Green artificial intelligence adoption in industrial systems: A SWOT assessment of opportunities and challenges
産業システムにおけるグリーン人工知能の導入：機会と課題のSWOT評価 (AI 翻訳)
Jayesh Rane
🤖 gxceed AI 要約
日本語
本論文は、産業システムにおけるグリーンAI導入の機会と課題をSWOT分析で評価。質問票とインタビューによる混合手法を用い、組織の意欲、技術的準備、規制基準が導入に正の影響を与えることを示した。エネルギー効率的なアルゴリズムや再生可能コンピューティングの機会を特定する一方、初期投資やスキル不足が障壁となる。
English
This paper assesses green AI adoption in industrial systems via SWOT analysis using mixed methods. Findings show organizational willingness, technological readiness, and regulatory standards positively influence adoption (73.4% variance). Opportunities include energy-efficient algorithms and renewable computing, while barriers include high initial investment and skill gaps.
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GX政策ではAIのエネルギー消費対策はまだ十分に議論されていないが、本論文は産業システムにおけるグリーンAI導入の成功要因と障壁を分析しており、今後の政策立案や企業戦略に示唆を与える。
In the global GX context
As AI adoption accelerates globally, this paper provides a structured SWOT analysis of green AI implementation, offering actionable insights for policymakers and practitioners balancing innovation with climate goals.
👥 読者別の含意
🔬研究者:Provides a theoretical framework combining environmental, technological, and organizational factors for green AI adoption.
🏢実務担当者:Offers implementation roadmaps and critical success factors for industrial practitioners.
🏛政策担当者:Highlights the role of regulatory standards and policies in promoting green AI.
📄 Abstract（原文）
Exponential growth of artificial intelligence (AI) has already introduced a level of energy consumption and carbon emission that would have never been seen before, and hence causing a severe sustainability paradox in the industrial system. As AI technologies can bring about operational efficiencies and productivity gain, its environmental footprint endangers world climatic and sustainable development agendas. This paper provides a SWOT analysis of the green implementation of artificial intelligence in industrial systems under the scope of the current urgent demand on environmentally friendly AI implementation patterns. We used a mixed-method approach by analyzing the data of industrial organizations in manufacturing, energy, logistics, and technology sectors using the convenient sample of structured questionnaires and semi-structured interviews. The methodology combines Structural Equation Modeling and Partial Least Squares regression, Multi-Criteria Decision Analysis (based on Analytical Hierarchy Process) in order to consider the most critical success factors and the barriers to implementation. Findings indicate that organizational willingness, technological readiness, and regulatory standards all contribute positively (73.4%- variance) to the difference in the levels of green AI adoption. The SWOT analysis recognizes the high opportunities on the energy efficient algorithms, renewable computing infrastructure and integration of the circular economy whereas the barriers to change are high initial investment, skills deficiency and technology hurdles. The research has a theoretical impact in that it creates a framework of Green AI Adoption that combines the three aspects of environmental, technological and organizational and is practical due to its offering of the implementation roadmaps that can be used by industrial practitioners. The study forms a baseline of knowledge regarding sustainable AI change in industries by providing essential understanding to the policy-makers, AI developers and business executives in the current complicated field of artificial intelligence invention and climate management.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.70593/deepsci.0201002first seen 2026-05-15 20:13:50
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