Green Manufacturing and Supply Chains and the Role of Green IT
Green Software Foundation · 2025-01-14
In this CXO Bytes episode, Sanjay Podder and May Yap, SVP & CIO of Jabil, discuss the intersection of green IT, responsible AI, and sustainable manufacturing, highlighting Jabil’s integration of renewable energy, circular economy principles, and AI-driven solutions into its operations, its commitment to carbon neutrality by 2045, and key initiatives like energy-efficient manufacturing, water conservation, and e-waste management, while emphasizing the role of green IT practices and AI in driving sustainable innovation.
Green Software Foundation. 2025-01-14. Green Manufacturing and Supply Chains and the Role of Green IT. https://www.youtube.com/watch?v=55fecE4CYH8 (AI & Environment Resource Hub; record video-047; collection snapshot 2026-09-15).
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Shared topics: Cross-cutting sustainability; Governance and society; Energy and electricity; Climate and greenhouse gases; Water; E-waste and circularity; Biodiversity and ecosystems
In the Environmental Impacts of Data Centers 101 course, you will learn to analyze the environmental impacts of data centers using a life cycle assessment (LCA) perspective that goes beyond what you see in the news. This will include energy, water, land use, carbon emissions, global supply chains, e-waste concerns, ecological impacts, and environmental justice case studies. Whether you work in tech, sustainability, policy, or are just curious about how AI systems operate behind the scenes, this course gives you the clarity and frameworks to understand these impacts from end-to-end.
Nathaniel Burola
Shared topics: Cross-cutting sustainability; Governance and society; Energy and electricity; Climate and greenhouse gases; Water; E-waste and circularity
This paper examines how AI has emerged as a contested force in climate discourse, with some championing its potential for renewable energy and emissions monitoring while others highlight its carbon footprint, water use, and e-waste. The authors apply the concept of Jevons' Paradox to AI, arguing that efficiency gains may paradoxically spur increased consumption and that understanding these second-order rebound effects requires combining lifecycle assessments with socio-economic analyses. The paper argues that AI's environmental trajectory depends on systemic governance choices rather than technical optimization alone.
FAccT '25: Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency
Shared topics: Cross-cutting sustainability; Governance and society; Energy and electricity; Climate and greenhouse gases; Water; Biodiversity and ecosystems
Artificial intelligence (AI) is in the media spotlight for its potential to transform the economic and research sectors, among others. This drives funding bodies to support AI-based innovation, with for example the Horizon Europe and Digital Europe programmes run by the European Union, or France’s investment strategy France 2030 (national strategy for AI). On the other hand, the environmental impacts of AI are now better understood, and we cannot ignore the role of AI on electricity and water usage, mineral resource depletion, and greenhouse gas emissions1,2. To bring together innovation and sustainability, the French Department for the Environment (Ministère en charge de la Transition Écologique) has decided to require the use of the Green Algorithms tool for funding applications on the topic of AI and climate change. Applicants now have to include estimates of the carbon footprint and energy usage of the different development phases of the proposed AI solution. This was tested on a first funding call “Demonstrators of frugal AI for sustainable development of local communities”. The first applications were received in December 2023, with positive feedback from the different stakeholders. Applicants in particular approved of this new criterion, as they understood its necessity, found the tool easy to use, and did not consider this to slow down innovation. Following this successful implementation in a first funding call, it was decided to include the Green Algorithms tool more systematically in the application guidelines of other AI-related funding calls run by the Department. The goal of this piece is to reflect on the inclusion of environmental criteria in AI funding calls and share the lessons learned with other funding bodies internationally to promote similar initiatives across the AI ecosystem.
Loic et al.
Shared topics: Cross-cutting sustainability; Governance and society; Energy and electricity; Climate and greenhouse gases; Water; Biodiversity and ecosystems
This course will help you understand AI's climate implications and identify practical next steps within your organization. The course begins with demystifying the connection between AI, Large Language Models (LLMs), data centers, and energy and water demand. Then you will learn about AI's environmental footprint, the related environmental and community impacts, you will evaluate real-world applications of AI across climate adaptation, energy transition, and nature conservation, and understand the business and policy landscape shaping corporate decisions.
