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Record W22866897 · doi:10.1017/s1355770x16000097

Adaptation to climate change: how does heterogeneity in adaptation costs affect climate coalitions?

2016· article· en· W22866897 on OpenAlexaff
Itziar Lazkano, Walid Marrouch, Bruno Nkuiya

Bibliographic record

VenueEnvironment and Development Economics · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsUniversity of AlbertaCenter for Interuniversity Research and Analysis on Organizations
Fundersnot available
KeywordsIncentiveAdaptation (eye)Climate changeEconomicsCarbon leakageAffect (linguistics)Natural resource economicsPublic economicsDeveloping countryBusinessEnvironmental resource managementMicroeconomicsClimate policyEcologyEconomic growth

Abstract

fetched live from OpenAlex

Abstract Adaptation costs to climate change vary widely across countries, especially between developed and developing countries. Adaptation costs also influence a country's decision to abate and join international environmental agreements (IEAs). In this paper, the authors study how these cost differences affect participation incentives. Their model identifies two channels through which adaptation affects free-riding incentives: carbon leakage and cost asymmetry in adaptation. In contrast with the common view, the authors find that the presence of adaptation is not necessarily an impediment to cooperation on abatement. They also present conditions under which adaptation can strengthen or weaken free-riding incentives. The results serve as a cautionary tale to policy makers and suggest that policies directed at reducing carbon leakage and/or cost differences between developed and developing countries may also affect the success and failure of IEAs.

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)0.003
metaresearch head score (Gemma)0.024
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.000

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 head0.100
GPT teacher head0.238
Teacher spread0.138 · 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
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

Citations36
Published2016
Admission routes1
Has abstractyes

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