Unveiling the Cognitive Structure of Renewable Energies, Sustainability, and Environment Research: A Large-Scale Longitudinal Co-Word Analysis
再生可能エネルギー、持続可能性、環境研究の認知構造の解明：大規模縦断的共語分析 (AI 翻訳)
Martínez JG, López-Leyva S, Vargas-Quesada B, Chinchilla-Rodríguez Z
🤖 gxceed AI 要約
日本語
本研究は、ScopusのASJC 2105に分類される再生可能エネルギー・持続可能性・環境研究（RESER）分野の大規模な共語分析を行い、5つの主要な研究フロント（バイオ燃料生成、再生可能エネルギー貯蔵、太陽光発電、持続可能性政策、再生可能エネルギー構造）を特定した。さらに、最近の期間（2020-2022年）では、人工知能と再生可能エネルギー応用に関する第6のフロントが出現していることを明らかにした。この研究は、分野の認知構造の経時的変化を捉える再現可能なプロトコルを提供している。
English
This study applies a large-scale co-word analysis to the Renewable Energies, Sustainability, and Environment Research (RESER) field, identifying five major research fronts: Biofuel Generation, Renewable Energy Storage, Solar Power Generation, Sustainability Policy, and Renewable Energy Structure. A sixth emerging front centered on artificial intelligence and renewable energy applications is identified in the most recent period (2020-2022). The study provides a reproducible protocol for longitudinal cognitive mapping of large research fields.
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
日本は再生可能エネルギー導入拡大を進めており、本分析が示す研究フロントの変化（AI×再エネの台頭）は、日本のエネルギー政策や研究戦略に示唆を与える。特に、太陽光発電や蓄電技術の研究動向は日本の産業競争力に直結する。
In the global GX context
This study offers a comprehensive map of renewable energy research fronts globally, highlighting the growing intersection of AI and renewables. It serves as a tool for science policy analysts and research managers to track knowledge evolution and identify emerging areas for investment.
👥 読者別の含意
🔬研究者:Use this cognitive map to identify emerging research fronts and collaboration opportunities in renewable energy.
🏛政策担当者:Research funding agencies can use the temporal mapping to allocate resources to frontier areas like AI for renewables.
📄 Abstract（原文）
<title>Abstract</title> <p>The field of Renewable Energies, Sustainability, and Environment Research (RESER), as represented in Scopus subject category ASJC 2105, has expanded substantially over the past two decades, reaching nearly 780,000 publications indexed in Scopus between 2000 and 2022. Yet its internal cognitive structure, how research fronts emerge, consolidate, and evolve over time, remains insufficiently mapped at a global scale. This study addresses that gap by applying a systematic and replicable co-word analysis protocol to a large Scopus-defined corpus. The approach combines a thesaurus of 25,728 descriptors, VOSviewer-based network clustering, sensitivity analysis of clustering parameters, and a second-derivative method for data-driven temporal segmentation. The analysis produces a large-scale cognitive map of the RESER field comprising five major research fronts: Biofuel Generation, Renewable Energy Storage, Solar Power Generation, Sustainability Policy, and Renewable Energy Structure. This five-front structure is subsequently projected onto three independently analyzed time periods (2000–2012, 2013–2019, and 2020–2022). Independent sensitivity analyses confirm the five-front structure in the first two periods and identify a sixth emerging front centered on artificial intelligence and renewable energy applications in the most recent period. The findings provide a comprehensive and empirically grounded cognitive map of how a large, interdisciplinary and institutionally defined research field reorganizes its knowledge base over time. Beyond the empirical case, the study contributes a transparent, reproducible, and applicable protocol for large-scale longitudinal cognitive mapping that can support comparative analysis of other large-scale research fields, offering a practical tool for research managers, funding agencies, and science policy analysts.</p>
🔗 Provenance — このレコードを発見したソース
- Research Square https://doi.org/10.21203/rs.3.rs-10046939/v1first seen 2026-07-22 04:38:41
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