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Promoting the adoption and maintenance of low-carbon consumption behaviors (LCBs) is a global climate action. Most prior studies have considered only the adoption of LCBs and not explored how behavioral characteristics and emotions influence individual behaviors. To address these limitations, this study builds a dual-level integrated model to detect the maintenance rate of various LCBs and identify what factors affect behavioral maintenance. Using data collected from 2052 residents in China, generalized linear mixed models are employed to examine the effects of behavioral and individual-level factors and of social interaction on the maintenance of LCBs. Robustness is checked using the instrumental variable method, least-squares dummy variable method, and Probit approach. The estimated maintenance rate for different LCBs ranges between 29.02 % and 83.49 %, while the estimated cessation rate ranges between 2.31 % and 6.58 %. Maintenance of LCBs is positively affected by context stability but negatively influenced by effort expectancy. Anticipated guilt is identified as the strongest driver (positive) of maintenance of LCBs for all tested variables, followed by symbol expectancy. However, neither behavior routinization nor anticipated pride is found to be significantly associated with behavioral maintenance. The effects of anticipated guilt, effort expectancy, and symbol expectancy on behavioral maintenance are moderated by social interaction. Regarding demographics, family size is positively associated with behavioral maintenance, while females are more likely than males to maintain LCBs. These findings address the under-studied effects of behavioral characteristics and emotions on behavioral maintenance, and provide new insights for redefining target interventions to engage the public in maintaining LCBs. [Display omitted] • Maintenance of low-carbon consumption behaviors (LCBs) is studied. • A dual-level integrated model incorporating social interaction is built. • Maintenance rate of LCBs is estimated between 29.02 % and 83.49 %. • Anticipated guilt is the strongest driver (positive) of maintenance of LCBs. • Symbol expectancy and context stability contribute to maintaining of LCBs. [ABSTRACT FROM AUTHOR]

About 3 billion people still rely on traditional sources of fuel. Biomass-based fuels were responsible for more than 4 million deaths in 2012. Household energy consumption is steadily increasing across cities due to the urbanization of rural areas, more economic and travel activities. In this review, Multiple Correspondence Analysis (MCA) and other bibliometric tools were applied to determine the conceptual structure and typological thematic areas of household emissions in urban areas. The current state of knowledge and opportunities towards creating carbon-neutral cities indicated that while most of the research was focussed on cities and regions of developed countries and China, the mitigation activities grouped under three broad heads - reducing emissions, consumption and impact of household emissions. Strong heterogeneity between the urban and rural areas, fuel options and human choices gave rise to fuel-stacking in developing countries. Integrated modelling and inclusion of environmental and human behavioural components into economic models were recognised as emerging fields oriented towards a systems approach. Accessibility and availability of clean energy and clean energy appliances were identified as the biggest challenges. To create carbon-neutral urban areas, integrated modelling should include categorization of carbon policy according to local conditions, analysis of feedback loops, embedded emissions, the global and regional impact of household consumption patterns, and uncertainty associated with mitigation measures. [Display omitted] • Carbon neutral cities should have access equality despite income inequality. • Multiple Correspondence Analysis indicated mitigation research focused on emissions, consumption and impacts. • Integrated modelling and general equilibrium models were the two emerging themes. • Risk, uncertainty, embedded emissions and feedback loops should be integrated into emission models. • Availability and accessibility to cleaner fuels is the biggest challenge for creating carbon-neutral cities. [ABSTRACT FROM AUTHOR]