IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i16p5957-d400073.html
   My bibliography  Save this article

A Carbon Emission Measurement Method for Individual Travel Based on Transportation Big Data: The Case of Nanjing Metro

Author

Listed:
  • Wei Yu

    (College of Automobile and Traffic Engineering, Nanjing Forestry University, Longpan Road 159#, Nanjing 210037, China)

  • Tao Wang

    (School of Architecture and Transportation, Guilin University of Electronic Technology, Jinji Road 1#, Guilin 541004, China)

  • Yujie Xiao

    (School of Marketing and Logistics Management, Nanjing University of Finance & Economics, Wenyuan Road 3#, Nanjing 210046, China)

  • Jun Chen

    (School of Transportation, Southeast University, Dongnandaxue Road 2#, Jiangning Development Zone, Nanjing 210096, China)

  • Xingchen Yan

    (College of Automobile and Traffic Engineering, Nanjing Forestry University, Longpan Road 159#, Nanjing 210037, China)

Abstract

With the strengthening of environmental awareness, the government pays much more attention to environmental protection and thus implements carbon trading schemes to promote the reduction of global carbon dioxide emissions. The carbon Generalized System of Preferences (GSP) is an incentive mechanism for citizens to value their energy conservation and carbon reduction. Individual travel needs to rely on various means of transportation, resulting in energy consumption. Carbon tax or subsidy can only be carried out after carbon GSP accurately measures individual carbon emissions. The big data acquired from the smart cards of passengers’ travels provide the possibility for carbon emission accounting of individual travel. This research proposes a carbon emission measurement of individual travel. Through establishing the network model of the Nanjing metro with a complex method, the shortest path of the passengers’ travels is obtained. Combined with the origination–destination (OD) records of the smart cards, the total distance of the passengers’ travels is obtained. By selecting the operation table to estimate the carbon emissions generated by the daily operation of the subway system, the carbon emissions per kilometer or per time of passenger travel are finally obtained. With the accurate tracking of carbon emissions for individual travel, the government may establish a comprehensive monitoring system so as to establish a carbon tax and carbon supplement mechanism for citizens.

