Municipal waste in Poland: analysis of the spatial dimensions of determinants using geographically weighted regression

Authors

  • Elżbieta Antczak University of Lodz, Faculty of Economics and Sociology, Department of Spatial Econometrics, ul. POW 3/5, 90-255 Łódź, Poland

DOI:

https://doi.org/10.18778/1231-1952.26.2.09

Keywords:

municipal waste, Polish districts, regional heterogeneity and spatial interactions, socio-economic factors, geographically weighted regression

Abstract

This article provides a quantification of the territorially varied relation between socio-economic factors and the amount of municipal waste in Polish districts. For this purpose, eight causes were identified: revenue budgets, the number and area of uncontrolled dumping sites, population density, the share of working-age population, average gross monthly wages, registrations for permanent residence, and the number of tourists accommodated. The preliminary data analysis indicated that to understand waste generation in Poland at the local level it is necessary to consider regional specificity and spatial interactions. To increase the explained variability of phenomena, and emphasise local differences in the amount of waste, geographically weighted regression was applied.

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References

ADVISORY GROUP TEST HUMAN RESOURCES (2014), Labour Market and Human Resources in Kraków, Business in Małopolska. Orange Report, http://businessinmalopolska.pl/public/upload/fck/orange_raport_7.pdf [accessed on: 10.10.2018].
Google Scholar

AKAIKE, H. (1973), ‘Information theory and the maximum likelihood principle’ [in]: PETROV, B.N. and CSAKI, F. (eds.), Selected Papers of Hirotugu Akaike. Springer Series in Statistics (Perspectives in Statistics). New York: Springer, pp. 199–213.
Google Scholar

ANDY, M. (2005), The ESRI Guide to GIS Analysis. Volume 2: Spatial Measurements and Statistics and Zeroing In: Geographic Information Systems at Work in the Community. Boston: ESRI Press.
Google Scholar

ANSELIN, L. (2010), ‘Thirty years of spatial econometrics’, Papers in Regional Science, 89, pp. 3–25.
Google Scholar DOI: https://doi.org/10.1111/j.1435-5957.2010.00279.x

ANTCZAK, E. (2014), ‘Economic Development and Transfrontier Shipments of Waste in Poland – Spatio-Temporal Analysis’, Comparative Economic Research, 17 (4), pp. 6–21.
Google Scholar DOI: https://doi.org/10.2478/cer-2014-0029

BACH, H., MILD, A., NATTER, M. and WEBER, M. (2004), ‘Combining socio-demographic and logistic factors to explain the generation and collection of waste paper’, Resources, Conservation and Recycling, 41 (1), pp. 65–73.
Google Scholar DOI: https://doi.org/10.1016/j.resconrec.2003.08.004

BEIGL, P., LEBERSORGER, S. and SALHOFER, S. (2008), ‘Modelling municipal solid waste generation: A review’, Waste Management, 28 (1), pp. 200–214.
Google Scholar DOI: https://doi.org/10.1016/j.wasman.2006.12.011

BEIGL, P., SALHAFER, S., WASSERMAN, G., MAĆKÓW, I., SEBASTIAN, M. and SZPADT, R. (2004), ‘Forecasting municipal solid waste generation in major European cities’, International Congress: Complexity and Integrated Resources Management, Osnabrück, Germany. https://pdfs.semanticscholar.org/6d33/e9d43a906be1dfe52f97b1fea27375183a78.pdf [accessed on: 10.05.2019].
Google Scholar

BOER DEN, E., JĘDRCZAK, A., KOWALSKI, Z., KULCZYCKA, J. and SZPADT, R. (2010), ‘A review of municipal solid waste composition and quantities in Poland’, Waste Management, 30 (3), pp. 369–377.
Google Scholar DOI: https://doi.org/10.1016/j.wasman.2009.09.018

BOWMAN, A. (1984), ‘An Alternative Method of Cross-Validation for the Smoothing of Density Estimate’, Biometrika, 71, pp. 353–360.
Google Scholar DOI: https://doi.org/10.1093/biomet/71.2.353

