アフリカにおける気候変動緩和のための人工知能活用：持続可能なアプローチ
Leveraging Artificial Intelligence for Climate Change Mitigation in Africa: A Sustainable Approach (原題)
Ogunade Josiah Ayodele
🤖 gxceed AI 要約
日本語
本論文は、アフリカにおける気候適応・緩和へのAI活用を、文献・制度枠組み・事例研究の質的統合により検討する。機械学習とリモートセンシングによる環境モニタリング、土地利用分類、水資源管理、早期警報システムへの応用を評価し、ENACTSプログラムを代表事例として分析する。データ不足やインフラ・人材の制約、プライバシーやアルゴリズムバイアス等の課題を指摘し、気候データ基盤強化や人材育成に向けた政策提言を行う。
English
This qualitative study examines AI's role in Africa's climate adaptation and mitigation, synthesizing literature, institutional frameworks, and case studies. It highlights machine learning and remote sensing for environmental monitoring, land-use classification, water management, and early warning, analyzing the ENACTS program as a leading case. It identifies barriers—data scarcity, weak infrastructure, limited capacity, and ethical risks—and offers policy recommendations for climate data infrastructure and human capital.
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
日本企業・投資家にとって直接の開示規制接点は薄いが、AI×気候データ基盤の整備は、サプライチェーン上流のアフリカ拠点における気候リスク評価や適応投資判断に示唆を与える。SSBJ・TCFD対応の物理的リスク評価を新興国データで補完する視点として参考になる。
In the global GX context
While not a disclosure-framework paper, it speaks to the global push for AI-enabled climate data infrastructure that underpins physical-risk assessment under TCFD/ISSB. It adds a Global South perspective on data equity and ethical governance that is largely absent from CSRD/ISSB-centric scholarship, relevant to transition finance in emerging markets.
👥 読者別の含意
🔬研究者:AI×気候適応の質的統合とENACTS事例は、新興国データ基盤研究の出発点として有用。
🏢実務担当者:アフリカ拠点を持つ企業は、気候データ・早期警報の活用可能性と現地インフラ制約を把握できる。
🏛政策担当者:気候データ基盤整備とAI人材育成への投資優先度を検討する際の政策根拠となる。
📄 Abstract（原文）
As climate change intensifies environmental and socio-economic vulnerabilities across Africa, there is a growing need for innovative, scalable solutions to support climate resilience. This study critically examines the role of Artificial Intelligence (AI) in enhancing climate adaptation and mitigation efforts on the continent. Employing a qualitative, multi-source research methodology, the paper synthesizes academic literature, institutional frameworks, and applied case studies to assess the integration of AI into environmental monitoring and resource management systems. Particular emphasis is placed on the use of machine learning and remote sensing technologies to support real-time data analysis, land-use classification, water resource management, and early warning systems. The Enhancing National Climate Services (ENACTS) program is analyzed as a leading case, demonstrating how the fusion of satellite and groundbased data can significantly improve climate services in sectors such as agriculture and public health. The paper also identifies critical challenges—including data scarcity, inadequate infrastructure, limited technical capacity, and ethical risks such as data privacy and algorithmic bias—that hinder the effective deployment of AI. In response, the study offers strategic policy recommendations to strengthen climate data infrastructure, promote cross-sector collaboration, and build human capital in AI and climate science. The findings underscore the importance of African-led innovation, ethical governance, and context-specific solutions in ensuring that AI contributes meaningfully and equitably to sustainable development and climate resilience.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.56201/wjimt.v9.no6.2025.pg212.229first seen 2026-10-03 04:46:48
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