As machine learning (ML) continues its rapid expansion, the environmental cost of model training and inference has become a critical societal concern. Existing benchmarks overwhelmingly focus on standard performance metrics such as accuracy, BLEU, or mAP, while largely ignoring energy consumption and carbon emissions. This single-objective evaluation paradigm is increasingly misaligned with the practical requirements of large-scale deployment, particularly in energy-constrained environments such as mobile devices, developing regions, and climate-aware enterprises. In this paper, we propose AI-CARE, an evaluation tool for reporting energy consumption, and carbon emissions of ML models. In addition, we introduce the carbon-performance tradeoff curve, an interpretable tool that visualizes the Pareto frontier between performance and carbon cost. We demonstrate, through theoretical analysis and empirical validation on representative ML workloads, that carbon-aware benchmarking changes the relative ranking of models and encourages architectures that are simultaneously accurate and environmentally responsible. Our proposal aims to shift the research community toward transparent, multi-objective evaluation and align ML progress with global sustainability goals. The tool and documentation are available at https://github.com/USD-AI-ResearchLab/ai-care.
Figures & tables
Fig. 1 : Sequence-style control-flow diagram of the proposed reporting framework. The main process spawns external monitoring and carbon-emission services and executes models on the evaluation dataset. During execution, energy consumption E(m) is measured periodically, while carbon-emission values are retrieved asynchronously. After execution completes, performance, energy, and carbon metrics are normalized and aggregated to generate final reports. The framework operates purely as a reporting layer and does not influence model execution, training, or optimization.
Fig. 2 : Global carbon–performance tradeoff across all evaluated model–dataset pairs. Each subplot reports task performance (Accuracy, Precision, Recall, and F1-Score) versus total carbon emissions (training + inference) shown on a logarithmic scale. Colors indicate datasets, while marker shapes denote model families.
Fig. 3 : Metric-wise scalar carbon-aware scores across datasets and model families. Rows correspond to evaluation metrics (accuracy, precision, recall, and F1-score), while columns correspond to datasets (MNIST, Fashion-MNIST, CIFAR-10, CIFAR-100, and ImageNet-100). Each score integrates normalized task performance with total carbon emissions (training + inference). The grouped layout enables direct cross-metric and cross-dataset comparison of carbon-aware model rankings.