Invited paper Evolving consumer trends: positioning animal proteins for a sustainable future.
Bibliographic record
Abstract
Meat has held a central role in human diets and cultural practices, yet growing societal concerns regarding health, animal welfare, and environmental sustainability are shifting consumer perceptions and behaviours. This short communication synthesizes recent literature (2022–2024) on meat consumption across global regions to identify trends, key drivers, and implications for positioning animal proteins in a sustainable future. Three consistent consumption drivers appear worldwide: sensory quality, affordability, and cultural significance. Taste, tenderness, and juiciness remain critical for sustaining demand, while price and purchasing power influence both type and quantity of meat consumed. Cultural and symbolic roles of meat reinforce its dietary centrality, with variation across regions; for example, meat represents national identity and social cohesion in Latin America, socialization and body image in Ghana, and visual quality cues in Japan. Regional trends highlight a heterogeneous consumer landscape: Western Europe exhibits gradual reduction patterns driven by health, welfare, and sustainability concerns; Latin America, the United States, and New Zealand display strong attachment to meat rooted in tradition, taste, and social identity; emerging Asia and Africa show rising demand driven by population and income growth, urbanization, aspiration, status, and identity signalling. Demographic variables, including age, gender, and education, further modulate attitudes and behaviours, underscoring a gap between intentions and actual consumption. Positioning meat for a sustainable future entails maintaining and enhancing meat’s sensory and nutritional qualities, addressing societal concerns around human health, animal welfare, and environmental sustainability, and simultaneously adapting communication and product differentiation to diverse cultural contexts and consumer segments. Understanding the interplay of sensory, economic, cultural, and ethical drivers of meat consumption provides critical insights for industry and policy stakeholders seeking to align meat production and marketing with evolving societal expectations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.039 | 0.011 |
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".