Bibliographic record
Abstract
My dissertation is composed of three chapters that explore the drivers of firms' environmental performance, with a particular focus on the role of internal factors and their interaction with external drivers. The first chapter of my dissertation explores how firms respond to increases in fuel prices, which is an important aspect of climate change policies. In this study, I propose that adjustments to changes in fuel prices are the result of complex organizational decisions, and that the adoption of structured management practices may shape a firm's response to higher fuel prices and its environmental performance. I find that higher fuel prices are associated with lower CO2 emissions; however, these responses are heterogenous and depend on firms' management practices. Firms adopting structured management practices are more responsive to increases in fuel prices, especially when they feature a decentralized structure. In the second chapter, I examine how labor unions, which have been recognized as key stakeholders in environmental protection, affect firms' innovation activities in environmentally friendly technologies. While labor unions have historically advocated against pollution to protect their members from environmental hazards, they are also seen as potentially opposing investments in clean technologies, citing concerns that such costs will reduce the economic benefits available to their members. I find no evidence of union influence on firms' innovation activities to protect the environment. Moreover, I also present evidence that unionization has no significant effect on pollution outcomes, particularly toxic chemical emissions. Overall, the findings suggest that unionization does not seem to be a primary driver of firms' environmental technology strategies. Finally, the third chapter investigates how firms adapted to Japan's electricity-saving campaigns launched in response to the Fukushima nuclear disaster, focusing on manufacturing plants within energy-intensive sectors. The findings show a notable reduction in electricity usage among firms; however, firms, particularly larger ones, significantly increased their on-site electricity generation to substitute grid-supplied electricity. Despite these adaptations, larger firms still faced significant production disruptions, particularly those that lacked self-generation capabilities prior to the disaster and the implementation of the electricity-saving campaigns.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".