アフリカにおけるグリーンファイナンス、エネルギー転換、炭素強度:ファイナンス・エネルギー・炭素ネクサスの解明
Green finance, energy transition, and carbon intensity in Africa: unpacking the finance–energy-carbon nexus (原題)
Amegnaglo CJ, Nonvide GMA, Adangbedou EJ
🤖 gxceed AI 要約
日本語
アフリカ35カ国・2000〜2023年のパネルデータを用い、クリーンエネルギー金融が風力・太陽光発電を促進する一方、水力には負の効果を持つことを示した。グリーンファイナンスは再生可能エネルギー経由で炭素強度を低下させるが、石炭・石油依存が残る国では効果が限定的。制度的・適応的レディネスがエネルギー転換の構造的前提であることを強調し、次世代NDCでの脆弱国への資金誘導を提言する。
English
Using a 35-country African panel (2000–2023), this study finds clean energy finance robustly boosts wind and solar generation but reduces hydropower, showing technology-specific effects. Green finance lowers carbon intensity partly via renewables, yet coal/oil dependence sustains emissions in some economies. Institutional and adaptive readiness emerges as a structural precondition for transition, with reverse causality showing established renewables attract more finance.
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・投資家にとっては、新興国向けトランジション金融の有効性条件(制度レディネス・技術選択)を示す点で、アジア向けGXファイナンス戦略やNDC連動の資金配分を考える際の参照材料となる。SSBJ・有報の気候関連開示においても、地域別リスク・機会の評価に示唆を与える。
In the global GX context
This paper contributes to global transition-finance scholarship by quantifying how institutional readiness conditions the effectiveness of green finance—a key theme for ISSB/TCFD-aligned disclosure and the design of next-generation NDCs. It reinforces that climate finance allocation must account for absorptive capacity, relevant to multilateral banks and blended-finance frameworks.
👥 読者別の含意
🔬研究者:グリーンファイナンスと再生可能エネルギー普及の因果関係を、技術別・地域別に識別した実証手法が参考になる。
🏢実務担当者:新興国での再生可能エネルギープロジェクト投資を検討する際、制度レディネスと技術選択の重要性を評価する材料になる。
🏛政策担当者:次世代NDC設計や気候資金の脆弱国への誘導において、制度・適応能力への投資を優先すべき根拠を提供する。
📄 Abstract(原文)
<title>Abstract</title> <p>This study analyses the interconnected relationship between green finance, energy transition, and carbon intensity in Africa. Using a panel of 35 African countries over the period 2000-2023, this study employs fixed effects, Driscoll-Kraay standard errors regression, and two-stage least squares instrumental variables to examine the effects of clean energy finance on wind, solar, and hydropower generation, the implications of the resulting energy mix for carbon intensity, and the potential feedback effect of renewable energy development on climate finance. The results show that clean energy finance has a positive and robust effect on wind and solar generation but a negative and robust effect on hydropower, indicating a technology-specific rather than uniform effect. However, sub-regional analysis reveals that green finance-renewable energy relationship is far from uniform across the continent. Clean energy finance also contributes to lower significantly carbon intensity, operating partly through this renewable-generation channel, although persistent coal- and oil-rent dependence continues to sustain emissions intensity in several economies. The strong and consistent role of climate resilience/ readiness in promoting renewable energy and thereby reducing carbon emissions suggests that investments in institutional and adaptive readiness are not merely a co-benefit of climate policy but a structural precondition for energy transition and the effective absorption of green finance. A reverse-causality test further finds that countries with more established wind and solar capacity attract significantly more clean energy finance. These findings indicate that the effectiveness of green finance depends on institutional readiness, existing energy infrastructure, and regional context, and call for redirecting climate finance toward vulnerable countries and scalable renewable technologies within next-generation NDCs.</p>
🔗 Provenance — このレコードを発見したソース
- Research Square https://doi.org/10.21203/rs.3.rs-9474000/v1first seen 2026-10-08 04:31:52 · last seen 2026-10-11 04:21:07
- openalex https://doi.org/10.21203/rs.3.rs-9474000/v1first seen 2026-10-09 04:46:00
🔔 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。