Farmers’ climate change perceptions in central Colombia: A propensity score matching approach using protection motivation theory and psychological distance

• Farmers’ perceptions of climate change vary by type of extreme weather event. • Farmers in landslide-prone villages doubt more that climate change is occurring. • Droughts increase farmers’ perceived severity, vulnerability, and proximity. • Context and resources shape how farmers view climate change risks. This study investigates how farmers’ experiences with extreme weather events, specifically landslides and droughts, shape their perceptions of climate change in central Colombia, and their implications for climate risk management. Using Protection Motivation Theory and psychological distance as frameworks, we surveyed 360 farmers in 2022–2023 to assess their perceptions of climate change severity, vulnerability, and proximity. To control for confounding factors, we employed propensity score matching, comparing farmers in villages affected by landslides and droughts with those in unaffected villages. Our findings reveal that while landslides do not significantly alter farmers’ perceptions, droughts heighten awareness of climate change, with statistically significant differences observed in 10 out of 16 perception categories. This suggests that the nature of extreme weather events plays a crucial role in shaping climate change perceptions. Notably, farmers affected by drought perceive climate change as more severe, feel more vulnerable, and report closer psychological distance to its impacts compared to those in landslide-affected areas. These results imply that climate risk management strategies should be tailored to the specific types of extreme weather events affecting a region. Furthermore, by comparing drought and landslide events, this study provides new insight into how different climatic shocks shape farmers’ perceptions, contributing to a more nuanced understanding of climate change adaptation. This research highlights how propensity score matching, by better balancing groups and reducing bias from confounding, offers a methodological improvement over conventional approaches in climate perception studies.