Green IT and Sustainability

Latest papers

• Improving the Energy-Efficiency of the Code Generated by LLMs through Effective Prompting

  Ritika Rekhi, Bing Zhang, Md Arman Islam, Jaya Krishna Pasham, Yeswanth Chitturi, Akshay Paramesha, Isha Valiveti, Asif Imran, Bekir Turkkan, Tevfik Kosar · 5 October 2026

  As AI-assisted programming becomes increasingly mainstream, the environmental impact of AI-generated software has emerged as an important consideration. This motivates evaluating LLM-generated code beyond functional correctness by considering execution efficiency and energy consumption. However, des…

• Quantifying Ethereum Energy Consumption via Network Mapping

  Yahn Costa Hackspacher, Cornelius Ihle, Vasundhara Shaw, Dennis Trautwein, Geerd-Dietger Hoffmann, Bela Gipp, Moritz Schubotz · 5 October 2026

  Ethereum's electricity use fell by about 99.95% after the move from proof of work to proof of stake. Service providers still need to report operational energy use, e.g. under the EU Markets in Crypto-Assets Regulation (MiCAR). Existing estimates either apply one typical wattage to every node or star…

• A Holistic Assessment of the Carbon Footprint of Noor, a Very Large Arabic Language Model

  Imad Lakim, Ebtesam Almazrouei, Ibrahim Abu Alhaol, Merouane Debbah, Julien Launay · 2 October 2026

  As ever larger language models grow more ubiquitous, it is crucial to consider their environmental impact. Characterised by extreme size and resource use, recent generations of models have been criticised for their voracious appetite for compute, and thus significant carbon footprint. Although repor…

• Exposing the Cost of Deep Learning Audio Development

  Constance Douwes, Paul Magron, Romain Serizel · 2 October 2026

  The environmental impact of deep learning has attracted increasing attention over the past decade. Existing studies mainly focus on the energy and carbon emissions of model training and inference, while the whole development phase is often overlooked. Yet, architecture prototyping and intensive expe…

• Beyond State-of-the-Art: Standardising Environmental Impact Metrics for AI Research

  Lachlan McGinness, Dan Pagendam, Robert Offner · 2 October 2026

  As the capabilities and ubiquity of Large Language Models (LLMs) grow, so does their environmental footprint. Despite calls for responsible AI, the machine learning community lacks standardised practices for carbon accounting. Our automated literature review of the 5,285 papers accepted to NeurIPS 2…

• Beyond Energy: When Sustainability Dimensions Reshape LLM Serving Decisions

  Tianyao Shi, Xipeng Shen, Yi Ding · 29 September 2026

  Large language model (LLM) serving has environmental impacts across energy consumption, carbon emission, water consumption, and biodiversity loss. Yet these dimensions are largely evaluated in isolation, leaving it unclear when and how they lead to different optimization decisions. We present PRISM,…

• Greenpixie's AI Token Methodology: Assessing the Energy, Water and $\mathrm{CO_2\text{-}eq}$ Impact of AI Tokens for Open and Closed Weight Models

  Joshua Horswill, Ross Hunter, Matt Clifford, James Hall · 29 September 2026

  We describe a methodology for estimating the per-token energy cost of cloud-hosted large language model (LLM) inference, separating between input (prefill) and output (decode) tokens. Graphics processing unit (GPU) energy usage is measured during inference benchmarking with open-weights models on a …

• Environmental Impact of Generative and Agentic AI: An in-Depth Analysis and Green Solutions

  Abderaouf Bahi, Amel Ourici, Ibtissem Gasmi · 29 September 2026

  The proliferation of generative and agentic artificial intelligence (AI) systems has introduced computational demands whose environmental consequences are substantial yet underexamined. This paper examines the environmental footprint of modern AI systems across energy consumption, carbon emissions, …

• How can AI accelerate the green transition?

