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The Diffusion Effect of MSW Recycling

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  • Yi-Tui Chen

    (Department of Health Care Management, National Taipei University of Nursing and Health Sciences, Taipei 108, Taiwan)

  • Fu-Chiang Yang

    (Department of Business Administration, HungKuo Delin University of Technology, New Taipei City 236, Taiwan)

  • Shih-Heng Yu

    (Department of Business Administration, National Central University, Taoyuan City 320, Taiwan)

Abstract

The purpose of this paper is to compare the recycling performance for some waste fractions selected including food waste, bulk waste, paper, metal products, plastics/rubber and glass products and then to develop some directions for the future improvements. The priority of each waste fraction for recycling is also analyzed by using an importance-performance analysis. Traditionally, the recycling rate that is calculated by the ratio of waste recycled to waste collected is used as an indicator to measure recycling performance. Due to a large variation among waste fractions in municipal solid waste (MSW), the recycling rate cannot reflect the actual recycling performance. The ceiling of recycling rate for each waste fraction estimated from the diffusion models is incorporated into a model to calculate recycling performance. The results show that (1) the diffusion effect exists significantly for the recycling of most recyclables but no evidence is found to support the diffusion effect for the recycling of food waste and bulk waste; (2) the recycling performance of waste metal products ranks the top, compared to waste paper, waste glass and other waste fractions; (3) furthermore, an importance-performance analysis (IPA) is employed to analyze the priority of recycling programs and thus this paper suggests that the recycling of food waste should be seen as the most priority item to recycle.

Suggested Citation

  • Yi-Tui Chen & Fu-Chiang Yang & Shih-Heng Yu, 2017. "The Diffusion Effect of MSW Recycling," Sustainability, MDPI, vol. 10(1), pages 1-12, December.
  • Handle: RePEc:gam:jsusta:v:10:y:2017:i:1:p:40-:d:124296
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    References listed on IDEAS

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    1. Turk, Tomaž & Trkman, Peter, 2012. "Bass model estimates for broadband diffusion in European countries," Technological Forecasting and Social Change, Elsevier, vol. 79(1), pages 85-96.
    2. Kogut, Bruce & Macpherson, J. Muir, 2011. "The mobility of economists and the diffusion of policy ideas: The influence of economics on national policies," Research Policy, Elsevier, vol. 40(10), pages 1307-1320.
    3. Rajkumar Venkatesan & Trichy V. Krishnan & V. Kumar, 2004. "Evolutionary Estimation of Macro-Level Diffusion Models Using Genetic Algorithms: An Alternative to Nonlinear Least Squares," Marketing Science, INFORMS, vol. 23(3), pages 451-464, August.
    4. Redmond, William H., 2004. "Interconnectivity in diffusion of innovations and market competition," Journal of Business Research, Elsevier, vol. 57(11), pages 1295-1302, November.
    5. Bohlin, Anders & Gruber, Harald & Koutroumpis, Pantelis, 2010. "Diffusion of new technology generations in mobile communications," Information Economics and Policy, Elsevier, vol. 22(1), pages 51-60, March.
    6. Fok, Dennis & Franses, Philip Hans, 2007. "Modeling the diffusion of scientific publications," Journal of Econometrics, Elsevier, vol. 139(2), pages 376-390, August.
    7. René Kemp, 1997. "Environmental Policy and Technical Change," Books, Edward Elgar Publishing, number 1187.
    8. Marco Ragazzi & Riccardo Catellani & Elena Cristina Rada & Vincenzo Torretta & Xavier Salazar-Valenzuela, 2014. "Management of Municipal Solid Waste in One of the Galapagos Islands," Sustainability, MDPI, vol. 6(12), pages 1-16, December.
    9. Dalla Valle, Alessandra & Furlan, Claudia, 2014. "Diffusion of nuclear energy in some developing countries," Technological Forecasting and Social Change, Elsevier, vol. 81(C), pages 143-153.
    10. Christophe Van den Bulte & Gary L. Lilien, 1997. "Bias and Systematic Change in the Parameter Estimates of Macro-Level Diffusion Models," Marketing Science, INFORMS, vol. 16(4), pages 338-353.
    11. Christophe Van den Bulte & Stefan Stremersch, 2004. "Social Contagion and Income Heterogeneity in New Product Diffusion: A Meta-Analytic Test," Marketing Science, INFORMS, vol. 23(4), pages 530-544, July.
    12. Peres, Renana & Muller, Eitan & Mahajan, Vijay, 2010. "Innovation diffusion and new product growth models: A critical review and research directions," International Journal of Research in Marketing, Elsevier, vol. 27(2), pages 91-106.
    13. van den Bulte, C. & Stremersch, S., 2003. "Contagion and heterogeneity in new product diffusion: An emperical test," ERIM Report Series Research in Management ERS-2003-077-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
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