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Analyzing the Effect of BRT Policy Strategies on CO2
Emissions: a Case Study of Beijing
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Xumei Chen, Ph.D.
Professor (Corresponding Author)
MOE Key Laboratory for Transportation Complex Systems Theory and Technology,
School of Traffic and Transportation, Beijing Jiaotong University
Beijing 100044, P.R. China
Phone: 86-10-51684022; Fax: 86-10-51684022; Email: tcxm@ 263.net
Lei Yu, Ph.D., P.E.
Yangtze River Scholar of Beijing Jiaotong University and Professor of Texas Southern
University
College of Science and Technology, Texas Southern University
3100 Cleburne Avenue, Houston, Texas 77004
Phone: 713-313-7007; Fax: 713-313-1853; Email: [email protected]
and
Ying Wang, Graduate Research Assistant
MOE Key Laboratory for Transportation Complex Systems Theory and Technology,
School of Traffic and Transportation, Beijing Jiaotong University
Beijing 100044 China
Phone: 86-10-51688570; Fax: 86-10-51684022; Email: [email protected]
Submitted for Presentation at the 93rd Transportation Research Board Annual Meeting
Washington, D.C.
January 2014
TRB 2014 Annual Meeting
Word Count: 4972 (Text) + 250*4 (Tables) + 250*1 (Figures) =6222
Submission Date: July 30, 2013
Paper revised from original submittal.
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ABSTRACT
Public transportation systems play an important role in reducing CO2 emissions from
transportation sectors because they deliver low carbon trips per capita. Bus rapid transit
(BRT) system, as a newly thriving bus service in Asia and the first, and so far the only,
mass transit technology certified under the Kyoto Protocol, has been considered as a
crucial solution to achieving low-carbon urban mobility. Different policy strategies in
the development of BRT may result in different levels of modal shifts, which affect the
amount of reductions of CO2 emissions from buses. However, few existing studies have
been conducted to analyze the effect of BRT policy strategies on CO2 reductions in
China. This paper presents an in-depth analysis of effects of BRT policy strategies on
CO2 reductions based on a case study in Beijing. Potential policy strategies for BRT
development are identified and analyzed, under which three scenarios are designed. A
CO2 emissions estimation method suitable for complicated vehicle classes and operating
patterns is established to assess the effect of BRT policy strategies on CO2 emissions.
Results indicate that BRT system has a great potential to reduce CO2 emissions in
Beijing if a positive development policy is adopted.
Key words: Bus Rapid Transit; Policy Strategy; CO2; Modal Share
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INTRODUCTION
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With the rapid urbanization and economic development, enhancing the environmental
sustainability while meeting the demand for mobility is becoming a critical strategic
goal in many Asian cities. Cumulative CO2 emissions are a major cause of global
warming. Public transportation systems can play an important role in reducing CO2
emissions from transportation sectors because they deliver low carbon CO2 trips per
capita. In public transportation systems, BRT is an innovative mode. Due to its ecofriendliness, BRT is considered as one of the best transit strategies to reduce
transportation related CO2 emissions and mitigate the negative environmental impacts,
according to a recent analysis (1). It is the first, and so far the only, mass transit
technology certified under the Kyoto Protocol (2).
In China, more and more BRT systems have been implemented and put into
operations in recent years. Beijing was the first city where a BRT system was ever
planned and implemented in China. One of purposes in designing a BRT system was to
mitigate the greenhouse gas emissions in Beijing. However, the role of BRT in CO2
mitigation remains under-investigated. There are several frequently asked questions
including: (1) how much net CO2 is reduced by the BRT system in Beijing from the
current level of public transit services being offered; (2) how much additional CO2
reductions can be achieved if different development policy strategies for BRT system
are adopted; and (3) what would be the significance of non-public transportation
commuters being attracted to use the BRT system? In this context, this research is
intended to conduct an in-depth analysis of effects of BRT policy strategies on CO2
reductions based on a case study in Beijing. Potential policy strategies for BRT
development are identified and analyzed, under which three scenarios are designed. A
CO2 emissions estimation method suitable for complicated vehicle classes and operating
patterns is established to assess the effect of BRT policy strategies on CO2 emissions.
