## 5 Prediction of Household Carbon Emission in China
### Prediction Model  
In recent years, data-driven approaches have gained significant attention in household carbon emission forecasting, focusing on leveraging machine learning or statistical regression to uncover relationships between high-dimensional features and carbon emissions from complex, multi-source data [111]. Representative methods include algorithms such as XGBoost, random forests, and support vector machines [106], as well as traditional statistical models like multiple regression [109] and ridge regression [105]. These models often demonstrate high predictive accuracy and can provide interpretability through tools like SHAP values or regression coefficients [101]. However, the complexity of household carbon emissions, involving variables such as demographic structure, behavioral patterns, and policy interventions, presents challenges in model interpretability and adaptability to temporal variations, as shown in Table 5. As a result, data-driven models require sufficiently large and high-quality training datasets, along with thorough hyperparameter tuning and feature selection, to accurately predict carbon emissions under high-uncertainty scenarios. \begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **Article** & **Study Area** & **Prediction Method** & **Mark** \\ \hline
1 & [PERSON] and [PERSON], 2024 & Fujian province & Kaya-LMDI-SD-MC & [103] \\
2 & [PERSON] et al., 2024 & Whole country & Kaya identity & [107] \\
3 & [PERSON] et al., 2024 & Provinces & XGBoost-TPE & [101] \\
4 & [PERSON] and [PERSON], 2024 & Beijing City & Ridge Regression & [105] \\
5 & [PERSON] et al., 2024 & Wuhan City & LEAP model & [104] \\
6 & [PERSON] et al., 2023 & Whole country & Multiple machine learning & [106] \\
7 & [PERSON]. [PERSON] et al., 2023 & Whole country & Linear extrapolation & [108] \\
8 & [PERSON] et al., 2023 & Whole country & Multiple regression & [109] \\
9 & [PERSON] et al., 2023 & Whole country & IPCC and citation data & [99] \\
10 & [PERSON] et al., 2022 & Provinces & STIRPAT model & [102] \\
11 & [PERSON] et al., 2021 & Whole country & SD-LEAP & [100] \\
12 & [PERSON] et al., 2021 & Whole country & IPCC and citation data & [110] \\
13 & [PERSON] et al., 2019 & Whole country & Multiple regression & [111] \\ \hline \end{tabular}
\end{table} 
Table 4: Literature on household carbon emission prediction. \begin{table}
\begin{tabular}{c c c c} \hline
**Category** & **Advantages** & **Disadvantages** & **Predictive Accuracy** \\ \hline Machine Learning & Handles high-dimensional, non-linear data; fits complex relationships. & Requires large datasets; lacks interpretability; promoting to overfitting. & High accuracy with sufficient data; good for medium/short-term predictions. \\ Traditional Statistical Models & Interpretable; robust with small datasets. & Difficulty with non-linear relationships; relies on assumptions. & Accurate for simple/linear relationships; limited in complex contexts. \\ Top-Down Approach & Captures macro-level drivers like population and economy. Provides detailed insights into household energy use. & Fails to capture household-level behaviors. & Good for trend estimation; weak for specific details. \\  & & Provides detailed insights into household energy use. & Needs high-quality data; hard to generalize. & Accurate with sufficient data; limited scalability. \\ Coupled Models & & Combines strengths of different models; addresses both macro and micro perspectives. & Complex structure; high costs; & Improves accuracy by integrating models; sensitive to data quality. \\ \hline \end{tabular}
\end{table} 
Table 5: Comparison of prediction models. Compared to data-driven methods, \"top-down\" and \"bottom-up\" approaches focus on capturing the operational mechanisms of the household system or actual energy activities, respectively, from macro or micro perspectives. The former typically decomposes or regresses using macro indicators and overall economic structures, such as the Kaya identity [107] or the STIRPAT model [102].