In this chapter, we explore the environmental impact of translation technologies, particularly focusing on energy consumption within the digital translation industry. While much of the more technical research on these translation technologies emphasises optimising translation quality and productivity, limited attention is given to the environmental considerations or the ecological footprint of the information and communication technology (ICT) infrastructure that supports these systems. Digital translation tools, including machine translation (MT), computer-assisted translation (CAT) and translation management systems (TMS), are heavily dependent on computational resources, contributing to a significant energy use and carbon emissions. We delve into the environmental consequences of modern translation workflows, examining the power demands of state-of-the-art neural MT models, which require large amounts of data and energy. We advocate for awareness and a more responsible use of translation technologies by offering practical recommendations to reduce the models’ environmental impact. These include power-monitoring software, careful consideration of hardware requirements, and adopting green practices in training and using models. Ultimately, we advocate for a balanced approach to translation technology development – one that considers long-term ecological sustainability alongside technical performance. The adoption of greener policies and practices across the ICT and translation industries is essential to minimise the negative environmental effects of translation technologies.