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Modelling US light pollution

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  • Reed N. Olsen
  • Terrel Gallaway
  • David Mitchell

Abstract

This paper uses a unique US dataset to study the economic causes of light pollution at the local (county) level. Light pollution has been shown to have negative consequences for both wildlife and humans. Light pollution is a form of pollution commonly ignored by environmental professionals. Traditionally, light-pollution models focus on population-based explanations. This paper confirms the importance of population in understanding light pollution. However, the results highlight the importance of economic variables, especially local economic development, in explaining the existence and extent of light pollution. Estimated models show, for example, that local employment patterns, personal income, roads and energy use are all important explanatory variables. By highlighting the connections between light pollution and specific types of local economic activity, this paper provides policy makers with additional information that they can use to improve public policies intended to safeguard the environment and local wildlife.

Suggested Citation

  • Reed N. Olsen & Terrel Gallaway & David Mitchell, 2014. "Modelling US light pollution," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 57(6), pages 883-903, June.
    • Handle: RePEc:taf:jenpmg:v:57:y:2014:i:6:p:883-903
    DOI: 10.1080/09640568.2013.774268
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    References listed on IDEAS

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    Cited by:

    1. Boslett, Andrew & Hill, Elaine & Ma, Lala & Zhang, Lujia, 2021. "Rural light pollution from shale gas development and associated sleep and subjective well-being," Resource and Energy Economics, Elsevier, vol. 64(C).
    2. Beaudet, Chloé & Tardieu, Léa & David, Maia, 2022. "Are citizens willing to accept changes in public lighting for biodiversity conservation?," Ecological Economics, Elsevier, vol. 200(C).
    3. Murray, Alan T. & Feng, Xin, 2016. "Public street lighting service standard assessment and achievement," Socio-Economic Planning Sciences, Elsevier, vol. 53(C), pages 14-22.

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