IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v40y2013icp936-942.html
   My bibliography  Save this article

The impact of federal incentives on the adoption of hybrid electric vehicles in the United States

Author

Listed:
  • Jenn, Alan
  • Azevedo, Inês L.
  • Ferreira, Pedro

Abstract

Starting in 2004, the federal government in the United States offered several nationwide incentives to consumers to increase the adoption of hybrid electric vehicles. This study assesses the effectiveness of the Energy Policy Act of 2005 in this regard using econometric methods and data between 2000 and 2010. Our model accounts for network externalities by using lagged sales as an independent variable. This approach helps to capture the exponential initial growth associated with the diffusion of new technologies and avoids overestimating the effect of the policy incentives. Our results show that the Energy Policy Act of 2005 increased the sales of hybrids from 3% to 20% depending on the vehicle model considered. In addition, we find that this incentive is only effective when the amount provided is sufficiently large.

Suggested Citation

  • Jenn, Alan & Azevedo, Inês L. & Ferreira, Pedro, 2013. "The impact of federal incentives on the adoption of hybrid electric vehicles in the United States," Energy Economics, Elsevier, vol. 40(C), pages 936-942.
    • Handle: RePEc:eee:eneeco:v:40:y:2013:i:c:p:936-942
    DOI: 10.1016/j.eneco.2013.07.025
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988313001709
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.eneco.2013.07.025?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kahn, Matthew E., 2007. "Do greens drive Hummers or hybrids? Environmental ideology as a determinant of consumer choice," Journal of Environmental Economics and Management, Elsevier, vol. 54(2), pages 129-145, September.
    2. James P. Lesage, 2008. "An Introduction to Spatial Econometrics," Revue d'économie industrielle, De Boeck Université, vol. 0(3), pages 19-44.
    3. Geroski, P. A., 2000. "Models of technology diffusion," Research Policy, Elsevier, vol. 29(4-5), pages 603-625, April.
    4. Hannan, Timothy H & McDowell, John M, 1990. "The Impact of Technology Adoption on Market Structure," The Review of Economics and Statistics, MIT Press, vol. 72(1), pages 164-168, February.
    5. Kelejian, Harry H & Prucha, Ingmar R, 1998. "A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 99-121, July.
    6. Jaffe Adam B. & Stavins Robert N., 1995. "Dynamic Incentives of Environmental Regulations: The Effects of Alternative Policy Instruments on Technology Diffusion," Journal of Environmental Economics and Management, Elsevier, vol. 29(3), pages 43-63, November.
    7. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    8. Lee, Lung-fei, 2007. "The method of elimination and substitution in the GMM estimation of mixed regressive, spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 140(1), pages 155-189, September.
    9. Diamond, David, 2009. "The impact of government incentives for hybrid-electric vehicles: Evidence from US states," Energy Policy, Elsevier, vol. 37(3), pages 972-983, March.
    10. Luc Anselin, 2010. "Thirty years of spatial econometrics," Papers in Regional Science, Wiley Blackwell, vol. 89(1), pages 3-25, March.
    11. Conley, T. G., 1999. "GMM estimation with cross sectional dependence," Journal of Econometrics, Elsevier, vol. 92(1), pages 1-45, September.
    12. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
    13. Lee, Lung-fei, 2007. "GMM and 2SLS estimation of mixed regressive, spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 137(2), pages 489-514, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. repec:asg:wpaper:1045 is not listed on IDEAS
    2. Grodner, Andrew & Kniesner, Thomas J. & Bishop, John A., 2011. "Social Interactions in the Labor Market," Foundations and Trends(R) in Microeconomics, now publishers, vol. 6(4), pages 265-366, September.
    3. Daron Acemoglu & Suresh Naidu & Pascual Restrepo & James A. Robinson, 2019. "Democracy Does Cause Growth," Journal of Political Economy, University of Chicago Press, vol. 127(1), pages 47-100.
    4. Sarafidis, Vasilis & Yamagata, Takashi & Robertson, Donald, 2009. "A test of cross section dependence for a linear dynamic panel model with regressors," Journal of Econometrics, Elsevier, vol. 148(2), pages 149-161, February.
