Beyond the Carbon Tax: Personal Carbon Trading and British Columbia's Climate Policy
Bibliographic record
Abstract
This thesis proposes a policy framing, communication and implementation model for personal carbon trading in British Columbia. Personal carbon trading is a scheme under which all individuals are allocated a number of free carbon allowances forming a personal carbon budget. Persons whose carbon emissions are lower than their carbon budgets can sell their surplus to persons who have exceeded theirs. As distributed allowances are reduced annually, consumers are encouraged to modify their behaviour and/or adopt technologies in order not to exceed their carbon budget. Personal carbon trading and carbon taxes are both carbon pricing instruments that, using different policy framings, aim to reduce greenhouse gas emissions. Comparative experiments in the United Kingdom tested the hypothesis that, due to economic, social and psychological drivers, personal carbon trading would have greater potential to deliver emission reductions than taxation alone. This thesis explores that hypothesis in the context of British Columbia’s climate policy. It builds on an analysis of the BC carbon tax, international examples of carbon pricing instruments, and strategies for behavioural change such as social networking, loyalty management, apps development and gamification. Interviews were conducted with experts in financial services, energy efficiency, and the green economy, as well as with specialists in climate, health and taxation policy. They offered opinions on the potential of personal carbon trading to increase individuals’ participation in carbon emission reductions in BC. Their input, together with a review of the theoretical literature and practical case studies, informed the proposed design of a personal carbon trading system for BC. The thesis concludes with policy recommendations for increasing individual engagement, carbon budgeting and collective action by linking personal carbon trading to social, financial and health incentives.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".