Agricultural energy transition pathways: Differential impacts of fine and coarse cereals on GHG emissions in India
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Bibliographic record
Abstract
Understanding how agricultural energy use and cereal production choices—particularly between fine and coarse cereals—shape greenhouse gas (GHG) emissions is crucial for designing effective mitigation strategies in light of agriculture’s major contribution to national emissions and growing climate-induced productivity concerns. This study investigates the dynamic relationships between these factors in India using an Autoregressive Distributed Lag (ARDL) model on data spanning 1975-2019. Pre-analysis (Unit root, an ideal lag length, and co-integration testing) and post-analysis (serial correlation, heteroscedasticity, and recursive residuals) assumptions for ARDL model estimation were tested which came aligned with the research questions. The model robustness statistical diagnostic tests CUSUM (cumulative sum), CUSUMSQ (cumulative sum of squares), and variance decomposition testing were carried out and found to be satisfactory. The study aimed to provide comprehensive analysis of how different cereal types i.e. fine versus coarse cereals influence agricultural energy-emissions relationship and their long run effects on agricultural production-emission scenario of India. Our analysis reveals significant differences in the emissions impacts of different cereal types: while rice and wheat production contribute positively to emissions in the short run (0.06% and 0.01% respectively), coarse cereals demonstrate a substantial negative impact (-2.08%) in the long run. The energy-emissions relationship shows increasing coupling over time, with elasticity rising from 0.02% in the short run to 1.06% in the long run. Variance decomposition analysis identifies rice production as the dominant contributor to emissions variability, accounting for 34.43% of future fluctuations. These findings suggest that strategic crop diversification, particularly increased cultivation of coarse cereals, could significantly reduce agricultural emissions while maintaining food security. The study recommends a three-pronged approach i.e., investing in energy-efficient agricultural technologies, developing policy frameworks to incentivize coarse cereal adoption, and strengthening institutional mechanisms for technology transfer. These insights contribute to the development of targeted policies for sustainable agricultural energy transition in India.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 it