Prediction of China’s Industrial Solid Waste Generation Based on the PCA-NARBP Model

Hong-Mei Liu, Hong-Hao Sun, Rong Guo, Dong Wang, Hao Yu, Diana Do Rosario Alves and Wei-Min Hong
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Hong-Mei Liu: School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China
Hong-Hao Sun: School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China
Rong Guo: School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China
Dong Wang: School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China
Hao Yu: School of Mechanical Engineering, Nantong University, Nantong 226019, China
Diana Do Rosario Alves: School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China
Wei-Min Hong: School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China

Sustainability, 2022, vol. 14, issue 7, 1-15

Abstract: Industrial solid waste (ISW) accounts for the most significant proportion of solid waste in China. Improper treatment of ISW will cause significant environmental pollution. As the basis of decision-making and the management of solid waste resource utilization, the accurate prediction of industrial solid waste generation (ISWG) is crucial. Therefore, combined with China’s national conditions, this paper selects 14 influential factors in four aspects: society, economy, environment and technology, and then proposes a new prediction model called the principal component analysis nonlinear autoregressive back propagation (PCA-NARBP) neural network model. Compared with the back propagation (BP) neural network model and nonlinear autoregressive back propagation (NARBP) neural network model, the mean absolute percentage error (MAPE) of this model reaches 1.25%, which shows that it is more accurate, includes fewer errors and is more generalizable. An example is given to verify the effectiveness, feasibility and stability of the model. The forecast results show that the output of ISW in China will still show an upward trend in the next decade, and limit the total amount to about 4.6 billion tons. This can not only provide data support for decision-makers, but also put forward targeted suggestions on the current management situation in China.

Keywords: PCA-NARBP neural network; industrial solid waste; management suggestions; generation prediction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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