Bibliographic record
Abstract
This dissertation studies questions in environmental economics by exploring the mechanisms through which government and private decisions interact in the transportation and housing markets. These have important environmental and distributional consequences in terms of mitigation of and adaptation to climate change. In Chapter 1, I compare new vehicle sales in the United States and Canada to determine whether updated EPA fuel economy labels introduced in 2012 succeeded in altering consumers' new vehicle purchase choices. I find small savings in gasoline consumption through a 1.5 percentage point increase in small car market shares, a corresponding decrease in SUV shares, and a 6% increase in the valuation of small SUVs' fuel economies. In Chapter 2, I study gasoline price volatility in California by estimating the gasoline price elasticity of demand for driving, and show that this parameter is both highly inelastic and likely to vary over time. Chapter 3 focuses on the impacts of hurricanes on the Florida housing market. I show that hurricanes cause an equilibrium increase in home prices and a concurrent decrease in transaction probability, lasting up to three years. With supplementary evidence from demographic trends, I conclude that the main driver of these dynamics is a negative transitory shock to the housing supply in the aftermath of hurricanes as homes recover from physical damages. I further observe that new homeowners have higher incomes, resulting in a permanent shift in the demographic composition of disaster-prone areas, and suggesting important implications about the expected costs and distributional impacts of future federal disaster relief spending.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".