Analysis results are discussed and strategies for increasing BRT's contribution to
greenhouse gas reductions are suggested.
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EXISTING STUDIES
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In recent years, some studies have been conducted to assess the impact of BRT vehicles
on greenhouse gases or other pollutant emissions, which provide valuable insight into
the impacts of BRT development policies and strategies on the production of
greenhouse gas emissions.
Vincent and Jerram (1) examined BRT as a near-term strategy for reducing CO2
emissions in a typical medium-sized U.S. city. They compared expected CO2 emissions
from three scenarios to meet the city’s growth in work trips by 2011 and calculated a
CO2 emissions inventory for each scenario. They found that BRT offers the greatest
potential for greenhouse gas reductions and BRT vehicles generally offer lower CO2
emissions per passenger mile than Light Rail Transit. They suggested that further study
to enhance a methodology to estimate expected CO2 reductions with BRT would be
valuable.
The work of McDonnell et al. (3) analyzed the role of BRT as a tool for
mitigating transport related CO2 emissions. A Quality Bus Corridor (QBC),
implemented in Dublin, Ireland, in 1999, was selected as a case study. BRT is in
keeping with the concept of a QBC. McDonnell et al. estimated CO2 emissions
associated with differing levels of bus priority for the period 1998-2003 and for the
Kyoto commitment period (2008-2012). They found that, in the absence of a QBC,
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peak-time emissions for the sample population would have been 50% higher than in the
factual scenario. Shi (4) analyzed the characteristics of residential trips in Beijing. He
assumed three BRT development scenarios. Air pollutant emissions including HC, CO,
and NOX under different scenarios were estimated and BRT’s impacts on emissions
were analyzed. In this study, CO2 emissions were not analyzed.
Hook et al. (5) attempted to develop a common methodology for estimating CO2
abatement potential of BRT projects at the project level. They used three BRT systems:
Bogotá, Mexico City and Jakarta in their studies. The CO2 estimation methodologies
adopted in these three BRT systems were compared and the accuracy of different
estimation methodologies was discussed.
Trigg and Fulton (6) analyzed three different scenarios for the increased use of
BRT by 2050, compared to a business-as-usual scenario, to estimate impacts of BRT on
CO2 and its cost-effectiveness as a CO2 mitigation intervention. The International
Energy Agency’s mobility model was used as an integrated modeling framework to
analyze the CO2 benefits of the worldwide deployment of BRT. In order to properly
analyze BRT, a comprehensive database of all BRT systems in the world was used.
Among scenarios, cumulative reductions of CO2 emissions were estimated to be 1727% in the transportation sector by 2050. Annual savings of CO2 emissions in the year
of 2050 were estimated to be in the range of 25-39%.
Studies identified in the literature review have offered insights into the
quantified assessment of BRT’s role in greenhouse gas reduction and factors
contributing to the CO2 reduction. They suggested that the modal shift is a key factor.
Different scenarios are designed to be concurrent with different development policy
strategies. A methodology to estimate expected CO2 reductions from BRT is needed.
However, little effort has been made to analyze the BRT’s potential to mitigate the CO2
emissions in China, especially in Beijing. The work in this paper has expanded the
previous research on assessing the role of BRT in CO2 reductions by using the densely
populated Beijing as a case study. The methodology of the modal-share-based
estimation for CO2 emissions for different modes is implemented. Recommendations
for development strategies for the BRT system in Beijing are presented.
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METHOD AND APPROACH
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Estimation method for CO2 emissions
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The transportation system is highly interconnected. CO2 emissions from the
transportation sector are influenced by a group of factors, such as the driving cycle, fuel
category, modal structure, and passenger travel activities. The relative importance of
each of factor to total changes in emissions varies with different levels of estimation (5,
7). At a city level, only the most important and easy-to-monitor factors should be used,
including the modal share, load factor, and CO2 emission factor. The function used to
estimate CO2 emissions from different modes at the city level is formulated in the
following Equation (1):
(1)
Ei  N * Si * Di * M i
Where Ei = CO2 emissions for the mode i (metric tons),
N = residential trips (trips),
Si = modal share for mode i (%),
Di = average trip distance for mode i (km), and
M i = average CO2 emissions per capita per km for mode i (g).
