Decision-making Related to Critical Materials Supported by Spatial Information on Environmental and Economic Performance
環境・経済パフォーマンスに関する空間情報に支援された重要鉱物に関わる意思決定 (AI 翻訳)
Xiaoyu Zhou
🤖 gxceed AI 要約
日本語
本論文は、米国の州レベルで電気自動車（EV）や風力タービンなどの新エネルギー技術に必要なリチウム、コバルトなどの重要鉱物（CM）需要を2050年まで予測。地域別の技術経済評価（TEA）およびライフサイクル評価（LCA）により、立地条件（労働コスト、電力コスト、税制等）が経済性と環境影響に大きく影響することを示し、サプライチェーン強靭化のための空間分析の重要性を指摘する。
English
This paper projects critical material demand (lithium, cobalt, etc.) for EV and renewable energy technologies in the U.S. at state level through 2050. Using regionalized techno-economic assessment and life cycle assessment, it shows that facility location factors (labor, electricity, land costs, tax policy) significantly affect economic viability and environmental impacts, highlighting the need for spatially resolved analyses to build supply chain resilience.
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
米国事例ではあるが、日本でもEV普及や蓄電池・風力発電に必要な重要鉱物の安定的確保は喫緊の課題。地域ごとの経済性・環境影響の差異を考慮した立地戦略は、日本のGX政策における鉱物資源戦略やサプライチェーン強靭化にも示唆を与える。
In the global GX context
This study provides a spatially explicit framework for evaluating critical material supply chains, relevant to global efforts in building resilient clean energy supply chains. While US-focused, the methodology can inform similar assessments in other regions, supporting transition finance and policy decisions.
👥 読者別の含意
🔬研究者:Provides a methodological framework combining TEA and LCA with spatial analysis for critical materials.
🏢実務担当者:Useful for companies in battery or renewable energy supply chains to assess location-specific economic and environmental trade-offs.
🏛政策担当者:Informs regional industrial policy and infrastructure investment decisions for securing critical material supply chains.
📄 Abstract（原文）
The adoption of innovative energy technologies, e.g., electrified transport, energy storage, and renewable power sources, is accelerating at an unprecedented pace, driven by climate mitigation targets and industrial emission reduction. As deployment expands, the primary barrier to transformational energy solutions is shifting from technological feasibility to the availability, processing, and governance of critical materials. The emerging technologies — including electric vehicle (EV) batteries, wind turbines, and power electronics — depend on materials such as lithium, cobalt, nickel, rare earth elements, and platinum group metals. This research investigates the demand for critical materials (CM) and examines the economic and environmental performance of their manufacturing technologies at regional level for securing a domestic supply chain in the U.S. The study addresses the questions of how alternative energy technology adoption and critical material demand vary by state, and how regional factors influence the economic viability and environmental impact of emerging CM manufacturing technologies. The study projects the EV demand in each state through 2050, revealing significant geographic variation in EV adoption rates and corresponding critical material demand across the U.S at the state level. Regionalized techno-economic assessment (TEA) demonstrates that economic feasibility of CM technologies is sensitive to facility location indicators such as labor costs, electricity costs, land costs, and tax policy. Consequently, net present value and internal rate of return (IRR) vary significantly across regions. Corresponding life cycle assessment (LCA) results show that environmental impacts are affected by regional grid composition and transportation distances. This research highlights the necessity of spatially resolved analyses of CM technologies to inform strategic infrastructure location selection, optimize resource allocation, and build supply chain resilience in support of innovative energy technology adoption.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.25394/pgs.32772336first seen 2026-07-15 04:49:16
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。