Inventory of Reports on Sustainable AI
Discover a selection of reports on sustainable AI published by our members. This page brings together publications essential to understanding advances, challenges and best practices in the design, use and environmental impact of artificial intelligence.
Opinions expressed in these reports are those of the issuing organization and do not necessarily reflect the views of the members of the Coalition.
Key reports & publications on Green AI
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.
Frugal AI: Introduction, Concepts, Development and Open Questions
This report provides a comprehensive overview of Frugal AI, emphasizing its role in promoting cost-effective and sustainable innovation within resource-constrained environments. It explores the environmental impact of AI technologies, the importance of optimizing AI systems for efficiency and accessibility, and discusses various strategies and open questions related to the development and implementation of Frugal AI.
ICT energy evolution: Telecom, data centers, and AI
A detailed analysis of the ICT sector’s electricity consumption, highlighting that while data traffic has grown exponentially, energy use has increased only modestly due to efficiency improvements, with future growth influenced by AI developments.
Energy and AI
This report aims to fill this gap based on new global and regional modelling and datasets, as well as extensive consultation with governments and regulators, the tech sector, the energy industry and international experts. It includes projections for how much electricity AI could consume over the next decade, as well as which energy sources are set to help meet it. It also analyses what the uptake of AI could mean for energy security, emissions, innovation and affordability.
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.
Artificial intelligence, data, computation: what infrastructure for a low-carbon world?
An interim report exploring the environmental impact of AI and data centers, focusing on energy consumption and carbon emissions.
Power Hungry Processing: Watts Driving the Cost of AI Deployment?
Recent years have seen a surge in the popularity of commercial AI products based on generative, multi-purpose AI systems promising a unified approach to building machine learning (ML) models into technology. However, this ambition of “generality” comes at a steep cost to the environment, given the amount of energy these systems require and the amount of carbon that they emit. In this work, we propose the first systematic comparison of the ongoing inference cost of various categories of ML systems, covering both task-specific (i.e. finetuned models that carry out a single task) and `general-purpose’ models, (i.e. those trained for multiple tasks).
Measuring the environmental impacts of artificial intelligence compute and applications
This report aims to improve understanding of the environmental impacts of AI, and help measure and decrease AI’s negative effects while enabling it to accelerate action for the good of the planet. It distinguishes between the direct environmental impacts of developing, using and disposing of AI systems and related equipment, and the indirect costs and benefits of using AI applications. It recommends the establishment of measurement standards, expanding data collection, identifying AI-specific impacts, looking beyond operational energy use and emissions, and improving transparency and equity to help policy makers make AI part of the solution to sustainability challenges.
A Systemic Review of Green AI
With the ever-growing adoption of AI-based systems, the carbon footprint of AI is no longer negligible. AI researchers and practitioners are therefore urged to hold themselves accountable for the carbon emissions of the AI models they design and use. This led in recent years to the appearance of researches tackling AI environmental sustainability, a field referred to as Green AI. Despite the rapid growth of interest in the topic, a comprehensive overview of Green AI research is to date still missing. To address this gap, in this paper, we present a systematic review of the Green AI literature.
Artificial Intelligence and electricity: A system dynamics approach
A detailed examination of how artificial intelligence affects electricity consumption, exploring various scenarios and sustainability implications.
Navigating AI’s Thirst in a Water-Scarce World: A Governance Agenda for AI and the Environment
A report highlighting the significant water and energy demands of AI and data centers, and emphasizing the urgent need for holistic environmental disclosure and governance to mitigate risks to ecosystems and communities in a context of global water scarcity.
Measuring and Standardizing AI’s Energy and Environmental Footprint to Accurately Assess Impacts
The rapid expansion of artificial intelligence (AI) is driving a surge in data center energy consumption, water use, carbon emissions, and electronic waste—yet these environmental impacts, and how they will change in the future, remain largely opaque. Without standardized metrics and reporting, policymakers and grid operators cannot accurately track or manage AI’s growing resource footprint. This policy memo proposes a set of congressional and federal executive actions to establish comprehensive, standardized metrics for AI energy and environmental impacts across model training, inference, and data center infrastructure.
Smarter, smaller, stronger: resource-efficient generative Al & the future of digital transformation
This report explores the dual role of artificial intelligence in environmental sustainability. While AI offers immense potential for reducing environmental impact through resource optimization and climate modeling, the rise of generative AI poses urgent challenges in terms of the consumption of energy, water and critical minerals. The report highlights the need to design AI systems “clean by design”, integrating energy and resource efficiency from the outset.
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
Generative AI could dramatically increase productivity and transform workloads in many industries. It can be used to respond to questions in customer service chats, create schedules, summarize information, produce Internet content—and more. But generative AI also poses potential risks to people and the environment. For example, the IT equipment that powers generative AI needs a lot of water and electricity to function efficiently and avoid overheating. Also, generative AI could replace workers or be used to create dangerous deepfakes. Our Technology Assessment discusses these and other challenges and offers options for policymakers to consider.
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.
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.
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.
