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

Area of Science:

• Environmental Science
• Public Policy
• Data Science

Background:

• Waste management (WM) complexity in developing countries leads to numerous policy challenges.
• Identifying similarities in WM systems is crucial for simplifying policy responses and fostering stakeholder discussion.
• WM performance measurement alone is insufficient; background factors influencing system characteristics must be analyzed.

Purpose of the Study:

• To apply multivariate statistical analysis to identify underlying characteristics of efficient WM systems in developing countries.
• To reduce the complexity of WM scenarios by extracting similarities between countries.
• To facilitate policy development and cooperation among developing nations through a clearer understanding of WM systems.

Main Methods:

• Bivariate correlation analysis to identify drivers of improved WM system performance.
• Combined Principal Component Analysis (PCA) and Hierarchical Clustering to map countries based on WM system characteristics.
• Examination of thirteen variables to extract similarities and group countries.

Main Results:

• Twelve significant drivers associated with controlled solid waste management were identified.
• Three homogenous clusters of countries were identified based on their WM system characteristics.
• The identified clusters showed a strong correlation with global classifications based on income and Human Development Index (HDI).

Conclusions:

• The multivariate statistical approach effectively clarifies underlying characteristics that facilitate efficient WM scenario development.
• The clustering of countries based on WM system characteristics simplifies complex scenarios and promotes inter-country cooperation.
• This method provides a valuable framework for developing targeted and effective waste management policies in developing countries.