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Ecological Indicators
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Analysis of sustainability of Chinese cities based on network big data of city rankings

Authors: Tao Lin; Guoqin Zhang; Meixia Lin; Zhiwei Zeng; Yu Zhao; Li Xing; Caige Sun; +2 Authors

Analysis of sustainability of Chinese cities based on network big data of city rankings

Abstract

Background: Achieving urban sustainability is the ultimate destination of urban development. City rankings as one of the sustainability assessment tools have received increasing attention from the scientific community. However, few study assesses Chinese cities’ sustainability performance using the big data of existing city rankings. Aim: This study aims to assess Chinese cities’ sustainability performances based on the outcomes of the existing internet big data of city rankings. Methods: The outcomes of city rankings were used as the raw dataset. The “sustainability” of city rankings, city’s appearance frequency, and its ranking place were comprehensively considered during evaluation processes. By considering the above factors, the scores of different cities were calculated in terms of overall sustainability and domain sustainability. Furthermore, the GeoDetector was applied to explore the association between social-economic and overall ranking scores as well as the interrelation among TBL dimensions. Results: Chinese cities’ sustainability performance was extremely uneven in spatial distribution. In terms of overall and domain sustainability, well-performing cities were aggregated in the Beijing-Tianjin-Hebei, the Yangtze River Delta, and the Pearl River Delta metropolitan regions. The top ten sustainable cities were Hangzhou, Beijing, Shenzhen, Guangzhou, Zhuhai, Hong Kong, Tianjin, Suzhou, and Xiamen. Most cities did not reach good coordination among the TBL dimensions, instead of developing well in one or two aspects. The results also revealed that current city rankings eyeing more economic and social development, while considering less environmental dimension. Moreover, TBL dimensions mutually reinforce each other in sustainable city construction. The environmental pillar played a critical role and interacting with other dimensions significantly enhanced urban sustainability. Conclusion: The outcomes of existing city rankings can be used as a new resource to evaluate cities’ sustainability performance. Current city rankings in China are not systematically considered in terms of TBL dimensions. Cities should enhance the coordination among TBL pillars, and increase the attention on environmental dimension. More empirical studies involving big data of city rankings will contribute to a new perspective to promote the practice of sustainable urbanization in China.

Country
Netherlands
Keywords

Ecology, Chinese city, Big data, City rankings, Triple bottom line (TBL), Sustainability, QH540-549.5

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    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    16
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
16
Top 10%
Top 10%
Top 10%
gold