Climate change mitigation policies in agriculture: An overview of sociopolitical barriers
Bibliographic record
Abstract
Abstract The realization of the economic and technical potential of climate mitigation policies in agriculture is influenced by how sociopolitical issues are considered in policy development and implementation. Based on a narrative review of the literature, this article provides an overview of common sociopolitical barriers facing supply‐side and demand‐side mitigation measures in agriculture. Understanding these sociopolitical issues can provide opportunities for the full realization of mitigation policy potentials. They are presented under four themes: local context, adoption capacity, and distributional impacts; food security, costs, and choices in food consumption; political considerations related to electoral weight and lobbying; and international aspects regarding emissions metrics, trade, and big agriculture. Designing complementary policies and second‐best options, incorporating local knowledge in policy design, recognizing women's voice and role in sustainable agriculture, planning for job transitions, engaging stakeholders through multiscalar platforms, and appropriately framing and communicating policies in a digestible manner are some considerations to address these sociopolitical barriers. This article is categorized under: Climate Economics > Economics and Climate Change Climate and Development > Social Justice and the Politics of Development Climate Economics > Economics of Mitigation
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".