Quantifying the greenhouse gas emissions of New Zealand households’ food purchases: An analysis by demographic variables
Notice bibliographique
Résumé
New Zealand has committed to a 50% reduction in greenhouse gas emissions (GHGEs) from 2005 levels by 2030. Dietary changes within New Zealand could simultaneously improve population health and contribute towards the nation's emissions reduction target, as globally, food production is estimated to account for between one quarter and one third of GHGEs. This research aimed to quantify the GHGEs associated with household purchases of major food groups in New Zealand and identify the demographic characteristics that are associated with per capita household dietary emissions. Household dietary emissions were estimated using the Nielsen Homescan(R) consumer panel — a large sample of households within New Zealand (N = 1775) who report purchasing data of take-home food and beverages. The sample is nationally representative in terms of broad geographical regions and selected key demographic characteristics. Carbon emission estimates were assigned to 1,908,485 total purchases in 2019 using a process-based life cycle assessment (LCA) dataset initially constructed in the United Kingdom (UK) and adapted for New Zealand. The emissions from purchases of major food groups were then estimated. Multiple linear regression was used to examine the relationships between household variables and per capita dietary emissions. Purchases of red and processed meat (35%) and dairy products (19%) were responsible for the greatest proportion of emissions using a 100-year time horizon. The age group of the primary household shopper as well as household size were predictors of per capita dietary emissions — households with primary shoppers >65 years had, on average, 33% (95% CI: 19%–49%) higher per capita dietary emissions, compared to households with primary shoppers ≤ 34 years; and every additional household member was associated with, on average, 11% (95% CI: 9%–13%) lower per capita dietary emissions. We have shown in this large representative sample of New Zealand households that purchases of just two food groups — red and processed meat, and dairy — were responsible for 54% of dietary greenhouse gas emissions in 2019. Larger households had lower per capita dietary greenhouse gas emissions, and older shoppers had relatively higher greenhouse gas emissions. Whilst similar associations have been reported elsewhere, more research is needed to confirm these latter findings. With enhanced understanding of the observed association between age of a household's primary shopper and per capita dietary emissions, interventions may be devised that encourage shoppers to purchase lower-emitting foods, particularly less meat and dairy.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».