Bibliographic record
Abstract
This study shows that competition drives corporate innovation under intense environmental regulatory pressure. Using the nonattainment status of U.S. counties as an exogenous variation in regulation, we find that competition spurs green innovation as firms respond to stricter policies. Firms are particularly motivated to innovate in clean technology when operating in pollution-intensive industries, facing high relocation costs, and possessing a strong history of innovation. Regulation-driven green innovation allows firms to differentiate their products, enhance their environmental, social, and governance (ESG) reputation, and attract more corporate customers, leading to higher sales growth, increased market share, and improved profitability, although not necessarily higher valuation. Stricter regulations in competitive environments not only curb pollution but also serve as a catalyst for sustainable long-term innovation. These findings emphasize the vital role of environmental regulations in promoting sustainable practices and operational benefits, underscoring the importance of well-designed policies to drive long-term economic and environmental progress. This paper was accepted by Bo Becker, finance. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.00600 .
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".