The field of computing is shifting towards a more sustainable and energy-efficient approach. Researchers are exploring innovative ways to reduce energy consumption and carbon footprint in various aspects of computing, including cloud computing, AI workloads, and data centers. One of the key directions is the development of carbon-aware frameworks and tools that can optimize energy usage and minimize environmental impact. These frameworks use real-time and forecasted carbon intensity, power consumption, and energy efficiency metrics to dynamically rank resources and optimize workloads. Another area of focus is the measurement and analysis of energy consumption and carbon emissions associated with AI workloads, with the goal of promoting more sustainable 'Green AI' practices. Additionally, researchers are investigating the use of power capping mechanisms and dynamic power management to improve energy efficiency in servers and data centers. Noteworthy papers in this area include: MAIZX, a carbon-aware framework that achieved an 85.68% reduction in CO2 emissions, and WattsOnAI, a comprehensive software toolkit for measuring and analyzing energy use and carbon emissions of AI workloads.