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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.128
Threshold uncertainty score1.000

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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