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Record W3122085084

A practical approach to offset permits in post Kyoto climate policy

2011· preprint· en· W3122085084 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueMADOC (University of Mannheim) · 2011
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsMarginal abatement costGreenhouse gasClean Development MechanismChinaOffset (computer science)Emerging marketsNatural resource economicsMarginal costDeveloping countryTransaction costKyoto ProtocolClimate changeCarbon leakageBusinessCarbon offsetEconomicsInternational economicsClimate policyInternational tradeGeographyEconomic growthFinance
DOInot available

Abstract

fetched live from OpenAlex

International Carbon Offsets from developing countries and emerging economies such as permits from the Clean Development Mechanism (CDM) will potentially play an important role for cost containment in domestic greenhouse gas regulation schemes in industrialised countries. We analyse the potential role of offset permits assuming that major emitters such as the USA, Canada, Japan, Australia and New Zealand install domestic greenhouse gas regulation schemes to achieve the emissions reductions pledged in the Copenhagen Accord and seek cost containment. We estimate a potential demand for offset permits of 627 to 667 MtCO2e p.a. from industrialised countries. To describe the supply structure, we derive marginal abatement cost curves for developing countries and emerging economies. We find that developing countries and emerging economies can supply 627 to 667 MtCO2e p.a. at costs of approximately EUR 10 (in 2004 EUR), neglecting transaction costs and country specific risks. The highest potentials for the generation of carbon offsets are present in China, India and the rest of Asia.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.754
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.182
GPT teacher head0.269
Teacher spread0.087 · 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