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A comparison of grid-level residential electricity demand scenarios in Japan for 2050

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  • Yamagata, Yoshiki
  • Murakami, Daisuke
  • Seya, Hajime

Abstract

Scenarios of future population at the grid level (e.g., 1km2) is very important for various types of regional planning, including energy planning. Though the most often used method for population projection is the so-called cohort-component method, if our focus is world level, not country or finer level, we must rely on much simpler methods. In this study, we compare the performance of several typical simple population projection methods using the residential electricity demand derived from each population scenario, especially focusing on spatial patterns. We show that different projection methods may produce different spatial patterns such as “compact” or “dispersed” urban form, which is quantified using the Gini coefficient. Also, we show that the projection method for gravity-based residential electricity demand is very flexible in that it can create the most compact and the most dispersed urban form among the focused methods. Furthermore, we point out the usefulness of model ensemble in projection.

Suggested Citation

  • Yamagata, Yoshiki & Murakami, Daisuke & Seya, Hajime, 2015. "A comparison of grid-level residential electricity demand scenarios in Japan for 2050," Applied Energy, Elsevier, vol. 158(C), pages 255-262.
    • Handle: RePEc:eee:appene:v:158:y:2015:i:c:p:255-262
    DOI: 10.1016/j.apenergy.2015.08.079
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    References listed on IDEAS

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    Cited by:

    1. Sueyoshi, Toshiyuki & Qu, Jingjing & Li, Aijun & Liu, Xiaohong, 2021. "A new approach for evaluating technology inequality and diffusion barriers: The concept of efficiency Gini coefficient and its application in Chinese provinces," Energy, Elsevier, vol. 235(C).
    2. Rauner, Sebastian & Eichhorn, Marcus & Thrän, Daniela, 2016. "The spatial dimension of the power system: Investigating hot spots of Smart Renewable Power Provision," Applied Energy, Elsevier, vol. 184(C), pages 1038-1050.
    3. Rafael Sánchez-Durán & Julio Barbancho & Joaquín Luque, 2019. "Solar Energy Production for a Decarbonization Scenario in Spain," Sustainability, MDPI, vol. 11(24), pages 1-29, December.
    4. Rizzati, Massimiliano & De Cian, Enrica & Guastella, Gianni & Mistry, Malcolm N. & Pareglio, Stefano, 2022. "Residential electricity demand projections for Italy: A spatial downscaling approach," Energy Policy, Elsevier, vol. 160(C).
    5. Daisuke Murakami & Yoshiki Yamagata, 2019. "Estimation of Gridded Population and GDP Scenarios with Spatially Explicit Statistical Downscaling," Sustainability, MDPI, vol. 11(7), pages 1-18, April.
    6. Qiushi Feng & Zhenglian Wang & Simon Choi & Yi Zeng, 2020. "Forecast Households at the County Level: An Application of the ProFamy Extended Cohort-Component Method in Six Counties of Southern California, 2010 to 2040," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 39(2), pages 253-281, April.

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