IDEAS home Printed from https://ideas.repec.org/a/eee/juipol/v95y2025ics0957178725000608.html

Dynamics and regional heterogeneity in the generation efficiency of Japan's photovoltaic power plants focusing on new market entrants

Author

Listed:
  • Eguchi, Shogo
  • Nakamoto, Yuya
  • Takayabu, Hirotaka

Abstract

The installation capacity of photovoltaic (PV) power generation systems in Japan in 2022 was 84 GW. Furthermore, 26.2 GW of PV power generation systems are expected to be newly installed by 2030, which indicates there will be several new entrants into the PV power generation market in the near future. We applied data envelopment analysis and the metafrontier global Malmquist index (MGMI) to Japan's PV power generation plant data (2016–2020) to investigate the static power generation efficiency and its growth considering new entrants and regional heterogeneity. The results demonstrated that the western region showed the highest static efficiency. Conversely, the eastern region experienced the largest increase in MGMI, with an average annual growth rate of 1.2 %. The decomposition analysis results of MGMI reveal that technological innovation within the same region is the primary driver of the growth in MGMI in all regions, while the catch-up effect has a negative effect. These results indicate that while advanced PV power plants in all regions contribute to advancing frontier technologies, others are insufficiently catching up. Policymakers should, therefore, encourage technology spillover between innovative power plants and others to promote the catch-up effect. We additionally identified innovative power plants that promote frontier technology, and the results demonstrated that plants that started operation before 2016 primarily contributed to technological innovation, indicating that the learning-by-doing effect of existing plants and the availability of favorable sites have a greater impact on power generation efficiency than introducing advanced facilities.

