Does energy follow form? The case of household travel in Jinan, China

Mitigation and Adaptation Strategies for Global Change(2014)

引用 11|浏览7
暂无评分
摘要
Rapidly increasing transportation energy use in China poses challenges to national energy security and the mitigation of greenhouse gas emissions. Meanwhile, the development of automobile oriented neighborhood structures, such as superblock housing, currently dominates urban expansion, and construction in Chinese cities. This research takes an empirical approach to understanding the relationship between neighborhood type and household travel energy use in Jinan, China, by examining nine neighborhoods that represent the four types of urban community commonly found in Chinese cities: traditional, grid, enclave, and superblock. After conducting a survey, we derive disaggregate household transport energy uses from the’ self-reported weekly travel diaries. Comparative analysis and two-step instrumental variable models are employed. Results show that, all else being equal, households located in superblock neighborhoods consume more transportation energy than those in other neighborhood types, because such households tend to own more cars and travel longer distances. Proximity to transit corridors and greater distance from the city center are also associated with higher household transport energy use in these neighborhoods, although both impacts are minor, partially because of the offsetting effects of car ownership. Overall, the analysis suggests that, to help chart a more energy-efficient future in urban China, policymakers should (1) examine past neighborhood designs to find alternatives to the superblock, (2) focus on strategic infill development, (3) encourage greater use of bicycles and e-bikes as a substitute for larger motorized vehicles, (4) improve the efficiency of public transportation, and (5) consider ways to shape citizens’ preferences for more energy-efficient modes of travel.
更多
查看译文
关键词
China, Climate change, Energy consumption, Transportation, Urban form
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要