China’s energy transition through a resource–fiscal–environmental lens: economic drivers and R&D threshold mediation
資源・財政・環境のレンズを通した中国のエネルギー転換：経済的推進力とR&D閾値媒介 (AI 翻訳)
Qian He, Ying Jin, Chen Chen, Nan Liu
🤖 gxceed AI 要約
日本語
本研究は1990〜2023年の中国を対象に、貿易開放、構造変革、政府効率性が石炭使用やエネルギー枯渇コストに与える影響と、それらがCO2排出やエネルギー強度に及ぼす経路を、LASSOや因果森などの機械学習手法で分析。R&D投資がGDP比2%を超えると、教育支出が排出削減に有効になる閾値を発見。環境クズネッツ仮説に新たな知見を加え、政策提言を行う。
English
This study examines China from 1990-2023 using machine learning methods (LASSO, causal forest, Gaussian process) to analyze how trade openness, structural transformation, and government effectiveness affect coal usage and energy costs, ultimately impacting CO2 emissions and energy intensity. It finds a threshold effect: when R&D investment exceeds 2% of GDP, education spending significantly reduces emissions and energy intensity. The findings refine the Environmental Kuznets Curve and innovation-threshold models, offering policy implications for promoting R&D.
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
本論文は中国を対象とするが、日本でもGX投資におけるR&Dの閾値効果は示唆に富む。特にSSBJ対応やエネルギー転換政策において、教育投資とイノベーションの連携が重要であることを示す手法は参考になる。
In the global GX context
This paper contributes globally to the Environmental Kuznets Curve literature by integrating resource depletion and fiscal reactions, and refining innovation-threshold models with precise boundary conditions (R&D >2% GDP). The use of causal forest and Gaussian process offers a methodological framework applicable to other countries' energy transition analysis.
👥 読者別の含意
🔬研究者:Demonstrates ML-based threshold analysis in environmental economics, providing a framework for studying R&D moderation in energy transition.
🏢実務担当者:The threshold finding (R&D >2% GDP) may inform corporate R&D investment strategies to enhance energy efficiency and reduce emissions.
🏛政策担当者:Suggests that boosting R&D investment beyond a critical threshold can amplify the environmental benefits of education spending, offering a policy lever for sustainable development.
📄 Abstract（原文）
This study examines the relationship between macroeconomic drivers and environmental outcomes, with a specific focus on China over the period 1990–2023. The research investigates how trade openness, structural transformation, and government effectiveness influence coal usage, energy depletion costs, and education spending, and how these factors ultimately impact CO₂ emissions and energy intensity. Using a multi-stage analytical approach, including penalized regressions (LASSO, Ridge), causal-forest analysis, and Gaussian process modelling, we uncover a multistage serial mediation pathway that identifies the moderating role of R&D investment. Our key findings reveal that at low levels of R&D investment, education spending correlates with rising CO₂ emissions and energy intensity. However, once R&D investment exceeds 2.0% of GDP, education spending significantly reduces both emissions and energy intensity. This study contributes to Environmental Kuznets Curve theory by integrating resource-depletion dynamics and fiscal reactions, while refining innovation-threshold models by establishing precise boundary conditions for sustainable growth. The study provides actionable policy implications, advocating for governments to promote R&D investment and align education spending with innovation goals to achieve sustainable environmental outcomes.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1057/s41599-026-07883-9first seen 2026-06-21 05:23:19
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