Replacing cars by green spaces: an assessment of the mortality benefits in Paris
Moutet, L.; Adelaide, L.; Claron, C.; Bahri, K.; Ben Halima, M. A.; Lepeule, J.; Pascal, M.; Temime, L.; Jean, K.
Show abstract
Increasing urban vegetation coverage is associated with improved human health and well-being, reduced environmental impact of cities and enhanced urban resilience to climate change. To support evidence-based urban planning, this study quantifies the mortality benefits, equity implications and cost-benefit ratio of several scenarios of green space development in Paris by 2040, including the replacement of car-dedicated surfaces with green spaces and a best-case scenario. This quantitative health impact assessment is based on estimated changes in the Normalized Difference Vegetation Index (NDVI), obtained through the estimation of the dynamic effects over time using a Difference-in-Differences approach based on previous public greening interventions, and on an exposure-response relationship linking NDVI and all-cause mortality. It was conducted at the sub-municipal level (IRIS) and incorporates a social deprivation index to assess health equity implications. Vegetation costs are drawn from a previous French study estimating urban soil restoration prices. Replacing surplus on-street parking and 20% of street space with vegetation could reduce all-cause mortality by around 0.8%, while reaching 15% of vegetation coverage in each IRIS could prevent around 3% of deaths yearly in Paris as early as 2040. For all scenarios, these benefits were approximatively equally distributed across deprivation levels. Predicted monetised health benefits outweigh intervention costs by 2035, with further impacts representing net gain. In conclusion, greening interventions targeting car-dedicated space in Paris would equitably improve health while supporting more sustainable and resilient cities.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Effect of park use and landscape structure on COVID-19 transmission rates 92%
- The phenological response of European vegetation to urbanisation is mediated by macrobioclimatic factors 92%
- An individual, mechanistic and dynamical model to simulate urban tree growth and ecosystem services supply under future scenarios 91%
Similar papers in this journal
- A higher ratio of green spaces means a lower racial disparity in severe acute respiratory syndrome coronavirus 2 infection rates: A nationwide study of the United States 92%
- Residential exposure to green and blue spaces over childhood and cardiometabolic health outcomes: The Generation XXI birth cohort 90%
- A spatio-temporal framework for modelling wastewater concentration during the COVID-19 pandemic 90%
Similar papers in this journal
- Urban greenspace and anxiety symptoms during the COVID-19 pandemic: A 20-month follow up of 19,848 participants in England 92%
- The interplay between air pollution, built environment, and physical activity: perceptions of children and youth in rural and urban India 89%
- The association between socioeconomic status and mobility reductions in the early stage of England’s COVID-19 pandemic 88%
Similar papers in this journal
- Intersecting vulnerabilities: Climatic and demographic contributions to future population exposure to Aedes-borne viruses in the United States 92%
- Unveiling the benefits and gaps of wild pollinators on nutrition and income 90%
- Land-use change from food to energy: meta-analysis unravels effects of bioenergy on biodiversity and amenity 90%
Similar papers in this journal
- Impacts Of Deforestation And Land Use/Land Cover Change On Carbon Stock And Dynamics In The Jomoro District, Ghana 89%
- Meta-analysis of livestock effects on tree regeneration in oak agroforestry systems 87%
- Frass fertilizers from mass-reared insects: species variation, heat treatment effects, and implications for soil application 87%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.