Influencing Factors Determination of MSW Clearance Volume Based on Spatial Dependency Consideration

Abstract:

Previous studies usually used regression analysis on OLS model to determine the influencing factors of MSW clearance volume, but OLS model did not take into account the spatial dependency of the dependent variable. In this study, firstly, as the dependent variable, the spatial autocorrelation of MSW clearance volume is tested to explore its spatial effect, and spatial regression model is introduced to establish a MSW-SEM (spatial error model) model. Secondly, variance inflation factor and partial correlation coefficient are used to remove the multicollinearity of 33 potential factors. Then using MSW-OLS model and MSW-SEM model respectively analyzes the influencing factors of MSW clearance volume in China mainland. Finally, comparative analysis of above two models results is taken by Akaike Information Criterion, determination coefficient, significance of parameter estimation, and spatial dependence of residuals. Result shows that community health center visits, accommodation enterprises main business profit, passenger capacity, investment enterprises profit rate of Hong Kong/Macao/Taiwan/overseas and urban road lighting were dominant influencing factors of MSW clearance volume. Although MSW-OLS and MSW-SEM models have similar regression coefficients, MSW-SEM model shows better model fitting because of its lower AIC, higher R2, and smaller global Morans I value. In summary, compared with MSW-OLS model, MSW-SEM model is more successful in identifying the dominant factors of MSW clearance volume due to its consideration of spatial dependency.

• Periodical: Advanced Materials Research (Volume 878)
• Pages: 513-519
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.878.513
• Online since: January 2014
• Authors: Yan Yu, Yuan Jun Huang, Jian Hua He, Jing Hui Zhao
• Keywords: Influencing Factor, MSW, OLS, SEM, Spatial Dependency

References

[1] S. Li, MSW crisis highlighted, how to resolve the plight of waste Siege, Policy Research & Exploration. 09(2012) 55-57.

[2] L. Chen, G. Wu, Y. Zhang, W. Zuang, Research on Provincial Distribution of City Garbage Generation and Disposal Investment, Journal of Wuhan University of Technology(Social Sciences Edition). 06(2012) 868-871.

[3] Y. Qu, Q. Zhu, Study on Resident Behavior Intention of Separating Their Waste at Source, Management Review. 21(2009) 108-113.

[4] O. Buenrostro, G. Bocco, J. Vence, Forecasting generation of urban solid waste in developing countries-A case study in Mexico, Journal Of The Air & Waste Management Association. 51(2001) 86-93.

DOI: 10.1080/10473289.2001.10464258

[5] A. Mohanmmed, H. Hou, M. Zhao, Application of Grey Theory to Prediction of Urban Garbage Amount, Environmental Science and Technology. 03(2005) 83-84.

[6] J. Navarro-Esbri, E. Diamadopoulos, D. Gine star, Time series analysis and forecasting techniques for municipal solid waste management, Resources, Conservation and Recycling. 35(2002) 201-214.

DOI: 10.1016/s0921-3449(02)00002-2

[7] B. Dyson, Ni-Bin Chang, Forecasting municipal solid waste generation in a fast-growing urban region with system dynamics modeling, Waste Management. 25(2005) 669-679.

DOI: 10.1016/j.wasman.2004.10.005

[8] X. Yang, J. Chu, C. Lu, The Prediction of MSW Yields Based on BP Neural Network, Journal of Xi'an University of Technology. 04(2003) 335-339.

[9] D. Hockett, Douglas J. Lober, Keith Pilgrim, Determinants of Per Capita Municipal Solid Waste Generation in the Southeastern United States, Journal of Environmental Management. 45 (1995) 205-217.

DOI: 10.1006/jema.1995.0069

[10] M. Z. Alikhan , F. A. Burney, Forecasting Solid Waste Composition—an Important Consideration in Resource Recovery and Recycling, Resources, Conservation and Recyling. 3(1989) 1-17.

DOI: 10.1016/0921-3449(89)90010-4

[11] J. Zhang, J. Zhang, Prediction of Municipal Solid Wastes Quantity of Chengdu Based on Grey Theory and BP Neural Network, Environmental Science and Management. 08(2012) 52-56.

[12] H. W. Chen，Ni-Bin Chang, Prediction analysis or solid waste generation based on grey fuzzy dynamic modeling, Resources, Conservation and Recyling. 29(2000) 1-18.

DOI: 10.1016/s0921-3449(99)00052-x

[13] G. Chu, J. Xia, The Grey Prediction of the Output of Hangzhou Domestic Garbage and its Correlation Analysis, Jour Geol & Min Res North China. 02(1994).

[14] W. Tobler, A computer movie simulating urban growth in the Detroit region, Economic Geography. 46(1970) 234-240.

DOI: 10.2307/143141

[15] X. Li, L. Gu, Spatial Autoregressive Model and its Estimation, Statistical Research. 06(2004) 48-51.

[16] Z. Li, S. Zhou, H. Zhang, X. Yao, W. Wu, Exploring the Factors Impacting on the Residential Land Price and Measuring Their Marginal Effects Based on Geographically Weighted Regression Model: A Case Study of Nanjing, China Land Science. 10(2009).

DOI: 10.1109/icmss.2009.5301077

[17] D. Lai, T. Xiong, Space Regression Analysis of Population Growth and Economic Development, Statistics and Decision. 12(2006) 72-73.

[18] D. A. Belsley, E. Kuh, R. E. Welsch, Regression Diagnostics：Identifying Influential Data and Sources of Collinearity, Wiley, New York, (1980).

DOI: 10.1002/0471725153

[19] R. J. Freund, W. J. Wilson, Regression Analysis：Statistical Modeling of a Response Variable, Academic Press, San Diego, (1998).

[20] H. M. Park, 2003．Multicollinearity in Regression Models, Jeeshim and KUCC625. <http: /www. masil. org/documents/multicollinearity. pdf> (accessed in November，2009).

[21] X. Zhou, H. Lin , Moran's I, in: Shekhar, I.S., Xiong, H. (Eds. ), Encyclopedia of GIS, Springer, New York, 2008, PP. 725.

[22] C. Gangodagamage, X. Zhou, H. Lin, Autocorrelation, Spatial , in: Shekhar S，Xiong H (Eds. ), Encyclopedia of GIS, Springer, New York, 2008, PP. 32-37.

DOI: 10.1007/978-0-387-35973-1_83

[23] R. S. Bivand, E. J. Pebesma, V. Gomez-Rubio, Applied Spatial Data Analysis with R, Springer, New York, (2008).

[24] D. W. Michael, S. G. Kristian, Spatial Regression Models, SAGE Publications, Los Angeles, (2008).

[25] H. Zeng, P. Yang, Spatial Regression Analysis of Housing Prices in Nanjing, Journal of Southwest University (Natural Science Edition). 05(2012) 141-145.

[26] B. Tai, Relationship between the Urbanization of Population and the Per Capita Added Value of Tertiary Industry, Journal of Xi'an Jiaotong University (Social Sciences). 03(2007) 24-27.