Study on the Maximum Level of Disposable Plastic Product Waste

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.

1. Introduction

Since the 1950s, the manufacturing of plastics has grown exponentially because of its variety of uses, such as food packaging, consumer products, medical devices, and construction. While there are significant benefits, the negative implications associated with increased production of plastics are concerning. Plastic products do not readily break down, are difficult to dispose of, and only about 9% of plastics are recycled (Geyer et al., 2017) [1]. Effects can be seen in the approximately 4–12 million tons of plastic waste that enter the oceans each year (Maiti et al., 2022) [2]. Plastic waste has severe environmental consequences, and it is predicted that if our current trends continue, the oceans will be filled with more plastic than fish by 2050 (Jambeck et al., 2015) [3]. The effect on marine life has been studied (Li et al., 2016) [4], but the effects on human health are not yet completely understood (Mendoza et al., 2018) [5]. The rise of single-use and disposable plastic products has resulted in entire industries dedicated to creating plastic waste. It also suggests that the amount of time the product is useful is significantly shorter than the time it takes to properly mitigate the plastic waste (Dhiman et al., 2022) [6]. Due to their high resistance to degradation and diversity of chemical components, plastics pose a great threat to human health and the living environment. According to the investigation and research, more than 1 billion tons of plastic waste enter the ocean every year, and the recycling rate is low, which has a negative impact on the survival of marine organisms and the sustainable development of the environment (Sivan, 2011) [7]. Consequently, to solve the plastic waste problem, we need to slow down the flow of plastic production and improve how we manage plastic waste.

Overall, this paper addresses the problem that there is a lot of plastic waste and its impact on the environment by (1) establishing a model that estimates the maximum level of single-use or disposable plastic product waste; (2) discussing the extent to which plastic waste can be reduced to reach the level of environmental safety; (3) based on the above model, discussing the objective set for the lowest attainable level of global waste of single-use or disposable plastic products and the impact of reaching this level; (4) discussing the equity issues that arise from the global crisis and intended solutions; (5) writing a memo to the International Council of Plastic Waste Management (ICM) about the actual minimum achievable level of global disposable or disposable plastic product waste, as well as any situation that may accelerate or hinder my achievement of my goals and schedule. For problem one, Lan (2010) [8] used the Analytic Hierarchy Process (AHP) to evaluate the weight of air, water, solid waste, and noise pollution in the overall environmental pollution prevention and control plan. After that, a risk assessment model for environmental pollution based on AHP and the Fuzzy Comprehensive Evaluation Method was developed, which is also used in shipwreck, psychology, and economics (Xing et al., 2011; Demattè et al., 2018) [9,10]. However, they did not emphasize the plastic waste and assess the maximum level of pollution that can safely be mitigated without further environmental damage. Thus, we should collect data on the impact of waste on the atmosphere, soil, and ocean. At the same time, it shows the current development history of plastics and offers a simple forecast of the future trend. Finally, the AHP model and entropy weight method are used to determine the evaluation index weight of the pollution impact of plastic waste. At the same time, the damage ability and recovery ability of plastic in the environment are used to make auxiliary judgments, and the highest level is determined by comparing it with the known threshold.

For problems two and three, this paper chooses to establish an evaluation system for environmental safety levels. Previous studies only considered environmental factors but did not consider human factors (Buckley, 1982; Terr, 2003; Dai et al., 2012) [11,12,13]. This system is divided into two dimensions: environmental sensitivity and human interference, and each dimension has specific measures (Costantini & Monni, 2006; Preston et al., 2013) [14,15]. According to the requirements of the question, this paper uses the gray correlation method to determine the weight value of the evaluation index and finally obtain the level of environmental safety and its level, which can also be compared and analyzed, considering regional-specific constraints may make some policies more effective than others (Li et al., 2015; Zhu & Li, 2017) [16,17]. However, this paper speculates that there is a linear relationship between the factors in the second question, so through the collinearity diagnosis, we can select the representative indicators. Then, according to the calculation formula for environmental safety level, the critical value when the environment reaches moderate safety is obtained, and the relationship with the above indexes is discussed.

For problems four and five, this paper selects six indicators and sample data from ten countries in the third question, establishes a BP neural network model, and calculates the level of security and responsibility-bearing intensity (Ding et al., 2011; Wu et al., 2016) [18,19]. According to the scores of the first question and the above bearing strength, we put forward our suggestions. Furthermore, taking the current level of global environmental safety as an example and assuming that there is no human interference, this paper predicts the future level of environmental safety and makes a timetable for ICM.

The contribution of this paper is that there is no simple choice of stratification method, but combined with the weight given to optimization analysis, there are many factors considered in the model that can measure the global security level well. In this paper, through the method of index selection, the factors with great influence are selected, and a neural network learning model can be established to measure the level of environmental security in various countries.

2. Parameter Definitions

CI: Consistency Indicators

CR: Consistency Ratio

RI: Average Random Consistency Index

AHP: Analytic Hierarchy Process

WS: Water Resource Sensitivity Index

SS: Soil Resource Sensitivity Index

AS: Air Resource Sensitivity Index

To facilitate subsequent discussion, the meaning of the symbols used is given in

Table 1. Symbol Statement.

Symbol	Meaning
Hi	Weight of Comprehensive Evaluation
Mi	Weight in AHP
Ni	Weight in Entropy Method
V	Environmental Safety Index
S	Environmental Sensitivity Index
D	Human Interference Index
Ci	Sensitivity Level Value
Xj	Corresponding Classification Value
Wj	Weight Value of the Interference Index

.

