用集成模型精准预测台湾十年碳排放，助力政策制定
Decade-long Emission Forecasting with an Ensemble Model in Taiwan
- 融合FFNN、SVM、RFR等最优模型，构建堆叠集成方法
- 十年排放预测平均绝对百分比误差仅1.407%，无过拟合
- 适合关注环境政策与长期碳排放预测的研究者
台湾人口密集且严重依赖化石燃料，导致空气污染严重，主要温室气体为二氧化碳（CO2）。本研究开展可复现的综合性案例分析，比较了21种常用时间序列模型在排放预测中的表现，涵盖单变量与多变量方法。其中，前馈神经网络（FFNN）、支持向量机（SVM）和随机森林回归器（RFR）表现最佳。为进一步提升稳健性，将上述最优模型通过自定义堆叠泛化集成技术与线性回归结合。所提出的集成模型在十年预测中达到1.407的SMAPE，且未出现过拟合迹象。研究提供了高精度的十年排放预测结果，可为政策制定提供数据支持。
原文摘要 · Abstract (English)
Taiwan's high population and heavy dependence on fossil fuels have led to severe air pollution, with the most prevalent greenhouse gas being carbon dioxide (CO2). There-fore, this study presents a reproducible and comprehensive case study comparing 21 of the most commonly employed time series models in forecasting emissions, analyzing both univariate and multivariate approaches. Among these, Feedforward Neural Network (FFNN), Support Vector Machine (SVM), and Random Forest Regressor (RFR) achieved the best performances. To further enhance robustness, the top performers were integrated with Linear Regression through a custom stacked generalization en-semble technique. Our proposed ensemble model achieved an SMAPE of 1.407 with no signs of overfitting. Finally, this research provides an accurate decade-long emission projection that will assist policymakers in making more data-driven decisions.
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