Inventory of Reports on Sustainable AI
Explore a curated selection of reports on sustainable AI, published by our members. This page showcases key publications that shed light on the latest advances, challenges, and best practices in the design, deployment, and environmental impact of artificial intelligence.
The opinions expressed in these reports are those of the issuing organization and do not necessarily reflect the views of the Coalition’s members.
Key Reports & Publications on Green AI
Energy Consumption
Sustainable Procurement Guidelines for Data Centres and Servers
UNEP’s U4E developed these Guidelines to support government institutions and procurement authorities to establish clear, measurable criteria for improving the energy efficiency of both new and existing data infrastructure. The recommendations are based on key performance indicators such as power and water usage effectiveness, IT equipment efficiency, and cooling performance.
ICT energy evolution: Telecom, data centers, and AI
This analysis examines electricity consumption within the ICT sector, revealing that although data traffic has surged exponentially, overall energy use has risen only modestly thanks to significant efficiency gains. The report also considers how future energy trends may be shaped by the rapid evolution and deployment of AI technologies.
Energy and AI
This International Energy Agency (IEA) report addresses that gap through new global and regional modelling, enriched by consultations with governments, regulators, tech companies, and energy experts. It projects AI’s electricity consumption over the next decade, identifies the energy sources likely to support it, and explores implications for energy security, emissions, innovation, and affordability.
Power Hungry Processing: Watts Driving the Cost of AI Deployment?
In recent years, the rise of commercial AI products built on generative, multi-purpose systems has marked a shift toward a unified approach to integrating machine learning (ML) into technology. These models promise versatility across tasks, but this ambition of generality comes with significant environmental costs particularly in terms of energy consumption and carbon emissions.
Artificial Intelligence and electricity: A system dynamics approach
This study explores the impact of artificial intelligence on electricity consumption, covering everything from local inference to large-scale model training. It evaluates both the direct energy demands of data centers and the broader ripple effects across industries, emphasizing the key trade-offs, opportunities, and policy actions required to ensure AI advancements support rather than undermine global climate objectives.
Generational Growth: AI, Data Centers and the Coming US Power Demand Surge.
Goldman Sachs’ report, “Generational Growth: AI, Data Centers, and the Coming U.S. Power Surge,” warns that the explosive growth of AI and data centers will trigger a major spike in U.S. electricity demand by 2030. Driven by expanding digital infrastructure and AI adoption, the report underscores the mounting pressure on power grids and urges significant investments in energy production and grid modernization to keep pace with soaring demand.
Power Hungry: How AI Will Drive Energy Demand
According to the International Monetary Fund, the AI and data center boom is causing a surge in U.S. electricity demand, with AI sectors expanding three times faster than the rest of the economy. Between 2019 and 2023, electricity costs for major firms nearly doubled. The IMF projects that by 2030, AI-driven demand could raise U.S. electricity prices by 8.6% and increase U.S. emissions by 5.5% unless renewable energy and grid infrastructure scale up rapidly.
Smarter, smaller, stronger: resource-efficient generative Al & the future of digital transformation
The UNESCO report examines the environmental challenges posed by the rise of generative artificial intelligence, especially large language models (LLMs). It highlights their high energy consumption and suggests solutions like model compression and query optimization. The study advocates for “clean by design” systems and the use of smaller, specialized models. It calls for collective action toward a sustainable and inclusive digital transformation.
How Hungry is AI? Benchmarking Energy, Water, and Carbon Footprint of LLM Inference
Cornell University’s new benchmarking framework evaluates the environmental impact of 30 LLM models in commercial data centers. o3 and DeepSeek-R1 are the most energy-intensive, while Claude-3.7 Sonnet stands out for eco-efficiency. Scaling GPT-4o’s 700 million daily queries significantly amplifies its environmental footprint. The study calls for standardized sustainability assessments and greater accountability in AI deployment.
Developing Sustainable Gen AI
The Capgemini Research Institute’s latest report, “Developing Sustainable Gen AI,” highlights generative AI’s escalating environmental impactyet many organizations are failing to track this footprint, risking their ESG goals. As businesses balance Gen AI’s growth potential against its environmental cost, the report proposes actionable steps to build a responsible and sustainable AI strategy.
