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Record W1979606812 · doi:10.1108/14777830810894238

Economic instruments and the conservation of biodiversity

2008· article· en· W1979606812 on OpenAlexaff
Collins Ayoo

Bibliographic record

VenueManagement of Environmental Quality An International Journal · 2008
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBiodiversityOriginalityNatural resource economicsMeasurement of biodiversityValue (mathematics)Biodiversity conservationEnvironmental resource managementEconomicsBusinessPublic economicsEnvironmental planningGeographyEcologySociologyBiologySocial science

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to describe and examine the problem of biodiversity loss and to explain its underlying causes and the possibilities of using economic instruments to conserve it. Design/methodology/approach The research in this paper was undertaken through a review of the literature and an analysis of data on the trends of various measures of biodiversity worldwide. Findings The loss of biodiversity is occurring worldwide at a rapid rate that has the potential to significantly undermine the prospects for sustainable development. Although the main proximate cause of biodiversity loss is land conversion, the fundamental causes are rooted in economic, institutional, and social factors and include market failures and the lack of property rights. Practical implications This paper presents arguments in support of using economic instruments to conserve biodiversity and explains the conditions under which the use of these instruments is likely to be most successful. Originality/value This paper illuminates the economic, social and institutional factors that underlie the rapid loss of biodiversity and outlines some ways in which economic instruments can be used to stem the loss of biodiversity.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.746

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.105
GPT teacher head0.240
Teacher spread0.135 · 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

Citations13
Published2008
Admission routes1
Has abstractyes

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