Antecedents for Circular Economy in Sugar Industrial Ecology in Emerging Economy
Bibliographic record
Abstract
Sugar industrial ecology involves the efficient reuse, recycling, and repair of byproducts, employing advanced technologies to foster the development of sustainable urban environments.Once known for its detrimental impact, the sugar factory needs to look into the circular economy and the procurement of byproducts.Therefore, the study attempts to answer the research question -What are the antecedents for a circular economy in the sugar industry in the emerging economy?This research delves into the circular economy practices within the sugar industrial ecosystem, utilizing a comprehensive analysis of input-output relationships and structural aspects.Data collection involved a combination of secondary data and insights from focus group discussions with eight key officials representing the top eight sugar factories.The analytical approach encompasses multiple regression and descriptive statistics to extract meaningful insights from the secondary data.A key revelation is the significant role byproducts play in creating construction materials such as cement, bricks, paver blocks, and activated binders.Utilizing secondary by-products from the sugar industry in manufacturing these materials mitigates the construction sector's carbon footprint and enhances the final products' quality.The study also signifies procuring sugarcane and other byproducts for sugar, ethanol, and electricity generation.It highlights the transformative potential of embracing sustainable practices within the sugar industry, demonstrating how such initiatives can positively impact the environment and the quality of goods produced in related sectors.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".