Socioeconomic and Environmental Prospects of the Food Industry
Bibliographic record
Abstract
Food production systems and consumption patterns are significant contributors to the social, economic, and environmental impacts of the industry, which swap with changing population demographics. The life cycle assessment approach has been increasingly utilized to evaluate the agricultural and food processing systems to ensure reliable and evidence-based support for decision-making for both industry stakeholders and policymakers. This study discusses the key social, economic, and environmental impacts of various food processing sectors, especially greenhouse gas (GHG) emissions, land, water, and energy use. Impacts vary widely depending on the types of foods, their sources, and supply chains. The animal (excluding poultry) slaughtering, rendering, and processing category has the highest contributions in both socioeconomic and environmental impacts out of all food and beverage processing industries. The food industry touches transdisciplinary policy domains and is recognized as dynamic and complex. It is thus important to adopt an integrated approach involving stakeholders from all policy domains associated with food supply chains to ensure the sustainability of the food industry. A broader sustainability check must be adopted for any strategic change in the food industry to reduce the risks to its sustainability and avoid rebound effects on society.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".