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Record W2292919614

Climate change: economic sense and non-sense of carbon mitigation policies

2006· preprint· en· W2292919614 on OpenAlexaboutno aff
multiple Or Corporate Authorship .

Bibliographic record

VenueRePEc: Research Papers in Economics · 2006
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsGreenhouse gasKyoto ProtocolIncentiveClimate changeNatural resource economicsStatus quoNegotiationMontreal ProtocolBusinessEconomicsEnvironmental resource managementPolitical scienceGeographyMarket economyMeteorology
DOInot available

Abstract

fetched live from OpenAlex

It is predicted that climate change caused by human activities will raise global average temperatures by between 1.5 and 5 degrees Celsius over the next 100 years. This could raise sea levels by one metre or more and lead to a number of other catastrophic climate changes and related phenomena. It could also have some benefits. • Humanity’s main response to this problem is the United Nations’ climate negotiation process. The most important milestone of that process so far is the ‘Kyoto protocol’ which has set targets for reductions in greenhouse gas emissions and which came into force in February 2005. • Despite some attractive design elements, the Kyoto protocol alone is unlikely to make much impact on greenhouse gas emissions. This is mainly due to the failure of the agreement to include most of the world’s current and future emissions, which will arise in China, India and the United States. • If, as seems likely, this status quo continues, then research and development (R&D), which leads to innovations that can both reduce the intensity of carbon emissions and reduce costs, will become an even more important part of the strategy to fight climate change. • While the Kyoto strategy of internationally agreed emission targets might create some incentives to develop these technologies, the incentives are probably insufficient. This suggests that some additional direct support from governments is required. Another problem with targets is that by the nature of things, they have to be based on very unreliable forecasts of what can be achieved in the future at reasonable costs. Pressing ahead with ambitious targets – as is the current UK strategy – might therefore risk wasting large amounts of public and private money without having much impact on climate change. • To avoid the danger of excessive costs or politically disastrous non-compliance, target schemes should include a ‘safety valve’ mechanism. • We propose an innovative solution of a safety valve mechanism operating through a Global Environmental R&D Fund. Countries could convert excess carbon into contributions to a research fund that would be used to develop technologies to reduce climate change.

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)0.010
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 score0.014
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0040.043
Scholarly communication0.0140.017
Open science0.0010.008
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0030.001

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 head0.077
GPT teacher head0.314
Teacher spread0.237 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

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