive effect and shifts in consumption patterns offer substantial mitigation potential. The low-carbon development path accelerates the decline of indirect carbon emissions and slows the increase of direct carbon emissions, while the fossil fuel-driven path slows the decline of indirect carbon emissions and accelerates the increase of direct carbon emissions. The study underscores the necessity of integrated optimized urban planning, climate policies, technological innovation and the adoption of clean energy to mitigate emissions and achieve long-term sustainability goals.