The effect of Import Solid Waste Ban on China’s Economy and Environment based on Grey Prediction Model

(1)

*Correspondence to Author: Boying Lv

College of Hydraulic & Environ- mental Engineering, China Three Gorges University, Yichang 443002, China.

How to cite this article:

Boying Lv, Botao Liu, Yishuai Tian, Wanting Zheng. The effect of Import Solid Waste Ban on

China’s Economy and

Environment Based on Grey Prediction Model. Global journal of Economics and Business Administration, 2020; 4:29

eSciPub LLC, Houston, TX USA. Website: http://escipub.com/

Boying Lv et al,. GJEBA, 2020; 4:29

Global journal of Economics and Business Administration

(ISSN:2475-6350)

Research Article GJEBA (2020) 4:29

The effect of Import Solid Waste Ban on China's Economy and

Environment based on Grey Prediction Model

In order to explore the impact of imported solid waste ban on China’s economy and environment, a grey prediction model was established in this paper. GDP, comprehensive income from the utilization of waste resources, the number of imported solid waste treatment enterprises, and the discharge of polluting waste gas and waste water were selected as the indicators to measure the economy and environment. The model was used to predict the data of the unreal garbage ban and compare it with the data after the actual implementation, and get the proportion of the impact on China’s economy and environment after the introduction of the policy. The results showed that before and after the promulgation of the ban on foreign waste, China’s environmental pollution had improved significantly, the total exhaust gas emissions have been reduced by 34.1%, and the number of imported waste enterprises had decreased by 66.1%, stimulating the rise of the domestic waste utilization industry, and the overall domestic economy had grown steadily.

Keywords: Grey Prediction; Import solid waste ban; Chinese

economy and environment

Boying Lv1_{*, Botao Liu}1_{, Yishuai Tian}1_，_{Wanting Zheng}2

1_{College of Hydraulic & Environmental Engineering, China Three Gorges University, Yichang}

443002, China. 2_{College of Electrical Engineering & New Energy, China Three Gorges University,} Yichang, 443002, China.

ABSTRACT

(2)

GJEBA: https://escipub.com/global-journal-of-economics-and-business-administration/

1. Introduction

For China, "imported solid waste" refers to the solid waste imported from other countries. Since 2004, China has become the largest garbage producer in the world, importing more than half of the global trade volume every year [1]_{. In April} 2018, the Ministry of Ecology and Environment issued a notice on the adjustment of the cata-logue of imported wastes, and decided to adjust 32 varieties of solid wastes, such as waste hard-ware, scrap ships and scrap automobile press-ing parts, to "import ban" [2]_{. 32 varieties of solid} waste will be banned by the end of 2019. After part of the ban came into effect, it led to a large-scale restructuring of the waste industry chain around the world [3]_{. The ban on importing} gar-bage will not only improve China's environment, but the economy of imported solid waste-related industries may also fluctuate.

After the ban, many scholars have discussed its impact on China's future environment and econ-omy. For example, Zhang et al. [4]_{through the} quantitative analysis of Granger causality test, it was concluded that there is no causal relation-ship between producer price index and unsorted waste paper import in papermaking and paper products industry. Sun et al. [5]_{qualitatively} ana-lyzed the changes of the garbage ban on China and the global garbage trading system. At pre-sent, most of the studies were theoretical analy-sis, and the analysis of data is less. This paper established a model to predict the data that do not implement the "imported solid waste ban", compared it with the data actually implemented by the "imported solid waste ban", and analyzes the impact of the ban on China's economy and

environment. The purpose of this study was to provide a relevant basis for the study of the im-pact of imported solid waste ban.

2. Definition of grey prediction model Grey prediction modeling technology is an im-portant part of grey system theory [6]_{. By} regard-ing the discrete data scattered on the time axis as a group of continuously changing series, the unknown factors in the grey system are weak-ened and the influence degree of the known fac-tors is strengthened by the way of accumulation and reduction. Finally, a continuous differential equation with time as a variable is constructed, and the parameters in the equation are deter-mined by mathematical method, so as to achieve the purpose of prediction. Without a large number of data samples, the short-term prediction effect is good, and the operation pro-cess is simple.

2.1 Data collection and Analysis

We have collected data on the amount of im-ported waste, the discharge of waste gas and waste water, the comprehensive income of waste resources, the number of imported waste enterprises and the number of harmless treat-ment plants in the past five or six years. These are compiled from the China data Yearbook and authoritative platforms such as the World Bank. Fig.1 shows the total amount and changes of im-ported solid waste in China from 2013 to 2018. Fig.2 shows China's emissions from 2012 to 2018, and Fig.3 shows China's recycling bene-fits and changes in the number of imported solid waste treatment enterprises from 2014 to 2017.

