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Record W3123667106

Pollution Control Under a Possible Future Shift in Environmental Preferences

2018· article· en· W3123667106 on OpenAlexaff
Bruno Nkuiya, Christopher Costello

Bibliographic record

VenueSSRN Electronic Journal · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDamagesDiscountingNatural resource economicsPollutionParadigm shiftEnvironmental pollutionEconomicsEnvironmental scienceEnvironmental protectionEcologyLawPolitical science
DOInot available

Abstract

fetched live from OpenAlex

We examine how the possibility of a future shift in environmental preferences affects optimal pollution emissions. For example, is a more stringent carbon cap called for if excessive carbon emissions today may trigger a shift toward greener preferences in the future? In contrast to the related literature, which largely focuses on regime shifts in damages, not preferences, we find that the possibility of a shift in environmental preferences can induce higher, or lower, optimal current emissions. This hinges on an economically interesting interplay between the magnitude of the possible shift, whether the shift is endogenous, and the magnitude of environmental damages. This helps reconcile the tension between the conservationists’ view of pollution reduction (to reduce damage and lessen the likelihood of regime shift) and the traditional economic rationale emphasizing risk and discounting.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.000

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 head0.025
GPT teacher head0.222
Teacher spread0.198 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations0
Published2018
Admission routes1
Has abstractyes

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