Pillars of smart cities include smart environment, mobility and economy. We explore impacts on these to enhance smart cities, heading towards a smart planet. Our motivation emerges from the need to decarbonize transportation. In this context, ride-sharing companies deploy electric vehicles (EVs). These should be managed by various factors: battery demand, EV charging station location, service availability, and charging time. Ride-sharing EV s aim to maximize profits via more rides. Our paper explores game theory in AI here. We propose E-Ride-Minimax, adapting the Minimax algorithm, treating EV ride-sharing companies as players. We hypothesize one player choosing its next move via total passenger-travel distance (longer the distance, larger the profit); and another player via battery usage (ratio of total passenger-travel distance to vehicle-passenger distance: optimizing this ratio enables more travel without recharging). Experimental results reveal that rising passenger numbers yield maximum battery savings (e.g. rush hours / major events); followed by stable and falling numbers. Our findings indicate that E-Ride-Minimax can reduce battery usage in some circumstances by 64%, losing 1 % profit. This is vital, given global emphasis on climate change. It increases cost-effectiveness, consumer participation and passengers per mile; reduces energy use and greenhouse gas emissions; and thus helps decarbonize transportation.