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Average CO2 emissions per capita per km for different modes can be derived
from CO2 emission factor, load factor, and rated passenger capacity, as shown in the
following Equation (2):
EF i
Mi 
(2)
Li * Pi
Where EFi = CO2 emissions factor for mode i (g/km),
Li = load factor for mode i, which determines the average occupancy (%), and
Pi = rated passenger capacity for mode i (persons).
Therefore, total CO2 emissions in a city can be further calculated by Equation
(3):
E   N * Si * Di *
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i
EFi
Li * Pi
(3)
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Where E = total CO2 emissions in a city (metric tons).
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Approach to determine emission rate
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In this research, several modes including conventional bus, BRT, subway, taxi, and car
are considered. A CO2 emission factor can be calculated using the standard
Intergovernmental Panel on Climate Change (IPCC) coefficients or other energy
conversion factors used to convert fuel (or electricity) consumption to carbon emissions
(7). However, in cities of developing country, vehicle numbers are growing rapidly, and
their patterns of usage are changing as cities sprawl outwardly. Except for well defined,
centrally operated vehicle fleets, the transportation sector fuel consumption data is
physically difficult to be collected due to the highly decentralized decision making
process for transportation activities (7). Hence, we use electricity-consumption-basedmethod to estimate the CO2 emission factor only for subway, which uses electricity as
the power. We do not choose a fuel-consumption-based approach to estimate CO2
emission factor for the fuel-intensive modes of transportation (i.e., conventional bus,
BRT, taxi, and car) because such data are not easily collected and not reliable either. An
emission-model-based approach to determine the emission rate for fuel-intensive modes
of transportation has been used in this research. This approach can capture the speed,
idling, or acceleration characteristics of vehicles, which reflects real operating patterns
in the roadway network.
For subway, the CO2 emission factor is determined in following steps. To
produce the electricity used by the subway operation, 123,400 tons of standard coal has
been consumed per year recently in Beijing according to the statistics from Beijing
metro system (8, 9). 2.834 tons of CO2 will be emitted when one ton of standard coal is
burned (10, 11). Multiplying the 2.834 by the standard coal consumption of 123,400
tons, we derive that Beijing subway produce 349,715.6 tons of CO2 emissions annually.
In 2010, the total subway mileage was 0.212 billon vehicle kilometers according to
2011 Beijing Transport Annual Report (12). Dividing 349,715.6 tons of annual CO2
emissions by 0.212 billon vehicle kilometers yields 1,649.6 grams of CO2 per kilometer.
For the conventional bus, BRT, taxi, and car, a portable emissions measurement
system (PEMS) system (OEM-2100) has been used to collect real time CO2 emission
data and a GPS device to collect driving activity data. More than 500 million groups of
data were collected. The vehicle specific power approach is used to establish the
emission estimation model, which determines the CO2 emission factor (13). Resulting
CO2 emission factors for different modes are summarized in Table 1.
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TABLE 1 CO2 Emission Factors for Different Modes
Modes
Conventional bus
BRT
Subway
Taxi
Car
CO2 emission
Factors (g/km)
682.832
506.401
1, 649.6
231.953
303.767
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ANALYSIS OF DIFFERENT BRT DEVELOPMENT POLICY STRATEGIES
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Factors that influence BRT development policy strategies in Beijing
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Various factors affect BRT development policy strategies, which further impact CO2
emissions. The BRT system in Beijing only carries 1.6% of passenger trips in the urban
transportation system. BRT is underdeveloped and its role to provide large scale highquality services has not been fully realized at present. In the future, economic, political,
land use, transit priority, and urban transportation goal factors may all influence BRT
development policy strategies in Beijing.
