Application of spatial and non-spatial data analysis in determination of the factors that impact municipal solid waste generation rates in Turkey
- Department of Environmental Engineering, Middle East Technical University, 06800 Ankara (Turkey)
- Department of Mining Engineering, Middle East Technical University, 06800 Ankara (Turkey)
Highlights: Black-Right-Pointing-Pointer Spatial autocorrelation exists in municipal solid waste generation rates for different provinces in Turkey. Black-Right-Pointing-Pointer Traditional non-spatial regression models may not provide sufficient information for better solid waste management. Black-Right-Pointing-Pointer Unemployment rate is a global variable that significantly impacts the waste generation rates in Turkey. Black-Right-Pointing-Pointer Significances of global parameters may diminish at local scale for some provinces. Black-Right-Pointing-Pointer GWR model can be used to create clusters of cities for solid waste management. - Abstract: In studies focusing on the factors that impact solid waste generation habits and rates, the potential spatial dependency in solid waste generation data is not considered in relating the waste generation rates to its determinants. In this study, spatial dependency is taken into account in determination of the significant socio-economic and climatic factors that may be of importance for the municipal solid waste (MSW) generation rates in different provinces of Turkey. Simultaneous spatial autoregression (SAR) and geographically weighted regression (GWR) models are used for the spatial data analyses. Similar to ordinary least squares regression (OLSR), regression coefficients are global in SAR model. In other words, the effect of a given independent variable on a dependent variable is valid for the whole country. Unlike OLSR or SAR, GWR reveals the local impact of a given factor (or independent variable) on the waste generation rates of different provinces. Results show that provinces within closer neighborhoods have similar MSW generation rates. On the other hand, this spatial autocorrelation is not very high for the exploratory variables considered in the study. OLSR and SAR models have similar regression coefficients. GWR is useful to indicate the local determinants of MSW generation rates. GWR model can be utilized to plan waste management activities at local scale including waste minimization, collection, treatment, and disposal. At global scale, the MSW generation rates in Turkey are significantly related to unemployment rate and asphalt-paved roads ratio. Yet, significances of these variables may diminish at local scale for some provinces. At local scale, different factors may be important in affecting MSW generation rates.
- OSTI ID:
- 21612941
- Journal Information:
- Waste Management, Vol. 32, Issue 3; Other Information: DOI: 10.1016/j.wasman.2011.10.017; PII: S0956-053X(11)00476-4; Copyright (c) 2011 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.; Country of input: International Atomic Energy Agency (IAEA); ISSN 0956-053X
- Country of Publication:
- United States
- Language:
- English
Similar Records
Co-gasification of municipal solid waste and material recovery in a large-scale gasification and melting system
Waste management under multiple complexities: Inexact piecewise-linearization-based fuzzy flexible programming
Related Subjects
12 MANAGEMENT OF RADIOACTIVE WASTES, AND NON-RADIOACTIVE WASTES FROM NUCLEAR FACILITIES
ASPHALTS
DATA ANALYSIS
EMPLOYMENT
LEAST SQUARE FIT
MINIMIZATION
SOLID WASTES
TURKEY
URBAN AREAS
WASTE MANAGEMENT
ASIA
BITUMENS
DEVELOPING COUNTRIES
MANAGEMENT
MATHEMATICAL SOLUTIONS
MAXIMUM-LIKELIHOOD FIT
MIDDLE EAST
NUMERICAL SOLUTION
OPTIMIZATION
ORGANIC COMPOUNDS
OTHER ORGANIC COMPOUNDS
TAR
WASTES