Sustainability and efficiency have become essential considerations in the development and deployment of Artificial Intelligence systems, but existing regulatory practices for Green AI still lack standardized, model-agnostic evaluation protocols. Recently, sustainability auditing pipelines for ML and usual practices by researchers show three main pitfalls: 1) they disproportionally emphasize epoch/batch learning settings, 2) they do not formally model the long-term sustainability cost of adapting and re-training models, and 3) they effectively measure the sustainability of sterile experiments, instead of estimating the environmental impact of real-world, long-term AI lifecycles. In this work, we propose a novel evaluation protocol for assessing the long-term sustainability of ML models, based on concepts inspired by Online ML, which measures sustainability and performance through incremental/continual model retraining parallel to real-world data acquisition. Through experimentation on diverse ML tasks using a range of model types, we demonstrate that traditional static train-test evaluations do not reliably capture sustainability under evolving datasets, as they overestimate, underestimate and/or erratically estimate the actual cost of maintaining and updating ML models. Our proposed sustainability evaluation pipeline also draws initial evidence that, in real-world, long-term ML life-cycles, higher environmental costs occasionally yield little to no performance benefits.