Developing sustainable global value chain: role of multi-stakeholder collaborations and digitalization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Global value chains (GVCs) channel roughly two-thirds of world trade, yet the efficiency they create is increasingly offset by climate risk, social inequity, and governance gaps. To clarify how digitalisation and multi-stakeholder collaboration might reverse this trajectory, we conducted a PRISMA-guided systematic review of 59 peer-reviewed articles published between 2014 and 2024, retrieved with a five-item quality checklist. Thematic coding, bibliometric mapping, and mechanism-focused process tracing reveal three persistent blind spots: scant causal evidence connecting specific digital tools—blockchain traceability, AI-driven analytics, industrial digital twins—to triple-bottom-line outcomes; under-specified governance mechanisms for scaling collaboration beyond tier-one suppliers; and weak integration of sustainability-linked finance with real-time traceability data. To bridge these gaps, we advance the conceptual model of the Enhanced Sustainable GVCs Framework in which digital infrastructures make social and environmental externalities auditable. At the same time, coalitions of buyers, suppliers, investors, regulators, and NGOs convert that visibility into collective action. The framework extends GVC governance and stakeholder theories by incorporating algorithmic coordination, radical visibility, and data-liquidity capabilities. Policy implications point to pairing mandatory due diligence laws with investments in open data standards; managerial guidance emphasises interoperable architectures and sector-wide standards alliances; and a future research agenda calls for quasi-experimental causal identification, cross-level data integration, and boundary-condition analysis. Together, these insights outline an evidence-based pathway for transforming GVCs from vectors of ecological externality into engines of inclusive, low-carbon growth.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it