Implications of Electric Bicycle Use in
China: Analysis of Costs and Benefits
Volvo Center Workshop-Berkeley
7/24/2006
Track 1
Christopher R. Cherry
PhD Candidate
Institute of Transportation Studies
Department of Civil and Environmental Engineering
University of California, Berkeley
Partnership with:
Pan Haixiao-Tongji University
Xiong Jian-Kunming University of Science and Technology
Yang Xinmiao-Tsinghua University
www.its.berkeley.edu/volvocenter
Outline
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•
•
•
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Brief Introduction
Research Question
Approach and Methodology
Data
Conclusion/Expected Results
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Emergence of Electric TwoWheelers in large Chinese Cities
•
Most large Chinese cities have banned or heavily
restricted gasoline motorcycles in the city center. In
response, electric bicycles and motorcycles that
can ride in the bike lane have gained popularity
and mode share.
12,000,000
Production (unit)
.
Bicycle style electric bike (BSEB)
E-bikes
All Autos
10,000,000
Personal Cars
8,000,000
6,000,000
4,000,000
2,000,000
05
20
04
20
03
20
02
20
01
20
00
20
99
19
19
98
0
Sources: Jamerson (2004) LuYuan Electric Bike Company (2006), Yu (2004), China Statistical Yearbook (2005)
Scooter style electric bike (SSEB)
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Emergence of Electric Bicycles in
large Chinese Cities
• These bikes are regulated by speed
and size by the central government
• What are the effects of these bikes on
the transportation system?
– Environmental implications
• Energy use and emissions
– -Production and Use
• Hazardous Waste-Lead Acid Batteries
– Safety of electric bikes and others in lanes
– Increased mobility and accessibility
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Research Question
• Do electric bikes provide greater relative benefits in
terms of mobility than environmental costs
compared to alternative modes?
–
–
–
–
Energy
Environment
Safety
Mobility
• Compared to what modes? Bus and Bike
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Research Approach
• Quantify the costs and benefits of electric bicycle and compare to
standard bicycle and bus to inform appropriate policy on regulation.
Case Study of Kunming (3M) and Shanghai (14+M)
Costs
Energy Use
•Production, Use
City Level Data
Electricity Mix
Environmental
Emissions
•Production, Use
Mode Split
Mortality
Morbidity
Lead Emissions
Average Speed by Mode
Safety Impacts
Mobility changes
Benefits
Quantify
Benefits
In terms of increased
Accessibility
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Environmental Impacts-Production
Weight of Electric Bike Materials
BSEB
•
Production Energy
Use and Emissions
–
–
–
–
Raw Materials
Assembly Processes
Assumes 5 batteries
over lifespan, and 3
sets of tires (10 year
lifespan)
Note: does not (yet)
include solid waste
from disposal or
energy/pollution
impacts of non-ferrous
metal mining, glass or
battery acid
manufacturing
Total Steel
SSEB
18.15
46.1%
26.18
46.5%
5.67
14.4%
15.22
27.0%
Total Lead
10.28
26.1%
14.70
26.1%
Total Fluid
2.94
7.5%
4.20
7.5%
Total Copper
2.55
6.5%
3.46
6.1%
Total Rubber
1.14
2.9%
1.22
2.2%
Total Aluminum
0.52
1.3%
0.58
1.0%
Total Glass
0.00
0.0%
0.16
0.3%
Total Weight
41.25
Total Plastic
65.73
Associated Energy and Emissions of Manufacturing Processes
Energy Use (tonne SCE)
0.061
0.077
Air Pollution (SO2) (g)
131
141
Air Pollution (PM) (g)
84
89
Waste Water (kg)
206
222
Solid Waste (kg)
378
493
Greenhouse Gas (CO2eq)
Sources: China statistical yearbook (2004, 2005), China
industrial yearbook (2004), China Data Online, Mao et al. (2006),
Price et al. (2001)
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Environmental Impacts-Use
•
E-bike Energy Use
–
–
–
–
–
–
–
•
For example: 350W motor, 48V/14 Ah battery, 50km
range
Current=Power/Voltage=350W/48V≈7.3 A
Drain Time=14Ah/7.3A=1.9 hours
Energy=Power*Time=350W*1.9h=670Wh=0.67kWh
Energy/Distance=0.67Wh/50km=0.13Wh/km
=1.3kWh/100km
6.6% electricity transmission loss (national average)
50,000 km life=695kWh=0.085 tonne SCE
Emissions from Electricity Production
–
–
–
Kunming1: 52% hydro, 48% coal
Shanghai: 2% hydro, 98% coal
All China: 15% hydro, 75% coal, 8%gas, 2%nuclear
Electric bike Emissions (g/km)
Kunming
SO2
0.066
0.137
NOX
0.015
0.031
PM
0.0033
0.007
6.105
12.808
Carbons
1.
