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Mapping the Nexus of Sustainability, Innovation, and Renewable Energy: A Bibliometric Analysis of Green Technology Research

持続可能性、イノベーション、再生可能エネルギーの連関の解明：グリーンテクノロジー研究の計量書誌学的分析 (AI 翻訳)

null Muhammad Adnan Afzal, null Saif Ur Rahman, null Muhammad Toseef Aslam

The Critical Review of Social Sciences Studiesプレプリント2025-05-12#再生可能エネルギーOrigin: Global
DOI: 10.59075/2p6sns08
原典: https://doi.org/10.59075/2p6sns08

🤖 gxceed AI 要約

日本語

本論文は、2004年から2025年第1四半期までのグリーンテクノロジー研究498件を計量書誌学的に分析し、技術中心からシステム転換科学への進化を明らかにした。中国が論文数・被引用数で首位だが、国際共同研究が密集。計量経済学的手法（Pesaranの共通相関効果推定量、Westerlundのパネル共和分検定）が基盤をなし、機械学習・スマートグリッドが高密度ニッチテーマとして浮上。政策立案者は技術導入とグリーン経済政策の連携を、研究者はシステムダイナミクスや機械学習モデルの活用、社会的公平性・金融面の研究拡充を求められる。

English

This bibliometric analysis of 498 green technology articles (2004-2025Q1) reveals a shift from technology-centric to systemic-transition science. China leads in output and citations but is embedded in dense international collaborations. Econometric methods (Pesaran's CCE, Westerlund's cointegration) underpin the field, while machine learning-smart grid emerges as a high-density niche. Policymakers should pair technology deployment with green economy instruments; researchers should complement panel econometrics with system dynamics and machine learning, and explore social equity and finance dimensions.

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

日本はグリーンテクノロジー研究で存在感が薄いが、本分析は国際的な研究ネットワークやテーマの変遷を俯瞰する上で参考になる。特に、計量経済学的手法と機械学習の融合や、南南・南北協力の重要性は、日本のGX政策や研究戦略にも示唆を与える。

In the global GX context

This study provides a global landscape of green technology research, highlighting China's dominance and the evolution toward systemic transition science. It underscores the need for integrating econometric and machine learning methods, and for strengthening South-South-North knowledge corridors—relevant for global GX collaboration and policy design.

👥 読者別の含意

🔬研究者:Use this to identify leading authors, methods, and thematic clusters in green technology research, and to spot emerging niches like machine learning-smart grid.

🏢実務担当者:Limited direct applicability; may inform R&D strategy by highlighting collaboration patterns and technology themes.

🏛政策担当者:Consider pairing technology deployment with green economy instruments and fostering international knowledge corridors for context-specific transitions.

📄 Abstract（原文）

Purpose: This study examines the intellectual, thematic, and geographical evolution of green technology in the nexus of sustainability, innovation, and renewable energy (2004 – 2025Q1), clarifying the field’s transition from technology-centric enquiry to systemic-transition science. Methodology: A bibliometric review of 498 Scopus-indexed articles was conducted in Biblioshiny. Performance indicators (publication and citation trends) were paired with science mapping techniques: coauthor and affiliation networks, citation  matrices, keyword co-occurrence , and strategic diagram  analysis. Centrality, density, and clustering metrics were used to identify leading actors, methods, and knowledge domains. Findings: Research output and impact are strongly skewed toward China (25 % of papers; 3 516 citations), yet Chinese work is embedded in dense multi country collaborations. Environmental Science & Pollution Research and Sustainability (Switzerland) constitute the journal’s core, while Ahmad and Pesaran M. Anchor authorship and methodological influence. Pesaran’s (2006) common correlated effects estimator and Westerlund’s (2007) panel cointegration tests underpin the econometric principle. Themes centered on innovation, sustainability, alternative energy, sustainable development, and renewable energies serve as basic integrators; machine learning–smart grid forms a high-density niche; and biomass performance is declining. Originality: By integrating multi-layer evidence, sources, authors, geography, methods, and thematic dynamics, this study reveals how econometric refinement, policy discourse, and South South collaboration co evolve. Strategic diagram analysis exposes latent trajectories in which niche topics migrate toward the motor quadrant, a pattern overlooked by earlier, more siloed reviews. Implications: Policymakers should pair technology deployment with green economy instruments to convert innovation into measurable decarbonization. Researchers are encouraged to complement panel econometrics with system dynamics and machine learning  models, and to foreground social equity and finance dimensions that remain under examined. Strengthening South South–North knowledge corridors can accelerate context-specific  transitions and diffuse best practices globally.

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