IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i7p3052-d1623848.html
   My bibliography  Save this article

Towards Sustainable Cities: A KPI-Based Method to Compare Cities’ Performance and Encourage the Spread of Electric Cars

Author

Listed:
  • Alvaro Menendez Agudin

    (DC Systems, Energy Conversion and Storage Group, Department of Electrical Sustainable Energy, Delft University of Technology, 2600 AA Delft, The Netherlands)

  • Claudia Caballini

    (Department DIATI-Transport Systems, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy)

  • Francesco Paolo Deflorio

    (Department DIATI-Transport Systems, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy)

  • Gregorio Fernandez Aznar

    (CIRCE, Centro de Investigación de Recursos y Consumos Energéticos, Parque Empresarial Dinamiza, Avda. Ranillas, 50018 Zaragoza, Spain)

  • Leopold Herman

    (Faculty of Electrical Engineering, University of Ljubljana, Kongresni trg 12, 1000 Ljubljana, Slovenia)

  • Klemen Knez

    (Faculty of Electrical Engineering, University of Ljubljana, Kongresni trg 12, 1000 Ljubljana, Slovenia)

Abstract

European cities have adopted different solutions to address the challenges of charging infrastructure for electric vehicles, depending on their specific characteristics and needs. The widespread adoption of effective solutions could accelerate the transition towards more sustainable urban mobility. However, as cities differ in socio-economic, infrastructural, and environmental aspects, a one-size-fits-all approach may not be suitable. Currently, there is a lack of studies in the literature that identify similarities among cities to support the development of shared strategies for sustainable electric mobility. This paper contributes to filling this gap by proposing a methodology based on Key Performance Indicators (KPIs) to classify and compare cities according to their electric vehicle infrastructure. Using quantitative data from 80 European cities across civil, social, and transport-related factors, as well as electric vehicle charging characteristics, we identified five reference city clusters. A sensitivity analysis, conducted across 30 scenarios, validated the robustness of the KPI framework. This approach provides a tool for policymakers to monitor the evolution of charging infrastructure, supporting data-driven decision-making for sustainable urban mobility. By promoting efficient and adaptable electric vehicle policies, this study aligns with the objectives of the 2030 Agenda for Sustainable Development, particularly in fostering sustainable cities and clean energy adoption.

