Digital technology's environmental footprint

AI FOOTPRINT: ENVIRONMENTAL IMPACT IN NEW OECD REPORT by Raffaella Aghemo, 2023

In November last year, the OECD published a report on the environmental impact of Artificial Intelligence use, entitled ‘Measuring the environmental impacts of Artificial Intelligence compute and applications the AI footprint’.

2023

As AI/ML models, including Large Language Models, continue to scale with massive datasets, so does their consumption of undeniably limited natural resources, and impact on society. In this collaboration between AI, Sustainability, HCI and legal researchers, we aim to enable a transition to sustainable AI development by enabling stakeholders across the AI value chain to assess and quantitfy the environmental and societal impact of AI. We present the ESG Digital and Green Index (DGI), which offers a dashboard for assessing a company's performance in achieving sustainability targets. This includes monitoring the efficiency and sustainable use of limited natural resources related to AI technologies (water, electricity, etc). It also addresses the societal and governance challenges related to AI. The DGI creates incentives for companies to align their pathway with the Sustainable Development Goals (SDGs). The value, challenges and limitations of our methodology and findings are discussed in the paper.

arXiv (Cornell University), 2023

As AI/ML models, including Large Language Models, continue to scale with massive datasets, so does their consumption of undeniably limited natural resources, and impact on society. In this collaboration between AI, Sustainability, HCI and legal researchers, we aim to enable a transition to sustainable AI development by enabling stakeholders across the AI value chain to assess and quantitfy the environmental and societal impact of AI. We present the ESG Digital and Green Index (DGI), which offers a dashboard for assessing a company's performance in achieving sustainability targets. This includes monitoring the efficiency and sustainable use of limited natural resources related to AI technologies (water, electricity, etc). It also addresses the societal and governance challenges related to AI. The DGI creates incentives for companies to align their pathway with the Sustainable Development Goals (SDGs). The value, challenges and limitations of our methodology and findings are discussed in the paper.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

Futures, 2020

with Vit Stritecky. The environmental costs and energy constraints have become emerging issues for the future development of Machine Learning (ML) and Artificial Intelligence (AI). So far, the discussion on environmental impacts of ML/AI lacks a perspective reaching beyond quantitative measurements of the energy-related research costs. Building on the foundations laid down by Schwartz et al., 2019 in the GreenAI initiative, our argument considers two interlinked phenomena, the gratuitous generalisation capability and the future where ML/AI performs the majority of quantifiable inductive inferences. The gratuitous generalisation capability refers to a discrepancy between the cognitive demands of a task to be accomplished and the performance (accuracy) of a used ML/AI model. If the latter exceeds the former because the model was optimised to achieve the best possible accuracy, it becomes inefficient and its operation harmful to the environment. The future dominated by the non-anthropic induction describes a use of ML/AI so all-pervasive that most of the inductive inferences become furnished by ML/AI generalisations. The paper argues that the present debate deserves an expansion connecting the environmental costs of research and ineffective ML/AI uses (the issue of gratuitous generalisation capability) with the (near) future marked by the all-pervasive Human-Artificial Intelligence Nexus.

IEEE Intelligent Systems, 2011

When preparing for a March 2007 talk at the US National Science Foundation (NSF), I searched the Web for scholarly work on AI and climate change, the natural environment, and sustainability. My search was not exhaustive, largely based on keywords, but it wasn't trivial either. Still, little turned up in the intersection of AI and sustainability in early 2007, and most of what did, as I recall, was in environmental science publications and appeared to be dominated by European researchers using evolutionary computation for the purposes of optimization. 1 AI and sustainability has grown substantially in the last few years. To some extent, this tracks with increasing interest in sustainability and computing more generally. However, AI is helping to drive this larger movement, rather than simply riding along. Indeed, it's hard to imagine that AI would not be central to understanding and managing the great complexity of maintaining a healthy planet in the face of pervasive and transformative human activity. A visible and scientifically significant landmark in this growth of AI and sustainability is the establishment of the Computational Sustainability Institute, 2 with its focus on AI and many sustainability areas, such as biodiversity and alternative energy. The institute grew from a 2008 Expedition in Computing Award from the NSF to Cornell University, Oregon State University, Bowdoin College, Howard University, and other partners, quickly attracting other researchers, educators, government, and industry. The first conference on computational sustainability took place in 2009, followed by a second in 2010 and leading in 2011 to a special track on Computational Sustainability at the Association for the Advancement of Artificial Intelligence (AAAI) conference. Coinciding with the institute's founding was a groundswell of activity to include sustainability tracks at other AI-related conferences. Machine learning and data mining have been strong among these, and in 2010, a second sustainability-focused Expedition in Computing award was given to the University of Minnesota and its partners for data-driven understanding of climate change and related phenomena. Forthcoming articles in this new IEEE Intelligent Systems AI and Sustainability Department will elaborate on AI's deployment in many areas of sustainability as well as the challenges and opportunities that sustainability issues bring to AI research, education, and practice. This opening article will touch upon the main themes at the intersection of AI and sustainability, but it will primarily concentrate on the larger contexts of sustainability, and on computing and sustainability, thereby setting the stage for articles to come. Sustainability The United Nations' Bruntland report contains a popular and succinct definition of sustainability: "Sustainable development is development that meets the needs of the present

SBS Swiss Business School, 2025

The net gain of AI and sustainable development remains a critical area of inquiry, as Artificial Intelligence (AI) offers opportunities and challenges in advancing sustainability. AI offers benefits like optimized energy use and improved resource efficiency, but its rapid adoption also results in high energy consumption, increased e-waste, and resource depletion. This contradiction is referred to as the Sustainability Paradox and calls for a structured evaluation of AI’s impact. The Sustainable AI Impact Assessment Framework (SAIAF) serves as a tool to measure AI’s role in sustainability while accounting for its unintended consequences. It assesses AI across three dimensions: environmental (carbon footprint, energy use), social (labor market changes, ethical issues), and economic (cost efficiency, long-term resilience). Case studies in precision agriculture and smart energy grids demonstrate how SAIAF aids policymakers and industries in minimizing negative impacts while enhancing the sustainability gains of AI. However, fragmented global policies complicate the effective implementation of AI for sustainability, leading to inconsistent regulations and misaligned objectives. This paper highlights the importance of cohesive AI governance and shared sustainability standards. By incorporating SAIAF into policies and industry practices, AI can shift from being resourceheavy to becoming a strategic sustainability ally. The study suggests further research on the sustainability of AI lifecycles, adaptive policies, and innovations in energy-efficient AI systems for a more balanced and responsible future.

Sustainability, 2022

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

Scientific African, 2021

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AI and Ethics

This paper outlines the ethical implications of AI from a climate perspective. So far, much of the discussion around AI ethics have focused on bias, unexplainable outcomes, privacy and other social impacts of such systems. The role and contribution of AI towards climate change and the ethical implications of its contribution to an unjust distribution of impact on the planet, humans and flora and fauna have not yet been covered in detail within the technical community. Within this paper, we aim to raise some of the issues of AI associated with climate justice and we propose a framework that will allow the AI and ICT industries to measure their true impact on the planet, propose an organisational structure to take this work forward and propose future research areas for this important topic.