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Rebound effect with energy efficiency determinants: a two-stage analysis of residential electricity consumption in Indonesia

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  • Adha, Rishan
  • Hong, Cheng-Yih
  • Firmansyah, M.
  • Paranata, Ade

Abstract

This study aims to estimate the economy-wide rebound effect using the determinants of household energy demand in Indonesia. Identifying the size of the rebound effect is essential for the government's energy efficiency and carbon emission reduction programs. The estimation of the rebound effect uses a two-stage analysis with panel data of every province in Indonesia from 2002 to 2018. We employ the Input Demand Function of the Stochastic Frontier Analysis to measure the energy efficiency of residential aggregate in Indonesia. In the second stage, we adopt the dynamic panel data model to estimate the economy-wide rebound effect. The estimated dynamic panel data model reveals that the magnitudes of the short-run and long-run rebound effects were 87.2% and -45.5%, respectively. In other words, a 1% increase in household energy efficiency results in a reduction in energy consumption of 0.13% in the short term and 1.45% in the long term. Our research also discovers that a backfire rebound effect exists in provinces with high energy efficiency. Therefore, we prove to backfire claims that improving energy efficiency will increase energy use. Henceforth, energy efficiency programs in the household sector still need to be implemented, followed by increasing technological innovation and improving housing policy.

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  • 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.
    • Handle: RePEc:pra:mprapa:110444
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    2. 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.
    3. Peng, Hua-Rong & Qin, Xiong-Feng, 2024. "Digitalization as a trigger for a rebound effect of electricity use," Energy, Elsevier, vol. 300(C).
    4. Karakaya, Etem & Alataş, Sedat & Erkara, Elif & Mert, Betül & Akdoğan, Tuğba & Hiçyılmaz, Burcu, 2024. "The rebound effect of material and energy efficiency for the EU and its major trading partners," Energy Economics, Elsevier, vol. 134(C).
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    6. Jun Liu & Yu Qian & Yuanjun Yang & Zhidan Yang, 2022. "Can Artificial Intelligence Improve the Energy Efficiency of Manufacturing Companies? Evidence from China," IJERPH, MDPI, vol. 19(4), pages 1-18, February.
    7. Zhang, Weike & Zeng, Ming, 2024. "Is artificial intelligence a curse or a blessing for enterprise energy intensity? Evidence from China," Energy Economics, Elsevier, vol. 134(C).
    8. Karakurt, Izzet & Aydin, Gokhan, 2023. "Development of regression models to forecast the CO2 emissions from fossil fuels in the BRICS and MINT countries," Energy, Elsevier, vol. 263(PA).

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    More about this item

    Keywords

    Electricity demand; energy efficiency; rebound effect; stochastic frontier analysis;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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