济南市主城区私家车日常出行碳排放特征及影响因素

Characteristics and determinants of carbon emissions from daily private cars travel in central area of Jinan

私家车出行方式对城市交通碳排放的影响巨大,如何促使居民出行行为的转变是城市实现绿色交通和低碳发展的关键问题。基于济南市主城区居民日常出行的调查数据,本文分析了私家车日常出行碳排放的基本特征,并应用Heckman两步估计法计量模型,研究了私家车碳排放的影响因素,结果发现:(1)私家车日常出行年碳排放量的平均值为2.22t,空间分布呈现中心低、外围高的差异化格局,个体分布则符合60/20的量化分布;(2)与社会经济因素相比,建成环境因素对私家车碳排放的影响更为显著,公共交通可达性水平的提高和职住距离的缩短虽然有助于降低私家车出行概率,但对私家车碳排放的减少并无显著影响,而提高居住地人口密度、完善商服设施供给、促进周边土地利用的多元化则可有效降低长距离的出行需求,减少碳排放量。

As car travel has a great impact on urban transport carbon emissions, promoting a change in residents＇ daily travel behavior is the key to achieving urban green transportation and low-carbon development. Based on daily travel survey data of residents in central area of Jinan we analyzed the characteristics and determinants of carbon emissions from daily private cars. We found that private cars are the main means of transportation for residents in Jinan, and the average annual carbon emissions from private cars is 2.22 tons, and the spatial distribution of carbon emissions from private cars reveals a pattern of differentiation increasing from the center outward.The individual distribution is consistent with the quantification principle of 60/20. A Heckman twostep model was used to explore influencers factors of carbon emissions from private cars, and results indicate that urban built environment factors play a more important role in carbon emissions from private cars compared with socio-economic factors. Regarding socio-economic factors, the increase in income level and family size increases the probability of private car travel rather than the carbon emissions of private cars. For urban built environment factors, both the improvement of transport infrastructure and optimization of job-housing distance significantly decrease the probability of private car travel but have no significant effect on carbon emissions from private cars. However, increasing the residential population density, improving the supply of commercial service facilities and promoting diversity of land use in surrounding communities can effectively reduce long distance travel demands and carbon emissions.