Dietary GHG emissions from 2.7 billion people already exceed the personal carbon footprint needed to achieve the 2 °C climate goal
Bibliographic record
Abstract
Abstract Our current global food system is failing to feed the world while simultaneously emitting between 26%–34% of greenhouse gases (GHGs) that alone could preclude us from meeting the Paris climate agreement goal of limiting warming to 1.5 °C or 2 °C above pre-industrial levels. But emissions from food consumption are not uniform amongst the world’s inhabitants and thus, we estimate those differentiated responsibilities. As expected, the emissions from those barely eating enough to survive are among the lowest. But the interplay of production practices, trade, dietary preferences, the nutrition transition, and within-country inequality in access to food shape the variations in global food system emissions. By combining the most recent estimates of access to food by income decile with trade-adjusted GHG emissions data for food, we present estimates of the inequality in emissions from food consumption on a global scale. We find that the top 15% of emitters account for 30% of the total emissions, equalling the contribution of the bottom 50%. Furthermore, we assess the reductions required from the top emitters to achieve two goals. First, to yield space for increased emissions to those not meeting basic dietary requirements to thrive; we find that only an additional 0.4% of the population in 2012 would need to cap their emissions so that 8.8% of the population can increase their emissions and be able to thrive. Second, to reduce agricultural GHG emissions to meet the 2 °C goal, we find that, between 40%–45% of the world’s population in 2012 consumed diets above a target per capita cap, while 89%–91% consumed diets above a target per capita cap calculated using a future 2050 population. This means that efforts to reduce emissions from the food system will be part of almost everyone’s life up to 2050 but for at least 40% that responsibility starts now.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".