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<p>Urban planners continuously face the challenge of reducing artificial lighting to protect biodiversity while ensuring urban residents’ comfort and safety at night. Striking this balance is crucial for supporting urban residents broadly, yet it remains insufficiently explored in current research. Here we integrate remote sensing and ecological modeling to assess species’ requirements around light-pollution reduction with socioeconomic modeling to evaluate human residents’ acceptance of various street-lighting adjustments, aiming to identify the optimal lighting compromises for Montpellier, France, a midsized European city. We show that, depending on the spatial context, both tradeoffs and synergies can emerge when implementing light-pollution-mitigation measures. By integrating results into an RShiny application, we enabled urban planners to prioritize actions for each streetlight. Our findings underscore the importance of tailoring lighting policies to the specific environmental and social context rather than adopting a universal ‘one-size-fits-all’ approach.</p>

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Planning sustainable urban lighting for biodiversity and society

  • 期刊论文

  • 发表日期：

    2025年6月20日

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  • Léa Tardieu,
  • Chloé Beaudet,
  • Sarah Potin,
  • Julie Chaurand,
  • Léa Mariton,
  • Vincent Delbar,
  • Maia David

摘要

Urban planners continuously face the challenge of reducing artificial lighting to protect biodiversity while ensuring urban residents’ comfort and safety at night. Striking this balance is crucial for supporting urban residents broadly, yet it remains insufficiently explored in current research. Here we integrate remote sensing and ecological modeling to assess species’ requirements around light-pollution reduction with socioeconomic modeling to evaluate human residents’ acceptance of various street-lighting adjustments, aiming to identify the optimal lighting compromises for Montpellier, France, a midsized European city. We show that, depending on the spatial context, both tradeoffs and synergies can emerge when implementing light-pollution-mitigation measures. By integrating results into an RShiny application, we enabled urban planners to prioritize actions for each streetlight. Our findings underscore the importance of tailoring lighting policies to the specific environmental and social context rather than adopting a universal ‘one-size-fits-all’ approach.