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Downscaling long term socio-economic scenarios at city scale: A case study on Paris

Author

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  • Vincent Viguie

    (CIRED - centre international de recherche sur l'environnement et le développement - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement - EHESS - École des hautes études en sciences sociales - AgroParisTech - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique)

  • Stéphane Hallegatte

    (World Bank - World Bank)

  • Julie Rozenberg

    (CIRED - centre international de recherche sur l'environnement et le développement - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement - EHESS - École des hautes études en sciences sociales - AgroParisTech - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique)

Abstract

The NEDUM-2D model is used to downscale four global socioeconomic scenarios at city scale and simulate the evolution of the Paris urban area between 1900 and 2100. It is based on a dynamic extension of the classical urban economic theory, to explain the spatial distribution of land and real estate values, dwelling surfaces, population density and buildings heights and density. A validation over the 1900-2010 period shows that the model reproduces available data and captures the main determinants of city shape evolution. From four global scenarios and additional local inputs, 32 local scenarios are created and analyzed. Main drivers of urban sprawl and climate and flood vulnerability appear to be local demographic growth and local policies; global factors, such as energy and transport prices, even including possible peak-oil and carbon taxes, have only a limited influence on them. Conversely, transport-related greenhouse gases emissions are mainly driven by global factors, namely vehicle efficiency changes, not by land use. As a consequence, very strict urban policies — including reconstruction — would become necessary to control emissions from urban transportation if technologies reveal unable to do so. These scenarios are a useful input for the design and assessment of mitigation and adaptation policies at local scale.

Suggested Citation

  • Vincent Viguie & Stéphane Hallegatte & Julie Rozenberg, 2014. "Downscaling long term socio-economic scenarios at city scale: A case study on Paris," Post-Print hal-01136217, HAL.
  • Handle: RePEc:hal:journl:hal-01136217
    DOI: 10.1016/j.techfore.2013.12.028
    Note: View the original document on HAL open archive server: https://enpc.hal.science/hal-01136217
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    Cited by:

    1. Zhen Chu & Mingwang Cheng & Ning Neil Yu, 2022. "Development potential of Chinese smart cities and its spatio‐temporal pattern: A new hybrid MADM method using combination weight," Growth and Change, Wiley Blackwell, vol. 53(4), pages 1546-1566, December.
    2. Cheng, M.N. & Wong, Jane W.K. & Cheung, C.F. & Leung, K.H., 2016. "A scenario-based roadmapping method for strategic planning and forecasting: A case study in a testing, inspection and certification company," Technological Forecasting and Social Change, Elsevier, vol. 111(C), pages 44-62.
    3. Lomborg, Bjorn, 2020. "Welfare in the 21st century: Increasing development, reducing inequality, the impact of climate change, and the cost of climate policies," Technological Forecasting and Social Change, Elsevier, vol. 156(C).
    4. Giovanni Matteo & Pierfrancesco Nardi & Stefano Grego & Caterina Guidi, 2018. "Bibliometric analysis of Climate Change Vulnerability Assessment research," Environment Systems and Decisions, Springer, vol. 38(4), pages 508-516, December.
    5. Kılkış, Şiir, 2022. "Urban emissions and land use efficiency scenarios towards effective climate mitigation in urban systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    6. Coulombel, Nicolas, 2018. "Why housing and transport costs should always be considered together: A monocentric analysis of prudential measures in housing access," Transport Policy, Elsevier, vol. 65(C), pages 89-105.
    7. Oliveira, Inês A.S.J. & Carayannis, Elias G. & Ferreira, Fernando A.F. & Jalali, Marjan S. & Carlucci, Daniela & Ferreira, João J.M., 2018. "Constructing home safety indices for strategic planning in residential real estate: A socio-technical approach," Technological Forecasting and Social Change, Elsevier, vol. 131(C), pages 67-77.
    8. Alison Rothwell & Brad Ridoutt & William Bellotti, 2016. "Greenhouse Gas Implications of Peri-Urban Land Use Change in a Developed City under Four Future Climate Scenarios," Land, MDPI, vol. 5(4), pages 1-23, December.
    9. McDermott, T.K.J. & Surminski, S., 2018. "Normative interpretations of climate risk assessment and how it affects local decision making – a study at the city scale in Cork, Ireland," Working Papers 309607, National University of Ireland, Galway, Socio-Economic Marine Research Unit.
    10. Biao Yin & Liu Liu & Nicolas Coulombel & Vincent Viguie, 2018. "Appraising the environmental benefits of ride-sharing: The Paris region case study," Post-Print hal-01695082, HAL.
    11. Avner, Paolo & Rentschler, Jun & Hallegatte, Stephane, 2014. "Carbon price efficiency : lock-in and path dependence in urban forms and transport infrastructure," Policy Research Working Paper Series 6941, The World Bank.
    12. Sokolov, Alexander & Veselitskaya, Natalia & Carabias, Vicente & Yildirim, Onur, 2019. "Scenario-based identification of key factors for smart cities development policies," Technological Forecasting and Social Change, Elsevier, vol. 148(C).

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    Keywords

    urban planning; urban sprawl; carbon tax; mitigation; adaptation; scenarios;
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