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Impact of feed-in tariffs on electricity consumption

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Abstract

The diffusion of renewable energy sources is an important policy issue for all countries. In particular, feed-in tariffs (FITs) are a major policy instrument used to diffuse renewable energy sources in developed countries. A few recent studies have found a rebound effect from the installation of solar photovoltaic (PV) systems. However, consumer behavior in relation to electricity consumption following the installation of solar PV systems is largely unknown. In particular, previous studies do not effectively reveal the FIT effect on electricity consumption. Therefore, we set up a model to measure this effect and conduct empirical analysis to confirm the theoretical contribution of the improved model. Our estimation results based on the matching method show that the FIT scheme increases the consumption of electricity purchased from electricity companies if the FIT rate exceeds the electricity price. This finding is important for better understanding the true cost-benefit of FITs.

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Notes

  1. Since the end of the official FITs, a voluntary FIT program has been implemented in some states.

  2. We summarize the diffusion policies in Australia based on some previous studies (Chapman et al. 2016; Macintosh and Wilkinson 2011) and official information of the government of Australia.

  3. The respondents who answered that their income equals zero may have answered incorrectly or may not have wanted to reveal their income. This is another reason we omit the data of such respondents.

  4. After we omit samples who answer that their income is equal to zero, the total sample becomes 8,101. Additionally, some variables include missing values because the respondents do not answer. Table 2 show detail about the number of observations of each variable.

  5. The summary statistics of each group are shown in Appendix 2.

  6. As mentioned above, we also run another regression model that includes the attributes as independent variables. We show the results in Appendix 3.

  7. We implement Wald test between estimated coefficients of FIT_ele in Table 5. The test results do not show significant differences between the coefficients.

  8. In this estimation, we add the cross term to model 1 in the previous section.

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Acknowledgements

We gratefully acknowledge financial support from the Grant-in-Aid for Scientific Research (JSPS KAKENHI JP17K13737 and JP20H00648).

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Authors and Affiliations

  1. Faculty of Economics, Musashi University, 1-26-1 Toyotamakami, Nerima-ku, Tokyo, 176-8534, Japan

    Kenta Tanaka

  2. Queensland University of Technology, 2 George St, Brisbane, QLD, 4000, Australia

    Clevo Wilson

  3. Urban Institute, Kyushu University, 744 Motooka Nishi-ku, Fukuoka, 819-0395, Japan

    Shunsuke Managi

Authors
  1. Kenta Tanaka
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  2. Clevo Wilson
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  3. Shunsuke Managi
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Correspondence to Kenta Tanaka.

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Appendices

Appendix 1

See Table 7.

Table 7 List of variables in the analysis
Full size table

Appendix 2

See Table 8.

Table 8 Summary statistics of the treatment and control groups after matching
Full size table

Appendix 3

See Table 9.

Table 9 Estimation results of sample matching (including the attributes of matching)
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Tanaka, K., Wilson, C. & Managi, S. Impact of feed-in tariffs on electricity consumption. Environ Econ Policy Stud 24, 49–72 (2022). https://doi.org/10.1007/s10018-021-00306-w

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