Speaker
Description
Electric vehicles (EVs) and hydrogen-based energy systems are emerging as transformative solutions in the transportation sector, offering the potential to significantly reduce carbon emissions and promote a cleaner environment. By 2030, hydrogen is expected to complement other renewable energy sources, playing a pivotal role in the energy transition alongside EVs (Tran et al., 2023). However, the integration of Twin Transition in transport depends on the equitable distribution of EV charging stations (EVCS) and hydrogen refueling station (HRS). To address social inequalities in infrastructure placement, this study is guided by research question: What are the socio-economic, environmental, and demographic factors that quantify spatial inequity in EVCS and HRS access?
This study presents a decision-making framework for developing policy recommendations to evaluate the optimal siting EVCS and HRS based on incorporating stakeholders’ preference (i.e., cost, congestion, environmental considerations) and socioeconomic and demographic factors. Recent studies conducted studies on spatial planning of EVCS and HRS focused on minimizing investment costs (Xu et al., 2018), optimizing traffic flow (Tran et al., 2023), and managing charging schedule (Gong, D., 2019). However, a significant gap persists in addressing equity concerns related to the spatial distribution of EVCS and HRS infrastructure. We propose a GIS-based Multi-Criteria Decision-Making (MCDM) model that integrates socio-economic, environmental, and demographic factors to assess spatial inequities in access to EVCS and HRD (Rashmitha et al., 2024). The model’s efficacy is validated through a city Warangal (India), recognized for promoting sustainable mobility (PM E-DRIVE, 2023). The Warangal city covers an area of 46.821 km², and its existing network of EVCS and HRS provides a suitable setting for validating the proposed framework.
Preliminary results identify vehicle ownership, population, household income, education level, major road connectivity, and proximity to utility areas as key factors influencing placement of EVCS and HRS. The regions in Warangal are categorized into low, medium, and high inequity levels. Approximately 63% of the region falls within high-inequity zones and are characterized by low vehicle ownership, high population density, poverty, limited education access, and inadequate median household income. These areas also lack proper power grids and road connectivity, increasing EVCS installation costs and discouraging investment. However, the majority of the population in these areas is aged 20–40 years, indicating a strong potential for future EV demand. The current and predicted EVCS and HRS placements also failed to account for evolving EV adoption, policy shifts, and grid constraints, worsening infrastructure disparity. Additionally, limited awareness and affordability create further inequities.
The integration of EV and hydrogen infrastructure is essential for achieving the Twin Transition in transport, where green and digital innovations converge to create resilient, inclusive mobility systems. This study aims to address emerging disparities in EVCS and HRS accessibility by developing a generalizable framework to quantify inequities in station placements. The framework enables policymakers to identify underserved communities and direct targeted infrastructure investments to ensure widespread EV and hydrogen vehicle adoption. By incorporating socioeconomic parameters, such as income levels, population density, and major road connectivity, the study ensures that infrastructure planning aligns with the needs of lower-middle-income populations, improving accessibility and equity over time. In future study, we aim to extends these findings by integrating ground truth data from existing EVCS and HRS to refine predictive modeling. AI-driven GIS models can generate predictive maps of EVCS and hydrogen infrastructure placements based on the identified environmental, socioeconomic and demographic factors. These predictions can be validated against real-world infrastructure data, ensuring accurate, data-driven decision-making. To conclude, this study created policy recommendations that contribute to both environmentally sustainable and socially equitable mobility system.
References
Gong, D., et al. (2019). Achieving sustainable transport through resource scheduling : A case study for electric vehicle charging stations. Advances in Production Engineering & Management, 14, 65–79.
He, S. Y., Kuo, Y. H., & Wu, D. (2016). Incorporating institutional and spatial factors in the selection of the optimal locations of public electric vehicle charging facilities: A case study of Beijing, China. Transportation Research Part C: Emerging Technologies, 67, 131–148.
Rashmitha, Y., Sushma, M. B., & Roy, S. (2024). A novel multi-criteria framework for selecting optimal sites for electric vehicle charging stations from a sustainable perspective: evidence from India. Environment, Development and Sustainability, 0123456789.
Tran, C. Q., Keyvan-Ekbatani, M., & Ngoduy, D. (2023). Towards clean transportation systems: Infrastructure planning for EVs charging while driving. Sustainable Cities and Society, 96(May).
Xu, J., Zhong, L., Yao, L., & Wu, Z. (2018). An interval type-2 fuzzy analysis towards electric vehicle charging station allocation from a sustainable perspective. Sustainable Cities and Society, 40(December 2017), 335–351.
PM E-DRIVE (2023), https://pib.gov.in/PressReleaseIframePage.aspx?PRID=1942506, Accessed in Jan 3, 2025
| Keywords | spatial planning; energy transition; social inequalities |
|---|---|
| Best Congress Paper Award | Yes |