  Jacques Sainte-Marie (LJLL), Marie-Fleur Simmet, Fanny Terrier · 28 September 2026

  In his essay The Gift, published in 1925, the sociologist Marcel Mauss emphasised the 'total' scope of the phenomenon of gift-and counter-gift-giving, noting that it 'expresses, simultaneously and all at once, all manner of institutions: religious, legal and moral (...)\,; economic (...)\,; not to m…

• Beyond the Last Truffula Tree: SustainAI - A Water-Aware, Closed-Loop Framework for Environmentally Accountable AI

  Farnaz Farid, Tashfia Towkee, Sania Nasreen, Sami bin Azad · 28 September 2026

  As artificial intelligence (AI) becomes embedded in everyday life, its environmental footprint, particularly water consumption remains largely invisible. While energy and carbon impacts are widely recognized, the substantial freshwater demands of data center cooling and electricity generation receiv…

• Beyond PUE: A Local Impact Audit Framework for Data Center Environmental Accountability

  Sharifa Sultana, Syed Ishtiaque Ahmed · 22 September 2026

  Standard data center sustainability metrics, including Power Usage Effectiveness (PUE), Water Usage Effectiveness (WUE), and Carbon Usage Effectiveness (CUE), measure a facility's resource use and emissions intensity, normalized to IT energy use, without directly representing local resource scarcity…

• Toward a Time-Aware Assessment Framework for the Carbon Cost of AI-Enabled Decarbonization

  Chenrui Xu, Burcu Akinci, Christopher McComb · 17 September 2026

  AI is increasingly used to support decarbonization decisions across the built environment, yet the development, training, and use of AI consume energy and induce CO2e emissions. However, existing assessments often report physical-system savings while omitting AI-side emissions. Moreover, they rarely…

• Toward Sustainable AI Deployment: A Carbon-Aware Decision Framework for Enterprise Supply Chain Systems

  Haoran Yu, Lifei Liu, Danping Zhang · 15 September 2026

  Enterprises deploying AI for supply chain decisions commonly default to the largest available language model, a procurement heuristic that neglects both empirical performance and environmental cost. We benchmark six large language models across 520 supply chain tasks, simultaneously measuring decisi…

• LOCO 2026 Lightning Talk Abstracts: 2nd International Workshop on Low Carbon Computing

  Ignatius Ezeani, Adrian Friday, Abdessalam Elhabbash, John Vidler, Daniel King, Paul Dempster · 15 September 2026

  This volume contains the accepted lightning-talk contributions from the 2nd International Workshop on Low Carbon Computing (LOCO 2026), held at Lancaster University, United Kingdom, on 10-11 September 2026. The collection brings together 14 short papers presenting emerging research, early-stage resu…

• The Battery Price of edge AI: A study of the Environmental Impact of LLM Inference on Mobile Devices

  \'Edouard Gu\'egain, Tristan Coignion · 14 September 2026

  The rapid diffusion of generative artificial intelligence raises privacy, latency, and performance concerns that motivate a shift toward "local-first" AI, where inferences are performed on the user's device instead of on remote cloud servers. This paradigm also places a significant computational loa…

• GreenPassport: Request-Level Carbon Accounting for Cross-Border AI Inference

  Rui Lu · 9 September 2026

  AI inference often crosses regional boundaries as prompts travel to remote data centers and generated tokens return to users. Regional averages cannot represent the resulting differences in serving hardware, electricity, and network delivery. Request-level accounting needs a common boundary for the …

• A Checklist to assess the energy and carbon impacts of ML/AI applications in Earth System Modeling

  Filippo Dainelli, Amirpasha Mozaffari, Marina Casta\~no, Aina Gaya i \`Avila, Llu\'is Palma Garcia, Alessio Melli, Oscar Dimdore Miles, Amanda Duarte · 2 September 2026

