Shrinking household size and CO₂ emissions in China evidence from machine learning and input output models

Owing to variations in expenditure structure among residents of different household sizes, their impacts on production and consumption activities within the economic system lead to significant variations in carbon footprint characteristics. Based on machine learning and input-output models, we quantify the carbon footprints of residents across different household sizes from 2010 to 2020 and analyze the underlying mechanisms. Per capita expenditure and population scale in one-person households are the main drivers for the increase in direct and indirect CO₂ emissions, respectively. From 2010 to 2020, housing expenditure accounts for 71.9% (115.22 Mt) of the increase in indirect CO₂ emissions associated with one-person households, followed by transportation (14.7%, 23.54 Mt) and household equipment (10.4%, 16.59 Mt). Notably, the per capita expenditure level effect in the transportation sector significantly contributes to rising emissions, and this increase may not be fully offset by the mitigation effects from improving emission intensity and optimizing production structure.