摘要
文章以广州市公共交通为例,运用长期替代能源规划模型(LEAP)分析了2016~2035年不同情景下的能源需求和主要温室气体排放。结果表明:如果政府不能合理引导客运发展,其能源需求和温室气体排放将迅速上升,在基准情景下,2035年的能源需求和排放约为2016年的1.5倍;机动车控制和新能源车推广情景影响巨大,同比基准情景,2035年节能减排、能源需求和温室气体排放降低17%~26%,所以机动车控制和新能源车推广的组合是广州城市客运交通部门低碳发展的一个明智选择;道路拥堵缓解、燃料税和共享交通发展情景也同样有节能减排的较大潜力,特别是“互联网+”共享经济形式的发展已成为未来社会发展的必然方向。综上,发展公共交通控制机动车数量,同时积极推进新能源车在私人交通的发展以及适当放宽对网约车的限制,将能够有效地降低能源需求和温室气体排放,极大地改善了广州市的城市交通状况。
This paper takes Guangzhou’s public transport as an example and uses the Long-range Energy Alternatives Planning(LEAP)model to analyze the energy demand and the main greenhouse gas(GHG)emissions under different scenarios during the period 2016~2035.The results show that the energy demand and GHG emissions will rise rapidly without government guiding for the development of the passenger transport.Under the baseline scenario,the energy demand and emissions in 2035 are about 1.5 times of those in 2016.Controlling the normal vehicles and developing the new energy vehicles have a significant effect on the energy saving and emission reduction.In 2035,the energy demand and GHG emissions decreases by 17%~26% compared with that of the baseline scenario,thus indicating this way is a good choice for the low carbon development of the passenger transport sector in Guangzhou.In addition,easing road congestion,fuel tax and transport sharing have a more potential for the energy saving and emission reduction.Especially,the development of the“Internet Plus”sharing economic form has become a necessary direction for the future social development.In summary,developing the public transportation,controlling the number of private vehicles,promoting the development of new energy vehicles and reducing the restrictions on network-appointed cars appropriately will effectively reduce the energy demand and greenhouse gas emissions,thus greatly improving the urban traffic situation in Guangzhou.
作者
周健
邓一荣
庄长伟
ZHOU Jian;DENG Yirong;ZHUANG Changwei(Guangdong Provincial Academy of Environmental Science,Guangzhou 510045,China)
出处
《环境保护科学》
CAS
2021年第2期122-127,共6页
Environmental Protection Science
基金
国家自然科学基金资助项目(41601616)
国家重点研发计划项目(2018YFC1800806,2018YFC1800205)。