## 5 Prediction of Household Carbon Emission in China
### Scenario Design and Factor Selection  
Scenario design typically starts with baseline development trends and integrates policy planning and findings from related studies to construct multiple variant scenarios with different assumptions. Collecting quantitative or qualitative data for key indicators is critical to ensure the scientific validity and reliability of the models.  
The baseline scenario serves as the foundation for setting other scenarios. [PERSON] et al. set a baseline and low-carbon scenario to reflect Wuhan's efforts in accelerating energy conservation and carbon reduction [104]. [PERSON] et al. constructed conventional, low-carbon, and high-carbon scenarios to simulate the impacts of different economic development paths and energy usage trends on carbon emissions [100]. [PERSON] et al. defined four scenarios, building on the baseline scenario while considering changes in consumption patterns and different assumptions about energy intensity [110].  
Population change is also a key focus in scenario design. [PERSON] et al. set five scenarios to comprehensively analyze the complex impacts of changes in population and consumption structures on carbon emissions [99]. [PERSON] et al. compared scenarios of constant fertility rates and the two-child policy to explore the potential impact of population changes on carbon emissions [111]. [PERSON] et al. proposed as many as 27 scenarios, covering various factors such as different carbon peak times, technological advancement paths, and cumulative carbon budgets, offering a rich set of hypothetical pathways for carbon emission forecasting [101]. Overall, a well-designed scenario can skillfully integrate multiple dimensions such as technology, policy, population, and consumption behavior, reflecting various future possibilities and providing support for HErc forecasting.  
Regardless of the model or scenario used, the selection of factors is the foundation of reliable predictions. Some factors are directly applied in carbon emission accounting. Household characteristics such as household size and labor status are commonly included to capture behavioral and demand diversity [107]. Economic attributes such as income levels and consumption expenditures show significant driving effects in most studies and are frequently incorporated into prediction models [108]. Energy consumption features,including energy structure, energy intensity, and specific household energy use behaviors, have the most direct impact on carbon emissions and are typically core variables in model calculations [104]. These core variables have received significant attention, with some studies refining them to an hourly scale for predicting dynamic carbon emission changes [113].  
Although awareness and norms, as well as policies and interventions, have been considered in some studies [100], they are often overlooked or inadequately addressed in many models. This is primarily due to difficulties in data collection and the complexity of quantifying these factors, which often results in their simplification during model construction. Currently, these factors are generally not directly used in carbon emission calculations and are considered background factors. However, they still play an important role in scenario design. Carbon accounting factors and background factors are closely interconnected, forming a complex network that influences carbon emissions. Their interactions highlight the multiple uncertainties in household carbon emission forecasting. Future research should not only continue to address traditional factors such as energy consumption and population size but also focus on quantifying background factors.