Skip to main content
Springer Nature Link
Log in

Choice Modelling and Forecasting Demand for Alternative-Fuel Tractors

  • Conference paper
Advances in Data Mining. Applications and Theoretical Aspects (ICDM 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8557))

Included in the following conference series:

  • 5144 Accesses

Abstract

This paper presents a study focused on potential demand for agricultural multi-functional electric tractor. In this context, the willingness-to-pay is investigated in order to establish the potential diffusion of an electrical solar tractor, by considering different levels of key attributes related to environmental, technical and economical characteristics of different version of alternative fuel tractors. The study is carried out through a choice-experiment and the application of multinomial discrete choice models, by considering heteroscedasticity of the respondent and heterogeneity across alternatives.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Alvarez-Daziano, R., Bolduc, D.: Canadian consumers’ perceptual and attitudinal responses towards green automobile technologies: an application of hybrid choice models. In: 2009 EAERE-FEEM-VIU European Summer School in Resources and Environmental Economics: Economics, Transport and Environment, Venice International University (2009)

    Google Scholar 

  2. Beggs, S., Cardell, S., Hausman, J.: Assessing the potential demand for electric cars. Journal of Econometrics 16, 1–19 (1981)

    Google Scholar 

  3. Bernal-Agustin, J.L., Dufo-Lopez, R.: Economical and environmental analysis of grid connected photovoltaic systems in Spain. Renew. Energ. 31, 1107–1128 (2006)

    Article  Google Scholar 

  4. Berni, R., Lombardi, G.V.: Agricultural multi-functional vehicles and the environment: choice experiments and random utility models for investigating renewable energies. Statistica Applicata-Italian Journal of Applied Statistics 22, 363–374 (2012)

    Google Scholar 

  5. Berni, R., Rivello, R.: Choices and conjoint analysis: critical aspects and recent developments. In: Bini, M., Monari, P., Piccolo, D., Salmaso, L. (eds.) Statistical Methods and Models for the Evaluation of Educational Services and Products’ Quality. Contribution to Statistics, pp. 119–137. Springer (2009)

    Google Scholar 

  6. Bhat, C.R.: A heteroschedstic extreme value model of intercity travel mode choice. Transport. Res. B-Meth. 29, 471–483 (1995)

    Article  Google Scholar 

  7. Bliemer, M., Rose, J., Hess, S.: Approximation of bayesian efficiency in experimental choice designs. Journal of Choice Modelling 1, 98–127 (2008)

    Article  Google Scholar 

  8. Boxall, P.C., Adamowicz, W.L.: Understanding heterogeneous preferences in random utility models: a latent class approach. Environmental and Resource Economics 23, 421–446 (2002)

    Article  Google Scholar 

  9. Brownstone, D., Bunch, D.S., Golob, T.F., Ren, W.: A transactions choice model for forecasting demand for alternative-fuel vehicles. Research in Transportation Economics 4, 87–129 (1996)

    Article  Google Scholar 

  10. Burgess, L., Street, D.J.: Optimal designs for choice experiments with asymmetric attributes. Journal of Statistical Planning and Inference 134, 288–301 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  11. Dagsvik, J.K., Wennemo, T., Wetterwald, D.G., Aaberge, R.: Potential demand for alternative fuel vehicles. Transportation Research Part B 36, 361–384 (2002)

    Article  Google Scholar 

  12. Daziano, R.A., Bolduc, D.: Incorporating pro-environmental preferences towards green automobile technologies through a Bayesian hybrid choice model. Transportmetrica A: Transport Science 9, 74–106 (2013)

    Google Scholar 

  13. Greene, W.H., Hensher, D.A.: A latent class model for discrete choice analysis: contrasts with mixed logit. Transport. Res. B-Meth. 37, 681–698 (2003)

    Article  Google Scholar 

  14. Haab, T.C., Hicks, R.L.: Accounting for choice set endogeneity in random utility models of recreation demand. Journal of Environmental Economics and Management 34, 127–147 (1997)

    Article  MATH  Google Scholar 

  15. Hensher, D.A., Greene, W.H.: The Mixed Logit model: the state of practice. Transportation 30, 133–176 (2003)

    Article  Google Scholar 

  16. Hole, A.: A discrete choice model with endogeneous attribute attendance. Economics Letters 110, 203–205 (2011)

    Article  MathSciNet  Google Scholar 

  17. Hynes, S., Hanley, N., Scarpa, R.: Effects on welfare measures of alternative means of accounting for preference heterogeneity in recreational demand models. American Journal of Agricultural Economics 901, 1011–1027 (2008)

