🤖 AI Summary
Rapid AI advancement has exacerbated challenges in assessing environmental impacts and lacks transparency in corporate carbon footprints. Method: This study develops an enterprise-level AI environmental impact assessment framework covering the full lifecycle—training, inference, hardware manufacturing, and end-of-life—enabling non-LCA experts to quantify carbon footprints. It introduces a novel multidimensional projection model integrating scaling laws, semiconductor energy-efficiency trends, and grid decarbonization pathways, alongside the first “Environmental Return on Emissions” (EROE) metric to align AI development with net-zero goals. Results: Combining streamlined LCA, IPCC electricity scenarios, empirical compute analysis, and generative AI adoption forecasts, we find: (1) large language models consume 4,600× more energy than traditional models; (2) under high-adoption scenarios, global AI electricity demand will increase 24.4× by 2030; and (3) isolated technological optimizations are insufficient to meet climate targets.
📝 Abstract
The rapid growth of artificial intelligence (AI), particularly Large Language Models (LLMs), has raised concerns regarding its global environmental impact that extends beyond greenhouse gas emissions to include consideration of hardware fabrication and end-of-life processes. The opacity from major providers hinders companies' abilities to evaluate their AI-related environmental impacts and achieve net-zero targets.In this paper, we propose a methodology to estimate the environmental impact of a company's AI portfolio, providing actionable insights without necessitating extensive AI and Life-Cycle Assessment (LCA) expertise. Results confirm that large generative AI models consume up to 4600x more energy than traditional models. Our modelling approach, which accounts for increased AI usage, hardware computing efficiency, and changes in electricity mix in line with IPCC scenarios, forecasts AI electricity use up to 2030. Under a high adoption scenario, driven by widespread Generative AI and agents adoption associated to increasingly complex models and frameworks, AI electricity use is projected to rise by a factor of 24.4.Mitigating the environmental impact of Generative AI by 2030 requires coordinated efforts across the AI value chain. Isolated measures in hardware efficiency, model efficiency, or grid improvements alone are insufficient. We advocate for standardized environmental assessment frameworks, greater transparency from the all actors of the value chain and the introduction of a"Return on Environment"metric to align AI development with net-zero goals.