Sustainable Diets, Population Growth & Regional Food Production: A Case Study of Waterloo Region, Ontario
Notice bibliographique
Résumé
The industrialized food system poses significant human health challenges, while simultaneously \ncompromising planetary boundaries that we depend on for human flourishing. In 2019, the Canada Food \nGuide was updated to represent a more nutritious and environmentally sustainable diet, consistent with the \n2019 EAT-Lancet Report’s Planetary Health Diet recommendations surrounding the human and planetary \nhealth nexus. Both recommendations notably put less emphasis on meats and dairy, and more emphasis on \nplant-based protein and fresh vegetables and fruits. One way to encourage the transition to more nutritious \nfood consumption is to develop and enhance the regional food environment. The food environment \ndetermines in part what the population eats, and in turn, drives demand. ‘Food environments’ are created \nby social environments and are the physical, social, economic, cultural, and political factors that impact the \naccessibility, availability, and adequacy of food within a community or region (Rideout et al., 2015). They \nare often responsible for affecting how consumers make food decisions. COVID-19 exposed vulnerabilities \nin our industrialized just-in-time system, including challenges in food security and optimal nutrition as \nimport-dependent foods faced risks in supply due to labour and supply chain disruptions. Increased political \nattention on local and regional self-sufficiency at regional and national scales may offer a solution to \nenhance resilience within socio-ecological systems. An optimum nutritional environment (ONE) \nassessment bridges nutritional needs with environmental sustainability through regional planning. For this \nthesis, a case study foodshed analysis of Waterloo Region (WR), Ontario, was conducted in order to \nunderstand the potential for regional sufficiency in nutrient-dense food (according to the 2019 Canadian \nFood Guide guidelines). The nutritional requirements were then compared to the local production capacity \nfor the population in 2020 and the projected population in 2040 and 2060. The research objectives were (1) \nto estimate the quantity of locally grown vegetables, fruits, legumes, and whole grains needed to meet the \nRegion of Waterloo population’s optimal nutritional requirements in 2020, 2040, and 2060; (2) to estimate \nhow much of these healthy food requirements for the WR population could realistically be produced \nthrough regional agriculture by the year 2040 and 2060. \nThis study used Canadian databases to quantify and predict the opportunities and potential for WR \nto meet its growing population's nutritional needs within regional boundaries. The results show that \nconsumption and production levels in fruits, vegetables, whole grains, and plant-based protein are \ninsufficient in 2020, 2040 and 2060. There were changes in comparison to the 2006 and 2019 Canada Food \nGuide’s recommendations, specifically a reduction in starchy vegetables, wheat and oats, and an increase \nof tree nuts and meat alternatives. Agricultural land requirements that align with nutritional \nrecommendations could be met with a 4% conversion of current agricultural land in use in 2040 and 6% in \n2060. One possibility to meet these recommendations is converting land that is currently dedicated to soy \nand corn production. One limitation of the study is the exclusion of livestock and dairy, which contributes \nto a large proportion of land use. This study contributes to current foodshed analysis research, providing a \nreplicable case study methodology for other regions to identify the current status of local food provisioning \nand its relationship to nutritional needs, as well as to predict and plan for future scenarios with an enhanced \nfood environment. This research suggests that collaborative and simultaneous effort from various \nstakeholders is needed to support the transition to sustainable diets in Waterloo Region.
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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,005 |
| Études des sciences et des technologies | 0,008 | 0,002 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
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 ».