The first BRT line (BRT Line 1) was opened in 2005. From then on, sufficient
financial support was available to open BRT Lines 2 and 3 due to Beijing 2008 Olympic
Games. However, currently the economic growth slows down; there will be no largescale special events equivalent to Olympic Game to be held in the near future; and the
land price has increased to a considerably high level. Meanwhile, the reality of the
limited roadway space and rapidly deteriorating traffic congestion has pressured the
government to reconsider transit priority strategies and the license plates controlling
policy. A stricter emission control standard will be implemented gradually with the
2020 as the target year. In view of impacts from these factors, three possible BRT
development policy strategies are identified for years up to 2020 in Beijing.
(1) Conservative development policy strategy
Beijing will maintain a steady economic growth from now to 2020. There will
be no significant development policy strategy provided for the BRT systems and no
more travel demand management measures undertaken for cars. Urban sprawl will not
be effectively controlled. The development of the BRT system maintains a natural
growth trend because “Let it be” strategy is adopted. Load factors for different modes
remain same levels. Stricter control regulations for CO2 emissions from both BRT
vehicles and other vehicles will be implemented. It is assumed that such regulations will
reduce the CO2 emission factor by 3-7%.
(2) Proactive development policy strategy
Due to the rapid economic growth and strong land use control, the BRT system
development will be enhanced by the Beijing municipal government since it is a
financially affordable system. Travel demand management measures, such as congestion
toll collection and low emission zone, will be implemented. No competitive local
services will be provided along BRT routes. A good publicity on BRT will encourage
more usage of BRT systems. The BRT becomes a main component of the ground
transportation system in Beijing and a BRT network with a comprehensive coverage is
developed. Load factors for both BRT and conventional bus systems are improved
because service levels increase. Compared with the first policy strategy, the CO2
emission factor for BRT vehicles and other vehicles is assumed to decrease by 3-7%.
(3) Moderate development policy strategy
The economy of Beijing will develop at a moderate speed. BRT systems have
been defined as a role to supply the feeder services supplemental to subway systems in
TRB 2014 Annual Meeting
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the urban transportation strategy. Travel demand management measures which promote
private mode users to switch to public modes will be implemented. Mixed-use and highdensity development patterns along structural axes will encourage the usage of subwayconnected-BRT-services. More and more communities will be attracted to such services.
Hence, load factors for both BRT and subway systems are increased. It is assumed that
the impetus to maintain a sustainable and eco-friendly urban transportation system
makes CO2 emission factor for all modes decrease by 8-12% with better running
conditions and vehicle technologies.
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Scenario design under different BRT development policy strategies
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Because of the uncertainty of BRT development policy strategies, scenario-based
analysis can be a good option. This paper relies upon ridership, mode-shift, and other
parameters in Equation (3) to design scenarios. These parameters will be estimated
under different BRT development policy strategies for the year of 2020. Actual
operation and performance data are collected to estimate these parameters. Based on the
estimated parameters, three BRT development scenarios will be designed and their
resulting CO2 emission will be further analyzed.
In Equation (3), several parameters including residential trips N , average trip
distance Di , and rated passenger capacity Pi , are assumed unchanged values under three
scenarios in 2020. They need to be estimated for 2020.
Residential trips N will be estimated for 2020 using an empirical equation, as
shown in Equation (4).
N '  N0*
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T'
* a
T0
(4)
Where N ' = residential trips for 2020 (trips),
N 0 = residential trips for base year 2010 (trips),
T ' = average trips per day per capita for 2020 (trips),
T 0 = average trips per day per capita for base year 2010 (trips), and
a = ratio between urban land area for 2020 and base year 2010.
According to 2010 Beijing Household Travel Survey and Urban Master Plan of
Beijing (14, 15), N 0 =1,653,800,000 (trips), T ' =3.05 (trips), T 0 =2.82 (trips), and
a =1.552. We can then derive residential trips for 2020 N ' as 22.2847 billion (trips).
