Essays on the effectiveness of environmental and urban transportation policies
Bibliographic record
Abstract
This dissertation is a collection of three essays that study the efficiency of policies targeting environmental and urban transportation issues. The first essay investigates a government incentive that subsidizes the purchase of electric vehicles(EVs) and asks a question of whether subsidizing public charging facility would be more cost-effective. It estimates a discrete choice model of EVs which relies on both EV characteristics and individual demographic information, using micro-level data from the California Clean Vehicle Rebate Program. Results show: (1) EVs with smaller battery capacity are more reliant on the public charging network; (2) consumers with higher income are less price sensitive. Such results support subsidizing more to public charging facility and reducing the EV purchase subsidy to the more affluent consumers. The second essay builds on the literature of road congestion and addresses the importance of schedule delay cost occurred due to uncertain traffic time. We propose the notion of a "reliability standard" that commuters use to calculate their schedule time --- the buffer time added to a commute so as to ensure being at work on time most of the time. With this tool, we conduct an extensive simulation study to gain further insights into the role of commuter composition, time cost differences, and the degree of inflexibility on optimal road tolls. While the cost of commuting time reliability is economically important, we find that the difference between a full-information road toll and a limited-information road toll is smaller than one would expect. Our study points to how future stated preference studies can be designed to elicit more meaningful information that identifies commuter heterogeneity, and in turn will lead to better designs for mobility pricing. The third essay examines the relationship between firms' voluntary disclosure in environmental performance information and their level of institutional ownership. Empirical results indicate that US S&P 500 companies with higher institutional ownership ratio are less likely to disclose to the Carbon Disclosure Project. Moreover, disclosure behavior also leads to a lower ratio of institutional ownership. This negative relationship suggests adverse selection in the disclosure decision, hence such voluntary disclosure policy might be of limited value to the public.
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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".