Traçabilité des émissions de gaz à effet de serre dans l'industrie minière : étude de cas sur la blockchain
Bibliographic record
Abstract
RÉSUMÉ: Ce mémoire a examiné l'application de la technologie blockchain pour relever le défi crucial de la traçabilité des émissions de gaz à effet de serre (GES) dans l'industrie minière. L'étude a démontré que la blockchain peut fournir une solution efficace, transparente et sécurisée pour suivre, consigner et authentifier les données relatives aux émissions de GES tout au long de la chaîne d'approvision-nement. L'analyse des pratiques actuelles dans le secteur minier a révélé un retard technologique important au Québec en matière de gestion des émissions de GES, mais aussi une opportunité pour adopter des technologies de pointe comme la blockchain. Les avantages clés identifiés comprennent la traçabilité complète des émissions de bout en bout, la réduction des fraudes et du double comptage, ainsi que l'amélioration de la transparence et de l'automatisation des audits environnementaux. Le cas d'étude de la mine de lithium Sayona, située à Val-d’Or, a montré que l'intégration de la blockchain permet non seulement de répondre aux exigences réglementaires croissantes, mais également de réduire les coûts liés aux audits tout en augmentant la fiabilité des bilans carbone. Grâce à cette technologie, les entreprises minières peuvent non seulement mieux gérer leur empreinte carbone, mais aussi renforcer leur crédibilité en matière de durabilité et répondre aux attentes des parties prenantes. En conclusion, cette recherche souligne que la blockchain pourrait devenir un standard pour la traçabilité des émissions de GES dans l'industrie minière, offrant une solution adaptée aux besoins d'automatisation et de transparence, et contribuant à la transition vers des pratiques plus écoresponsables. ABSTRACT: This brief examined the application of blockchain technology to address the critical challenge of tracking greenhouse gas (GHG) emissions in the mining industry. The study demonstrated that blockchain can provide an efficient, transparent, and secure solution for tracking, logging, and authenticating GHG emissions data throughout the supply chain. An analysis of current practices in the mining sector revealed a significant technological gap in Quebec when it comes to managing GHG emissions, but also an opportunity to adopt cutting-edge technologies such as blockchain. Key benefits identified include full end-to-end traceability of emissions, reduced fraud and double-counting, and improved transparency and automation of environmental audits. The case study of the Sayona lithium mine, located in Val-d'Or, showed that integrating blockchain not only helps meet growing regulatory requirements, but also reduces audit-related costs while increasing the reliability of carbon footprints. Thanks to this technology, mining companies can not only better manage their carbon footprint but also strengthen their sustainability credibility and meet stakeholder expectations. In conclusion, this research highlights that blockchain could become a standard for GHG emissions traceability in the mining industry, offering a solution tailored to the need for automation and transparency and contributing to the transition towards more eco-responsible practices.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".