A cloud database uses thousands of nodes to exchange data in order to process query requests from different applications. It inevitably happens that some nodes stored hot records are facing high frequent query requests while the others are rarely visited or even idle. Therefore, how data are dynamically allocated and migrated at the runtime has crucial impact on the query load distribution and the system performance. In this paper, we simulate the management of carbon assets in the global carbon trading market, and regard the cloud database as global carbon trading market, where data nodes are regarded as countries around the world and the query load as carbon quota. The global carbon trading market will achieve balance through the trading of query load between countries, ultimately achieving carbon peak, which means the global equilibrium in cloud database. Therefore, we propose a Carbon Trading Behavior-based Strategy for query load balance in cloud database, taking into account the computing capacity, disk volume, bandwidth, etc. We further apply this strategy to MongoDB and conduct experiments on synthetic dataset. Experimental results show that the strategy we propose significantly enhances the query efficiency of MongoDB, and over 65% improvement is achieved for efficiency.