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<p>The growing number of EVs in Varanasi is notable, yet the development of EVCSs is lagging behind this trend. Thus, this study uses MCDM techniques, including AHP, FAHP, CRITIC, and MULTIMOORA to assess site suitability and find alternate EVCS development sites in Varanasi. The AUC-ROC curve validated the model, while sensitivity analysis verified reliability. The study is particularly significant as it addresses both urban and rural areas in a metropolitan region of the Global South. Findings revealed 226.5 to 279.09 sq.km. of land was suitable to highly suitable for EVCS development. From these areas, fifteen sites from urban and rural locations were chosen to strategically address the urban–rural EVCS infrastructure gap. The ranking analysis identified the top rural sites as R14, R9, R12, R2, and R6, while urban sites included U10, U11, U3, U15, and U2. Notably, the FAHP model outperformed others with an AUC value of 0.918, demonstrating its robustness. The minimal variation of 0.79% between the highest (PtAM) and lowest mean sensitivity index (PtGS) values further confirms the reliability of the FAHP model. The study’s findings help urban planners and municipal authorities strategically place EV charging stations to maximize user accessibility and enhance the city’s emerging EV infrastructure.</p>

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Harnessing GIS-based hybrid MCDM techniques for optimal electric vehicle charging sites selection: bridging the urban–rural divide in a metropolitan region of the Global South

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月6日

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  • Bhaskar Mandal,
  • Sharmistha Mondal

摘要

The growing number of EVs in Varanasi is notable, yet the development of EVCSs is lagging behind this trend. Thus, this study uses MCDM techniques, including AHP, FAHP, CRITIC, and MULTIMOORA to assess site suitability and find alternate EVCS development sites in Varanasi. The AUC-ROC curve validated the model, while sensitivity analysis verified reliability. The study is particularly significant as it addresses both urban and rural areas in a metropolitan region of the Global South. Findings revealed 226.5 to 279.09 sq.km. of land was suitable to highly suitable for EVCS development. From these areas, fifteen sites from urban and rural locations were chosen to strategically address the urban–rural EVCS infrastructure gap. The ranking analysis identified the top rural sites as R14, R9, R12, R2, and R6, while urban sites included U10, U11, U3, U15, and U2. Notably, the FAHP model outperformed others with an AUC value of 0.918, demonstrating its robustness. The minimal variation of 0.79% between the highest (PtAM) and lowest mean sensitivity index (PtGS) values further confirms the reliability of the FAHP model. The study’s findings help urban planners and municipal authorities strategically place EV charging stations to maximize user accessibility and enhance the city’s emerging EV infrastructure.