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Exploring Sustainable Planning Strategies for Carbon Emission Reduction in Beijing’s Transportation Sector: A Multi-Scenario Carbon Peak Analysis Using the Extended STIRPAT Model

Author

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  • Yuhao Yang

    (School of Architecture, Tianjin University, Tianjin 300072, China
    Sichuan Hongtai Tongji Architectural Design Co., Ltd., Meishan 620020, China
    These authors contributed equally to this work.)

  • Ruixi Dong

    (School of Architecture and Art Design, Hebei University of Technology, Tianjin 300130, China
    Key Laboratory of Healthy Human Settlements in Hebei Province, Tianjin 300130, China
    These authors contributed equally to this work.)

  • Xiaoyan Ren

    (School of Architecture, Tianjin University, Tianjin 300072, China)

  • Mengze Fu

    (School of Architecture, Zhengzhou University, Zhengzhou 450001, China)

Abstract

The transportation sector plays a pivotal role in China’s efforts to achieve CO 2 reduction targets. As the capital of China, Beijing has the responsibility to lead the era’s demand for low-carbon development and provide replicable and scalable low-carbon transportation development experience and knowledge for other cities in China. This study calculates the CO 2 emissions of the transportation sector in Beijing from 1999 to 2019, constructs an extended STIRPAT model (population, affluence, technology, and efficiency), employs ridge regression to mitigate the effects of multicollinearity among the eight indicators, reveals the extent and direction of influence exerted by different indicators on CO 2 emissions, and predicts the development trends, peak times, and quantities of transportation CO 2 emissions in nine scenarios for Beijing from 2021 to 2035. Finally, adaptive low-carbon planning strategies are proposed for Beijing pertaining to population size and structure, industrial layout optimization, urban functional reorganization and adjustment, transportation infrastructure allocation, technological research and promotion, energy transition planning, and regional collaborative development. The results are as follows: (1) The total amount of CO 2 emissions from Beijing’s transportation sector exhibits a trend of gradually stabilizing in terms of growth, with a corresponding gradual deceleration in the rate of increase. Kerosene, gasoline, and diesel are the main sources of transportation CO 2 emissions in Beijing, with an annual average proportion of 95.78%. (2) The degree of influence of the indicators on transportation CO 2 emissions, in descending order, is energy intensity, per capita GDP, population size, GDP by transportation sector, total transportation turnover, public transportation efficiency, possession of private vehicles, and clean energy structure. Among them, the proportion of clean energy structure and public transportation efficiency are negatively correlated with transportation CO 2 emissions, while the remaining indicators are positively correlated. (3) In the nine predicted scenarios, all scenarios, except scenario 2 and scenario 4, can achieve CO 2 emission peaks by 2030, while scenarios 7 and 9 can reach the peak as early as 2025. (4) The significant advancement and application of green carbon reduction technologies have profound implications, as they can effectively offset the impacts of population, economy, and efficiency indicators under extensive development. Effective population control, sustainable economic development, and transportation efficiency improvement are viable means to help achieve carbon peaking and peak value in the transportation sector.

Suggested Citation

  • Yuhao Yang & Ruixi Dong & Xiaoyan Ren & Mengze Fu, 2024. "Exploring Sustainable Planning Strategies for Carbon Emission Reduction in Beijing’s Transportation Sector: A Multi-Scenario Carbon Peak Analysis Using the Extended STIRPAT Model," Sustainability, MDPI, vol. 16(11), pages 1-23, May.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:11:p:4670-:d:1405767
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    References listed on IDEAS

    as
    1. Huaping Sun & Lingxiang Hu & Yong Geng & Guangchuan Yang, 2020. "Uncovering impact factors of carbon emissions from transportation sector: evidence from China’s Yangtze River Delta Area," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 25(7), pages 1423-1437, October.
    2. Zhenggang Huo & Xiaoting Zha & Mengyao Lu & Tianqi Ma & Zhichao Lu, 2023. "Prediction of Carbon Emission of the Transportation Sector in Jiangsu Province-Regression Prediction Model Based on GA-SVM," Sustainability, MDPI, vol. 15(4), pages 1-15, February.
    3. Suyi Kim, 2019. "Decomposition Analysis of Greenhouse Gas Emissions in Korea’s Transportation Sector," Sustainability, MDPI, vol. 11(7), pages 1-16, April.
    4. York, Richard & Rosa, Eugene A. & Dietz, Thomas, 2003. "STIRPAT, IPAT and ImPACT: analytic tools for unpacking the driving forces of environmental impacts," Ecological Economics, Elsevier, vol. 46(3), pages 351-365, October.
    5. Yan SUN & Yu ZHANG & Xuemin LIU, 2020. "Driving Factors of Transportation CO2 Emissions in Beijing: An Analysis from the Perspective of Urban Development," Chinese Journal of Urban and Environmental Studies (CJUES), World Scientific Publishing Co. Pte. Ltd., vol. 8(03), pages 1-17, September.
    6. Changzheng Zhu & Meng Wang & Yarong Yang, 2020. "Analysis of the Influencing Factors of Regional Carbon Emissions in the Chinese Transportation Industry," Energies, MDPI, vol. 13(5), pages 1-20, March.
    7. Hong, Sungjun & Chung, Yanghon & Kim, Jongwook & Chun, Dongphil, 2016. "Analysis on the level of contribution to the national greenhouse gas reduction target in Korean transportation sector using LEAP model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 549-559.
    8. Zeng, Yuan & Tan, Xianchun & Gu, Baihe & Wang, Yi & Xu, Baoguang, 2016. "Greenhouse gas emissions of motor vehicles in Chinese cities and the implication for China’s mitigation targets," Applied Energy, Elsevier, vol. 184(C), pages 1016-1025.
    9. Gen Li & Shihong Zeng & Tengfei Li & Qiao Peng & Muhammad Irfan, 2023. "Analysing the Effect of Energy Intensity on Carbon Emission Reduction in Beijing," IJERPH, MDPI, vol. 20(2), pages 1-19, January.
    10. Wu, Ning & Liu, ZuanKuo, 2021. "Higher education development, technological innovation and industrial structure upgrade," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    11. Xiaodong Li & Ai Ren & Qi Li, 2022. "Exploring Patterns of Transportation-Related CO 2 Emissions Using Machine Learning Methods," Sustainability, MDPI, vol. 14(8), pages 1-21, April.
    12. Emami Javanmard, M. & Tang, Y. & Wang, Z. & Tontiwachwuthikul, P., 2023. "Forecast energy demand, CO2 emissions and energy resource impacts for the transportation sector," Applied Energy, Elsevier, vol. 338(C).
    13. Pan, Xiuzhen & Wei, Zixiang & Han, Botang & Shahbaz, Muhammad, 2021. "The heterogeneous impacts of interregional green technology spillover on energy intensity in China," Energy Economics, Elsevier, vol. 96(C).
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