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Data-driven governance, smart urbanism and risk-class inequalities: Security and social credit in China

Author

Listed:
  • Dean Curran

    (University of Calgary, Canada; London School of Economics, UK)

  • Alan Smart

    (University of Calgary, Canada)

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.

Suggested Citation

  • Dean Curran & Alan Smart, 2021. "Data-driven governance, smart urbanism and risk-class inequalities: Security and social credit in China," Urban Studies, Urban Studies Journal Limited, vol. 58(3), pages 487-506, February.
    • Handle: RePEc:sae:urbstu:v:58:y:2021:i:3:p:487-506
    DOI: 10.1177/0042098020927855
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    References listed on IDEAS

    as
    1. Fulong Wu, 2020. "Adding new narratives to the urban imagination: An introduction to ‘New directions of urban studies in China’," Urban Studies, Urban Studies Journal Limited, vol. 57(3), pages 459-472, February.
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    4. Yue Ray Gong, 2016. "Rental housing management as surveillance of Chinese rural migrants: the case of hillside compound in Dongguan," Housing Studies, Taylor & Francis Journals, vol. 31(8), pages 998-1018, November.
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    6. Harvey, David, 2007. "A Brief History of Neoliberalism," OUP Catalogue, Oxford University Press, number 9780199283279.
    7. Vito Albino & Umberto Berardi & Rosa Maria Dangelico, 2015. "Smart Cities: Definitions, Dimensions, Performance, and Initiatives," Journal of Urban Technology, Taylor & Francis Journals, vol. 22(1), pages 3-21, January.
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    Citations

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    Cited by:

    1. Ryan Burns & Victoria Fast & Anthony Levenda & Byron Miller, 2021. "Smart cities: Between worlding and provincialising," Urban Studies, Urban Studies Journal Limited, vol. 58(3), pages 461-470, February.
    2. Mikołaj Biesaga & Anna Domaradzka & Magdalena Roszczyńska-Kurasińska & Szymon Talaga & Andrzej Nowak, 2023. "The effect of the pandemic on European narratives on smart cities and surveillance," Urban Studies, Urban Studies Journal Limited, vol. 60(10), pages 1894-1914, August.
    3. Lingyue Li & Surong Zhang & Jinfeng Wang & Xiaoming Yang & Lan Wang, 2023. "Governing public health emergencies during the coronavirus disease outbreak: Lessons from four Chinese cities in the first wave," Urban Studies, Urban Studies Journal Limited, vol. 60(9), pages 1750-1770, July.
    4. Federico Cugurullo & Federico Caprotti & Matthew Cook & Andrew Karvonen & Pauline MᶜGuirk & Simon Marvin, 2024. "The rise of AI urbanism in post-smart cities: A critical commentary on urban artificial intelligence," Urban Studies, Urban Studies Journal Limited, vol. 61(6), pages 1168-1182, May.
    5. Abuljadail, Mohammad & Khalil, Ashraf & Talwar, Shalini & Kaur, Puneet, 2023. "Big data analytics and e-governance: Actors, opportunities, tensions, and applications," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    6. Byron Miller & Kevin Ward & Ryan Burns & Victoria Fast & Anthony Levenda, 2021. "Worlding and provincialising smart cities: From individual case studies to a global comparative research agenda," Urban Studies, Urban Studies Journal Limited, vol. 58(3), pages 655-673, February.
    7. Shengchen Du & Hongze Tan, 2022. "Location Is Back: The Influence of COVID-19 on Chinese Cities and Urban Governance," Sustainability, MDPI, vol. 14(6), pages 1-13, March.

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