3. Modeling and Solving

3.1. Problem One

3.1.1. Plastic Waste Pollution Indicators and Data Acquisition

This paper classifies indicators according to their different effects on the atmosphere, soil, and ocean and finally divides them into eight indicators: temperature, carbon dioxide concentration, PM2.5, pH, vegetation coverage, cultivated land area, micro-medium plastic concentration, and oil spill. This paper has gathered the corresponding data reflecting the impact of plastic waste pollution, which is collected in

Table A1. Relevant Environmental Data of Some Countries.

Factor	China	Canada	Japan	India	USA
Agricultural Waste (1000 ton)	183.00	130.00	30.00	240.00	60.00
Industrial Waste (1000 ton)	948.32	193.26	101.00	560.00	133.56
Proportion of Urban Solid Waste (%)	21.50	37.00	3.90	1.60	23.00
Incineration Ratio (%)	39.33	12.00	77.95	23.00	12.77
Landfill Ratio (%)	4.30	12.00	15.00	8.90	24.00
Dumping Ratio (%)	55.93	65.00	0.99	67.00	52.48
Recovery Rate (%)	8.60	79.97	50.29	0.20	99.60
Natural Degradation Rate (%)	66.47	0.20	79.72	99.60	66.47

in Appendix A.

According to the question, defining the so-called maximum levels of single-use or disposable plastic product waste refers to the critical point that the environment will not be further damaged when the impact of plastic waste pollution on the environment reaches a certain degree of damage and the damage capacity and recovery capacity of the environment are balanced. In this regard, I collected relevant data to show the global plastic waste pollution situation and fit the data through MATLAB to predict the future trend, as shown in the following Figure 1:

3.1.2. Establishing the AHP Model

AHP is a simple method for quantitative analysis of non-quantitative events in system engineering, and it is also a method for subjective judgment and objective description (Kriksciuniene & Sakalauskas, 2017) [20]. Its basic idea is to organize all kinds of influencing factors in the overall phenomenon by dividing them into interrelated and orderly levels according to the overall goal of evaluation. The AHP can be established as Figure 2 follows:

First, according to Saaty (1997) [21]’s 1–9 scale method, this paper determines that the judgment matrix of the comparison criteria layer is: $w_1=(0.32, 0.22, 0{.46)}^T$. Secondly, after matrix normalization, line-by-line summation, and standardization, the final weight value is: ${(0.65, 0.21, 0.14)}^T$. Thirdly, I tested the consistency of judgment matrix. By using the function in MATLAB, this paper finally calculates the eigenvalue of the judgment matrix ${\chi }_{\max }=3$. By searching in

Table A2. Saaty’s 1–9 Scale Method.

n	1	2	3	4	5	6	7	8	9
RI	0	0	0.58	0.9	1.12	1.24	1.32	1.41	1.45

in Appendix A, when n = 3, the corresponding average random consistent indicator RI = 0.58. Then, I calculated CI and CR as follows (Herrick et al., 2013) [22]:

$$CI=\frac{{\chi }_{\max }-n}{n-1}=0$$

(1)

$$CR=\frac{CI}{RI}=0<0.1$$

(2)

This paper reaches the conclusion that the consistency of the judgment matrix is acceptable. Finally, calculate the AHP weight of the target layer relative to the criterion layer. In the same way, the judgment matrix from index layer to criterion layer can also be obtained, which is as follows:

$$\left(\begin{array}{ccc}1& 1/4& 1/2\\ 4& 1& 3\\ 2& 1/3& 1\end{array}\right)\left(\begin{array}{ccc}1& 3& 1/3\\ 1/3& 1& 1\\ 3& 1& 1\end{array}\right)\left(\begin{array}{ccc}1& 1/3& 5\\ 3& 1& 7\\ 1/5& 1/7& 1\end{array}\right)$$

(3)

Additionally, $w_1=(0.14 ,0.63, 0.23){}^T$, $w_2=(0.32, 0.22, 0.46){}^T,$ and $w_3=(0.28, 0.65, 0.07){}^T$. According to the above methods, the weight results of each index are as

Table 2. The Weight Results in AHP.

Criterion Level	Criterion Weight	Index Level	Index Weight
Atmosphere	0.65	Temperature	0.09
		Carbon Dioxide Concentration	0.41
		PM2.5	0.15
Soil	0.21	PH	0.06
		Vegetation Coverage	0.04
		Cultivated Land Area	0.09
Ocean	0.14	Micro Medium Plastic Concentration	0.04
		PH	0.11
		Oil Spill	0.01

follows:

3.1.3. Comprehensive Evaluation Calculation

This paper uses AHP and entropy method to determine the evaluation index weight of plastic waste pollution impact to reduce the deviation between subjective weight and objective weight. At the same time, it uses plastic to judge the environmental damage and recovery abilities.

At first, this paper uses entropy method to determine the impact degree of plastic waste. Entropy is a measure of the disorder degree of the system; the larger the information entropy of the index is, the larger the information provided by the index is, and the greater the role it plays in the comprehensive evaluation, the higher the weight should be (Qian et al., 2018) [23]. Therefore, information entropy can be used to calculate the weight of each index and provide the basis for the comprehensive evaluation of multiple indexes. Because there is no consistent measurement standard between each index, according to the content of the model, the gathered data are collected and sorted out, standardized, and normalized, and then the weight of each index is calculated according to the specific requirements of entropy weight method (Mahbub et al., 2017; Zhao et al., 2018) [24,25]. The result is as

Table 3. The Index Weight in Entropy Method.

First Level Index	Second Level Index	Index Weight
Atmosphere	Temperature	0.09
	Carbon Dioxide Concentration	0.13
	PM2.5	0.06
Soil	PH	0.13
	Vegetation Coverage	0.10
	Cultivated Land Area	0.11
Ocean	Micro Medium Plastic Concentration	0.18
	PH	0.11
	Oil Spill	0.09

follows:

Then, the weight of comprehensive evaluation is as follows:

$$H_i=\frac{M_i{N}_i}{{\displaystyle \sum _{i=1}^n{M}_i{N}_i}}$$

(4)

In style, H_i is the weight of comprehensive evaluation, M_i is the weight in AHP, and N_i is the weight in entropy method. The resulting weights are shown in

Table 4. The Weight of Comprehensive Evaluation.

