Comparative Analysis of Model Evaluation Metrics in Energy Systems, Environmental Modeling, and Sustainability Science

Model evaluation is essential for ensuring the reliability, transparency, and practical usefulness of simulations in energy systems, environmental modeling, and sustainability science. However, the frequent misuse, misinterpretation, and isolated application of common performance metrics particularly root mean square error (RMSE), mean absolute percentage error (MAPE), and R 2 continue to undermine model credibility and lead to misleading conclusions. This study (i) reviews and defines five widely used evaluation metrics (mean absolute error [MAE], RMSE, mean bias error [MBE], MAPE, and R 2 ), including their mathematical foundations and conceptual meaning, (ii) compares their characteristics, strengths, and limitations, and (iii) provides context‐specific guidelines for selecting appropriate indicators based on modeling objectives and data behavior. Using a combined methodology consisting of a structured literature review, a controlled illustrative dataset, and sensitivity analysis, the study demonstrates how each metric responds to outliers and zero‐observation values. Results show that RMSE and MAE increase more under outlier conditions, MAPE becomes undefined when observed values reach zero, and R 2 collapses from 0.82 to below 0.05 when data irregularities are introduced. These findings highlight critical vulnerabilities that can distort evaluation if metrics are used in isolation. The study contributes a consolidated comparative framework, practical recommendations for multimetric reporting, and guidance for improving evaluation transparency in energy, environmental, and sustainability applications. Adopting a context‐aware, multimetric strategy enhances credibility, interpretability, and policy relevance of model‐based assessments in support of sustainable development goals (SDGs).