Household Travel CO2 emissions (kg/week)

The average weekly household CO2 emissions

whereTij =the number of trips between residential neighborhood i and employment center jEj = the size of employment center j in number of jobsβ = the impedance parameter, we set it to be 0.01 which is a default value in conven-tional travel modelsdij =the distance between residential neighborhood i and employment center j (3) The average distance from household i to the three employment centers j weighted by the number of trips made to each center can be calculated as:

Polycentrism or transit-oriented development?The influence of urban spatial structure on household travel CO2 emissions in Jinan, ChinaYang JiangChina Sustainable Transportation Center / Tsinghua University

Yulin ChenTsinghua University

Xin ZhengMassachusetts Institute of Technology

Kebin HE Tsinghua University

Qizhi MaoTsinghua University

ABSTRACTDeveloping climate policies from a spatial perspective is desirable for realizing effective mitigation of CO2 emissions in urbanizing China. Literatures in US and Europe have generated debates on whether polycentric urban spatial structure could lead to less car driving and help reduce household travel CO2 emissions. Yet few empirical studies on Chinese cities exist. This paper takes a case study on the Jinan city with multiple centers and a comprehensive bus rapid transit network. 2579 households from 104 neighborhoods were surveyed. Based on the distribution of population and non-residential buildings across the entire city, Moran’s I analysis was conducted to identify population centers and employment centers. TOBIT modeling results indicate that controlling for household socio-demographics, residential self-selection and neighborhood-level form characteristics, dwelling on bus rapid transit corridors is as-sociated with lower travel CO2 emissions, whereas the proximity to population or employment centers show no significant effect.

Keywords: urban structure, residential location, travel, CO2, China

1 Introduction· Continuous increase in car ownership and motorized travels in China.· Transformation of urban spatial structure and form.· Significant investments to develop new sub-centers or new towns and city-wide mass transit.· This paper attempts to test the hypothesis that, locating households close to city main and sub centers and transit corridors could help mitigate travel CO2 emis-sions.

2 Data and Method· The study boundary includes most built-up areas of Jinan covering 826 sq km, bounded by the highways. The arterial road network cut the study area into 379 blocks, which were used as basic spatial units for analysis on center identification.· Data on the block-level population density and non-residential building density (FAR, Floor Area Ratio) are obtained to explore their distribution patterns in Jinan.

Moran’s I hotspot center recognition· GeoDa software is used to calculate local Moran’s I with 95% confidence level.· The significant units of the local Moran’s I value are classified into four types, HH, LL, LH and HL. The adjacent H-H spatial unit is regarded as a hotspot center.

Calculation of the Average Distance to Centers(1) Calculate the distance from the centroid point of each neighborhood to the three employment centers respectively.(2) Calculate the number of trips from residential neighborhood to each center.

3 Sub Center Identification

4 Result

· The weekly travelled distance of the households on BRT corridors is shorter than that of the off-BRT-corridor households by 35.9%. · The overall travel distance of the grid neighborhoods is the shortest, the walking distance is the longest.

5 Conclusion(1) Dwelling on bus rapid transit corridors is associated with lower travel CO2 emissions.(2) However, the proximity to population and employment centers show no sig-nificant effect. (3) Indeed, the two models of transit-orient development and polycentrism in-fluence mutually. The key is to match spatial development density with the transit supply capacity. (4)Mixed use, walkable environments and good local bus service will be equal-ly critical for low carbon urbanization.

6 AcknowledgeThis work is supported by the National Natural Science Foundation of China (No.51378278) and the Energy Foundation China Sustainable Cities Program.

Moran’s I cluster map of population density

Interrelationship between distance from center and average CO2 emission per week

Relationship between the BRT corridor and weekly household travel distance

Relationship between the neighborhood type and household weekly travel distance

Moran’s I cluster map of non-residential building density

Identification result of population cen-ters and employment centers of Jinan

Results of TOBIT models predicting log-transformed household weekly total travel CO2 emissions

· There is no dominant pattern for the spatial distribution of different intensity of travel CO2 emission.

For Jinan, three residential centers and three employment centers are identified