IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0196331.html
   My bibliography  Save this article

Dynamic linear modeling of monthly electricity demand in Japan: Time variation of electricity conservation effect

Author

Listed:
  • Keita Honjo
  • Hiroto Shiraki
  • Shuichi Ashina

Abstract

After the severe nuclear disaster in Fukushima, which was triggered by the Great East Japan earthquake in March 2011, nuclear power plants in Japan were temporarily shut down for mandatory inspections. To prevent large-scale blackouts, the Japanese government requested companies and households to reduce electricity consumption in summer and winter. It is reported that the domestic electricity demand had a structural decrease because of the electricity conservation effect (ECE). However, quantitative analysis of the ECE is not sufficient, and especially time variation of the ECE remains unclear. Understanding the ECE is important because Japan’s NDC (nationally determined contribution) assumes the reduction of CO2 emissions through aggressive energy conservation. In this study, we develop a time series model of monthly electricity demand in Japan and estimate time variation of the ECE. Moreover, we evaluate the impact of electricity conservation on CO2 emissions from power plants. The dynamic linear model is used to separate the ECE from the effects of other irrelevant factors (e.g. air temperature, economic production, and electricity price). Our result clearly shows that consumers’ electricity conservation behavior after the earthquake was not temporary but became established as a habit. Between March 2011 and March 2016, the ECE on industrial electricity demand ranged from 3.9% to 5.4%, and the ECE on residential electricity demand ranged from 1.6% to 7.6%. The ECE on the total electricity demand was estimated at 3.2%–6.0%. We found a seasonal pattern that the residential ECE in summer is higher than that in winter. The emissions increase from the shutdown of nuclear power plants was mitigated by electricity conservation. The emissions reduction effect was estimated at 0.82 MtCO2–2.26 MtCO2 (−4.5% on average compared to the zero-ECE case). The time-varying ECE is necessary for predicting Japan’s electricity demand and CO2 emissions after the earthquake.

Suggested Citation

  • Keita Honjo & Hiroto Shiraki & Shuichi Ashina, 2018. "Dynamic linear modeling of monthly electricity demand in Japan: Time variation of electricity conservation effect," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-23, April.
    • Handle: RePEc:plo:pone00:0196331
    DOI: 10.1371/journal.pone.0196331
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0196331
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0196331&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0196331?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
    ---><---

