AI & Environment Resource HubAuthor or publisher unknown · 2025-06-13
The recent Sustainability Council webinar was an insightful and thought-provoking session led by Emma Herd, Partner at Ernst & Young’s Climate Change and Sustainability team and co-leader of EY’s Net-Zero Centre. The session tackled a highly relevant and timely topic: how emerging technologies like AI can be both a powerful tool and a complex challenge in the sustainability journey. Emma covered a wide range of topics, from the exponential growth of AI and its energy demands to its potential to transform ESG outcomes across sectors like manufacturing, logistics, finance and health.
Author unknown. 2025-06-13. Putting the AI in SustAInability. https://www.drinksassociation.com.au/events?Event=PuttingtheAIinSustAInability# (AI & Environment Resource Hub; record pod-0085; collection snapshot 2026-09-15).
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Shared topics: Cross-cutting sustainability; Governance and society; Energy and electricity; Climate and greenhouse gases; Transport and logistics
This study shows that AI-driven transportation systems can significantly reduce urban carbon emissions and improve energy efficiency by optimizing routes, easing congestion, and enhancing public transit, while offering policy guidance for sustainable implementation.
Shared topics: Cross-cutting sustainability; Governance and society; Energy and electricity; Climate and greenhouse gases; Transport and logistics
As the global climate crisis intensifies, governments, industries, and communities worldwide face an increasing pressure to accelerate efforts toward achieving Net Zero emissions. Although the rapid adoption of Artificial Intelligence (AI) is transforming the Information and Communications Technology (ICT) landscape, it presents both opportunities and challenges. While AI offers powerful tools to optimize energy use, […]
Shared topics: Cross-cutting sustainability; Governance and society; Energy and electricity; Climate and greenhouse gases
This study presents a behavior-driven decision framework that helps AI developers choose models balancing accuracy and environmental sustainability by quantifying energy use and emissions during fine-tuning and applying behavioral decision theories.
Shared topics: Cross-cutting sustainability; Governance and society; Energy and electricity; Climate and greenhouse gases
This paper explores green AI as a path to environmental sustainability by promoting energy-efficient models, democratized access, eco-friendly AI applications, and regulatory frameworks to align machine learning practices with global climate goals.