Shanghai
China Statistical Yearbook 2005, Energy Foundation China 2005
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Environmental Impacts-Lead
• Battery Pollution
– 95% of electric bikes use lead acid batteries
– Lead batteries last about 300 recharges or 1-2 years (10,000 km)
– China Lead Acid Battery Recycling/Loss Rates1
• 4.8% Loss Rate During Manufacture
• 27.5% Loss Rate During Mining, Smeltering and Recycling
• 62% Recycling Rate
– 36V (10.3kg), 48V (14.7kg) lead content
– 36V-3.214 kg lost during manufacture, 3.914 kg lost due to low recycle rate
– 48V-4.689 kg lost during manufacture, 5.586 kg lost due to low recycling rate
• Electric bikes indirectly emit 712-1028mg/km into environment!
• If 100% recycled, still 321-469mg/km into environment
– For Sake of Comparison-in the USA:
• 4% loss from virgin production, 2% from recycling and 1% from manufacturing
• A 7.9L/100km (30mpg) car running on leaded fuel emits 33mg/km
1Mao
et al. (2006) 2Lave et al.(1995)
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Safety Impacts
• One of the issues cited for regulation
– China Bicycle Association1
• Crash Rate is 0.17% for E-Bike (crashes/veh pop)
• Crash Rate is 1.6% for cars
– Kunming
• 2005-171,000 ebikes2 -98 crashes, 102 injuries, 5 fatalities3
– 0.05% crash rate
– 2400 vkt/year (survey data)
– 0.012 fatalities/1,000,000 vkt
– Zhejiang province 2004
Motor vehicle
Bicycle
Electric bike
Fatalities4
Injuries4
Veh pop5
Vkt/yr6
3731
1194
129
29884
7148
1660
1.81m
24.9m
1.5m
18100m
53012m
3255m
Fatality Rate
(fatalities/m- vkt)
0.206
0.023
0.036
1 Ribet (2005), 2 Kunming Public Security Bureau-Vehicle Registration Division, 3 Kunming Public Security Bureau-Traffic Safety Division, 4 Secondary source Zhejiang Public Security
Bureau, Zhejiang Bicycle Association, 5 Zhejiang and China Statistical Yearbooks 2005
6 10,000 vkt/year/veh assumed for motor vehicles, average of Kunming and Shanghai survey data for bicycle and e-bike used for two-wheelers
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Mobility
• Mobility can be defined in terms of speed
– Measure operating speed of electric motorcycle and compare to other
modes
• Floating vehicle studies
• Travel time savings can be calculated using value of time methodology
• We can also use mobility as a proxy for accessibility
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GPS Travel Time Study
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GPS Travel Time Study-Kunming
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Speed Distribution PDF
From Secondary Data
• Average Bus Speed1,2
PDF of Speeds in Shanghai
0.18
bike
0.16
–
–
electric bike
0.14
11.1 13.0 14.5
0.12
18.2
39% ↑
↑31%
0.1
Kunming-16km/hr
Shanghai-<20km/hr
0.08
0.06
0.04
PDF of Speeds in Kunm ing
0.02
0.16
electric bike
0.14
bicycle
32
30.5
29
27.5
26
23
speed km/h
24.5
21.5
20
17
18.5
15.5
14
12.5
11
9.5
8
6.5
5
3.5
2
0.5
0
0.12
10.9 12.8 14.7
0.1
17.9
40%↑
0.08
0.06
35%↑
0.04
0.02
32.5
30.5
28.5
26.5
24.5
22.5
20.5
18.5
16.5
14.5
12.5
10.5
8.5
6.5
4.5
2.5
0.5
0
Speed km /hr
Kunming University of Science and Technology (2005), Shanghai transit agency
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Mobility to Accessibility
• Mobility can be defined in
terms of speed, but
accessibility is measured
in the number of
opportunities reached in a
specific amount of travel
time
– Given land use data and
average travel speed on
links, accessibility
differences can be identified
Image source: Cervero (2005)
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Survey of Two Wheeler Users
• Travel Survey in Shanghai and Kunming
– In order to calculate the difference in transportation costs and
benefits, mode shift and vehicle use characteristics must be
identified.
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•
•
•
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Travel Diary of previous day (Tuesday through Thursday)
How many trips are made per day
What is the average vehicle-kilometer-traveled per day/week/year
Determine alternative mode if e-bike was not available
Demographics of users
Identify travel time and distance of all modes and trips
Can compare time savings if alternative modes were taken
– Survey Bicycle Users, Electric Bike Users and LPG scooter
(Shanghai)
– overall sample size 1200
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Preliminary Descriptive Statistics
Average VKT for Environmental Analysis and Mobility Valuation
Shanghai
Kunming
Bike
E-bike
LPG
Bike
Ebike
Number of trips
1.98
1.94
2.01
2.23
2.53
Trip Length (km)
4.29
4.84
6.65
3.37
3.62
Weekday VKT
8.51
9.41
13.33
7.51
9.16
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Descriptive Statistics
Stated Mode Preference for Comparative Environmental Analysis
What Mode Would You Take Otherwise?
