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Network externality perspective of feed-in-tariffs (FIT) instruments--Some observations and suggestions

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  • Shum, Kwok L.
  • Watanabe, Chihiro

Abstract

Existing energy policy frameworks revolving around the acceleration of deployment1 of renewable energy technology can be broadly classified as the quantity vs. price approach. With this brief viewpoint, this paper suggests another perspective of viewing these instruments in terms of a more fundamental basis: whether the deployment in capacity is in terms of a cost minimization approach or a network externality approach. We suggest that the generic price or feed in tariff (FIT) approach in subsidizing renewable electricity generation and associated income would create a bandwagon or self-propagation effect among users rendering the renewable energy technology spreads like a software or information technology. Our objective is to raise awareness of this technology dynamics oriented perspective in renewable deployment supplementing the conventional installation subsidies perspective. We hope that it would inspire more empirical works and studies relating to the policy implications of this viewpoint.

Suggested Citation

  • Shum, Kwok L. & Watanabe, Chihiro, 2010. "Network externality perspective of feed-in-tariffs (FIT) instruments--Some observations and suggestions," Energy Policy, Elsevier, vol. 38(7), pages 3266-3269, July.
    • Handle: RePEc:eee:enepol:v:38:y:2010:i:7:p:3266-3269
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    References listed on IDEAS

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    1. Lauber, Volkmar, 2004. "REFIT and RPS: options for a harmonised Community framework," Energy Policy, Elsevier, vol. 32(12), pages 1405-1414, August.
    2. Nagamatsu, Akira & Watanabe, Chihiro & Shum, Kwok L., 2006. "Diffusion trajectory of self-propagating innovations interacting with institutions--incorporation of multi-factors learning function to model PV diffusion in Japan," Energy Policy, Elsevier, vol. 34(4), pages 411-421, March.
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    Cited by:

    1. Harkonen, Janne & Haapasalo, Harri & Hanninen, Kai, 2015. "Productisation: A review and research agenda," International Journal of Production Economics, Elsevier, vol. 164(C), pages 65-82.
    2. Jin, Wei & Zhang, ZhongXiang, 2015. "Levelling the playing field: On the missing role of network externality in designing renewable energy technology deployment policies," Working Papers 249514, Australian National University, Centre for Climate Economics & Policy.
    3. Newell, Richard G. & Pizer, William A. & Raimi, Daniel, 2019. "U.S. federal government subsidies for clean energy: Design choices and implications," Energy Economics, Elsevier, vol. 80(C), pages 831-841.
    4. Mercure, J.-F. & Pollitt, H. & Chewpreecha, U. & Salas, P. & Foley, A.M. & Holden, P.B. & Edwards, N.R., 2014. "The dynamics of technology diffusion and the impacts of climate policy instruments in the decarbonisation of the global electricity sector," Energy Policy, Elsevier, vol. 73(C), pages 686-700.
    5. Rocha, Luiz Célio Souza & Aquila, Giancarlo & Rotela Junior, Paulo & Paiva, Anderson Paulo de & Pamplona, Edson de Oliveira & Balestrassi, Pedro Paulo, 2018. "A stochastic economic viability analysis of residential wind power generation in Brazil," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 412-419.
    6. Shum, Kwok L., 2017. "Renewable energy deployment policy: A transition management perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1380-1388.

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