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Record W2901707827 · doi:10.1093/ajae/aay076

Spatial Renewable Resource Extraction under Possible Regime Shift

2018· article· en· W2901707827 on OpenAlexafffund
Christopher Costello, Bruno Nkuiya, Nicolas Quérou

Bibliographic record

VenueAmerican Journal of Agricultural Economics · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicEcosystem dynamics and resilience
Canadian institutionsUniversity of Alberta
FundersSocial Sciences and Humanities Research Council of CanadaEuropean Association of Environmental and Resource EconomistsUniversity of California, Santa BarbaraAgence Nationale de la RechercheUniversity of Alberta
KeywordsResource (disambiguation)Renewable energyExtraction (chemistry)Computer scienceRenewable resourceNatural resource economicsEngineeringComputer networkElectrical engineeringEconomics

Abstract

fetched live from OpenAlex

Abstract How will countries harvesting mobile natural resources react to the possibility of regime shift? We address the non‐cooperative exploitation of a migratory renewable resource in the presence of possible regime shift that affects its movement. Motivated by the anticipated effects of climate change, we model a regime shift that will alter the spatial movement patterns of the resource at some point in the future. We develop a stochastic spatial bioeconomic model to address the effects of this class of regime shift on non‐cooperative harvest decisions made by decentralized owners such as countries exploiting a migratory fish or other natural resource stock. We find that the threat of a future shift modifies the standard “golden rule”, and may induce more aggressive harvest everywhere, irrespective of whether the owner will be advantaged or disadvantaged by the shift. We also identify conditions under which the threat of regime shift induces owners to reduce harvest rates in advance of the shift. Our analysis suggests that different property rights structures (single ownership vs common property) or heterogeneous growth can give rise to previously unexplored incentives and can even reverse conventional wisdom about how countries will react to the prospect of future environmental change.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.778
Threshold uncertainty score0.349

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.198
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2018
Admission routes2
Has abstractyes

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