title: Development and application of a multiple linear regression model to consider the impact of weekly waste container capacity on the yield from kerbside recycling programmes in Scotland.
abstract: This article describes the development and application of a multiple linear regression model to identify how the key elements of waste and recycling infrastructure, namely container capacity and frequency of collection, affect the yield from municipal kerbside recycling programmes. The overall aim of the research was to gain an understanding of the factors affecting the yield from municipal kerbside recycling programmes in Scotland with an underlying objective to evaluate the efficacy of the model as a decision-support tool for informing the design of kerbside recycling programmes. The study isolates the principal kerbside collection service offered by all 32 councils across Scotland, eliminating those recycling programmes associated with flatted properties or multi-occupancies. The results of the regression analysis model have identified three principal factors which explain 80% of the variability in the average yield of the principal dry recyclate services: weekly residual waste capacity, number of materials collected and the weekly recycling capacity. The use of the model has been evaluated and recommendations made on ongoing methodological development and the use of the results in informing the design of kerbside recycling programmes. We hope that the research can provide insights for the further development of methods to optimise the design and operation of kerbside recycling programmes.
authors: Glasgow Caledonian University, Glasgow, UK. j.baird@gcu.ac.uk
authors: Jim
authors: J
authors: Baird
authors: Robin
authors: R
authors: Curry
authors: Tim
authors: T
authors: Reid
mesh_terms: D003625:Data Collection; D006794:Household Articles; D006795:Household Products; D016014:Linear Models; D009111:Local Government; D059027:Recycling / Q000379:methods*; D012037:Refuse Disposal / Q000379:methods; D012044:Regression Analysis; D012606:Scotland