Addressing climate change impacts on food supply chain operations: An integrated framework for sustainable optimization
Bibliographic record
Abstract
Climate change exerts significant and multifaceted impacts on food supply chains, disrupting operations from production to consumption. This study investigates how climate-related vulnerabilities such as extreme weather events and climatic variability affect the efficiency, cost structure, and overall resilience of food supply chains, with a particular emphasis on disruptions that pose risks to the stability of food supply under uncertain climate conditions. These dimensions remain insufficiently explored in the current literature. To address this gap, a novel multi-objective optimization model is developed, incorporating Climate Vulnerability Indices (CVI) into the strategic planning of food supply chain networks. The model is formulated and solved using GAMS with the CPLEX solver, drawing on parameters derived from prior research in sustainable supply chain management. Results illustrate that integrating the CVI into supply chain decision-making enhances the model's ability to account for climate-related risks, enabling more informed trade-offs among economic, environmental, and social objectives. Moreover, through its adaptive structure, the model promotes the long-term sustainability of food supply chains and supports continuity under climate-induced operational challenges. This study offers an innovative, resilience-focused modeling framework that supports sustainable and adaptive supply chain configurations. The findings underscore the critical need for climate-aware optimization approaches to enhance the resilience and sustainability of food systems amid escalating climate risks.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".