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Impacts of Urban Transportation Mode Split on CO 2 Emissions in Jinan, China

Author

Listed:
  • Dongquan He

    (The Energy Foundation, Room 1903, CITIC Building, Jianguomenwai Avenue, Beijing 100004, China)

  • Fei Meng

    (Tsinghua University, Department of Environmental Science and Engineering, Beijing 100084, China)

  • Michael Q. Wang

    (Argonne National Laboratory, Center for Transportation Research, Building 362, 9700 South Cass Avenue, Argonne, IL 60439, USA)

  • Kebin He

    (Tsinghua University, Department of Environmental Science and Engineering, Beijing 100084, China)

Abstract

As the world’s largest developing country, China currently is undergoing rapid urbanization and motorization, which will result in far-reaching impacts on energy and the environment. According to estimates, energy use and carbon emissions in the transportation sector will comprise roughly 30% of total emissions by 2030. Since the late 1990s, transportation-related issues such as energy, consumption, and carbon emissions have become a policy focus in China. To date, most research and policies have centered on vehicle technologies that promote vehicle efficiency and reduced emissions. Limited research exists on the control of greenhouse gases through mode shifts in urban transportation—in particular, through the promotion of public transit. The purpose of this study is to establish a methodology to analyze carbon emissions from the urban transportation sector at the Chinese city level. By using Jinan, the capital of China’s Shandong Province, as an example, we have developed an analytical model to simulate energy consumption and carbon emissions based on the number of trips, the transportation mode split, and the trip distance. This model has enabled us to assess the impacts of the transportation mode split on energy consumption and carbon emissions. Furthermore, this paper reviews a set of methods for data collection, estimation, and processing for situations where statistical data are scarce in China. This paper also describes the simulation of three transportation system development scenarios. The results of this study illustrate that if no policy intervention is implemented for the transportation mode split (the business-as-usual (BAU) case), then emissions from Chinese urban transportation systems will quadruple by 2030. However, a dense, mixed land-use pattern, as well as transportation policies that encourage public transportation, would result in the elimination of 1.93 million tons of carbon emissions—approximately 50% of the BAU scenario emissions.

Suggested Citation

  • Dongquan He & Fei Meng & Michael Q. Wang & Kebin He, 2011. "Impacts of Urban Transportation Mode Split on CO 2 Emissions in Jinan, China," Energies, MDPI, vol. 4(4), pages 1-15, April.
    • Handle: RePEc:gam:jeners:v:4:y:2011:i:4:p:685-699:d:12143
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    References listed on IDEAS

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    1. He, Kebin & Huo, Hong & Zhang, Qiang & He, Dongquan & An, Feng & Wang, Michael & Walsh, Michael P., 2005. "Oil consumption and CO2 emissions in China's road transport: current status, future trends, and policy implications," Energy Policy, Elsevier, vol. 33(12), pages 1499-1507, August.
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    Cited by:

    1. 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.
    2. Daniel Neves Schmitz Gonçalves & Renata Albergaria de Mello Bandeira & Mariane Gonzalez da Costa & George Vasconcelos Goes & Tássia Faria de Assis & Márcio de Almeida D’Agosto & Isabela Rocha Pombo Le, 2020. "A Multitier Approach to Estimating the Energy Efficiency of Urban Passenger Mobility," Sustainability, MDPI, vol. 12(24), pages 1-18, December.
    3. Chuyu Xia & Yan Li & Yanmei Ye & Zhou Shi & Jingming Liu, 2017. "Decomposed Driving Factors of Carbon Emissions and Scenario Analyses of Low-Carbon Transformation in 2020 and 2030 for Zhejiang Province," Energies, MDPI, vol. 10(11), pages 1-16, October.
    4. Arne Höltl & Cathy Macharis & Klaas De Brucker, 2017. "Pathways to Decarbonise the European Car Fleet: A Scenario Analysis Using the Backcasting Approach," Energies, MDPI, vol. 11(1), pages 1-20, December.
    5. Marcelo Maciel & Luiz Rosa & Fernando Correa & Ursula Maruyama, 2012. "Energy, Pollutant Emissions and Other Negative Externality Savings from Curbing Individual Motorized Transportation (IMT): A Low Cost, Low Technology Scenario Analysis in Brazilian Urban Areas," Energies, MDPI, vol. 5(3), pages 1-27, March.
    6. Yang, Zhenshan & Jia, Peng & Liu, Weidong & Yin, Hongchun, 2017. "Car ownership and urban development in Chinese cities: A panel data analysis," Journal of Transport Geography, Elsevier, vol. 58(C), pages 127-134.
    7. Maria La Gennusa & Patrizia Ferrante & Barbara Lo Casto & Gianfranco Rizzo, 2015. "An Integrated Environmental Indicator for Urban Transportation Systems: Description and Application," Energies, MDPI, vol. 8(10), pages 1-19, October.
    8. Yang Li & Lu Miao & Ying Chen & Yike Hu, 2019. "Exploration of Sustainable Urban Transportation Development in China through the Forecast of Private Vehicle Ownership," Sustainability, MDPI, vol. 11(16), pages 1-18, August.

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