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Record W2061215019 · doi:10.1017/s1355770x13000582

Production-based versus consumption-based emission targets: implications for developing and developed economies

2013· article· en· W2061215019 on OpenAlexaff
Madanmohan Ghosh, Manmohan Agarwal

Bibliographic record

VenueEnvironment and Development Economics · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Impact and Sustainability
Canadian institutionsCentre for International Governance InnovationEnvironment and Climate Change Canada
Fundersnot available
KeywordsEconomicsProduction (economics)Consumption (sociology)Computable general equilibriumNatural resource economicsEnergy consumptionGreenhouse gasEnvironmental scienceInternational economicsEnvironmental economicsMacroeconomics

Abstract

fetched live from OpenAlex

Abstract This paper evaluates how the marginal abatement cost (MAC) and the efficiency cost of policies will change at the regional and global level if reduction targets are based on consumption-based emissions (CBEs) rather than on production-based emissions (PBEs). Using a CGE model, this paper finds that the MAC of CBEs is in general higher than that of PBEs, mainly due to limited substitution possibilities between energy and non-energy goods in final consumption compared to those in the choice of inputs in production activities under PBEs. Interestingly, when policies such as border carbon adjustments (BCAs) are introduced to reduce CBEs, net importers of emissions are better off, while net exporters of emissions are worse off in this approach compared to the PBEs target. If border tariffs are not allowed, the CBEs target turns out to be worse both for net importers and exporters of emissions.

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)0.002
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.028
GPT teacher head0.239
Teacher spread0.211 · 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

Citations5
Published2013
Admission routes1
Has abstractyes

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