With the increase of the residential travel demand, the average trip distance will
increase due to the extension of urban rail transit network. We analyze the average trip
distance in 2000, 2005, and 2010. From 2000 to 2010, the average trip distance for bus
and taxi did not change significantly with only a slight fluctuation and the average trip
distance for cars and subway increased by 12.75% and 15.38% respectively. These
increasing rates have been used for estimating the average trip distance from 2010 to
2020. Therefore, we can obtain the average trip distance Di for all modes for 2020.
Rated passenger capacity Pi is related to the vehicle design. We assume that this
parameter will not change in the near future since no significant changes on vehicle
seats or space size are predicted.
Other parameters in Equation (3), such as, modal share Si , CO2 emission
factor EFi , and load factor Li , are different under three scenarios. These parameters need
to be estimated for different scenarios in 2020 respectively.
Modal share values from 2007 to 2010 are analyzed to estimate the modal
share Si in 2020. From 2007 to 2009, the modal share for buses including BRT
increased slowly, but decreased slightly in 2010. The modal share for the subway
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changed with an average growth of 18%, the modal share for taxi decreased with an
average rate of 5%, and the modal share for cars increased with an average growth of
1.6%. Modal shares in 2020 are set in three scenarios in Table 2. In order to better
analyze effects of BRT development policy strategies on CO2 emissions, an increase
range of 3 to 7% for the BRT modal share is designed in Scenario 1. Modal shares for
other modes in Scenario 1 have been changed with the average change rate from 2007
to 2009. For Scenarios 2 and 3, the model share for public transit system will increase
considering the improvement of public transit infrastructure in the future. The modal
share for BRT in Scenario 2 increases within a range of 18 to 22%. In Scenario 3, a
modal share of 10% which reflects trips using subway-connected-BRT-services is
considered.
Beijing has begun to introduce Phase 5 emission standards recently. CO2
emission factor EFi under all three scenarios will be lower in consideration of the
implementation of stringent emission control standards, the enhancement of fuel
efficiency, and the improvement of engine technologies. Table 2 shows the EFi values
for different scenarios.
Load factor Li was also assumed according to the identification of three possible
BRT development policy strategies in 2020. As shown in Table 2, the load factor under
Scenario 1 does not change. Under Scenario 2, the load factor for the conventional bus
increases by 50% and the load factor for BRT increases by 45%. Load factors for both
BRT and the subway are increased by 5% under Scenario 3.
TABLE 2 Parameters for Different Scenarios
Modes
Unchanged
parameters for
three scenarios
Different
parameters for
scenario 1
Different
parameters for
scenario 2
Different
parameters for
scenario 3
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Conventional bus BRT
Subway
N ' (trips)
Taxi
Car
2,228,470,000
Di (km)
10.8
12
20.77
9.3
12.97
Pi (persons)
112
160
310
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4
26.00%
3-7%
13.57%
6.27%
34.75%
Si1
EFi1 (g/km) 635.03-662.35
470.95-491.21
1534.13-1600.11 215.72-224.99
282.50-294.65
L1i
44.00%
33.00%
35.00%
30.20%
33.50%
Si2
24.00%
18-22.00%
13.57%
6.27%
20.00%
EFi 2 (g/km) 635.03-662.35
L2i
50.00%
Si3
25.00%
EFi 3 (g/km) 600.89-628.21
L3i
44.00%
470.95-491.21
1534.13-1600.11 215.72-224.99
45.00%
8-12.00%
35.00%
10.00%*
12.50%
445.63-465.89
1451.65-1517.63
38.00%
40.00%
282.50-294.65
30.20%
33.50%
6.80%
20.00%
204.12-213.40 267.31-279.47
30.20%
33.50%
Note: 10.00%* represents a modal share in which the trips use subway-connected-BRT-services. In such
trips, the trip distances for BRT and subway are 8 and 16 km respectively.
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RESULTS AND ANALYSIS
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CO2 emission in 2010
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2010 has been chosen as the base year. 2010 Beijing Household Travel Survey shows
that Beijing residents made 16.538 billion annual trips in 2010. Other parameters are
listed in Table 3 and results on CO2 emission are shown in Table 4.
