Who is more willing to use shared autonomous vehicles in first-mile-last-mile? A heterogeneity study on carbon incentive policy from China

Encouraging and motivating travelers to opt for more efficient and low-carbon last-mile transportation options is a crucial strategy for increasing the share of public transportation. This study aims to under-stand travelers’ preferences for the new travel mode combination of “shared autonomous vehicles + subway” and to explore effective incentive policies to encourage heterogeneous population with di-verse demographics to adopt this mode. Grounded in social cognitive theory, the study establishes a structural equation model encompassing four latent variables: low-carbon knowledge, low-carbon habits influenced by policy incentives, external environmental factors, and low-carbon travel intention, to analyze the factors influencing individual transportation mode choice. Prospect theory is proposed to calculate prospect values rather than utility values, and a discrete choice model is constructed to estimate the risk preference coefficients of various traveler types under different incentive measures, facilitating a comparison of the effectiveness of these incentives. The findings indicate that residents of mega-cities and low-income groups are more responsive to policy incentives and more inclined to choose the combined transportation mode. In mega-cities, travelers show a higher preference for public transportation recharge rewards, whereas cash rewards are more attractive to travelers in second-tier cities and low-income groups. High-income groups exhibit a stronger preference for commodity shop-ping vouchers. Incorporating these insights into the incentive measures of decarbonization platforms will enhance the promotion and adoption of the combined transportation mode.