気候持続可能性への非線形経路：デジタル技術、クリーンエネルギー、構造変化の役割
Nonlinear pathways to climate sustainability: the role of digital technologies, clean energy, and structural change (原題)
Shangjie Liu, Agyemang Kwasi Sampene, Hazrat Hassan
🤖 gxceed AI 要約
日本語
本研究は、中国・日本・ドイツ・米国・韓国の5カ国を対象に、デジタル技術、クリーンエネルギー移行、産業構造の高度化が気候変動に与える非線形な影響を、多層ニューラルネットワークとDeep SHAPを用いて分析した。結果、ロボット導入や環境技術、ICTの発展は排出削減に寄与するが、経済成長と都市人口増加は排出を増加させる。再生可能エネルギーと産業イノベーションが最も影響力のある予測因子である。
English
This study analyzes nonlinear effects of digital technologies, clean energy transition, and industrial upgrading on climate change in five major robot-adopting economies (China, Japan, Germany, US, South Korea) using a multilayer neural network and Deep SHAP. Findings show robotics, environmental tech, and ICT reduce emissions with maturity, while renewables and innovation are key; economic growth and urbanization increase emissions.
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
日本はSSBJ開示やGX推進政策が進む中、デジタル技術と再生可能エネルギーの非線形効果を示す本研究成果は、日本の産業構造転換やエネルギー政策の設計に示唆を与える。特に、技術成熟度に応じた投資の閾値効果は、日本のGX投資戦略に有用。
In the global GX context
Globally, this paper contributes to understanding how digitalization and clean energy interact in climate mitigation, relevant for ISSB-aligned disclosure and transition finance. The multi-country comparison offers insights for policymakers balancing economic growth with decarbonization, emphasizing coordinated industrial-energy-digital strategies.
👥 読者別の含意
🔬研究者:Provides empirical evidence on nonlinear climate pathways using ML, useful for further research on technology diffusion and climate policy.
🏢実務担当者:Highlights the importance of investing in renewable energy and digital technologies for corporate decarbonization strategies.
🏛政策担当者:Suggests coordinated industrial-energy-digital policies and threshold-targeted green technology investment for effective climate action.
📄 Abstract（原文）
Introduction Climate change remains one of the most pressing global challenges, particularly in highly industrialized and technologically advanced economies. This study examines how sustainable digital technologies, clean energy transition, industrial structural upgrading, and socio-economic dynamics influence climate change in the world’s five largest robot-adopting economies: China, Japan, Germany, the United States, and South Korea. Method The study uses panel data from 2000 to 2024, and a multilayer neural network (MNN) framework combined with surface slope analysis and Deep SHAP interpretation to capture nonlinear relationships between key variables and climate indicators. Result The findings reveal strong nonlinear effects across the four dimensions. Robotics adoption, environmental technologies, and ICT development contribute to emission reduction as technological maturity increases. Modern renewable energy and access to clean energy technologies significantly reduce emissions, while nuclear power shows weaker and heterogeneous effects. Industrial structural upgrading, represented by industrialization and research and development, improves environmental outcomes when technological innovation supports cleaner production systems. However, economic growth and urban population growth continue to exert upward pressure on emissions. The Deep SHAP results highlight renewable energy, robotics, and industrial innovation as the most influential predictors of climate outcomes. Discussion Theoretically, the findings align with Ecological Modernization Theory and Innovation and Technology Diffusion Theory, demonstrating that climate mitigation emerges from cumulative technological diffusion and structural transformation rather than isolated interventions. Policy implications highlight the need for coordinated industrial–energy–digital strategies, threshold-targeted green technology investment, and adaptive urban development frameworks.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3389/fenvs.2026.1865275first seen 2026-09-05 05:18:14
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