BRUNSDON, C., FOTHERINGHAM, A.S. and CHARLTON, M.E. (1996), ‘GWR: A Method for Exploring Spatial Nonstationarity’, Geographical Analysis, 28 (4), pp. 281–298.
Google Scholar DOI: https://doi.org/10.1111/j.1538-4632.1996.tb00936.x

CHARLTON, M. and FOTHERINGHAM, A.S. (2009), Geographically weighted regression. Maynooth: National Centre for Geocomputation.
Google Scholar

CHEBA, K. (2014), ‘Methods of Forecasting Changes of Municipal Waste Production in case of Cities’, Acta Universitatis Lodzensis. Folia Oeconomica, 3 (302), pp. 223–229.
Google Scholar

CLEVELAND, W.S. (1979), ‘Robust Locally Weighted Regression and Smoothing Scatterplots’, Journal of American Statistical Association, 74 (368), pp. 829–836.
Google Scholar DOI: https://doi.org/10.1080/01621459.1979.10481038

COUNCIL OF MINISTRY (2016), National Waste Management Plan 2022, Annex to the Resolution No 88 of the Council of Ministers of 1 July 2016 (item 784), Warsaw. https://www.mos.gov.pl/komunikaty/szczegoly/news/krajowy-plan-gospodarki-odpadami-2022/ [accessed on: 12.11.2018].
Google Scholar

CYRANKA, M., JURCZYK, M. and PAJĄK, T. (2016), ‘Municipal Waste-to-Energy plants in Poland–current projects’. E3S Web Conf. Volume 10, 1st International Conference on the Sustainable Energy and Environment Development (SEED 2016).
Google Scholar DOI: https://doi.org/10.1051/e3sconf/20161000070

ERTUR, C. and LE GALLO, J. (2008), ‘Regional Growth and Convergence: Heterogenous reaction versus interaction in spatial econometric approaches’, LEO Working Papers/DR LEO 1423, Orleans Economics Laboratory, Orleans: University of Orleans.
Google Scholar

EUROPEAN COMISSION (2008), Directive 2008/98/EC. http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2008:312:0003:0030:en:PDF [accessed on: 12.10.2018].
Google Scholar

EUROPEAN COMISSION (2018), Eastern Poland more attractive for inhabitants and investors. http://ec.europa.eu/regional_policy/en/newsroom/news/2018/03/23-03-2018-eastern-polandmore-attractive-for-inhabitants-and-investors [accessed on: 12.10.2018].
Google Scholar

FINGLETON, B. (1999), ‘Spurious Spatial Regression: Some Monte Carlo Results with a Spatial Unit Root and Spatial Cointegration’, Journal of Regional Science, 39 (1), pp. 1–19.
Google Scholar DOI: https://doi.org/10.1111/1467-9787.00121

FOTHERINGHAM, A.S., BRUNSDON, C. and CHARLTON, M.E. (2000), Quantitative geography: perspectives on spatial data analysis. London: Sage.
Google Scholar

FOTHERINGHAM, A.S., BRUNSDON, C. and CHARLTON, M.E. (2002), Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Chichester: Wiley.
Google Scholar

GENEROWICZ, A., KOWALSKI, Z. and KULCZYCKA, J. (2011), ‘Planning of Waste Management Systems in Urban Area Using Multi-criteria Analysis’, Journal of Environment Protection, 2 (6), pp. 736–743.
Google Scholar DOI: https://doi.org/10.4236/jep.2011.26085

GETIS, A. and ALDSTADT, J. (2004), ‘Constructing the Spatial Weights Matrix Using a Local Statistic’, Geographical Analysis, 36 (2), pp. 90–104.
Google Scholar DOI: https://doi.org/10.1111/j.1538-4632.2004.tb01127.x

GOLLINI, I., LU, B., CHARLTON, M., BRUNSDON, C. and HARRIS, P. (2015), ‘GWR model: An R Package for Exploring Spatial Heterogeneity Using Geographically Weighted Models’, Journal of Statistical Software, 63 (17), pp. 1–50.
Google Scholar DOI: https://doi.org/10.18637/jss.v063.i17

HAGE, O. and SÖDERHOLM, P. (2008), ‘An econometric analysis of regional differences in household waste collection: the case of plastic packaging waste in Sweden’, Waste Management, 28 (10), pp. 1720–1731.
Google Scholar DOI: https://doi.org/10.1016/j.wasman.2007.08.022