    References listed on IDEAS

    as
    1. MORITA Tamaki & MANAGI Shunsuke, 2013. "Japanese Consumers' WTP for the Source of Electricity after the Great East Japan Earthquake," Discussion Papers (Japanese) 13066, Research Institute of Economy, Trade and Industry (RIETI).
    2. Hunt, Lester C. & Judge, Guy & Ninomiya, Yasushi, 2003. "Underlying trends and seasonality in UK energy demand: a sectoral analysis," Energy Economics, Elsevier, vol. 25(1), pages 93-118, January.
    3. Adeyemi, Olutomi I. & Hunt, Lester C., 2014. "Accounting for asymmetric price responses and underlying energy demand trends in OECD industrial energy demand," Energy Economics, Elsevier, vol. 45(C), pages 435-444.
    4. Mourshed, Monjur, 2011. "The impact of the projected changes in temperature on heating and cooling requirements in buildings in Dhaka, Bangladesh," Applied Energy, Elsevier, vol. 88(11), pages 3737-3746.
    5. Inglesi-Lotz, R., 2011. "The evolution of price elasticity of electricity demand in South Africa: A Kalman filter application," Energy Policy, Elsevier, vol. 39(6), pages 3690-3696, June.
    6. Osamu Kimura and Ken-Ichiro Nishio, 2016. "Responding to electricity shortfalls: Electricity-saving activities of households and firms in Japan after Fukushima," Economics of Energy & Environmental Policy, International Association for Energy Economics, vol. 0(Number 1).
    7. Commandeur, Jacques J.F. & Koopman, Siem Jan, 2007. "An Introduction to State Space Time Series Analysis," OUP Catalogue, Oxford University Press, number 9780199228874.
    8. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    9. Harvey,Andrew C., 1991. "Forecasting, Structural Time Series Models and the Kalman Filter," Cambridge Books, Cambridge University Press, number 9780521405737, September.
    10. Dilaver, Zafer & Hunt, Lester C., 2011. "Industrial electricity demand for Turkey: A structural time series analysis," Energy Economics, Elsevier, vol. 33(3), pages 426-436, May.
    11. Büyükalaca, Orhan & Bulut, Hüsamettin & YIlmaz, Tuncay, 2001. "Analysis of variable-base heating and cooling degree-days for Turkey," Applied Energy, Elsevier, vol. 69(4), pages 269-283, August.
    12. Dordonnat, V. & Koopman, S.J. & Ooms, M. & Dessertaine, A. & Collet, J., 2008. "An hourly periodic state space model for modelling French national electricity load," International Journal of Forecasting, Elsevier, vol. 24(4), pages 566-587.
    13. Lester C. Hunt & Yasushi Ninomiya, 2003. "Unravelling Trends and Seasonality: A Structural Time Series Analysis of Transport Oil Demand in the UK and Japan," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 63-96.
    14. Cho, Seong-Hoon & Tanaka, Katsuya & Wu, Junjie & Robert, Roland K. & Kim, Taeyoung, 2016. "Effects of nuclear power plant shutdowns on electricity consumption and greenhouse gas emissions after the Tohoku Earthquake," Energy Economics, Elsevier, vol. 55(C), pages 223-233.
    15. Fujimi, Toshio & Chang, Stephanie E., 2014. "Adaptation to electricity crisis: Businesses in the 2011 Great East Japan triple disaster," Energy Policy, Elsevier, vol. 68(C), pages 447-457.
    16. Shiraki, Hiroto & Nakamura, Shogo & Ashina, Shuichi & Honjo, Keita, 2016. "Estimating the hourly electricity profile of Japanese households – Coupling of engineering and statistical methods," Energy, Elsevier, vol. 114(C), pages 478-491.
    17. Hunt, Lester C. & Ninomiya, Yasushi, 2005. "Primary energy demand in Japan: an empirical analysis of long-term trends and future CO2 emissions," Energy Policy, Elsevier, vol. 33(11), pages 1409-1424, July.
    18. Chang, Yoosoon & Kim, Chang Sik & Miller, J. Isaac & Park, Joon Y. & Park, Sungkeun, 2014. "Time-varying Long-run Income and Output Elasticities of Electricity Demand with an Application to Korea," Energy Economics, Elsevier, vol. 46(C), pages 334-347.
    19. Takeda, Hisashi & Tamura, Yoshiyasu & Sato, Seisho, 2016. "Using the ensemble Kalman filter for electricity load forecasting and analysis," Energy, Elsevier, vol. 104(C), pages 184-198.
    20. Yoosoon Chang & Chang Sik Kim & J. Isaac Miller & Joon Y. Park & Sungkeun Park, 2014. "Time-varying Long-run Income and Output Elasticities of Electricity Demand," Working Papers 1409, Department of Economics, University of Missouri.
    21. Arisoy, Ibrahim & Ozturk, Ilhan, 2014. "Estimating industrial and residential electricity demand in Turkey: A time varying parameter approach," Energy, Elsevier, vol. 66(C), pages 959-964.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. A Uemura & L Hamabe & R Tanaka, 2021. "Stress burden for dogs in a simple soundproof cage: Heart rate variability and behavioural analyses," Veterinární medicína, Czech Academy of Agricultural Sciences, vol. 66(9), pages 376-384.
    2. Said, Fathin Faizah & Babatunde, Kazeem Alasinrin & Md Nor, Nor Ghani & Mahmoud, Moamin A. & Begum, Rawshan Ara, 2022. "Decarbonizing the Global Electricity Sector through Demand-Side Management: A Systematic Critical Review of Policy Responses," Jurnal Ekonomi Malaysia, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, vol. 56(1), pages 71-91.
    3. Kaneko, Nanae & Fujimoto, Yu & Hayashi, Yasuhiro, 2022. "Sensitivity analysis of factors relevant to extreme imbalance between procurement plans and actual demand: Case study of the Japanese electricity market," Applied Energy, Elsevier, vol. 313(C).
    4. Shi, Changfeng & Zhao, Yi & Zhang, Chenjun & Pang, Qinghua & Chen, Qiyong & Li, Ang, 2022. "Research on the driving effect of production electricity consumption changes in the Yangtze River Economic Zone - Based on regional and industrial perspectives," Energy, Elsevier, vol. 238(PA).
    5. Kaneko, Nanae & Fujimoto, Yu & Kabe, Satoshi & Hayashida, Motonari & Hayashi, Yasuhiro, 2020. "Sparse modeling approach for identifying the dominant factors affecting situation-dependent hourly electricity demand," Applied Energy, Elsevier, vol. 265(C).

