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Dynamic stock modelling: A method for the identification and estimation of future waste streams and emissions based on past production and product stock characteristics

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  • Elshkaki, Ayman
  • van der Voet, Ester
  • Timmermans, Veerle
  • Van Holderbeke, Mirja

Abstract

Large quantities of products, materials and substances have accumulated in society. This article investigates the dynamic behaviour of these societal reservoirs or stocks in order to explore future emissions and waste streams. We argue that the stock dynamics are mainly determined by its inflow and outflow characteristics. The stock’s inflow is determined by socio-economic factors, which can be quantified using regression analysis. Two processes determine the stock’s outflow: leaching and delay. Leaching occurs during use and can be modelled as a function of the stock’s size. Delay is related to the discarding of products after use and can be modelled as a delayed inflow distributed over time. This approach is illustrated by the case of lead as applied in cathode ray tubes in the European Union (EU). By applying this model to other lead applications and combining the results, the dynamic behaviour of the total lead stock in society can be described.

Suggested Citation

  • Elshkaki, Ayman & van der Voet, Ester & Timmermans, Veerle & Van Holderbeke, Mirja, 2005. "Dynamic stock modelling: A method for the identification and estimation of future waste streams and emissions based on past production and product stock characteristics," Energy, Elsevier, vol. 30(8), pages 1353-1363.
  • Handle: RePEc:eee:energy:v:30:y:2005:i:8:p:1353-1363
    DOI: 10.1016/j.energy.2004.02.019
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    References listed on IDEAS

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    1. Kleijn, Rene & Huele, Ruben & van der Voet, Ester, 2000. "Dynamic substance flow analysis: the delaying mechanism of stocks, with the case of PVC in Sweden," Ecological Economics, Elsevier, vol. 32(2), pages 241-254, February.
    2. Gijbels, Irène & Rousson, Valentin, 2001. "A nonparametric least-squares test for checking a polynomial relationship," Statistics & Probability Letters, Elsevier, vol. 51(3), pages 253-261, February.
    3. Moore, David J. & Tilton, John E. & Shields, Deborah J., 1996. "Economic growth and the demand for construction materials," Resources Policy, Elsevier, vol. 22(3), pages 197-205, September.
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    Cited by:

    1. Wang, Minxi & Chen, Wu & Zhou, Yang & Li, Xin, 2017. "Assessment of potential copper scrap in China and policy recommendation," Resources Policy, Elsevier, vol. 52(C), pages 235-244.
    2. Kleijn, René & van der Voet, Ester & Udo de Haes, Helias A., 2008. "The need for combining IEA and IE tools: The potential effects of a global ban on PVC on climate change," Ecological Economics, Elsevier, vol. 65(2), pages 266-281, April.
    3. Shigetomi, Yosuke & Nansai, Keisuke & Kagawa, Shigemi & Kondo, Yasushi & Tohno, Susumu, 2017. "Economic and social determinants of global physical flows of critical metals," Resources Policy, Elsevier, vol. 52(C), pages 107-113.
    4. Tsiliyannis, Christos Aristeides, 2018. "Markov chain modeling and forecasting of product returns in remanufacturing based on stock mean-age," European Journal of Operational Research, Elsevier, vol. 271(2), pages 474-489.
    5. Taulo, J.L. & Sebitosi, A.B., 2016. "Material and energy flow analysis of the Malawian tea industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1337-1350.
    6. Liu, Jian & An, Rui & Xiao, Rongge & Yang, Yongwei & Wang, Gaoshang & Wang, Qian, 2017. "Implications from substance flow analysis, supply chain and supplier’ risk evaluation in iron and steel industry in Mainland China," Resources Policy, Elsevier, vol. 51(C), pages 272-282.
    7. Wu, Jiechen & Franzén, Daniel & Malmström, Maria E., 2016. "Nutrient flows following changes in source strengths, land use and climate in an urban catchment, Råcksta Träsk in Stockholm, Sweden," Ecological Modelling, Elsevier, vol. 338(C), pages 69-77.

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