Data-driven governance, smart urbanism and risk-class inequalities: Security and social credit in China. (February 2021)

• Record Type: Journal Article
• Authors: Curran, Dean; Smart, Alan
• Other Names: Miller Byron guest-editor.; Burns Ryan guest-editor.; Fast Victoria guest-editor.; Levenda Anthony guest-editor.
• Is Part Of: Urban studies. Volume 58:Number 3(2021)
• Page Start: 487
• Page End: 506
• Publication Date: 2021-02
• Subjects: actually persisting smart cities -- Chinese smart cities -- data-driven governance -- risk-class -- social credit; 实际坚持智慧城市 -- 中国智慧城市 -- 数据驱动型治理 -- 风险等级 -- 社会信用; Cities and towns -- Periodicals; City planning -- Periodicals; 307.1216
• DOI: 10.1177/0042098020927855
• Languages: English
• ISSNs: 0042-0980

Abstract

This article contributes to the politicisation of smart urbanism and data-driven governance by making visible some of the potential inequalities emerging from these transitions through a provisional risk-class analysis. To pursue this analysis, it focuses on the case of smart urbanism and its associated process of data-driven governance in China. It looks specifically at the manner in which Chinese smart urbanism, in terms of its security measures, including widespread use of facial recognition and the roll-out of social credit scoring, is affecting inequalities. This article proposes risk-class analysis as a toolbox that can pose new questions in the search for what types of potential risks and inequalities emerge from the smart urbanism and data-driven governance being rolled out in the Chinese context.