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.