IDEAS home Printed from https://ideas.repec.org/p/eti/dpaper/22093.html
   My bibliography  Save this paper

Impact of the Rapid Expansion of Renewable Energy on Electricity Market Price: Using machine learning and shapley additive explanation

Author

Listed:
  • LI Chao
  • MANAGI Shunsuke

Abstract

The positive effects of greenness in living environments on human well-being are known. As a widely used proxy, the nighttime light (NTL) indicates the regional socio-economic status and development level. Higher development levels and economic status are related to more opportunity and higher income, ultimately leading to greater human well-being. However, whether simple increases in greenness and NTL always produce positive results remains inconclusive. Here, we demonstrate the complex relationships between human well-being and greenness and NTL by employing the random forest method. The accuracy of this model is 81.83%, exceeding most previous studies. According to the analysis results, the recommended ranges of greenness and NTL in living environments are 10.91% - 32.99% and 0 – 17.92 nW/cm 2 ・sr , respectively. Moreover, the current average monetary values of greenness and NTL are 3351.96 USD/% and 658.11 USD/(nW/cm 2 ・sr) , respectively. The residential areas are far away from the abundant natural resources, which makes the main population desire more greenness in their living environments. Furthermore, high urban development density, represented by NTL, has caused adverse effects on human well-being in metropolitan areas. Therefore, retaining a moderate development intensity is an effective way to achieve a sustainable society and improve human well-being.

Suggested Citation

  • LI Chao & MANAGI Shunsuke, 2022. "Impact of the Rapid Expansion of Renewable Energy on Electricity Market Price: Using machine learning and shapley additive explanation," Discussion papers 22093, Research Institute of Economy, Trade and Industry (RIETI).
  • Handle: RePEc:eti:dpaper:22093
    as

    Download full text from publisher

    File URL: https://www.rieti.go.jp/jp/publications/dp/22e093.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Paul L Joskow, 2019. "Challenges for wholesale electricity markets with intermittent renewable generation at scale: the US experience," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 35(2), pages 291-331.
    2. James Bushnell & Kevin Novan, 2018. "Setting with the Sun: The Impacts of Renewable Energy on Wholesale Power Markets," NBER Working Papers 24980, National Bureau of Economic Research, Inc.
    3. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    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. Shimomura, Mizue & Keeley, Alexander Ryota & Matsumoto, Ken'ichi & Tanaka, Kenta & Managi, Shunsuke, 2024. "Beyond the merit order effect: Impact of the rapid expansion of renewable energy on electricity market price," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    2. Fabra, Natalia, 2021. "The energy transition: An industrial economics perspective," International Journal of Industrial Organization, Elsevier, vol. 79(C).
    3. Javier L'opez Prol & Wolf-Peter Schill, 2020. "The Economics of Variable Renewables and Electricity Storage," Papers 2012.15371, arXiv.org.
    4. Sirin, Selahattin Murat & Yilmaz, Berna N., 2020. "Variable renewable energy technologies in the Turkish electricity market: Quantile regression analysis of the merit-order effect," Energy Policy, Elsevier, vol. 144(C).
    5. Sophie-Charlotte Klose & Johannes Lederer, 2020. "A Pipeline for Variable Selection and False Discovery Rate Control With an Application in Labor Economics," Papers 2006.12296, arXiv.org, revised Jun 2020.
    6. Hindriks, Jean & Serse, Valerio, 2022. "The incidence of VAT reforms in electricity markets: Evidence from Belgium," International Journal of Industrial Organization, Elsevier, vol. 80(C).
    7. Shoshan, Vered & Hazan, Tamir & Plonsky, Ori, 2023. "BEAST-Net: Learning novel behavioral insights using a neural network adaptation of a behavioral model," OSF Preprints kaeny, Center for Open Science.
    8. Juergen Deppner & Marcelo Cajias, 2024. "Accounting for Spatial Autocorrelation in Algorithm-Driven Hedonic Models: A Spatial Cross-Validation Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 68(2), pages 235-273, February.
    9. Keppler, Jan Horst & Quemin, Simon & Saguan, Marcelo, 2022. "Why the sustainable provision of low-carbon electricity needs hybrid markets," Energy Policy, Elsevier, vol. 171(C).
    10. Stephane Helleringer & Chong You & Laurence Fleury & Laetitia Douillot & Insa Diouf & Cheikh Tidiane Ndiaye & Valerie Delaunay & Rene Vidal, 2019. "Improving age measurement in low- and middle-income countries through computer vision: A test in Senegal," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 40(9), pages 219-260.
    11. Simshauser, Paul, 2024. "On static vs. dynamic line ratings in renewable energy zones," Energy Economics, Elsevier, vol. 129(C).
    12. Csereklyei, Zsuzsanna & Qu, Songze & Ancev, Tihomir, 2019. "The effect of wind and solar power generation on wholesale electricity prices in Australia," Energy Policy, Elsevier, vol. 131(C), pages 358-369.
    13. Naguib, Costanza, 2019. "Estimating the Heterogeneous Impact of the Free Movement of Persons on Relative Wage Mobility," Economics Working Paper Series 1903, University of St. Gallen, School of Economics and Political Science.
    14. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    15. Akash Malhotra, 2018. "A hybrid econometric-machine learning approach for relative importance analysis: Prioritizing food policy," Papers 1806.04517, arXiv.org, revised Aug 2020.
    16. Thiemo Fetzer & Stephan Kyburz, 2024. "Cohesive Institutions and Political Violence," The Review of Economics and Statistics, MIT Press, vol. 106(1), pages 133-150, January.
    17. Dang, Hai-Anh & Carletto, Calogero & Gourlay, Sydney & Abanokova, Kseniya, 2024. "Addressing Soil Quality Data Gaps with Imputation: Evidence from Ethiopia and Uganda," GLO Discussion Paper Series 1445, Global Labor Organization (GLO).
    18. Tobias Götze & Marc Gürtler & Eileen Witowski, 2020. "Improving CAT bond pricing models via machine learning," Journal of Asset Management, Palgrave Macmillan, vol. 21(5), pages 428-446, September.
    19. Sascha O. Becker & Thiemo Fetzer, 2018. "Has Eastern European Migration Impacted UK-born Workers?," CAGE Online Working Paper Series 376, Competitive Advantage in the Global Economy (CAGE).
    20. Bailliu, Jeannine & Han, Xinfen & Kruger, Mark & Liu, Yu-Hsien & Thanabalasingam, Sri, 2019. "Can media and text analytics provide insights into labour market conditions in China?," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1118-1130.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:eti:dpaper:22093. 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: TANIMOTO, Toko (email available below). General contact details of provider: https://edirc.repec.org/data/rietijp.html .

    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.