再生可能エネルギー、環境持続可能性と経済成長：MINT諸国の機械学習分析
Renewable Energy, Environmental Sustainability and Economic Growth: A Machine Learning Analysis of MINT Countries (原題)
Gazi Polat
🤖 gxceed AI 要約
日本語
本研究はMINT諸国（メキシコ・インドネシア・ナイジェリア・トルコ）の2000〜2023年データを用い、再エネ消費・炭素排出・医療支出・観光・経済成長の非線形関係を機械学習のKRLSで分析した。再エネ消費は平均的に経済成長と負の関連を示し、クリーンエネルギー移行に伴う短期的な移行コストの存在を示唆する。ただし限界効果は観測値間で大きく異なり、観光は成長と正に関連する。政策面では技術革新・省エネ・長期資金による再エネ投資支援の必要性を強調する。
English
Using 2000–2023 World Bank data for MINT countries, this study applies Kernel Regularized Least Squares (a machine-learning method) to examine nonlinear links among renewable energy, carbon emissions, health expenditure, tourism, and growth. Renewable consumption shows a significant negative average association with growth, suggesting short-term transition costs, though marginal effects are highly heterogeneous. Tourism is positively linked to growth; policy implications stress innovation, efficiency, and long-term financing to lower transition costs.
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
日本企業が新興国で再エネ事業や脱炭素投資を展開する際、移行コストと成長のトレードオフを定量的に把握する材料となる。ただし日本固有の政策・開示制度との直接の接点は薄い。
In the global GX context
Adds emerging-market empirical evidence to the global green-growth debate, quantifying transition costs of renewables in MINT economies—relevant to transition finance and just-transition discussions under ISSB/TCFD-adjacent frameworks, though not a disclosure study itself.
👥 読者別の含意
🔬研究者:機械学習KRLSによる非線形・異質性を考慮した成長・環境ネクサス分析の実証例として参照できる。
🏢実務担当者:新興国での再エネ調達・投資判断における短期的な成長コストの存在を踏まえたリスク評価に活用できる。
🏛政策担当者:再エネ移行コストを緩和する技術革新・省エネ・長期資金支援策の設計根拠として参考になる。
📄 Abstract（原文）
The transition toward sustainable energy systems and environmentally responsible development has become a central policy objective for emerging economies. Within the framework of green growth, this study investigates the nonlinear relationships among renewable energy consumption, carbon emissions, health expenditures, tourism, and economic growth in MINT countries (Mexico, Indonesia, Nigeria, and Türkiye) over the period 2000–2023. Annual data obtained from the World Bank were analyzed using the Kernel Regularized Least Squares (KRLS) approach, a machine learning technique capable of capturing heterogeneous and nonlinear relationships without imposing restrictive functional form assumptions. The findings reveal that renewable energy consumption is statistically significantly associated with lower economic growth on average, suggesting the existence of short-term transition costs associated with the shift toward cleaner energy systems. However, the magnitude of this negative association decreases across the distribution of the estimated marginal effects, indicating substantial heterogeneity across observations. Tourism is positively associated with economic growth, whereas the estimated marginal association between health expenditures and economic growth varies across observations. Although carbon emissions do not exhibit a statistically significant average marginal association with economic growth, their estimated marginal association also varies across observations, highlighting the heterogeneous nature of the growth–environment nexus. The results provide important implications for energy and environmental policies in developing economies. They emphasize the need to support renewable energy investments through technological innovation, energy efficiency improvements, and long-term financing mechanisms to reduce transition costs and promote sustainable growth.
🔗 Provenance — このレコードを発見したソース
- openalex https://dergipark.org.tr/en/pub/ijes/article/1966839first seen 2026-10-18 04:42:53
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