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Scenario-based forecast for the electricity demand in Qatar and the role of energy efficiency improvements

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  • Khalifa, Ahmed
  • Caporin, Massimiliano
  • Di Fonzo, Tommaso

Abstract

We model the electricity consumption in the market segment that compose the Qatari electricity market. We link electricity consumption to GDP growth and Population Growth. Building on the estimated model, we develop long-range forecasts of electricity consumption from 2017 to 2030 over different scenarios for the economic drivers. In addition, we proxy for electricity efficiency improvements by reducing the long-run elasticity of electricity consumption to GDP and Population. We show that electricity efficiency has a crucial role in controlling the future development of electricity consumption. Energy policies should consider this aspect and support both electricity efficiency improvement programs, as well as a price reform.

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  • Khalifa, Ahmed & Caporin, Massimiliano & Di Fonzo, Tommaso, 2019. "Scenario-based forecast for the electricity demand in Qatar and the role of energy efficiency improvements," Energy Policy, Elsevier, vol. 127(C), pages 155-164.
    • Handle: RePEc:eee:enepol:v:127:y:2019:i:c:p:155-164
    DOI: 10.1016/j.enpol.2018.11.047
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    Cited by:

    1. Xin Ma & Yubin Cai & Hong Yuan & Yanqiao Deng, 2023. "Partially Linear Component Support Vector Machine for Primary Energy Consumption Forecasting of the Electric Power Sector in the United States," Sustainability, MDPI, vol. 15(9), pages 1-26, April.
    2. Chen, Hai-Bao & Pei, Ling-Ling & Zhao, Yu-Feng, 2021. "Forecasting seasonal variations in electricity consumption and electricity usage efficiency of industrial sectors using a grey modeling approach," Energy, Elsevier, vol. 222(C).
    3. Shayma Al Bannay & Satoshi Takizawa, 2022. "Decoupling of Water Production and Electricity Generation from GDP and Population in the Gulf Cooperation Council (GCC) Countries," Sustainability, MDPI, vol. 14(9), pages 1-20, April.
    4. Abulibdeh, Ammar, 2022. "Time series analysis of environmental quality in the state of Qatar," Energy Policy, Elsevier, vol. 168(C).
    5. Yusri Syam Akil & Hajime Miyauchi & Saiful Mangngenre & Kifayah Amar, 2020. "Perceptions and Determinants of SMEs Consumers Behaviors for Electricity Saving: Evidence from Indonesia," International Journal of Energy Economics and Policy, Econjournals, vol. 10(3), pages 168-174.
    6. Zhixiong Weng & Yuqi Song & Hao Ma & Zhong Ma & Tingting Liu, 2023. "Forecasting energy demand, structure, and CO2 emission: a case study of Beijing, China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(9), pages 10369-10391, September.
    7. Angel Manuel Benitez Rodriguez & Ian Michael Trotter, 2019. "Climate change scenarios for Paraguayan power demand 2017–2050," Climatic Change, Springer, vol. 156(3), pages 425-445, October.

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    More about this item

    Keywords

    O13; Q47; C32; C53; Electricity consumption; Electricity efficiency; Scenario-based forecast;
    All these keywords.

    JEL classification:

    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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