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Record W2404643609 · doi:10.1108/ijse-10-2014-0206

Framework for valuing the utilization of the environment

2016· article· en· W2404643609 on OpenAlexaboutno aff
Seck Tan

Bibliographic record

VenueInternational Journal of Social Economics · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainable Development and Environmental Policy
Canadian institutionsnot available
Fundersnot available
KeywordsCommodityOriginalityEconomicsCapital (architecture)Value (mathematics)Function (biology)Resource (disambiguation)Kuznets curvePoint (geometry)EconomyEconomic growthMarket economySociologyComputer science

Abstract

fetched live from OpenAlex

Purpose – The purpose of this paper is to apply a methodology (drawn from the literature) to value the environment as a capital. Design/methodology/approach – The main analytic framework considered is a factor utilization function, which traditionally deals with labour and manufactured capital. The development of a three-factor function in terms of labour, manufactured capital and environmental capital enables the display of mistaken notions of economic performance. Findings – The purpose of this illustration is to identify the patterns of environmental capital utilization as an economy grows. Although there are some differences in the patterns of environment utilization between Australia and Canada, the patterns observed are in line with that of the environmental Kuznets curve (EKC). Practical implications – This illustration is made with reference to two commodity-driven economies, namely, Australia and Canada. The findings could be used as a point of reference for resource rich economies. Originality/value – This paper illustrates an approach for valuing the environment as an economy grows. This approach is applied to two selected resource rich economies and the findings demonstrate traits similar to that of the EKC.

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 score0.521
Threshold uncertainty score0.544

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 head0.022
GPT teacher head0.262
Teacher spread0.240 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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