森林持続可能性の強化：サハラ以南アフリカにおける森林火災、制度の質、再生可能エネルギー導入、気候変動が森林被覆に与える影響の検討
Augmenting Forest Sustainability: Investigating the Effects of Forest Fires, Institutional Quality, Renewable Energy Adoption, and Climate Change on Forest Cover in Sub‐Saharan Africa (原題)
Abdul Salami Bah, Yongqiang Wang, Yuchun Zhu, Roman Romashkin, Nazir Muhammad Abdullahi
🤖 gxceed AI 要約
日本語
サハラ以南アフリカ35カ国・2000〜2023年のパネルデータを用い、森林火災・制度の質・再生可能エネルギー・気候変動が森林被覆に与える影響をMMQRとGMMで分析。森林火災・農業政策・気温上昇は森林被覆を減少させ、再エネと降水は増加させる。制度の質はR&D・ICT・環境保護を通じ間接的にも作用し、機械学習（RF・XGBoost）が森林減少の主要予測因子を特定した。
English
Using balanced panel data for 35 Sub-Saharan African countries (2000–2023), this study applies MMQR and GMM to examine how forest fires, institutional quality, renewable energy, and climate change affect forest cover. Forest fires, agricultural policy, and rising temperatures reduce forest cover, while renewable energy and precipitation increase it. Institutional quality acts both directly and indirectly via R&D, ICT, and environmental protection; ML models (RF, XGBoost) confirm arable land, fires, and rural population as key predictors of forest loss.
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
日本企業にとって直接的な開示規制との接点は薄いが、自然資本・生物多様性（TNFD）やサプライチェーン上の森林リスク評価に関心を持つ読者には、SSA地域の実証エビデンスとして参考になる。
In the global GX context
Adds empirical evidence on forest cover determinants in a region underrepresented in global disclosure scholarship, relevant to TNFD/nature-related risk framing and to ISSB-linked natural capital discussions where governance quality and clean energy interact with deforestation.
👥 読者別の含意
🔬研究者:森林被覆の決定要因をMMQR・GMM・MLで多角的に検証した手法面の参考になる。
🏢実務担当者:SSA調達・森林リスクを持つ企業の自然資本・TNFD対応の背景情報として活用可能。
🏛政策担当者:制度の質と再エネ導入が森林保全に寄与する点は、森林・気候政策設計の示唆となる。
📄 Abstract（原文）
ABSTRACT Amid growing global concern over climate change, addressing deforestation and promoting forest sustainability has become a major policy and research priority. Despite the ecological and economic importance of forests, deforestation in Sub‐Saharan Africa (SSA) persists, driven by unsustainable land use, weak institutions, and rising climate variability. This study examines the effects of forest fires, institutional quality (IQ), renewable energy adoption, and climate change on forest cover in SSA, a region facing severe environmental and governance challenges. Using balanced panel data for 35 countries from 2000 to 2023, the study applies the method of moments quantile regression (MMQR) and generalized method of moments (GMM) to address distributional and endogeneity issues. Machine learning (ML) algorithms, including random forest (RF), XGBoost (XGB), and gradient boosting (GB), are used to evaluate predictive performance and identify key determinants of forest cover. The results show that forest fires, agricultural policy, and temperature increase have negative and significant effects on forest cover, while renewable energy and precipitation have positive impacts. IQ influences forest cover both directly and indirectly through research and development (R&D), information and communication technology (ICT), and environmental protection (EP). The Dumitrescu Hurlin causality test reveals bidirectional links between forest cover, IQ, forest fires, and temperature. ML results confirm that arable land, forest fires, and rural population predict forest loss, while strong institutions and renewable energy enhance sustainability, with RF and XGB yielding the highest predictive accuracy. Strengthening governance, promoting clean energy, and integrating digital monitoring are essential for sustainable forest resilience in SSA.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1002/sd.71626first seen 2026-09-15 04:39:50
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