A new experience mining approach for improving low carbon city development

Developing low carbon city (LCC) has been widely appreciated as an important strategy for sustainable development. In line with this, an increasing number of cities globally have launched low carbon practices in recent years and gained various types of experience. However, it appears that existing studies do not present methods of how to use these valuable LCC experience in solving new problems. This study therefore introduces an experience mining approach to assist decision‐makers in reusing previous experience when tailoring LCC development strategies. The mining approach consists of three processes, namely, collecting historical cases which have been experiencing LCC, establishing LCC experience base, and mining similar experience cases. This study innovates the existing experience mining approach by introducing a two‐step mining process with considering the perspective of problem‐based urban characteristics (PBUCs) and the perspective of solution‐based urban characteristics (SBUCs). The application of the introduced mining approach has been demonstrated by a case study, where Shenyang’s energy structure is adopted as the target problem. The new experience mining approach provides a valuable reference for decision‐makers to retrieve similar cases for improving LCC development with the consideration of city characteristics.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156189/2/sd2046_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156189/1/sd2046.pd

Energy and Environmental Efficiency Evaluation of Transportation Systems in China’s 255 Cities

China’s transportation sector suffers from excessive energy consumption and serious pollutant emissions. There is increasing pressure to improve energy and environmental efficiency (EEE). This paper researches the EEE of transportation systems in 255 Chinese cities from 2015 to 2019 with the assistance of the super-efficiency SBM model. Research results show that the five-year average EEE of the Chinese transportation system is 0.4420, indicating an overall low performance, with most regions still needing improvement. There are significant differences in the transportation system EEE between cities, with Guangzhou, Maoming, and Zhoushan ranking in the top three, and Heihe, Xining, and Taiyuan in the bottom. Cities with a better economic base, developed water systems and more relevant policy documents do better in energy use and environmental protection compared to other cities. Moreover, the development of the transportation systems is uneven, with noticeable regional differences. The general trend is that cities located in the eastern have better transportation systems EEE than cities in other economic zones. The findings should have a far-reaching impact on the sustainable development of cities. It also provides an essential reference for the research on EEE efficiency of transportation systems in China and other countries

A Novel Approach for Assessing the Performance of Sustainable Urbanization Based on Structural Equation Modeling: A China Case Study

The rapid urbanization process has brought problems to China, such as traffic congestion, air pollution, water pollution and resources scarcity. Sustainable urbanization is commonly appreciated as an effective way to promote the sustainable development. The proper understanding of the sustainable urbanization performance is critical to provide governments with support in making urban development strategies and policies for guiding the sustainable development. This paper utilizes the method of Structural equation modeling (SEM) to establish an assessment model for measuring sustainable urbanization performance. Four unobserved endogenous variables, economic variable, social variable, environment variable and resource variable, and 21 observed endogenous variables comprise the SEM model. A case study of the 31 provinces in China demonstrates the validity of the SEM model and the analysis results indicated that the assessment model could help make more effective policies and strategies for improving urban sustainability by recognizing the statue of sustainable urbanization

Critical risk factors of public building green retrofit projects- an empirical study in Chongqing, China

In order to discuss the critical risk factors of green retrofit project in existing public buildings, this study identifies the risk factors from the whole life cycle firstly, and the experts are invited to assess the probability of the occurrence, the degree of the severity, and the follow-up effect of the risk occurrence, respectively. In addition, a Choquet integral-based FMEA (Failure mode and effect analysis) model is introduced to assess and analyze the risks in which the descriptive language results are transformed into triangle fuzzy number to analyze the risk assessment results quantitatively, and the relative preference relations are used to rank the risk factors, empirical study is conducted focusing on Chongqing. Based on the proposed risk assessment model, the top 10 high-risk factors in green retrofit projects of public buildings are extracted, the possible potential causes are analyzed, and the corresponding risk response measures are proposed, which provide references for the improvement of risk management in urban renewal

Document-Level Sentiment Analysis Using Attention-Based Bi-Directional Long Short-Term Memory Network and Two-Dimensional Convolutional Neural Network

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

A Novel Approach for Assessing the Performance of Sustainable Urbanization Based on Structural Equation Modeling: A China Case Study

