摘要
为了解决道路交通带来的能源消耗、温室气体及大气污染物排放问题,采用情景分析法,以京津冀地区为例,应用长期能源替代规划系统(LEAP)模型构建了京津冀地区道路交通部门的能源与环境排放模型,对不同情景下2015—2030年的能源消耗、CO_2及污染物的排放情况进行了预测分析.结果表明:京津冀地区各政策情景中,提高燃油经济性情景的节能效果明显,节能率为22.4%.高排放车的淘汰短期节能效果最佳,节能率为19.1%,但长期效果不明显.北京市新能源汽车推广情景的CO_2、NOx长期减排效果最佳,天津市、河北省提高燃料经济性情景的CO_2、CO、NOx、PM2.5减排效果最好.相对于基准情景,2030年综合情景下京津冀地区的节能率为37.1%,CO_2减排率可达到36.8%,其中对污染物CO、HC的减排效果最佳,减排率为45.7%、43.8%.
In order to solve problems of energy consumption,greenhouse gases and air pollutants emissions caused by road transport, anurban road transport model for energy consumption and emission wa s developed in this paper based on long range energy alternatives planning system （ LEAP）. And then the energy consumption reduction potential and the emissions reduction potential of CO 2 a n d air pollutants of different control strategies and policies using scenario analysis in the Beijing-Tianjin-Hebei in 2015 -2030 were assessed. Results show that the advanced fuel economy scenario is the most effective measure to reducing energy demand, and it c a n save 22. 4% of energy consumption. The high-emission vehicle elimination can reduce energy consumption more effectively in short-term than in long-term, the ratio of saving energy is 19. 1%. Further intensifying efforts to green energy vehicle promotion scenario would have better effect on reduction of CO 2 and NOx emission in long-term in Beijing. Advanced fuel economy has a better effect on reduction of CO 2 ,CO ,NOx a n d PM 2 5 emission in Tianjin a n d Hebei. Comparing with BAU scenario,the Integrated Scenario saved 37. 1% of energy consumption in the Beijing-Tianjin-Hebei areas in 2030,and the emissions of CO 2 decreased by 36. 8 %. Ithad a better effect on reduction of CO a n d HC , and the ratio of emission reduction was 45. 7% and 43. 8 % respectively.
出处
《北京工业大学学报》
CAS
CSCD
北大核心
2017年第11期1743-1749,共7页
Journal of Beijing University of Technology
基金
北京市自然科学基金资助项目(9152001)
环保公益型行业科研专项(201409007)
关键词
LEAP模型
节能减排
交通部门
情景分析
long range energy alternatives planning system （ LEAP） model
energy-saving and emission- reduction
transport sector
scenario analysis
