The impact of environmental regulations on industry growth : an analysis of the Norwegian salmon farming industry
Bibliographic record
Abstract
Increasing the production of farmed salmon in Norway is an aspiration of the Norwegian government and domestic salmon farming companies. However, given the growing relevance of the environmental movement worldwide, no industry should be operated without considering the correlation between environmental externalities and industry growth. This thesis navigates through the extensive landscape of the salmon farming industry in Norway to understand the dynamic connections between environmental externalities, innovation, and industry growth.\nFurthermore, this thesis utilizes a qualitative approach to demonstrate why the industry deems sea lice to be the most significant negative externality and barrier to growth. The importance of ecological innovations in addressing this externality is introduced, and innovations are classified based on the public and private benefits they provide. A detailed overview of the organizations and funding involved in developing these innovations is also presented.\nHowever, the development of innovations is not enough. To achieve environmental goals, companies must adopt them. This thesis aims to understand if widespread adoption of a sustainability enhancing innovations would be possible. A simulation using game theory was conducted to predict companies' strategic behavior in response to environmental regulations.\nThe analysis revealed that sustainability enhancing innovations that are socially desirable might not achieve widespread adoption. To overcome this, an increase in pre-competitive collaboration amongst salmon farming companies is suggested as a solution.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".