First Level Index	Second Level Index	Mi	Ni	Hi
Atmosphere	Temperature	0.09	0.09	0.03
	Carbon Dioxide Concentration	0.41	0.13	0.47
	PM2.5	0.15	0.06	0.08
Soil	PH	0.06	0.13	0.08
	Vegetation Coverage	0.04	0.10	0.04
	Cultivated Land Area	0.09	0.11	0.10
Ocean	Micro Medium Plastic Concentration	0.04	0.18	0.06
	PH	0.11	0.11	0.09
	Oil Spill	0.01	0.09	0.01

below:

At the same time, considering the dynamic impact of plastic waste on the environment, an index system of plastic damage ability and environmental recovery ability is established. According to the source, use, treatment means, and current severity of plastic waste, an evaluation system of damage ability is established. The ability of environmental recovery includes the ability of human restriction and the ability of environmental self-recovery. The specific index system is as

Table 5. The Weight of Environmental Damage and Resilience Capacity.

First Level Index	Second Level Index	Weight
Environmental Damage Capacity	Amount of Agricultural Plastic Waste	0.14
	Industrial Plastic Waste	0.24
	Proportion of Plastic Waste in the Whole Urban Waste	0.16
	Proportion of Plastic Waste in Landfill	0.25
	Proportion of Burned Plastic Waste	0.13
	Proportion of Plastic Waste Dumped	0.08
Environmental Resilience	Recyclable Rate	0.65
	Natural Degradation Rate	0.35

follows:

3.1.4. Estimating the Maximum Levels of Waste

After the factors and weights of plastic waste are determined, the specific values are substituted, and the judgment value is compared with the known threshold value to determine the highest level. Taking the PM2.5 index affected by plastic waste as an example, assuming that the threshold value of PM2.5 index is 80, if the PM2.5 index is more than 80 caused by plastic product waste in a certain amount, then it cannot be, and plastic waste pollution has the greatest degree of damage to the environment. When the degree of environmental damage is at the threshold of each index, the maximum impact level of plastic waste pollution on the environment can be determined (Bennewitz, 2009) [26].

At the same time, taking the United States as an example, under the maximum impact level of plastic waste pollution on the environment, when the environmental damage ability and environmental recovery ability are balanced, the environment will not be further damaged at this time. According to the scores of environmental recovery ability of the United States at this time, assuming that other factors remain unchanged, it is estimated that the current proportion of plastic waste in the United States to urban solid waste is 47.78%, and the total plastic waste reaches the maximum value, which is estimated to be 103.0845 million tons. The corresponding data are in Table A1 in Appendix A.

3.2. Problem Two

3.2.1. Composition of Environmental Safety Level

Environmental safety is a measure of the degree of harmony between humans and the environment, which is based on adaptation to survival (Lemly, 2004) [27]. The struggle between human beings and disasters threatening environmental security is essentially accompanied by the whole process of human development. The level of environmental safety is a method to estimate and evaluate the environmental safety of a certain region, country, or world.

In this regard, this paper has sorted out the dimensions and factors that affect the level of environmental safety, as shown in Figure 3.

3.2.2. Strength and Type of Environmental Safety Level

According to the connotation of environmental safety, it is divided into two dimensions: environmental sensitivity and human interference. The two dimensions are divided into three levels: low, medium, and high, and the score range of each level is determined (Zhao et al., 2017) [28]. Combination of multiple security types is in

Table A3. Security Level.

Security Type			Interference Degree
			Low	Medium	High
			(1~4)	(4~6)	(6~9)
Susceptibility	low	(1~4)	low—low	low—medium	low—high
	medium	(4~6)	medium—low	medium—medium	medium—high
	high	(6~9)	high—low	high—medium	high—high

in Appendix A.

Quantitative evaluation shall be carried out for the safety strength index, and the specific formula is as follows:

$$V=\sqrt{S\ast D}$$

(5)

In the formula, V is the environmental safety index, S is the environmental sensitivity index, and D is the human interference index.

According to the comprehensive score of safety, it is divided into four levels of safety intensity: extremely high, high, medium, and low. Characteristics of environmental safety level reflected by different safety intensities are in

Table A4. Safety Level and Score.

Safety Level and Score	Features
Low (7~9)	It shows that the environmental system has been unable to maintain normal structure and function, the health of the environmental system has been seriously damaged, and the environmental system is difficult to restore to its original state.
Medium (5~7)	It shows that the environmental system has been seriously damaged, and the health has been damaged to some extent. It is difficult for the environmental system to recover to its initial state.
High (3~5)	It shows that the system is greatly affected by human beings and health is threatened, but the environmental system carrying pressure has not exceeded the critical threshold.
Extremely high (1~3)	It means that the environmental system is less affected by human activities. On this condition, it can maintain normal ecological structure and function, and the environmental system is in a healthy state.

in Appendix A (Othmar & Lillian, 1997) [29].

3.2.3. Establishing the Evaluation Index System

Based on the specific analysis of the characteristics of each factor and its influencing factors, this paper has constructed the evaluation index system for environmental safety level, as shown in the following Figure 3:

Figure 3. The Evaluation Index System.

Figure 3. The Evaluation Index System.

3.2.4. Evaluation Index Standard

The evaluation indexes and classification standards of water resources, soil resources, and air resources sensitivities are shown in

Table 6. Evaluation Indexes and Classification Standards of Sensitivity.

