ABSTRACT
With an increasing awareness of global climate change, the effect of urban spatial organization, at both city and neighborhood scales, on urban CO2 emission reduction has attracted much scholarly and practical attention. Using Beijing as a case study, this article examines the extent to which neighborhood-scale urban form may contribute to reduction of travel-related CO2 emissions in the context of rapid urbanization and spatial transformation. We derive complete travel-activity records of 1,048 residents from an activity diary survey conducted in 2007. Analysis using structural equation models finds that residents living in a neighborhood with higher land use mix, public transit accessibility, and more pedestrian-friendly street design tend to travel in a “low-carbon” manner and emit less CO2 in daily travel, even controlling for residential and travel preferences. This article offers empirical evidence that sheds light on debates about policy measures to facilitate China’s transition toward sustainable and low-carbon urban development.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes
1. In London, ground transportation is responsible for 22% of the city’s total CO2 emissions, or 44% if aviation is included (Greater London Authority, Citation2007). Transportation also contributes nearly 40% of the energy-related CO2 emissions in Portland (City of Portland Bureau of Planning and Sustainability, Citation2009).
2. The energy experts we interviewed indicate that this estimation remains an ongoing research but it has not yet produced final results. The report published by Lvyuan Company can be found at: http://wenku.baidu.com/view/ad60fee79b89680203d825fc.html.
3. In other words, if one resident drives 10 km in a day, his/her total travel-related CO2 emissions would be 1.786 kg, whereas the total CO2 emissions from daily travel would be 738 g or 91 g if the resident chooses public transportation. CO2 emissions for walking or bicycle are zero.
4. The total effect is the total of direct and indirect effects from a “cause” variable to an “outcome” variable. To take the subway accessibility variable as an example, as seen in , the total effect of subway accessibility on travel modal choices is 0.046, which equals the total of its direct effect (0.012) and indirect effect (0.024). The indirect effect is the effect of a “cause” variable on an “outcome” variable through an intermediate variable. For instance, living in a neighborhood with a nearby subway station indirectly increases the probability of using a low-carbon travel mode by 2.4%, through the intermediate effects of travel distance and number of trips (i.e. 0.024 = 0.405 × 0.023 + (−0.268) × (−0.053), see .