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.
How Do Household Energy Interventions Work?
Sam Harper, Jill Baumgartner, Ellison Carter, Shu Tao, Yuanxan Zhang
McGill University, Montreal, Quebec, Canada
Background and Objectives: An estimated 1.2 million yearly premature deaths in China are attributed to air pollution. Improving air quality has become a policy priority in China. One of many programs is an ambitious plan to transition up to 70% of all households in northern China away from highly-polluting coal heating stoves. To meet this target the Beijing municipal government announced a two-pronged program that designates coal-restricted areas and simultaneously offers large subsidies to night-time electricity rates and for the purchase and installation of electric-powered, air-source heat pumps to replace traditional coal-heating stoves. The program is being rolled out on a village-by-village basis and variability in when the policy is applied to each village allows us to treat the roll-out of the program as a quasi-randomized intervention. This study aims to address the following aims: 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: To understand how Beijing’s policy works we are using a quasi-experimental study design, taking advantage of the staggered rollout of the policy across multiple villages to estimate its impact on health outcomes and understand the mechanisms through which it works. To understand the overall impact of the policy, the study will use a difference-in-difference approach, comparing outcomes before and after an intervention in a treated group (one “difference”) relative to the same outcomes measured in a control group (the second “difference”).
The data collection for this project will include outdoor PM2.5 and personal PM exposure, indoor and outdoor household temperature measures, blood pressure and central hemodynamics measures, respiratory measures, anthropomorphic measures and measures of serum glucose/lipids and cardiovascular biomarkers.
Results: We completed two winter data collection campaigns prior to the coronavirus pandemic (winters in 2019 and 2020), in addition to a reduced third campaign during the pandemic (winter 2021). Initial data cleaning and analysis have been conducted across several study domains. A subset of blood samples from seasons 1 and 2 were submitted for analysis. Descriptive summaries for average 24-hour concentrations of outdoor, indoor and personal exposure to PM2.5 were created. Distributions of household temperatures in study villages during season 1 and 2 were also created. Plots of diurnal temperatures trends in 18 study villages were created with season 1 and 2 data. Plots were also created for mean heating hours per day, per energy source type and income as well as cumulative distribution of life satisfaction by village treatment status.
Conclusions: The next phases of this study will include initial analysis of first follow-up air pollution data, including indoor and outdoor sensor-based measurements, and prepare for submission to peer-reviewed journals, pilot testing of new survey software for season 4 and preparing for season 4 data collection. Qualitative data on the implementation of the coal ban for each village will also be summarized and coded and statistical models for mediation analysis and Bayesian analysis of the complete data will be developed.