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Modeling Future Energy Demand and CO 2 Emissions of Passenger Cars in Indonesia at the Provincial Level

Author

Listed:
  • Qodri Febrilian Erahman

    (Chemical Engineering Department, Faculty of Engineering, Universitas Indonesia, Depok 16424, Indonesia)

  • Nadhilah Reyseliani

    (Chemical Engineering Department, Faculty of Engineering, Universitas Indonesia, Depok 16424, Indonesia)

  • Widodo Wahyu Purwanto

    (Chemical Engineering Department, Faculty of Engineering, Universitas Indonesia, Depok 16424, Indonesia)

  • Mahmud Sudibandriyo

    (Chemical Engineering Department, Faculty of Engineering, Universitas Indonesia, Depok 16424, Indonesia)

Abstract

The high energy demand and CO 2 emissions in the road transport sector in Indonesia are mainly caused by the use of passenger cars. This situation is predicted to continue due to the increase in car ownership. Scenarios are arranged to examine the potential reductions in energy demand and CO 2 emissions in comparison with the business as usual (BAU) condition between 2016 and 2050 by controlling car intensity (fuel economy) and activity (vehicle-km). The intensity is controlled through the introduction of new car technologies, while the activity is controlled through the enactment of fuel taxes. This study aims to analyze the energy demand and CO 2 emissions of passenger cars in Indonesia not only for a period in the past (2010–2015) but also based on projections through to 2050, by employing a provincially disaggregated bottom-up model. The provincially disaggregated model shows more accurate estimations for passenger car energy demands. The results suggest that energy demand and CO 2 emissions in 2050 will be 50 million liter gasoline equivalent (LGE) and 110 million tons of CO 2 , respectively. The five provinces with the highest CO 2 emissions in 2050 are projected to be West Java, Banten, East Java, Central Java, and South Sulawesi. The projected analysis for 2050 shows that new car technology and fuel tax scenarios can reduce energy demand from the BAU condition by 7.72% and 3.18% and CO 2 emissions by 15.96% and 3.18%, respectively.

Suggested Citation

  • Qodri Febrilian Erahman & Nadhilah Reyseliani & Widodo Wahyu Purwanto & Mahmud Sudibandriyo, 2019. "Modeling Future Energy Demand and CO 2 Emissions of Passenger Cars in Indonesia at the Provincial Level," Energies, MDPI, vol. 12(16), pages 1-25, August.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:16:p:3168-:d:258625
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    References listed on IDEAS

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    1. Dargay, Joyce & Gately, Dermot, 1997. "Vehicle ownership to 2015: Implications for energy use and emissions," Energy Policy, Elsevier, vol. 25(14-15), pages 1121-1127, December.
    2. Lu, I.J. & Lewis, Charles & Lin, Sue J., 2009. "The forecast of motor vehicle, energy demand and CO2 emission from Taiwan's road transportation sector," Energy Policy, Elsevier, vol. 37(8), pages 2952-2961, August.
    3. Cheah, Lynette & Heywood, John, 2011. "Meeting U.S. passenger vehicle fuel economy standards in 2016 and beyond," Energy Policy, Elsevier, vol. 39(1), pages 454-466, January.
    4. Deendarlianto, & Widyaparaga, Adhika & Sopha, Bertha Maya & Budiman, Arief & Muthohar, Imam & Setiawan, Indra Chandra & Lindasista, Alia & Soemardjito, Joewono & Oka, Kazutaka, 2017. "Scenarios analysis of energy mix for road transportation sector in Indonesia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 13-23.
    5. Tian Wu & Hongmei Zhao & Xunmin Ou, 2014. "Vehicle Ownership Analysis Based on GDP per Capita in China: 1963–2050," Sustainability, MDPI, vol. 6(8), pages 1-23, August.
    6. 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.
    7. Baptista, Patrícia C. & Silva, Carla M. & Farias, Tiago L. & Heywood, John B., 2012. "Energy and environmental impacts of alternative pathways for the Portuguese road transportation sector," Energy Policy, Elsevier, vol. 51(C), pages 802-815.
    8. Eom, Jiyong & Schipper, Lee, 2010. "Trends in passenger transport energy use in South Korea," Energy Policy, Elsevier, vol. 38(7), pages 3598-3607, July.
    9. Ko, Ahyun & Myung, Cha-Lee & Park, Simsoo & Kwon, Sangil, 2014. "Scenario-based CO2 emissions reduction potential and energy use in Republic of Korea’s passenger vehicle fleet," Transportation Research Part A: Policy and Practice, Elsevier, vol. 59(C), pages 346-356.
    10. Erahman, Qodri Febrilian & Purwanto, Widodo Wahyu & Sudibandriyo, Mahmud & Hidayatno, Akhmad, 2016. "An assessment of Indonesia's energy security index and comparison with seventy countries," Energy, Elsevier, vol. 111(C), pages 364-376.
    11. Joyce Dargay & Dermot Gately & Martin Sommer, 2007. "Vehicle Ownership and Income Growth, Worldwide: 1960-2030," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 143-170.
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