Rethinking Machine Learning Evaluation in Waste Management Research

Reliable model evaluation is critical in waste management research, where machine learning is increasingly used to inform policy, circular economy strategies and progress towards the United Nations Sustainable Development Goals. However, common evaluation practices often fail to account for key methodological challenges, risking misleading conclusions. This study presents a theoretical analysis supplemented with a practical example of municipal solid waste generation in Ireland to demonstrate how standard evaluation metrics can produce distorted results. In particular, the widespread use of the R2 in waste management/sustainability machine learning is examined, showing its susceptibility to inflation when data exhibit strong correlations, temporal dependence or non-linear model structures. The findings show that reliance on the R2 misrepresents model performance under conditions typical of waste datasets. In the Irish example, the R2 often suggested a degradation of predictive ability even when error-based metrics, such as root mean squared error (RMSE) and mean absolute error (MAE), indicated improvement or stability. These results demonstrate the need for evaluation frameworks that move beyond single, correlation-based metrics. Future work should focus on developing and standardising robust practices to ensure that machine learning can support transparent, reliable and effective decision-making in waste management and circular economy contexts.