This work presents a novel approach to neural architecture search (NAS) that aims to increase carbon efficiency for the model design process.

Shaping multi-megawatt loads, such as data centers, impacts generator dispatch on the electric grid, which in turn affects system CO2 emissions and energy cost. Substantiating the effectiveness of prevalent load shaping strategies, such as those based on grid-level average carbon intensity, locational marginal price, or marginal emissions, is challenging due to the lack of detailed counterfactual data required for accurate attribution. This study uses a series of calibrated granular ERCOT day-ahead direct current optimal power flow (DC-OPF) simulations for counterfactual analysis of a broad set of load shaping strategies on grid CO2 emissions and cost of electricity. In terms of annual grid level CO2 emissions reductions, LMP-based shaping outperforms other common strategies, but can be significantly improved upon. Examining the performance of practicable strategies under different grid conditions motivates a more effective load shaping approach: one that "cherry-picks" a daily strategy based on observable grid signals and historical data. The cherry-picking approach to power load shaping is applicable to any large flexible consumer on the electricity grid, such as data centers, distributed energy resources and Virtual Power Plants (VPPs).

Abstract--Integrated Satellite-Aerial-Terrestrial Networks (ISATNs) are envisioned as key enablers of 6G, providing global connectivity for applications such as autonomous transportation, Industrial IoT, and disaster response. ISATN-specific control knobs, including carbon-aware handovers, UA V duty-cycling, and renewable-aware edge placement, are exploited to reduce emissions. I. Introduction The rapid evolution of next-generation communication systems is driving the integration of Satellite, Aerial, and Terrestrial Networks (ISATNs) into a unified infrastructure capable of delivering seamless global connectivity. This convergence is critical for enabling emerging applications such as autonomous transportation, Industrial Internet of Things (IIoT), remote healthcare, and disaster response, where reliable, low-latency, and high-capacity communication is essential [ 1 ]. However, the energy consumption associated with operating dense terrestrial base stations, satellite constellations, and aerial platforms introduces significant carbon emissions, posing new challenges for designing energy-efficient and environmentally sustainable integrated networks. As communication networks scale toward 6G and beyond, addressing carbon emissions and energy optimization has become a priority. The increasing reliance on renewable energy sources and fluctuating carbon intensity in power grids demand intelligent orchestration mechanisms capable of balancing Quality of Service (QoS) with environmental impact. S. Javaid is with the College of Electronics and Information Engineering, Tongji University, Shanghai 201804, and the State Key Laboratory of Autonomous Intelligent Unmanned Systems, Tongji University, Shanghai 201210, China N. Saeed is with the Department of Electrical and Communication Engineering, College of Engineering, UAE University, Al-Ain 15551, UAE (e-mail: mr.nasir.saeed@ieee.org).

E-14 F Reward Evaluation and Customization F-19 F.1 Load Shifting Penalty ( LS F-19 F.2 Default Reward Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F-19 F.3 Customization of Reward Formulations . . . . . . . . . . . . . . . . . . . . . . . Current Workload - The current workload level, which includes both flexible and non-flexible components. The data center modeled is illustrated in Figure 1. The hot air exits the cabinets and returns to the CRAH via the ceiling.

This work presents a novel approach to neural architecture search (NAS) that aims to increase carbon efficiency for the model design process.

Due to increased computing use, data centers consume and emit a lot of energy and carbon. These contributions are expected to rise as big data analytics, digitization, and large AI models grow and become major components of daily working routines. To reduce the environmental impact of software development, green (sustainable) coding and claims that AI models can improve energy efficiency have grown in popularity. Furthermore, in the automotive industry, where software increasingly governs vehicle performance, safety, and user experience, the principles of green coding and AI-driven efficiency could significantly contribute to reducing the sector's environmental footprint. We present an overview of green coding and metrics to measure AI model sustainability awareness. This study introduces LLM as a service and uses a generative commercial AI language model, GitHub Copilot, to auto-generate code. Using sustainability metrics to quantify these AI models' sustainability awareness, we define the code's embodied and operational carbon.

Edge computing systems struggle to efficiently manage multiple concurrent deep neural network (DNN) workloads while meeting strict latency requirements, minimizing power consumption, and maintaining environmental sustainability. This paper introduces Ecomap, a sustainability-driven framework that dynamically adjusts the maximum power threshold of edge devices based on real-time carbon intensity. Ecomap incorporates the innovative use of mixed-quality models, allowing it to dynamically replace computationally heavy DNNs with lighter alternatives when latency constraints are violated, ensuring service responsiveness with minimal accuracy loss. Additionally, it employs a transformer-based estimator to guide efficient workload mappings. Experimental results using NVIDIA Jetson AGX Xavier demonstrate that Ecomap reduces carbon emissions by an average of 30% and achieves a 25% lower carbon delay product (CDP) compared to state-of-the-art methods, while maintaining comparable or better latency and power efficiency.