Sustainable Supply Chain Risk Management in a Climate-Changed World: Review of Extant Literature, Trend Analysis, and Guiding Framework for Future Research
Bibliographic record
Abstract
In the face of climate change (CC), “business as usual” is futile. The increased frequency and intensity of extreme weather events (e.g., hurricanes, floods, droughts, and heatwaves) have hurt lives, displaced communities, destroyed logistics networks, disrupted the flow of goods and services, and caused delays, capacity failures, and immense costs. This study presents a strategic approach we term “Climate-Change Resilient, Sustainable Supply Chain Risk Management” (CCR-SSCRM) to address CC risks in supply chain management (SCM) pervading today’s business world. This approach ensures supply chain sustainability by balancing the quadruple bottom line pillars of economy, environment, society, and culture. A sustainable supply chain analytics perspective was employed to support these goals, along with a systematic literature network analysis of 699 publications (2003–2022) from the SCOPUS database. The analysis revealed a growing interest in CC and supply chain risk management, emphasizing the need for CCR-SSCRM as a theoretical guiding framework. The findings and recommendations may help to guide researchers, policymakers, and businesses. We provide insights on constructing and managing sustainable SCs that account for the accelerating impacts of CC, emphasizing the importance of a proactive and comprehensive approach to supply chain risk management in the face of CC. We then offer directions for future research on CCR-SSCRM and conclude by underlining the urgency of interdisciplinary collaboration and integration of climate considerations into SCM for enhanced resilience and sustainability.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.018 | 0.032 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".