1 Okayama University, Graduate school of Environmental and Life Science, Department of Environmental Science 3-1-1 Tsushima, Kita, Japan

2 Waste Management Research Center Okayama University, 3-1-1 Tsushima, Kita, Okayama 700-8530, Japan

3 Faculty of Environmental Engineering, National University of Civil Engineering, 55 Giai Phong Road, Hai Ba Trung, Ha Noi, Viet Nam


A prognosis model has been developed for solid waste generation from households in Hoi An City, a famous tourist city in Viet Nam. Waste sampling, followed by a questionnaire survey, was carried out to gather data. The Bayesian model average method was used to identify factors significantly associated with waste generation. Multivariate linear regression analysis was then applied to evaluate the impacts of significant factors on household waste production. The model obtained from this study indicated that household location, household size, house area per person, and family economic activity are important determinants of the waste generation rate. The models could explain about 34% of the variation of the per capita daily waste generation rate. Diagnostic tests and model validation results showed that the regression model could provide reliable results of estimated household waste. The study revealed that per capita urban household waste generation is 70–80% higher compared to a rural household. The models also showed that if a family ran a business from home, the household waste generation rate would increase by about 35%. This result provides reliable information for better waste collection and management planning. Two other significant variables (family size and house area per capita) do not contribute much (less than 20%) to waste generation. Variables accounting for household income, presence of a garden, number of rooms in a house, and percentage of members of different ages were proven to be not significant. The study provides a reliable method for estimating household waste generation, providing decision makers useful information for waste management policy development.

Graphical Abstract

Predicting waste generation using Bayesian model averaging


  • The waste generation predictive model is proposed as multivariable linear regression  
  • Urban-rural topology is the most important explanatory variables of waste generation
  • Home business has a significant effect on waste management in the future in Hoi An


Main Subjects

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