Suggested Citation

  • Eguchi, Shogo & Nakamoto, Yuya & Takayabu, Hirotaka, 2025. "Dynamics and regional heterogeneity in the generation efficiency of Japan's photovoltaic power plants focusing on new market entrants," Utilities Policy, Elsevier, vol. 95(C).
    • Handle: RePEc:eee:juipol:v:95:y:2025:i:c:s0957178725000608
    DOI: 10.1016/j.jup.2025.101945
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0957178725000608
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jup.2025.101945?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Ghosh, Santosh & Yadav, Vinod Kumar & Mukherjee, Vivekananda & Yadav, Pankaj, 2017. "Evaluation of relative impact of aerosols on photovoltaic cells through combined Shannon's entropy and Data Envelopment Analysis (DEA)," Renewable Energy, Elsevier, vol. 105(C), pages 344-353.
    2. Zhang, Ning & Choi, Yongrok, 2013. "Total-factor carbon emission performance of fossil fuel power plants in China: A metafrontier non-radial Malmquist index analysis," Energy Economics, Elsevier, vol. 40(C), pages 549-559.
    3. Azadeh, A. & Ghaderi, S.F. & Maghsoudi, A., 2008. "Location optimization of solar plants by an integrated hierarchical DEA PCA approach," Energy Policy, Elsevier, vol. 36(10), pages 3993-4004, October.
    4. Azadeh, A. & Sheikhalishahi, M. & Asadzadeh, S.M., 2011. "A flexible neural network-fuzzy data envelopment analysis approach for location optimization of solar plants with uncertainty and complexity," Renewable Energy, Elsevier, vol. 36(12), pages 3394-3401.
    5. Ufuk Akcigit & William R. Kerr, 2018. "Growth through Heterogeneous Innovations," Journal of Political Economy, University of Chicago Press, vol. 126(4), pages 1374-1443.
    6. Martin Neil Baily & Charles Hulten & David Campbell, 1992. "Productivity Dynamics in Manufacturing Plants," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 23(1992 Micr), pages 187-267.
    7. Nakamoto, Yuya & Eguchi, Shogo, 2024. "How do seasonal and technical factors affect generation efficiency of photovoltaic power plants?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    8. Pastor, Jesus T. & Lovell, C.A. Knox, 2005. "A global Malmquist productivity index," Economics Letters, Elsevier, vol. 88(2), pages 266-271, August.
    9. Huaimo You & Hong Fang & Xu Wang & Siran Fang, 2018. "Environmental Efficiency of Photovoltaic Power Plants in China—A Comparative Study of Different Economic Zones and Plant Types," Sustainability, MDPI, vol. 10(7), pages 1-17, July.
    10. Ederer, Nikolaus, 2015. "Evaluating capital and operating cost efficiency of offshore wind farms: A DEA approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 1034-1046.
    11. Dong-hyun Oh & Jeong-dong Lee, 2010. "A metafrontier approach for measuring Malmquist productivity index," Empirical Economics, Springer, vol. 38(1), pages 47-64, February.
    12. Nakamoto, Yuya & Eguchi, Shogo & Takayabu, Hirotaka, 2024. "Efficiency and benchmarks for photovoltaic power generation amid uncertain conditions," Socio-Economic Planning Sciences, Elsevier, vol. 94(C).
    13. Fare, Rolf & Grosskopf, Shawna & Yaisawarng, Suthathip & Li, Sung Ko & Wang, Zhaoping, 1990. "Productivity growth in Illinois electric utilities," Resources and Energy, Elsevier, vol. 12(4), pages 383-398, December.
    14. Acemoglu, Daron & Cao, Dan, 2015. "Innovation by entrants and incumbents," Journal of Economic Theory, Elsevier, vol. 157(C), pages 255-294.
    15. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    16. Feng, Chao & Zhang, Hua & Huang, Jian-Bai, 2017. "The approach to realizing the potential of emissions reduction in China: An implication from data envelopment analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 859-872.
    17. Wang, Chunhua & Cao, Xiaoyong & Mao, Jie & Qin, Ping, 2019. "The changes in coal intensity of electricity generation in Chinese coal-fired power plants," Energy Economics, Elsevier, vol. 80(C), pages 491-501.
    18. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    19. Christopher O’Donnell & D. Rao & George Battese, 2008. "Metafrontier frameworks for the study of firm-level efficiencies and technology ratios," Empirical Economics, Springer, vol. 34(2), pages 231-255, March.
    20. Shogo Eguchi, 2017. "Understanding productivity declines of resource accumulation in the prefectures of Japan," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 19(2), pages 337-357, April.
    21. Eguchi, Shogo & Takayabu, Hirotaka & Lin, Chen, 2021. "Sources of inefficient power generation by coal-fired thermal power plants in China: A metafrontier DEA decomposition approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    22. Fare, Rolf & Shawna Grosskopf & Mary Norris & Zhongyang Zhang, 1994. "Productivity Growth, Technical Progress, and Efficiency Change in Industrialized Countries," American Economic Review, American Economic Association, vol. 84(1), pages 66-83, March.
    23. William W. Cooper & Lawrence M. Seiford & Kaoru Tone, 2007. "Data Envelopment Analysis," Springer Books, Springer, edition 0, number 978-0-387-45283-8, March.
    24. Yang, Hongliang & Pollitt, Michael, 2010. "The necessity of distinguishing weak and strong disposability among undesirable outputs in DEA: Environmental performance of Chinese coal-fired power plants," Energy Policy, Elsevier, vol. 38(8), pages 4440-4444, August.
    25. Ogunrinde, Olawale & Shittu, Ekundayo, 2023. "Efficiency and productivity of renewable energy technologies: Evidence from U.S. investor-owned utilities across regional markets," Utilities Policy, Elsevier, vol. 82(C).