Classification	Sensitivity Index	Insensitive	Slightly Sensitive	Moderately Sensitive	Highly Sensitive	Extremely Sensitive
Sensitivity	Grade Assignment (S)	1	3	5	7	9
Water Resources Sensitivity	Per Capita Waste Water Volume (m3/year)	<35	35~255	255~600	600~1550	>1550
	Renewable Water Resources Per Capita (m3/year)	>19,170	4000~19,170	1600~4000	500~1600	<500
Soil Resources Sensitivity	Forest Coverage (%)	>60	35~60	25~35	15~25	<15
	Percentage of Protected Land (%)	>30	14~30	8~14	5.5~8	<5.5
Air Resources Sensitivity	Carbon Dioxide Emission Per Capita (ton)	<0.6	0.6~1.90	1.90~4.3	4.3~7.9	>7.9
	EPI	>66	60~66	52~60	44.2~52	<44.2

below:

The value of each evaluation index of human interference degree conforms to normal distribution, and the classification standard is obtained through analysis, as shown in

Table 7. Evaluation Indexes and Grading Standards of Human Interference Degree.

Classification	No Pressure	Low Pressure	Medium Pressure	High Pressure	Extremely High Pressure
Grade Assignment (D)	1	3	5	7	9
Plastic Damage Ability	<0.25	0.25~0.35	0.35~0.45	0.45~0.6	>0.6
Restore Destructive Power	>0.65	0.5~0.65	0.35~0.5	0.25~0.35	<0.25
Waste Per Capita (m3/year)	<35	35~255	255~600	600~1550	>1550
Hazardous Waste Recovery (ton)	<45	45~240	240~1150	1150~30,000	>30,000
GDP Per Capita (yuan)	<12,000	12,000~24,000	24,000~48,000	48,000~72,000	>72,000
Population Density (person·km−2)	<100	100~200	200~400	400~1000	>1000
Land Development and Utilization Degree(%)	<10	10~18	18~32	32~60	>60
Utilization Rate of Water Resources(%)	<10	10~30	30~60	60~80	>80

below:

3.2.5. Determination of Weight Value of Evaluation Index

This paper uses the gray correlation method to determine the weight value of the evaluation index. First, the data related to the evaluation system is collected and collated (in

Table A5. Raw Data on Sensitivity.

Factor	China	Canada	Japan	India	USA
Waste Water Per Capita (Ton/10,000 population)	289.2516	253.1264	47.85808	59.56201	664.2469
Per Capita Share of Renewable Water Resources (m3/person/year)	1971	79238	3373	1427	9459
Forest Coverage (%)	21.36	44	64	23	33
Percentage of Protected Land (%)	15.3	11.1	6.8	5.2	25.9
Carbon Dioxide Emissions Per Capita (Ton)	7.55	13.53	9.76	1.59	16.4
EPI	50.74	72.18	74.69	30.57	71.19

in Appendix A). Then the data are dimensionless processed (in

Table A6. Standardization Data of Human Interference Index in Some Countries.

Factor	Reference	China	Canada	Japan	India	USA
Plastic damage ability	0.328759	0.6029	0.382467	0.328759	0.423448	0.321177
Restore destructive ability	0.878509	0.291172	0.516809	0.607238	0.354472	0.878509
Waste water per capita (Ton/10,000 population)	37.85808	290	253.1264	37.85808	59.56201	664.2469
Hazardous waste recovery per capita (Ton/10,000 population)	9758.004	289.2516	62.78	9758.004	90.5	104.91
GDP per capita (dollar)	65,895.68	10,986.47	50,525.16	42,049.62	2354.68	65,895.68
Population density (Person/km2)	196.32	144.3	4.3	347	450.4	35.6
Land development and utilization degree (%)	21.18	14.4	5.3	12.3	57	16.9
Utilization rate of water resources (%)	78	46	30	78	44	54

in Appendix A), and a new matrix is obtained by dividing the original matrix by the reference sequence. Finally, the gray correlation degree is calculated to determine the weight value. According to the above steps, the weight values of environmental sensitivity and human interference are obtained as

Table 8. Weight Value of Evaluation Index.

Dimension	Indicators	Weight
Human Interference Degree	Plastic Damage Ability	0.1282
	Restore Destructive Power	0.1102
	Waste Per Capita (m3/year)	0.1282
	Hazardous Waste Recovery (ton)	0.1282
	GDP Per Capita (yuan)	0.1282
	Population Density (person·km−2)	0.1070
	Land Development and Utilization Degree(%)	0.1418
	Utilization Rate of Water Resources(%)	0.1282
Environmental Sensitivity	Water Resources Sensitivity	0.3822
	Soil Resources Sensitivity	0.2190
	Air Resources Sensitivity	0.3988

follows:

3.2.6. Evaluation Method

The level of environmental safety includes two dimensions: environmental sensitivity and human interference. Environmental sensitivity includes three aspects: water resource sensitivity, soil resource sensitivity, and air resource sensitivity. The comprehensive index of environmental sensitivity is calculated by calculating the weighted average of each index according to the evaluation results of each single item (Vartiainen et al., 2011) [30]. The specific calculation method is as follows:

$$S={\displaystyle \sum _{i=1}^3{S}_j\ast W_j}$$

(6)

$$WS=\sqrt{{\displaystyle \prod _{i=1}^2{C}_i}}$$

(7)

$$SS=\sqrt{{\displaystyle \prod _{i=1}^2{C}_i}}$$

(8)

$$AS=\sqrt{{\displaystyle \prod _{i=1}^2{C}_i}}$$

(9)

In the formula, S is the comprehensive index of water ecosystem sensitivity; S_j is each sensitive index; and W_j is the weight value of the j sensitive index. WS is the water resource sensitivity index; SS is the soil resource sensitivity index; AS is the ocean resource sensitivity index; and C_i is the sensitivity level value of index i.

