人工知能と持続可能なエネルギー転換：イノベーション・活用経路と政治的・金融的・経済的安定性の条件付き役割
Artificial Intelligence and Sustainable Energy Transition: Innovation–Utilization Pathways and the Contingent Roles of Political, Financial, and Economic Stability (原題)
Rongrong Li, Yuanfan Li, Qiang Wang
🤖 gxceed AI 要約
日本語
60カ国・2010〜2022年のパネルデータを用い、AI発展と再生可能エネルギー消費シェアの関係を固定効果・動学的閾値モデルで分析。AIは再エネ比率を高めるが、その効果は高所得国で強く、中所得国では応用チャネルが有意でない。政治的・金融的安定性が関係を強化し、経済安定性は限界的逓減を示す。AI主導の転換には安定したガバナンスと強靭な金融が不可欠と結論。
English
Using balanced panel data for 60 countries (2010–2022), this study examines how AI development relates to renewable energy consumption via fixed-effects, dynamic threshold, and scenario models. AI shows a positive association with renewable share, stronger in high-income countries; the technology channel outperforms the application channel, which is insignificant in middle-income economies. Political and financial stability strengthen the AI–energy transition link, while economic stability shows diminishing marginal effects.
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
日本はAI・デジタル基盤と再エネ導入の双方で先進国に位置し、GX推進には技術チャネルと安定した政策・金融環境の組み合わせが鍵となる。本稿は中所得国との比較から、日本の強みと課題を相対化する視点を提供する。
In the global GX context
For global GX scholarship, this paper links AI capability to energy transition outcomes and shows that governance and financial stability are contingent enablers—relevant to transition finance debates and to ISSB/TCFD-adjacent discussions on enabling environments for decarbonization.
👥 読者別の含意
🔬研究者:AIと再エネ転換の条件付き効果を国別パネルで示し、閾値・シナリオ分析の枠組みを提供する。
🏢実務担当者:AI投資やデジタル化が再エネ調達・エネルギー効率に寄与する条件を、自社の立地・制度環境と照らして検討する材料になる。
🏛政策担当者:AI普及だけでなく、政治的・金融的安定とデジタル基盤整備を同時に進める政策設計の必要性を示唆する。
📄 Abstract（原文）
ABSTRACT Artificial intelligence (AI) is increasingly linked to sustainable energy transitions, but its role can be highly contextual on country‐specific technological pathways and macro‐level stability. Using balanced panel data for 60 countries from 2010 to 2022, this study examines the relationship between AI indices and renewable energy consumption outcomes through fixed‐effects models, dynamic panel threshold models, and scenario‐based regressions. The results support the positive association between AI development and renewable energy consumption share, with stronger effects in high‐income countries than in middle‐income countries. The technology channel yields a stronger impact than the application channel, while AI application is insignificant in middle‐income countries, suggesting constraints from weak digital infrastructure and absorptive capacity. Threshold results indicate that political and financial stability strengthen the AI–energy transition relationship, whereas economic stability shows a diminishing marginal pattern. Scenario analysis further suggests that AI is most effective under jointly low political, financial, and economic risks. These findings highlight that intelligence‐enabled energy transition requires not only AI development, but also stable governance, resilient finance, and adaptive macroeconomic policies.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1002/sd.71691first seen 2026-10-02 04:58:48
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