Environmental R&D in the Presence of an Eco-Industry
Bibliographic record
Abstract
We compare the performance of R&D cooperation and R&D competition within the eco-industry using a model of vertical relationship between a polluting industry and the eco-industry. The polluting industry is assumed perfectly competitive and the eco-industry is a duopoly in the market for abatement goods and services, with one fi?rm acting as a Stackelberg leader and the other fi?rm as a follower. When there are full information sharing under R&D cooperation and involuntary information leakages under R&D competition, we ?find that the only case where government intervention is needed is the case where R&D cooperation yields a higher welfare but smaller pro?fits for the follower eco-industrial fi?rm than R&D competition. Furthermore, because of the market power that the eco-industry enjoys, we show that more total R&D efforts under R&D competition do not necessarily translate into more abatement activities and larger social welfare. When there are no involuntary leakages of information under R&D competition, this result occurs because R&D competition can induce more total R&D efforts than R&D cooperation even for signi?ficantly high R&D spillovers if the marginal environmental damage is large.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| 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".