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Record W2621368369 · doi:10.5539/enrr.v7n2p112

The Simple Economics of Conservation Clubs

2017· article· en· W2621368369 on OpenAlexvenueno aff
Arthur J. Caplan

Bibliographic record

VenueEnvironment and Natural Resources Research · 2017
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsnot available
FundersScheme for Promotion of Academic and Research CollaborationUtah Agricultural Experiment StationUtah State University
KeywordsClubBenchmark (surveying)Energy conservationBusinessFunction (biology)Order (exchange)Key (lock)Public economicsEnvironmental economicsWelfareEconomicsMarketingEnvironmental resource managementComputer scienceEcologyGeographyMarket economyFinance

Abstract

fetched live from OpenAlex

This paper examines how a regulatory authority might ideally promote the formation of “conservation clubs” among households in order to initiate and empower voluntary household-level water and energy conservation efforts. We characterize a socially optimal conservation benchmark and derive the conditions necessary for a club to effectively attain this benchmark on behalf of the wider community. Both theoretical and numerical analyses are used to demonstrate ways in which households choose to become club members and are subsequently empowered to undertake conservation efforts. The avenues through which club membership might empower households include (1) information provision/education that is assumed to alter key parameters of the household’s welfare function, thereby inducing the household to build a stronger “conservation ethic,” and (2) bulk-pricing arrangements that reduce the prices of applicable conservation technologies. Our results highlight key relationships between the regulator and households, as well as between the club and the marketplace, that should be measured empirically before efforts are made to establish conservation clubs in practice.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.613
Threshold uncertainty score0.484

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 studies0.0010.000
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 head0.025
GPT teacher head0.254
Teacher spread0.229 · 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 designNot applicable
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

Citations0
Published2017
Admission routes1
Has abstractyes

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