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A green intensity index to better assess the multiple functions of urban vegetation with an application to Paris metropolitan area

Author

Listed:
  • Martin Seidl

    (University Paris-Est)

  • Manal Saifane

    (CNRS - Sorbonne University)

Abstract

Due to its multiple functions, urban green is nowadays seen as a possible solution to mitigate global changes, getting so more attention from urban planners. This article develops a systemic function approach through a green intensity index with validation in two characteristic urban areas of Paris conurbation. Their vegetation was qualified and quantified with a shortened species classification system, based on three vertical layers, using field visits, remote sensing and street imagery. The collected data were transformed into urban green intensity index (UGII), using simplified functions based on species number, leaf surface and soil occupation for the five main functions of urban green as identified through bibliometric analysis: hydrology, climate, biodiversity, well-being and air pollution. Central city parts of the metropolis scored hereby low (UGII 3.7–8.9%) due to low biodiversity and absence of infiltration surface, while the suburbs with more green obtained higher values (UGII 36–38%), thanks to better biodiversity and more green per inhabitant. The proposed index allows a rapid assessment of urban green present and classification of three different types of neighbourhood according to their particular green functions biodiversity, hydrology and well-being. Moreover, the differentiation between public and private sectors makes it possible to assess the contribution of public green to the quality of the living environment, while incorporation of species in the index differentiates summer from winter cover, enabling adjustment of municipal green policies.

Suggested Citation

  • Martin Seidl & Manal Saifane, 2021. "A green intensity index to better assess the multiple functions of urban vegetation with an application to Paris metropolitan area," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(10), pages 15204-15224, October.
    • Handle: RePEc:spr:endesu:v:23:y:2021:i:10:d:10.1007_s10668-021-01293-4
    DOI: 10.1007/s10668-021-01293-4
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    Cited by:

    1. Jiayu Yan & Huiping Liu & Shangyuan Yu & Xiaowen Zong & Yao Shan, 2023. "Classification of Urban Green Space Types Using Machine Learning Optimized by Marine Predators Algorithm," Sustainability, MDPI, vol. 15(7), pages 1-18, March.
    2. Mohammad Reza Khalilnezhad & Francesca Ugolini & Luciano Massetti, 2021. "Attitudes and Behaviors toward the Use of Public and Private Green Space during the COVID-19 Pandemic in Iran," Land, MDPI, vol. 10(10), pages 1-22, October.

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