MétaCan
Menu
Back to cohort
Record W1554798108 · doi:10.3386/w16878

The Possibilities For Global Poverty Reduction Using Revenues From Global Carbon Pricing

2011· report· en· W1554798108 on OpenAlexaff
James Davies, Xiaojun Shi, John Whalley

Bibliographic record

VenueNational Bureau of Economic Research · 2011
Typereport
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsWestern University
Fundersnot available
KeywordsPoverty reductionRevenueReduction (mathematics)Natural resource economicsEconomicsPovertyCarbon fibersEnvironmental economicsBusinessEnvironmental sciencePublic economicsComputer scienceEconomic growthMathematicsFinance

Abstract

fetched live from OpenAlex

Global carbon pricing can yield revenues which are large enough to create significant global pro-poor redistributive opportunities.We analyze alternative multidecade growth trajectories for major global economies with carbon tax rates designed to stabilize emissions in the presence of both continued country growth and autonomous energy use efficiency improvement.In our central case analysis, revenues from globally internalizing carbon pricing rise to 7% and then fall to 5% of gross world product.High growth in India and China is the major equalizing force globally over time, but the incremental redistributive effects that can be achieved using global carbon pricing revenues are large both in absolute and relative terms.Revenues from carbon pricing depend on growth and energy efficiency improvement parameters as well as on the price elasticity of demand for fossil fuels.

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)0.001
metaresearch head score (Gemma)0.002
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: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.670
GPT teacher head0.515
Teacher spread0.155 · 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
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueNational Bureau of Economic ResearchSame topicClimate Change Policy and EconomicsFrench-language works237,207