Analysis of factors influencing carbon emissions of urban households: A case study of old urban community in northern China

CO2 emissions reduction of urban residents is one of the main ways to mitigate climate change. Clarifying the key factors of carbon emissions can provide a reference basis for the government to formulate carbon reduction policies. The old urban community that widely exist in most cities in northern China is taken as the research object. Based on the fuzzy interpretive structural model and structural equation model, a new method is proposed to explore the influencing factors of household carbon emissions (HCEs). It is found that behavior change attitude and low-carbon personal cognition are the two most important factors affecting HCE. Elderly families, high-income families, and low-education families are the groups with higher per capita HCE. Different suggestions were proposed to promote HCE reduction for the high-income young tenants and low-income elderly local residents in the community. In addition, by introducing multi-agent judgment, the coupling model proposed in this study reduces the subjectivity caused by excessive reliance on single agent judgment in previous studies. It enhances the credibility of research conclusions and provides a good methodological reference for research in similar fields.