Incentivizing Alternatives to Agricultural Waste burning in Northern India: Trust, Awareness, and Access as Barriers to Adoption
Bibliographic record
Abstract
Abstract The burning of agricultural residue from previous season’s rice crop is a key contributor to poor air quality during the winter across North India, primarily in the states of Punjab and Haryana. Air quality can deteriorate to catastrophic levels during the Agricultural Waste Burning (AWB) season in October-November, when fine particulate matter (PM2.5) concentrations can exceed WHO daily maxima over a sustained period by an order of magnitude or more, over a large swathe of the Indo-Gangetic plain. Over the past decade, attempts by Indian governments to change farmer behavior by incentivizing the use of novel technologies for managing rice residue without burning it have been met with little success. This paper uses farmer and expert interviews, as well as secondary data, to examine the barriers to adoption of these technologies in the state of Punjab. We analyze how operational factors (such as farm size, timing, technology availability, and choice) affect a farmers’ decision to choose (or not) a rice residue management practice. We develop a financial model for analyzing the costs of residue management technologies that are consistent with the decision-making process of both small and large farmers. We find that more sustainable residue management practices can be cost effective relative to residue burn, especially when existing subsidies are applied. However, difficulties in accessing technological alternatives to AWB and subsidies for their use, and a lack of trust in the government’s ability to deliver the full benefits of subsidies, all contribute the low adoption of technological alternatives to AWB.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".