I am a postdoctoral scholar at Stanford University, working with Marshall Burke as a part of the ECHO (Environmental Change and Human Outcomes) Lab. My research interest is in environmental and energy policies with a global focus on issues involving air pollution, climate change and energy systems. I use causal inference, machine learning, and atmospheric chemistry modeling to study the sustainability challenges at the intersection of energy, pollution and climate using real-world data.
I received my PhD degree from MIT’s Institute for Data, Systems, and Society on September 2021, advised by Noelle Selin. I also worked closely with my committee members: Valerie Karplus, Cory Zigler and Colette Heald. I received bachelor degrees in environmental sciences and economics from Peking University in Beijing.
Honors & Awards
Outstanding Student Presentation Awards (OSPA), American Geophysical Union Fall Meeting (2021)
Fellow, Martin Family Society of Fellows for Sustainability (2020)
Young Scientists Summer Program, IIASA (2019)
Marshall Burke, Postdoctoral Faculty Sponsor
- Statistical and machine learning methods for evaluating trends in air quality under changing meteorological conditions ATMOSPHERIC CHEMISTRY AND PHYSICS 2022; 22 (16): 10551-10566
- Using snapshot measurements to identify high-emitting vehicles ENVIRONMENTAL RESEARCH LETTERS 2022; 17 (4)
Improving Evaluation of Energy Policies with Multiple Goals: Comparing Ex Ante and Ex Post Approaches
ENVIRONMENTAL SCIENCE & TECHNOLOGY
2020; 54 (24): 15584-15593
Sustainability policies are often motivated by the potential to achieve multiple goals, such as simultaneously mitigating the climate change and air quality impacts of energy use. Ex ante analysis is used prospectively to inform policy decisions by estimating a policy's impact on multiple objectives. In contrast, ex post analysis of impacts that may have multiple causes can retrospectively evaluate the effectiveness of policies. Ex ante analyses are rarely compared with ex post evaluations of the same policy. These comparisons can assess the realism of assumptions in ex ante methods and reveal opportunities for improving prospective analyses. We illustrate the benefits of such a comparison by examining a case of two energy policies in China. Using ex post analysis, we estimate the impacts of two policies, one that targets energy intensity and another that imposes quantitative targets on SO2 emissions, on energy use and pollution outcomes in two major energy-intensive industrial sectors (cement, iron and steel) in China. We find that the ex post effects of the energy intensity policy on both energy and pollution outcomes are very limited on average, while the effects of the SO2 emissions policy are large. Compared with ex ante analysis, ex post estimates of benefits of the energy intensity policy are on average smaller, and differ by location in both sign and magnitude. Accounting for firm-level heterogeneity in production processes and policy responses, as well as the use of empirically grounded counterfactual baselines, can improve the realism of ex ante analysis and thus provide a more reliable basis for policy design.
View details for DOI 10.1021/acs.est.0c01381
View details for Web of Science ID 000600100400003
View details for PubMedID 33263386
The contribution of the Beijing, Tianjin and Hebei region's iron and steel industry to local air pollution in winter
2019; 245: 1095-1106
The Beijing, Tianjin and Hebei region (BTH) in China is a highly populated area that has recently experienced frequent haze episodes in winter. With high production capacities, the iron and steel industry (ISI) has long been a key source of air pollutants in BTH and is thus considered responsible for the degradation of local air quality. Here, we conducted a cross-disciplinary research combining the Weather Research and Forecasting with Chemistry (WRF/Chem) model, the multiregional input-output model (MRIO) and the health assessment model to explore the impacts of the ISI on air pollution in the BTH region in January 2012. Our results show large increases in air pollution due to direct ISI emissions, with up to a 90 μg/m3 monthly average of fine particulate matter (PM2.5) and sulfur dioxide (SO2) in eastern Tangshan and western Handan. In addition to direct emissions, the ISI has induced large quantities of indirect emissions from upstream sectors (e.g., the electricity and transportation sectors), leading to PM2.5, SO2 and NOx increases of 2-10 μg/m3 in BTH. Considering the direct and indirect emissions, we estimated that 275 (233-313) PM2.5-related mortalities occurred in January, and approximately 42% of these premature deaths occurred in Tangshan. A high rate of premature deaths also occurred in urban Beijing due to its high population density. Revealing the great health burden caused by the ISI, our results underscore the necessity for the Chinese government to reduce air pollutant emissions from the ISI and its upstream industries in BTH.
View details for DOI 10.1016/j.envpol.2018.11.088
View details for Web of Science ID 000457511900117
View details for PubMedID 30682744