  As machine learning and artificial intelligence find their way into nearly every aspect of climate, weather, and Earth system modeling, it is worth pausing to consider what our design decisions imply for the science and for the computational resources we consume. A growing body of literature address…

• GreenBench: Benchmarking Energy Efficiency and Carbon Footprint of Open-Source LLM Inference on Apple Silicon

  Rajeswari Kannan, Raj Firke, Shreya Bengle, Srushti Deshmukh · 1 September 2026

  The rapid proliferation of Large Language Models (LLMs) has raised concerns about their environmental impact during inference. While Green AI research has focused on datacenter GPUs and embedded platforms, the energy profile of LLM inference on Apple Silicon, with its unified memory architecture, re…

• PCFBench: A Diagnostic Benchmark for Product Carbon Footprint Estimation

  Krishna Rao, Andrew Dumit, Shaena Ulissi, Jacob Feintzeig, P. James Joyce, Daniel Frank, Steven Watson, Jonathan Glidden, Gizem Ilayda Dinc, Travis M. Kwee · 31 August 2026

  AI systems are being deployed on high-stakes, domain-specific workflows that demand correctness not just in the final output, but at every intermediate step. One such workflow is estimating a product carbon footprint (PCF), the greenhouse-gas emissions attributable to a physical product. AI agents a…

• The Accuracy-Efficiency Paradox Quantifying Net Energy Loss in on-Device Energy Forecasting

  Jaeik Jeong, Tai-Yeon Ku, Wan-Ki Park · 28 August 2026

  Energy forecasting aims to maximize accuracy to ensure energy efficiency by reducing energy waste, an objective that applies equally to on-device forecasting for mission-critical edge environments, including military systems. However, this paper identifies the Accuracy-Efficiency Paradox: high-preci…

• Understanding the Energy Scaling of Large Language Model Inference Across Context Lengths and Attention Architectures

  Molka Chkir, Syed Muhammad Danish, Jos H\"oll, Arghavan Asad · 27 August 2026

  The growing adoption of large language models (LLMs) has raised increasing concerns about the energy consumption and environmental impact of inference. This paper presents a systematic empirical study of decode-phase energy consumption across representative open-source LLMs employing Multi-Head Atte…

• When LLMs Slow Down: How Environmental Impacts Mediate University Students' LLM Usage

  Hyeonwook Kim, Xuesi Chen, Alex Cabral, Cindy Kaiying Lin, Udit Gupta, Josiah Hester · 26 August 2026

  Large Language Models (LLMs) are increasingly being embedded into all facets of society, from search to education, industrial, and financial applications. These systems' carbon and water footprints raise important sustainability concerns, particularly with adoption rates exceeding 80% among universi…

• Energy and CO2 Footprint of Climate Model Intercomparison Projects

  Sergi Palomas, Pablo Aparici, Gladys Utrera, Mario Acosta · 25 August 2026

  Earth System Models (ESMs) rely heavily on High-Performance Computing (HPC) resources to simulate global climate. As these models evolve, their computational demands continue to grow, driven by three factors: (1) finer spatial grid resolutions, (2) the integration of complex biogeochemical processes…

• Enabling Organisational Change Through Ground-Up Initiatives: A Case Study from the STFC Scientific Computing Department

  Jessica Huntley, David McDonagh · 25 August 2026

  Transforming digital research infrastructure (DRI) to align with UK Net Zero targets requires significant action from organisations in this space. Although high level strategies and recommendations exist, it is not always obvious how to translate these into concrete results. Here we present a case s…

• Green BOA: Determining the environmental break-even point for ML-based data compression

  Caterina Doglioni, Akshat Gupta, Thomas Elliott, Hanzila Hussain, Sanjiban Sengupta · 21 August 2026

  We summarise the outcome of two summer internship projects based at the University of Manchester, focused on the break-even point in terms of environmental sustainability for ML-based data compression algorithms. Using the example of a ML-based lossless compression algorithm, we compare estimates fo…