The rapid urbanization process has brought problems to China, such as traffic congestion, air pollution, water pollution and resources scarcity. Sustainable urbanization is commonly appreciated as an effective way to promote the sustainable development. The proper understanding of the sustainable urbanization performance is critical to provide governments with support in making urban development strategies and policies for guiding the sustainable development. This paper utilizes the method of Structural equation modeling (SEM) to establish an assessment model for measuring sustainable urbanization performance. Four unobserved endogenous variables, economic variable, social variable, environment variable and resource variable, and 21 observed endogenous variables comprise the SEM model. A case study of the 31 provinces in China demonstrates the validity of the SEM model and the analysis results indicated that the assessment model could help make more effective policies and strategies for improving urban sustainability by recognizing the statue of sustainable urbanization

Document-Level Sentiment Analysis Using Attention-Based Bi-Directional Long Short-Term Memory Network and Two-Dimensional Convolutional Neural Network

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

An Innovative Approach to Determining High-Risk Nodes in a Complex Urban Rail Transit Station: A Perspective of Promoting Urban Sustainability

Public safety presents high importance in urban sustainable development. Transportation safety is a significant section in public safety. Over the last couple of decades, as a sustainable means of public transportation, urban rail transit presents a rapid development in China. Increasing initiatives and practices have been engaged with views to facilitating people’s travel and intensive utilizing land resources. Echoing this, rail transit stations with multi-floor structure have been built and show structure complexity. Due to this complexity, there is a need to focus on risk management for the stations to guarantee operation safety. Accordingly, this research introduces an innovative approach to identify high-risk nodes in the complex rail transit stations. The high-risk nodes are determined according to two aspects, which are the key nodes of the station and presenting large passenger volumes. Complex network analysis and field investigation were adopted in this study. The Lianglukou rail transit station in Chongqing, China was selected for case study. The research results in this study indicate that (1) in platform floors, stairs/escalators are almost high-risk nodes; (2) columns and metal fences that have been determined as high-risk nodes are located near stairs/escalators; (3) in concourse floor, the determined high-risk nodes present relative high degree centrality and low betweenness centrality compared with nodes in platform floor. The obtained high-risk nodes are helpful for the management firms to develop risk mitigation measures and re-allocate their resources to create a safe environment for passengers in the stations. The guarantee for the rail transit station operation safety plays an important role in enhancing urban sustainability

Country review on the main building energy-efficiency policy instrument

Building energy-efficiency (BEE) is the key to drive the promotion of energy saving in building sector. A large variety of building energy-efficiency policy instrument exist. Some are mandatory, some are soft scheme, and some use economic incentives from country to country. This paper presents the current development of implementing BEE policy instruments by examining the practices of BEE in seven selected countries and regions. In the study, BEE policy instruments are classified into three groups, including mandatory administration control instruments, economic incentive instruments and voluntary scheme instruments. The study shows that different countries have adopted different instruments in their practices for achieving the target of energy-saving and gained various kinds of experiences. It is important to share these experiences gained

Measuring Crowdedness between Adjacent Stations in an Urban Metro System: a Chinese Case Study

The urban metro system has been widely appreciated as the most important component in urban infrastructures. It plays a critical role in promoting urban social and economic development, and particularly in reducing the urban traffic congestion. However, there are various inherent problems with operating metro systems, which typically involve the crowdedness both at stations and inside vehicles. Both policymakers and academic researchers in China have paid little attention to the crowdedness between metro stations. In order to solve the problem of crowdedness, it is necessary to develop a method to evaluate the level of crowdedness. This work establishes a model to measure the crowdedness between adjacent stations in a metro system based on the load factor principle, passenger standing density, and other factors such as the metro operation schedule and estimations of passenger flows. The Chongqing Metro Line 3 in China is used as a case study to demonstrate the application of the evaluation model. The case study reveals that the model introduced in this study can assist with assessing the crowdedness level between adjacent stations in a metro line. The model is an effective tool for helping the metro management and administration understand the level of crowdedness, apply proper methods to mitigate the crowdedness, and thus improve the quality of the service for those utilizing the metro system