Powering artificial intelligence
A study of AI’s environmental
footprint today and tomorrow
The Deloitte report states that global data centers consumed over 380 TWh of electricity in 2023, largely due to AI. By 2030, this could triple to 1,000 TWh, and potentially reach 3,550 TWh by 2050 under high AI adoption. Without advances in energy efficiency and renewable energy, this growth risks undermining climate goals. The report urges action to align AI expansion with sustainability.
How Data Centers Are Shaping the Future of Energy Consumption
Jefferies Equity Research Team reports that AI and GPU-driven data center demand is fueling over 30% annual growth in key markets—but supply lags due to power and grid constraints. This surge is accelerating renewable energy contracts (PPAs) and investments in power infrastructure, particularly in the U.S., Europe, and Asia. However, regions face critical challenges: aging grids, regulatory hurdles, and the need for massive capital investments to support this explosive growth.
Powering Intelligence: Analyzing Artificial Intelligence and Data Center Energy Consumption
The EPRI report, “U.S. National Electrification Assessment,” examines the impacts of accelerated electrification on the U.S. power grid by 2050. It focuses on challenges and opportunities in the energy transition, including renewable integration, demand management, and infrastructure needs. The study highlights the urgency of modernizing the grid to support a decarbonized, resilient economy.
Frugal AI
Frugal AI: Introduction, Concepts, Development and Open Questions
This report offers a comprehensive exploration of Frugal AI, highlighting its potential to drive cost-effective and sustainable innovation in resource-constrained settings. It examines the environmental footprint of AI technologies, underscores the need to optimize systems for efficiency and accessibility, and presents a range of strategies and open questions surrounding the development and deployment of Frugal AI.
A Systemic Review of Green AI
The University of Florence highlights AI’s growing carbon footprint, driving the rise of Green AI a field focused on sustainable AI development. To address the lack of a unified research framework, this study provides a systematic review, mapping current efforts and identifying pathways for more eco-friendly AI innovation.
Smarter, smaller, stronger: resource-efficient generative Al & the future of digital transformation
According to UNESCO, AI plays a dual role in sustainability: it can cut environmental impact through optimization and climate modeling, but generative AI’s surging demand for energy, water, and minerals poses major challenges. The report calls for “Clean by Design” AI systems, embedding efficiency and sustainability from the very start.
Key challenges for the environmental performance of AI
In the months leading up to the AI Action Summit, a wide range of stakeholders were consulted to elaborate collaboratively a position paper on the current key challenges to foster environmental performance and reduce the environmental impact of AI. The goal is to align internationally on the major challenges to achieve environmental performance of hardware and software and extend the lifespan of equipment and software used for AI.
Small Language Models are the Future of Agentic AI
NVIDIA Research and Georgia Tech argue that Small Language Models (SLMs) outperform LLMs for most agentic AI tasks offering greater efficiency, flexibility, and cost savings while reducing energy and infrastructure demands. They propose hybrid systems (SLMs for routine tasks, LLMs for complex ones) and a conversion algorithm to ease adoption, positioning SLMs as key to scalable, sustainable AI.
Environmental Impact & Metrics
Semiconductor Emission Explorer: Tracking Greenhouse Gas Emissions from Chip Production (2015-2023)
Analysis of greenhouse gas emissions from global chip manufacturers between 2015 and 2023, highlighting key trends and the need for improved reporting standards. This analysis examines greenhouse gas emissions from global chip manufacturers between 2015 and 2023, highlighting major trends and emphasizing the urgent need for more robust and standardized reporting practices.
Artificial intelligence, data, computation: what infrastructure for a low-carbon world?
This report investigates the growing environmental footprint of artificial intelligence and data infrastructure, with a particular focus on energy consumption and carbon emissions. As AI adoption accelerates, so too does the demand for electricity especially in data centers that power large-scale model training and inference. The report highlights key trends, quantifies energy use across AI systems, and examines the implications for sustainability, emissions targets, and energy policy.