Suggested Citation

  • Wei Yu & Tao Wang & Yujie Xiao & Jun Chen & Xingchen Yan, 2020. "A Carbon Emission Measurement Method for Individual Travel Based on Transportation Big Data: The Case of Nanjing Metro," IJERPH, MDPI, vol. 17(16), pages 1-15, August.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:16:p:5957-:d:400073
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/16/5957/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/17/16/5957/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chen, Zhenni & Du, Huibin & Li, Jianglong & Southworth, Frank & Ma, Shoufeng, 2019. "Achieving low-carbon urban passenger transport in China: Insights from the heterogeneous rebound effect," Energy Economics, Elsevier, vol. 81(C), pages 1029-1041.
    2. Peng, Bin-Bin & Xu, Jin-Hua & Fan, Ying, 2018. "Modeling uncertainty in estimation of carbon dioxide abatement costs of energy-saving technologies for passenger cars in China," Energy Policy, Elsevier, vol. 113(C), pages 306-319.
    3. Yu Wei & Sun Ning, 2018. "Establishment and Analysis of the Supernetwork Model for Nanjing Metro Transportation System," Complexity, Hindawi, vol. 2018, pages 1-11, December.
    4. Song, Siqi & Diao, Mi & Feng, Chen-Chieh, 2016. "Individual transport emissions and the built environment: A structural equation modelling approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 92(C), pages 206-219.
    5. Fei Ma & Wenlin Wang & Qipeng Sun & Fei Liu & Xiaodan Li, 2018. "Ecological Pressure of Carbon Footprint in Passenger Transport: Spatio-Temporal Changes and Regional Disparities," Sustainability, MDPI, vol. 10(2), pages 1-17, January.
    6. Kanwar, Kushal & Kumar, Harish & Kaushal, Sakshi, 2019. "Complex network based comparative analysis of Delhi Metro network and its extension," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
    7. Wang, Bo & Sun, Yefei & Chen, Qingxiang & Wang, Zhaohua, 2018. "Determinants analysis of carbon dioxide emissions in passenger and freight transportation sectors in China," Structural Change and Economic Dynamics, Elsevier, vol. 47(C), pages 127-132.
    8. Xiaohong Jiang & Xiucheng Guo, 2020. "Evaluation of Performance and Technological Characteristics of Battery Electric Logistics Vehicles: China as a Case Study," Energies, MDPI, vol. 13(10), pages 1-23, May.
    9. Wei Yu & Hua Bai & Jun Chen & Xingchen Yan, 2019. "Analysis of Space-Time Variation of Passenger Flow and Commuting Characteristics of Residents Using Smart Card Data of Nanjing Metro," Sustainability, MDPI, vol. 11(18), pages 1-19, September.
    10. Gupta, Monika & Bandyopadhyay, Kaushik Ranjan & Singh, Sanjay K., 2019. "Measuring effectiveness of carbon tax on Indian road passenger transport: A system dynamics approach," Energy Economics, Elsevier, vol. 81(C), pages 341-354.
    11. Wei Yu & Jun Chen & Xingchen Yan, 2019. "Space‒Time Evolution Analysis of the Nanjing Metro Network Based on a Complex Network," Sustainability, MDPI, vol. 11(2), pages 1-17, January.
    12. Li, Junlong & Li, Xuhong & Chen, Dawei & Godding, Lucy, 2018. "Assessment of metro ridership fluctuation caused by weather conditions in Asian context: Using archived weather and ridership data in Nanjing," Journal of Transport Geography, Elsevier, vol. 66(C), pages 356-368.
    13. Zhang, Shaojun & Wu, Ye & Un, Puikei & Fu, Lixin & Hao, Jiming, 2016. "Modeling real-world fuel consumption and carbon dioxide emissions with high resolution for light-duty passenger vehicles in a traffic populated city," Energy, Elsevier, vol. 113(C), pages 461-471.
    14. Rebeca Fontanilla Andong & Edsel Sajor, 2017. "Urban sprawl, public transport, and increasing CO2 emissions: the case of Metro Manila, Philippines," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 19(1), pages 99-123, February.
    15. De Zhao & Wei Wang & Chenyang Li & Yanjie Ji & Xiaojian Hu & Wenfu Wang, 2019. "Recognizing metro-bus transfers from smart card data," Transportation Planning and Technology, Taylor & Francis Journals, vol. 42(1), pages 70-83, January.
    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. Wei Wang & Hongming Zhong & Yu Zeng & Yachao Liu & Jun Chen, 2021. "A Carbon Emission Calculation Model for Roadside Parking," IJERPH, MDPI, vol. 18(4), pages 1-21, February.
    2. Ao Lv & Baofeng Sun, 2022. "Multi-Objective Robust Optimization for the Sustainable Location-Inventory-Routing Problem of Auto Parts Supply Logistics," Mathematics, MDPI, vol. 10(16), pages 1-22, August.
    3. Xiaohong Jiang & Jianxiao Ma & Huizhe Zhu & Xiucheng Guo & Zhaoguo Huang, 2020. "Evaluating the Carbon Emissions Efficiency of the Logistics Industry Based on a Super-SBM Model and the Malmquist Index from a Strong Transportation Strategy Perspective in China," IJERPH, MDPI, vol. 17(22), pages 1-19, November.

