The commons problem in the presence of negative externalities
Bibliographic record
Abstract
Purpose While the commons problem and the issues related to the negative externalities of harvesting have been studied extensively, there remains a need to bridge these two streams of studies to comprehensively investigate the implications of the strategic interactions among resource harvesters in the presence of such negative externalities. This paper aims to fill this gap. Design/methodology/approach The authors study a common-pool harvest problem when the extractive activities leave behind negative externalities which affect the resource growth rate and reduce the stock beyond the extracted levels. Markov perfect noncooperative and optimal solutions are presented under different scenarios regarding considerations of negative externalities into harvest decisions. Findings Results of the study suggest that, in the presence of such externalities, all parties must scale down their extraction in accordance with their externalities. The resource can be preserved by implementation of such harvest rule. However, failure to incorporate the externalities exacerbates the commons problem and can even lead to exhaustion of the biomass even if countries manage to cooperate and coordinate their harvest. Suggesting that if such externalities are large enough – which empirical literature suggests they are – then recognition and consideration of these externalities in the harvest decisions is as crucial as cooperation. Originality/value This paper provides a framework that is capable of incorporating the negative externalities of harvest activities into a bioeconomic game theoretic model and thereby providing a more real-world representation of the state of the common-pool resource management. While, the authors extend a well-known simple model, the model of this research study has the capacity to explain the widespread incidences of resource collapses. Therefore, the important policy implication is that agents should rigorously work together to understand the extent of the negative externalities of their harvests on the resources.
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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 | 0.010 |
| 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 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".