Fig.1 Imported solid waste volume and imported waste enterprises 5485

4960 ₄₆₉₈

4658 ₄₃₇₀

2338 2527 ₂₃₁₇

2189

1766 ₁₅₉₇

855

0 1000 2000 3000 4000 5000 6000

2013 2014 2015 2016 2017 2018

Total weight of imported solid waste (10000 tons)

(3)

GJEBA: https://escipub.com/global-journal-of-economics-and-business-administration/ Fig.2 Discharge of waste gas and waste water in China from 2012 to 2018

From 2017 to 2018, before and after the promul-gation of the ban on foreign waste, the total amount of imported waste in China decreased by 50%, and the number of enterprises dealing with imported waste also decreased by nearly half, indicating that the effect of the ban on for-eign waste is more obvious. The reduction of

garbage import leads to a decrease in the num-ber of corresponding enterprises. At the same time, the emissions of nitrogen oxides, sulfur di-oxide, smoke and dust decreased year by year from 2016 to 2018, and the total emissions in 2018 were close to 1/3 of those in 2012, and en-vironmental pollution was greatly reduced.

Fig.3 Income from Comprehensive Utilization of waste and the number of harmless Chemical plants in China

Apart from a marked reduction in emissions, the ban has brought about industrial changes in the economy. There are two distinct scenes of the rise of domestic waste resources utilization dustry, the annual increase of comprehensive in-come and the decline of enterprises dealing with imported waste.

2.2 Stage ratio test

In order to ensure the feasibility of the data and the feasibility of the modeling method [7]_{, it is} necessary to verify the collected data, so let the reference data be (0) (0) (0)

(1), (2), ( )

x x x n The

series formula for calculating the sequence is

0 -1 (0)

( ) k ( 1, 2, , )

k

x

k k n

x

(4)

( )k

 stands for grade ratio. By calculating the grade ratio of each series, it can be found that the( )k _{ratio of all series falls within the}

cover-age range of ( 2_n ₁, 2_n ₁)

e + e +

− , so the data has passed the grade ratio test and can be predicted by grey system.

2.3 Establishment of model Let (0)

x have n observations

(0) (0) (0)

(1), (2), ( )

x x x n and then add up to (0)

x to

generate a new series (1)

x . The expression of its

elements is as follows:

(

)

(1) (0)

1

( ) ( ) 1, 2,..., i

m

x i x m i n

=



= ，

Where (1) (1) (1) (1) [ (1), (2),..., ( )]

x = x x x n .

The albino differential equation is established. By expanding the above formula according to

n

i=1,2,..., _{, the relationship between the original} sequence and the generated sequence can be

obtained: (1)( ) (1)( 1) (0)( )

n x n

x n

x = − + . For the

first order generated sequencex(1)(k), the whit-ening differential equation in the form of GM (1,1) prediction model is as follows:

u ax dt dx

= + (1) ) 1 (

Time response function. Let the initial value

(1) (0)

(1) (1)

x =x , then the solution of the time res-

ponse function is:

a u e a u x k

x  ak+

     ₋ = + − ) 1 ( ) 1

( (0)

) 1 (

The estimated parameters of the two models are expressed in vector form _

     = u a

aˆ .According to the parameter estimation method of multivariate linear regression, the discrete equations of the

expanded time response function are solved by the least square method. We get

N T T_B _B _Y

B

a_ˆ=₍ ₎−1 _{,among them the}

Band YN for-mula is as follows:

              + − − + − + − = 1 2 / )) ( ) 1 ( ( ... ... ... ... 1 2 / )) 3 ( ) 2 ( ( 1 2 / )) 1 ( ) 1 ( ( ) 1 ( ) 1 ( ) 1 ( ) 1 ( ) 1 ( ) 1 ( n x n x x x x x B

(0) (0) (0)

(2), (3),..., ( ) T

N

Y = x x x n _

By substituting the obtained aˆ moment into the

equation of the prediction model and solving the

differential equation, the formula of GM(1 , 1) prediction model is obtained as follows:

a u e a u x k

x  ak+

     ₋ = + − ) 1 ( ) 1

( (1)

) 1 (





),... 1 ( ˆ ), ( ˆ ),..., 2 ( ˆ ), 1 ( ˆ

ˆ(0) = (0) (0) (0) (0) + n x n x x x x

wherexˆ(0)(k)=xˆ(1)(k)−xˆ(1)(k−1).

