Weng Qinnan, Zhu Suling, Luo Lijiao, Liu Bowen, Zhang Zhenhua
Institute of Health Statistics and Intelligent Analysis, School of Public Health, Lanzhou University, Lanzhou, 730000, China; Department of Epidemiology and Health Statistics, School of Public Health, Lanzhou University, Lanzhou, 730000, China.
Institute of Health Statistics and Intelligent Analysis, School of Public Health, Lanzhou University, Lanzhou, 730000, China; Big Data Research Center, Lanzhou University, Lanzhou, 730000, China.
J Environ Manage. 2025 May;382:125388. doi: 10.1016/j.jenvman.2025.125388. Epub 2025 Apr 17.
Evaluating the synergistic environmental and health effects of energy control policies is essential for scientifically informed policy optimization. Given that air quality is affected by factors such as meteorological conditions and economic growth, relying solely on air quality monitoring data is insufficient to assess the environmental impacts of these policies accurately. To objectively examine the synergistic effects of energy control policies on both environment and public health, this study introduces the CEEMDAN-OA-SVR-DID model from a predictive perspective. The model forecasts PM concentrations as a counterfactual scenario without the implementation of the Northern Winter Clean Heating Policy (NWCHP). By integrating the difference-in-difference approach, exposure-response functions, and economic evaluation methods, including the value of statistical life and the cost of illness, the model evaluates the environmental and health outcomes of the policy. Results demonstrate the CEEMDAN-GWO-SVR model's high predictive accuracy, with mean absolute percentage error values ranging from 3.2 % to 8.8 % across 10 pilot cities. Overall, the NWCHP significantly reduced PM concentrations (by 3.7 %-31.7 %) in these cities, reducing premature deaths, respiratory and cardiovascular hospitalizations, outpatient visits, and other health issues related to PM exposure (decreased by 7.7 %-52.9 %). The policy also averted approximately 3.197 billion yuan in economic losses. These findings offer essential evidence to refine heating policies further and provide valuable insights for developing countries advancing environmental governance and energy transitions.
评估能源控制政策对环境和健康的协同影响对于基于科学依据进行政策优化至关重要。鉴于空气质量受气象条件和经济增长等因素影响，仅依靠空气质量监测数据不足以准确评估这些政策的环境影响。为了客观检验能源控制政策对环境和公众健康的协同效应，本研究从预测角度引入了CEEMDAN-OA-SVR-DID模型。该模型预测了在未实施北方冬季清洁取暖政策（NWCHP）的反事实情景下的PM浓度。通过整合双重差分法、暴露-反应函数以及包括统计生命价值和疾病成本在内的经济评估方法，该模型评估了该政策的环境和健康结果。结果表明，CEEMDAN-GWO-SVR模型具有较高的预测准确性，在10个试点城市中的平均绝对百分比误差值在3.2%至8.8%之间。总体而言，NWCHP显著降低了这些城市的PM浓度（降低了3.7%-31.7%），减少了过早死亡、呼吸和心血管疾病住院、门诊就诊以及其他与PM暴露相关的健康问题（减少了7.7%-52.9%）。该政策还避免了约31.97亿元的经济损失。这些发现为进一步完善供暖政策提供了重要证据，并为推进环境治理和能源转型的发展中国家提供了宝贵的见解。
