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サウジアラビアにおける再生可能エネルギー消費のマクロ経済決定要因:計量経済学と機械学習のハイブリッド調査

Macroeconomic Determinants of Renewable Energy Consumption in Saudi Arabia: A Hybrid Econometric-Machine Learning Investigation (原題)

Omer S, Ghanim H, Ahmed I, Aba Alkhayl A, Elhaj M, Yousif GM

Research Squareプレプリント2026-09-02#エネルギー転換対象セクター: power
DOI: 10.20944/preprints202609.0166.v1
原典: https://doi.org/10.20944/preprints202609.0166.v1

🤖 gxceed AI 要約

日本語

サウジアラビアの再生可能エネルギー消費の決定要因を1990-2025年についてARDLとランダムフォレストで分析。消費の経路依存性が強く、貿易開放度は負の効果。Vision 2030以降モデルの説明力が向上。技術革新の影響は未成熟。

English

This study analyzes determinants of renewable energy consumption in Saudi Arabia (1990-2025) using ARDL and Random Forest with SHAP. Results show strong path dependence and negative effect of trade openness, with improved model fit after Vision 2030. Innovation proxies are not yet significant drivers.

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

日本では再生可能エネルギー導入拡大が課題であり、本論文の政策転換前後の構造変化分析は、日本のエネルギー政策評価に示唆を与える。ただし、サウジの炭素ロックイン状況は日本と異なる点に留意。

In the global GX context

This paper contributes to global energy transition literature by demonstrating how policy shifts (Vision 2030) can alter the drivers of renewable adoption, relevant for countries with fossil fuel dependence. The hybrid econometric-ML approach offers a methodological template for analyzing energy transitions.

👥 読者別の含意

🔬研究者:Methodological insights on combining ARDL and ML for energy policy analysis.

🏢実務担当者:Understanding macro factors influencing renewable energy investment decisions.

🏛政策担当者:Evidence on how policy frameworks can accelerate renewable energy adoption.

📄 Abstract(原文)

This paper investigates the macroeconomic determinants of renewable energy consumption in Saudi Arabia during 1990–2025 using a hybrid approach of ARDL bounds testing and Random Forest machine learning with SHAP (SHapley Additive ExPlanations) to improve model interpretability. We employ annual data from the World Development Indicators to examine the effect of high-technology exports, trade openness, foreign direct investment, inflation and GDP on renewable energy consumption. Our results indicate that renewable energy consumption is highly path dependent, with previous adoption playing a significant role in current consumption (β = 0.776, p < 0.01). Trade openness also shows a negative contemporaneous effect ( = 0.0013, p 0.05) in line with the carbon lock-in hypothesis that typifies hydrocarbon-dependent economies. High-technology exports as a proxy for technological innovation do not appear to be a significant driver, indicating that the innovation-led energy transition in Saudi Arabia is still maturing. The Random Forest model confirms the importance of persistence effects explaining about 73 % of the variance. The analysis of the different time periods indicates that, after 2016 (also known as Vision 2030), there has been a significant change in the structure of the analyzed data, with the statistical model of research becoming much stronger as its explanatory power has reached up to 83%. The analysis of the SHAP allows understanding of the obtained results by measuring the influence of each individual variable. The findings that were obtained pertain to Sustainable Development Goal 7 (which focuses on developing cheap and clean energy), Sustainable Development Goal 9 (which focuses on industry, innovation, and infrastructure), and Sustainable Development Goal 13 (which focuses on climate action). It is our proposal that Saudi Arabia adopt focused innovation strategies, careful management of its trade integration, and a commitment to the institutional framework in order to accelerate the transition to renewable energy. However, it is important to note that this recommendation is suggestive and not definitive.

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