Essays in Environmental and Energy Economics
Bibliographic record
Abstract
This thesis contains three chapters associated with the study of environmental and energy economics. The first chapter studies the impact of activism by non-governmental organizations (NGOs) on the stock returns of the firms they target, measured through their cumulative abnormal returns (CARs). I find that campaign events on environmental concerns substantially reduce CARs, and that there is no significant effect on campaigns related to non-environmental issues. I additionally test whether NGOs strategically time their campaigning activities and find evidence that NGOs avoid days with newsworthy events that may shift public attention away from their activity. Taken together, the results of this paper provide comprehensive evidence of the effectiveness of activism by NGOs in impacting firms. The second chapter investigates trends in energy use in Canada, relating them to economic output using a key metric: energy intensity. We first derive the major determinants of energy intensity in Canada between 1997 and 2017 using a traditional decomposition framework, namely the activity and efficiency indices. We then find through a non-parametric estimation framework that energy intensity trends downwards, with an average reduction of 1.5% per year, and improvements in efficiency of roughly 1% per year. Our results demonstrate that non-parametric estimation methods are effective in analyzing improvements in energy intensity. The third chapter models the interaction and strategic decision-making between activist NGOs and the firms they target. Firms respond to pressure from NGOs by choosing "green" or "brown" technologies, or by window-dressing; engaging in costly deception to avoid paying the full cost of complying to green technology. I examine how NGOs and firms interact in a monopolistically competitive environment. I derive an NGO-firm game in which firms can choose their technology in response to NGO activism, and the NGO chooses its monitoring intensity. I find that increases in payoffs to the NGO's arm that monitors the use of brown technology lead to an increase in window-dressing. Meanwhile, increases in payoffs to the arm that monitors window-dressing cause a rise in brown technology adoption.
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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 | 0.003 |
| 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 | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 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".