The field of computing is shifting towards more sustainable and energy-efficient solutions. Researchers are exploring innovative approaches to reduce carbon emissions and minimize the environmental impact of computing systems. A key area of focus is the development of dynamic load balancing and task scheduling strategies that optimize resource utilization and reduce energy consumption. Another important aspect is the investigation of the carbon footprint of computing in space and the development of carbon-aware design principles for digital infrastructure. Additionally, there is a growing interest in extending neural scaling laws to incorporate carbon footprint and developing more sustainable AI programming practices. Noteworthy papers in this area include: A Dynamic Approach to Load Balancing in Cloud Infrastructure, which introduces a novel Score-Based Dynamic Load Balancer that enhances resource utilization and overall system efficiency. CarbonScaling, which presents an analytical framework that extends neural scaling laws to incorporate both operational and embodied carbon in LLM training. Energy-Aware Code Generation with LLMs, which evaluates the performance and energy efficiency of small language models against large language models and efficient human-written Python code.