AI-Driven Energy Efficiency versus AI-Induced Energy Demand: A Dynamic Computable General Equilibrium (CGE) Analysis of Vietnam’s Twin Transition
AI主導のエネルギー効率化とAI誘発のエネルギー需要：ベトナムのツイン・トランジションの動学的応用一般均衡（CGE）分析 (AI 翻訳)
Anh Bui-Tuyet, Nhat Duy Lai, Quyen Hua-Thi-Ngoc
🤖 gxceed AI 要約
日本語
AIはエネルギー転換に二面性を持つ。重工業での生産性向上はエネルギー原単位を下げるが、データセンターの電力需要が増大する。ベトナムの23部門CGEモデルで2035年までシミュレーションし、Green AIはGDPを+1.79%押し上げる一方、Brown AIは消費を0.42%減少させる。ツイン・トランジションはマクロ的には実現可能だが、繊維・皮革など労働集約産業に調整コストが集中する。
English
AI has a dual role in energy transition: productivity gains in heavy industry reduce energy intensity, while data centers surge electricity demand. Using a 23-sector CGE model for Vietnam through 2035, Green AI boosts GDP by +1.79%, while Brown AI cuts consumption by 0.42%. The twin transition is macro-feasible but adjustment costs fall on labor-intensive sectors like textiles and footwear.
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活用による省エネとデータセンター電力需要の両立が課題。本分析は、AI投資が産業構造に与える分配効果を示し、日本の産業政策やSSBJ開示におけるリスク評価に示唆を与える。
In the global GX context
Globally, this study informs the AI-energy nexus debate, relevant for ISSB/CSRD disclosure on climate risk and transition planning. It highlights distributional impacts of AI-driven productivity gains, useful for policymakers balancing decarbonization and industrial competitiveness.
👥 読者別の含意
🔬研究者:Provides a quantitative framework for modeling AI's dual energy effects in emerging economies.
🏢実務担当者:Highlights sectoral risks and opportunities from AI adoption, useful for transition planning.
🏛政策担当者:Shows macro-feasibility of twin transition but warns of uneven adjustment costs, guiding policy design.
📄 Abstract（原文）
Artificial Intelligence (AI) acts as a double-edged sword in the energy transition: its deployment in heavy industry generates productivity dividends that reduce energy intensity (the “Green AI” effect), while the data centers and hardware required to run it surge electricity demand and strain emerging-economy power grids (the “Brown AI” effect). This paper quantifies both effects and their interaction for Vietnam using a 23-sector recursive dynamic Computable General Equilibrium (CGE) model calibrated to the 2019 Input-Output Table and simulated through 2035. Three scenarios are evaluated: Green AI (S2), modelled as a Total Factor Productivity (TFP) shock to heavy manufacturing and electricity; Brown AI (S3), modelled as an exogenous investment surge in Information Technology (IT) hardware and services; and a combined Twin Transition (S4). Green AI delivers a compounding GDP dividend of +0.98% by 2030 and +1.79% by 2035, with consumption rising +1.17% and +2.03%. Brown AI is macro-neutral in GDP terms but imposes a consumption cost of 0.42% by 2030 as infrastructure investment crowds out household expenditure. The Twin Transition is approximately macro-additive (GDP +1.06% by 2030 and +1.95% by 2035), with consumption recovering to +1.70% above baseline by 2035. The dominant structural trade-off is distributional: real exchange rate appreciation generated by Green AI’s export surge displaces Textiles ( 5.5%) and Leather and Footwear ( 16.1%), even as Heavy Manufacturing (+12.9%) and IT Hardware (+10.9%) expand substantially. Under S3 alone the Electricity sector expands only +0.10% by 2030 against a 14.87% per year IT investment surge, indicating a binding generation capacity that the Green AI productivity shock relaxes in S4. The Twin Transition is macro-feasible, but its adjustment costs fall unevenly on the manufacturing workforce.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1051/e3sconf/202672303002/pdffirst seen 2026-07-31 05:11:39
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