Research on Green Campus Evaluation in Cold Areas Based on AHP-BP Neural Networks

The green campus agenda is a specific manifestation of sustainable development and China’s basic strategy of developing the country through science and education. As a result of the differences in the climate environments and topographies of various places, the requisites for site planning and energy consumption by colleges and universities are different among regions, especially cold regions. However, China’s current green campus evaluation standard, GB/T 51356-2019, does not refine the evaluation indicators according to the different regions. Therefore, it is important to develop a green campus evaluation system appropriate to the region. Firstly, based on the relevant literature and standards, this paper clarifies the four evaluation criteria of campus sustainable land use, resource utilization, healthy environment, and safety. Nine first_level evaluation indicators for campuses—master planning, energy utilization, indoor environment, etc.—and twenty-one second-level evaluation indicators for campus siting—such as the use of water-saving appliances and renewable energy—were determined. Secondly, expert scoring and hierarchical analysis (AHP) were utilized to calculate the weights of the evaluation indicators by inputting the experts’ scores into the neural network model and testing the evaluation results using a back propagation neural network (BP) to finally establish a green campus evaluation model for cold regions based on an AHP-BP neural network. Finally, a university building in Xi’an, a cold region, was selected as a case study, and the errors in the green campus evaluation results were between 0.0001 and 0.001, which verifies the precision and practicability of the assessment system and the AHP-BP model. This paper’s findings serve as significant references for the improvement in assessment criteria for green campuses in the future.

This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met.