Decision: Decarbonising global supply chains: building a sustainable future — R1/PR6
Bibliographic record
Abstract
Modern supply chains are vital to global commerce, but they are also major contributors to greenhouse gas (GHG) emissions. As climate change intensifies, achieving carbon neutrality – particularly through supply chain decarbonisation – has become a global imperative. While organisations have made strides in reducing direct emissions, addressing indirect supply chain emissions presents greater complexity and urgency. We invite academic contributions that examine the challenges, enablers, potential risks, strategic approaches and innovative practices related to decarbonisation across a wide range of sectors, including manufacturing, service industries and humanitarian logistics. Emphasis is placed on holistic, multi-stakeholder approaches aligned with the GHG Protocol. The issue welcomes interdisciplinary research employing varied methodologies – ranging from empirical studies to conceptual frameworks – to inform practice, policy and sustainability transitions. By showcasing sector-specific insights and cross-cutting solutions, this issue aims to advance knowledge and action in building low-carbon, resilient supply chains.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.018 | 0.009 |
| Insufficient payload (model declined to judge) | 0.079 | 0.041 |
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".