Landslides present remarkable hazards in the Himalayan region, particularly in areas with young and fragile topography. Mitigating vulnerability requires assessing susceptibility, which relies heavily on the accuracy of susceptibility maps generated through various approaches that consider different conditioning factors at various resolutions. This study, conducted in Jajarkot District within the Karnali Province of Nepal and covering 2230 km2, aims to identify suitable conditioning factors at appropriate resolutions. Sixteen factors, encompassing topography, hydrology, geology, and anthropogenic activities, were analyzed alongside a landslide inventory of 159 occurrences compiled from satellite imagery, the literature, and field surveys. A genetic algorithm (GA) was employed to determine the optimal set of conditioning factors, while Maximum Entropy (Maxent) modeling produced landslide susceptibility maps (LSM) at spatial resolutions ranging between 12.5 and 200 m. Resolution selection was guided by Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUC) analyses. Multicollinearity testing identified 15 influential factors, with land use ranking highest at 22.7%, followed by stream power index (SPI), drainage density, and aspect. The GA consistently highlighted land use and slope as effective factors across subset sizes. The results indicated resolutions finer than one hundred meters enhanced discrimination between landslide and non-landslide areas, emphasizing the need to balance resolution with computational resources and data availability. This study emphasizes the intricate interplay of conditioning factors, the GA’s efficacy in subset selection, and the crucial role of resolution in the improvement of susceptibility models. The findings provide practical insights for policymakers and disaster management authorities, aiding evidence-based decision making in the mitigation of landslide risk in Jajarkot and similar regions.

Climate change perceptions are shaped by the interplay between formal knowledge—acquired through formal education and mass media—and lived experiences of direct environmental impacts. This study investigates how these complementary knowledge sources influence climate awareness, risk perception, and pro-environmental behavior among diverse socio-professional groups in Central Nepal. A mixed-methods survey of 300 respondents across three districts (Kathmandu, Sindhupalchowk, and Chitwan) reveals an epistemological divide: professionals emphasize global-scale risks (e.g., glacier melt) while farmers focus on immediate local impacts (e.g., crop failure, erratic rainfall). Regression analysis shows that internalized personal norms (β = 1.01, p < 0.001) are the strongest predictors of pro-environmental behavior, with formal education playing a statistically non-significant role (β = −0.007, p = 0.746). The findings highlight the need for integrated adaptation strategies that integrate global scientific insights with local experiential knowledge, recommending community-led action and the recognition of indigenous knowledge systems. This research informs climate communication and policy design in Global South contexts by advocating for a multi-knowledge framework to climate adaptation.