Enhancing urban ecosystem services: A stakeholder-centric analysis of green supply chain management and urban forest management quality in Palangka Raya
Bibliographic record
Abstract
This quantitative study employs Structural Equation Modeling (SEM) to investigate the intricate relationships between stakeholder participation, green supply chain management, urban forest management quality, and ecosystem service quality in Palangkaraya, Indonesia. Survey data collected from stakeholders engaged in urban forest management and environmental conservation efforts were subjected to Confirmatory Factor Analysis (CFA) to validate the measurement model. Path Analysis was then conducted to explore direct and mediated effects, with a focus on the mediating role of urban forest management quality as assessed through SEM. The findings reveal significant positive relationships between stakeholder participation, green supply chain management, urban forest management quality, and ecosystem service quality. Notably, urban forest management quality emerges as a mediator between stakeholder participation and ecosystem service quality, as well as between green supply chain management and ecosystem service quality. This study contributes to the empirical understanding of urban environmental management dynamics, offering insights that can inform policy and practice for promoting environmental sustainability and enhancing ecosystem service provision in Palangkaraya.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".