To address the problem of the difficulty of monitoring and controlling enterprise emissions， a Vertical Federated Learning Enterprise Emission Prediction （VFL-EEP） model with integration of electricity data was proposed by considering the premise of secure data sharing and privacy protection. Firstly， within the framework of Vertical Federated Learning （VFL）， the logistic regression model was enhanced to allow the separation of data usage and model training without leaking the monitoring data of electricity and environmental protection enterprises. Then， the logistic regression algorithm was improved to incorporate with Paillier encryption technology for ensuring the security of model parameter transmission， thereby solving the issue of insecure communication among participants in VFL effectively. Finally， through experiments on simulated data， the pollution prediction results of the proposed model were compared with those of the centralized logistic regression model. The results show that the proposed model integrates electricity data under the premise of privacy security， and has the accuracy， recall， precision， and F1 value improved by 8.92%， 7.62%， 3.95%， and 11.86%， respectively， realizing the balance between privacy protection and model performance effectively.