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

Abstract

This master thesis investigates the determinants behind adoption intentions of agri cultural climate mitigation technologies with a focus on rice farmers in Kerala, India. The agricultural sector faces significant challenges in reducing GHG emissions. However, the adoption of proven and cost-effective technologies is often constrained due to information asymmetry 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 implemen tation at the individual level. We investigate which behavioural barriers, such as social norms, perceived effort, and risk aversion, affect the intention of adopting these tech nologies and whether training interventions can lower the barriers. This is measured by two experiments and survey questions. We combined a vignette experiment with a field experiment, consisting of 831 rice farmers. These experiments aim to measure the effect of training through information framing and practical training on adoption intentions of climate 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 information that a large majority use microbiomes had a significantly higher adoption intention com pared to those who received information that a low share had adopted microbiomes. Additionally, the majority-vignette significantly increased the initial intention to adopt microbiomes. Furthermore, practical training showed no significant effect on adoption intentions. Our findings imply that future policy implications should prioritise interventions on social mechanisms, such as highlighting early adopters, rather than only focusing on tech nical training and information.1 1AI (Gemini) was used during the writing process for language refinement and technical assistance with LaTex formatting. All findings, interpretations, and conclusions are the original work of the authors