Data-driven governance, smart urbanism and risk-class inequalities: Security and social credit in China
Dean Curran and
Alan Smart
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Dean Curran: University of Calgary, Canada; London School of Economics, UK
Alan Smart: University of Calgary, Canada
Urban Studies, 2021, vol. 58, issue 3, 487-506
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.
Keywords: actually persisting smart cities; Chinese smart cities; data-driven governance; risk-class; social credit; å®žé™…å šæŒ æ™ºæ…§åŸŽå¸‚; ä¸å›½æ™ºæ…§åŸŽå¸‚; æ•°æ ®é©±åŠ¨åž‹æ²»ç †; 风险ç‰çº§; 社会信用 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (7)
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Persistent link: https://EconPapers.repec.org/RePEc:sae:urbstu:v:58:y:2021:i:3:p:487-506
DOI: 10.1177/0042098020927855
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