    5. Lee, Lung-fei & Yu, Jihai, 2010. "Some recent developments in spatial panel data models," Regional Science and Urban Economics, Elsevier, vol. 40(5), pages 255-271, September.
    6. Vasilis Sarafidis & Tom Wansbeek, 2012. "Cross-Sectional Dependence in Panel Data Analysis," Econometric Reviews, Taylor & Francis Journals, vol. 31(5), pages 483-531, September.
    7. Sarafidis, Vasilis, 2009. "GMM Estimation of Short Dynamic Panel Data Models With Error Cross-Sectional Dependence," MPRA Paper 25176, University Library of Munich, Germany.
    8. Kripfganz, Sebastian, 2014. "Unconditional Transformed Likelihood Estimation of Time-Space Dynamic Panel Data Models," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100604, Verein für Socialpolitik / German Economic Association.
    9. Kelejian, Harry H. & Prucha, Ingmar R., 2010. "Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances," Journal of Econometrics, Elsevier, vol. 157(1), pages 53-67, July.
    10. Lee, Lung-fei & Yu, Jihai, 2014. "Efficient GMM estimation of spatial dynamic panel data models with fixed effects," Journal of Econometrics, Elsevier, vol. 180(2), pages 174-197.
    11. Moscone, Francesco & Tosetti, Elisa & Canepa, Alessandra, 2014. "Real estate market and financial stability in US metropolitan areas: A dynamic model with spatial effects," Regional Science and Urban Economics, Elsevier, vol. 49(C), pages 129-146.
    12. repec:asg:wpaper:1013 is not listed on IDEAS
    13. Andrew Grodner & Thomas J. Kniesner, 2008. "Labor supply with social interactions: econometric estimates and their tax policy implications," Research in Labor Economics, in: Work, Earnings and Other Aspects of the Employment Relation, pages 1-23, Emerald Group Publishing Limited.
    14. Luc Anselin, 2010. "Thirty years of spatial econometrics," Papers in Regional Science, Wiley Blackwell, vol. 89(1), pages 3-25, March.
    15. Pavlyuk, Dmitry, 2012. "Maximum Likelihood Estimator for Spatial Stochastic Frontier Models," MPRA Paper 43390, University Library of Munich, Germany.
    16. Yu, Jihai & de Jong, Robert & Lee, Lung-fei, 2008. "Quasi-maximum likelihood estimators for spatial dynamic panel data with fixed effects when both n and T are large," Journal of Econometrics, Elsevier, vol. 146(1), pages 118-134, September.
    17. Wang Lu & Hao Yu & Wei Yi-Ming, 2017. "How Do Regional Interactions in Space Affect China’s Mitigation Targets and Economic Development?," Working Papers 2017.21, Fondazione Eni Enrico Mattei.
    18. Harald Badinger & Peter Egger, 2008. "GM Estimation of Higher-Order Spatial Autoregressive Processes in Cross-Section Models with Heteroskedastic Disturbances," CESifo Working Paper Series 2356, CESifo.
    19. Sarafidis, Vasilis, 2016. "Neighbourhood GMM estimation of dynamic panel data models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 526-544.
    20. Zhou, Yiwei & Wang, Xiaokun & Holguín-Veras, José, 2016. "Discrete choice with spatial correlation: A spatial autoregressive binary probit model with endogenous weight matrix (SARBP-EWM)," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 440-455.
    21. Seya, Hajime & Yamagata, Yoshiki & Tsutsumi, Morito, 2013. "Automatic selection of a spatial weight matrix in spatial econometrics: Application to a spatial hedonic approach," Regional Science and Urban Economics, Elsevier, vol. 43(3), pages 429-444.
    22. Mustafa Koroglu & Yiguo Sun, 2016. "Functional-Coefficient Spatial Durbin Models with Nonparametric Spatial Weights: An Application to Economic Growth," Econometrics, MDPI, vol. 4(1), pages 1-16, February.

    More about this item

    Keywords

    Hybrid electric vehicle; Policy incentive; Technology adoption;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • H23 - Public Economics - - Taxation, Subsidies, and Revenue - - - Externalities; Redistributive Effects; Environmental Taxes and Subsidies
    • L98 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Government Policy
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:eneeco:v:40:y:2013:i:c:p:936-942. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eneco .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.