Decarbonization through supply chain innovation: Role of supply chain collaboration and mapping
Bibliographic record
Abstract
The urgency of reducing carbon emissions has intensified amid escalating climate change concerns. Supply chain innovation practices are increasingly recognized as critical enablers of decarbonization by fostering efficiency, sustainability , and carbon reduction strategies. Against this backdrop, this study examines the role of SCIP in supply chain decarbonization. We also explore how supply chain collaboration and supply chain mapping can play a role in mediating the impact of SCIP, if any, on decarbonization. The study is contextualized in Electrical and Electronics sector of Malaysia. Data were collected through close-ended questionnaire from 156 firms. We employed Partial Least Squares Structural Equation Modeling to analyze these relationships. The results confirm a significant direct impact of SCIP on SCD, underscoring the pivotal role of innovation in sustainability efforts. However, contrary to conventional wisdom, SCC does not significantly mediate this relationship, suggesting that collaboration alone may not directly enhance decarbonization outcomes. In contrast, SC mapping plays a crucial mediating role, highlighting its importance in translating SCIP into effective carbon reduction strategies. These findings provide theoretical contributions to supply chain sustainability literature by distinguishing between collaboration and mapping as enablers of decarbonization. Practically, the study underscores the need for firms to invest in digital supply chain mapping tools to enhance visibility and strategic decision-making for decarbonization. Future research should explore industry-specific variations and the role of emerging digital technologies in strengthening supply chain sustainability.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".