In order to continuously take into account the disturbance factors entering the system, the metabolic series is introduced, that is, each newly obtained data is sent into the original x (0) series, and the oldest data is removed to keep the number of data unchanged. Therefore, the

above model is modified. The initial valuexˆ(1)(1) is taken as the latest data xˆ(1)(k) the special so-lution is obtained, and then the formula of the prediction model can be obtained.

) ( ˆ ] ) ( [ ) 1 (

ˆ(0) (1) (1)

k x a u e a u k x k

(5)

3. Results and Discussion

Each selected index data is substituted into the above prediction model formula, which is real-ized based on MATLAB platform operation [8]_. Forecast the economic and environmental indi-

cators for 2018 without the implementation of the "foreign waste ban", and compare the 2018 pre-dicted values with the measured values, as shown in Fig.4 and Fig.5.

700 750 800 850 900

1100 1120 1140 1160 1180 400

500

600

700 708 709 710 711 712 713

Wastewater discharge (trillion tons)

Smoke dust (10,000 tons)

Nitrogen oxides (tonnes) Sulfur dioxide

emissions (10,000 tons)

Predictive value Actual value

Fig.4 Comparison between Forecast and actual value of Chinese Environmental Indexes

In terms of the environment, the ban on imported solid waste has reduced the degree of environ-mental pollution. As can be seen from Fig.4 the degree of environmental pollution has been sig-nificantly reduced after the implementation of the ban, in which air pollution has a significant down-

ward trend, especially smoke and dust has been reduced by 2.103 million tons, by 31%, and sul-fur dioxide by 1.151 million tons by 13%. At the same time, wastewater and nitride were reduced by 0.4% and 4.8%, respectively.

880000 885000 890000 895000 900000 905000

800 1000

1200 1400

1600 4374

4376 4378 4380 4382

Waste Resource Utilization comprehensive income (100 million yuan)

Number of companies handling imported waste GDP(Trillion yuan)

Actual value Predictive value

(6)

On the aspect of economy, although the "ban on foreign waste" has brought about an economic boost to the waste recycling industry, it has dealt a blow to the development of imported waste treatment enterprises. To a certain extent, the re-duction of imported garbage has stimulated the growth of the domestic "garbage recycling in-dustry" and surrounding industries, with a GDP growth value of 16743 trillion. Among them, the economy of abandoned resources has only in-creased by 0.1%, indicating that the stimulus of the industry is not obvious in just one year, and it is possible that the monopoly of domestic gar-bage imports will further stimulate economic growth in the next few years. As a result, the ban

has no negative impact on the overall economy to some extent, but it has dealt a severe blow to the imported waste disposal industry, with the number of enterprises reduced by 39%.

4. Model Test

Variance ratio test is an important method to de-tect model prediction. According to the ratio of mean square error, the accuracy of the model is very good when the ratio is less than 0.35. When the ratio is between 0.35 and 0.5, it is qualified, and when the ratio is greater than 0.65, it is not qualified. By calculating the variance ratio of each forecast index, as shown in figure 6:

Fig.6 Each error index

The above figure clearly shows the relative error of each index and the corresponding accuracy interval of the model. Compared with the stand-ard scale, it can be found that only smoke and dust is grade 3, that is, barely. The other inspec-tion grades are grade 1 and grade 2. Therefore, it can be explained that the overall grey predic-tion effect is good, and the model is reasonable. 5. Conclusion

In this paper, a grey prediction model is estab-lished to reasonably quantify the impact of ban on China's environment and economy. GDP, comprehensive income from the utilization of waste resources, the number of imported solid

(7)

has no negative impact on the overall economic improvement, but it has dealt a severe blow to the imported waste disposal industry.

6. References

1. The most stringent ban on foreign waste in his-tory has been implemented for one year: forcing China's recycled plastics industry to upgrade. Resource Saving and Environmental Protection, 2018 (08): page 1.

2. Xuechen Wang. China's decision to ban foreign waste for one year affects the global solid waste treatment system [J]. Resource Recycling, 2018 (09): p. 1215.

3. With the ban on "imported solid waste", how should China's recycling industry move forward [J]? China Petroleum and Chemical Economic Analysis, 2019 (04): 1.

4. Bin Zhang, Liping Li and Li Zhang. Study on the economic impact of measures to ban imported solid waste entry-- take unsorted waste paper as an example[J]. Environmental Protection, 2018 (23): 64-68.

5. Qianwen Sun, China's ban on imported solid waste promotes a new pattern of global garbage trade [J]. Financial Economics, 2019 (12): 135-136.

6. Bo Zeng. Grey Prediction Modeling Technology Research [D]. Nanjing University of Aeronautics and Astronautics, 2012.

7. Yi Lu. Research and Application of Grey Predic-tion Model [D]. Zhejiang University of Technol-ogy, 2014.