Aligning Artificial Intelligence with Economic Policy for Decarbonisation: A Multi-Level Simulation Framework
脱炭素化のための人工知能と経済政策の連携：マルチレベルシミュレーションフレームワーク (AI 翻訳)
Madalina Ana BURDUJA, Dorel Mihai PARASCHIV
🤖 gxceed AI 要約
日本語
本研究は、AIと行動経済学・厚生経済学を統合した多層的脱炭素モデル（AEDM）を提案する。家庭・企業のミクロ行動、都市インフラの中間、ガバナンスのマクロの各レベルで政策評価を行い、マサチューセッツ州とソウル市の比較シミュレーションを通じて、AI適応的政策が炭素税や現状維持よりも排出削減・コスト・公平性で優れることを示した。アルゴリズム政策の倫理的課題も議論。
English
This study introduces the AI-Enhanced Decarbonisation Model (AEDM), integrating AI with behavioral and welfare economics for adaptive climate policy. Simulations in Massachusetts and Seoul show that AI-driven policy feedback outperforms static carbon tax and business-as-usual scenarios in emissions reduction, cost-effectiveness, and equity. The framework supports evidence-based, equitable climate transitions and discusses ethical challenges.
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、SSBJや有報での気候関連開示が進む中、AIを活用した政策シミュレーションは、企業や自治体の脱炭素計画立案に有用。特に、炭素税や適応的政策の効果を動的に評価する本モデルは、日本のGX政策にも示唆を与える。
In the global GX context
This paper demonstrates the potential of AI to enhance climate policy effectiveness by integrating behavioral economics. It offers a scalable model that can inform ISSB-aligned scenario analysis and transition planning globally, bridging the gap between static policy and dynamic system behavior.
👥 読者別の含意
🔬研究者:Provides a novel multi-level simulation framework combining AI and welfare economics for policy evaluation, suitable for further empirical testing.
🏢実務担当者:Corporate sustainability teams can use the AEDM for dynamic scenario analysis in transition planning and climate risk management.
🏛政策担当者:Policymakers can leverage AI-adaptive policies to improve cost-effectiveness and equity in decarbonisation strategies, informing carbon pricing and regulatory design.
📄 Abstract（原文）
Climate change is accelerating, yet policy responses remain fragmented, slow, and often outdated. This study presents the AI-Enhanced Decarbonisation Model (AEDM), a multi-level framework that integrates artificial intelligence with behavioural and welfare economics to support adaptive climate policymaking. AEDM evaluates decarbonisation strategies across micro (household and firm behaviour), meso (urban infrastructure), and macro (governance) levels. Using comparative simulations of Massachusetts (USA) and Seoul (South Korea), the model tests three scenarios: Business-as-Usual, Carbon Tax, and AI-Adaptive Policy. Results show that AI-driven policy feedback improves emissions reductions, lowers abatement costs, and enhances equity outcomes. The model consistently outperforms static interventions by aligning policy actions with dynamic system behaviour and social realities. This research contributes a scalable, policy-oriented tool that supports evidence-based, equitable climate transitions. Ethical and governance challenges of algorithmic policymaking are also discussed, with recommendations for future interdisciplinary research.
🔗 Provenance — このレコードを発見したソース
- crossref https://doi.org/10.24818/icess/2025/013first seen 2026-05-22 04:54:25 · last seen 2026-05-27 05:05:39
🔔 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。