Determinants of Households’ Willingness to Pay for Improved Solid Waste Management in Asmara, Eritrea: A Binary Logistic Regression Analysis

Rapid urbanization, population growth, and shifting consumption habits have overwhelmed solid waste management in many Sub-Saharan African cities, including Asmara, Eritrea’s capital. Despite government investments in equipment and personnel, inadequate collection, poor disposal practices, and minimal recycling continue to strain the limited infrastructure. Based on data collected from 373 households which are selected using multi-stage sampling techniques, this study examines determinants of households’ willingness to pay (WTP) for an improved solid waste management plan (ISWMP) using a binary logistic regression model. Results show a high proportion (89.3%) of respondents are willing to pay an additional amount, with a mean of Nakfa 26.65 (~USD 1.77) per month. The binary logistic regression result revealed that higher income, greater weekly waste generation, longer distance to disposal sites, marital status (married heads), awareness about health and environmental risks, trust in neighbors’ cooperation, and satisfaction with the existing services positively and significantly influenced households’ WTP. Conversely, larger household size and the presence of any current collection service negatively impacted households’ WTP, possibly reflecting budget constraints or perceived sufficiency of the status quo. The findings highlight substantial latent demand for better waste services and underscore the importance of community involvement beyond government action alone. Policy recommendations include strengthening public awareness of waste-related risks, improving baseline service reliability to build trust, encouraging collective responsibility, and designing affordable fee structures that reflect households’ stated preferences and local socioeconomic conditions. Engaging residents financially and behaviorally could pave the way for more sustainable urban waste management in Asmara and similar settings.