The human interference index is calculated by the weighted average of each index, that is, the sum of the product of the weight of index factor and index factor index. The calculation formula is:

$$D={\displaystyle \sum _{i=1}^8{X}_j\ast W_j}$$

(10)

In the formula, D is the human interference index; X_j is the corresponding classification value of the j interference index; and W_j is the weight value of the j interference index.

3.2.7. Discussing the Extent of Environmental Safety

The model established in this study quantifies the degree of environmental damage and human interference in a country. According to the determination of index factors in the model, it can be found that in a country’s own environmental situation, atmospheric resources have the greatest impact on the level of environmental safety. If a country wants to keep its environment at a safe level, it needs to focus on managing its water and air resources.

A country’s environmental security level is also related to its own constraints, which are called human interference in this paper. The level of plastic waste pollution, hazardous waste pollution, economic and social level, and resource utilization will have an impact on the level of environmental safety. According to the model, the environmental sensitivity, human interference, and environmental safety scores of the following five countries are calculated as

Table 9. Dimensions and Environmental Safety Score.

Dimension	China	Canada	Japan	India	USA
Environmental Sensitivity	5.706184	2.706596	3.166367	5.561961	3.437576
Human Interference	4.061600	3.712400	4.752400	4.472000	4.104000
Environmental Safety	4.814170	3.169853	3.879155	4.987293	3.756037

follows:

Because of the different geographical environments and social and economic levels, each country has a different impact on environmental security. Taking China as an example, the sensitivity of the Chinese environment is relatively high, and the policy treatment should focus on the protection of the environment. The degree of human disturbance in Japan and India is relatively high, which can reduce the degree of human disturbance.

In addition, taking China and Japan as examples, this paper analyzes measures taken by two countries in their plastic policies:

China: in June 2008, the plastic restriction order was formally implemented. Taking the data from 2007 and 2010 for comparison and assuming that other factors remain unchanged, the environmental damage ability score of China will be reduced from 0.602900 to 0.520867. It can be seen that the plastic waste pollution has been alleviated by 13.6%.

Japan: relevant policies and laws have been issued for a long time, as shown in Figure 4:

At this time, keeping other factors unchanged, Japan’s environmental damage capacity will be reduced from 0.3288 to 0.2493, and it can be seen that plastic waste pollution has been alleviated by 24.2%. It can be estimated that even the same policy on plastic pollution will play a different role in each region because of different implementation areas and different years.

3.3. Problem Three

3.3.1. Verification of Correlation Coefficient Matrix

After analysis, this question is intended to express the impact of the global environment below the level of safety on people’s lives. Therefore, we can draw from the model in the second question that there are many factors that affect the level of environmental safety, and there is likely to be a correlation between these factors. In order to verify the idea, the correlation coefficient matrix is used (in

Table A7. Correlation Matrix.

	A	B	C	D	E	F	G	H	I	J	K	L	M	N	O	P	Q
A	1	−0.48075	0.623826	−0.75871	−0.34773	−0.17578	0.061269	−0.28749	0.988019	0.74318	0.366717	−0.31967	−0.16167	0.56391	−0.56119	0.623092	0.298478
B	−0.48075	1	0.150663	0.430956	−0.24622	0.875545	−0.27039	0.849603	−0.40576	−0.00519	0.432239	−0.63494	−0.42403	−0.51439	0.9232	0.152229	−0.97679
C	0.623826	0.150663	1	−0.66367	−0.65793	0.159816	0.417025	0.534192	0.589116	0.95289	0.893014	−0.83423	−0.81887	0.506264	−0.15596	0.857484	−0.25356
D	−0.75871	0.430956	−0.66367	1	0.740464	0.441368	−0.60499	0.04542	−0.73285	−0.8424	−0.60133	0.316891	0.174591	−0.95116	0.521777	−0.32277	−0.31389
E	−0.34773	−0.24622	−0.65793	0.740464	1	−0.09382	−0.46652	−0.5066	−0.39038	−0.78364	−0.87648	0.674264	0.305257	−0.6718	−0.16914	−0.22602	0.339795
F	−0.17578	0.875545	0.159816	0.441368	−0.09382	1	−0.63078	0.600561	−0.0787	0.022341	0.317504	−0.64676	−0.32783	−0.64355	0.841017	0.343823	−0.9301
G	0.061269	−0.27039	0.417025	−0.60499	−0.46652	−0.63078	1	0.231831	−0.03813	0.448005	0.418169	−0.05183	−0.35395	0.778575	−0.46042	0.023877	0.340295
H	−0.28749	0.849603	0.534192	0.04542	−0.5066	0.600561	0.231831	1	−0.27317	0.371448	0.745212	−0.78946	−0.75417	−0.08967	0.616482	0.378495	−0.81759
I	0.988019	−0.40576	0.589116	−0.73285	−0.39038	−0.0787	−0.03813	−0.27317	1	0.726167	0.37888	−0.35667	−0.09848	0.516103	−0.44791	0.585134	0.208285
J	0.74318	−0.00519	0.95289	−0.8424	−0.78364	0.022341	0.448005	0.371448	0.726167	1	0.880756	−0.75243	−0.61143	0.689646	−0.23308	0.724258	−0.12764
K	0.366717	0.432239	0.893014	−0.60133	−0.87648	0.317504	0.418169	0.745212	0.37888	0.880756	1	−0.91271	−0.71327	0.475	0.198677	0.608594	−0.51757
L	−0.31967	−0.63494	−0.83423	0.316891	0.674264	−0.64676	−0.05183	−0.78946	−0.35667	−0.75243	−0.91271	1	0.757929	−0.11088	−0.40575	−0.73824	0.736828
M	−0.16N167	−0.42403	−0.81887	0.174591	0.305257	−0.32783	−0.35395	−0.75417	−0.09848	−0.61143	−0.71327	0.757929	1	−0.08364	−0.04605	−0.81525	0.424818
N	0.56391	−0.51439	0.506264	−0.95116	−0.6718	−0.64355	0.778575	−0.08967	0.516103	0.689646	0.475	−0.11088	−0.08364	1	−0.58755	0.101537	0.454788
O	−0.56119	0.9232	−0.15596	0.521777	−0.16914	0.841017	−0.46042	0.616482	−0.44791	−0.23308	0.198677	−0.40575	−0.04605	−0.58755	1	−0.15501	−0.9073
P	0.623092	0.152229	0.857484	−0.32277	−0.22602	0.343823	0.023877	0.378495	0.585134	0.724258	0.608594	−0.73824	−0.81525	0.101537	−0.15501	1	−0.25999
Q	0.298478	−0.97679	−0.25356	−0.31389	0.339795	−0.9301	0.340295	−0.81759	0.208285	−0.12764	−0.51757	0.736828	0.424818	0.454788	−0.9073	−0.25999	1

