From Farm to Fork: The Role of AI in Advancing Sustainable Business Practices
Bibliographic record
Abstract
This study examines how artificial intelligence (AI) supports environmental and social sustainability in the Icelandic food industry across the full value chain-from production to retail.Using a qualitative design, five semi-structured interviews were conducted with senior executives from food production, processing, and distribution firms.Thematic analysis revealed that AI is primarily applied for automation, quality control, energy efficiency, and workplace safety.Although not initially adopted for sustainability purposes, AI delivers indirect benefits such as reduced material use, lower energy and water consumption, and minimized food waste.For instance, AI-based quality control improved efficiency in meat and fish processing, while predictive tools enhanced inventory accuracy in retail.Importantly, no evidence was found of rebound effects or increased resource use.Instead, AI contributes to sustainability mainly through operational improvements.Its adoption is largely driven by external factors such as labor shortages, regulatory pressures, and consumer expectations.Despite its promise, challenges remain, particularly in data-sharing, implementation costs, and public trust.The study highlights the importance of embedding AI in broader ethical and sustainability frameworks to ensure its long-term contribution to responsible food system transformation.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".