基于组合集成学习模型的区域碳排放预测方法研究

在低碳发展的大背景下，区域碳排放预测模型研究对未来双碳目标任务制定与实施的具有重要指导意义。Elas-ticNet-XGBRegressor 模型是一种组合集成学习模型，其中ElasticNet模型为特征筛选模型，XGBRegressor模型为区域碳排放预测模型。通过采用STIRPAT模型原理和IPCC排放因子法构建包含25个特征的原始数据集，并验证提出模型的有效性，以实证对照实验的方式进行，ElasticNet-XGBRegressor模型作为实验组，Spearman特征筛选和常见机器学习方法组合作为对照。结果表明，ElasticNet-XGBRegressor 模型在RMSE、MAPE和R2等模型评价指标上全面优于对照组，说明了ElasticNet-XGBRegressor 模型在区域碳排放预测中的优越性。通过创新性的将回归模型与决策树集成学习模型相结合，利用ElasticNet模型的特征筛选能力和集成学习的高准确性与鲁棒性提高了预测模型的精度和稳定性。

Regional carbon emission prediction method based on combined ensemble learning model

In the context of low-carbon development, the study of regional carbon emission prediction models is of great significance in guiding the formulation and implementation of future dual carbon target tasks. The ElasticNet- XGBRegressor model, which combines the ElasticNet model as a feature selection model and the XGBRegressor model for regional carbon emission prediction, is a type of ensemble learning model. Based on the principles of the STIRPAT model and the IPCC emission factor method, an original dataset containing 25 features is constructed for the study of regional carbon emission prediction. To validate the effectiveness of the proposed model, an empirical controlled experiment was conducted,with the ElasticNet- XGBRegressor model as the experimental group, and Spearman feature selection and common machine learning methods as the control group. The results showed that the ElasticNet- XGBRegressor model out performed the control group in terms of model evaluation metrics such as RMSE, MAPE, and R2,demonstrating the superiority of the ElasticNet- XGBRegressor model in regional carbon emission prediction. Regression models are innovatively combined with decision tree-based ensemble learning models, leveraging the feature selection capability of the ElasticNet model and the high accuracy and robustness of ensemble learning to improve the accuracy and stability of the prediction model.