Urbanization is commonly described as the process of population flow from rural to urban areas. As the largest developing country, China has experienced an unprecedentedly fast and large urbanization process since 1980s, which will continue for the coming future. The immense scale of the process has brought multidimensional benefits across all sectors in the country, yet also consumed a vast amount of resources and caused various types of environmental problems. The conflict between limited resources and an unstoppable urbanization process has become a pressing issue, which presents the urgent need for efficiency pursuance in the process of urbanization in order to ensure sustainable urban development. It is considered that the improvement of urbanization efficiency in large developing countries such as China has great implications for global sustainability. There is little existing study conducted to understand what efficiency achieved in the current fast urban development era in China. This study investigates the urbanization efficiency and its changes in the contemporary China. A set of input-output indicators are employed for analyzing the efficiency, in which both desirable and undesirable outputs are considered. The Super-efficiency Slack-based Measure (SBM) model and DEA-based Malmquist Production Index (MPI) are adopted collectively for conducting data analysis. The research is conducted at provincial level in China and the data collected for analysis are from 30 provinces for the period of 2006–2015. The results from this study show that the overall urbanization efficiency in China during the surveyed period is low, although certain improvement has been achieved. The difference between good and poor performers is considerable. In general, those provinces with better social and economic background have better urbanization efficiency performance. East China is much better than the rest of China, whilst Southwest region has the poorest performance.

Extremely heavy rainfall has posed a significant hazard to urban growth as the most common and disaster-prone natural calamity. Due to its unique geographical location, the metro system is more vulnerable to waterlogging caused by rainstorm disaster. Research on resilience to natural disasters has attracted extensive attention in recent years. However, few studies have focused on the resilience of the metro system against rainstorms. Therefore, this paper aims to develop an assessment model for evaluating metro stations’ resilience levels. Twenty factors are carried out from dimensions of resistance, recovery and adaptation. The methods of ordered binary comparison, entropy weight and cloud model are proposed to build the assessment model. Then, taking Chongqing metro system in china as a case study, the resilience level of 13 metro stations is calculated. Radar charts from dimensions of resistance, recovery, and adaptation are created to propose recommendations for improving metro stations’ resilience against rainstorms, providing a reference for the sustainable development of the metro system. The case study of the Chongqing metro system in china demonstrates that the assessment model can effectively evaluate the resilience level of metro stations and can be used in other infrastructures under natural disasters for resilience assessment.

Due to outstanding feature extraction ability, neural networks have recently achieved great success in sentiment analysis. However, one of the remaining challenges of sentiment analysis is to model long texts to consider the intrinsic relations between two sentences in the semantic meaning of a document. Moreover, most existing methods are not powerful enough to differentiate the importance of different document features. To address these problems, this paper proposes a new neural network model: AttBiLSTM-2DCNN, which entails two perspectives. First, a two-layer, bidirectional long short-term memory (BiLSTM) network is utilized to obtain the sentiment semantics of a document. The first BiLSTM layer learns the sentiment semantic representation from both directions of a sentence, and the second BiLSTM layer is used to encode the intrinsic relations of sentences into the document matrix representation with a feature dimension and a time-step dimension. Second, a two-dimensional convolutional neural network (2DCNN) is employed to obtain more sentiment dependencies between two sentences. Third, we utilize a two-layer attention mechanism to distinguish the importance of words and sentences in the document. Last, to validate the model, we perform an experiment on two public review datasets that are derived from Yelp2015 and IMDB. Accuracy, F1-Measure, and MSE are used as evaluation metrics. The experimental results show that our model can not only capture sentimental relations but also outperform certain state-of-the-art models.

Improving the ability of the urban rail transit system to cope with rainstorm disasters is of great significance to ensure the safe travel of residents. In this study, a model of the hierarchical relationship of the influencing factors is constructed from the resilience perspective, in order to research the action mechanisms of the influencing factors of urban rail transit stations susceptible to rainstorm disaster. Firstly, the concept of resilience and the three attributes (resistance, recovery, and adaptability) are interpreted. Based on the relevant literature, 20 influencing factors are discerned from the 3 attributes of the resilience of urban rail transit stations. Subsequently, an interpretative structural model (ISM) is utilised to analyse the hierarchical relationship among the influencing factors. The key influencing factors of station resilience are screened out using social network analysis (SNA). Combined with ISM and SNA for analysis, the result shows that the key influencing factors are: “Flood prevention monitoring capability”; “Water blocking capacity”; “Flood prevention capital investment”; “Personnel cooperation ability”; “Emergency plan for flood prevention”; “Flood prevention training and drill”; “Publicity and education of flood prevention knowledge”; and “Regional economic development level”. Therefore, according to the critical influencing factors and the action path of the resilience influencing factors, station managers can carry out corresponding flood control work, providing a reference for enhancing the resilience of urban rail transit stations.

Metro systems are gradually becoming more and more crucial in promoting the economy and society in cities. However, various challenges such as financial resources and the efficiency of utilizing these metro plans bring difficulties for metro construction. Hence, accurately evaluating the urban metro system’s development condition seems significant for the sustainable development of the urban metro system. Therefore, a comprehensive evaluation indicator system of metro development conditions containing 25 indicators from dimensions of demand and supply is established in this study, and a coupling coordination degree model combined with the entropy weight method and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method is proposed to analyze the level of metro development conditions and coupling coordination conditions of 35 cities in China. According to the calculation results, 35 cities are divided into six categories, and radar charts are constructed to promote the sustainable development of the metro system.

Although the acceleration of urbanization brings social and economic development, it also produces various problems. Accurate evaluation of sustainable urbanization performance can help local governments summarize experiences and solve problems. Sustainable urban development should focus not only on modern construction, but also on original natural ecosystems and traditional cultural protection. This paper develops a holistic framework based on an “origin” and “modernization” perspectives and uses the multilevel extension method and the Analytic Hierarchy Process (AHP) method for accurately evaluating sustainable urbanization performance. A case study of Chongqing City in China demonstrates the process of using the holistic framework and evaluation method. The empirical results from this study indicate that Chongqing has a medium level of sustainable urbanization. The city is considered as a medium level in “origin” performance and the “modernization” performance is good, while uncoordinated. The case study reveals that the proposed framework and the method are effective theoretical bases for policy-makers and practitioners to understand the performance of urban sustainability and for promoting urbanization toward better sustainability. Beyond the application case, the holistic framework and method can be applied to other cities.