Sustainable Food Cold Chains: Opportunities, Challenges and the Way Forward
Bibliographic record
Abstract
An estimated 14 percent of the total food produced for human consumption is lost, while 17 per cent is wasted. This is enough to feed around 1 billion people in a world where currently 811 million people are hungry and 3 billion cannot afford a healthy diet. The lack of effective refrigeration is a leading contributor to this challenge, resulting in the loss of 12 percent of total food production, in 2017. Moreover, the food cold chain is responsible for 4 percent of global greenhouse gas emissions, including from cold chain technologies and food loss and waste due to lack of refrigeration. This report explores how food cold chain development can become more sustainable and makes a series of important recommendations. These include governments and other cold chain stakeholders collaborating to adopt a systems approach and develop National Cooling Action Plans, backing plans with financing and targets, implementing and enforcing ambitious minimum efficiency standards. The Montreal Protocol on Substances that Deplete the Ozone Layer - a universally ratified multilateral environmental agreement - can contribute to mobilizing and scaling up solutions for delivering sustainable, efficient, and environmentally friendly cooling through its Kigali Amendment and Rome Declaration. Reducing non-CO2 emissions, including refrigerants used in cold chain technologies is key to achieve the Paris Agreement targets, as highlighted in the latest mitigation report from the Intergovernmental Panel on Climate Change (IPCC). At a time when the international community must act to meet the Sustainable Development Goals, sustainable food cold chains can make an important difference.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".