TABLE 3 Parameters for Estimate 2010 Beijing CO2 Emission in Beijing
Modes
Conventional bus
BRT
Subway
Taxi
Car
Si
26.6%
1.6%
11.5%
6.8%
34.2%
Di (km)
10.8
10.8
18
9.3
11.5
EFi (g/km)
682.832
506.401
1,649.6
231.953
303.767
Li
44%
33%
35%
30.2%
33.5%
Pi (persons)
112
160
310
5
4
9
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Note: Modal share Si and average trip distance Di are from 2010 Beijing Household Travel Survey.
Load factor Li are from the survey by public transportation agency in Beijing.
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TABLE 4 CO2 Emission Results for Different Modes in 2010
Modes
Conventional
bus
BRT
Subway
CO2 emission
(metric ton)
658,320
27,410
520,480
per capita
(g/person)
39.806
1.657
31.472
97.144
891.579
1061.658
Percentage
3.75%
0.16%
2.96%
9.15%
83.98%
100.00%
Taxi
Car
Total
1,606,580 14,745,060
17,557,850
CO2 emission
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As presented in Table 4, CO2 emissions for the transportation sector in Beijing
have reached 17,557,850 metric tons. This means an average CO2 emission per capita of
1061.658g/person. Cars emit 83.98% of CO2 emissions, which are substantially higher
than those from the conventional bus, BRT, and subway. CO2 emissions from BRT are
the lowest among all modes. It can be observed that the public transportation system,
especially the BRT system, has a great potential for CO2 emission reductions. Hence,
CO2 emissions from BRT under different BRT policy strategies will be analyzed and
the effect of BRT policy strategies on CO2 emissions in Beijing will be assessed in the
following section.
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CO2 emissions under different development policy strategies
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CO2 emissions under different development policy strategies are calculated using
Equation (3). Figures 1(a) and 1(b) illustrate the results for 2010 and three scenarios for
2020.
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CO2 Emission
2800.000
CO2 Emission (Metric Ton)
2020 Scenario 1
25,963,300 (+41.23%)
2600.000
24,797,230 (+47.87%)
2400.000
2200.000
2000.000
2010 Base Year
1800.000
17,557,850
16,658,190 (-5.21%)
15,901,290 (-9.43%)
1600.000
2020 Scenario 2
1400.000
2020 Scenario 3
16,186,340 (-7.81%)
15,404,190 (-12.27%)
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(a)
CO2 Emission Per Capita (g/person)
CO2 Emission Per Capita
1300.000
2020 Scenario 1
1200.000
2010 Base Year
1100.000
1061.658
1165.072 (+4.81%)
1112.746 (+9.74%)
1000.000
900.000
2020 Scenario 2
800.000
747.517 (-29.59%)
713.551 (-32.79%)
700.000
2020 Scenario 3
726.343 (-31.58%)
691.244 (-34.89%)
600.000
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(b)
FIGURE 1 Comparison of CO2 emissions for 2010 and three scenarios in 2020.
As shown in Figure 1, when the conservative development policy strategy for
BRT is adopted (BRT modal share increases within a range of 3 to 7% and modal
shares for other modes in Scenario 1 are changed in a natural growth), which
corresponds Scenario 1, CO2 emissions will increase to 24,797,230-25,963,300 metric
tons. Compared with CO2 emissions in 2010, 7,239,380-8,405,440 metric tons are
added with an increase of 41.23 to 47.87%. Meanwhile, the average CO2 emission per
capita also increases from 1061.658g/person to 1112.746-1165.072 g/person with an
increase of 4.81 to 9.74%. It can be observed that CO2 emissions from the transportation
section in Beijing will increase significantly when no effective measures to encourage
modal shift towards public transportation modes including BRT are implemented.
When a proactive development policy strategy for BRT system is implemented,
the BRT network can be extended at a rapid speed. With a better marketing speed and
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an improved reliability, the BRT modal share will increase and the load factor will be
improved. Total CO2 emissions can reach 15,901,290-16,658,190 metric tons. This is a
decrease of 899,660-1,656,560 metric tons. The percentage of decrease is 5.12 to 9.43%.
