A Scoping Review of Energy Consumption and Sustainability Benefits in Renewable Energy Applications
再生可能エネルギー応用におけるエネルギー消費と持続可能性の利益に関するスコーピングレビュー (AI 翻訳)
Sofian Lusa, Aghnia Nadhira Aliya Putri, Myrza Rahmanita, Rahmat Inkadijaya
🤖 gxceed AI 要約
日本語
本スコーピングレビューは、再生可能エネルギーシステムにおけるAIの導入がもたらすエネルギー消費と持続可能性のトレードオフを、2014～2025年の76の査読研究から総合的に分析。AIのエネルギー消費はバリューチェーン全体で不均一であり、効率向上の恩恵はモデル複雑化の増大によって相殺されることを指摘。AIのエネルギー消費と持続可能性をバランスする枠組みを提案し、政策立案者や実務者へのエビデンスベースの指針を提供する。
English
This scoping review synthesizes 76 peer-reviewed studies (2014–2025) to examine the paradox of AI integration in renewable energy: while AI optimizes efficiency, its computational demands impose environmental costs. It finds that AI's energy consumption varies across value chain stages and that benefits are offset by model complexity. The study proposes a framework for balancing AI energy use and sustainability, offering evidence-based guidance for policymakers and practitioners.
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
日本では再生可能エネルギー導入拡大に伴い、AI活用の効率化とその環境負荷のバランスが重要課題。本レビューはAI導入判断のエビデンスを提供し、日本のエネルギートランジション政策（GX実現に向けたエネルギー需給構造改革）に示唆を与える。ただし、日本特有のデータは含まれていない。
In the global GX context
Globally, this review addresses the critical tension between AI's benefits in renewable energy and its environmental cost, relevant to TCFD/ISSB climate risk assessments and energy transition planning. It challenges simplistic narratives and provides a framework that can inform corporate sustainability strategies and policy design, especially for integrating AI in grid management and renewable forecasting.
👥 読者別の含意
🔬研究者:Provides a synthesized evidence base on AI's energy trade-offs in renewables, highlighting gaps for future research.
🏢実務担当者:Offers a framework for balancing AI deployment decisions with sustainability goals, useful for energy companies and tech vendors.
🏛政策担当者:Informs evidence-based policies on AI regulation and renewable energy planning to avoid unintended environmental costs.
📄 Abstract（原文）
The rapid integration of artificial intelligence (AI) in renewable energy systems presents a paradox: while AI optimizes energy efficiency and forecasting accuracy, its computational demands impose substantial environmental costs. From this perspective, the approaches proposed by researchers to address this issue are of interest, as they aim to ensure that the benefits outweigh the costs. Progress in their implementation will determine whether AI ultimately accelerates or hinders renewable energy transitions and transforms from a potentially double-edged technology into a genuinely sustainable catalyst for decarbonization. This scoping review addresses a critical knowledge gap at the intersection of digital innovation and environmental sustainability. It synthesizes evidence from 76 peer-reviewed studies (2014–2025) to examine AI's energy footprint, operational benefits, and trade-off dynamics. These findings challenge simplistic narratives about AI as either uniformly beneficial or harmful for sustainability in relation to the studied sector. AI’s energy consumption is not evenly distributed across the various stages of the value chain, and the benefits of increased equipment efficiency are offset by the growing complexity of the AI models. The study proposes a framework for balancing AI energy consumption and sustainability, providing evidence-based guidance for policymakers and practitioners navigating AI deployment decisions in renewable energy transitions.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.17323/fstig.2026.30069first seen 2026-06-25 04:34:59
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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。