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Methodology
This section will present the study methodology. First, the household sampling and characteristics
of the samples in the two cities will be discussed. Then, the calculation method of the
transportation CO2 emission and its sensitivity analysis will be presented. Next, the Tobit
models will be developed to investigate factors of the commuting CO2 emissions. Also, wewill present the method of using the spatial join module in ArcGIS to show the spatial
distribution of the home-based commuting CO2 emission by zone in the entire urban area of
Xi’an and Bangalore.
3.1 Data collection
Household surveys were carried out in the urban area of Xi’an and Bangalore to collect data of
commuting CO2 emissions. The statistical method from Meyer and Miller (2001) was used to
determine the sample size as shown in Eq. (1). At least 1476 and 756 commuting trip
observations in Xi’an and Bangalore are needed, respectively, to achieve the precision within
±5 % (r = 0.05) of the real value at 95 % of the time (α= 0.05).
n=[Z1-(1/2)α]2 (1-p)/r2p
where r is the margin of error or precision and is assumed to be 0.05 (assuming an estimate of
the sample size within ±5 % of the real value at 95 % of the time), p is the observed value of
the proportion of the commuting trips in the urban passenger transportation, and Z1-(1/2)α is the
standard normal statistic corresponding to the (1-α) confidence level.
In Xi’an, simple random sampling was implemented in each zone in 2012. On average, 9 to
10 households were surveyed in each zone and a total of 1501 households were surveyed. In
Bangalore, the survey was carried out on the basis of a stratified (economic status) random
selection procedure during 2011–2012. The validation of the sampled data was conducted
during 2012–2013. The survey covered 1967 households representing heterogeneous population
belonging to different income, education, and social aspects. The distributions of the
sampled households and the areas, population, and densities between the ring roads in Xi’an
and Bangalore are shown in Table 1.
Commuting mode, trip distance, commuting frequency, household location, and workplace
were included in the questionnaire. Furthermore, socio-economic characteristics of the households
and individuals, including household annual income, household tenure, car availability,
age, work unit type, and education level of the household members, were also surveyed.
Table 2 presents the levels of each characteristic in the survey. It is found that some common
characteristics exist in both Xi’an and Bangalore
have college degrees or above,
respectively); (2) there is a high percent of commuters working in private enterprises (41.7 %
Table 1 Area, population, density, and sample distributions by ring roads in Xi’an and Bangalore
| Xi’an |
1st Ring
Road |
1st–2nd Ring
Road |
2nd–3rd Ring
Road |
Outside 3rd Ring Road |
| Area (km2) |
10.78 |
64.51 |
271.57 |
|
| Population (million people) |
0.396 |
1.324 |
1.791 |
|
| Population density (thousand people/km2) |
36 |
20 |
6.5 |
|
| Sample size (household/%) |
93/6.2 % |
469/31.2 % |
848/56.5 % |
91/6.1 % |
| Bangalore |
CBD |
CBD-Outer Ring
Road |
Outer Ring Road-
Peripheral Ring
Road |
Outside Peripheral
Ring Road |
| Area (km2) |
15.65 |
203.35 |
493.97 |
|
| Population (million people) |
0.193 |
4.036
|
1.610 |
|
| Population density (thousand people/km2) |
12.3 |
19.8 |
3.2 |
|
| Sample size (household/%) |
12/0.6 % |
962/48.9 % |
993/50.5 % |
|
in Xi’an and 25.4 % in Bangalore); and (3) a high percentage of commuters have their own
houses/apartments (81.2 % in Xi’an and 58.2 % in Bangalore). However, (1) Xi’an has higher
household annual incomes (72.1 % more than $10,000), a higher rate of car availability
(54.4 %), and a higher percentage of commuting by car (28.56 %) than Bangalore has. (2)
Bangalore has more household members (averagely 4.53 per person in one household) and a
higher two-wheeler ownership rate (55.4 %).
3.2 Calculation of commuting CO2 emissions and sensitivity analysis
The commuting CO2 emissions were calculated as the emission factor (by mode, fuel type, and
occupancy) multiplied by trip distance (IPCC 1997), as shown in Eq. (2).
where C is the CO2 emission (kg/passenger/km), EF is the emission factor (by mode, fuel type,
and occupancy), and L is the trip distance (km).
Well-to-wheel (WTW) CO2 emission intensity by fuel types suggested by Huo et al. (2012)
was applied in this study. The fuel consumed (e.g., liter of gasoline consumed per 100 km) by
vehicles is associated with uncertainty because it is affected by several factors such as driving
speed. For instance, during commuting hours, driving may become less fuel efficient due to congestions (Huo et al. 2011). The actual vehicle occupancy is not a fixed value either. Hence,
we collected local data on the range of these values in Xi’an and Bangalore through surveys
and the related literatures to calculate the ranges of the CO2 emission factors by mode, fuel
type, and occupancy, as shown in Eq. (3).