    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. Wei Yu & Xiaofei Ye & Jun Chen & Xingchen Yan & Tao Wang, 2020. "Evaluation Indexes and Correlation Analysis of Origination–Destination Travel Time of Nanjing Metro Based on Complex Network Method," Sustainability, MDPI, vol. 12(3), pages 1-21, February.
    2. Cui, Yin & Li, Zhiyong & Sun, Yu & Sun, Weizheng, 2023. "Environmental performance of an urban passenger transport system and influencing factors: A case study of Tianjin, China," Utilities Policy, Elsevier, vol. 80(C).
    3. Ming Li & Wei Yu & Jun Zhang, 2023. "Clustering Analysis of Multilayer Complex Network of Nanjing Metro Based on Traffic Line and Passenger Flow Big Data," Sustainability, MDPI, vol. 15(12), pages 1-17, June.
    4. Wei Yu & Hua Bai & Jun Chen & Xingchen Yan, 2019. "Analysis of Space-Time Variation of Passenger Flow and Commuting Characteristics of Residents Using Smart Card Data of Nanjing Metro," Sustainability, MDPI, vol. 11(18), pages 1-19, September.
    5. Liping Ge & Malek Sarhani & Stefan Voß & Lin Xie, 2021. "Review of Transit Data Sources: Potentials, Challenges and Complementarity," Sustainability, MDPI, vol. 13(20), pages 1-37, October.
    6. Shuanfeng Zhao & Chao Wang & Pei Wei & Qingqing Zhao, 2020. "Research on the Deep Recognition of Urban Road Vehicle Flow Based on Deep Learning," Sustainability, MDPI, vol. 12(17), pages 1-16, August.
    7. Chen Shi & Yujiao Xian & Zhixin Wang & Ke Wang, 2023. "Marginal abatement cost curve of carbon emissions in China: a functional data analysis," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 28(2), pages 1-25, February.
    8. Nusirat Ojuolape Gold & Fauziah Md. Taib & Yaxin Ma, 2022. "Firm-Level Attributes, Industry-Specific Factors, Stakeholder Pressure, and Country-Level Attributes: Global Evidence of What Inspires Corporate Sustainability Practices and Performance," Sustainability, MDPI, vol. 14(20), pages 1-28, October.
    9. Zhonghua Cheng & Xiaowen Hu, 2023. "The effects of urbanization and urban sprawl on CO2 emissions in China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(2), pages 1792-1808, February.
    10. Zhen, Wei & Qin, Quande & Miao, Lu, 2023. "The greenhouse gas rebound effect from increased energy efficiency across China's staple crops," Energy Policy, Elsevier, vol. 173(C).
    11. Hu, Yi & Yin, Zhifeng & Ma, Jian & Du, Wencui & Liu, Danhe & Sun, Luxi, 2017. "Determinants of GHG emissions for a municipal economy: Structural decomposition analysis of Chongqing," Applied Energy, Elsevier, vol. 196(C), pages 162-169.
    12. Deng, Taotao & Wang, Dandan & Hu, Yukun & Liu, Shuang, 2020. "Did high-speed railway cause urban space expansion? ——Empirical evidence from China's prefecture-level cities," Research in Transportation Economics, Elsevier, vol. 80(C).
    13. Shiraki, Hiroto & Matsumoto, Ken'ichi & Shigetomi, Yosuke & Ehara, Tomoki & Ochi, Yuki & Ogawa, Yuki, 2020. "Factors affecting CO2 emissions from private automobiles in Japan: The impact of vehicle occupancy," Applied Energy, Elsevier, vol. 259(C).
    14. Solaymani, Saeed, 2019. "CO2 emissions patterns in 7 top carbon emitter economies: The case of transport sector," Energy, Elsevier, vol. 168(C), pages 989-1001.
    15. Christian Martin Mützel & Joachim Scheiner, 2022. "Investigating spatio-temporal mobility patterns and changes in metro usage under the impact of COVID-19 using Taipei Metro smart card data," Public Transport, Springer, vol. 14(2), pages 343-366, June.
    16. Du, Huibin & Li, Qun & Liu, Xi & Peng, Binbin & Southworth, Frank, 2021. "Costs and potentials of reducing CO2 emissions in China's transport sector: Findings from an energy system analysis," Energy, Elsevier, vol. 234(C).
    17. Hongchang Li & Jack Strauss & Lihong Liu, 2019. "A Panel Investigation of High-Speed Rail (HSR) and Urban Transport on China’s Carbon Footprint," Sustainability, MDPI, vol. 11(7), pages 1-24, April.
    18. Elbaum Jean-David, 2021. "The effect of a carbon tax on per capita carbon dioxide emissions: evidence from Finland," IRENE Working Papers 21-05, IRENE Institute of Economic Research.
    19. Hasselwander, Marc & Bigotte, Joao F. & Antunes, Antonio P. & Sigua, Ricardo G., 2022. "Towards sustainable transport in developing countries: Preliminary findings on the demand for mobility-as-a-service (MaaS) in Metro Manila," Transportation Research Part A: Policy and Practice, Elsevier, vol. 155(C), pages 501-518.
    20. Michał Urbaniak & Ewa Kardas-Cinal, 2021. "Optimization of Train Energy Cooperation Using Scheduled Service Time Reserve," Energies, MDPI, vol. 15(1), pages 1-17, December.

    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:gam:jijerp:v:17:y:2020:i:16:p:5957-:d:400073. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.