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Study on Carbon Emissions from Road Traffic in Ningbo City Based on LEAP Modelling

Author

Listed:
  • Yan Lu

    (College of Architecture and Transportation, Ningbo University of Technology, Ningbo 315201, China
    Automobile College, Chang’an University, Xi’an 710064, China)

  • Lin Guo

    (College of Architecture and Transportation, Ningbo University of Technology, Ningbo 315201, China
    Automobile College, Chang’an University, Xi’an 710064, China)

  • Runmou Xiao

    (Automobile College, Chang’an University, Xi’an 710064, China)

Abstract

Rapid urbanization in China is intensifying travel demand while making transport the nation’s third-largest source of carbon emissions. Anticipating continued growth in private-car fleets, this study integrates vehicle-stock forecasting with multi-scenario emission modeling to identify effective decarbonization pathways for Chinese cities. First, Kendall rank and grey relational analyses are combined to screen the key drivers of car ownership, creating a concise input set for prediction. A Lévy-flight-enhanced Sparrow Search Algorithm (LSSA) is then used to optimize the smoothing factor of the Generalized Regression Neural Network (GRNN), producing the Levy flight-improved Sparrow Search Algorithm optimized Generalized Regression Neural Network (LSSA-GRNN) model for annual fleet projections. Second, a three-tier scenario framework—Baseline, Moderate Low-Carbon, and Enhanced Low-Carbon—is constructed in the Long-range Energy Alternatives Planning System (LEAP) platform. Using Ningbo as a case study, the LSSA-GRNN outperforms both the benchmark Sparrow Search Algorithm optimized Generalized Regression Neural Network (SSA-GRNN) and the conventional GRNN across all accuracy metrics. Results indicate that Ningbo’s car fleet will keep expanding to 2030, albeit at a slowing rate. Relative to 2022 levels, the Enhanced Low-Carbon scenario delivers the largest emission reduction, driven primarily by accelerated electrification, whereas public transport optimization exhibits a slower cumulative effect. The methodological framework offers a transferable tool for cities seeking to link fleet dynamics with emission scenarios and to design robust low-carbon transport policies.

Suggested Citation

  • Yan Lu & Lin Guo & Runmou Xiao, 2025. "Study on Carbon Emissions from Road Traffic in Ningbo City Based on LEAP Modelling," Sustainability, MDPI, vol. 17(9), pages 1-26, April.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:9:p:3969-:d:1644705
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    References listed on IDEAS

    as
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    3. Junjie Wang & Yuan Li & Yi Zhang, 2022. "Research on Carbon Emissions of Road Traffic in Chengdu City Based on a LEAP Model," Sustainability, MDPI, vol. 14(9), pages 1-15, May.
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