EFmax;min = CI * FCmax;min/VOmin;max
where CI is the WTW CO2 emission intensity by fuel type, FC is the fuel consumption, and
VO is the vehicle occupancy
Levels
|
Xi’an
|
|
|
Bangalore
|
|
|
Number
|
Percent
|
|
Number
|
Percent
|
Age |
<35 years old |
1378 |
47.9 % |
547 |
13.9 % |
|
35–55 years old |
1364 |
47.4 % |
3119 |
79.3 % |
|
>55 years old |
137 |
4.8 % |
268 |
6.8 % |
Work unit type |
Government |
113 |
4.2 % |
593 |
19.9 % |
|
Public institution |
511 |
19.0 % |
234 |
7.8 % |
|
Foreign company |
32 |
1.2 % |
152 |
5.1 % |
|
Private enterprise/local company |
1120 |
41.7 % |
758 |
25.4 % |
|
State-owned company |
453 |
16.9 % |
83 |
2.8 % |
|
Others |
454 |
16.9 % |
1166 |
39.0 % |
Education level |
Middle school graduate |
307 |
10.7 % |
322 |
8.5 % |
|
Graduated from the high school or technical |
671 |
23.4 % |
1106 |
29.3 % |
|
secondary school |
|
|
|
|
|
|
Graduated from college |
664 |
23.1 % |
413 |
10.9 % |
|
Bachelor’s degree |
1029 |
35.9 % |
1391 |
36.8 % |
|
Master’s degree |
167 |
5.8 % |
426 |
11.3 % |
|
Ph.D. degree |
31 |
1.1 % |
119 |
3.2 % |
Household members |
Average number of household members |
3.22 |
|
4.53 |
|
Household traffic |
Household car availability |
817a |
54.4%a |
|
1415b |
71.9%b |
vehicles |
|
|
|
|
|
|
Household annual |
<US$2,000 |
9 |
0.6 % |
197 |
10.0 % |
income |
US$2,000–6,000 |
77 |
5.2 % |
839 |
42.7 % |
|
|
US$6,000–10,000 |
326 |
22.1 % |
381 |
19.4 % |
|
US$10,000–16,000 |
580 |
39.3 % |
306 |
15.6 % |
|
US$16,000–20,000 |
311 |
21.1 % |
64 |
3.3 % |
|
US$20,000–40,000 |
139 |
9.4 % |
125 |
6.4 % |
|
>US$40,000 |
34 |
2.3 % |
55 |
2.8 % |
Housing tenure |
House owner occupied |
1158 |
81.2 % |
1136 |
58.2 % |
|
House is rented |
268 |
18.8 % |
817 |
41.8 % |
Household location |
Inside the 1st Ring Road/CBD |
93 |
6.20 % |
12 |
0.6 % |
|
1st–2nd Ring Road/CBD-Outer Ring Road |
469 |
31.25 % |
962 |
48.9 % |
|
2nd–3rd Ring Road/Outer-Peripheral Ring Road |
848 |
56.50 % |
993 |
50.5 % |
|
Outside the 3rd Ring Road/Peripheral Ring Road |
91 |
6.1 % |
|
|
|
 
Table 2 Socio-economic characteristics and sample distributions by ring roads in Xi’an and Bangalore
a In Xi’an, household car availability refers to people owning car or willing to buy car
b The car availability of the household in Bangalore’s questionnaires includes both cars and two-wheelers; twowheelers
account for 77.1 % of these two types of vehicles (BT 2015)
The calculation results of CO2 emission factors are presented in Table 3. The average value
of each CO2 emission factor was used in the calculation of commuting CO2 emissions for
Xi’an and Bangalore.
