Comprehensive evaluation of urban air quality using the relative entropy theory and improved TOPSIS method

Lin, Hong; Pan, Tianhong; Chen, Shan

doi:10.1007/s11869-020-00930-7

Cited by 18 publications

(7 citation statements)

References 34 publications

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“…The entropy value method is an objective weight calculation method and can effectively overcome information superposition among indicators [50]. After standardizing the number of broadband internet access users, the proportion of urban employees in the computer services and software industry, the total number of telecommunication services per capita, and the number of cell phone subscribers per 100 persons and considering the China Digital Inclusive Finance Index, the entropy value method was used to calculate the weights of each indicator.…”

Section: Entropy Value Methodsmentioning

confidence: 99%

Has Industrial Upgrading Improved Air Pollution?—Evidence from China’s Digital Economy

Wang

et al. 2022

Sustainability

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Air pollution has seriously hindered China’s sustainable development. The impact mechanism of industrial upgrading on air pollution is still unclear, given the rapid digital economy. It is necessary to analyze the impact of industrial structure upgrading on air pollution through the digital economy. To investigate the impact of industrial upgrading and the digital economy on air pollution, this paper selected the industrial advanced index and the digital economy index to construct a panel regression model to explore the improvement effect of industrial upgrading on air pollution and selected China’s three typical areas to construct a zonal regression model. The concentrations of air pollutants showed a downward trend during 2013–2020. Among them, the SO2 concentration decreased by 63%, which is lower than the PM2.5 and NO2 concentrations. The spatial pattern of air pollutants is heavier in the north than in the south and heavier in the east than in the west, with the North China Plain being the center of gravity. These air pollutants have significant spatial spillover effects, while local spatial correlation is dominated by high-high and low-low clustering. Industrial upgrading has a stronger suppressive effect on the PM2.5 concentration than the suppressive effect on the SO2 and NO2 concentrations, while the digital economy has a stronger improvement effect on the SO2 concentration than its improvement effect on the PM2.5 and NO2 concentrations. Industrial upgrading has a stronger improvement effect on air pollution in the Yangtze River Delta urban agglomeration than in Beijing–Tianjin–Hebei and its surrounding areas, while the improvement in air pollution attributable to the digital economy in Beijing–Tianjin–Hebei and its surrounding areas is stronger than in the Yangtze River Delta urban agglomeration. There are significant differences in the effects of industrial upgrading and the digital economy on the various types of air pollutants.

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Section: Entropy Value Methodsmentioning

confidence: 99%

Has Industrial Upgrading Improved Air Pollution?—Evidence from China’s Digital Economy

Wang

et al. 2022

Sustainability

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“…For example, Lin, Pan & Chen (2020) used an Entropy and TOPSIS mixed method to evaluate Urban air quality, which used the entropy to calculate the weights and then used the TOPSIS method to measure the air quality level [ 40 ]. Subba et al (2020) studied quality criteria for groundwater use through the Entropy index and PCA method, which used the Entropy index to quantify water quality and use the PCA method to explain the interrelated variance variables dimensionality of the datasets [ 41 ].…”

Section: Literature Reviewmentioning

confidence: 99%

A comparative analysis of the principal component analysis and entropy weight methods to establish the indexing measurement

