With the development of the bike-sharing system (BSS) and the introduction of green and low carbon development, the environmental impacts of BSS had received increasing attention in recent years. However, the emissions from the rebalancing of BSS, where fossil-fueled vehicles are commonly used, are usually neglected, which goes against the idea of green travel in a sharing economy. Previous studies on the bike-sharing rebalancing problem (BRP), which is considered NP-hard, have mainly focused on algorithm innovation instead of improving the solution model, thereby hindering the application of many existing models in large-scale BRP. This study then proposes a method for optimizing the CO₂ emissions from BRP and takes the BSS of Beijing as a demonstration. We initially analyze the spatial and temporal characteristics of BSS, especially the flow between districts, and find that each district can be independently rebalanced. Afterward, we develop a rebalancing optimization model based on a partitioning strategy to avoid deciding the number of bikes being loaded or unloaded at each parking node. We then employ the tabu search algorithm to solve the model. Results show that (i) due to over launch and lack of planning in rebalancing, the BSS in Beijing shows great potential for optimization, such as by reducing the number of vehicle routes, CO₂ emissions, and unmet demands; (ii) the CO₂ emissions of BSS in Beijing can be reduced by 57.5% by forming balanced parking nodes at the end of the day and decreasing the repetition of vehicle routes and the loads of vehicles; and (iii) the launch amounts of bikes in specific districts, such as Shijingshan and Mentougou, should be increased.