Key reports & publications on AI for Green
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
Artificial Intelligence (AI) can play a powerful role in supporting climate action while boosting sustainable and inclusive economic growth. However, limited research exists on the potential influence of AI on the low-carbon transition. Here we identify five areas through which AI can help build an effective response to climate threats. We estimate the potential for greenhouse gas (GHG) emissions reductions through AI applications in three key sectors—power, food, and mobility—which collectively contribute nearly half of global emissions. This is compared with the increase in data centre-related emissions generated by all AI-related activities.
Leveraging artificial intelligence to enhance early action towards the Kunming-Montreal Global Biodiversity Framework
Developed on an on-demand basis through the Early Action Support (EAS) Project implemented by UNDP, NBSAP Target Similarity Assessments offer customized insights on synergies between global and national biodiversity targets and provide recommendations for enhanced alignment to bring about a transformation in our societies’ relationship with biodiversity by 2030. Developed with governments to address their unique needs, these assessments can foster dynamic, inclusive, and effective national stakeholder engagement to fill gaps, raise political will, and improve sectoral collaboration, resulting in accelerated progress towards global biodiversity commitments. This publication releases the methodology behind the National Biodiversity Strategies and Action Plans (NBSAP) Target Similarity Assessments and identifies key lessons learned and opportunities for future applications.
Tackling Climate Change with Machine Learning
Climate change is one of the greatest challenges facing humanity, and we, as machine learning (ML) experts, may wonder how we can help. Here we describe how ML can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by ML, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the ML community to join the global effort against climate change.
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..
Artificial Intelligence for Climate Action in Developing Countries: Opportunities,
Challenges and Risks
This information note has been prepared by the Technology Executive Committee (TEC) as part of the TEC rolling workplan 2023-2027. It 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
Assessing and shaping the effects of artificial intelligence (AI) and machine learning (ML) on climate change mitigation demands a concerted effort across research, policy, and industry. However, there is great uncertainty regarding how ML may affect present and future greenhouse gas (GHG) emissions. This is owed in part to insufficient characterization of the different mechanisms through which such emissions impacts may occur, posing difficulties in measuring and forecasting them. We therefore introduce a systematic framework for describing ML’s effects on GHG emissions
Biodiversity and Artificial Intelligence: Opportunities & Recommendations for Action
Biodiversity loss is one of the most critical issues facing humanity, requiring urgent and coordinated action. Despite ongoing conservation efforts, biodiversity has declined dramatically in recent decades. Artificial intelligence (AI) is one tool that offers opportunities to accelerate action on biodiversity conservation. However, it must be deployed in a way that supports a paradigm shift to new, sustainable models of development, rather than entrenching business as usual. However AI is not asilver bullet and needs to be deployed as part of wider applications and efforts that support action.
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.
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.
The potential for AI to revolutionize conservation: a horizon scan
Artificial Intelligence (AI) is an emerging tool that could be leveraged to identify the effective conservation solutions demanded by the urgent biodiversity crisis. We present the results of our horizon scan of AI applications likely to significantly benefit biological conservation. An international panel of conservation scientists and AI experts identified 21 key ideas. These included species recognition to uncover dark diversity’, multimodal models to improve biodiversity loss predictions, moni toring wildlife trade, and addressing human–wildlife conflict.
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 solutions. 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
Climate modelling and analysis are facing new demands to enhance projections and climate information. Here we argue that now is the time to push the frontiers of machine learning beyond state-of-the-art approaches, not only by developing machine-learning-based Earth system models with greater fidelity, but also by providing new capabilities through emulators for extreme event projections with large ensembles, enhanced detection and attribution methods for extreme events, and advanced climate model analysis and benchmarking. Utilizing this potential requires key machine learning challenges to be addressed, in particular generalization, uncertainty quantification, explainable artificial intelligence and causality.
Machine Learning for Sustainable Energy Systems
In recent years, machine learning has proven to be a powerful tool for deriving insights from data. In this review, we describe ways in which machine learning has been leveraged to facilitate the development and operation of sustainable energy systems. We first provide a taxonomy of machine learning paradigms and techniques, along with a discussion of their strengths and limitations. We then provide an overview of existing research using machine learning for sustainable energy production, delivery, and storage. Finally, we identify gaps in this literature, propose future research directions, and discuss important considerations for deployment.
ICEF Artificial Intelligence for Climate Change Mitigation Roadmap
The ICEF Artificial Intelligence for Climate Change Mitigation Roadmap (Second Edition) comprehensively updates all chapters in last year’s ICEF Roadmap (on topics including the power sector, food system and materials innovation) while adding six new chapters (on aviation, buildings, carbon capture, nuclear power, large language models and extreme weather response). Each chapter highlights 5-10 specific, actionable recommendations, providing the most detailed and comprehensive set of recommendations to date on how artificial intelligence (AI) can be used to respond to climate change.
Tackling Climate Change with Machine Learning
Climate change is one of the greatest challenges facing humanity, and we, as machine learning (ML) experts, may wonder how we can help. Here we describe how ML can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by ML, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the ML community to join the global effort against climate change.
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.