HOCKETT, D., LOBER, D.J. and PILGRIM, K. (1995), ‘Determinants of Per Capita Municipal Solid Waste Generation in the Southeastern United States’, Journal of Environment Management, 45 (3), pp. 205–217.
Google Scholar DOI: https://doi.org/10.1006/jema.1995.0069

HUNG, M.L., MA, H.W. and YANG, W.F. (2007), ‘A novel sustainable decision making model for municipal solid waste management’, Waste Management, 27 (2), pp. 209–219.
Google Scholar DOI: https://doi.org/10.1016/j.wasman.2006.01.008

IOANNOU, T., LASARIDI, K. and KALOGIROU, S. (2010), ‘Spatial analysis of the recyclable municipal solid waste collection’. http://gisc.gr/docs/sk_papers/2_5_Ioannou_Lasaridi_Kalogirou_2010.pdf [accessed on: 5.10.2018].
Google Scholar

ISMAILIA, A.B., MUHAMMED, I., BIBI, U.M. and HUSAIN, M.A. (2015), ‘Modelling Municipal Solid Waste Generation Using Geographically Weighted Regression: A Case Study of Nigeria’, Journal of Environmental Science, 4 (8), pp. 98–108.
Google Scholar

JALIGOT, R. and CHENAL, J. (2018), ‘Decoupling municipal solid waste generation and economic growth in the canton of Vaud, Switzerland, Resources’, Conservation and Recycling, 130, pp. 260–266.
Google Scholar DOI: https://doi.org/10.1016/j.resconrec.2017.12.014

KESER, S. (2012), ‘Investigation of the Spatial Relationship of Municipal Solid Waste Generation in Turkey with Socio-Economic, Demographic and Climatic Factors’. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.632.5116&rep=rep1&type=pdf [accessed on: 5.10.2018].
Google Scholar

KESER, S., DUZGUN, S. and AKSOY, A. (2010), ‘Application of spatial and non-spatial data analysis in determination of the factors that impact municipal solid waste generation rates in Turkey’, Waste Management, 32, pp. 359–371.
Google Scholar DOI: https://doi.org/10.1016/j.wasman.2011.10.017

KHAN, D., KUMAR, A. and SAMADDER, S.R. (2016), ‘Impact of socioeconomic status on municipal solid waste generation rate’, Waste Management, 49, pp. 15–25.
Google Scholar DOI: https://doi.org/10.1016/j.wasman.2016.01.019

KLOJZY-KARCZMARCZYK, B. and MAKOUDI, S. (2017), ‘Analysis of municipal waste generation rate in Poland compared to selected European countries’. www.e3s-conferences.org/artic-les/e3sconf/abs/2017/07/e3sconf_eems2017_02025/e3sconf_eems2017_02025.html [accessed on: 5.10.2018].
Google Scholar DOI: https://doi.org/10.1051/e3sconf/20171902025

KOŁSUT, B. (2016), ‘Inter-Municipal Cooperation in Waste Management: The Case of Poland’, Quaestiones Geographicae, 35 (2), pp. 91–104.
Google Scholar DOI: https://doi.org/10.1515/quageo-2016-0018

KOSZEWSKA, M. (2016), ‘Analysis of the legal framework in the sustainability area across v4 countries’, Polish report prepared in the framework of the project Prospects of the Visegrad cooperation in promoting a sustainable consumption and production model. http://www.k48.p.lodz.pl/ecomarket/plik---reportlabels-polandgot_(V4EcoMarket--p54).pdf [accessed on: 20.07.2018].
Google Scholar

KUKUŁA, K. (2016), ‘Municipal Waste Management in Poland in the Light of Multi Dimensional Comparative Analysis’, Acta Scientiarum Polonorum Oeconomia, 15 (4), pp. 93–103.
Google Scholar DOI: https://doi.org/10.22630/ASPE.2017.16.4.48

LEUNG, Y., MEI, C.L. and ZHANG, W.X. (2000), ‘Statistical Tests for Spatial Nonstationarity Based on the Geographically Weighted Regression Model’, Environment and Planning A: Economy and Space, 32 (1), pp. 9–32.
Google Scholar DOI: https://doi.org/10.1068/a3162

