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Application of rough set theory to establish the amount of waste in households in rural areas

Autorzy
Identyfikatory
Warianty tytułu
PL
Zastosowanie teorii zbiorów przybliżonych do oceny ilości odpadów z gospodarstw domowych na wsi
Języki publikacji
EN
Abstrakty
EN
The method based on rough set theory (RST) was used in the study to establish the rate of mass accumulation of waste in households in rural areas, which are characterised by different economic types, in case of which traditional statistical analyses are usually hardy reliable. The following indicators available in the General Statistical Office’s statistics were used in the analysis: population density, income level, main source of income, economic type of the municipality, area of agricultural land, age of the buildings and participation of gaseous fuels in meeting heat demands. The method shown should not be considered as a competition for statistical methods, but it could complement them, especially in cases when there are few objects to analyse, the more so as it proves useful in cases where input data are general, imprecise and uncertain. As has been shown in the study, with such data and a small number of objects, the relative error of estimation was 13% on average.
Rocznik
Strony
311--325
Opis fizyczny
Bibliogr. 29 poz., tab., rys.
Twórcy
autor
  • Faculty of Production and Power Engineering, University of Agriculture in Krakow, ul. Balicka 116B, 30-149 Kraków, Poland, phone +48 12 662 46 47
autor
  • Faculty of Production and Power Engineering, University of Agriculture in Krakow, ul. Balicka 116B, 30-149 Kraków, Poland, phone +48 12 662 46 47
autor
  • Faculty of Production and Power Engineering, University of Agriculture in Krakow, ul. Balicka 116B, 30-149 Kraków, Poland, phone +48 12 662 46 47
Bibliografia
  • [1] Directive 2008/98/EC of the European Parliament and of the Council of 19 November 2008 on waste and repealing certain Directives. http://eur-lex.europa.eu/eli/dir/2008/98/oj.
  • [2] European Union Statistics. Eurostat. Waste statistics. 2016. http://ec.europa.eu/eurostat/statistics-explained/index.php/waste_statistics.
  • [3] Ministerstwo Ochrony Środowiska. Sprawozdanie z realizacji krajowego sytemu gospodarki odpadami - plan 2010. (The Ministry of Environmental Protection. Report on the implementation of the national waste management plan 2010). http://www.mos.gov.pl/g2/big/2011_11/935528872b22be4e27c786bc41a3c5fc.pdf.
  • [4] Główny Urząd Statystyczny. Bank Danych Lokalnych. (Central Statistical Office. Local Data Bank). http://stat.gov.pl/bdl/app/strona.html?p_name=indeks.
  • [5] KPGO Krajowy plan gospodarki odpadami 2022. (National plan of the waste disposal 2022). 2016. https://bip.mos.gov.pl/fileadmin/user_upload/bip/strategie_plany_programy/DGO/Krajowy_plan_gospodarki_odpadami_2022_____M.P._poz._784_.pdf.
  • [6] Boer E, Jędrczak A, Kowalski Z, Kulczycka J, Szpadt R. A review of municipal solid waste composition and quantities in Poland. Waste Manage. 2010;30:369-377. DOI: 10.1016/j.wasman.2009.09.018.
  • [7] Bach H, Mild A, Natter M, Weber A. Combining socio-demographic and logistic factors to explain the generation and collection of waste paper. Resources Conserv Recycl. 2004;41(1):65-73. DOI: 10.1016/j.resconrec.2003.08.004.
  • [8] Beigl P, Lebersorger S, Salhofer S. Modelling municipal solid waste generation: a review. Waste Manage. 2008; 28(1):200-214. DOI: 10.1016/j.wasman.2006.12.011.
  • [9] Hage O, Soderholm P. An econometric analysis of regional differences in household waste collection: the case of plastic packaging waste in Sweden. Waste Manage. 2008;28(10):1720-1731. DOI: 10.1016/j.wasman.2007.08.022.
  • [10] Hockett D, Lober DJ, Pilgrim K. Determinants of per capita municipal solid waste generation in the Southeastern United States. J Environ Manage. 1995;45(3):205-217. DOI: 10.1006/jema.1995.0069.
  • [11] Lebersorger S, Beigl P. Municipal solid waste generation in municipalities: Quantifying impacts of household structure, commercial waste and domestic fuel. Waste Manage. 2011;31:1907-1915. DOI: 10.1016/j.wasman.2011.05.016.
