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Residential past and future energy consumption: Potential savings and environmental impact

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  • Al-Ghandoor, A.
  • Jaber, J.O.
  • Al-Hinti, I.
  • Mansour, I.M.

Abstract

In order to identify main drivers behind changes in electricity and fuel consumptions in the household sector in Jordan, two empirical models are developed based on multivariate linear regression analysis. In addition, this paper analyzes and evaluates impacts of introducing some efficient measures, such as high efficiency lightings and solar water heating systems, in the housing stock, on the future fuel and electricity demands and associated reduction in GHG emissions. It was found that fuel unit price, income level, and population are the most important variables that affect demand on electrical power, while population is the most important variable in the case of fuel consumption. Obtained results proved that the multivariate linear regression models can be used adequately to simulate residential electricity and fuel consumptions with very high coefficient of determination. Without employing most effective energy conservation measures, electricity and fuel demands are expected to rise by approximately 100% and 23%, respectively within 10 years time. Consequently, associated GHG emissions resulting from activities within the residential sector are predicted to rise by 59% for the same period. However, if recommended energy management measures are implemented on a gradual basis, electricity and fuel consumptions as well as GHG emissions are forecasted to increase at a lower rate.

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  • Al-Ghandoor, A. & Jaber, J.O. & Al-Hinti, I. & Mansour, I.M., 2009. "Residential past and future energy consumption: Potential savings and environmental impact," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(6-7), pages 1262-1274, August.
    • Handle: RePEc:eee:rensus:v:13:y:2009:i:6-7:p:1262-1274
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    1. Al-Ghandoor, A. & Al-Hinti, I. & Jaber, J.O. & Sawalha, S.A., 2008. "Electricity consumption and associated GHG emissions of the Jordanian industrial sector: Empirical analysis and future projection," Energy Policy, Elsevier, vol. 36(1), pages 258-267, January.
    2. Al-Hinti, I. & Al-Ghandoor, A. & Akash, B. & Abu-Nada, E., 2007. "Energy saving and CO2 mitigation through restructuring Jordan's transportation sector: The diesel passenger cars scenario," Energy Policy, Elsevier, vol. 35(10), pages 5003-5011, October.
    3. Jaber, J. O., 2002. "Greenhouse gas emissions and barriers to implementation in the Jordanian energy sector," Energy Policy, Elsevier, vol. 30(5), pages 385-395, April.
    4. Lu, Wei, 2006. "Potential energy savings and environmental impact by implementing energy efficiency standard for household refrigerators in China," Energy Policy, Elsevier, vol. 34(13), pages 1583-1589, September.
    5. Michael Parti & Cynthia Parti, 1980. "The Total and Appliance-Specific Conditional Demand for Electricity in the Household Sector," Bell Journal of Economics, The RAND Corporation, vol. 11(1), pages 309-321, Spring.
    6. Jaber, J.O. & Jaber, Q.M. & Sawalha, S.A. & Mohsen, M.S., 2008. "Evaluation of conventional and renewable energy sources for space heating in the household sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(1), pages 278-289, January.
    7. Aydinalp, Merih & Ismet Ugursal, V. & Fung, Alan S., 2004. "Modeling of the space and domestic hot-water heating energy-consumption in the residential sector using neural networks," Applied Energy, Elsevier, vol. 79(2), pages 159-178, October.
    8. Aunan, Kristin & Fang, Jinghua & Vennemo, Haakon & Oye, Kenneth & Seip, Hans M., 2004. "Co-benefits of climate policy--lessons learned from a study in Shanxi, China," Energy Policy, Elsevier, vol. 32(4), pages 567-581, March.
    9. Dincer, Ibrahim & Dost, Sadik, 1996. "Energy intensities for Canada," Applied Energy, Elsevier, vol. 53(3), pages 283-298.
    10. Mahlia, T.M.I. & Masjuki, H.H. & Saidur, R. & Choudhury, I.A. & NoorLeha, A.R., 2003. "Projected electricity savings from implementing minimum energy efficiency standard for household refrigerators in Malaysia," Energy, Elsevier, vol. 28(7), pages 751-754.
    11. Jaber, J. O. & Al-Sarkhi, A. & Akash, B. A. & Mohsen, M. S., 2004. "Medium-range planning economics of future electrical-power generation options," Energy Policy, Elsevier, vol. 32(3), pages 357-366, February.
    12. Siller, Thomas & Kost, Michael & Imboden, Dieter, 2007. "Long-term energy savings and greenhouse gas emission reductions in the Swiss residential sector," Energy Policy, Elsevier, vol. 35(1), pages 529-539, January.
    13. Aydinalp, Merih & Ismet Ugursal, V. & Fung, Alan S., 2002. "Modeling of the appliance, lighting, and space-cooling energy consumptions in the residential sector using neural networks," Applied Energy, Elsevier, vol. 71(2), pages 87-110, February.
    Full references (including those not matched with items on IDEAS)

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    13. Al-Ghandoor, A., 2012. "Analysis of Jordan's industrial energy intensity and potential mitigations of energy and GHGs emissions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 4479-4490.
    14. Khoshnevisan, Benyamin & Rafiee, Shahin & Omid, Mahmoud & Yousefi, Marziye & Movahedi, Mehran, 2013. "Modeling of energy consumption and GHG (greenhouse gas) emissions in wheat production in Esfahan province of Iran using artificial neural networks," Energy, Elsevier, vol. 52(C), pages 333-338.
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    16. Asma' M. Bataineh & Hikmat H. Ali, 2021. "Improving Energy Efficiency of Multi-Family Apartment Buildings Case of Jordan," International Journal of Energy Economics and Policy, Econjournals, vol. 11(5), pages 244-254.
    17. Khoshnevisan, Benyamin & Rafiee, Shahin & Omid, Mahmoud & Mousazadeh, Hossein & Rajaeifar, Mohammad Ali, 2014. "Application of artificial neural networks for prediction of output energy and GHG emissions in potato production in Iran," Agricultural Systems, Elsevier, vol. 123(C), pages 120-127.
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    19. Yu, Yihua & Guo, Jin, 2016. "Identifying electricity-saving potential in rural China: Empirical evidence from a household survey," Energy Policy, Elsevier, vol. 94(C), pages 1-9.

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