The impact of energy metabolic pattern on household carbon emissions: A spatio-temporal perspective in Guangdong-Hong Kong-Macao Greater Bay Area

Abstract

The increasing energy metabolism of households and its role in carbon emissions underscore the critical need for comprehensive studies in meeting emission targets. Existing research often fails to incorporate these patterns holistically, leaving a gap in understanding how rising energy metabolism drives household carbon emissions within different regions. This study addressed this gap by employing a spatio-temporal LMDI model to investigate household CO₂ emissions in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) and its peripheral cities (PC) from 2010 to 2020. With a city-level inventory and MuSIASEM analysis of energy metabolic patter, this research found that, alongside with During this period, household CO₂ emissions nearly doubled, with total household growth and the increased energy metabolism rate (EMR) contributing 60 % and 32 % of the rise, respectively. Significant regional disparities were observed, particularly in the GBA regions, where factors such as smaller population sizes and higher housing prices amplified the effects of EMR and shifts in energy structure. These findings highlight the urgent need for tailored carbon reduction strategies that address both socioeconomic and geographical disparities in urban agglomerations, advocating for diversified energy policies and adaptive urban planning to mitigate carbon emissions effectively in densely populated cities worldwide.