Essays in Energy Economics and International Trade
Bibliographic record
Abstract
This dissertation consists of three independent essays examining how international trade in fossil fuels shapes global climate outcomes and local economic conditions. The first two chapters focus on the implications of global fossil fuel trade for global investment in power sector decarbonization. The first chapter develops and estimates a dynamic, multi-country model of power asset investment, where the carbon intensity of electricity generation is affected by the entry and exit of plants using alternative fuels and the local price of fossil inputs is determined in a global trade equilibrium. Using this model, in the second chapter I assess the climate impact of granting federal approval to all proposed U.S. liquified natural gas (LNG) export terminal projects, which would double U.S. export capacity by 2030. Results indicate a net decrease in global emissions through 2070, primarily due to higher local gas prices in the U.S., leading to lower domestic gas generation and accelerated renewable adoption. In the rest of the world, short-term emissions fall as reliance on coal drops, yet delayed renewable uptake drives long-term emissions up. Finally, the third chapter examines how oil price shocks affect the relative labor market outcomes of exposed individuals in exporting countries, studying the case of the 2014 oil price crash in Canada. Unique data linking post-secondary education records to tax files shows that a one-standard deviation decrease in school-major-specific labor demand due to the oil shock reduced graduates’ earnings by 2.3% and increased the likelihood of filing for unemployment insurance and engaging in self-employment. The shock also reduced dropout rates, highlighting the role of outside options in shaping school enrollment decisions.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".