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Record W4205930762 · doi:10.5539/eer.v12n1p1

Integrated Carbon Policy Design for Achieving Net-Zero Targets

2022· article· en· W4205930762 on OpenAlex
Abhijeet Acharya

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.

venuePublished in a venue whose home country is Canada.
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

VenueEnergy and Environment Research · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsGreenhouse gasRevenueCarbon footprintCarbon offsetCarbon priceBusinessClean Development MechanismOffset (computer science)Zero (linguistics)Environmental economicsCarbon fibersNatural resource economicsEconomicsFinanceComputer science

Abstract

fetched live from OpenAlex

Several countries have set net-zero targets, and many more will announce in the next few years. Countries have used carbon pricing as an instrument to cut Greenhouse Gas (GHG) emissions and provide a price signal to attract private investments to achieve net-zero targets. However, current carbon policy in countries with net-zero targets remains inadequate and asymmetrical to overcome net-zero challenges; there are visible gaps in the carbon price level, sectoral coverage, and mechanism to reward carbon-neutral initiatives. This paper proposed an integrated carbon policy design covering economic, technical, and social dimensions and discussed how an integrated policy design approach could be effective in helping countries achieve net-zero objectives. The paper makes recommendations for net-zero policymakers. First, a stable and appropriate carbon price must be in place to attract private investments in carbon offset measures and commercialize clean technologies. Second, governments should use an effective revenue recycling mechanism to engage firms and citizens in mitigating the side effects of the carbon price regime and win their trust. Third, countries should promote behavioral changes and carbon footprint reduction measures through citizen participation. 

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.825
Threshold uncertainty score0.653

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.175
GPT teacher head0.293
Teacher spread0.119 · 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