Measurement of Impacts on GHG
Chapter 4Emission Reductions by Smart City

More than 90 % out of 16 GHG
gas that cause global warming iscarbon dioxide. Therefore, reducing
carbon dioxide emissions from ouractivities is the central policy for low
carbonization and the most importanteffort to curb global warming.
This carbon dioxide is also emitted
by our fundamental activities such as
breathing, etc., but the main cause
is that it burns fossil fuels consistingof hydrocarbons, an oxidative actionthat combines carbon with oxygen
through the process of obtaining
energy. Therefore, Carbon dioxide
is emitted from all activities that use
fossil fuels as energy. Next to theindustrial sector, which requires a
lot of energy, and the household
sector, which is involved in our daily
activities, carbon dioxide emissions
from the internal combustion enginepowered transportation sector are
high. In particular, transportationsuch as automobiles is poweredby an internal combustion engine

that uses gasoline and diesel as its
main energy sources, so it is one ofa major source of carbon dioxide
emissions in cities that require a
lot of travel. Even in the case of
trains and electric vehicles, whichhave become increasingly popular
in recent years, carbon dioxide isemitted if thermal power is used togenerate electricity from view pointof life cycle. Therefore, in promoting
smart cities, the realization of atransportation system that is efficient,that is, reduces unnecessary
movement as much as possible, isthe key to reducing greenhouse gas
emissions.
Currently, cities consume over twothirds of the world’s energy andaccount for more than 70% of global
carbon dioxide emissions. Thus,
achieving smart cities and reducing
greenhouse gas emissions in cities is
the most important step in combating
global warming. Naturally, carbon

dioxide is emitted from various
activities in industry and homes evenin cities, but the proportion of theamount emitted from transportation isvery large. In particular, as cities growin size, they become more active,
have a wider range of travel, become
more crowded, consume moreenergy, or emit more carbon dioxide.
It is expected two-thirds of the global
population will settle in towns
and cities by 2050. As shown in
Figure 4-1 [ 1 ], 90% of this urban
growth will take place in lessdeveloped regions such as East Asia,
Sub-Saharan Africa, and South
Asia including India. In such areas,urbanization is largely unplanned,
fueling the continuous growth ofmostly poor informal settlements.
Currently home to some 1 billion
people, informal settlements arewhere the impact of climate change
is most acute. This situation will be
more serious.

23Handbook of Multimodal Transport for Smart City
Application of Sensing, Networking and Big Data Analysis

Figure 4-1 World Urbanization Prospects [1]Thus, urban development, especiallysmart city development will playsigniﬁcant role in climate action.
Urban density can also create thepossibility for a better quality of life
and a lower carbon footprint, forexample through improved standards
in house building and more efficient
infrastructure and planning. This is
one of the goals of smart cities. But
the most important measure is how
to achieve an efficient transportation
system. This will signiﬁcantly reduce
emissions from transportation, whichis the main source of carbon dioxide
emissions, and reduce carbon
emissions throughout the city.

Figure 4-2 Top-down and Bottom-up Approach24

How to estimate
carbon dioxide
emissions in a city?
In order to measure the low-carbon
effect of the realization of smart cities,
it is ﬁrst necessary to consider how
to estimate the amount of carbon
dioxide emitted from current cities.
Generally, there are two possible
methods for estimating carbon
dioxide emissions: a bottom-up
approach and a top-down approach(Figure 4-2). Due to the variety ofactivities taking place in cities,
it is quite complicated to estimate

overall emissions with a bottom-up
approach. Basically, the amount ofactivity for each activity is grasped,
and this is multiplied by the emission
factors to obtain it. If it can beorganized as an inventory for eachactivity, it can be accumulated andthe total emission amount can be
estimated, but ﬁnding all of them isa huge task. In addition, there is aboundary problem. Since there arevarious exchanges between cities
and other regions, it becomes adifficult problem to deﬁne how much
emissions are limited to cities.
On the other hand, the top-down
approach is a method of estimating
carbon dioxide emissions based onenergy consumption of the city as awhole. For example, if you know theenergy consumption of the country
as a whole, there is a way to estimate
the contribution of the city from theratio of the inhabitants living in thecity and the industry in which it islocated. Alternatively, if the carbon
dioxide emissions of the country as awhole are estimated, it is possible toestimate the contribution in the same
way mentioned above. If the energy
consumption of each city is directly
known, it is not so difficult to estimatethe carbon dioxide emissions from it.
If the above statistics are notChapter 4
Measurement of Impacts on GHG Emission Reductions by Smart City

