Emission Reduction and Socio-Economic Indicators as Driving Factors of West Sulawesi Economic Growth
Bibliographic record
Abstract
This study investigates the impact of implementing a green economy on the economic growth of West Sulawesi.A green economic system offers a pathway to achieving sustainable development goals by balancing economic, social, and environmental resilience in regional development strategies.By integrating sustainability principles, green economy policies aim to enhance growth while addressing environmental challenges.The research employs a mixedmethods approach, combining descriptive and inferential analysis.Data from 2012-2022 were analyzed using Structural Equation Modeling-Partial Least Squares (SEM-PLS).The findings demonstrate a significant positive relationship between green economic indicators and economic growth (p = 0.000), with an influence value of 0.88.This indicates that the adoption of green economic policies contributes substantially to economic development in West Sulawesi, reflecting the potential of such initiatives to drive sustainable progress.The study highlights the necessity of implementing well-designed green economy policies tailored to local challenges and opportunities.These policies should promote economic growth while safeguarding environmental conditions, ensuring alignment with the principles of sustainable development.Policymakers are encouraged to leverage these findings to enhance the region's economic growth through a green economy framework that fosters long-term resilience and sustainability.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".