Gray correlation analysis and prediction models of living refuse generation in Shanghai city

A better understanding of the factors that affect the generation of municipal living refuse (MLF) and the accurate prediction of its generation are crucial for municipal planning projects and city management. Up to now, most of the design efforts have been based on a rough prediction of MLF without any actual support. In this paper, based on published data of socioeconomic variables and MLF generation from 1990 to 2003 in the city of Shanghai, the main factors that affect MLF generation have been quantitatively studied using the method of gray correlation coefficient. Several gray models, such as GM(1,1), GIM(1), GPPM(1) and GLPM(1), have been studied, and predicted results are verified with subsequent residual test. Results show that, among the selected seven factors, consumption of gas, water and electricity are the largest three factors affecting MLF generation, and GLPM(1) is the optimized model to predict MLF generation. Through this model, the predicted MLF generation in 2010 in Shanghai will be 7.65 million tons. The methods and results developed in this paper can provide valuable information for MLF management and related municipal planning projects.