Publication detail

Forecasting plastic waste generation and interventions for environmental hazard mitigation

Fan, Y.V., Jiang, P., Tan, R.R., Aviso, K.B., You, F., Zhao, X., Lee, C.T., Klemeš, J.J.

Original Title

Forecasting plastic waste generation and interventions for environmental hazard mitigation

Type

journal article in Web of Science

Language

English

Original Abstract

Plastic waste and its environmental hazards have been attracting public attention as a global sustainability issue. This study builds a neural network model to forecast plastic waste generation of the EU-27 in 2030 and evaluates how the interventions could mitigate the adverse impact of plastic waste on the environment. The black-box model is interpreted using SHapley Additive exPlanations (SHAP) for managerial insights. The dependence on predictors (i.e., energy consumption, circular material use rate, economic complexity index, population, and real gross domestic product) and their interactions are discussed. The projected plastic waste generation of the EU-27 is estimated to reach 17 Mt/y in 2030. With an EU targeted recycling rate (55%) in 2030, the environmental impacts would still be higher than in 2018, especially global warming potential and plastic marine pollution. This result highlights the importance of plastic waste reduction, especially for the clustering algorithm-based grouped countries with a high amount of untreated plastic waste per capita. Compared to the other assessed scenarios, Scenario 4 with waste reduction (50% recycling, 47.6% energy recovery, 2.4% landfill) shows the lowest impact in acidification, eutrophication, marine aquatic toxicity, plastic marine pollution, and abiotic depletion. However, the global warming potential (8.78 Gt CO(2)eq) is higher than that in 2018, while Scenario 3 (55% recycling, 42.6% energy recovery, 2.4% landfill) is better in this aspect than Scenario 4. This comprehensive analysis provides pertinent insights into policy interventions towards environmental hazard mitigation.

Keywords

Clustering analysis; Environmental hazard mitigation; Machine learning; Microplastic; Plastic pollution; Scenario analysis

Authors

Fan, Y.V., Jiang, P., Tan, R.R., Aviso, K.B., You, F., Zhao, X., Lee, C.T., Klemeš, J.J.

Released

15. 2. 2022

Publisher

Elsevier B.V.

Location

ELSEVIER, RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS

ISBN

0304-3894

Periodical

Journal of Hazardous Materials

Number

424

State

Kingdom of the Netherlands

Pages from

127330

Pages to

127330

Pages count

14

URL

https://www.sciencedirect.com/science/article/pii/S0304389421022986?via%3Dihub

BibTex

@article{BUT172777,
  author="Yee Van {Fan} and Jiří {Klemeš}",
  title="Forecasting plastic waste generation and interventions for environmental hazard mitigation",
  journal="Journal of Hazardous Materials",
  year="2022",
  number="424",
  pages="127330--127330",
  doi="10.1016/j.jhazmat.2021.127330",
  issn="0304-3894",
  url="https://www.sciencedirect.com/science/article/pii/S0304389421022986?via%3Dihub"
}