Rooftop photovoltaic (RPV) is often understood as a niche contribution to climate change mitigation. However, the global potential of RPVs to mitigate global warming is unknown. Here we map the global rooftop area at 1-km resolution, quantifying 286,393 km² of rooftops worldwide through geospatial data mining and artificial intelligence techniques. Using nine advanced Earth system models from the coupled model intercomparison project phase 6, we reveal that RPVs could substantially contribute to reducing global temperatures by 0.05–0.13 °C before 2050. Region-specific analysis underscores the variability in RPV potential and the necessity of tailored approaches to optimize RPV deployment, considering local solar resources, existing infrastructure and grid carbon intensity. Our findings reveal that leveraging RPV systems offers a viable and impactful strategy for reducing carbon footprints and combating climate change globally, while advocating targeted interventions to enhance the benefits of RPVs, particularly in areas with high solar radiation or rapid urbanization.

The recent surge in electric vehicles (EVs), driven by a collective push to enhance global environmental sustainability, has underscored the significance of exploring EV charging prediction. To catalyze further research in this domain, we introduce UrbanEV — an open dataset showcasing EV charging space availability and electricity consumption in a pioneering city for vehicle electrification, namely Shenzhen, China. UrbanEV offers a rich repository of charging data (i.e., charging occupancy, duration, volume, and price) captured at hourly intervals across an extensive six-month span for over 20,000 individual charging stations. Beyond these core attributes, the dataset also encompasses diverse influencing factors like weather conditions and spatial proximity. Comprehensive experiments have been conducted to showcase the predictive capabilities of various models, including statistical, deep learning, and transformer-based approaches, using the UrbanEV dataset. This dataset is poised to propel advancements in EV charging prediction and management, positioning itself as a benchmark resource within this burgeoning field.

Vehicular traffic has strong implication in the severity and degree of Urban Heat Island (UHI) effect in a city. It is crucial to map and monitor the spatio-temporal heat patterns from vehicular traffic in a city. Data observed from traffic counting stations are readily available for mapping the traffic-related heat across the stations. However, macroscopic models utilizing traffic counting data to estimate dynamic directional vehicular flows are rarely established. Our work proposes a simple and robust cell-transmission-model to simulate all the possible cell-based origin-destination trajectories of vehicular flows over time, based on the traffic counting stations. Result shows that the heat patterns have notable daily and weekly periodical circulation/pattern, and volumes of heat vary significantly in different grid cells. The findings suggest that vehicular flows in some places are the dominating influential factor that make the UHI phenomenon more remarkable.