A Waste water per capita. B Hazardous waste recovery per capita. C GDP per capita. D Population density. E Land development and utilization degree. F Utilization rate of water resources. G Per capita share of renewable water resources. H Forest coverage. I Proportion of protected land. J Per capita carbon dioxide emissions. K EPI. L Agricultural waste. M Industrial waste. N Proportion of urban solid waste. O Incineration ratio. P Landfill ratio. Q Dumping ratio.

in Appendix A).

From the correlation coefficient matrix, it can be seen that the correlation coefficient of some indicators is very high. In this paper, through collinearity diagnosis and selection of indicators, the representative indicators are finally obtained as follows: x₁ is GDP Per Capita, x₂ is Population Density, x₃ is Land Development and Utilization Degree, x₄ is Utilization Rate of Water Resources, x₅ is Per Capita Share of Renewable Water Resources, and x₆ is Industrial Plastic Waste.

In this paper, the second question model is used to determine the impact on the above indicators after reaching the environmental safety level. According to the weight of each index to the level of environmental safety, therefore, in the study of the impact on the environment, this paper mainly considers the change in the share of renewable water resources of people; in the impact on people’s lives, this paper mainly considers the degree of land development and utilization; in the impact on society, this paper mainly considers the per capita GDP; in the impact on the plastic industry, this paper mainly considers the amount of industrial plastic waste.

3.3.2. Discussing Target Value and Influence Degree

According to the safety level of the second question, we can know that it is necessary to make the indicators of the whole environmental safety system reach the safety level to ensure the environment is moderately safe, that is, that human activities have a certain impact and the environment can self-recover.

According to the evaluation system for environmental safety level, the critical value is 5 points. By calculating the safety level, the scores for human interference and environmental sensitivity are also 5 points. The target value and influence degree are as follows (Table 10):

• In the case of considering environmental factors, assuming that other factors remain unchanged, the safety level score is reduced by 0.1 point, and the critical value of renewable water resource index per capita is as follows: If the sensitivity index of raw water resources = 1, the improvement degree of per capita renewable water resources must reach 69.2%. If the sensitivity index of raw water resources is higher, the improvement degree of per capita renewable water resources is lower.
• In the case of considering social factors, it is assumed that other factors remain unchanged. If the original per capita GDP index = 1, only by changing the per capita GDP can the safety level score be reduced by 0.1 point. If the original GDP per capita index is at other levels, to reduce the safety level score by 0.1 point, the GDP per capita needs to be reduced by 4680 US dollars.
• Considering people’s lives and assuming that other factors remain unchanged, if the original land development and utilization index = 1, only by adjusting the land development and utilization degree can the safety level score be reduced by 0.1 point. If the original land development and utilization index is at other levels, to reduce the safety level score by 0.1 point, the original land development and utilization degree needs to be reduced by 7.12%.
• In considering the impact on the plastics industry, it is assumed that other factors remain unchanged. If the original environmental damage capacity = 1, only by adjusting the amount of industrial plastic waste can the safety level score be reduced by 0.1 point. If the original environmental damage ability is at other levels, in order to reduce the safety level score by 0.1 point, the environmental damage ability needs to be reduced by 34.5%. According to the measurement method of environmental damage ability, the reduction degree of industrial plastic waste in the following countries is estimated.

Table 10. Example of Reduction of Industrial Plastic Waste.

Dimension	USA	Germany	Japan	Canada	China
Environmental Damage Capacity	0.321177	0.536771	0.328759	0.382467	0.602902
Environmental Damage Capacity after Change	0.210371	0.351585	0.215337	0.250516	0.394901
Industrial Waste Reduction	/	98.40%	/	/	96.90%

Table 10. Example of Reduction of Industrial Plastic Waste.

Dimension	USA	Germany	Japan	Canada	China
Environmental Damage Capacity	0.321177	0.536771	0.328759	0.382467	0.602902
Environmental Damage Capacity after Change	0.210371	0.351585	0.215337	0.250516	0.394901
Industrial Waste Reduction	/	98.40%	/	/	96.90%

3.3.3. Enlightens

• To improve the level of environmental safety, from the perspective of environmental impact, the greater the damage to the current environment itself, the worse the effect of improvement.
• In terms of social factors, to improve the level of environmental security, we need to weaken the level of national or regional economic development.
• In terms of people’s lives, it is necessary to reduce the degree of land development for each improvement of the corresponding environmental safety level.
• In terms of the impact on the plastic industry, if only reducing the level of the plastic industry to achieve a certain degree of safety level score, the impact on various countries is huge, and some countries cannot even meet this requirement.

