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Record W3122085084

A practical approach to offset permits in post Kyoto climate policy

2011· preprint· en· W3122085084 on OpenAlexaboutno aff
Peter Heindl, Sebastian Voigt

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.

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)0.004
metaresearch head score (Gemma)0.011
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.003
Scholarly communication0.0050.006
Open science0.0020.002
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0260.001

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

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

Citations2
Published2011
Admission routes1
Has abstractyes

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Same venueMADOC (University of Mannheim)Same topicClimate Change Policy and EconomicsFrench-language works237,207