IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v13y2024i7p1033-d1432243.html
   My bibliography  Save this article

Response of Low Carbon Level to Transportation Efficiency in Megacities: A Case Study of Beijing, China

Author

Listed:
  • Chang Gao

    (Key Laboratory for Urban Habitat Environmental Science and Technology, School of Urban Planning and Design, Peking University, Shenzhen 518055, China)

  • Yueyang Du

    (Key Laboratory for Urban Habitat Environmental Science and Technology, School of Urban Planning and Design, Peking University, Shenzhen 518055, China)

  • Yuhao Zhao

    (School of Geography and Planning, Huaiyin Normal University, Huai’an 223300, China)

  • Yingqiao Jia

    (College of Management and Economics, Tianjin University, Tianjin 300072, China)

  • Jiansheng Wu

    (Key Laboratory for Urban Habitat Environmental Science and Technology, School of Urban Planning and Design, Peking University, Shenzhen 518055, China
    Key Laboratory for Earth Surface Processes, Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China)

Abstract

Global warming caused by massive carbon dioxide emissions can lead to a chain of ecological disasters. As one of the main sources of carbon emissions, transportation is of great significance, and the evaluation of its connections with carbon emissions is necessary to achieve “carbon neutrality”. Taking Beijing as an example, this study evaluated traffic efficiency (TE) by utilizing principal component analysis and fuzzy comprehensive evaluation. Using the Tapio decoupling model and coupling coordination degree model, the corresponding relationship between urban low carbon level (LCL) and TE was explored. The results showed the following: (1) The total carbon emission (CE) level exhibited fluctuating variation from increasing to decreasing. The carbon emission intensity (CEI) continued to slow down, and the rapid growth of population density played a key role in low-carbon development. (2) The traffic operations continually showed a positive trend in development. TE increased from a step-like to a slow shape, until it declined in 2020 due to the pandemic. (3) TE and LCL both developed from low coordination to an extreme level of coordination. Per capita carbon emission (CEP) and TE presented an inverted U-shaped curve; meanwhile, with increases in TE, the decline in CEI slowed. In addition, the weak decoupling of TE changed to become strong, due to CE and CEP, and maintained a strong decoupling state from CEI. (4) There is a necessity for the rational planning of land use for transportation infrastructure, the encouragement of a combination of public and private transportation, and the strengthening of the maintenance of the relative infrastructure and the management of traffic behaviors to attain a win–win situation. The results provide a reference for optimizing the traffic structure to achieve “carbon neutrality”.

Suggested Citation

  • Chang Gao & Yueyang Du & Yuhao Zhao & Yingqiao Jia & Jiansheng Wu, 2024. "Response of Low Carbon Level to Transportation Efficiency in Megacities: A Case Study of Beijing, China," Land, MDPI, vol. 13(7), pages 1-21, July.
  • Handle: RePEc:gam:jlands:v:13:y:2024:i:7:p:1033-:d:1432243
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/13/7/1033/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2073-445X/13/7/1033/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhang, Qi & Gu, Baihe & Zhang, Haiying & Ji, Qiang, 2023. "Emission reduction mode of China's provincial transportation sector: Based on “Energy+” carbon efficiency evaluation," Energy Policy, Elsevier, vol. 177(C).
    2. Jinchao Huang & Shuang Meng & Jiajie Yu, 2023. "The Effects of the Low-Carbon Pilot City Program on Green Innovation: Evidence from China," Land, MDPI, vol. 12(8), pages 1-26, August.
    3. Tom J. Battin & Ronny Lauerwald & Emily S. Bernhardt & Enrico Bertuzzo & Lluís Gómez Gener & Robert O. Hall & Erin R. Hotchkiss & Taylor Maavara & Tamlin M. Pavelsky & Lishan Ran & Peter Raymond & Jud, 2023. "River ecosystem metabolism and carbon biogeochemistry in a changing world," Nature, Nature, vol. 613(7944), pages 449-459, January.
    4. Huang, Wencheng & Shuai, Bin & Sun, Yan & Wang, Yang & Antwi, Eric, 2018. "Using entropy-TOPSIS method to evaluate urban rail transit system operation performance: The China case," Transportation Research Part A: Policy and Practice, Elsevier, vol. 111(C), pages 292-303.
    5. Yacine Aït-Sahalia & Dacheng Xiu, 2019. "Principal Component Analysis of High-Frequency Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(525), pages 287-303, January.
    6. Ye, Bin & Jiang, JingJing & Li, Changsheng & Miao, Lixin & Tang, Jie, 2017. "Quantification and driving force analysis of provincial-level carbon emissions in China," Applied Energy, Elsevier, vol. 198(C), pages 223-238.
    Full references (including those not matched with items on IDEAS)

