Research Article of Journal of Modern Economy
Assessing the Impact of Garbage Classification on China’s Economic Environment
Baogang Song1*, Xing Li2, Weiming Jing3, Qian Zhang4
1College of Hydraulic & Environmental Engineering, China Three Gorges University, Yichang, 443002, China. 2College of Mechanical & Power Engineering, China Three Gorges University, Yichang, 443002, China. 3College of Computer & Information Technology, China Three Gorges University, Yichang, 443002, China. 4College of Arts & Communication, China Three Gorges University, Yichang, 443002, China.
This article mainly studies the impact of waste classification on China’s economy and environment. For the economy, the annual per capita waste removal volume, waste disposal cost, and waste disposal profit from 2008 to 2017 were selected as the three input indicators, and GDP was used as the output value. A BP neural network model based on GM (1,1) was established. The GM (1,1) model is used to predict the values of the three indicators in the next five years. The relationship between GDP and the three input indicators is determined using the BP neural network. The three indicators are substituted into the model to obtain the GDP in the next five years. value. As for the environment, the number of resource processing plants, resource processing capacity, and resource processing capacity are selected as three input indicators, and the per capita green space area is used to measure the impact on the environment. The same method is used to predict the per capita public green area in the next five years. The results show that garbage classification will have a beneficial impact on China’s economy and environment, but the impact will weaken year by year.
Keywords: Garbage classification; Gray prediction; BP neural network
How to cite this article:
Baogang Song, Xing Li, Weiming Jing, Qian Zhang. Assessing the Impact of Waste Classification on China’s economic environment Based on Gray Prediction and BP Neural Network Model. Journal of Modern Economy, 2020; 4:12. DOI: 10.28933/jme-2020-04-1005
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