Evaluating Sustainable Liveable City via Multi-MCDM and Hopfield Neural Network

Research on liveable cities has received increased attention in recent years because of the complexity and diversity of liveability standards. Evaluating the liveable environment is a multiple criteria decision-making (MCDM) problem, and the results can be used to control environmental pollution and protect human health. However, different evaluation methods can lead to different results; hence, determining how to effectively to obtain the consistent results is a main consideration of this study. The objective of this work is to design an optimal method based on the difference ratio concept. A hopfield neural network is selected to validate the experimental results. Referring to the liveable city rankings established by the Economist Intelligence Unit, thirteen large cities in China are used to illustrate the application of the model, evaluate the liveable urban environment, and demonstrate the effectiveness and feasibility of the proposed model. The results show that Hangzhou is the most liveable city and Beijing has the worst liveable urban environment. Therefore, a common policy should be strengthening environmental governance, with a special focus on the development of low-carbon cities, for which both the local and global environmental impacts could be mitigated.