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Record W4393317947 · doi:10.18280/ijsdp.190335

Assessing the Role of Carbon Taxes in Driving Low-Carbon Transformations: A Comparative Analysis of Implementation Policies

2024· article· en· W4393317947 on OpenAlexvenueno aff
Rumanintya Lisaria Putri, Desak Nyoman Sri Werastuti, Ni Wayan Rustiarini, Agung Sutoto, Eko Wahyono, Budi Wardono, Armen Zulham, Amos Lukas, R. Djoko Goenawan, Sofia Anita, I Ketut Ardana, I Nyoman Normal, Moehar Daniel, Lisa Yuniarti, Khojin Supriadi, Dodi Al Vayed

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsGreenhouse gasCarbon taxCarbon fibersEnvironmental economicsNatural resource economicsEconomicsEnvironmental scienceComputer scienceAlgorithm

Abstract

fetched live from OpenAlex

Innovations in the utilization of alternative energy sources to replace coal and oil-based production methods have a direct impact on the volume of carbon dioxide (CO2) emissions released into the atmosphere and subsequently contributing to the greenhouse effect.Addressing these negative externalities of greenhouse gas emissions is most effectively achieved through a universal global carbon tax system applied uniformly across all nations.This study seeks to explore the implementation of a carbon tax as an alternative policy for curbing carbon emissions and promoting a transition to a sustainable green economy.The research adopts a qualitative approach with a focus on comparative analysis, examining carbon tax policies across various countries in Europe, America, and Asia.Research data was primarily gathered through an extensive review of relevant literature, with a major data source being the World Bank's reports on the status and trends of carbon pricing.The study's findings underscore the efficacy of a carbon tax as a policy instrument to reduce carbon emissions.Furthermore, it has the potential to induce shifts in both household and industrial decisionmaking behaviors, leading to reduced energy consumption with high emissions.Ultimately, this policy approach can foster sustainable development and facilitate the transition to a green economy characterized by low-carbon practices, resource efficiency, and social inclusivity.These policies are instrumental in addressing environmental and social challenges, thus safeguarding the well-being of future generations.

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 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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.199
Threshold uncertainty score0.249

Codex and Gemma teacher scores by category

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

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 head0.058
GPT teacher head0.339
Teacher spread0.281 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations11
Published2024
Admission routes1
Has abstractyes

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