| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 1063524 | Resources, Conservation and Recycling | 2011 | 10 Pages |
Most cities are actually very concerned about the economic viability of waste management and also about the impact they may have on the environment. Economical, social and cultural factors in the population will determine the characteristics in waste and the value of the design parameters used in the calculations of a collection system. A clear understanding of these factors is fundamental to plan and to implement efficient and sustainable collecting strategies. Our goal in this work is to model municipal waste separation rates in Spanish cities with over 50,000 inhabitants taking their different socio-economic, demographic and logistic covariates into account. Several statistical regression models to manage continuous proportion data are compared, these being: Generalized linear models (GLM) with Binomial, Poisson and Gamma errors after several transformations of the data and Beta regression on the raw data. The best fits are obtained by using GLM Gamma and beta regression. Significant covariates for the different separation rates are obtained from these models, and the strength of the influence of all these factors on the response variable is calculated. All these results could be taken into account to design and to evaluate selective collection systems, and will allow us to make predictions on cities not included in this study.
► Our goal in this work is to model municipal waste separation rates of the main materials collected separately in Spanish medium and large-sized cities. ► Waste separation rates of paper, glass and lightweight packages are analyzed. ► Those separation rates in the different cities are modeled taking their socio-economic, demographic and logistic characteristics into account. ► Several statistical regression models to manage continuous proportion data are compared. ► Significant covariates for the different separation rates are obtained from these models, and the strength of the influence of all these factors on the response variables are calculated.
