College of New Energy and Environment, Jilin University, Changchun 130021, China.
Key Laboratory of Groundwater Resources and Environment, College of New Energy and Environment, Jilin University, Ministry of Education, Changchun 130021, China.
Int J Environ Res Public Health. 2022 Apr 11;19(8):4570. doi: 10.3390/ijerph19084570.
Climate heterogeneity has enormous impacts on CO emissions of the transportation sector, especially in cold regions where the demand for in-car heating and anti-skid measures leads to high energy consumption, and the penetration rate of electric vehicles is low. It entails to propose targeted emission reduction measures in cold regions for peaking CO emissions as soon as possible. This paper constructs an integrated long-range energy alternatives planning system (LEAP) model that incorporates multi-transportation modes and multi-energy types to predict the CO emission trend of the urban transportation sector in a typical cold province of China. Five scenarios are set based on distinct level emission control for simulating the future trends during 2017-2050. The results indicate that the peak value is 704.7-742.1 thousand metric tons (TMT), and the peak time is 2023-2035. Energy-saving-low-carbon scenario (ELS) is the optimal scenario with the peak value of 716.6 TMT in 2028. Energy intensity plays a dominant role in increasing CO emissions of the urban transportation sector. Under ELS, CO emissions can be reduced by 68.66%, 6.56% and 1.38% through decreasing energy intensity, increasing the proportion of public transportation and reducing the proportion of fossil fuels, respectively. Simultaneously, this study provides practical reference for other cold regions to formulate CO reduction roadmaps.
气候异质性对交通运输部门的 CO 排放有巨大影响，在寒冷地区尤为明显，这些地区对车内取暖和防滑措施的需求导致了高能耗，而电动汽车的渗透率较低。因此，需要为寒冷地区提出有针对性的减排措施，以尽快实现 CO 排放峰值。本文构建了一个综合的长距离能源替代规划系统（LEAP）模型，该模型结合了多种交通运输方式和多种能源类型，以预测中国典型寒冷省份城市交通部门的 CO 排放趋势。基于不同的排放控制水平，设定了五个情景来模拟 2017-2050 年期间的未来趋势。结果表明，峰值为 704.7-742.1 万吨（TMT），峰值时间为 2023-2035 年。节能低碳情景（ELS）是最优情景，其峰值为 2028 年的 716.6 TMT。能源强度在增加城市交通部门 CO 排放方面起着主导作用。在 ELS 情景下，通过降低能源强度、提高公共交通比例和降低化石燃料比例，可分别减少 68.66%、6.56%和 1.38%的 CO 排放。同时，本研究为其他寒冷地区制定 CO 减排路线图提供了实际参考。
