Bibliographic record
Abstract
Effective domestic policies are urgently needed to address climate change. A great deal of energy is devoted to selecting and designing the optimal policy instruments, with questions of environmental effectiveness and economic efficiency dominating the debate. However, it is equally important to consider how those policies will impact upon different segments of society and to ensure that they are designed in a way that is fair and does not further entrench systemic inequalities. This article approaches this social justice issue by examining carbon taxes from a feminist perspective, specifically considering how carbon taxes impact upon women. The article proposes the gender analysis of environmental taxes framework, which goes beyond the evaluation of distributional impacts to consider non-income impacts, implications of related mitigation, and revenue-use policies as well as the outcome of the measure. Applying the framework to British Columbia’s carbon tax and Quebec’s redevance annuelle reveals that women may bear a disproportionate burden of the increased prices created by carbon taxes. The chapter also demonstrates that policies designed to mitigate the impact of carbon taxes on low-income households do not address income disparities between women and men, nor do they take into account the socio-economic status of women. The author concludes with recommendations for developing carbon pricing policies that avoid perpetuating existing systemic inequalities between women and men and that might even help to overcome these inequalities.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".