Impacts of Energy and Environmental Policies on Air Quality: Bridging Observational Data, Statistical, and Atmospheric Models

As more countries have adopted regulations on ambient air pollution and announced commitments moving away from fossil fuel energy, assessing the impacts of adopted energy and environmental policies on air quality is essential to evaluating policy progress and informing future actions. The increasing amount of measurement data on pollutant concentrations and precursor emissions provides an opportunity for tracking the progress of policies in mitigating air pollution, but key challenges remain. Levels of measured air pollutants and its precursor emissions are subject to variability in both the natural environment and human activities. This thesis incorporates four studies that integrate research tools across disciplines - from statistical causal inference to atmospheric chemistry models - to assess the impacts of adopted energy and environmental policy on air quality, in support of decision making in energy, climate, and environmental governance. The first study estimates the impacts of energy policies on air quality in major energy-intensive industrial sectors in China with both prospective and retrospective methods. It finds that the realized effects of policy on energy and pollution outcomes are generally much smaller than the projected benefits. The differences between projected and realized benefits stem from how policy baselines are selected and reflect heterogeneity in firms' policy responses. The second study evaluates the impacts of wind power development on air quality and related environmental justice issues in the US. We find substantial air quality benefits from existing wind power, but benefits would increase four-fold if policies could prioritize displacing the most damaging units. The fraction of air quality benefits accruing to low income and minority populations fall below a new 40% goal for future US policies, suggesting targeted efforts are needed to address air pollution disparities. The third study designs a statistical method to estimate the average emission factors of vehicles (the relevant outcome for decision making) based on snapshot measurements (the quantity being measured in the field). We find that a much lower fraction of the measured fleet in Europe is in compliance with emission standards compared to previous estimates. We further quantify the uncertainty and effectiveness of detecting high-emitting vehicles with snapshot measurements. The fourth study evaluates the ability of statistical methods to attribute observed pollutant trends to emissions changes under meteorological variability. We show that widely-used regression methods do not perform well, and we propose a machine learning model that offers better performance. We further provide a lower bound of the estimation error due to interactions between meteorology and emissions.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Institute for Data, Systems, and Society

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2021

Author dc:creator

• Qiu, Minghao

Advisor dc:contributor.advisor

• Selin, Noelle E.

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/140152

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/140152