AI 中文总结
该研究结合多领域成果，估算ChatGPT类LLM单次查询最终碳成本约0.4美元、对应10gCO₂eq，引入QCC概念以凸显AI对地球健康的影响，为相关研究提供方向。
AI 中文摘要
本文综述并整合了产品生命周期分析领域[36,38]、基于Transformer的现代大语言模型（LLM）使用情况[6]，以及温室气体排放及其对后代的最终后果成本[2]等领域的研究成果。研究表明，LLM一次查询的碳成本，以环境破坏的形式体现为未来人类需承担的成本，约为每次查询0.4美元，对应排放量约为每次查询10克二氧化碳当量。该计算中最主要的未知因素是计算的token数量（1000至100000个token对应每次查询0.012美元至1.2美元）。该数值存在广泛的计算不确定性，不应视为确定事实，而更应作为数量级估计。该估计旨在通过将技术发展的后果以熟悉的单位呈现，揭示AI系统的必然后果，从而助力围绕AI系统的讨论。通过引入每次查询的最终碳成本（QCC），即便存在较大不确定性，也能凸显AI服务的使用发生在超复杂的相互依存系统中，且对地球健康有具体影响。提高对地球健康与AI使用相互依存关系的认识，有助于个体用户通过讨论形成政治观点[5]，并合理使用AI系统[31]。研究还确定了提高估计准确性的方法，例如纳入AI的水资源消耗成本或进一步确定查询的实际token数量，这些均为未来的研究目标。
英文摘要
This paper reviews and combines findings from the fields of product and life-cycle analysis [36, 38], the usage of modern transformer- based large language models (LLM) [6], as well as on greenhouse gas emissions and the ultimate cost of their subsequent consequences for future generations [2]. In this paper, it is shown that the carbon cost of a LLM query is in the order of magnitude of (USD) $0.4 per query for the future human population in the form of environmental disruptions. This corresponds to emissions in the magnitude of 10 gCO2eq/query. The most significant unknown factor in that calculation being the number of tokens computed (1k to 100k tokens equal 1.2 cent/query to 120 cent/query). This number is subject to a wide range of calculation uncertainties and is less to be seen as a matter of fact and more as an order of magnitude estimate. This estimate is aimed towards aiding the discourse surrounding AI systems by uncover- ing the inevitable consequences of technological development by the means of attaching a consequence in a familiar unit to it. By the introduction of the per query ultimate carbon cost (QCC), even if attached to great uncertainty, it is highlighted that the use of AI services happens within hypercomplex interdependent systems and has concrete consequences for our planetary health. The spread of the awareness about the interdependence of planetary health and AI usage can be useful for the individual user in the formation of political opinion through discourse [5] as well as a literate usage of AI systems [31]. Ways to increase the accuracy of the estima- tions, such as incorporating the cost of AIs water consumption or further determining the realistic token count of a query, have been identified as further research targets.
Comments5 pages,0 figures. Accepted at the 2nd International Workshop on Low Carbon Computing (LOCO 2026), Lancaster University, United Kingdom, 10-11 September 2026. Part of the LOCO 2026 proceedings, arXiv:LOCO2026/P15