    Article  Google Scholar 

  18. Mannering, F.L., Train, K.: Recent directions in automobile demand modeling. Transportation Research Part B 19, 265–274 (1985)

    Article  Google Scholar 

  19. Mousazadeh, H., Keyhani, A., Mobli, H., Bardi, U., Lombardi, G.V., El-Asmar, T.: Environmental assessment of RAMseS multipurpose electric vehicle compared to a conventional combustion engine vehicle. J. Clean. Prod. 17, 781–790 (2009)

    Article  Google Scholar 

  20. Mousazadeh, H., Keyhani, A., Mobli, H., Bardi, U., Lombardi, G.V., El-Asmar, T.: Technical and economical assessment of a multipurpose electric vehicle for farmers. J. Clean. Prod. 17, 1556–1562 (2009)

    Article  Google Scholar 

  21. McFadden, D., Train, K.: Mixed MNL for discrete response. J. Appl. Econom. 15, 447–450 (2000)

    Article  Google Scholar 

  22. OECD, Climate Change and Agriculture: Impacts, Adaptation and Mitigation, Paris (2010)

    Google Scholar 

  23. Sandor, Z., Wedel, M.: Profile construction in experimental choice designs for mixed logit models. Marketing Science 21, 445–475 (2002)

    Article  Google Scholar 

  24. Sandor, Z., Wedel, M.: Heterogeneous conjoint choice designs. Journal of Marketing Research 42, 210–218 (2005)

    Article  Google Scholar 

  25. Scarpa, R., Ruto, E.S.K., Kristjanson, P., Radeny, M., Druker, A.G., Rege, J.E.O.: Valuing indigenous cattle breeds in Kenya: an empirical comparison of stated and revelaed preference value estimates. Ecological Economics 45, 409–426 (2003)

    Article  Google Scholar 

  26. Scarpa, R., Thiene, M.: Destination choice models for rock climbing in the Northeastern Alps: a latent-class approach based on intensity of preferences. Land Economics 81, 426–444 (2005)

    Google Scholar 

  27. Scarpa, R., Thiene, M., Train, K.: Utility in WTP space: a tool to address confounding random scale effects in destination choice to the Alps. Working Paper in Economics. Dept. of Economics, Univ. of Waikato, New Zealand, vol. 15, pp. 1–22 (2007)

    Google Scholar 

  28. Street, D.J., Burgess, L.: Optimal and near-optimal pairs for the estimation of effects in 2-level choice experiments. Journal of Statistical Planning and Inference 118, 185–199 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  29. Toubia, O., Hauser, J.R.: On managerially efficient experimental designs. Marketing Science 26, 851–858 (2007)

    Article  Google Scholar 

  30. Train, K.E.: The potential market for non-gasoline-powered automobiles. Transportation Research Part A 14, 405–414 (1980)

    Article  Google Scholar 

  31. Train, K.E.: Recreation demand models with taste differences over people. Land Econ. 74, 230–239 (1998)

    Article  Google Scholar 

  32. Wardman, M.: A Comparison of revealed preference and stated preference models of travel behaviour. Journal of Transport Economics and Policy 22, 71–92 (1988)

    Google Scholar 

  33. Wen, C., Koppelman, F.: A Generalized Nested Logit model. Transportation Research: Part B: Methodological 35, 627–641 (2001)

    Article  Google Scholar 

  34. Yu, J., Goos, P., Vandebroek, M.L.: Efficient conjoint choice designs in the presence of respondent heterogeneity. Marketing Science 28, 122–135 (2009)

    Article  Google Scholar 

  35. Zwerina, K., Huber, J., Kuhfeld, W.F.: A general method for constructing efficient choice designs. Working paper-Fuqua School of Business-Duke University, Durham, vol. 27708, pp. 121–139 (1996)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept. of Economic and Business, University of Florence, Via delle Pandette 32, 50127, Florence, Italy

    Ginevra Virginia Lombardi

  2. Dept. of Statistics, Informatics, Applications, University of Florence, Viale Morgagni 59, 50134, Florence, Italy

    Rossella Berni

Authors
  1. Ginevra Virginia Lombardi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rossella Berni
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, IBaI, Leipzig, Germany

    Petra Perner

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Lombardi, G.V., Berni, R. (2014). Choice Modelling and Forecasting Demand for Alternative-Fuel Tractors. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2014. Lecture Notes in Computer Science(), vol 8557. Springer, Cham. https://doi.org/10.1007/978-3-319-08976-8_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-08976-8_9

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08975-1

  • Online ISBN: 978-3-319-08976-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics