Carbon Accounting and Beyond: An Evidence-Based Life Cycle Assessment of the Environmental Impacts of Data Center IT Equipment
カーボン・アカウンティングとその先：データセンターIT機器の環境影響のエビデンスに基づくライフサイクルアセスメント (AI 翻訳)
Meghann Smith, Manveer Mann, Pankaj Lal
🤖 gxceed AI 要約
日本語
本研究は、データセンターIT機器のライフサイクルアセスメント（LCA）と従来の企業GHG会計手法（平均データ法・支出基準法）を比較。LCAは0.710 kg CO2 eq/kWhと平均データ法（0.723）と近似したが、支出基準法は1.07と過大評価。シナリオ分析では系統脱炭素化が最も効果的だが、環境トレードオフも明示。包括的なLCAの重要性を示唆。
English
This study compares life cycle assessment (LCA) of data center IT equipment with conventional corporate GHG accounting methods (average-data and spend-based). LCA yields 0.710 kg CO2 eq/kWh, close to the average-data method (0.723 kg CO2 eq/kWh), while the spend-based method overestimates (1.07 kg CO2 eq/kWh). Scenario analysis identifies grid decarbonization as the most effective mitigation, but reveals environmental trade-offs. The findings advocate for comprehensive LCA to improve reporting accuracy and sustainability decision-making.
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
日本のデータセンター市場は拡大しており、Scope 2排出の正確な算定が重要。本論文のLCAと平均データ法の一致は、現行の有報/統合報告書における排出量開示の信頼性向上に寄与。また、系統脱炭素化シナリオは、日本政府のGX政策（非化石証書等）との連動を考える上で参考になる。
In the global GX context
As data center energy demand surges globally (especially with AI), accurate GHG accounting is critical for transition finance and climate disclosure. This paper empirically demonstrates limitations of spend-based methods (common in Scope 3) and aligns LCA with average-data methods, relevant for TCFD/ISSB reporting. The grid decarbonization scenario finding is particularly timely given global push for low-carbon electricity purchase agreements.
👥 読者別の含意
🔬研究者:This study provides a robust LCA framework and comparison of carbon accounting methods for data centers, useful for refining emission factors and methodology.
🏢実務担当者:Demonstrates the inaccuracy of spend-based methods for data center equipment; suggests using LCA or average-data methods for more reliable Scope 2/3 reporting.
🏛政策担当者:Highlights need for standardized LCA-based reporting guidance in data center sector, especially for evaluating grid decarbonization impacts.
📄 Abstract（原文）
Data centers are essential to modern infrastructure, but are significant contributors to greenhouse gas (GHG) and related environmental challenges. Despite energy efficiency improvements, rising electricity demands driven by technologies like AI pose challenges to sustaining low-emission sector growth. Regulatory requirements mandate data center operators to report electricity use and emissions, yet current methods, particularly pertaining to indirect sources, remain insufficient. This study explores how life cycle assessment (LCA)-based estimates of data center IT equipment impacts compared with commonly used corporate GHG accounting methods: average-data and spend-based. Environmental impacts were modeled at both grouped and granular levels to support enterprise-wide reporting and operational decision-making. Sensitivity and uncertainty analysis validate the robustness of the LCA models. Scenario analysis was also conducted to assess emission abatement strategies, including on-site renewable energy generation and operation with a low-carbon electricity grid. The results show that the LCA approach produced emissions of 0.710 CO2 eq/kWh, along with additional burdens that are not captured through carbon-only metrics. The LCA results are closely aligned with the average-data method (0.723 kg CO2 eq/kWh) while the spend-based method yielded substantially higher estimates (1.07 kg CO2 eq/kWh), highlighting the inaccuracies associated with volatile market prices. Scenario analysis identifies grid decarbonization as the most effective mitigation pathway, while also demonstrating environmental trade-offs across impact categories. Findings highlight the importance of comprehensive LCA-based assessment methods to improve emission reporting accuracy, transparency, and sustainability-focused decision-making in the data center sector.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/su18115671first seen 2026-06-06 05:10:36
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