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Frontiers of Environmental Science & Engineering

ISSN 2095-2201

ISSN 2095-221X(Online)

CN 10-1013/X

Postal Subscription Code 80-973

2018 Impact Factor: 3.883

Front. Environ. Sci. Eng.    2015, Vol. 9 Issue (4) : 702-711    https://doi.org/10.1007/s11783-014-0665-x
RESEARCH ARTICLE
Fuel type preference of taxi driver and its implications for air emissions
Feng WANG1,Beibei LIU1,2,*(),Bing ZHANG1,Jun BI1,*()
1. State Key Laboratory of Pollution Control & Resource Reuse, School of Environment, Nanjing University, Nanjing 210093, China
2. Department of Geography and Environmental Engineering, Johns Hopkins University, Baltimore MD 21218, USA
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Abstract

Natural gas became an available fuel for taxis in 2005 and had occupied a market share of 43.6% in taxi industry till 2010 in Nanjing, China. To investigate the energy replacement pattern as well as the pollutants reduction potential of the taxi industry, first, the fuel preference determinants of taxi drivers for their next taxis are analyzed. Results show that as an important alternative for the traditional gasoline, natural gas is widely accepted (75%) by taxi drivers. Different from the previous studies which focused on the early stage of cleaner fuel replacement, taxi drivers with various characteristics (such as age, working experience, and education level) are consistent with their fuel preference when they choose their next taxis. Result suggests that policies that concern consumers with specific characteristics may have little effects on the change of the market share, when the alternative fuel market has been developed well. In addition, the increased share of gas in the fuel market achieves a 7.2% reduction of energy consumption. Considering life cycle emissions, the following air pollutants, namely Greenhouse Gases (GHGs), carbonic oxide (CO), nitrogen oxide (NOx), particulate matters (PM) and hydrocarbons (CxHy), gain 10.0%, 3.5%, 20.5%, 36.1%, and 26.4% of reduction respectively. Assuming all taxi fleets powered by natural gas with local policy intervention, the energy conservation and the five major air pollutant emissions could achieve the maximum reductions with 12.2%, 16.0%, 8.8%, 22.5%, 44.2%, and 49.4% correspondingly.

Keywords fuel preference      energy replacement      environmental impacts      taxi     
Corresponding Author(s): Beibei LIU,Jun BI   
Online First Date: 07 March 2014    Issue Date: 25 June 2015
 Cite this article:   
Feng WANG,Beibei LIU,Bing ZHANG, et al. Fuel type preference of taxi driver and its implications for air emissions[J]. Front. Environ. Sci. Eng., 2015, 9(4): 702-711.
 URL:  
https://academic.hep.com.cn/fese/EN/10.1007/s11783-014-0665-x
https://academic.hep.com.cn/fese/EN/Y2015/V9/I4/702
variable groupvariablesdescription
individual characteristicsageage of the driver
experiencenumber of years that the driver has worked in the taxi industry
incomereported income per month (CNY)
education1-higher education; 0-primary education
work patternownership1-individually owned; 0-company owned
sec1- with a second driver; 0-otherwise
day driver1-yes, 0-otherwise
long distance1-yes, 0-otherwise
his fuel11-gas as current fuel, 0-otherwise
his fuel21-diesel as current fuel, 0-otherwise
vehicle ageage of the taxi (month)
Tab.1  Definition of the independent variables of the MNL model
fuel typeenergy intensity/(MJ·km-1)sourcesemission factor/(g·km-1)sources
GHGCONOxPMHC
gasoline4.27[29]3012.260.370.0450.37[29]
diesel4.01[30]3101.301.130.160.12[31]
natural gas3.39[29]2261.790.330.0270.12[29]
Tab.2  Energy intensity and emission factors from various fuels
Fig.1  Frequency of vehicle age
Fig.2  Frequency of categorical variables
variablesgas poweredgas powered’s marginal effectother fuelother fuel’s marginal effect
age-0.0262-0.000537-0.0333-0.00103
(0.230)(0.00257)(0.0286)(0.00230)
experience0.0116-0.002780.04300.00371
(0.333)(0.00361)(0.0404)(0.00318)
income-0.7390.0271-0.363*-0.0338*
(0.110)(0.0168)(0.174)(0.0164)
edu-0.232-0.0235-0.1200.0110
(0.292)(0.0319)(0.357)(0.0283)
sec0.5900.04390.508-0.00364
(0.366)(0.0496)(0.473)(0.0422)
ind-0.789*-0.0691-0.6160.0115
(0.390)(0.0582)(0.514)(0.0491)
day driver-0.291-0.0336-0.1140.0181
(0.291)(0.0317)(0.354)(0.0281)
long journey-0.891*-0.124*-0.3130.0641
(0.374)(0.0553)(0.453)(0.0482)
hisf12.30**0.143**2.03**-0.0111
(0.449)(0.0356)(0.508)(0.0305)
hisf21.51*-0.175*2.85**0.234**
(0.752)(0.0741)(0.787)(0.0732)
vehicle age-0.008960.000758-0.0202*-0.00137*
(0.00697)(0.000770)(0.00854)(0.000680)
cons2.97**2.53*
(1.04)(1.35)
LR Chi2(22)101.01**
pseudo R20.113
observations618
Tab.3  MNL model results and the marginal effects on the probability of fuel type preference
yeargasoline vehiclesdiesel vehiclesnatural gas vehicles
20104495(46.77%)930(9.68%)4185(43.55%)
20113471(36.12%)899(9.35%)5240(54.53%)
20122525(26.27%)264(2.75%)6821(70.98%)
20132229(23.19%)109(1.13%)7272(75.67%)
20142102(21.87%)0(0%)7508(78.13%)
Tab.4  Current and future (five-year) mix of Nanjing taxis
Yeargasolinedieselnatural gastotal
20102225.4432.61645.24303.3
20111718.5418.22060.04196.6
20121250.1122.82681.54054.4
20131103.650.72858.84013.1
20141040.702951.63992.3
NG scenario003777.93777.9
Tab.5  Energy consumption of the current and future taxi fleet in Nanjing/ (TJ a))
Fig.3  GHG and air pollutant emissions of the taxi fleet in Nanjing
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