LAUSR

With the global resource and environment problems becoming increasingly prominent and the government procurement scale growing rapidly, the implementation of green procurement for government public project will have a significant impact on the protection of resources and environment. Identifying the key influential factors of bid evaluation in government public project green procurement is of great significance for promoting the construction of government public project green procurement system. Based on the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method, Back Propagation (BP) neural network is introduced to determine the direct-relation matrix, optimizing the traditional DEMATEL method. This paper uses the BP-DEMATEL model to identify key influential factors of bid evaluation in government public project green procurement by algorithmic design. Among 56 indicators, which have formed a set of index system of bid evaluation in government public project green procurement, 20 key influential factors are identified, providing reference for the government to implement green procurement in the field of public project. The research results not only enrich and improve the theoretical and practical research in the field of government public project green procurement, but also solve the defects of traditional DEMATEL method that the method is too subjective and tedious when obtaining the influence degree between each pair of factors to construct direct-relation matrix through questionnaire surveys or expert scoring method, thus expanding the scope of the application of the DEMATEL method.