The average CO2 emission per capita declines to 713.551-747.517g/person with a 29.59
to 32.79% reduction. It can be concluded that when the development of BRT is paid
more attention and its role in Beijing ground transportation system is defined as the
main public transportation mode, there can be a decrease of CO2 emissions although the
travel demand increases. Moreover, the average CO2 emission per capita can be reduced
significantly, which facilitates a transition towards a low-carbon or decarbonized urban
mobility essentially.
If the Beijing government initiates a moderate development policy strategy for
BRT, the BRT system will be considered as the supplement of the subway, in which
BRT routes collect and distribute passenger flows for the subway system. Then, total
CO2 emissions can vary from 17,557,850 to 15,404,190-16,186,340 metric tons.
Compared with CO2 emissions in 2010, 1,371,510-2,153,670 metric tons of CO2
emissions are reduced with a decrease of 7.81 to 12.27%. The average CO2 emission per
capita can decrease to 691.244-726.343 g/person, which represents a decrease of 31.58
to 34.89%. It can be observed that promoting BRT as the supplement of the subway
system and shifting mode to high quality multimodal and integrated public
transportation systems have very high contributions to reducing CO2 emissions in the
urban transport system. This facilitates low carbon trips for Beijing.
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CONCLUSIONS
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Low carbon mobility with low energy consumption is essential for a sustainable and
competitive future for Asian cities. Different BRT development policies and strategies
implemented by the governments will yield different results in emissions of carbon
dioxide from the urban transportation sector. In this paper, we employed a scenario
analysis approach to the case study of Beijing. A CO2 emission estimation method
suitable for Beijing that has complicated vehicle classes and operating patterns, has
been established to assess the effectiveness of the BRT policy in reducing CO2
emissions. Factors that influence BRT development policy strategies in Beijing are
analyzed and three potential policy strategies for BRT development are identified.
Accordingly, three scenarios in the target year of 2020 are designed and results are
analyzed.
Urban travel demand in Beijing is estimated to increase by 34.75% from 2010 to
2020, which brings a great challenge to mitigating CO2 emissions. The analysis in this
paper suggested that if the conservative development policy strategy for BRT is adopted
(BRT modal share increases within a range of 3 to 7% and modal shares for other
modes are changed in a natural growth), there will be a significant increase in CO2
emissions. A proactive development policy strategy for the BRT system can be
implemented to control CO2 emissions with significant effects. Significant CO2
emission reductions can be resulted by a moderate development policy strategy for BRT,
in which BRT systems have been defined as a role to supply the feeder services to the
subway system. Most of reductions are achieved by an attractive intermodal public
transit system. We can conclude that the BRT system has a great potential to reduce
CO2 emissions for Beijing. As a result, it is recommended that positive development
policies for BRT be adopted in Beijing. BRT can be deployed more quickly, and in
greater quantities, than the urban rail systems. This increases opportunities to shift trips
away from private modes. Whether the BRT system is considered as a main public
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transportation mode or a supplement of the subway system, CO2 emissions can be
controlled and significant CO2 emission per capita reductions can be delivered. Such
policies and related measures are comparatively at low costs.
The analysis presented here is limited to a set of data collected in Beijing. It
should be noted that localized factors, such as electricity generation mix, driving
activities, emission and efficiency standards (16), and travel demand management
policies, will all affect the results in a particular city. The study attempts to develop a
methodology for quantifying CO2 emissions for those cities facing similar problems in
Beijing. Future studies should consider using better and more extensive data to
determine the sensitivity of parameters on estimating CO2 emissions, thereby offering
more practical strategies to monitor and improve the control of CO2 emissions. The CO2
emission analysis for other cities that have different development patterns should also
be explored.
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ACKNOWLEDGEMENTS
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The authors acknowledge that this paper is prepared based on NSFC #71373018,
“National Basic Research Program of China”(No. 2012CB725403), “Program for New
Century Excellent Talents in University” (NCET-12-0763), and “the Fundamental
Research Funds for the Central Universities” (No. 2012JBM054). This research is
partially supported by the National Science Foundation (NSF) under grant #1137732.
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