Measuring the environmental impacts of artificial intelligence compute and applications
The OECD report assesses AI’s environmental impact, distinguishing direct (energy use, emissions) from indirect (sector-wide) effects. It calls for standardized metrics, expanded data, and broader risk analysis beyond energy use to ensure AI advances global sustainability equitably. A roadmap for policymakers to harness AI’s potential responsibly.
Measuring and Standardizing AI’s Energy and Environmental Footprint to Accurately Assess Impacts
According to the Federation of American Scientists, the rapid growth of AI is driving rising energy use, water consumption, emissions, and e-waste, but these impacts remain unclear. Without standardized metrics, policymakers and grid operators cannot manage AI’s footprint. This memo proposes congressional and executive actions to establish such metrics across training, inference, and data center infrastructure.
Measuring What Matters: How to Assess AI’s Environmental Impact
The report offers a comprehensive overview of current approaches to evaluating the environmental impacts of AI systems. The review focuses on identifying which components of AI’s environmental impacts are being measured, evaluating the transparency and methodology soundness of these measuring practices, and determining their relevance and actionability.
Artificial Intelligence:Generative AI’s Environmental and Human Effect
According to the U.S. Government Accountability Office, generative AI could boost productivity and transform industries by powering customer service, scheduling, content creation, and more. Yet it also carries risks: high energy and water use, job displacement, and harmful deepfakes. This Technology Assessment reviews these challenges and outlines options for policymakers.
Artificial Intelligence (AI) end-to-end: The Environmental Impact of the Full AI Lifecycle Needs to be Comprehensively Assessed – Issue Note
According to the United Nations Environment Programme (UNEP), the environmental dimensions of digital technologies must be assessed, including their role in sustainability and their impacts. This note highlights key areas of AI’s environmental footprint across its lifecycle, aiming to inform Member States, civil society, the private sector, and the public, while encouraging research to develop methods for measuring AI’s impact.
Measuring the environmental impact of delivering AI at Google Scale
This paper presents a comprehensive method for measuring the environmental impact of AI inference at Google scale. It shows that a single Gemini Apps text prompt uses just 0.24 Wh of energy far less than public estimates. Thanks to software optimizations and clean energy sourcing, Google reduced energy use by 33× and carbon footprint by 44× in one year. The authors advocate for standardized metrics to drive efficiency across the entire AI serving stack.
Transparency
Generative AI Environmental Impact Study
This report allows to uncover AI’s carbon impact and strategies for sustainable innovation, by discovering best practices for balancing innovation and responsibility, understanding rising environmental demands and calculating the impact of two AI marketing use cases.
Contribution to a global environmental standard for AI
Mistral AI has conducted a first-of-its-kind comprehensive study to quantify the environmental impacts of our LLMs. This report aims to provide a clear analysis of the environmental footprint of AI, contributing to set a new standard for our industry.
Water Consumption
Navigating AI’s Thirst in a Water-Scarce World: A Governance Agenda for AI and the Environment
According to Nature Finance, artificial intelligence and data centers demand substantial water and energy, straining ecosystems and communities amid global water scarcity. The report urges comprehensive environmental disclosure and governance to ensure transparency, accountability, and sustainable resource use, calling for urgent action to align AI with planetary boundaries and sustainability goals.
Making AI Less ‘Thirsty’
According to the ACM Digital Library, AI technologies especially large models like GPT-4 significantly increase datacenter energy and water use, with water largely overlooked despite its impact. Datacenters consume billions of liters of freshwater annually, a demand projected to grow, making transparency, holistic strategies, and optimized training essential to mitigate scarcity.
Climate Action
Artificial Intelligence for Climate Action:
Advancing Mitigation and Adaptation in Developing Countries
This technical paper offers a comprehensive overview for policymakers, practitioners, and researchers navigating opportunities, challenges, and risks of the use of AI for climate action in developing countries, with a focus on LDCs and SIDS as these countries face unique vulnerabilities to climate change. AI driven solutions can become potential enablers for adapting to climate impacts and reducing GHG emissions. However, risks and challenges also exist, which need to be addressed for the effective and sustainable use of AI in climate action.
