U.S. State Energy Transition Dynamics: Renewable Adoption, Fossil Displacement, and Forecasting with Ensemble Machine Learning
米国の州別エネルギー転換のダイナミクス：再生可能エネルギーの導入、化石燃料の代替、アンサンブル機械学習による予測 (AI 翻訳)
Christopher Odedina, Tochuckwu Akaegbusi
🤖 gxceed AI 要約
日本語
1990～2024年の米国50州のデータを用い、再生可能エネルギー導入パターンをパネル計量経済学、クラスタリング、機械学習で分析。州間での大きな不均一性と2008年を転機とする構造変化を確認。早期導入州、大規模導入州、漸進的移行州、化石燃料依存州の4類型を特定。再エネ比率の上昇はエネルギー支出を削減し、過去の再エネ比率が将来導入の主要な予測因子である経路依存性を明らかにした。予測精度はR²≈0.77。
English
This study analyzes U.S. state-level energy transition from 1990 to 2024 using panel econometrics, clustering, and machine learning. It identifies four state archetypes (early movers, progressive large-scale adopters, gradually transitioning, and fossil-locked). Higher renewable share reduces energy expenditure. Historical renewable share is the dominant predictor of future adoption, indicating strong path dependence. Predictive accuracy reaches R²≈0.77.
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
本論文は米国の事例だが、日本のエネルギー転換においても、州／県ごとの不均一性や経路依存性は重要な示唆を与える。特に、再エネ導入がエネルギー費用削減につながる点は、日本の政策立案や企業のコスト評価に参考となる。また、機械学習による予測手法は、日本の地域別エネルギーモデリングにも応用可能。
In the global GX context
This paper contributes to global energy transition scholarship by quantifying state-level heterogeneity and path dependence in renewable adoption using advanced ML techniques. The four archetypes provide a framework applicable to other countries. The finding that past adoption drives future adoption underscores the importance of early policy push to overcome inertia, relevant for ISSB and TCFD scenario analysis and national decarbonization strategies.
👥 読者別の含意
🔬研究者:The four state archetypes and the demonstration of path dependence via ML interpretability offer a methodological template for studying regional energy transitions.
🏢実務担当者:Energy companies and investors can use the archetype classification to benchmark transition risks and opportunities across U.S. states.
🏛政策担当者:The finding that renewable adoption reduces total energy expenditure supports policies that accelerate deployment, while path dependence suggests early intervention is critical.
📄 Abstract（原文）
The study investigates the dynamics of the United States’ energy transition in all 50 states (including the District of Columbia), over the period 1990 to 2024, using the United States Energy Information Administration’s State Energy Datasets. Using a combination of panel econometrics, clustering, and machine learning, this study aims to identify major patterns in the uptake of renewable energy sources in the United States, along with their economic effects. The preliminary results show significant cross-state heterogeneity and structural change in 2008 as the major turning point in the transition path. In addition, four major archetypes of states are identified, namely: early movers, progressive large-scale adopters, gradually transitioning states, and fossil-locked states, which are characterized by their high dependence on conventional sources of energy. Furthermore, a higher share of renewable energy reduces total energy expenditure, taking into account factors such as energy demand and prices. Based on the machine learning models, this study demonstrates a high degree of predictive accuracy (R² ≈ 0.77). Also, the interpretability analysis of the results demonstrates that lagged renewable share is the dominant predictor of future adoption. This implies a high degree of path dependence in which historical energy structures strongly constrain current transition dynamics. Thus, the U.S. energy transition is heterogeneous, cost-reducing, and structurally persistent, with inertia in renewable adoption representing a key barrier to accelerated decarbonization.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.64388/irev9i10-1716160first seen 2026-05-17 04:39:36
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