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Frontiers in Energy

ISSN 2095-1701

ISSN 2095-1698(Online)

CN 11-6017/TK

邮发代号 80-972

2019 Impact Factor: 2.657

Frontiers in Energy  2018, Vol. 12 Issue (3): 389-399   https://doi.org/10.1007/s11708-018-0578-7
  本期目录
分解菲律宾交通运输能源消耗和二氧化碳排放的驱动因素:以发展中国家为例
LOPEZ Neil Stephen1(), 周安东尼2, BIONA Jose Bienvenido Manuel1
1. 菲律宾马尼拉德拉萨大学机械工程系(1004)
2. 菲律宾马尼拉德拉萨大学工业工程系(1004)
Decomposing drivers of transportation energy consumption and carbon dioxide emissions for the Philippines: the case of developing countries
Neil Stephen LOPEZ1(), Anthony S.F. CHIU2, Jose Bienvenido Manuel BIONA1
1. Mechanical Engineering Department, De La Salle University, Manila 1004, the Philippines
2. Industrial Engineering Department, De La Salle University, Manila 1004, the Philippines
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摘要:

从1990年到2014年,全球二氧化碳排放量增加了57.9%,其中21%来自交通运输部门。根据政策发展,可以利用分解分析技术来确定能源消耗和排放的驱动力。然而,对发展中国家的交通运输部门进行此类研究所需信息的细节可能具有挑战性。本研究试图建立一个考虑到发展中国家数据可用性和局限性的分解分析框架。此外,还提出了利用平均油价调整运输活动数据的建议。在菲律宾进行的一项说明性案例研究表明,最重要的驱动力是运输活动,其次是能源强度,然后是人口增长,这与以前在发达国家和快速城市化国家进行的研究既相似又相反,后者指出运输活动是主要的贡献力量。对菲律宾来说,运输活动是一种抑制因素,而能源强度是主要的促进因素。这种差异可以通过不同国家的模式占比和生活质量的差异来解释。从私人车辆的所有权数据来看,农村的增长率高于城市中心。根据研究结果,制定一项全面的公共交通规划,为未来增长地区、城市公共交通服务的扩展和现代化以及交通政策的战略部署提供建议。

Abstract

Global CO2 emissions increased by 57.9% from 1990 to 2014, of which 21% is known to be from the transportation sector. In line with policy development, driving forces to energy consumption and emissions may be determined using decomposition analysis techniques. However, the detail of information required to perform such studies for the transportation sector in developing countries can be challenging. An attempt was made in this study to formulate a decomposition analysis framework considering data availability and limitation in developing countries. Furthermore, a suggestion of adjusting transport activity data using average oil price was proposed. An illustrative case study in the Philippines revealed that the most significant driver was transport activity, followed by energy intensity, and then population growth, which was both similar and contrary to all previous studies performed in developed and rapidly urbanizing countries, which pointed out to transport activity as the primary contributing force. For the Philippines, transport activity was an inhibiting force, whereas energy intensity was the primary contributing factor. The difference could be explained by the differences in mode shares and quality of life between countries. Looking at private vehicle ownership data, it is observed that growth rates are higher in the rural, than in the urban centers. Deriving from the findings, developing a comprehensive public transport plan is recommend for future growth areas, expansion and modernization of public transport services in the city, and strategic deployment of transport policies.

Key wordstransportation    LMDI    decomposition    developing country    emissions
收稿日期: 2017-12-31      出版日期: 2018-09-05
通讯作者: LOPEZ Neil Stephen     E-mail: neil.lopez@dlsu.edu.ph
Corresponding Author(s): Neil Stephen LOPEZ   
 引用本文:   
LOPEZ Neil Stephen, 周安东尼, BIONA Jose Bienvenido Manuel. 分解菲律宾交通运输能源消耗和二氧化碳排放的驱动因素:以发展中国家为例[J]. Frontiers in Energy, 2018, 12(3): 389-399.
Neil Stephen LOPEZ, Anthony S.F. CHIU, Jose Bienvenido Manuel BIONA. Decomposing drivers of transportation energy consumption and carbon dioxide emissions for the Philippines: the case of developing countries. Front. Energy, 2018, 12(3): 389-399.
 链接本文:  
https://academic.hep.com.cn/fie/CN/10.1007/s11708-018-0578-7
https://academic.hep.com.cn/fie/CN/Y2018/V12/I3/389
Factor Expression
Transport activity As an activity/affluence term:
travel ?distancepopulation?or ? passenger-kmGDP
Transport mode share As a structure term:
(passenger-km)itotal?passenger-km,?for? mode?choice?i
Vehicle technology mix As a structure term:
(No. of?vehicles)itotal? No. of?vehicles,?for ?vehicles?technology ?i
Vehicle engine capacity As a structure term:
(No. of?vehicles)itotal?No. of?vehicles ,?for?engine ?capacity?i
Transport energy intensity (energy per passenger-kilometer) As an intensity term:
energypassenger-km
Vehicle ownership As a population term
Number of trips As a population term
Tab.1  
Fig.1  
Fig.2  
Fig.3  
Fig.4  
Fig.5  
Fig.6  
Fig.7  
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