Carbon Footprint Based on Household Consumption: Case Study on Cocoa Farmer’s Household in Polewali Mandar
Bibliographic record
Abstract
Sustainable development has become an interesting issue in the 21st century. The main pillar of sustainable development is the economic sustainability, social, and environmental. Since the industrial revolution, there is a trade-off between economic growth and environment. The main environmental problem nowadays is a huge amount of carbon dioxide in the atmosphere. This study aims to analyze the determinant of carbon footprint formation through household consumption approach, with the case of cacao farmers in Polewali Mandar. This study employed OLS and quantile regression as the method. A combined GTAP-E data, I-O, and the calculation of carbon footprint survey used in this study. The result shows that fuel light consumption and transportation are the most carbon footprint formers. Furthermore, household income determines the most carbon footprint. The higher household income, the higher carbon footprint produced. The control variables that influence the carbon footprint are marriage status, poverty level and household size.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".