Notice bibliographique
Résumé
Information and Communications Technology (ICT) is a large and growing contributor to the world’s GHG emissions. Left unchecked, annual growth in this sector is expected to continue at 12%, as more and more computing resources and storage are required to support the move to a digital economy and the dematerialization of goods and services. Implicit in this growth is the additional demands for energy to run the storage and computing power systems (data centres). The challenge becomes one of meeting the increasing demand for technology, information storage, and computing power, while reducing the overall impact on the environment. CANARIE has recognized this challenge and is sponsoring research into the opportunities for green or carbon-neutral computing. This report seeks to address two questions: • How can universities and other organizations create and maintain carbon neutral computing facilities that are cost-competitive and scalable? • Should an organization or user move its servers (co-located or rented) to a low-carbon region, and can that move be financed through carbon offsets? Six alternative scenarios for creating a carbon-neutral data centre were developed and evaluated using financial analysis and Net Present Value (NPV), as well as a life cycle assessment and GHG audit to factor in materials, construction, and use-phase emissions. Two key drivers emerged as critical factors for creating low-carbon computing; location and cost. Location - The electricity generation mix in Canada varies considerably from one province to the next, and the dirtiest region (Alberta) is over 70 times as GHG intense as the cleanest (Quebec). Location is therefore a key determinant of how clean or dirty a data centre’s GHG emissions profile is. Data centres located in regions with access to renewable energy provide opportunities for the data centre to tap into the clean energy source, either by connecting to the regional electricity grid or through direct investment in renewable energy projects. Cost - Data centres and the required electrical supply and infrastructure constitute a significant capital investment. Large data centres require a significant amount of electricity, and can therefore justify investments in dedicated renewable energy projects and other forms of direct investment to secure clean electricity. Small data centres, including those evaluated in this report, are less able to justify and support large investments in renewable energy. These owners should therefore leverage indirect investments in clean electricity to achieve carbon-neutrality, be it through Renewable Energy Credits (RECs) or premiums, or carbon offsets. The recommendation, aimed at small and medium sized data centres, is to build in low-carbon regions where opportunities exist to tap into renewable energy sources. Data centres and any renewable energy sources should be connected to the region’s electricity grid in order to manage variability and maximize investments. In this way, investments in renewable energy are spurred on by increasing regional demand while any new sources are connected to the regional grid. The second question deals with existing data centre users (co-located) looking to achieve carbon-neutrality for their operations. Carbon offsets and credits offer opportunities for projects to receive financing to support projects that achieve carbon reductions, however the opportunity to use carbon financing and the decision of whether to move locations must be evaluated on a case-by-case basis. Overall it is recommended that data centres located in low to medium carbon-intensity regions should simply invest in RECs or carbon offsets rather than financing a relocation through carbon credits. Only those data centres that are currently located in dirty or high-carbon intensity areas have a significant opportunity to capitalize on carbon credit financing.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,035 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,004 | 0,004 |
| Communication savante | 0,015 | 0,008 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,005 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».