Once Bitten, Twice Shy: The Impact of Natural Disasters on the Adoption of Green Production Technologies by Farmers Based on the Risk Aversion Perspective
Bibliographic record
Abstract
Purpose— This study investigates the low adoption rate of green agricultural technologies among farmers, focusing on the impact of natural disasters and farmers’ risk aversion variability. This research also aims to explore potential response strategies for these challenges. Design/methodology/approach— This study employs a dynamic perspective to assess the influence of natural disasters on farmers’ adoption of green agricultural technology. It uses a series of robustness checks, including substitution of the explained variable, to ensure the reliability of the findings. The underlying mechanism is explored through the lens of risk aversion, and a heterogeneity analysis is conducted to understand the differential impacts across various farmer types. This study also evaluates the mitigating effects of agricultural subsidies and insurance on the negative impacts of natural disasters. Findings— This study revealed the following: 1. Natural disasters significantly inhibit the likelihood and degree of farmers’ adoption of green agricultural technologies, with the conclusion remaining robust after the robustness checks. 2. The frequency of natural disasters increases farmers’ risk aversion, which in turn affects their decisions to adopt green agricultural technologies, with higher risk aversion correlating with lower adoption and engagement rates. 3. The heterogeneity analysis shows that natural disasters suppress the adoption behaviors of small-scale farmers, those with lower farming income proportions, and those with fewer types of agricultural machinery. 4. Agricultural subsidies and participation in agricultural insurance can reduce the negative effects of natural disasters on farmers’ adoption of green technology. Originality/value— This study provides original insights into the overlooked consequences of natural disasters on the adoption of green agricultural technologies and the role of risk aversion in farmers’ decision-making. It offers valuable policy recommendations for enhancing natural disaster warning and response mechanisms, intensifying technical training, and improving agricultural insurance systems to foster green and sustainable development of agriculture in China. This study contributes to the literature by highlighting the need for targeted support mechanisms to address the challenges faced by different farmer demographics during natural disasters.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".