Suggested Citation

  • Alvaro Menendez Agudin & Claudia Caballini & Francesco Paolo Deflorio & Gregorio Fernandez Aznar & Leopold Herman & Klemen Knez, 2025. "Towards Sustainable Cities: A KPI-Based Method to Compare Cities’ Performance and Encourage the Spread of Electric Cars," Sustainability, MDPI, vol. 17(7), pages 1-27, March.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:7:p:3052-:d:1623848
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/7/3052/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/17/7/3052/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xydas, Erotokritos & Marmaras, Charalampos & Cipcigan, Liana M. & Jenkins, Nick & Carroll, Steve & Barker, Myles, 2016. "A data-driven approach for characterising the charging demand of electric vehicles: A UK case study," Applied Energy, Elsevier, vol. 162(C), pages 763-771.
    2. Zhou, Guangyou & Zhu, Zhiwei & Luo, Sumei, 2022. "Location optimization of electric vehicle charging stations: Based on cost model and genetic algorithm," Energy, Elsevier, vol. 247(C).
    3. He, Sylvia Y. & Kuo, Yong-Hong & Sun, Ka Kit, 2022. "The spatial planning of public electric vehicle charging infrastructure in a high-density city using a contextualised location-allocation model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 160(C), pages 21-44.
    4. Chandra, Minal, 2022. "Investigating the impact of policies, socio-demography and national commitments on electric-vehicle demand: Cross-country study," Journal of Transport Geography, Elsevier, vol. 103(C).
    5. Zhang, Qi & Li, Hailong & Zhu, Lijing & Campana, Pietro Elia & Lu, Huihui & Wallin, Fredrik & Sun, Qie, 2018. "Factors influencing the economics of public charging infrastructures for EV – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 500-509.
    6. Giles-Corti, Billie & Lowe, Melanie & Arundel, Jonathan, 2020. "Achieving the SDGs: Evaluating indicators to be used to benchmark and monitor progress towards creating healthy and sustainable cities," Health Policy, Elsevier, vol. 124(6), pages 581-590.
    7. Morrissey, Patrick & Weldon, Peter & O’Mahony, Margaret, 2016. "Future standard and fast charging infrastructure planning: An analysis of electric vehicle charging behaviour," Energy Policy, Elsevier, vol. 89(C), pages 257-270.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wu, Jiabin & Li, Qihang & Bie, Yiming & Zhou, Wei, 2024. "Location-routing optimization problem for electric vehicle charging stations in an uncertain transportation network: An adaptive co-evolutionary clustering algorithm," Energy, Elsevier, vol. 304(C).
    2. Hu, Dingding & Zhou, Kaile & Li, Fangyi & Ma, Dawei, 2022. "Electric vehicle user classification and value discovery based on charging big data," Energy, Elsevier, vol. 249(C).
    3. Chengxiang Zhuge & Chunfu Shao & Xia Li, 2019. "Empirical Analysis of Parking Behaviour of Conventional and Electric Vehicles for Parking Modelling: A Case Study of Beijing, China," Energies, MDPI, vol. 12(16), pages 1-21, August.
    4. Wei Wei & Ming Cao & Qianling Jiang & Sheng-Jung Ou & Hong Zou, 2020. "What Influences Chinese Consumers’ Adoption of Battery Electric Vehicles? A Preliminary Study Based on Factor Analysis," Energies, MDPI, vol. 13(5), pages 1-15, February.
    5. Khaleghikarahrodi, Mehrsa & Macht, Gretchen A., 2023. "Patterns, no patterns, that is the question: Quantifying users’ electric vehicle charging," Transport Policy, Elsevier, vol. 141(C), pages 291-304.
    6. Kacperski, Celina & Ulloa, Roberto & Klingert, Sonja & Kirpes, Benedikt & Kutzner, Florian, 2022. "Impact of incentives for greener battery electric vehicle charging – A field experiment," Energy Policy, Elsevier, vol. 161(C).
    7. Zhu, Lijing & Wang, Peize & Zhang, Qi, 2019. "Indirect network effects in China’s electric vehicle diffusion under phasing out subsidies," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    8. Ma, Shao-Chao & Fan, Ying, 2020. "A deployment model of EV charging piles and its impact on EV promotion," Energy Policy, Elsevier, vol. 146(C).
    9. Andrenacci, N. & Genovese, A. & Ragona, R., 2017. "Determination of the level of service and customer crowding for electric charging stations through fuzzy models and simulation techniques," Applied Energy, Elsevier, vol. 208(C), pages 97-107.
    10. Fischer, David & Harbrecht, Alexander & Surmann, Arne & McKenna, Russell, 2019. "Electric vehicles’ impacts on residential electric local profiles – A stochastic modelling approach considering socio-economic, behavioural and spatial factors," Applied Energy, Elsevier, vol. 233, pages 644-658.
    11. Simona Bigerna & Silvia Micheli, 2018. "Attitudes Toward Electric Vehicles: The Case of Perugia Using a Fuzzy Set Analysis," Sustainability, MDPI, vol. 10(11), pages 1-14, November.
    12. Helmus, J.R. & Spoelstra, J.C. & Refa, N. & Lees, M. & van den Hoed, R., 2018. "Assessment of public charging infrastructure push and pull rollout strategies: The case of the Netherlands," Energy Policy, Elsevier, vol. 121(C), pages 35-47.
    13. Shi, Lefeng & Hao, Ying & Lv, Shengnan & Cipcigan, Liana & Liang, Jun, 2021. "A comprehensive charging network planning scheme for promoting EV charging infrastructure considering the Chicken-Eggs dilemma," Research in Transportation Economics, Elsevier, vol. 88(C).
    14. Mingdong Sun & Chunfu Shao & Chengxiang Zhuge & Pinxi Wang & Xiong Yang & Shiqi Wang, 2022. "Uncovering travel and charging patterns of private electric vehicles with trajectory data: evidence and policy implications," Transportation, Springer, vol. 49(5), pages 1409-1439, October.
    15. Chen, Yu & Lin, Boqiang, 2022. "Are consumers in China’s major cities happy with charging infrastructure for electric vehicles?," Applied Energy, Elsevier, vol. 327(C).
    16. Xiong, Siqin & Yuan, Yi & Yao, Jia & Bai, Bo & Ma, Xiaoming, 2023. "Exploring consumer preferences for electric vehicles based on the random coefficient logit model," Energy, Elsevier, vol. 263(PA).
    17. Baresch, Martin & Moser, Simon, 2019. "Allocation of e-car charging: Assessing the utilization of charging infrastructures by location," Transportation Research Part A: Policy and Practice, Elsevier, vol. 124(C), pages 388-395.
    18. Natascia Andrenacci & Roberto Ragona & Antonino Genovese, 2020. "Evaluation of the Instantaneous Power Demand of an Electric Charging Station in an Urban Scenario," Energies, MDPI, vol. 13(11), pages 1-19, May.
    19. Javier García López & Raffaele Sisto & Javier Benayas & Álvaro de Juanes & Julio Lumbreras & Carlos Mataix, 2021. "Assessment of the Results and Methodology of the Sustainable Development Index for Spanish Cities," Sustainability, MDPI, vol. 13(11), pages 1-29, June.
    20. Wolbertus, Rick & van den Hoed, Robert & Kroesen, Maarten & Chorus, Caspar, 2021. "Charging infrastructure roll-out strategies for large scale introduction of electric vehicles in urban areas: An agent-based simulation study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 148(C), pages 262-285.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:17:y:2025:i:7:p:3052-:d:1623848. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.