An Integrated Algorithm for Low-Carbon City Evaluation Based on Neural Network and Analytic Hierarchy Process

Abstract

The low-carbon city evaluation has great significance to promote the urban construction management and sustainable development of city. In order to establish a suitable low-carbon city evaluation method to measure the performance of low-carbon city planning and construction, based on the index system of low-carbon city evaluation in the Pressure Status Response (PSR) framework, an integrated algorithm for low-carbon city evaluation combining BP neural network with the Analytic Hierarchy Process (AHP) is proposed. Furthermore, the possible ways for optimization have been discussed. The model and algorithm have the following properties: rapid, flexible and feasible.