Bibliographic record
Abstract
This article proposes the ‘contractual carbon fee’ as a novel governance instrument to guide non-state climate change mitigation efforts. At its core, the contractual carbon fee is a privatized carbon tax: one contracting party agrees to pay a fee on its greenhouse gas (GHG) emissions, while another agrees to enforce the commitment to pay the contractual carbon fee. The enforcing party may recover unpaid carbon fees through a stipulated remedy clause. This instrument increases the credibility of a firm’s environmental commitments and helps fill gaps in environmental governance. Due to its binding nature, the contractual carbon fee holds non-state actors accountable for their GHG emissions goals and targets. This article provides advice on how to draft an enforceable contractual carbon fee under Canadian common law and further argues that the contractual carbon fee may be beneficial to self-interested economic actors. Indeed, a contractual carbon fee can help reduce a firm’s GHG emissions, lead to marginal cost savings, help finance green investments, and mitigate climate-related risks.
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 | 0.023 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.018 | 0.013 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".