3.4. Problem Four

3.4.1. Sample Index Selection and Data Acquisition

It can be seen from the first question that plastic pollution has the greatest impact on the concentration of carbon dioxide emissions in the atmosphere. In the second question, the level of plastic pollution, waste pollution, economic and social level, and resource utilization of human interference in various countries will have an impact on the overall environmental safety. According to the third question, among the factors affecting the level of environmental safety, the six indicators of per capita GDP, population density, land development and utilization degree, water resource utilization rate, per capita share of renewable water resources, and industrial waste have the greatest impact. Therefore, this paper collects relevant data from the United States, Germany, Japan, Canada, Russia, China, India, North Korea, South Africa, and Afghanistan, as shown in Table 11.

3.4.2. Establishing the BP Neural Network Model

A neural network is a complex network formed by a large number of simple processing units connected to each other. It is a knowledge processing system based on numerical calculation. Its model is built on the basis of a case study and adopts parallel reasoning method, which has the characteristics of association, memory, and induction (Werbos, 1989) [31].

According to the level of national environmental safety, the higher the score, the greater the degree of responsibility, which can be quantified. In order to conveniently measure the degree of responsibility of each country, this paper sets up a BP neural network model, which can measure the degree of national security with a small number of indicators. Taking the above 10 countries as samples, the learning model of BP neural network is established. The input layer is also the six indicators selected above, and the output layer is the score of the responsibility intensity of the country, as shown in the table below:

Table 11. Relevant Data of Influencing Factors of Environmental Safety Level.

Country	Per Capita GDP	Population Density	Land Development and Utilization Degree	Water Resource Utilization Rate	Per Capita Share of Renewable Water Resources	Industrial Waste	Intensity of Responsibility
USA	65,896	35.6	16.9	54	9459	0.04	3.756
Germany	42,542	230	19.3	48	8726	0.823	3.947
Japan	42,050	347	12.3	78	3373	0.002	3.879
Canada	50,525	4.3	5.3	30	79,238	0.11	3.17
Russia	24,310	8	14.4	35	3847	0.224	4.706
China	10,986	144.3	14.4	46	1971	0.996	4.814
India	2354.7	450.4	57	44	1427	0.54	4.987
North Korea	31,229	190	24.3	43	2458	0.278	5.129
South Africa	12,179	36	65.3	13	872	0.032	5.291
Afghanistan	1626.8	46	78.1	22	432	0.001	5.091

Table 11. Relevant Data of Influencing Factors of Environmental Safety Level.

Country	Per Capita GDP	Population Density	Land Development and Utilization Degree	Water Resource Utilization Rate	Per Capita Share of Renewable Water Resources	Industrial Waste	Intensity of Responsibility
USA	65,896	35.6	16.9	54	9459	0.04	3.756
Germany	42,542	230	19.3	48	8726	0.823	3.947
Japan	42,050	347	12.3	78	3373	0.002	3.879
Canada	50,525	4.3	5.3	30	79,238	0.11	3.17
Russia	24,310	8	14.4	35	3847	0.224	4.706
China	10,986	144.3	14.4	46	1971	0.996	4.814
India	2354.7	450.4	57	44	1427	0.54	4.987
North Korea	31,229	190	24.3	43	2458	0.278	5.129
South Africa	12,179	36	65.3	13	872	0.032	5.291
Afghanistan	1626.8	46	78.1	22	432	0.001	5.091

Based on the established neural network learning model, by inputting the specific values of the above six indicators of a country, we can estimate the responsibility intensity of that country.

3.4.3. The Test of BP Neural Network Model

From output results, the fitting degree of the model is shown in

Table 12. Fitting Degree of BP Neural Network Model.

Hidden Layer	1
Number of Neurons	2
Prediction Error Rate of Training Set	0.40%
Prediction Error Rate of Test Set	1%

:

It can be seen from the above table that the model has a good fitting degree (Wang et al., 2019) [32].

3.5. Plastic Waste Memo

Memorandum

To: ICM

From: University of Science and Technology of China

Subject: Waste Pollution of Plastic Products

Date: 7th May 2023

As we know, waste pollution from plastic products is becoming more and more serious. In this regard, I would like to make some comments on the minimum achievable level of global single-use or disposable plastic product waste.

To begin, please look at the current global environmental level, as shown in

Table 13. The Current Global Environmental Level.

Human Interference	5.042400
Environmental Sensitivity	5.406977
Environmental Safety Level	5.221507

below:

From the table, we can see that environmental sensitivity is higher than human interference. Moreover, the environmental safety level is at the middle level (see Table A4 in Appendix A), indicating that the environmental system has been seriously damaged and the health system has been damaged to some extent. After that, I consider the future changes in global environmental levels and calculate the future environmental sensitivity and environmental safety levels under the assumption that human beings do not interfere with the environment. I use the data between 1950 and 2019 to fit and forecast the specific indicators of environmental sensitivity so as to predict the values of each indicator in the future, as shown in Figure 5, Figure 6 and Figure 7:

The fitting results show that the carbon dioxide emission per capita is on an upward trend and slightly decreases after reaching its peak as the years increase; similarly, the wastewater per capita is also on an upward trend; however, the forest coverage stays on a stable trend. The values are estimated as

Table 14. Fit Forecast.

Factor	2025	2030	2035	2040
Waste Water Per Capita (MT/10,000 population)	296.78	379.28	498.76	633.81
Per Capita Share of Renewable Water Resources (m3/year)	18,880.31	26,369.81	36,830.26	51,440.20
Forest Coverage(%)	30.896	30.952	31.008	31.064
Percentage of Protected Land(%)	14.062	16.836	20.156	24.132
Carbon Dioxide Emissions Per Capita (metric tons)	8.71	6.9778	4.9292	2.5642
EPI	54.5203	57.52488	60.52946	63.53404

follows:

If there is no human interference, the future environmental safety level can reach the following levels (

Table 15. The Future Environmental Safety Level.

