An improved multi-variable grey model for forecasting China's finished products from comprehensive waste utilization.

Area of Science:

• Environmental Science
• Resource Management
• Data Science

Background:

• Accurate prediction of finished products from waste recycling and reprocessing is vital for sustainable resource management and pollution reduction.
• Comprehensive Waste Utilization (CWU) creativity reflects the efficiency of waste reprocessing.

Purpose of the Study:

• To forecast the finished products in China's Comprehensive Waste Utilization (CWU) using an improved multi-variable grey model.
• To enhance the accuracy of waste reprocessing predictions compared to traditional models.

Main Methods:

• An improved multi-variable grey model was developed, incorporating a linear correction term and grey action quantity.
• The degree of grey incidence was used for explanatory variable selection and multicollinearity elimination.
• The model's response function and parameter estimation methods were deduced and proven.

Main Results:

• The improved grey model achieved a mean relative simulation percentage error of only 0.0001%.
• This represents a significant improvement over the traditional GM(1,N) (12.1232%) and GM(1,1) (8.8402%) models.
• Key factors influencing finished products were identified as general industrial solid waste utilization and the number of industrial enterprises.

Conclusions:

• The improved grey model provides highly accurate predictions for finished products in China's CWU.
• Future trends (2020-2025) for finished products from CWU are projected to be unstable.
• The study highlights the importance of industrial solid waste management and enterprise numbers in waste reprocessing outcomes.