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Accurately quantifying carbon mitigation of operational photovoltaics (PVs) influenced by dynamic geo-environment is crucial for developing suitable initiatives on renewable energy transition. However, previous studies made strong assumptions to avoid modelling spatial heterogeneity of carbon footprints or ignore weather and shadowing effects on PV electricity generation, making the estimated results unreliable and even causing false policymaking. To tackle this challenge, we developed a novel model coupling multi-sourced data modelling and life-cycle assessment to estimate spatially heterogenous carbon mitigation of all the operational rooftop PVs in an entire city. It is built by three hierarchal modules: (i) segmenting PV areas from high-resolution satellite imagery, by using Deep Solar PV Refiner, an advanced semantic segmentation network; (ii) estimating electricity generation in the segmented PV areas, by using a well-developed 3D solar irradiation model that considers the effects of land surface solar irradiation influenced by weather and shadowing effects produced by 3D buildings; (iii) quantifying carbon mitigation potential of PVs, by developing a spatial-aware life-cycle model to track the life-cycle carbon footprints of PVs from production, transportation, operation, to decommission. Investigating Singapore by 2020, we reveal that industrial, airport, and residential areas have the largest rooftop PV installation. We also suggest a carbon emission rate of 13.20 g-CO 2 /kWh, a carbon payback time of 0.81 years, and an energy payback time of 0.94 years, showing an improved carbon mitigation capability compared to the past years. This study contributes to GIS data modelling and helps understand the geospatial characteristics of urban-scale PV carbon mitigation. • Developed life-cycle-assessment integrated multi-sourced geospatial data model. • Segmented rooftop PV areas using an advanced semantic segmentation model. • Estimated installed PV electricity output using 3D solar estimation model. • Suggested significant carbon mitigation potential from rooftop PVs in Singapore. • Contributes to renewable energy transition and Information Geography development. [ABSTRACT FROM AUTHOR]

Scooter-sharing has been introduced as a new transportation mode. However, e-scooters have a limited battery capacity and require frequent charging, which causes the operational cost significantly high and hinders the viability of the service. To tackle this problem, this study proposes a solar charging solution with the creation of a real-time shareability network that maximizes the scooter-sharing capability and minimizes the total trip distance, constrained by e-scooters with real-time battery levels. Specifically, hourly solar potential is simulated based on a three-dimensional solar irradiation model so that photovoltaic (PV) electricity generation can be estimated when PV modules are installed at the parking stations, which enables solar charging when the origin–destination matrix of scooter-sharing trips is clustered and associated to the charging stations. As a case study in Singapore, the proposed solar charging system only needs 1–3 m 2 PV modules at each station and 24%–67% of the total number of e-scooters to support almost all the real trips with a 98% reduction of trips used for charging. The system also supports 90% of on-demand mobility for at least three consecutive days without solar charging, which suggests the resilience of the system and inspires us to promote the proposed solar charging in the other global cities. • Developed a solar charging system to charge e-scooters at the parking stations. • Developed a battery-level-aware real-time shareability network for scooter-sharing. • Estimated spatio-temporal distribution of solar irradiation on 3D urban surfaces. • Only needs 24%–27% of the fleet size to obtain 98% reduction of charging trips. • Only needs 1–3 m2 PV module at each station to save the charging cost dramatically. [ABSTRACT FROM AUTHOR]