The Influencing Factors and Emission Reduction Pathways for Carbon Emissions from Private Cars: A Scenario Simulation Based on Fuzzy Cognitive Maps

The promotion of carbon reduction in the private car sector is crucial for advancing sustainable transportation development and addressing global climate change. This study utilizes vehicle trajectory big data from Guangdong Province, China, and employs machine learning, an LDA topic model, a gradient descent-based fuzzy cognitive map model, and grey correlation analysis to investigate the influencing factors and emission reduction pathways of carbon emissions from private cars. The findings indicate that (1) population density exhibits the strongest correlation with private car carbon emissions, with a coefficient of 0.85, rendering it a key factor influencing emissions, (2) the development of public transportation emerges as the primary pathway for carbon reduction in the private car sector under a single-factor scenario, and (3) coordinating public transport with road network density and fuel prices with traffic congestion are both viable pathways as well for reducing carbon emissions in the private car sector. This study attempts to integrate multiple factors and private car carbon emissions within a unified research framework, exploring and elucidating carbon reduction pathways for private cars with the objective of providing valuable insights into the green and low-carbon transition of the transportation sector.