A Policy Framework and Prediction on Low Carbon Development in the Agricultural Sector in Indonesia
Bibliographic record
Abstract
Currently, Indonesia has adopted Low Carbon Development (LCD) in its Medium-Term Development Plan (RPJMN) 2020-2024. One of the priority activities is agriculture, which accounts for 12.21% of total greenhouse gas emissions. The agricultural sector is the victim affected by CO2 emissions, such as degradation, shrinkage of agricultural resources, land and water, shifting planting seasons, crop failures, decreased food production due to rising air temperatures, floods, and droughts. Greenhouse gas emissions are predicted to continue to increase along with the increasing demand for food. The purpose of this study is to predict and find an alternative policy framework for low-carbon development in the agricultural sector in Indonesia. This study uses a quantitative and qualitative approach by Artificial Neural Network (ANN), and multicriteria policy (MULTIPOL) analysis. The data were obtained through secondary data in 2014-2018, and the primary data are in-depth interviews, Focus Group Discussions (FGD), and field observations. The results of ANN show that the predictions of provinces that need to adopt low-carbon development in Indonesia are dominated in production centers such as Java Island, so an alternative policy framework using MULTIPOL is needed. Furthermore, this research establishes three scenarios, eight policies, twenty-six actions, and nine evaluative criteria in analyzing the LCD of the agricultural sector. The results indicate that LCD can be conducted by integrating the speed scenario (S2) with a value ranging from 6.3 (policy to increase capacity and quality of human resources) to 18.7 (circular economy). This scenario accommodates policies related to low carbon reduction and agricultural production increase, such as a circular economy, co-benefit adaptation strategies, low carbon technology innovation, and strengthening low carbon networks.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".