Factor	2019	2025	2030	2035	2040
Water Resource Sensitivity	3	3.872983	2.236068	2.236068	2.645751
Oil Resource Sensitivity	5	3.872983	3.872983	3.872983	3.872983
Air Resource Sensitivity	7.937254	6.708204	5.91608	4.582576	3.872983
Environmental Sensitivity	5.406977	5.003669	4.062141	3.53034	3.403935
Environmental Safety Level	5.221507	5.022997	4.525808	4.219169	4.142946

):

From the above table, I would like to remind you of the following points:

• By 2025, the environmental safety level will basically reach the safety level. There will be some human interference with the environment, but the environmental recovery capacity can withstand such interference. Among them, water resources, soil resources, and air resources should be protected.
• By 2030, it will reach the level of environmental safety. Among them, we need to focus on the protection of water resources and atmospheric resources.
• By 2035, the level of environmental safety will have been further improved. In these five years, the protection of atmospheric resources will be the main concern.
• By 2040, the level of environmental security will have reached a stable level, which focuses on the sensitivity of water resources and atmospheric resources.
• According to the analysis of the third question, human life styles and social and economic conditions will have an impact on environmental security. Therefore, slowing down the development of the human economy, such as by reducing the degree of industrialization, reducing the output of hazardous waste and plastic waste, and improving the ability of waste treatment to help natural recovery, can speed up the improvement of environmental safety levels. At the same time, excessive land development or rapid population growth will cause pressure on the environment, thus slowing down the process of improving the level of environmental security.

Finally, I wish our earth healthy growth, far away from waste!

4. Discussion

In summary, the global plastic waste management problem is not caused by any single factor. On the contrary, this is an interdisciplinary problem consisting of a set of variables. First, based on the pollution index data of plastic waste, this paper uses the AHP and entropy weight model to determine the evaluation index weight of plastic waste pollution impact and reduce the deviation between subjective and objective weight. At the same time, it uses plastic to judge environmental damage and environmental recovery abilities. To sum up, take the United States as an example: the largest level of disposable plastic waste pollution is 103.0845 million tons. Secondly, in order to measure the level of environmental safety, this paper establishes an evaluation index system and uses the gray correlation method to determine the weight value of the evaluation index. By calculating the environmental safety scores of each country, the use of plastic resources, regional restrictions, national policies, and other factors were evaluated. Thirdly, it makes a collinearity diagnosis of the indicators that affect the level of environmental safety in the second problem and obtains representative indicators for an in-depth study. Then, it assumes that the minimum achievable target for environmental safety level is 5 points. According to the calculation method of the safety level, it discusses the influence of environmental factors, social factors, people’s lives, and the plastic industry on the level. Fourthly, according to the second index system, it selects the relevant data from 10 countries, establishes the BP neural network model, and calculates the level of security and the intensity of responsibility. Moreover, based on the conclusion of the first question, this paper puts forward suggestions for different types of countries in combination with international policies and offers a memorandum with a schedule to discuss the measures needed to achieve this goal and the factors to be considered.

Based on the first question, plastic pollution has a greater impact on the concentration of carbon dioxide emissions in the atmosphere. According to the score, the higher the score, the greater the level of impact on carbon dioxide emissions. Based on the United Nations Framework Convention on Climate Change, this paper puts forward the following suggestions: For industrialized countries, these countries have promised to cut emissions on the basis of 1990 emissions. Undertake the obligation to reduce greenhouse gas emissions. If the reduction task cannot be completed, emission targets can be purchased from other countries. For developed countries, these countries do not undertake specific reduction obligations but undertake financial and technical assistance obligations for developing countries. In developing countries, they will not undertake the obligation of reduction so as not to affect economic development. They can accept financial and technical assistance from developed countries but must not sell emissions targets. Furthermore, by adopting the basic pillars of a circular economy (reuse, recycling, refurbishment, remanufacturing, replacement, etc.) and using alternative materials (i.e., biological composite materials) to replace or minimize their use in all fields, changing the “business as usual” mindset has the most important and valuable significance for transferring waste from landfills. It is necessary to develop a universal circular business model for composite materials, but a quality agreement on the final waste standards for each material mixture is also required (Chatziparaskeva et al., 2022) [33].

The innovation of this paper is that there is no simple choice of stratification method, but combined with the weight given to optimization analysis, there are many factors considered in the model that can measure the global security level well. In this paper, through the method of index selection, the factors with great influence are selected, and a neural network learning model can be established to measure the level of environmental security in various countries.

However, there are also some disadvantages to models. At first, the learning data of each model is not rich enough, which makes it easy to affect the effect to a certain extent. Moreover, when there are too many indicators, the data statistics are large and the weight is difficult to determine. Additionally, the structure of the neural network is different, and the convergence speed of the algorithm is slow.

In future improvements, firstly, for the evaluation system and the neural network model, if the sample data are large enough and the data are processed, the classification level of each layer of indicators can be more detailed, and our evaluation of environmental safety level will be more sensitive. Secondly, future evaluation models should incorporate cost analysis factors, maximize plastic processing at controllable costs, and score the utilization of funds. Thirdly, we can conduct policy analysis in the future, focusing on the environmental policies proposed by international organizations, analyzing the implementation situation in mainstream countries, and proposing targeted suggestions. Fourthly, it is also possible to predict the rate of plastic usage in the future based on the current social development speed in order to modify the formula model. Additionally, although plastic waste pollution is a global problem, the root causes of the problem vary from country to country, as do the impacts or hazards. As this is a fairness issue, the concept of sustainable development coefficient can be introduced to calculate the average sustainable development coefficient and variance of all countries on each continent separately, and the smaller the variance value, the fairer the world.