Study on the Maximum Level of Disposable Plastic Product Waste

Yizhao Hong ()
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Yizhao Hong: School of Management, University of Science and Technology of China, Hefei 230026, China

Sustainability, 2023, vol. 15, issue 12, 1-20

Abstract: Plastic is a widely used material in daily life that has brought huge social benefits to society with the advantages of low-cost manufacturing and mildness. However, due to their high resistance to degradation and diversity of chemical components, plastics pose a great threat to human health and the living environment. Aiming to address the problem that there is a lot of plastic waste and its impact on the environment, this paper puts forward an effective plan to reduce plastic waste and tests the relevant models. First, based on the pollution index data of plastic waste, it uses the Analytic Hierarchy Process and the entropy weight model to determine the evaluation index weight of plastic waste pollution impact and judge the environmental damage ability and environmental recovery ability. Secondly, in order to measure the level of environmental safety, it establishes an evaluation index system and uses the gray correlation method to determine the weight value of the evaluation index and calculate the environmental safety scores of each country. Thirdly, according to the second index system, it selects the relevant data from 10 countries, establishes a BP neural network model, and calculates the level of security and the intensity of responsibility. Finally, based on the results of the model and the global goal of achieving the minimum level of plastic waste, it offers a memorandum with a schedule and discusses the measures needed to achieve this goal and the factors to be considered. Overall, compared with the existing research, this paper presents a different approach to the assessment and measurement of the use of the environment and its capacity for pollution, combining multi-disciplinary influencing factors.

Keywords: plastic waste; environmental sustainability development; analytic hierarchy process; entropy weight; gray correlation; BP neural network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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