Considering the rapid growth of motorization in Chinese and Indian cities, it is necessary to
investigate the impact of the reduction in vehicle occupancy and the increase in traffic
congestions on commuting CO2 emissions. Thus, the sensitivity of CO2 emission factor
Table 3 Well-to-wheel CO2 emission factors by fuel type, travel mode, and occupancy in Xi’an and Bangalore
Travel mode |
Fuel type |
WTW CO2
intensity(t CO2 eq./unit of fuel)a
| Fuel consumptions
per 100 kmb |
Occupancy |
WTW CO2 |
Average WTW CO2
|
| Xi’an |
Car |
Gasoline |
3.87/t |
7.80~10.45 (L) |
1~3 |
0.073~0.295 |
0.184 |
Normal coach |
Diesel |
3.94/t |
39.12~42.38 (L) |
20~50 |
0.027~0.072 |
0.050 |
Taxi |
Compressed natural gas (CNG) |
2.76/1000 m3 |
8~10 (m3) |
2~5 |
0.044~0.138 |
0.091 |
Bus |
CNG |
2.76/1000 m3 |
52~58 (m3) |
60~100 |
0.014~0.027 |
0.021 |
Metro |
Electricity |
0.83/1000 kWh |
3340~3350 (kWh)c |
1100~1600/train |
0.017~0.025 |
0.021 |
Electric bicycled |
Electricity |
0.83/1000 kWh |
1.1~1.25 (kWh)e |
1~2 |
0.005~0.010 |
0.008 |
Electric motorf |
Electricity |
0.83/1000 kWh |
1.6~1.7 (kWh)g |
1~2 |
0.007~0.014 |
0.011 |
Bangalore |
|
|
|
|
|
|
Car or two-wheeleri |
Gasoline |
3.87/t |
1.23~10.45 (L) |
1~3 |
0.032~0.184 |
0.067 |
Taxi |
Gasoline |
3.87/t |
7.80~10.45 (L) |
2~5 |
0.044~0.148 |
0.096 |
Bus |
Diesel |
3.94/t |
36~39 (L) |
30~50 |
0.024~0.044 |
0.034 |
m3 cubic meter, kWh kilowatt hour, L liter
a Data was from Huo et al. (2012)
b Data was from Liu and Hou (2009), Huo et al. (2011), Zhang et al. (2014a), and Ramachandra et al. (2015); the fuel consumptions considered the factor of vehicle speed in the peak hours
c Data was collected from the survey of Xi’an Metro Co., Ltd
d The highest speed of the electric bicycle is 20 km/h
e Data was from National Standard of the People’s Republic of China, GB17761-1999, Electric bicycles—general technical requirements, issued by China State Bureau of Quality and
Technical Supervision
f The highest speed of the electric motor is more than 20 km/h
g Data was collected from the questionnaire surveys of the electric motor users in Xi’an
h Data came from Xi’an Transport Development Annual Report in 2012 and field surveys in Bangalore in 2012
i In the Bangalore’s questionnaires of the travelmode, car and two-wheeler were combined in one choice option; thus, the CO2 emission factor of car or two-wheeler is weighted by proportions
of car and two-wheeler, which are 22.9 % for cars and 77.1 % for two-wheelers (BT 2015). The average value of CO2 emission factor for car and two-wheeler is 0.067 kg/passenger/km to vehicle occupancy and fuel consumption was analyzed. That is, the change of CO2
emission factor due to the changes of vehicle occupancy and fuel consumption was calculated using Eq. (4).
ΔEF = CI * FCaverage * 1 þ ΔFC /VOaverage * 1 + ΔVO
where ΔEF is the change of CO2 emission factor due to the changes of vehicle
occupancy and fuel consumption, ΔFC is the change of fuel consumption, and ΔVO
is the change of vehicle occupancy.
In the future, in both China and India, if the total number of vehicles increases, the vehicle
occupancy decreases, and the traffic congestions increase, the CO2 emissions will increase
significantly. Therefore, to estimate this tendency, the CO2 emissions in the future from major
transportation modes (electric motors, buses, and cars) in Chinese and Indian cities were also
calculated based on the past increasing trend on CO2 emissions.
3.3 Spatial distribution of CO2 emissions by commuters
The spatial join module in the ArcGIS software was used to explore the characteristics of
spatial distributions of the household and individual commuting CO2 emissions by zone in the
urban area of Xi’an and Bangalore and to find where the high commuting CO2 emissions are
from.
3.4 Tobit modeling
To explore the impact factors of the emissions and the characteristics of the high and lowemitters in the urban areas of the two cities, the relationships were modeled between the
household/individual commuting CO2 emissions and the urban spatial and household/
individual socio-economic characteristics. The distributions of the household and individual
commuting CO2 emissions were analyzed. The significance levels for the normality
test are all smaller than 0.05 as shown in Table 4, which means that it is hard to say
these distributions follow normal distributions. Also, it is observed that the household
and individual commuting CO2 emissions are left censored at zero. Therefore, the Tobit
model was established to analyze the relationships between the CO2 emissions by commuters and their factors.
| |
Kolmogorov-Smirnov |
Shapiro-Wilk |
| |
Statistic Significance |
Statistic Significance |
| Individual emissions (Bangalore) |
0.181 0.000 |
0.827 0.000 |
| Household emissions (Bangalore) |
0.165 0.000 |
0.843 0.000 |
| Individual emissions (Xi’an) |
0.272 0.000 |
0.665 0.000 |
| Household emissions (Xi’an) |
0.245 0.000 |
0.709 0.000 |
Table 4 Normality test for CO2 emissions by commuters in Xi’an and Bangalore
Citation : Yuanqing Wang, Liu Yang, Sunsheng Han, Chao Li and Ramachandra T V, 2016. Urban CO2emissions in Xi’an and Bangalore by commuters: implications for controlling urban transportation carbon dioxide emissions in developing countries, Mitig Adapt Strateg Glob Change, 21(113): , DOI 10.1007/s11027-016-9704-1
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