ARTIFICIAL INTELLIGENCE–BASED SYSTEMS FOR CLIMATE CHANGE MODELING AND PREDICTION
気候変動モデリングと予測のための人工知能ベースシステム (AI 翻訳)
Komal Bamugade and Archana Jadhav
🤖 gxceed AI 要約
日本語
本論文は、気候変動モデリングと予測におけるAI（機械学習、深層学習等）の活用を体系的にレビュー。特にカーボンフットプリントの高精度算出や気候パターン理解への応用可能性を示す。データ品質や解釈可能性などの課題も指摘し、政策立案者や環境ステークホルダーへの実践的示唆を提供。
English
This review systematically examines AI (ML, deep learning, etc.) applications in climate change modeling and prediction, with emphasis on improving carbon footprint accuracy and climate pattern understanding. It identifies challenges like data quality and interpretability, offering practical implications for policymakers and environmental stakeholders.
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対応やTCFD開示で気候リスク分析が重要視されており、本論文のAIによる気候モデリング手法は、企業のシナリオ分析精度向上に貢献し得る。特にカーボンフットプリント精度はScope3算定にも直結する。
In the global GX context
Globally, AI-driven climate modeling supports TCFD/ISSB scenario analysis and enhances the reliability of climate disclosures. This review provides a foundation for integrating AI into corporate sustainability reporting and climate resilience planning.
👥 読者別の含意
🔬研究者:Provides a comprehensive overview of AI methods for climate modeling, identifying research gaps in data quality and model interpretability.
🏢実務担当者:Highlights AI's potential to improve carbon footprint accuracy and climate projections, useful for corporate sustainability teams.
🏛政策担当者:Emphasizes the need for transparent and scalable AI solutions to strengthen climate resilience and inform policy.
📄 Abstract（原文）
One of the most important worldwide issues of the twenty-first century is climate change, which has profound effects on ecosystems, societies, and the economy. Predicting the future course of climate change and its associated effects is a highly complex task, requiring sophisticated modeling techniques. By providing predictions that are more precise, quicker, and scalable, artificial intelligence (AI), particularly machine learning (ML), has the potential to improve conventional climate models. This study examines the use of artificial intelligence (AI) in climate change modeling and prediction, with a particular emphasis on the integration of AI methodologies into climate research, including supervised learning, deep learning, reinforcement learning, and ensemble approaches. We look at how AI can be used to improve weather prediction models, calculate carbon footprints more accurately, understand climate patterns, and produce more reliable climate projections. Furthermore, we examine the limitations, challenges, and future directions of AI in this domain, highlighting the importance of continued interdisciplinary collaboration to ensure its effective use in climate change mitigation and adaptation. Additionally, this study identifies key research gaps and discusses challenges related to data quality, model interpretability, and computational complexity. It also highlights the practical implications of AI-driven climate modeling for policymakers and environmental stakeholders, emphasizing the development of transparent, scalable, and sustainable solutions to strengthen global climate resilience.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.5281/zenodo.20553835first seen 2026-06-26 04:48:52 · last seen 2026-06-26 04:51:56
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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。