Examining Domestic Waste Separation Behaviors in Istanbul by Using Correspondence Analysis

Examining and comprehending the elements that impact waste recycling behaviour is crucial for enhancing domestic waste management separation practices. The recycling of used products represents a vital improvement in sustainability for households and related locations. Domestic waste separation relies on residents, whose behaviour in waste separation significantly influences the outcome of waste management efforts, whether successful or unsuccessful for the related country. While domestic waste separation is common in numerous countries, its adoption remains uncommon in Turkey. Even though there is limited research on household recycling behaviour in metropolitan cities such as Istanbul, gaining insight into these factors can pave the way for more efficient recycling programs. To this end, this paper investigates the factors altering the behaviour of individuals in the separation of domestic waste in a system for the collection of recyclable waste in Istanbul, Türkiye. The waste recycling behaviour study data was collected by Istanbul Municipality with a detailed questionnaire (face to face) to 50000 people (2023). Correspondence analysis, also known as reciprocal averaging, is a valuable multivariate statistical technique in data science used to visualize and understand relationships between categories. It involves creating a graph to display the relationships between two or more data points, providing a clear visual representation of the data. With this aim, Correspondence analysis with Euclid Distance was used to examine household domestic recycling behaviours, visualize the current behavioural direction with the help of two-dimensional maps, and display the cluster structures constructed. As a result, district-based waste separation behaviours have been determined, and it is aimed to reveal the structures of these reasonings with district characteristics.

Keywords:  Waste management; Recycling; Recycling behaviour; Correspondence analysis