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How do household energy interventions work?
This study will focus on a coal ban and heat pump subsidy program in the Beijing, China, region. They are building on an existing panel study that is following about 966 people who live in 50 villages around Beijing. Half the villages are subject to the policy, the other half are not.
Poster abstract for HEI Annual Conference 2023
How do household energy interventions work?
Sam Harper1, Jill Baumgartner1, Ellison Carter2, Xiaoying Li1, Zhongjie Fan3, Shu Tao4, Yuanxun Zhang5
1McGill University, Montreal, Quebec, Canada; 2Colorado State University, Fort Collins, Colorado, USA; 3Peking Union Medical College Hospital, Beijing, China; 4Peking University, Beijing, China; 5University of Chinese Academy of Sciences, Beijing, China
Background. Improving air quality is a policy priority in China. Starting in 2016, the Beijing municipal government designated coal-restricted areas and simultaneously offered subsidies for the purchase and installation of mostly electric-powered heat pumps to replace coal-heating stoves. This study aims to: a) estimate the contribution of changes in PM2.5, to the overall effect of the policy on health; b) quantify the contribution of changes in the chemical composition of PM2.5 from different sources to the overall effect on health outcomes, and c) quantify the impact of the policy on outdoor air quality and personal air pollution exposures.
Methods. In winter 2017, we enrolled households in 50 Beijing villages not currently in the coal ban (i.e., untreated) into a four-year longitudinal study. By winter 2022, 20 of 50 study villages were participating in the coal ban. We measured indoor and outdoor PM2.5 and personal PM exposure, indoor and outdoor household temperature, blood pressure, respiratory and sleep measures, anthropometrics, and blood biomarkers of oxidation stress and inflammation. To understand the impact of the policy, we use a difference-in-difference design, comparing outcomes before and after the policy in treated villages relative to the same outcomes measured in untreated villages.
Results. We completed four winter data collection campaigns with all study measurements (2018, 2019, 2021, and 2022) in 50 villages and a reduced third campaign (due to the coronavirus pandemic) in 2020 limited to air pollution and stove use assessment in 30 villages. A total of 1,234 households were included in the study, 941 of which participated in at least two full campaigns. At baseline, mean participant age was 60 y (SD = 9.2), 60% were female, and most (63%) were agricultural workers. The geometric mean of village-averaged personal exposures to PM2.5 and black carbon at baseline ranged from 23 to 387 μg/m3 and from 0.8 to 11 μg/m3, respectively. Indoor temperatures ranged from 0.0 to 28.0˚C and average systolic/diastolic blood pressure was 130/82 mmHg, respectively, and 61% of participants had hypertension. Preliminary (unadjusted) results indicate that treated households had greater increases in average indoor temperatures and greater reductions in mean indoor PM2.5 than untreated households between baseline and follow-up seasons, and that individuals in treated households had greater reductions in mean systolic and diastolic blood pressure than those in untreated households. Preliminary difference-in-differences models estimate that wintertime indoor PM2.5 (± SD) decreased by 37.5 (± 13.0) μg/m3 in treated households compared with untreated households, which represents a 44% (14.0% to 73.5%) reduction in mean indoor PM2.5 between seasons 2 and 4.
Conclusions. Next steps include finalizing laboratory analysis of filter-based samples for mass and chemical composition and of blood samples collected in the first two seasons, preparation of analyses for submission to peer-reviewed journals, and implementation of statistical models for estimating the total effect of the intervention and mediation analysis to provide insights on the mechanisms linking the intervention and health effects.
