Uppsats

Determinants of Adoption Intentions of Climate Mitigation Technologies: Experimental Evidence from India.

Master-uppsats

Göteborgs universitet/Graduate School

Publicerad: 2026-06-29

Språk: Engelska

Sammanfattning

This master thesis investigates the determinants behind adoption intentions of agricultural climate mitigation technologies with a focus on rice farmers in Kerala, India. Theagricultural sector faces significant challenges in reducing GHG emissions. However, theadoption of proven and cost-effective technologies is often constrained due to informationasymmetry and behavioural barriers.The study analyses two distinct innovations: Alternate Wetting and Drying (AWD),which requires coordination at the local level, and microbiomes, which allows implementation at the individual level. We investigate which behavioural barriers, such as socialnorms, perceived effort, and risk aversion, affect the intention of adopting these technologies and whether training interventions can lower the barriers. This is measured bytwo experiments and survey questions. We combined a vignette experiment with a fieldexperiment, consisting of 831 rice farmers. These experiments aim to measure the effectof training through information framing and practical training on adoption intentions ofclimate mitigation technologies.The results indicate that behavioural mechanisms, with social drivers in particular,have a significant effect on the adoption intentions of both AWD and microbiomes.Through the vignette experiment, we identified that farmers who received informationthat a large majority use microbiomes had a significantly higher adoption intention compared to those who received information that a low share had adopted microbiomes.Additionally, the majority-vignette significantly increased the initial intention to adoptmicrobiomes. Furthermore, practical training showed no significant effect on adoptionintentions.Our findings imply that future policy implications should prioritise interventions onsocial mechanisms, such as highlighting early adopters, rather than only focusing on technical training and information.11AI (Gemini) was used during the writing process for language refinement and technical assistance with LaTexformatting. All findings, interpretations, and conclusions are the original work of the authors

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