LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Languages: English
Types: Article
Subjects:
Technology changes rapidly over years providing continuously more options for computer alternatives and making life easier for economic, intra-relation or any other transactions. However, the introduction of new technology “pushes” old Information and Communication Technology (ICT) products to non-use. E-waste is defined as the quantities of ICT products which are not in use and is bivariate function of the sold quantities, and the probability that specific computers quantity will be regarded as obsolete. In this paper, an e-waste generation model is presented, which is applied to the following regions: Western and Eastern Europe, Asia/Pacific, Japan/Australia/New Zealand, North and South America. Furthermore, cumulative computer sales were retrieved for selected countries of the regions so as to compute obsolete computer quantities. In order to provide robust results for the forecasted quantities, a selection of forecasting models, namely (i) Bass, (ii) Gompertz, (iii) Logistic, (iv) Trend model, (v) Level model, (vi) AutoRegressive Moving Average (ARMA), and (vii) Exponential Smoothing were applied, depicting for each country that model which would provide better results in terms of minimum error indices (Mean Absolute Error and Mean Square Error) for the in-sample estimation. As new technology does not diffuse in all the regions of the world with the same speed due to different socio-economic factors, the lifespan distribution, which provides the probability of a certain quantity of computers to be considered as obsolete, is not adequately modeled in the literature. The time horizon for the forecasted quantities is 2014-2030, while the results show a very sharp increase in the USA and United Kingdom, due to the fact of decreasing computer lifespan and increasing sales.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Afroz, R., Masud, M.M., Akhtar, R. and Duasa, J.B. (2013), Survey and analysis of public knowledge, awareness and willingness to pay in Kuala Lumpur, Malaysia - a case study on household WEEE management, Journal of Cleaner Production, Vol. 52, pp. 185-193.
    • Akaike, H. (1987), Factor analysis and AIC, Psychometrika, Vol. 52 No. 3, pp. 317-332.
    • Anderson, O.D. (1977), Time series analysis and forecasting: Another look at the Box-Jenkins approach, Journal of the Royal Statistical Society, Vol. 26 No. 4, pp. 285-303.
    • Araújo, M.G., Magrini, A., Mahler C.F. and Bilitewski, B. (2012), A model for estimation of potential generation of waste electrical and electronic equipment in Brazil, Waste Management, Vol. 32 No. 2, pp. 335-342.
    • Babbitt, C. W., Kahhat, R., Williams, E., & Babbitt, G. A. (2009). Evolution of product lifespan and implications for environmental assessment and management: a case study of personal computers in higher education. Environmental science & technology, 43(13), 5106-5112.
    • Bass, F.M. (1969), A new product growth for model consumer durables, Management Science, Vol. 15 No. 5, pp. 215-227.
    • Bass, F.M., Krishnan, T.V. and Jain, D.C. (1994), Why the Bass model fits without decision variables, Marketing Science, Vol. 13 No. 3, pp. 203-223.
    • Chi, X., Wang, M.Y.L. and Reuter, M.A. (2014), E-waste collection channels and household recycling behaviors in Taizhou of China, Journal of Cleaner Production, Vol. 80, pp. 87-95.
    • Chung, S.S., Lau, K.Y. and Zhang, C. (2011), Generation of and control measures for, e-waste in Hong Kong, Waste Management, Vol. 31 No. 3, pp. 544-554.
    • Delignette-Muller, M.L., Dutang, C., Pouillot, R. and Denis J.-B. (2013), fitdistrplus: Help to fit of a parametric distribution to non-censored or censored data (Version 1.0-1), available at: http://cran.rproject.org/web/packages/fitdistrplus/index.html
    • Dwivedy, M. and Mittal, R.K. (2010), Estimation of future outflows of e-waste in India, Waste Management, Vol. 30 No. 3, pp. 483-491.
    • European Commission-RoHS Directive (2003), available at: http://ec.europa.eu/environment/waste/rohs_eee/legis_en.htm
    • European Commission-WEEE Directive (2003), available at: http://ec.europa.eu/environment/waste/weee/legis_en.htm
    • Fisher, J.C. and Pry, R.H. (1971), A simple substitution model of technological change, Technological Forecasting and Social Change, Vol. 3, pp. 75-88.
    • Gößling-Reisemann, S., Knak, M. and Schulz, B. (2009), Product lifetimes and copper content of selected obsolete electric and electronic products, General Energy Research Department, University of Bremen, Germany, available at: http://ewasteguide.info/files/Goessling_2009_R09.pdf
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article