Scholarlink

<p>With the rapid expansion of electric vehicles (EVs), optimizing charging infrastructure has become essential for advancing sustainable transportation. The integration of Geographic Information Systems (GIS) and Multi-Criteria Decision-Making (MCDM) methods provides a robust scientific framework for selecting electric vehicle charging station (EVCS) locations. This bibliometric study analyses 1336 WoS Core Collection records (2016–2025; 2025 partial-year) and highlights four thematic clusters. Annual output peaks in 2022 (18.039%) and 2024 (18.563%), with leading national shares from China (18.713%), India (16.692%), and Iran (13.323%). VOSviewer network mapping delineates four cohesive clusters—EVCS layout/planning, GIS–MCDM methods, spatial sustainability, and uncertainty/risk—providing a structured lens for evidence-based synthesis. A focused mapping restricted to GIS and MCDM for EVCS siting—read directly from VOSviewer co-occurrence structures and clusters—yields EVCS-specific evidence that domain-wide surveys do not resolve with comparable clarity. The analysis includes keyword co-occurrence, author collaboration networks, country and institutional influence, journal contributions, and citation networks. The study identifies core research themes, key contributors, and high-impact journals in this field. We position the contribution as an EVCS-specific, cluster-wise interpretation of GIS–MCDM developed within a transparent WoS-only pipeline with verbatim query disclosure, which strengthens traceability and is intended to facilitate reproducibility for planning-oriented evidence. The research insights aim to support the development of intelligent and sustainable charging networks.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Trends of GIS-based Multi-Criteria Decision-Making (GIS-MCDM) in site selection for electric vehicle charging stations: A bibliometric analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月8日

查看全文
  • Wenhao Li,
  • Narimah Samat,
  • Mou Leong Tan,
  • Mohd Amirul Mahamud

摘要

With the rapid expansion of electric vehicles (EVs), optimizing charging infrastructure has become essential for advancing sustainable transportation. The integration of Geographic Information Systems (GIS) and Multi-Criteria Decision-Making (MCDM) methods provides a robust scientific framework for selecting electric vehicle charging station (EVCS) locations. This bibliometric study analyses 1336 WoS Core Collection records (2016–2025; 2025 partial-year) and highlights four thematic clusters. Annual output peaks in 2022 (18.039%) and 2024 (18.563%), with leading national shares from China (18.713%), India (16.692%), and Iran (13.323%). VOSviewer network mapping delineates four cohesive clusters—EVCS layout/planning, GIS–MCDM methods, spatial sustainability, and uncertainty/risk—providing a structured lens for evidence-based synthesis. A focused mapping restricted to GIS and MCDM for EVCS siting—read directly from VOSviewer co-occurrence structures and clusters—yields EVCS-specific evidence that domain-wide surveys do not resolve with comparable clarity. The analysis includes keyword co-occurrence, author collaboration networks, country and institutional influence, journal contributions, and citation networks. The study identifies core research themes, key contributors, and high-impact journals in this field. We position the contribution as an EVCS-specific, cluster-wise interpretation of GIS–MCDM developed within a transparent WoS-only pipeline with verbatim query disclosure, which strengthens traceability and is intended to facilitate reproducibility for planning-oriented evidence. The research insights aim to support the development of intelligent and sustainable charging networks.