Factors associated with carbon tax awareness: a systematic review
Bibliographic record
Abstract
Carbon dioxide is the primary greenhouse gas responsible for approximatelythree-quarters of the world’s gas emissions.Carbon dioxide (CO2) is an important heat-trapping gas, or greenhouse gas that comes from the extraction and burning of fossil fuels.Its presence in the atmosphere warms the planet, but the rising concentration of greenhouse gases has led toclimate change due to hotter temperaturesacross the globe.Carbon tax is defined as a fee imposed on the burning of carbon-based fuels,such ascoal, oil,and gas,whichaims to reduce greenhouse gasses. Thus, thisstudy aimedto evaluate the factorsassociated with carbon tax awareness among the general population.This systematic reviewaimed to systematically evaluate factors associated with carbon tax awareness among the general population.A systematic search was conducted in Scopus and ScienceDirect. This review was conducted based onthe Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The literature search was conducted from1stNovember until 1stDecember 2022 using Scopus and ScienceDirect databases. The following keywords were used tosearch for related articles: “carbon tax policy” AND “awareness” AND “factor” OR “factor associated”. All retrieved articles were imported into EndNote20.A total of twostudies met the inclusion and exclusion criteria,with oneeligible article from Taiwanand1 from Canada. The analysed articles were published between 2020 and 2022. The identified associated factorsof carbon tax awareness, namely trust in the federal government, climate change concerns, environmental concerns, education, health consciousness, and age,can be used as a guide fordesigningevidence-based measures and policies that address these concerns.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".