アフリカの持続可能なエネルギー移行のための機械学習：マクロ経済・財政・環境要因からのエビデンス
Machine Learning for Sustainable Energy Transitions in Africa: Evidence from Macroeconomic, Fiscal, and Environmental Drivers (原題)
Ramzi Knani, Chaker Gabsi, Ismail Bengana
🤖 gxceed AI 要約
日本語
アフリカ25カ国のエネルギー移行を、DTWによるグループ分け、LightGBMによる変数重要度評価、パネルARDLによる短期・長期関係の推定で分析。LSTMとの一貫性も確認し、グループごとに異なる移行ダイナミクスを解明。政策立案への示唆を提供。
English
This study analyzes energy transitions in 25 African countries using DTW clustering, LightGBM for variable importance, and Panel ARDL for short- and long-term effects. Results show distinct dynamics across groups, with macroeconomic vulnerabilities hindering some, and provide policy implications for accelerating renewable energy adoption.
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
日本企業のアフリカ展開や国際協力において、再生可能エネルギー投資の有望国・リスク国を識別する手法として参考になる。また、AIと計量経済の組み合わせは、日本のエネルギー政策評価にも応用可能。
In the global GX context
This paper offers a robust methodological framework combining ML and econometrics for energy transition analysis, relevant for global climate policy and investment decisions in emerging markets. It contributes to understanding drivers of renewable energy adoption in Africa, informing international development strategies.
👥 読者別の含意
🔬研究者:Methodological insights on combining ML and econometrics for energy transition analysis.
🏢実務担当者:Identifies country-specific opportunities and risks for renewable energy investments in Africa.
🏛政策担当者:Provides evidence for tailoring energy transition policies to country contexts.
📄 Abstract（原文）
This article analyses the energy transition trajectories of 25 African countries, combining machine learning and econometric methods.Initially, a Dynamic Time Warping (DTW)-based partitioning approach is used to divide countries into three groups with distinct socio-economic and energy profiles.The Light Gradient Boosting Machine (LightGBM) model is then used to evaluate the significance of macroeconomic and structural variables.A Panel Autoregressive Distributed Lag (Panel ARDL) model is then applied to each group to examine the short-and long-term relationships between macroeconomic and structural variables and renewable energy consumption.The results demonstrate consistency in the importance of variables identified by a Long Short-Term Memory (LSTM) model and their long-term effects within the Panel ARDL framework, thereby showcasing the robustness of the approach.The analysis reveals different dynamics: the first group is hindered by macroeconomic vulnerabilities such as financial instability and high debt, whereas the second group enjoys more favourable conditions.These results provide a basis for developing policies tailored to the specific contexts of each group to accelerate the energy transition in Africa.Thus, the study contributes to a better understanding of the key factors and helps to guide sustainable development strategies on the continent.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.56578/ijei090406first seen 2026-08-23 04:37:35
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