    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. Kaneko, Nanae & Fujimoto, Yu & Kabe, Satoshi & Hayashida, Motonari & Hayashi, Yasuhiro, 2020. "Sparse modeling approach for identifying the dominant factors affecting situation-dependent hourly electricity demand," Applied Energy, Elsevier, vol. 265(C).
    2. Alptekin, Aynur & Broadstock, David C. & Chen, Xiaoqi & Wang, Dong, 2019. "Time-varying parameter energy demand functions: Benchmarking state-space methods against rolling-regressions," Energy Economics, Elsevier, vol. 82(C), pages 26-41.
    3. Salisu, Afees A. & Ayinde, Taofeek O., 2016. "Modeling energy demand: Some emerging issues," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1470-1480.
    4. Sharimakin, Akinsehinwa, 2021. "Modelling asymmetric price responses of industrial energy demand with a dynamic hierarchical model," Energy Economics, Elsevier, vol. 98(C).
    5. Ozturk, Ilhan & Arisoy, Ibrahim, 2016. "An estimation of crude oil import demand in Turkey: Evidence from time-varying parameters approach," Energy Policy, Elsevier, vol. 99(C), pages 174-179.
    6. Dilaver, Zafer & Hunt, Lester C., 2011. "Industrial electricity demand for Turkey: A structural time series analysis," Energy Economics, Elsevier, vol. 33(3), pages 426-436, May.
    7. Wang, Banban & Wei, Jie & Tan, Xiujie & Su, Bin, 2021. "The sectorally heterogeneous and time-varying price elasticities of energy demand in China," Energy Economics, Elsevier, vol. 102(C).
    8. Atalla, Tarek N. & Gasim, Anwar A. & Hunt, Lester C., 2018. "Gasoline demand, pricing policy, and social welfare in Saudi Arabia: A quantitative analysis," Energy Policy, Elsevier, vol. 114(C), pages 123-133.
    9. Wang, Nan & Mogi, Gento, 2017. "Industrial and residential electricity demand dynamics in Japan: How did price and income elasticities evolve from 1989 to 2014?," Energy Policy, Elsevier, vol. 106(C), pages 233-243.
    10. Yasunobu Wakashiro, 2019. "Estimating price elasticity of demand for electricity: the case of Japanese manufacturing industry," International Journal of Economic Policy Studies, Springer, vol. 13(1), pages 173-191, January.
    11. Agnolucci, Paolo & De Lipsis, Vincenzo & Arvanitopoulos, Theodoros, 2017. "Modelling UK sub-sector industrial energy demand," Energy Economics, Elsevier, vol. 67(C), pages 366-374.
    12. Yuo-Hsien Shiau & Su-Fen Yang & Rishan Adha & Syamsiyatul Muzayyanah, 2022. "Modeling Industrial Energy Demand in Relation to Subsector Manufacturing Output and Climate Change: Artificial Neural Network Insights," Sustainability, MDPI, vol. 14(5), pages 1-18, March.
    13. Dilaver, Zafer & Hunt, Lester C., 2011. "Turkish aggregate electricity demand: An outlook to 2020," Energy, Elsevier, vol. 36(11), pages 6686-6696.
    14. Jeyhun I. Mikayilov & Fakhri J. Hasanov & Carlo A. Bollino & Ceyhun Mahmudlu, 2017. "Modeling of Electricity Demand for Azerbaijan: Time-Varying Coefficient Cointegration Approach," Energies, MDPI, vol. 10(11), pages 1-12, November.
    15. Huntington, Hillard G. & Barrios, James J. & Arora, Vipin, 2019. "Review of key international demand elasticities for major industrializing economies," Energy Policy, Elsevier, vol. 133(C).
    16. Liddle, Brantley & Smyth, Russell & Zhang, Xibin, 2020. "Time-varying income and price elasticities for energy demand: Evidence from a middle-income panel," Energy Economics, Elsevier, vol. 86(C).
    17. Sharimakin, Akinsehinwa & Glass, Anthony J. & Saal, David S. & Glass, Karligash, 2018. "Dynamic multilevel modelling of industrial energy demand in Europe," Energy Economics, Elsevier, vol. 74(C), pages 120-130.
    18. Alkhathlan, Khalid & Javid, Muhammad, 2015. "Carbon emissions and oil consumption in Saudi Arabia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 105-111.
    19. Javid, Muhammad & Qayyum, Abdul, 2014. "Electricity consumption-GDP nexus in Pakistan: A structural time series analysis," Energy, Elsevier, vol. 64(C), pages 811-817.
    20. Daniel Morais de Souza & Rogerio Silva de Mattos & Alexandre Zanini, 2022. "Estimating Elasticities for the Residential Demand of Electricity in Brazil Using Cointegration Models," International Journal of Energy Economics and Policy, Econjournals, vol. 12(2), pages 315-324, March.

    More about this item

    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:plo:pone00:0196331. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.