Cutting carbon emissions from China’s food system by supply-demand coordination and optimizing spatial allocation of production
Bibliographic record
Abstract
Abstract Food systems, including supply chains, account for approximately one-third of global anthropogenic GHGs emissions. We construct a bottom-up GHG inventory of China’s food system from farm to fork for the period 1990–2018. The decomposition method is used to assess regional differentiated drivers. GHG emissions reduced by 6.8% between 1990 and 2000 due to energy structure changes in East, Central and Southwest China. They then increased by 2.3% (2001–2010) because of rapid economic growth. Further large increases of 13.1% (2011–2018) were driven by growing consumption expenditure and GHG-intensive food consumption. In 2018 total emissions from China’s food system, including supply chains, was 1.55 Gt CO2e yr-1 (95%CI 1.07-2.03 Gt CO2e yr−1). Scenario simulation shows that demand- and supply-side synergies can offset emissions increases from high-quality protein food demands. Results demonstrate the importance of supply-side production spatial optimization and green source food importation for mitigating GHGs emissions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".