80.0%
70.0%
60.0%
shanghai bike
50.0%
shanghai e-bike
shanghai lpg
40.0%
kunming bike
kunming e-bike
30.0%
20.0%
10.0%
ot
he
r
no
tri
p
e
ot
or
cy
cl
m
bu
s
pa
ny
co
m
Sc
oo
te
r
ca
r
y
ta
xi
k
wa
l
su
bw
a
LP
G
bi
c
yc
l
bu
s
e/
eb
ike
0.0%
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Descriptive Statistics
Most People Indicate that they choose e-bike because of speed, but don’t travel (much) farther.
Why Did You Choose This Mode?
shanghai e-bike
shanghai lpg
new job so longer commute
moved so longer commute
access to moto-restricted
areas
ride in bike lane
PT too expensive
cheaper than auto
PT too crowded
safer that motorcycle
Less effort
kunming e-bike
fast
90.0%
80.0%
70.0%
60.0%
50.0%
40.0%
30.0%
20.0%
10.0%
0.0%
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Why Do We Care?
• We tolerate environmental externalities only
because of improved mobility!
• Research Approach:
– Costs: increased emissions, battery pollution, and safety
– Benefit: reduced travel time/improved accessibility
– Case Study of Kunming and Shanghai
• Policy Implication:
– Rather than ban electric bikes-accurately price
externalities
• Lead battery tax=“pull” incentive to develop better lead battery or
levels the “economic playing field” of NiMH or Li batteries
• Clean up lead industry
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Conclusion and Expected
Results
• Policy decisions being made on perceived costs of
electric bikes
• This research:
–
–
–
–
Provides a framework to analyze a new mode in this context
Identifies use characteristics of this new, influential mode
Classifies costs that can be priced
I expect that this mode will outperform most other modes (except
perhaps a bicycle) in terms of low externalities and high mobility
gains, with the exception of lead emissions
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Still Ahead
• Public Health Impact Analysis of Power Plant Emissions
• Thorough Analysis of Survey Data
– Trip Length and Frequency by Purpose
– Mode Choice Modeling?
• Identification of Use/Environmental Characteristics of
Bus and Bike Modes for comparative analysis
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Questions?
Working Papers/Conferences:
Weinert, J., C. Cherry, Z.D. Ma. An Analysis of Key Factors for the Rapid Growth
of Electric Bikes in China. EVS22-The 22nd International Battery, Hybrid and
Fuel Cell Electric Vehicle Symposium and Exhibition. Yokohama, Japan.
October 23-28, 2006
Cherry, C., J. Weinert, Z.D. Ma. The Environmental Impacts of Electric Bikes in
China. TRB?
Cherry, C. The Costs and Benefits of Electric Bike Use in China. WCTRS 2007.
Chris Cherry
cherry@berkeley.edu
www.ce.berkeley.edu/~cherry
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Supplemental Slides
1Maramba
et al (2003), 2Suplido et al (2000), 3 US EPA (1997) 4Wang et al (2006)
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Environmental Impacts
• Health Impacts of Lead
– WHO/CDC Lead Blood Concentration Guidelines
• Men 40 μg/dL, Women 30 μg/dL, Children 10 μg/dL
• Population near recycling plant1
– +20% for adults, +30% for children
• Workers and families of battery maintenance and recycling2
– +330% for adults, +400% for children
– First order approximation of fiscal impact would be costs of hospitalization
• 23% of individuals near recycling plant have history of hospitalization vs. 4%
of control
• US EPA3 Quantify Health Effects of increased blood lead levels
1Maramba
et al (2003), 2Suplido et al (2000), 3 US EPA (1997) 4Wang et al (2006)
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Environmental Impacts
•
Converting Emissions into Intake
– Intake Fraction-A methodology to calculate exposure
• The fraction of pollutants emitted that people eventually inhale-unitless
• iF=f(mass emitted, population, breathing rate, concentration)
N
iF 
 ( P  C  BR )
i 1
i
i
Q
• Map concentrations to populations using emissions modeling
• CALPUFF dispersion model calibrated and used in Chinese context1,2,3
• From dispersion models, regression analysis was performed and iF calculated as a function
of population distribution and climatic conditions at a power plant
SO2
SOX
NOX
PM1
PM3
PM7
4.80E06
4.40E06
3.50E06
1.00E05
6.10E06
3.50E06
1Li
PM13
1.80E06
et al (2003), 2Zhou et al (2003), 3Zhou et al (2004)
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Impact area of Qujing Power Plant
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Environmental Impacts
•
Converting Intake into Public Health Effects
Intake Fraction
concentration changes
mortality and morbidity rates
– Concentration Response ΔC=C(ebΔP-1)
b=ln(relative risk)/(change in pollutant)
Relative Risk Factor (X% increase in mortality per μ/m3 concentration increase)
1Xu
et al (1995) 2Brajer et al (2003)
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Descriptive Statistics
Trip Purpose
70.0%
sh bike
60.0%
sh e-bike
50.0%
sh lpg
km bike
40.0%
km e-bike
30.0%
20.0%
10.0%
us
on
al
b
ot
he
r
s
in
es
ay
su
bw
pe
rs
al
ac
ce
ss
ed
ic
m
sit
vi
en
t
m
in
g
en
te
rt a
in
op
p
sh
ho
ol
sc
w
or
k
0.0%
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