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Record W2158692592 · doi:10.5539/enrr.v4n4p109

Experts Valuating the Climate Change Policies in Greece: Self-Interested Versus Ethically Motivated Values

2014· article· en· W2158692592 on OpenAlexvenueno aff
Vasileios Markantonis, K. Bithas

Bibliographic record

VenueEnvironment and Natural Resources Research · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsnot available
Fundersnot available
KeywordsValuation (finance)Climate changeIndividualismPositive economicsPublic economicsEconomicsContext (archaeology)Environmental ethicsPolitical scienceLawAccountingGeographyEcologyPhilosophy

Abstract

fetched live from OpenAlex

Understanding and estimating the climate change costs has been in focus of the scientific community in the last years, whereas several studies are dealing with this challenging issue. In this context, the present paper aims at valuating the climate change mitigation and adaptation measures in Greece. To achieve that, we carried out a contingent valuation survey. In addition it explores the coexistence of ethically motivated values and self-interested values held by Greek climate experts, a condition in economics underlying the existence of the “Bergson-Tintner-Samuelson (BTS) value formulation effect”. This is an experimental attempt in recent valuation literature and carries significant implications for the valuation issue and its policy implications. The results indicate that ethically motivated values of crucial environmental functions such as climate far exceed the individualistic ones. Furthermore, the coexistence of public ethically-based values alongside self-interested ones supports earlier findings in the literature and indicates that solely self-interested individual values do not reflect the real welfare contribution of crucial environmental functions and, therefore, should not form the exclusive guide for environmental policy.

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)0.003
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 score0.044
Threshold uncertainty score0.557

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 integrity0.0000.001
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 head0.214
GPT teacher head0.320
Teacher spread0.105 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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