4.6 Article

Ranking Sri Lanka among the World's Top Mismanaged Waste Polluters: Does Model Data Change the Story?

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SUSTAINABILITY
卷 15, 期 3, 页码 -

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MDPI
DOI: 10.3390/su15032687

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marine debris; mismanaged plastic waste; plastic pollution; ranking; Sri Lanka

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The accumulation of Mismanaged Plastic Waste (MPW) in the environment is a global concern. Estimation of waste generated by countries can be done using globally available data layers and/or empirical surveys. A model study ranked Sri Lanka as the world's fifth worst mismanaged plastic offender based on global metadata, but errors in the source data and parameters used for prediction have been identified. Therefore, Sri Lanka's position in the global ranking needs to be reconsidered, and model predictions should be supported by robust and unbiased surveys based on empirical data.
The accumulation of Mismanaged Plastic Waste (MPW) in the environment is a global concern. The amount of waste generated by countries is estimated using globally available data layers and/or empirical surveys. Unlike globally available metadata, MPW estimates based on empirical surveys allow for better visualization of amounts, potential pathways, and hotspots. A model study conducted in 2015, based on global metadata, ranked Sri Lanka in fifth position among the world's worst mismanaged plastic offenders. However, there is significant uncertainty in the source data on waste generation and the parameters used for model prediction, such as plastic usage (5.1 kg per person per day), since Sri Lanka is predominantly a service-based country with limited plastic-based manufacturing industries. The source data for plastic usage has been derived from a very limited study, biased toward waste hotspots that have not been verified. Our empirical data has shown that population density, one of the key parameters used for global ranking, is a weak predictor of debris densities. Therefore, we argue that the given plastic leakage data and the ranking is an error. Therefore, Sri Lanka's position in the global ranking deserves reconsideration. Further, we propose the need for model predictions that rely on global metadata to be backed by robust and unbiased designed surveys that are based on empirical data and undergo intense baseline data verification to generate more precise predictions on litter quantities.

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