ChatGPTクエリの究極の炭素コスト
The ultimate carbon cost of a ChatGPT query (原題)
Paul Kron
🤖 gxceed AI 要約
日本語
本論文は、大規模言語モデル（LLM）のクエリあたりの炭素コストをライフサイクル分析と温室効果ガス排出の観点から推定し、将来世代への環境影響を金銭換算して約0.4ドル/クエリ（約10gCO2eq/クエリ）と算出した。トークン数に大きな不確実性があるものの、AI利用が惑星の健康に与える影響を認識するための枠組みを提供する。
English
This paper estimates the carbon cost of a large language model (LLM) query using life-cycle analysis and greenhouse gas emissions, calculating an ultimate cost of approximately $0.4 per query (about 10 gCO2eq/query) for future generations. Despite significant uncertainty in token counts, it provides a framework for recognizing the planetary health impacts of AI usage.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本では、AI活用が進む一方で、その環境負荷の可視化が求められている。本論文のQCC指標は、企業のAI利用に伴うScope 3排出量の算定や、SSBJ開示における間接排出の考慮に役立つ可能性がある。
In the global GX context
Globally, this paper contributes to the discourse on AI's environmental footprint, offering a metric (QCC) that can inform corporate disclosure under frameworks like TCFD and ISSB, and support policy discussions on sustainable AI deployment.
👥 読者別の含意
🔬研究者:AIの環境影響評価の方法論と不確実性の扱いに関する洞察を提供。
🏢実務担当者:AIサービスの利用に伴う炭素コストを認識し、開示や削減策の検討に活用できる。
🏛政策担当者:AI技術の持続可能な発展に向けた政策立案の基礎データとして有用。
📄 Abstract（原文）
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.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.48550/arxiv.2608.16657first seen 2026-08-20 04:46:15
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