SynthesisScientific reports2025
Carbon footprint of food production: a systematic review and meta-analysis.
Synthesis in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Identifying Mitigation Pathways for Low-Carbon Strawberry Production under Mediterranean Conditions: A Life Cycle Assessment.Global challenges (Hoboken, NJ) · 2026Article
- Changes in Dietary Patterns and Environmental Footprints Among University Students: A Retrospective Study.International journal of environmental research and public health · 2026Article
- AnFrontiers in toxicology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
In the face of the urgent climate crisis, food production is a significant contributor to greenhouse gas emissions (GHG). We analyzed 118 life-cycle assessment (LCA) studies on GHG emissions of food production, considering LCA methods, life cycle phase, waste inclusion, and regional factors, including country, continent, and development status. Additionally, machine learning analysis identifies influential factors of GHG emissions of food production across seven categories: red meats, seafood, white meat, fruits & vegetables, animal products, other plant-based, and others (oils). Based on the gradient boosting algorithm, the LCA method choice ranks among the top determinants for GHG emissions in animal products, red meat, seafood, other plant-based products, and others food categories. Only 22% of studies include waste, revealing up to 39% higher emissions in some categories compared to those excluding waste. Our meta-analysis presents min-max-average GHG emission results for each food category, within countries, different scope settings, waste considerations, and LCA methods.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.