Reducing waste management scenario space for developing countries: A hierarchical clustering on principal components approach.

The complexity of waste management (WM) problems resulted in the explosion of scenarios that challenge focused discussion among stakeholders and hinder the integrity of policy responses in developing countries. Hence, drawing similarities is essential to reduce the number of scenarios to simplify the WM efforts. To extract similarities, measuring WM performance is not enough, but the background factors related to this performance should be incorporated. These factors form a unique system characteristic that facilitates or hinders WM functions. Thus, this study applied multivariate statistical analysis to clarify underlying characteristics that facilitate efficient WM scenario developments for developing countries. The study first analysed drivers associated with improved WM system performance using bivariate correlation analysis. As a result, twelve significant drivers associated with controlled solid waste were identified. Then, it mapped the countries based on their WM system characteristics using the combined principal component analysis and hierarchical clustering approach. Thirteen variables were examined to extract similarities between the countries. The results identified three homogenous clusters. The clusters were found considerably parallel to the global classifications based on income and human development index. Hence, the presented approach is efficient in explaining similarities that reduce WM scenarios and favours cooperation among countries.