The current growth in transportation-related greenhouse gas (GHG) emissions has been largely attributed to rapid urbanization, particularly for cities in developing countries. Studies on the contributing factors and analysis of the mechanisms by which they influence transportation GHG emissions could aid in better achievement of mitigation goals. Yet, comparative contributions of the different sources of these drivers have not been well quantitatively investigated. This study employs a wide range of indicators across urban form, socio-demographic characteristics and residents' travel attitudes. By integrating a questionnaire-based survey of 1125 household in 45 communities in Xiamen City, China, land-use-based urban form quantification, inventory-based GHG calculation, a path analysis model is built to identify the interactions among these indicators and investigate their comparative direct and indirect effects on residents' local transportation GHG emissions. It was found that many variables interactively influence the transportation GHG emissions, producing considerable effects, both direct and indirect. Urban form plays a leading role in transportation GHG emissions, in comparison to socio-demographics and travel attitudes. Population density, land use mix, road connectivity and bus accessibility, in urban form; education, in socio-demographics; and willingness to ride the bus, in travel attitudes; were found to have significant positive effects on reducing residents' local transportation GHG emissions. Urban density—characterized by population density here—is the primary influential factor, due not only to its large direct effects, but also to its wide indirect effects through its influence on other variables. The results of this study may help policy makers consider how they can effectively utilize these key indicators to formulate mitigations in the transportation sector, and particularly, how to design low-carbon-friendly urban forms, in urban planning.

Path analysis model with standardized coefficients (the thickness of the line indicates the magnitude of the effect; arrows indicate the direction of the effect. *** indicates coefficient is significant at the 0.001 level; **indicates coefficient is significant at the 0.01 level; * indicates coefficient is significant at the 0.05 level.)