This study uses the opening of the new Mass Rapid Transit (MRT) in stages between 2010 and 2012 in Singapore as the exogenous event to empirically test the impact of the new Circle Line (CL) on housing wealth. Applying a "differences-in-differences" approach to the non-landed private housing transaction data covering the period from 2009 to 2013, we find that the average housing prices increase by 1.6% in the post-opening of the CL. We find significant capitalization of the new CL into housing prices, especially households living within a 400-meter radius (the treatment zone) from the closest MRT stations on the CL. The treatment effects that are measured by the "marginal willingness to pay" for houses located within the treatment zone is 13.2% relative to houses located outside the treatment zone. The new CL opening creates an estimated S$1.23 billion housing wealth effects for households living in close proximity to the CL MRT stations. However, we do not find significant "anticipative" effects on house prices in the six-month window prior to the opening of CL. The strongest treatment effect is found after the opening of the phase 1 of CL, and the treatment intensity declines in phases 2 and 3 of the CL opening.

In the realm of evolving e-commerce sales channels, the e-commerce sale of agricultural products has become a vital avenue for cherry farmers. However, a notable discrepancy exists between the intentions and actual behaviors of cherry farmers regarding e-commerce participation. In this study, binary logistic regression and interpretive structural model were used, and the cherry producing area of Yantai City, Shandong Province, China, was taken as the study area, and a total of 501 actual valid questionnaires were returned, and the validity rate of the questionnaires was 95.1 per cent. The results of the study show that the deviation of cherry farmers’ willingness and behavior is mainly affected by age, frequency of online shopping, whether to participate in e-commerce training, and whether to join a cooperative in farmers’ individual characteristics, revenue expectations and profit expectations in behavioral attitudes, government publicity and neighborhood effects in subjective norms, e-commerce use in perceived behavioral attitudes, the number of agricultural population in household resource endowment and logistics costs and e-commerce training in external scenarios Impact. On this basis, the 11 influencing factors are analyzed in depth and three transmission paths are analyzed. The study further proposes recommendations to enhance the translation of cherry farmers’ e-commerce intentions into action, such as bolstering e-commerce promotion, increasing the frequency of training, improving supporting infrastructure, and reducing logistics costs.