Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Shaanxi Key Laboratory for Carbon Neutral Technology, Department of Urban Planning, College of Urban and Environmental Sciences, Northwest University, Xi'an 710127, China.
College of Transportation Engineering, Chang'an University, Xi'an 710064, China.
Int J Environ Res Public Health. 2022 Oct 22;19(21):13746. doi: 10.3390/ijerph192113746.
Exploring key impact factors and their effects on urban residents' transport carbon dioxide (CO) emissions is significant for effective low-carbon transport planning. Researchers face the model uncertainty problem to seek a rational and better explanatory model and the key variables in the model set containing various factors after they are arranged and combined. This paper uses the Bayesian Model Averaging method to solve the above problem, explore the key variables, and determine their relative significance and averaging effects. Beijing, Xi'an, and Wuhan are selected as three case cities for their representation of developing Chinese cities. We found that the initial key factor increasing transport emissions is the high dependence on cars, and the second is the geographical location factor that much more suburban residents suffer longer commuting. Developing satellite city rank first for reducing transport emissions due to more local trips with an average short distance, the second is the metro accessibility, and the third is polycentric form. Key planning strategies and policies are proposed: (i) combining policies of car restriction based on vehicle plate number, encouraging clean fuel cars, a carbon tax on oil uses, and rewarding public transit passengers; (ii) fostering subcenters' strong industries to develop self-contained polycentric structures and satellite cities, and forming employment and life circle within 5 km radius; and (iii) integrating bus and rail transit services in the peripheral areas and suburbs and increasing the integration level of muti-modes transferring in transport hubs. The findings will offer empirical evidence and reference value in developing cities globally.
探索关键影响因素及其对城市居民交通二氧化碳（CO）排放的影响，对于有效的低碳交通规划至关重要。研究人员在对各种因素进行排列和组合后，面临模型不确定性问题，需要寻求合理且更好的解释模型和模型集中的关键变量。本文使用贝叶斯模型平均方法解决上述问题，探索关键变量及其相对重要性和平均效应。选择北京、西安和武汉作为三个案例城市，以代表中国发展中城市。我们发现，初始增加交通排放的关键因素是对汽车的高度依赖，其次是地理位置因素，郊区居民通勤时间更长。发展卫星城因其平均短距离的更多本地出行而排名第一，其次是地铁可达性，第三是多中心形态。提出了关键规划策略和政策：（i）结合基于车牌的汽车限制政策、鼓励清洁燃料汽车、对石油使用征收碳税以及奖励公共交通乘客；（ii）培育次中心的强大产业，发展自给自足的多中心结构和卫星城市，在 5 公里半径范围内形成就业和生活圈；（iii）整合周边地区和郊区的公共汽车和轨道交通服务，提高交通枢纽中多模式换乘的整合水平。研究结果将为全球发展中城市提供经验证据和参考价值。
