A Realist Review of the Effectiveness of Sustainable Food Packaging Interventions in Food Waste and Healthy Eating
Bibliographic record
Abstract
The significant environmental and health impacts of food packaging underscore the need for more effective interventions to promote sustainable lifestyles and consumer well‐being. This study conducted a realist review of 96 articles published in top‐tier journals between 2019 and 2024, focusing on packaging interventions aimed at promoting healthy food consumption and food waste reduction. It explored the mechanisms and contextual factors that influence the effectiveness of such interventions in real‐world settings. Using directed content analysis guided by a context–mechanism–outcome (CMO) framework, the study identified how, for whom, under what circumstances, and why sustainable food packaging interventions succeed. The findings yielded several guiding principles to enhance intervention effectiveness, including: (1) improving clarity of packaging communication to help consumers assess food healthiness; (2) involving consumers in packaging innovation to increase acceptance of novel technologies; and (3) developing business models that share responsibility, benefits, and risks across the food supply chain. This research contributes to the literature by (1) offering deeper insights into the contextual and mechanistic pathways that shape outcomes of sustainable food packaging interventions and (2) proposing 12 actionable principles to support healthier eating and food waste reduction for key stakeholders.
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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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".