LOADER, C.R. (1999), ‘Bandwidth selection: classical or plug-in?’, The Annals of Statistics, 27 (2), pp. 415–438.
Google Scholar DOI: https://doi.org/10.1214/aos/1018031201

LU, B., YANG, W., GE, Y. and HARRIS, P. (2018), ‘Improvements to the calibration of a geographically weighted regression with parameter-specific distance metrics and bandwidths’, Computers, Environment and Urban Systems, 71, pp. 41–57.
Google Scholar DOI: https://doi.org/10.1016/j.compenvurbsys.2018.03.012

MATTHEWS, S.A. and YANG, T.C. (2016), ‘Mapping the results of local statistics: Using geographically weighted regression’, Demographic Research, 26, pp. 151–166.
Google Scholar DOI: https://doi.org/10.4054/DemRes.2012.26.6

MINISTRY OF THE ENVIRONMENT (2017). https://www.mos.gov.pl/fileadmin/user_upload/mos/Aktualnosci/2017/grudzien_2017/Raport_z_badania_dot._gospodarki_odpadami_2017_r._.pdf [accessed on: 20.10.2018].
Google Scholar

MORAN, P.A.P. (1950), ‘Notes on Continuous Stochastic Phenomena’, Biometrika, 37(1), pp. 17–23.
Google Scholar DOI: https://doi.org/10.1093/biomet/37.1-2.17

THE ORGANISATION FOR ECONOMIC CO-OPERATION AND DEVELOPMENT (2015), ‘Environmental Performance Reviews, Poland Highlights’. https://www.unece.org/env/epr.html [accessed on: 12.10.2015].
Google Scholar

PASIECZNIK I., BANASZKIEWICZ, K. and SYSKA, Ł. (2017), ‘Local community e-waste awareness and behavior. Polish case study’, Environmental Protection Engineering, 43 (3), pp. 287–303.
Google Scholar DOI: https://doi.org/10.37190/epe170320

RYBOVA, K. (2019), ‘Do Sociodemographic Characteristics in Waste Management Matter? Case Study of Recyclable Generation in the Czech Republic’, Sustainability, 11 (2030), pp. 1–15.
Google Scholar DOI: https://doi.org/10.3390/su11072030

RYBOVA, K, BURCIN, B. and SLAVIK, J. (2018), ‘Spatial and non-spatial analysis of socio-demographic aspects influencing municipal solid waste generation in the Czech Republic’, Detritus, 1, pp. 3–7.
Google Scholar

SCHULTZ, P., OSKAMP, S. and MAINIERI, T. (1995), ‘Who recycles and when? A review of personal and situational factors’, Journal of Environmental Psychology, 5 (2), pp. 105–121.
Google Scholar DOI: https://doi.org/10.1016/0272-4944(95)90019-5

STERNER, T. and BARTELINGS, H. (1999), ‘Household waste management in a Swedish municipality: determinants of waste disposal, recycling and composting’, Environmental and Resource Economics, 13 (4), pp. 473–491.
Google Scholar DOI: https://doi.org/10.1023/A:1008214417099

TAŁAŁAJ, I.A. (2011), ‘The influence of chosen socio-economical factors on change of waste quantity in Podlaskie province’, Inżynieria Ekologiczna, 25, pp. 146–156.
Google Scholar

UNITED NATIONS ENVIRONMENT PROGRAMME (2011), ‘Decoupling natural resource use and environmental impacts from economic growth’. http://hdl.handle.net/20.500.11822/9816 [accessed on: 20.07.2018].
Google Scholar

ZEMANEK, J, WOŹNIAK, A. and MALINOWSKI, M. (2011), ‘The role and place of solid waste transfer station in the waste management system’, Infrastruktura i Ekologia Terenów Wiejskich, 11, pp. 5–13.
Google Scholar

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Published

2019-12-31

How to Cite

Antczak, E. (2019). Municipal waste in Poland: analysis of the spatial dimensions of determinants using geographically weighted regression. European Spatial Research and Policy, 26(2), 177–197. https://doi.org/10.18778/1231-1952.26.2.09

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