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Enregistrement W7139615395

CFFS Procurement Strategy : Climate Mitigation

2022· report· en· W7139615395 sur OpenAlexaboutno aff
Rebecca Baron, Maya Bodnar, Nicola Bodnarchuk, Sandra Gurguis, Michelle Nifco

Notice bibliographique

RevuecIRcle (University of British Columbia) · 2022
Typereport
Langueen
Domaine
Thématique
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésProcurementGreenhouse gasFood systemsClimate changeAction planFocus groupGlobal warmingConsumption (sociology)
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

A review of past research indicates that global warming is an imminent issue for all Canadians — trends predict temperature values to rise 0.2°C per decade (IPCC, 2019). Food systems are responsible for 34% of global greenhouse gas (GHG) emissions and are a significant contributor to increased runoff, extreme weather events, and flooding throughout Canada, and globally (Crippa et al., 2021; IPCC, 2019). Canadian food consumption trends have led to long-term, broad impacts that have accelerated climate change (IPCC, 2019). Therefore, addressing these issues will require action from everyone, including The University of British Columbia (UBC). UBC is well-positioned to lead an integrated approach in creating a just and resilient campus-wide food system; however, food procurement strategies and consumption patterns have not yet been sufficiently analyzed. Our project informs the development of a campus-wide Climate-Friendly Food System (CFFS) Procurement Strategy to reduce GHGs and engage with UBC’s Climate Action Plan 2030 (CAP). Working alongside UBC Student Housing and Community Services (SHCS), and Campus + Community Planning (SEEDS), we developed a Climate-Friendly Food Systems (CFFS) procurement strategy in order to aid our community partners reach their goal of reducing food system-related GHG emissions by 50% in 2025. Our project achieved several goals, including (1) providing knowledge and information on campus-wide food-supply practices, (2) developing a Climate-Friendly Food System Procurement Strategy, and (3) providing recommendations to our community partners of Campus and Community Planning, and UBC Student Housing and Community Services that will help to reduce UBC’s GHG emissions. We conducted primary data collection through a focus group and interviews with individuals involved in UBC’s food system via Zoom, and secondary data collection through practitioner literature reviews to identify high impact opportunities, frameworks, policies, and promising practices to reduce procurement-related GHG emissions at UBC. Based on these results we categorized our results into three major areas of opportunity which were: plantbased, seasonality/locality, and monitoring. To give a broad overview of our primary and secondary results, they indicated that UBC would find it useful economically and environmentally to promote more plant-based menu offerings, conflicting evidence between primary and secondary data on the effects of procuring locally/seasonally, and to emphasize the monitoring of progress towards decreasing GHGs and ensure accountability throughout UBC’s food system. Our discussion revealed that plant-forward was a high impact opportunity for reducing GHGs, that we should focus on a variety of metrics for food procurement rather than focusing solely on locality/seasonality, and that monitoring can help inform the furthering of equity in UBC’s food systems. Overall, we acknowledge data limitations such as limited sample size, scope and sampling bias due to the short time frame of this project, but believe that our findings will still prove useful in developing a CFFS Food procurement strategy. Recommendations found through this project for reducing GHG emissions within the UBC Vancouver Campus Food System include short term recommendations of increasing the appeal and incentivization of plantbased foods, develop climate change accountability benchmarks, and promoting menu switches to less GHG emitting products, and develop assessment tools to determine the sustainability of the food procured at UBC. Longterm recommendations include increased monitoring of food waste to inform a procurement strategy that produces less waste (and therefore fewer emissions), increasing funding to the development of metrics that monitor the GHG emissions associated with foods at UBC, and utilize CFFS metrics to inform food sourcing. We also developed a comprehensive CFFS Procurement Strategy for the UBC Vancouver campus food system, which is provided externally, which involves climate food procurement targets, indicators, and actions that will help reduce GHG emissions in a holistic manner. Overall, we believe that our CFFS Procurement Strategy, informed by our primary and secondary research results, will prove valuable to our clients when furthering these important initiatives. Disclaimer: “UBC SEEDS provides students with the opportunity to share the findings of their studies, as well as their opinions, conclusions and recommendations with the UBC community. The reader should bear in mind that this is a student project/report and is not an official document of UBC. Furthermore readers should bear in mind that these reports may not reflect the current status of activities at UBC. We urge you to contact the research persons mentioned in a report or the SEEDS Coordinator about the current status of the subject matter of a project/report.”

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,005
score de la tête « metaresearch » (Gemma)0,004
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,753
Score d'incertitude au seuil0,491

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0050,004
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0020,003
Études des sciences et des technologies0,0030,001
Communication savante0,0040,003
Science ouverte0,0020,004
Intégrité de la recherche0,0020,002
Charge utile insuffisante (le modèle a refusé de juger)0,0250,005

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.

Tête enseignante Opus0,023
Tête enseignante GPT0,219
Écart entre enseignants0,196 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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