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Description

Promoting active transport (e.g., walking and cycling) reduces greenhouse gas emissions and is associated with improved health outcomes through increased physical activity. However, increasing heat exposure driven by climate change may pose health risks for people engaging in physical activity outdoors. Therefore, active transport infrastructure designed to reduce heat exposure (e.g. tree cover) can help minimise the risk of heat-related health impacts while encouraging active mobility.

Short method description 

This indicator estimates the proportion of cycling paths shaded by trees during peak solar hours. The proportion of shaded cycling infrastructure of each city was estimated.  

Main findings 

The mean canopy coverage during 2018–20 along all cycling paths was 17·2% and 30·5% along designated and shared pedestrian-cycle paths across 885 European cities.

Caveats 

A key limitation is that OpenStreetMap cycle paths are represented as centre lines without information on path width or exact location, which may introduce uncertainty in the estimated tree shading coverage. In addition, shading estimates are based on direct overhead alignment between tree canopies and cycle paths and may not fully capture actual shade availability during peak heat periods when shadows are longer.

Reference information

Websites:
Source:
  • OpenStreetMap (​​OSM) 

    OpenStreetMap contributors. Planet dump [data file] [Internet]. Cambridge, UK: OpenStreetMap Foundation; 2025 [cited 2025 Sep 17]. Available from: https://planet.osm.org 

  • ERA5-HEAT is an hourly, gridded global dataset with a spatial resolution of 0.25° × 0.25°, developed by the ​​Copernicus Climate Change Service

    Di Napoli C, Barnard C, Prudhomme C, Cloke HL, Pappenberger F. ERA5‐HEAT: A global gridded historical dataset of human thermal comfort indices from climate reanalysis. Geoscience Data Journal. 2021 Jun;8(1):2–10. doi:10.1002/gdj3.102

  • The Global Canopy Height Maps ​​dataset  

    Tolan J, Yang HI, Nosarzewski B, Couairon G, Vo HV, Brandt J, et al. Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on aerial lidar. Remote Sensing of Environment. 2024 Jan 1;300:113888. doi:10.1016/j.rse.2023.113888

Contributor:
Lancet Countdown in Europe

Published in Climate-ADAPT: Sep 29, 2026

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This translation is generated by eTranslation, a machine translation tool provided by the European Commission.

Exclusion of liability
This translation is generated by eTranslation, a machine translation tool provided by the European Commission.