Zhang

Yan

et al. 2022

PLoS ONE

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Background As the world’s largest coal producer, China was accounted for about 46% of global coal production. Among present coal mining risks, methane gas (called gas in this paper) explosion or ignition in an underground mine remains ever-present. Although many techniques have been used, gas accidents associated with the complex elements of underground gassy mines need more robust monitoring or warning systems to identify risks. This paper aimed to determine which single method between the PCA and Entropy methods better establishes a responsive weighted indexing measurement to improve coal mining safety. Methods Qualitative and quantitative mixed research methodologies were adopted for this research, including analysis of two case studies, correlation analysis, and comparative analysis. The literature reviewed the most-used multi-criteria decision making (MCDM) methods, including subjective methods and objective methods. The advantages and disadvantages of each MCDM method were briefly discussed. One more round literature review was conducted to search publications between 2017 and 2019 in CNKI. Followed two case studies, correlation analysis and comparative analysis were then conducted. Research ethics was approved by the Shanxi Coking Coal Group Research Committee. Results The literature searched a total of 25,831publications and found that the PCA method was the predominant method adopted, and the Entropy method was the second most widely adopted method. Two weighting methods were compared using two case studies. For the comparative analysis of Case Study 1, the PCA method appeared to be more responsive than the Entropy. For Case Study 2, the Entropy method is more responsive than the PCA. As a result, both methods were adopted for different cases in the case study mine and finally deployed for user acceptance testing on 5 November 2020. Conclusions The findings and suggestions were provided as further scopes for further research. This research indicated that no single method could be adopted as the better option for establishing indexing measurement in all cases. The practical implication suggests that comparative analysis should always be conducted on each case and determine the appropriate weighting method to the relevant case. This research recommended that the PCA method was a dimension reduction technique that could be handy for identifying the critical variables or factors and effectively used in hazard, risk, and emergency assessment. The PCA method might also be well-applied for developing predicting and forecasting systems as it was sensitive to outliers. The Entropy method might be suitable for all the cases requiring the MCDM. There is also a need to conduct further research to probe the causal reasons why the PCA and Entropy methods were applied to each case and not the other way round. This research found that the Entropy method provides higher accuracy than the PCA method. This research also found that the Entropy method demonstrated to assess the weights of the higher dimension dataset was higher sensitivity than the lower dimensions. Finally, the comprehensive analysis indicates a need to explore a more responsive method for establishing a weighted indexing measurement for warning applications in hazard, risk, and emergency assessments.

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“…e determined alternative scheme should be located at the shortest geometric distance from the optimal scheme and the longest geometric distance from the worst scheme [30]. As a multicriteria decision analysis method, TOPSIS has been successfully applied to industrial technology [31,32], computer science [33][34][35], medical problems [36], applied statistics [37][38][39][40], environmental protection [41,42], and other fields.…”

Section: Topsis Methodmentioning

confidence: 99%

Cognitive Matching of Design Subjects in Product Form Evolutionary Design

Zhang

Wang

Zhou

et al. 2021

Computational Intelligence and Neuroscience

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In actual product development, the cognitive differences between users and designers make it difficult for the designed products to be recognized by users. To reduce the cognitive differences between these two design subjects, this paper proposes a method of cognitive matching of the design subjects. First, we use the relevant methods of Kansei engineering to quantify the Kansei image cognition of the two design subjects and construct a cognitive matching model of the design subjects with information entropy and the technique for order preference by similarity to ideal solution (TOPSIS). Second, according to the Kansei image, the Kansei image prototype cluster is constructed, and the representative Kansei image prototype is obtained. Then, we combine an artificial neural network (ANN) with a cognitive matching model of the design subjects to construct a product Kansei image evaluation system; this is used to evaluate the evolved forms. Finally, a product Kansei image form evolution system is constructed based on the genetic algorithm (GA). To some extent, the system simulates the cognitive matching process between designers and users in product design, helps designers to more accurately understand the cognitive trends of the two design subjects, and provides a theoretical basis for the intelligent design of product forms through the cognitive balance of multiple design subjects. This paper takes a beverage bottle as an example to verify the feasibility of the model through a comparative study.

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Comprehensive evaluation of urban air quality using the relative entropy theory and improved TOPSIS method

Cited by 18 publications

References 34 publications

Has Industrial Upgrading Improved Air Pollution?—Evidence from China’s Digital Economy

Has Industrial Upgrading Improved Air Pollution?—Evidence from China’s Digital Economy

A comparative analysis of the principal component analysis and entropy weight methods to establish the indexing measurement

Cognitive Matching of Design Subjects in Product Form Evolutionary Design

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