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A concrete implementation case from a city or utility pilot in Generative AI environmental footprint, covering design choices, measured outcomes, and transferable lessons for other jurisdictions.
Essential KPIs for Compute, chips & energy demand across sectors, with benchmark ranges from recent deployments and guidance on meaningful measurement versus vanity metrics.
An in-depth analysis of the most dynamic subsegments within AI for scientific discovery, tracking where momentum is building, capital is flowing, and breakthroughs are emerging.
A comprehensive state-of-play assessment for AI for scientific discovery, evaluating current successes, persistent challenges, and the most promising near-term developments.
A comprehensive state-of-play assessment for AI-powered carbon accounting & MRV, evaluating current successes, persistent challenges, and the most promising near-term developments.
A comprehensive state-of-play assessment for Responsible AI & environmental impact, evaluating current successes, persistent challenges, and the most promising near-term developments.
A practical primer on AI for materials discovery & green chemistry covering key concepts, decision frameworks, and evaluation criteria for sustainability professionals and teams exploring this space.
A rigorous look at the most persistent misconceptions about AI for grid optimization & demand forecasting, with evidence-based corrections and practical implications for decision-makers.
Side-by-side analysis of common myths versus evidence-backed realities in AI for grid optimization & demand forecasting, helping practitioners distinguish credible claims from marketing noise.
Strategic analysis of value creation and capture in AI for grid optimization & demand forecasting, mapping where economic returns concentrate and which players are best positioned to benefit.
Strategic analysis of value creation and capture in AI for materials discovery & green chemistry, mapping where economic returns concentrate and which players are best positioned to benefit.
A forward-looking assessment of Responsible AI & environmental impact trends in 2026, identifying the signals that matter, emerging winners, and red flags that practitioners should monitor.