    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. Iara da Silva & Caroline Fernanda Hei Wikuats & Elizabeth Mie Hashimoto & Leila Droprinchinski Martins, 2022. "Effects of Environmental and Socioeconomic Inequalities on Health Outcomes: A Multi-Region Time-Series Study," IJERPH, MDPI, vol. 19(24), pages 1-22, December.
    2. Yong Bian & Zhi Yu & Xuelan Zeng & Jingchun Feng & Chao He, 2018. "Achieving China’s Long-Term Carbon Emission Abatement Targets: A Perspective from Regional Disparity," Sustainability, MDPI, vol. 10(11), pages 1-19, November.
    3. Xiaodong Yuan & Weiling Song, 2022. "Evaluating technology innovation capabilities of companies based on entropy- TOPSIS: the case of solar cell companies," Information Technology and Management, Springer, vol. 23(2), pages 65-76, June.
    4. Liu, Yajie & Dong, Feng & Wang, Yulong & Li, Jingyun & Qin, Chang, 2023. "Assessment of the energy-saving and environment effects of China's gasoline vehicle withdrawal under the impact of geopolitical risks," Resources Policy, Elsevier, vol. 86(PB).
    5. Xiang-Fei Ma & Ru Zhang & Yi-Fan Ruan, 2023. "How to Evaluate the Level of Green Development Based on Entropy Weight TOPSIS: Evidence from China," IJERPH, MDPI, vol. 20(3), pages 1-15, January.
    6. Hu, Dingding & Zhou, Kaile & Li, Fangyi & Ma, Dawei, 2022. "Electric vehicle user classification and value discovery based on charging big data," Energy, Elsevier, vol. 249(C).
    7. Geng, ZhiQiang & Dong, JunGen & Han, YongMing & Zhu, QunXiong, 2017. "Energy and environment efficiency analysis based on an improved environment DEA cross-model: Case study of complex chemical processes," Applied Energy, Elsevier, vol. 205(C), pages 465-476.
    8. Paolella, Marc S. & Polak, Paweł & Walker, Patrick S., 2021. "A non-elliptical orthogonal GARCH model for portfolio selection under transaction costs," Journal of Banking & Finance, Elsevier, vol. 125(C).
    9. Huibing Cheng & Shanshui Zheng & Jianghong Feng, 2022. "A Fuzzy Multi-Criteria Method for Sustainable Ferry Operator Selection: A Case Study," Sustainability, MDPI, vol. 14(10), pages 1-22, May.
    10. Liang Lv & Lidong Fan & Bin Meng & Mohammad Zoynul Abedin & Haoyue Feng, 2023. "A Combined Evaluation Method of Corporate Social Responsibility Based on the Difference and Similarity: A Case Study of Transportation Industry in China," Sustainability, MDPI, vol. 15(6), pages 1-25, March.
    11. Zhang, Xi & Geng, Yong & Shao, Shuai & Dong, Huijuan & Wu, Rui & Yao, Tianli & Song, Jiekun, 2020. "How to achieve China’s CO2 emission reduction targets by provincial efforts? – An analysis based on generalized Divisia index and dynamic scenario simulation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    12. Jiang, Jingjing & Ye, Bin & Liu, Junguo, 2019. "Peak of CO2 emissions in various sectors and provinces of China: Recent progress and avenues for further research," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 813-833.
    13. Benth, Fred Espen & Schroers, Dennis & Veraart, Almut E.D., 2022. "A weak law of large numbers for realised covariation in a Hilbert space setting," Stochastic Processes and their Applications, Elsevier, vol. 145(C), pages 241-268.
    14. YAMAMOTO, Yohei & 山本, 庸平, 2015. "Asymptotic Inference for Common Factor Models in the Presence of Jumps," Discussion Papers 2015-05, Graduate School of Economics, Hitotsubashi University.
    15. Ruijun Bu & Degui Li & Oliver Linton & Hanchao Wang, 2022. "Nonparametric Estimation of Large Spot Volatility Matrices for High-Frequency Financial Data," Working Papers 202212, University of Liverpool, Department of Economics.
    16. Fan, John Hua & Todorova, Neda, 2017. "Dynamics of China’s carbon prices in the pilot trading phase," Applied Energy, Elsevier, vol. 208(C), pages 1452-1467.
    17. Álvaro Costa & Carlos Oliveira Cruz & Joaquim Miranda Sarmento & Vitor Faria Sousa, 2021. "Empirical Analysis of the Effects of Ownership Model (Public vs. Private) on the Efficiency of Urban Rail Firms," Sustainability, MDPI, vol. 13(23), pages 1-14, December.
    18. Feng Dong & Jingyun Li & Yue-Jun Zhang & Ying Wang, 2018. "Drivers Analysis of CO 2 Emissions from the Perspective of Carbon Density: The Case of Shandong Province, China," IJERPH, MDPI, vol. 15(8), pages 1-24, August.
    19. Chen Zhang & Can Xu & Tianbao Huang & Liankai Zhang & Jinjiang Yang & Guiren Chen & Xiongwei Xu & Fuyan Zou & Zihao Liu & Zhenhui Wang, 2024. "Dynamic Replacement of Soil Inorganic Carbon under Water Erosion," Land, MDPI, vol. 13(7), pages 1-16, July.
    20. Sławomira Hajduk, 2021. "Multi-Criteria Analysis in the Decision-Making Approach for the Linear Ordering of Urban Transport Based on TOPSIS Technique," Energies, MDPI, vol. 15(1), pages 1-30, 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:jlands:v:13:y:2024:i:7:p:1033-:d:1432243. 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.