Exposure to air pollution is a leading cause of premature death worldwide. An increasing part of air pollution results from industrial activity and the production of energy. When unregulated, emissions of air pollutants constitute a market failure as polluters do not bear the costs imposed on society at large. My dissertation develops empirical methods to test the effectiveness and distributional effects of environmental policies designed to address this externality. To do so, I apply econometrics and data science techniques on large datasets from cutting-edge research in environmental science and engineering that I match with microeconomic data. The dissertation makes use of new datasets on air pollution derived from satellite imagery, as well as micro-level data on power plant operations and housing transactions across the United States.
Chapter 1 assembles unit-level data to disentangle the factors that led US power plants to achieve the unprecedented reductions in emissions of the past fifteen years. I calculate the costs incurred by the electricity generation sector and compare these costs to the correspond- ing health benefits. In hedonic regressions, I use these shocks to emissions to estimate the demand for clean air with micro-level data on housing transactions. Chapter 2 studies the causal impacts and evaluates the distributional effects of stringent emissions markets that were put in place to target power plants emissions of air pollutants in the Eastern US. Chapter 3 uses new satellite imagery to document the inequalities in the exposure to air pollution in American cities and their recent evolutions.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/d8-x0f0-ce82 |
| Date | January 2019 |
| Creators | Benatiya Andaloussi, Mehdi |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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