As the scale of machine learning models and the prevalence of AI workloads has grown, so have the computational, financial, and energy requirements of development and deployment. In response, recent research in efficient machine learning and Green AI has proposed interventions aimed at reducing the environmental resource consumption of machine learning, such as model compression, efficient training methods, and data distillation. Additionally, various tools and frameworks have facilitated reporting and measurement of metrics related to efficiency and environmental impact. However, holistic, bottom-up assessment of the end-to-end environmental impacts of ML remains elusive. Inspired by work from the environmental impact community, we propose that holistic lifecycle assessment (LCA) for analyzing language models. We identify use stages for studying LLM development and deployment, propose methods for measuring power utilization, and analysis for comparing the relative environmental costs of individual stages.