In the post-COVID-19 period, urban mobility patterns have changed significantly, making micromobility systems, particularly e-bikes, increasingly important for short-distance and last-mile travel. As the role of e-bikes in urban transportation continues to expand, energy efficiency has become a key issue affecting battery performance, operational feasibility, and the broader sustainability of micromobility systems. Among the factors influencing e-bike energy use, topography, especially road slope, is a key determinant of consumption and recovery potential.

This study presents a GIS-based framework for modelling slope-dependent energy consumption and regenerative energy recovery across an urban road network. Rather than relying solely on average energy values for the entire network, the study calculates segment-based consumption, recovery, and net energy indicators. It visualizes them as layered thematic maps in a GIS/Folium environment. This approach reveals the spatial heterogeneity of energy patterns and helps identify critical segments and corridors where short but steep transitions may generate disproportionately high energy costs.

The findings show a clear spatial relationship between slope and energy performance. The resulting net energy maps enable the identification of energy-advantaged and energy-disadvantaged parts of the network, thereby providing a more detailed understanding of urban topographic effects on micromobility systems. These outputs can support municipalities, micromobility operators, and urban mobility planners in developing more informed, place-sensitive, and energy-aware strategies for sustainable transport.

Although the framework is based on simplifying assumptions, excludes traffic-related operational conditions, and is limited to a single study area with constrained validation, it provides a practical basis for integrating topography-sensitive energy analysis into sustainable urban mobility planning.