Can Public Health Policies Drive Carbon Reduction? — Forecasting China's Carbon Emissions and Attribution of Policy Contributions Based on SHAP-BiLSTM

Vollständiger Abstract

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Growing public health imperatives are accelerating carbon mitigation efforts, and carbon emission forecasting constitutes a critical foundation for assessing the feasibility of such measures. However, carbon emission data exhibit pronounced nonlinear and dynamically time-varying characteristics, rendering accurate prediction difficult for traditional econometric models, and existing studies lack quantitative assessment of public health policy contributions. This study proposes the SHAP-BiLSTM analytical approach to ensure both high prediction accuracy and policy interpretability. Based on a daily dataset comprising 2,466 observations spanning January 2019 to September 2025 to conduct quantitative analysis of policy contributions; two public health policy dummy variables and two carbon reduction policy dummy variables are established in chronological order in accordance with IPCC standards. The inclusion of carbon reduction policies helps control for other policy shocks during the implementation period of public health policies. The results indicated that the BiLSTM model significantly outperformed the comparative models in prediction accuracy, demonstrating that excessive algorithmic stacking does not necessarily enhance prediction accuracy. SHAP analysis revealed that public health policies contributed to carbon emission reduction to a certain extent,reflecting that the relevant policies played an effective role in slowing growth during high-emission periods.