Towards Green Crypto Mining: Regulating Sustainability in Canada and Iceland — A Dual Approach with Lessons from Bitcoin and Copper Industries
Bibliographic record
Abstract
Cryptocurrency mining has emerged as a significant sector within the digital economy, characterised by its substantial energy consumption and its impact on environmental sustainability. The article undertakes a comparative analysis of the regulatory frameworks governing cryptocurrency mining in Canada and Iceland, with a particular emphasis on addressing energy consumption and environmental concerns. The article aims to provide valuable insights into crafting effective regulatory strategies that balance the growth of the crypto mining industry with sustainable energy practices. It will highlight the growing importance of regulating this industry to address these challenges effectively. L’extraction de crypto-monnaie est devenue un secteur important au sein de l’économie numérique, caractérisé par sa consommation d’énergie importante et son impact sur la durabilité environnementale. Cet article entreprend une analyse comparative des cadres réglementaires régissant l’extraction de cryptomonnaies au Canada et en Islande, en mettant particulièrement l’accent sur la consommation d’énergie et les préoccupations environnementales. Il vise à fournir des informations précieuses sur l’élaboration de stratégies réglementaires efficaces qui équilibrent la croissance de l’industrie minière de crypto avec des pratiques énergétiques durables. Il mettra en évidence l’importance croissante de réglementer cette industrie pour relever efficacement ces défis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".