Are Climate Change Policies Fair to Vulnerable Communities? the Impact of British Columbia's Carbon Tax and Australia's Carbon Pricing Policy on Indigenous Communities
Bibliographic record
Abstract
This paper compares carbon pricing policies in British Columbia and Australia in order to identify differences between carbon taxes and emissions trading schemes (ETS) from a fairness perspective. We examine how taxes and trading systems impact indigenous communities in both jurisdictions. While the regressivity of carbon pricing is a critical part of any fairness assessment, we argue that socioeconomic and cultural factors must also be taken into consideration. We discuss the importance of accompanying carbon pricing with policies that mitigate not only distributional impacts, but also additional impacts. These may be funded by the revenue generated by the policy or byother sources of government revenue. We argue in favour of devoting at least some portion of revenues generated by the instruments to climate change mitigation, versus tax cuts, since vulnerable communities are often disproportionately impacted by climate change. We conclude that carbon pricing policies have the potential to be designed in a way that is fair to indigenous communities. The devil is in the details. Both ETS and carbon taxes have cost implications for disadvantaged groups such as indigenous peoples, but they can both be designed in a way that compensates fairly for these impacts. Ultimately, it is a political choice.
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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 | 0.003 | 0.011 |
| 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 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".