Abstract: In the context of achieving "peak carbon" by 2023, the urban transportion sector has become a major contributor to carbon emissions. In order to alleviate the environmental impact of urban transportation carbon emissions, this paper uses the "down-top method" to measure the carbon emissions of urban transportation in Lhasa from 2013 to 2022, and selects the factors that have a high impact on carbon emissions, such as the total population, gross domestic product (GDP), motor vehicle ownership, and passenger and freight turnover of Lhasa, and analyzes the influencing factors by using the partial least squares regression analysis model. Therefore, it is concluded that freight turnover, motor vehicle ownership, passenger turnover, and gross national product have a promoting effect on carbon emissions, while the impact of local tourists, total population, and public transport passenger volume is relatively low. Therefore, energy conservation and emission reduction measures are proposed accordingly, which provides a favorable reference for the influencing factors of urban transportation carbon emissions in other regions.

Key words: Urban transportation; Carbon emissions; Partial least squares regression model; Influencing factors; Emissions reduction measures

摘要： 在 2023 年实现“碳达峰”的背景下，城市交通运输业成为碳排放的主要贡献者。为了 缓解城市交通碳排放所带来的环境影响，文章利用“自下而上法”测算了 2013－2022 年拉萨 市城市交通碳排放量，并且选取了拉萨市的人口总量、国内生产总值（GDP）、机动车保有量、 客货运周转量等对碳排放影响程度较高的因素，采用偏最小二乘回归分析模型对其影响因素 进行分析，从而得出货运周转量、机动车保有量、客运周转量、国民生产总值对碳排放有促 进作用，而当地旅游人数、总人口数量、公共交通客运量的影响程度相对较低。由此相应提 出节能减排措施，为其他区域城市交通碳排放影响因素提供有利参考。

关键词: 城市交通；碳排放；偏最小二乘回归模型：影响因素；减排措施