    26. Wang, Derek D. & Sueyoshi, Toshiyuki, 2017. "Assessment of large commercial rooftop photovoltaic system installations: Evidence from California," Applied Energy, Elsevier, vol. 188(C), pages 45-55.
    27. Sueyoshi, Toshiyuki & Goto, Mika, 2014. "Photovoltaic power stations in Germany and the United States: A comparative study by data envelopment analysis," Energy Economics, Elsevier, vol. 42(C), pages 271-288.
    28. Shogo Eguchi, 2022. "CO 2 Reduction Potential from Efficiency Improvements in China’s Coal-Fired Thermal Power Generation: A Combined Approach of Metafrontier DEA and LMDI," Energies, MDPI, vol. 15(7), pages 1-19, March.
    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. Nakamoto, Yuya & Eguchi, Shogo, 2024. "How do seasonal and technical factors affect generation efficiency of photovoltaic power plants?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    2. Nakamoto, Yuya & Eguchi, Shogo & Takayabu, Hirotaka, 2024. "Efficiency and benchmarks for photovoltaic power generation amid uncertain conditions," Socio-Economic Planning Sciences, Elsevier, vol. 94(C).
    3. Nakaishi, Tomoaki & Takayabu, Hirotaka & Eguchi, Shogo, 2021. "Environmental efficiency analysis of China's coal-fired power plants considering heterogeneity in power generation company groups," Energy Economics, Elsevier, vol. 102(C).
    4. Eguchi, Shogo & Takayabu, Hirotaka & Lin, Chen, 2021. "Sources of inefficient power generation by coal-fired thermal power plants in China: A metafrontier DEA decomposition approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    5. Shogo Eguchi, 2022. "CO 2 Reduction Potential from Efficiency Improvements in China’s Coal-Fired Thermal Power Generation: A Combined Approach of Metafrontier DEA and LMDI," Energies, MDPI, vol. 15(7), pages 1-19, March.
    6. Zhang, Ning & Zhao, Yu & Wang, Na, 2022. "Is China's energy policy effective for power plants? Evidence from the 12th Five-Year Plan energy saving targets," Energy Economics, Elsevier, vol. 112(C).
    7. Zhong, Shen & Li, Junwei & Chen, Xi & Wen, Hongmei, 2022. "A multi-hierarchy meta-frontier approach for measuring green total factor productivity: An application of pig breeding in China," Socio-Economic Planning Sciences, Elsevier, vol. 81(C).
    8. Shixiong Cheng & Jiahui Xie & De Xiao & Yun Zhang, 2019. "Measuring the Environmental Efficiency and Technology Gap of PM 2.5 in China’s Ten City Groups: An Empirical Analysis Using the EBM Meta-Frontier Model," IJERPH, MDPI, vol. 16(4), pages 1-22, February.
    9. Barnabé Walheer, 2022. "Global Malmquist and cost Malmquist indexes for group comparison," Journal of Productivity Analysis, Springer, vol. 58(1), pages 75-93, August.
    10. Arabi, Behrouz & Munisamy, Susila & Emrouznejad, Ali & Shadman, Foroogh, 2014. "Power industry restructuring and eco-efficiency changes: A new slacks-based model in Malmquist–Luenberger Index measurement," Energy Policy, Elsevier, vol. 68(C), pages 132-145.
    11. Chiu, Yung-Ho & Lee, Jen-Hui & Lu, Ching-Cheng & Shyu, Ming-Kuang & Luo, Zhengying, 2012. "The technology gap and efficiency measure in WEC countries: Application of the hybrid meta frontier model," Energy Policy, Elsevier, vol. 51(C), pages 349-357.
    12. Wang, Zhaohua & Feng, Chao, 2015. "Sources of production inefficiency and productivity growth in China: A global data envelopment analysis," Energy Economics, Elsevier, vol. 49(C), pages 380-389.
    13. Li, Mingquan & Wang, Qi, 2014. "International environmental efficiency differences and their determinants," Energy, Elsevier, vol. 78(C), pages 411-420.
    14. Adel Hatami-Marbini & Aliasghar Arabmaldar & John Otu Asu, 2022. "Robust productivity growth and efficiency measurement with undesirable outputs: evidence from the oil industry," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(4), pages 1213-1254, December.
    15. Lan-Bing Li & Cong-Cong Zhang & Jin-Li Hu & Ching-Ren Chiu, 2021. "Disaggregate productivity growth sources of regional industries in China," Empirical Economics, Springer, vol. 60(3), pages 1531-1557, March.
    16. Mohsen Afsharian & Mohammadreza Alirezaee & Peter Reichling, 2012. "The master Malmquist index measurement using DEA-based weighted average efficiency," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 4(1), pages 21-42.
    17. Zhigang Zhu & Xuping Zhang & Yujia Wang & Xiang Chen, 2021. "Energy Cost Performance of Thermal Power Industry in China Considering Regional Heterogeneity: A Meta-Frontier Cost Malmquist Productivity Decomposition Approach," Sustainability, MDPI, vol. 13(12), pages 1-19, June.
    18. Ying Li & Yung-ho Chiu & Tai-Yu Lin, 2019. "Energy and Environmental Efficiency in Different Chinese Regions," Sustainability, MDPI, vol. 11(4), pages 1-26, February.
    19. Yongqi Feng & Haolin Zhang & Yung-ho Chiu & Tzu-Han Chang, 2021. "Innovation efficiency and the impact of the institutional quality: a cross-country analysis using the two-stage meta-frontier dynamic network DEA model," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3091-3129, April.
    20. Barnabé Walheer, 2018. "Cost Malmquist productivity index: an output-specific approach for group comparison," Journal of Productivity Analysis, Springer, vol. 49(1), pages 79-94, February.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:juipol:v:95:y:2025:i:c:s0957178725000608. 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: https://www.sciencedirect.com/journal/utilities-policy .

    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.