探索人工智能可持续扩展的困境：企业人工智能环境影响的投射研究
Exploring the sustainable scaling of AI dilemma: A projective study of corporations' AI environmental impacts
AI总结：
本文提出一种估算企业AI组合环境影响的方法，预测到2030年AI电力使用在高采用情景下将增加24.4倍，并呼吁通过价值链协调、标准化框架和“环境回报”指标实现净零目标。
摘要：
人工智能（AI）的快速增长，特别是大型语言模型（LLMs），引发了人们对其全球环境影响的担忧，这种影响不仅限于温室气体排放，还包括硬件制造和生命周期终结过程。主要提供商的不透明性阻碍了企业评估其AI相关的环境影响并实现净零目标。在本文中，我们提出了一种方法来估算企业AI组合的环境影响，提供可操作的见解，而无需广泛的AI和生命周期评估（LCA）专业知识。结果证实，大型生成式AI模型消耗的能源比传统模型多4600倍。我们的建模方法考虑了AI使用量的增加、硬件计算效率的提升以及符合IPCC情景的电力组合变化，预测了到2030年的AI电力使用情况。在高采用情景下，由于生成式AI和智能体的广泛采用，以及与日益复杂的模型和框架相关联，预计AI电力使用将增加24.4倍。到2030年减轻生成式AI的环境影响需要整个AI价值链的协调努力。仅在硬件效率、模型效率或电网改进方面的孤立措施是不够的。我们提倡标准化的环境评估框架、价值链所有参与者更大的透明度，以及引入“环境回报”指标，以使AI发展与净零目标保持一致。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