Green and intelligent: the role of AI in the climate transition
According to the London School of Economics and Systemiq, AI can support climate action and sustainable economic growth, though research on its role in the low-carbon transition is limited. The study identifies five ways AI can address climate threats and estimates potential GHG reductions in power, food, and mobility sectors responsible for nearly half of global emissions while comparing this with AI-related datacenter emissions.
How AI Can Be a
Powerful Tool in the Fight
Against Climate Change
This report explores how advanced analytics and artificial intelligence (a pairing referred to the report simply as “AI”) are tools uniquely positioned to help manage the complex issues brought about by climate change. Three areas of application are particularly relevant: Mitigation, adaptation and resilience, and fundamentals.
Tackling Climate Change with Machine Learning
According to McGill University & MILA – AI Institute of Québec, machine learning can be a powerful tool to reduce greenhouse gas emissions and help society adapt to climate change. From smart grids to disaster management, ML can address high-impact problems, offering both research opportunities and business solutions, and the community is urged to join the global climate effort.
Artificial Intelligence for Climate Action in Developing Countries: Opportunities,
Challenges and Risks
This information note, prepared by the Technology Executive Committee (TEC) as part of its 2023–2027 rolling workplan, provides an overview of the opportunities, risks, and challenges of using artificial intelligence (AI) for climate action in developing countries, with a focus on least developed countries (LDCs) and small island developing States (SIDS).
Climate Change and AI: Recommendations for Government
The report highlights 48 specific recommendations for how governments can both support the application of AI to climate challenges and address the climate-related risks that AI poses. AI is already being used to support climate action in a wide range of use cases, several of which the report highlights. The authors also detail critical bottlenecks that are impeding faster adoption.
Aligning Artificial Intelligence with Climate Change Mitigation
According to ETH Zurich, assessing and shaping the effects of AI and machine learning on climate change mitigation requires coordinated research, policy, and industry efforts. Due to uncertainty and insufficient characterization of how ML affects greenhouse gas emissions, measuring and forecasting these impacts is difficult; this study introduces a systematic framework to describe ML’s effects on GHG emissions.
Climate Change and AI: Recommendations for Government
The report highlights 48 specific recommendations for how governments can both support the application of AI to climate challenges and address the climate-related risks that AI poses. AI is already being used to support climate action in a wide range of use cases, several of which the report highlights. The authors also detail critical bottlenecks that are impeding faster adoption.
UNDP Digital Guides : Climate
Signature Solution 4 – Environment
Digital technologies offer powerful tools to mitigate and adapt to climate change, supporting better data, planning, and local engagement. UNDP helps over 120 countries integrate these tools into their climate strategies. However, the benefits are unevenly distributed, and risks like digital inequality, misinformation, and increased emissions must be addressed to ensure a just and inclusive transition.
AI for Climate and Nature: Landscape Assessment
Transformative system-oriented changes are urgently needed to confront the world’s most intractable challenges such as escalating biodiversity loss and climate change. Artificial intelligence (AI) provides a way to better understand these vital and complex problems and if applied strategically to dramatically accelerate the pace of solution. This report examines how AI can help address these urgent challenges by enhancing our understanding and accelerating the implementation of impactful, scalable, and ethical solutions in climate and nature.
Pushing the frontiers in climate modelling and analysis with machine learning
According to Deutsches Zentrum für Luft- und Raumfahrt & University of Bremen, climate modeling faces new demands that call for advancing machine learning beyond current approaches. This includes developing ML-based Earth system models, emulators for extreme events, improved detection and attribution, and enhanced model analysis, while addressing challenges like generalization, uncertainty quantification, explainability, and causality.
Machine Learning for Sustainable Energy Systems
According to Carnegie Mellon University, machine learning has become a powerful tool for sustainable energy systems. This review outlines ML paradigms and techniques, surveys their use in energy production, delivery, and storage, and identifies research gaps, future directions, and deployment considerations.
ICEF Artificial Intelligence for Climate Change Mitigation Roadmap
According to ICEF, the second edition of the Artificial Intelligence for Climate Change Mitigation Roadmap updates all previous chapters and adds six new ones, covering sectors from aviation to extreme weather response. Each chapter provides 5–10 actionable recommendations, offering the most detailed guidance to date on using AI to address climate change.
