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Illegal waste fly-tipping in the COVID-19 pandemic: Enhanced compliance, temporal displacement and urban-rural variation

Author

Listed:
  • Dixon, Anthony
  • Farrell, Graham

    (University of Leeds)

  • Tilley, Nick

Abstract

Objective: Illegal dumping of household and business waste is a significant environmental crime, known as fly-tipping in the UK. News agencies reported major increases early in the pandemic when waste disposal services were closed or disrupted. This study examines the veracity of those claims. Method: A freedom of information request was sent to all local authorities in the UK asking for records of reported incidents of fly-tipping for before and after the first national lockdown. ARIMA modelling and year-on-year comparison was used to compare observed and expected levels of fly-tipping. Urban and rural local authorities were compared. Results: A statistically significant decline in fly-tipping during lockdown was followed by a similar increase when lockdown ended. The effects largely cancelled each other out. There was pronounced variation in urban-rural experience: urban areas, with higher rates generally, experienced most of the initial drop in fly-tipping while some rural authorities experienced an increase. Conclusion: Waste services promote compliance with laws against illegal dumping. When those services were disrupted during lockdown it was expected that fly-tipping would increase but, counter-intuitively, it declined. This enhanced compliance effect was likely due to increased perceived risk in densely populated urban areas. However, as lockdown restrictions were eased, fly-tipping increased to clear the backlog, indicating temporal displacement.

Suggested Citation

  • Dixon, Anthony & Farrell, Graham & Tilley, Nick, 2022. "Illegal waste fly-tipping in the COVID-19 pandemic: Enhanced compliance, temporal displacement and urban-rural variation," SocArXiv df5ue, Center for Open Science.
    • Handle: RePEc:osf:socarx:df5ue
    DOI: 10.31219/osf.io/df5ue
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    References listed on IDEAS

    as
    1. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    2. Langton, Samuel & Farrell, Graham & Dixon, Anthony, 2020. "Six Months In: Pandemic Crime Trends in England and Wales," SocArXiv t7ne8, Center for Open Science.
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