当前位置: X-MOL 学术Transp. Res. Part D Transp. Environ. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Two-stage DEA model to evaluate technical efficiency on deployment of battery electric vehicles in the EU countries
Transportation Research Part D: Transport and Environment ( IF 7.3 ) Pub Date : 2020-08-03 , DOI: 10.1016/j.trd.2020.102489
Sónia Almeida Neves , António Cardoso Marques , Vitor Moutinho

The transportation sector represents an important barrier to decarbonising economies. The introduction of electric vehicles seems to be a promising solution; however, the intensive use of such vehicles remains a challenge for economies. By using the two-stage Data Envelopment Analysis (DEA) method, this paper aims to provide useful insights to enlarge Battery Electric Vehicles (BEV) market share. In the first stage, it calculates the efficiency scores for 20 European countries for both BEV adoption and policies supporting electric mobility, considering an output-oriented DEA method with constant returns to scale, and using annual data from 2010 to 2018. It is a non-parametric method, which makes it possible to determine the technical efficiency of the countries under study, i.e., the ability of these countries to transform their inputs into outputs. It calculates the efficiency frontier and determines if the countries are (or not) on this frontier. In the second stage, it examines the role of some determinants of electric mobility using the efficiency scores previously calculated by applying a fractional regression model. The main findings show that few countries are performing on the efficiency frontier. Additionally, renewable electricity generation increases a countries’ DEA score and contributes to bringing the inefficient countries closer to the efficiency frontier. Contrary, the existence of peak periods of electricity consumption decreases the DEA score and moves the inefficient countries further away from the frontier. This paper highlights the need to design transport and electricity policies jointly in order to ensure that the intensive use of BEV contributes towards renewables accommodation.



中文翻译:

两阶段DEA模型,用于评估欧盟国家中部署电动汽车的技术效率

运输部门是经济脱碳的重要障碍。引入电动汽车似乎是一个有前途的解决方案。但是,大量使用这种车辆仍然是经济上的挑战。通过使用两阶段数据包络分析(DEA)方法,本文旨在提供有用的见解,以扩大电池电动汽车(BEV)的市场份额。在第一阶段,它会计算20个欧洲国家在采用BEV和支持电动汽车政策方面的效率得分,考虑采用具有规模报酬不变的面向输出的DEA方法,并使用2010年至2018年的年度数据。参数方法,可以确定所研究国家的技术效率,即 这些国家将其投入转化为产出的能力。它计算效率边界,并确定这些国家是否在该边界上。在第二阶段中,它使用先前通过应用分数回归模型计算出的效率得分来研究电动迁移率的一些决定因素的作用。主要发现表明,很少有国家在效率前沿方面表现良好。此外,可再生能源发电提高了一个国家的DEA得分,并有助于使效率低下的国家更接近效率前沿。相反,用电高峰期的存在降低了DEA得分,并使效率低下的国家远离边界。

更新日期:2020-08-03
down
wechat
bug