Evaluation of the Urban Low-Carbon Sustainable Development Capability Based on the TOPSIS-BP Neural Network and Grey Relational Analysis
With the development of industrialization and urbanization, cities have become the main carriers of economic activities. However, the long-term development of cities has also caused damage to resources and the environment. Hence, objective and scientific evaluation of urban low-carbon sustainable de...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/6616988 |
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| author | Wei Zhang Xinxin Zhang Fan Liu Yan Huang Yuwei Xie |
| author_facet | Wei Zhang Xinxin Zhang Fan Liu Yan Huang Yuwei Xie |
| author_sort | Wei Zhang |
| collection | DOAJ |
| description | With the development of industrialization and urbanization, cities have become the main carriers of economic activities. However, the long-term development of cities has also caused damage to resources and the environment. Hence, objective and scientific evaluation of urban low-carbon sustainable development capacity is very important. An index system of urban low-carbon sustainable development capability is constructed in this paper, and a TOPSIS-BP neural network model is established to evaluate the low-carbon sustainable development capability of Beijing, Shanghai, Shenzhen, and Guangzhou in China. At the same time, the difference degree of low-carbon sustainable development level in these four cities is analyzed by standard deviation and coefficient of variation, and the influencing factors of urban low-carbon sustainable development ability are extracted by grey correlation analysis. The results show that (1) the capability of low-carbon sustainable development in four cities is rising and the difference of low-carbon sustainable development capability is decreasing; (2) the general view that the higher the general investment in low-carbon sustainable development, the higher the level of low-carbon sustainable development in cities has not been verified; (3) with the change of time series, the factors affecting the capability of low-carbon sustainable development in the same city are different and the influence of the same factor on the capability of low-carbon sustainable development in different cities is different. |
| format | Article |
| id | doaj-art-9c1716b53b5c48d181a078a2bd8fe39e |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-9c1716b53b5c48d181a078a2bd8fe39e2025-02-03T01:05:10ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/66169886616988Evaluation of the Urban Low-Carbon Sustainable Development Capability Based on the TOPSIS-BP Neural Network and Grey Relational AnalysisWei Zhang0Xinxin Zhang1Fan Liu2Yan Huang3Yuwei Xie4School of Public Administration, Central China Normal University, Wuhan 430079, ChinaSchool of Public Administration, Central China Normal University, Wuhan 430079, ChinaSchool of Business Administration, Zhongnan University of Economics and Law, Wuhan 430073, ChinaSchool of Business Administration, Zhongnan University of Economics and Law, Wuhan 430073, ChinaSchool of Business Administration, Zhongnan University of Economics and Law, Wuhan 430073, ChinaWith the development of industrialization and urbanization, cities have become the main carriers of economic activities. However, the long-term development of cities has also caused damage to resources and the environment. Hence, objective and scientific evaluation of urban low-carbon sustainable development capacity is very important. An index system of urban low-carbon sustainable development capability is constructed in this paper, and a TOPSIS-BP neural network model is established to evaluate the low-carbon sustainable development capability of Beijing, Shanghai, Shenzhen, and Guangzhou in China. At the same time, the difference degree of low-carbon sustainable development level in these four cities is analyzed by standard deviation and coefficient of variation, and the influencing factors of urban low-carbon sustainable development ability are extracted by grey correlation analysis. The results show that (1) the capability of low-carbon sustainable development in four cities is rising and the difference of low-carbon sustainable development capability is decreasing; (2) the general view that the higher the general investment in low-carbon sustainable development, the higher the level of low-carbon sustainable development in cities has not been verified; (3) with the change of time series, the factors affecting the capability of low-carbon sustainable development in the same city are different and the influence of the same factor on the capability of low-carbon sustainable development in different cities is different.http://dx.doi.org/10.1155/2020/6616988 |
| spellingShingle | Wei Zhang Xinxin Zhang Fan Liu Yan Huang Yuwei Xie Evaluation of the Urban Low-Carbon Sustainable Development Capability Based on the TOPSIS-BP Neural Network and Grey Relational Analysis Complexity |
| title | Evaluation of the Urban Low-Carbon Sustainable Development Capability Based on the TOPSIS-BP Neural Network and Grey Relational Analysis |
| title_full | Evaluation of the Urban Low-Carbon Sustainable Development Capability Based on the TOPSIS-BP Neural Network and Grey Relational Analysis |
| title_fullStr | Evaluation of the Urban Low-Carbon Sustainable Development Capability Based on the TOPSIS-BP Neural Network and Grey Relational Analysis |
| title_full_unstemmed | Evaluation of the Urban Low-Carbon Sustainable Development Capability Based on the TOPSIS-BP Neural Network and Grey Relational Analysis |
| title_short | Evaluation of the Urban Low-Carbon Sustainable Development Capability Based on the TOPSIS-BP Neural Network and Grey Relational Analysis |
| title_sort | evaluation of the urban low carbon sustainable development capability based on the topsis bp neural network and grey relational analysis |
| url | http://dx.doi.org/10.1155/2020/6616988 |
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