AI-Driven Forecasting of Sustainable Energy Transitions Using Multi-Factor Green Index Modelling
多因子グリーン指標モデリングを用いた持続可能なエネルギー転換のAI駆動型予測 (AI 翻訳)
Bharati Amit Patil, Komal Korade, Deepashree K. Mehendale
🤖 gxceed AI 要約
日本語
本研究は、炭素排出量、再生可能エネルギー比率、エネルギー効率、経済成長、グリーン技術革新などの要素を統合した「知能グリーン転換指標（IGTI）」を開発し、AIモデルを用いて国のエネルギーシステムの進化を予測する。機械学習・深層学習により、主要な変化要因を特定し、政策シミュレーション（再エネ投資、炭素価格、水素導入など）を2040年まで行う。結果、再エネ投資、技術革新、強力な政策枠組みが持続可能な転換の主な推進力であることを示す。
English
This study develops the Intelligent Green Transition Index (IGTI), combining carbon emissions, renewable energy share, energy efficiency, economic growth, and green technology innovation into a unified sustainability measure. Using machine learning and deep learning, the system forecasts energy system evolution and identifies key drivers. Scenario simulations for 2040 show that renewable investments, technological innovation, and strong policy frameworks are the primary forces behind sustainable transitions.
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、2050年カーボンニュートラル目標に向け、本モデルを適用することで、再エネ投資や水素戦略などの政策効果を定量的に評価できる。企業のGX戦略立案や投資家のポートフォリオ評価にも活用可能な枠組みを提供する。
In the global GX context
This AI framework offers a novel, adaptive approach to model global energy transitions, complementing existing IAMs and providing scenario analysis for policymakers and investors. It integrates multiple GX levers (renewables, carbon pricing, hydrogen) into a single predictive index, useful for national and corporate decarbonization planning.
👥 読者別の含意
🔬研究者:Methodological contribution combining AI with multi-factor green indices for energy transition forecasting, applicable to further model development.
🏢実務担当者:Scenario simulation tool for corporate sustainability teams to evaluate the impact of renewable investments and carbon pricing strategies.
🏛政策担当者:Insights on key drivers (renewables, innovation, policy) and a framework to test policy packages for achieving carbon neutrality by 2040.
📄 Abstract（原文）
This study develops a smart AI-based framework to predict and understand global progress toward clean and sustainable energy. The proposed Intelligent Green Transition Index (IGTI) combines multiple factors such as carbon emissions, renewable energy share, energy efficiency, economic growth, and green technology innovation into one meaningful measure of sustainability. Using advanced machine learning and deep learning models, the system can forecast how countries’ energy systems will evolve and identify the most influential drivers of change. The model also includes a self-improving mechanism that updates its forecasts as new data and policies emerge, ensuring more accurate and timely insights. A built-in scenario simulation tool allows researchers to explore how actions like renewable investments, carbon pricing, or adoption of green hydrogen could shape the global energy landscape by 2040. Results show that renewable investments, technological innovation, and strong policy frameworks are the main forces behind sustainable transitions. Overall, this research offers a meaningful, data-driven, and adaptive AI approach to guide governments and industries in achieving carbon-neutral and energy-secure futures.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.47191/ijmcr/v14ispc3.33first seen 2026-05-15 20:03:31
🔔 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。