IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v18y2013icp31-42.html
   My bibliography  Save this article

An approach to energy savings and improved environmental impact through restructuring Jordan's transport sector

Author

Listed:
  • Al-Ghandoor, A.

Abstract

This paper illustrates a new approach to forecast the potential energy savings and environmental impact of adopting energy efficient practices in the Jordanian transportation sector. This approach is based on Adaptive Neuro-Fuzzy Inference System (ANFIS) and the double exponential smoothing techniques. The ANFIS model has been developed using socio-economic and transport related indicators based on annual number of vehicles, vehicle owner level, income level, and fuel prices in Jordan. The double exponential smoothing technique has been used to forecast the different transport indicators to feed the developed ANFIS model in order to forecast the transport energy demand for the next two decades. The model has been validated using testing data and has showed very accurate results of 97%. The results show that the transport energy demand is expected to increase at % 4.9yr−1 from years 2011–2030. As an example of the energy efficiency improvement in the transportation sector, this paper examines potential benefits that can be achieved through the introduction of diesel cars to the passenger cars market in Jordan. Five scenarios are suggested for implementation and investigated using the new approach on the basis of local and global trends over the period 2011–2030. It is demonstrated that introducing diesel passenger cars can slow down the growth of energy consumption in the transportation sector resulting in significant savings in the national fuel bill. It is also shown that this is an effective and feasible option for cutting down CO2 emissions.

