The Influence Path of Industry Collaboration Network and Policy Support on the Optimization of Sugarcane Bagasse Packaging Value Chain: An Empirical Study Based on Structural Equation Modeling
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Bibliographic record
Abstract
The rapid transition toward sustainable packaging highlights the need to understand how external enablers drive value chain optimization (VCO) in emerging green industries.This study systematically examined the influence paths of industry collaboration networks (ICN) and policy support (PS) on the optimization of the sugarcane bagasse packaging value chain, focusing on the mediating roles of technology integration capability (TIC) and green innovation (GI), and the moderating effect of environmental responsiveness.Drawing on Resource-Based View (RBV), Collaborative Network Theory, and Institutional Theory, primary data were collected from 463 industry participants and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM).Results indicated that ICN significantly enhance TIC ( = 0.593, p < 0.001) and GI ( = 0.256, p < 0.001), while PS more strongly promotes GI ( = 0.464, p < 0.001).Both technology integration ( = 0.338, p < 0.001) and GI ( = 0.416, p < 0.001) positively affect value chain optimization.Environmental responsiveness (ER) significantly moderated these relationships ( = 0.124 and 0.104, both p < 0.05), and mediation analyses confirmed both internal capabilities as key pathways.These findings clarified the mechanisms by which external collaboration and policy support optimize value chains through strengthening internal capabilities, with ER amplifying these effects.This research provided robust empirical evidence and actionable insights for advancing sustainable transformation in the agricultural by-product packaging sector.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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