  • [12] Miller I, Lauzon A, Wattle B, Ritter M, Hood J. Determinants of municipal solid waste generation and recycling in western New York communities. J Solid Waste Technol Manage. 2009;35(4):209-236. DOI: 10.5276/JSWTM.2009.209.
  • [13] Passarini F, Vassura I, Monti F, Morselli L, Villani B. Indicators of waste management efficiency related to different territorial conditions. Waste Manage. 2011;31(4):785-792. DOI: 10.1016/j.wasman.2010.11.021.
  • [14] Poll A.J. Variations in the composition of household collected waste. AEAT/ENV/R/1839, AEA Technology, Harwell, UK. 2004.
  • [15] Purcell M, Magette WL. Prediction of household and commercial BMW generation according to socio-economic and other factors for the Dublin region. Waste Manage. 2009;29(4):1237-1250. DOI: 10.1016/j.wasman.2008.10.011.
  • [16] Daskalopoulos E, Badr O, Probert SD. Municipal solid waste: a prediction methodology for the generation rate and composition in the European Union countries and the USA. Resources Conserv Recycl. 1998;24(1):155-166. DOI: 10.1016/S0921-3449(98)00032-9.
  • [17] Mazzanti M, Zoboli R. Waste generation, waste disposal and policy effectiveness: evidence on decoupling from the European Union. Resources, Conserv Recycl. 2008;52(10):1221-1234. DOI: 10.1016/j.resconrec.2008.07.003.
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  • [19] Keser S, Duzgun S, Aksoy A. Application of spatial and non-spatial data analysis in determination of the factors that impact municipal solid waste generation rates in Turkey. Waste Manage. 2012;32:359-371. DOI: 10.1016/j.wasman.2011.10.017.
  • [20] Armstrong JS. Principles of Forecasting: A Handbook for Researchers and Practitioners. Boston: Kluwer Academic Publishers; 2001. https://marketing.wharton.upenn.edu/files/?whdmsaction=public:main.file&fileID=794.
  • [21] Pawlak Z. Rough Sets. Theoretical Aspects of Reasoning about Data. Dordrecht: Kluwer Academic Press; 1991.
  • [22] Nutech Solution - Science for Business. 2005. http://www.nutechsolutions.com.pl/.
  • [23] Nguyen HS. Tolerance Rough Set Model and Its Applications in Web Intelligence. IEEE/WIC/ACM Int Conf Web Intelligence and Intelligent Agent Technol. Workshop Proceedings. IEEE Computer Society. 2013:237-244. DOI: 10.1109/WI-IAT.2013.189.
  • [24] Pawlak Z. Rough set approach to knowledge-based decision support. Europ J Operat Res. 1997;99(1):48-57. DOI: 10.1016/S0377-2217(96)00382-7.
  • [25] Polkowski L, Skowron A. Rough mereological calculi of granules: A rough set approach to computation. Computat Intelligence. 2001;17(3):472-492. DOI: 10.1111/0824-7935.00159.
  • [26] Renigier-Biłozor M, Wiśniewski R. The effectiveness of real estate market versus efficiency of its participants. Europ Spatial Res Policy. 2012;19(1):95-110. DOI: 10.2478/v10105-012-0008-5.
  • [27] d’Amato M. Rough Set Theory as Automated Valuation Methodology: The Whole Story. Int Seminar Advancess in Mass Appraisal in Deft. 2006. https://scholar.google.pl/scholar?q=d%E2%80%99Amato+M.+Rough+Set+Theory+as+Automated+Valuation+Methodology:+The+Whole+Story.&hl=pl&as_sdt=0&as_vis=1&oi=scholart&sa=X&ved=0ahUKEwjByNqFiYnNAhVI2ywKHQ4LDqIQgQMIGjAA.
  • [28] Renigier-Biłozor M. Zastosowanie teorii zbiorów przybliżonych do masowej wyceny nieruchomości na małych rynkach (Application of rough set theory for mass valuation of real estate in small markets). Acta Scientarum Polonorum Administratio Locorum. 2008;7(3):35-51. http://wydawnictwo.uwm.edu.pl/artykul/14/czytelnia.html.
  • [29] Bański J. Współczesne typologie obszarów wiejskich w Polsce - przegląd podejść metodologicznych (Contemporary typologies of rural areas in Poland - an overview of methodological approaches). Przegląd Geograficzny. 2014;84(4):441-470. http://www.rcin.org.pl/Content/51257/WA51_70537_r2014-t86-z4_Przeg-Geogr-Banski.pdf.
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f45cc145-0d07-4e4c-b656-35d34da27117
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