A multilevel Bayesian framework for predicting municipal waste generation rates

Area of Science:

• Environmental Science
• Statistics
• Waste Management

Background:

• Waste production prediction is crucial for effective waste management systems.
• Hierarchical data structures (e.g., municipal or county levels) are common in waste generation data.
• Traditional multilevel models can be limited by small datasets, leading to biased estimates.

Purpose of the Study:

• To propose a Bayesian multilevel framework for modeling municipal waste generation with hierarchical data.
• To compare the predictive accuracy of Bayesian multilevel models against aggregated and disaggregated Bayesian models.
• To evaluate the performance of Bayesian estimation versus frequentist approaches in this context.

Main Methods:

• Development of a Bayesian multilevel model framework.
• Application to a real-world dataset of municipal waste generation in Denmark.
• Comparison using socio-economic external variables and the leave-one-out information criterion.

Main Results:

• Bayesian multilevel models demonstrated superior predictive accuracy compared to aggregated and disaggregated models.
• The Bayesian approach showed more conservative coefficient estimation, shrinking estimates towards the grand mean.
• Bayesian models produced broader credible intervals, while frequentist models yielded narrower confidence intervals.

Conclusions:

• Bayesian multilevel modeling provides a powerful and accurate approach for predicting municipal waste generation.
• This method is particularly advantageous when dealing with hierarchical data and potential data limitations.
• The Bayesian approach offers a more stable and reliable estimation strategy compared to frequentist methods.