An environmental pollution management method based on extended belief rule base and data envelopment analysis under interval uncertainty

The increasing investment in environmental pollution management urgently needs the scientific utilization of environmental management costs. However, three challenges must be addressed in environmental pollution management. First, the reliability of environmental pollution data is often disregarded, which may produce unreliable inference results. Second, there are many uncertainties in actual practice, which are neglected in existing cost prediction and efficiency evaluation of environmental pollution management. Third, existing research studies mainly focused on either efficiency evaluation or cost prediction and ignored the importance of combining both for environmental pollution management. To address these, an extended belief rule base (EBRB) model that considers consequence reliability is proposed to predict the interval costs, followed by an interval data envelopment analysis (IDEA) model that considers undesirable output to evaluate interval efficiencies of environmental pollution management. Based on these improved models, an integrated model named as EBRB-IDEA model is further developed under interval uncertainty. To verify its practical usage, the environmental pollution data of 29 Chinese provinces from 2004 to 2017 were used to carry out a case study. The experimental results demonstrated that the EBRB-IDEA model did not only achieve the desired interval prediction costs and efficiency evaluation but also effectively distinguished regional differences in the efficiency of environmental pollution management compared with existing models.