Suggested Citation

  • Al-Ghandoor, A., 2013. "An approach to energy savings and improved environmental impact through restructuring Jordan's transport sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 18(C), pages 31-42.
  • Handle: RePEc:eee:rensus:v:18:y:2013:i:c:p:31-42
    DOI: 10.1016/j.rser.2012.09.026
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032112005278
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.rser.2012.09.026?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jaber, J.O. & Al-Ghandoor, A. & Sawalha, S.A., 2008. "Energy analysis and exergy utilization in the transportation sector of Jordan," Energy Policy, Elsevier, vol. 36(8), pages 2985-2990, August.
    2. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
    3. Hao, Han & Wang, Hewu & Yi, Ran, 2011. "Hybrid modeling of China’s vehicle ownership and projection through 2050," Energy, Elsevier, vol. 36(2), pages 1351-1361.
    4. Atabani, A.E. & Badruddin, Irfan Anjum & Mekhilef, S. & Silitonga, A.S., 2011. "A review on global fuel economy standards, labels and technologies in the transportation sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(9), pages 4586-4610.
    5. Liao, Chun-Hsiung & Lu, Chin-Shan & Tseng, Po-Hsing, 2011. "Carbon dioxide emissions and inland container transport in Taiwan," Journal of Transport Geography, Elsevier, vol. 19(4), pages 722-728.
    6. Haldenbilen, Soner & Ceylan, Halim, 2005. "Genetic algorithm approach to estimate transport energy demand in Turkey," Energy Policy, Elsevier, vol. 33(1), pages 89-98, January.
    7. Wang, Zhao & Jin, Yuefu & Wang, Michael & Wei, Wu, 2010. "New fuel consumption standards for Chinese passenger vehicles and their effects on reductions of oil use and CO2 emissions of the Chinese passenger vehicle fleet," Energy Policy, Elsevier, vol. 38(9), pages 5242-5250, September.
    8. Mayyas, Ahmad & Qattawi, Ala & Omar, Mohammed & Shan, Dongri, 2012. "Design for sustainability in automotive industry: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(4), pages 1845-1862.
    9. Zhang, Ming & Mu, Hailin & Li, Gang & Ning, Yadong, 2009. "Forecasting the transport energy demand based on PLSR method in China," Energy, Elsevier, vol. 34(9), pages 1396-1400.
    10. Limanond, Thirayoot & Jomnonkwao, Sajjakaj & Srikaew, Artit, 2011. "Projection of future transport energy demand of Thailand," Energy Policy, Elsevier, vol. 39(5), pages 2754-2763, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Pan, Hongye & Qi, Lingfei & Zhang, Zutao & Yan, Jinyue, 2021. "Kinetic energy harvesting technologies for applications in land transportation: A comprehensive review," Applied Energy, Elsevier, vol. 286(C).
    2. Fan, Yee Van & Klemeš, Jiří Jaromír & Walmsley, Timothy Gordon & Perry, Simon, 2019. "Minimising energy consumption and environmental burden of freight transport using a novel graphical decision-making tool," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    3. Al-Ghandoor, Ahmed & Jaber, Jamal & Al-Hinti, Ismael & Abdallat, Yousef, 2013. "Statistical assessment and analyses of the determinants of transportation sector gasoline demand in Jordan," Transportation Research Part A: Policy and Practice, Elsevier, vol. 50(C), pages 129-138.
    4. Wang, Zhaohua & Liu, Wei, 2015. "Determinants of CO2 emissions from household daily travel in Beijing, China: Individual travel characteristic perspectives," Applied Energy, Elsevier, vol. 158(C), pages 292-299.
    5. Ben Abdallah, Khaled & Belloumi, Mounir & De Wolf, Daniel, 2013. "Indicators for sustainable energy development: A multivariate cointegration and causality analysis from Tunisian road transport sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 25(C), pages 34-43.
    6. Hao, Han & Geng, Yong & Wang, Hewu & Ouyang, Minggao, 2014. "Regional disparity of urban passenger transport associated GHG (greenhouse gas) emissions in China: A review," Energy, Elsevier, vol. 68(C), pages 783-793.
    7. Hao, Han & Ou, Xunmin & Du, Jiuyu & Wang, Hewu & Ouyang, Minggao, 2014. "China’s electric vehicle subsidy scheme: Rationale and impacts," Energy Policy, Elsevier, vol. 73(C), pages 722-732.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Al-Ghandoor, Ahmed & Samhouri, Murad & Al-Hinti, Ismael & Jaber, Jamal & Al-Rawashdeh, Mohammad, 2012. "Projection of future transport energy demand of Jordan using adaptive neuro-fuzzy technique," Energy, Elsevier, vol. 38(1), pages 128-135.
    2. Manuel Llorca & José Baños & José Somoza & Pelayo Arbués, 2017. "A Stochastic Frontier Analysis Approach for Estimating Energy Demand and Efficiency in the Transport Sector of Latin America and the Caribbean," The Energy Journal, International Association for Energy Economics, vol. 0(Number 5).
    3. Llorca, Manuel & Baños, José & Somoza, José & Arbués, Pelayo, 2014. "A latent class approach for estimating energy demands and efficiency in transport: An application to Latin America and the Caribbean," Efficiency Series Papers 2014/04, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    4. Al-Ghandoor, Ahmed & Jaber, Jamal & Al-Hinti, Ismael & Abdallat, Yousef, 2013. "Statistical assessment and analyses of the determinants of transportation sector gasoline demand in Jordan," Transportation Research Part A: Policy and Practice, Elsevier, vol. 50(C), pages 129-138.
    5. Sahraei, Mohammad Ali & Duman, Hakan & Çodur, Muhammed Yasin & Eyduran, Ecevit, 2021. "Prediction of transportation energy demand: Multivariate Adaptive Regression Splines," Energy, Elsevier, vol. 224(C).
    6. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    7. Atul Anand & L Suganthi, 2018. "Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand," Energies, MDPI, vol. 11(4), pages 1-15, March.
    8. Huang, Chung-Neng & Chen, Yui-Sung, 2017. "Design of magnetic flywheel control for performance improvement of fuel cells used in vehicles," Energy, Elsevier, vol. 118(C), pages 840-852.
    9. Wu, Qunli & Peng, Chenyang, 2017. "A hybrid BAG-SA optimal approach to estimate energy demand of China," Energy, Elsevier, vol. 120(C), pages 985-995.
    10. Yu, Shiwei & Wei, Yi-Ming & Wang, Ke, 2012. "A PSO–GA optimal model to estimate primary energy demand of China," Energy Policy, Elsevier, vol. 42(C), pages 329-340.
    11. Sonmez, Mustafa & Akgüngör, Ali Payıdar & Bektaş, Salih, 2017. "Estimating transportation energy demand in Turkey using the artificial bee colony algorithm," Energy, Elsevier, vol. 122(C), pages 301-310.
    12. Geem, Zong Woo, 2011. "Transport energy demand modeling of South Korea using artificial neural network," Energy Policy, Elsevier, vol. 39(8), pages 4644-4650, August.
    13. Yu, Shi-wei & Zhu, Ke-jun, 2012. "A hybrid procedure for energy demand forecasting in China," Energy, Elsevier, vol. 37(1), pages 396-404.
    14. Zhao, Jingjing & Heydari, Shahram & Forrest, Michael & Stevens, Alan & Preston, John, 2023. "Investigating correlates of personal and freight road transport energy consumption: A case study of England," Journal of Transport Geography, Elsevier, vol. 112(C).
    15. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
    16. Muhammad Muhitur Rahman & Syed Masiur Rahman & Md Shafiullah & Md Arif Hasan & Uneb Gazder & Abdullah Al Mamun & Umer Mansoor & Mohammad Tamim Kashifi & Omer Reshi & Md Arifuzzaman & Md Kamrul Islam &, 2022. "Energy Demand of the Road Transport Sector of Saudi Arabia—Application of a Causality-Based Machine Learning Model to Ensure Sustainable Environment," Sustainability, MDPI, vol. 14(23), pages 1-21, December.
    17. Wei Sun & Yujun He & Hong Chang, 2015. "Forecasting Fossil Fuel Energy Consumption for Power Generation Using QHSA-Based LSSVM Model," Energies, MDPI, vol. 8(2), pages 1-21, January.
    18. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    19. Hafezi, Reza & Akhavan, AmirNaser & Pakseresht, Saeed & Wood, David A., 2019. "A Layered Uncertainties Scenario Synthesizing (LUSS) model applied to evaluate multiple potential long-run outcomes for Iran's natural gas exports," Energy, Elsevier, vol. 169(C), pages 646-659.
    20. Satrio Mukti Wibowo & Dedi Budiman Hakim & Baba Barus & Akhmad Fauzi, 2022. "Estimation of Energy Demand in Indonesia using Artificial Neural Network," International Journal of Energy Economics and Policy, Econjournals, vol. 12(6), pages 261-271, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:rensus:v:18:y:2013:i:c:p:31-42. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.