## 6 Discussion and Conclusions
### Conclusions  
This review systematically examines studies on urban household energy consumption and carbon emissions in China from 2000 to 2024. A bibliometric analysis of 96 selected papers reveals a steady increase in academic interest driven by global climate actions. The research in this field is interdisciplinary, spanning environmental science, energy, and economics, reflecting the complexity of addressing household carbon emissions. This study systematically identifies key influencing factors and their interactions, deepening the understanding of HErC within a multidisciplinary framework. Additionally, the comparative analysis of predictive methods provides methodological references for future research, particularly in the application of machine learning techniques and scenario-based modeling in carbon emissions studies. These findings offer valuable insights for policymakers in designing more targeted and effective carbon reduction policies. The identified influencing factors can serve as a foundation for developing incentive mechanisms that promote low-carbon consumption behaviors. Furthermore, understanding the advantages and limitations of different predictive methods can assist policymakers in selecting appropriate models for policy evaluation and scenario planning. This study also emphasizes the critical role of behavioral and social factors in climate policies and highlights their importance in enhancing the long-term effectiveness of policy measures. In terms of influencing factors, household characteristics such as household size and education level are identified as major drivers of HErC. Economic attributes, particularly income and household assets, affect energy use and carbon emissions. Energy efficiency and policy interventions are also widely recognized as effective emission reduction measures. Additionally, the interaction of environmental ideology, behavior attitudes, and social norms emphasizes the importance of cultivating a sustainable culture alongside technological and policy advancements. These key influencing factors have all been applied in HErC forecasting. Various predictive methods, including traditional statistical approaches, the Kaya identity, and the LEAP model, have been widely applied, each with its own advantages. The rise of data-driven machine learning algorithms, in particular, has significantly improved the accuracy of predictions. Scenario analysis is widely used to construct multiple future possibilities, exploring different carbon emission trajectories. In terms of forecasting results, it is generally believed that in the absence of strict intervention measures, household carbon emissions will continue to rise, and the peak will be delayed. Although there is significant potential for carbon reduction in households, its actual effectiveness remains highly dependent on the implementation of policies and technologies. To achieve China's carbon goals, it is urgent to adopt systematic and multi-level mitigation strategies. Improving building and energy efficiency, promoting low-carbon consumption and clean energy applications, and implementing multidimensional coordinated policies to address the diverse needs of regions and populations are considered the most effective and feasible pathways for emission reductions. Future research should focus on optimizing predictive models with more granular data, refining factor selection, quantitatively assessing the impacts of policy interventions, and fostering interdisciplinary collaboration to develop comprehensive solutions. **Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15062961/s1](https://www.mdpi.com/article/10.3390/app15062961/s1)]([https://www.mdpi.com/article/10.3390/app15062961/s1](https://www.mdpi.com/article/10.3390/app15062961/s1)), Table S1: Detailed categorization for the factors of interest in the literature. **Author Contributions:** Conceptualization, Q.Z. and T.W. ; methodology, Q.Z. and T.W. ; software, Q.Z. and T.W.