Detail publikace

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.

Originální název

Forecasting plastic waste generation and interventions for environmental hazard mitigation

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

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

Autoři

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

Vydáno

15. 2. 2022

Nakladatel

Elsevier B.V.

Místo

ELSEVIER, RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS

ISSN

0304-3894

Periodikum

Journal of Hazardous Materials

Číslo

424

Stát

Nizozemsko

Strany od

127330

Strany do

127330

Strany počet

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"
}