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How Large the Direct Rebound Effect for Residential Electricity Consumption When the Artificial Neural Network Takes on the Role? A Taiwan Case Study of Household Electricity Consumption

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  • Rishan Adha

    (Department of Business Administration, Chaoyang University of Technology, Taiwan,)

  • Cheng-Yih Hong

    (Faculty of Finance, Chaoyang University of Technology, Taiwan.)

Abstract

Amid the energy reform efforts by the Taiwan government, residential energy demand continues to face an escalating trend every year. This indicates the phenomenon of the energy efficiency gap. One of the factors that control the energy efficiency gap is the rebound effect. The rebound effect is related to the increase in energy consumption through efforts to reduce the use of energy itself. This can be due to the low cost of usage that causes a person to be encouraged to use more energy. This study aims to estimate the magnitude of the direct rebound effect of household electricity consumption in Taiwan using monthly time series data from January 1998 to December 2018 and to implement the artificial neural network (ANN) as an alternative approach to measure the direct rebound effect. Based on the simulation results, the direct rebound effect magnitude for household electricity consumption in Taiwan is in the range of 11.17% to 21.95%. GDP growth is the most important input in the model. Additionally, population growth and climate change are also critical factors and have significant implications in the model.

Suggested Citation

  • Rishan Adha & Cheng-Yih Hong, 2021. "How Large the Direct Rebound Effect for Residential Electricity Consumption When the Artificial Neural Network Takes on the Role? A Taiwan Case Study of Household Electricity Consumption," International Journal of Energy Economics and Policy, Econjournals, vol. 11(3), pages 354-364.
    • Handle: RePEc:eco:journ2:2021-03-43
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Adha, Rishan & Hong, Cheng-Yih & Firmansyah, M. & Paranata, Ade, 2021. "Rebound effect with energy efficiency determinants: a two-stage analysis of residential electricity consumption in Indonesia," MPRA Paper 110444, University Library of Munich, Germany.
    3. Rishan Adha & Cheng-Yih Hong & Somya Agrawal & Li-Hua Li, 2023. "ICT, carbon emissions, climate change, and energy demand nexus: The potential benefit of digitalization in Taiwan," Energy & Environment, , vol. 34(5), pages 1619-1638, August.
    4. Syamsiyatul Muzayyanah & Cheng-Yih Hong & Rishan Adha & Su-Fen Yang, 2023. "The Non-Linear Relationship between Air Pollution, Labor Insurance and Productivity: Multivariate Adaptive Regression Splines Approach," Sustainability, MDPI, vol. 15(12), pages 1-20, June.

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    More about this item

    Keywords

    energy efficiency gap; direct rebound effect; artificial neural network;
    All these keywords.

    JEL classification:

    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • E7 - Macroeconomics and Monetary Economics - - Macro-Based Behavioral Economics

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