IDEAS home Printed from https://ideas.repec.org/a/rss/jnljms/v6i5p2.html
   My bibliography  Save this article

A Model of Subsidies and Feed-In Tariffs for the Deployment of Photovoltaic Energy in the Residential Sector in Tunisia

Author

Listed:
  • Mahmoud Tnani
  • Hafedh Ben Abdennebi

Abstract

Through an analytical model, we investigate the effectiveness and efficiency of current government incentives in accelerating deployment of photovoltaic systems since 2010 in the Tunisian residential sector. Investor behavior takes into account the diffusion phenomenon, the learning phenomenon, and the net present value. We show that the currently adopted incentives do not provide a framework conducive to investment for both households and the PV industrial sector. Specifically, it was found that the effectiveness and efficiency of the incentive policy cannot be achieved while maintaining feed-in tariffs (FITs) equals to retail tariffs. Moreover, the sensitivity analysis results obtained from the model show that the net social benefits can be maximized when the capacities are installed during the early years, accompanied by incentive mechanisms through prices and subsidies. Our analysis also reveals that for Tunisia, which is a net importer of PV, the optimal net social benefit depends essentially on the social costs of subsidies and FITs, which are directly impacted by government policies. In contrast, the externalities, the benefits of consumers, the savings in natural gas, and the carbon credits are only slightly affected by government policies, since the objective in terms of installed capacity is reached at a time horizon.

Suggested Citation

  • Mahmoud Tnani & Hafedh Ben Abdennebi, 2015. "A Model of Subsidies and Feed-In Tariffs for the Deployment of Photovoltaic Energy in the Residential Sector in Tunisia," International Journal of Management Sciences, Research Academy of Social Sciences, vol. 6(5), pages 235-259.
  • Handle: RePEc:rss:jnljms:v6i5p2
    as

    Download full text from publisher

    File URL: http://rassweb.org/admin/pages/ResearchPapers/Paper%202_1497466955.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. McDonald, Alan & Schrattenholzer, Leo, 2001. "Learning rates for energy technologies," Energy Policy, Elsevier, vol. 29(4), pages 255-261, March.
    2. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    3. Geroski, P. A., 2000. "Models of technology diffusion," Research Policy, Elsevier, vol. 29(4-5), pages 603-625, April.
    4. Rao, K. Usha & Kishore, V.V.N., 2010. "A review of technology diffusion models with special reference to renewable energy technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(3), pages 1070-1078, April.
    5. Fouquet, Doerte & Johansson, Thomas B., 2008. "European renewable energy policy at crossroads--Focus on electricity support mechanisms," Energy Policy, Elsevier, vol. 36(11), pages 4079-4092, November.
    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. Saed Alizamir & Francis de Véricourt & Peng Sun, 2016. "Efficient Feed-In-Tariff Policies for Renewable Energy Technologies," Operations Research, INFORMS, vol. 64(1), pages 52-66, February.
    2. Lee, Chul-Yong & Huh, Sung-Yoon, 2017. "Forecasting the diffusion of renewable electricity considering the impact of policy and oil prices: The case of South Korea," Applied Energy, Elsevier, vol. 197(C), pages 29-39.
    3. Cong, Rong-Gang & Shen, Shaochuan, 2014. "How to Develop Renewable Power in China? A Cost-Effective Perspective," MPRA Paper 112209, University Library of Munich, Germany.
    4. van Blommestein, Kevin & Daim, Tugrul U. & Cho, Yonghee & Sklar, Paul, 2018. "Structuring financial incentives for residential solar electric systems," Renewable Energy, Elsevier, vol. 115(C), pages 28-40.
    5. Chen, Huayi & Ma, Tieju, 2014. "Technology adoption with limited foresight and uncertain technological learning," European Journal of Operational Research, Elsevier, vol. 239(1), pages 266-275.
    6. Ding, Hao & Zhou, Dequn & Zhou, P., 2020. "Optimal policy supports for renewable energy technology development: A dynamic programming model," Energy Economics, Elsevier, vol. 92(C).
    7. Huh, Sung-Yoon & Lee, Chul-Yong, 2014. "Diffusion of renewable energy technologies in South Korea on incorporating their competitive interrelationships," Energy Policy, Elsevier, vol. 69(C), pages 248-257.
    8. Bessi, Alessandro & Guidolin, Mariangela & Manfredi, Piero, 2021. "The role of gas on future perspectives of renewable energy diffusion: Bridging technology or lock-in?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    9. Vaidyanathan, Geeta & Sankaranarayanan, Ramani & Yap, Nonita T., 2019. "Bridging the chasm – Diffusion of energy innovations in poor infrastructure starved communities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 99(C), pages 243-255.
    10. Theoharakis Vasilis & Vakratsas Demetrios & Wong Veronica, 2004. "The Relationship between Market Share and Information in a High-Tech Industry," Review of Marketing Science, De Gruyter, vol. 2(1), pages 1-14, January.
    11. Edouard Civel & Marc Baudry, 2018. "The Fate of Inventions. What can we learn from Bayesian learning in strategic options model of adoption ?," EconomiX Working Papers 2018-47, University of Paris Nanterre, EconomiX.
    12. Comin, Diego & Rode, Johannes, 2013. "From Green Users to Green Voters," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 63678, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    13. Toka, Agorasti & Iakovou, Eleftherios & Vlachos, Dimitrios & Tsolakis, Naoum & Grigoriadou, Anastasia-Loukia, 2014. "Managing the diffusion of biomass in the residential energy sector: An illustrative real-world case study," Applied Energy, Elsevier, vol. 129(C), pages 56-69.
    14. 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.
    15. Hyoung Jun Kim & Su Jung Jee & So Young Sohn, 2021. "Cost–benefit model for multi-generational high-technology products to compare sequential innovation strategy with quality strategy," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-17, April.
    16. Brito, Thiago Luis Felipe & Islam, Towhidul & Stettler, Marc & Mouette, Dominique & Meade, Nigel & Moutinho dos Santos, Edmilson, 2019. "Transitions between technological generations of alternative fuel vehicles in Brazil," Energy Policy, Elsevier, vol. 134(C).
    17. Chen, Huayi & Ma, Tieju, 2017. "Optimizing systematic technology adoption with heterogeneous agents," European Journal of Operational Research, Elsevier, vol. 257(1), pages 287-296.
    18. Giovanni Dosi & Richard Nelson, 2013. "The Evolution of Technologies: An Assessment of the State-of-the-Art," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 3(1), pages 3-46, June.
    19. Al-Alawi, Baha M. & Bradley, Thomas H., 2013. "Review of hybrid, plug-in hybrid, and electric vehicle market modeling Studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 190-203.
    20. Cheng, Yu & Huang, Lucheng & Ramlogan, Ronnie & Li, Xin, 2017. "Forecasting of potential impacts of disruptive technology in promising technological areas: Elaborating the SIRS epidemic model in RFID technology," Technological Forecasting and Social Change, Elsevier, vol. 117(C), pages 170-183.

    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:rss:jnljms:v6i5p2. 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: Danish Khalil (email available below). General contact details of provider: http://www.rassweb.org .

    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.