Accelerating Climate Action with AI
AI holds major potential to tackle climate challenges boosting emissions reduction, energy efficiency, and resource managementwhile also highlighting associated risks. The report outlines a clear policy framework for guiding AI’s role, showcasing real-world examples where AI has already advanced climate action across industries. It urges public and private leaders to collaborate in scaling AI’s impact while ensuring responsible deployment for a sustainable future.
Harnessing Artificial
Intelligence for the Earth
According to the World Economic Forum & PwC, AI, central to the Fourth Industrial Revolution, can help reverse environmental damage but risks worsening crises without safeguards. The report emphasizes decentralized energy, climate modeling, and disaster resilience, while calling for global collaboration, new governance, ethical frameworks, and equitable access to ensure AI drives sustainability.
Climate AI– How artificial intelligence can power your climate action strategy
The Capgemini Research Institute finds AI has already cut GHG emissions by 13% and could help achieve 11–45% of Paris Agreement targets by 2030, yet only 3% of AI climate solutions are fully scaled. While 67% of organizations prioritize climate action, just 13% effectively integrate AI into their strategies, revealing a critical implementation gap. To unlock AI’s potential, companies must scale use cases, reduce AI’s own carbon footprint, and target Scope 3 emissions.
Machine Learning for Sustainable Energy Systems
Machine learning is transforming sustainable energy by enabling smarter production, delivery, and storage, with diverse techniques offering unique strengths and trade-offs. This review highlights key applications from optimizing renewables to grid management while identifying research gaps and challenges in real-world deployment. Future work should focus on scalability, robustness, and ethical deployment to maximize ML’s impact on energy sustainability.
How AI can enable a
Sustainable Future
According to the Microsoft and PwC report, AI can break the link between economic growth and carbon emissions, but only with clean energy policies, global cooperation, and equitable access to avoid widening inequalities.Its potential hinges on combining AI with IoT, renewables, and reskilling effortsyet risks leaving regions behind without inclusive strategies. Success demands cross-sector collaboration to ensure AI drives sustainable, just, and global progress.
Biodiversity
UNDP Digital Guides : Nature
Signature Solution 4 – Environment
Digital technology plays a crucial role in achieving the UNDP Nature Pledge and the Kunming-Montreal Global Biodiversity Framework by enabling smart monitoring, data transparency, and traceability. However, it also risks widening inequalities, especially for vulnerable groups. UNDP promotes an inclusive digital transformation to harness technology for biodiversity while mitigating its negative impacts.
The potential for AI to revolutionize conservation: a horizon scan
According to Cambridge University, AI is an emerging tool for addressing the urgent biodiversity crisis. A horizon scan by international conservation and AI experts identified 21 key applications, including species recognition, multimodal models for predicting biodiversity loss, wildlife trade monitoring, and managing human–wildlife conflict.
Leveraging artificial intelligence to enhance early action towards the Kunming-Montreal Global Biodiversity Framework
According to UNDP, NBSAP Target Similarity Assessments, developed through the Early Action Support Project, provide customized insights on aligning global and national biodiversity targets. These assessments support inclusive stakeholder engagement, enhance political will, improve sectoral collaboration, and accelerate progress toward global biodiversity commitments, with this publication detailing the methodology and key lessons learned.
Biodiversity and Artificial Intelligence: Opportunities & Recommendations for Action
According to the Global Partnership on AI, biodiversity loss is a critical issue requiring urgent action. AI offers opportunities to accelerate conservation but must be deployed to support sustainable development models and integrated with broader efforts, rather than as a standalone solution.
Ocean
AI for Ocean Conservation: The Role of Artificial Intelligence in Marine Sustainability
This paper explores the transformative role of AI in ocean conservation, highlighting its applications, benefits, and limitations. It emphasizes the necessity of integrating AI with existing marine conservation frameworks to enhance environmental sustainability, protect biodiversity, and mitigate the adverse effects of climate change on marine ecosystems..
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