Figure 4-3 Bottom-up Approach

Total CO2
Emission

Activity

Emission
Factoravailable, carbon dioxide can be
estimate the amount of emissions
by analyzing the relationshipbetween some activity and carbon
dioxide emissions in other cities
and extrapolating the activity ofthe target city to this relationship.
However, when adopting a top-down
approach, it is difficult to estimate theeffectiveness of individual reduction
measures. Therefore, this method
can be used when the emission
whole is calculated as a frame.

On the other hand, the amount ofactivity is calculated by the productof traffic volume and mileage.
Therefore, it is generally expressed
as a kilometer or a person kilometerfor each road section. In addition,
if the emission source is a functionof velocity, the average velocity
for each road section is also
required. These traffic volumes andaverage speeds can be calculated
by calculating the traffic volumedistribution.

1: Bottom-up approach

If you want to consider changes incarbon dioxide emissions due tovehicle driving conditions, apply
an emission model to the driving
conditions of each vehicle in a micro
traffic simulation to estimate changes
in carbon dioxide emissions in
considerable detail. It is also possible
to do.

The amount of carbon dioxide
emitted from each activity can becalculated by multiplying the basic
amount of activity by the emission
factor, as shown in Figure 4-3.
When estimating carbon dioxide
emissions in the transportationﬁeld, the emission factor is shownas emissions per unit distance, pervehicle, or per person, based on aninventory of emissions. In the case ofautomobiles, the amount of fossil fuelconsumed varies greatly dependingon the driving conditions, so theamount of carbon dioxide emissions
also changes. In such cases, theemission factor is expressed as afunction such as velocity. For such
emission factor, after creating adriving mode in each city, carry out
chassis dynamometer test according
to this mode to measure carbon
dioxide emissions, and create acurve showing emission factor byspeed.

2: Top-down approachWhen estimating carbon dioxide
emissions at the national or regional
level, a top-down approach isemployed. Among estimation
formulas based on a top-down
approach as shown in Figure 4-4,
the Kaya formula is the most widely
known.
In this formula, total CO2 emissions
can be expressed by multiplying(1) CO2 emissions per energy
consumption, (2) energy efficiencyof economic activities, (3) economic
level per capita, and (4) population.

By using this formula, it is possible toexplicitly incorporate the population
size and economic level of eachcountry into the estimation ofcarbon dioxide emissions, and it
is possible to analyze the impactof economic development on
the increase in carbon dioxideemissions. In this formula, except for(2) Energy efficiency of economicactivity, each value can be obtained
relatively easily by basic statistics
such as economic statistics anddemographic statistics. It should benoted that (2) Energy efficiency of
economic activities is affected by thetechnological level of each country.
When estimating emissions as acity, it is conceivable to apply theKaya equation and input data for thecity bell to estimate, as in the caseof estimation at the national level.
Alternatively, it is conceivable todivide the estimation results at thenational level into the ratio of theeconomic activity level of each city
to obtain the emissions at the citylevel. The former method cannot be
estimated without statistics showing
economic activity at the city level.
It should also be noted that theenergy efficiency of economic
activity can vary between cities andother regions, or from city to city.

CO2 Emission in Indiancities
There are many estimates of GHG
emissions for cities in the world.
For example, Moran, et al. [2]estimated carbon footprint of
13,000 cities including Indian cities
based on carbon footprint per capita.
Carbon footprints in top 25 India
cities are in Table 4-1. Since this isestimation of top-down approach,Figure 4-4 Top-down Approach

Total CO2
Emission

CO2
Emission

EnergyConsumption

Economic
Activities

EnergyConsumption

Economic
Activities

Population

Population

25

Handbook of Multimodal Transport for Smart City
Application of Sensing, Networking and Big Data Analysis

it is hard to identify the contribution
by sectors.

Table 4-1 Carbon Footprint of Top 25 India Cities [2]Urban cluster

On the other hand, Ramachandra,
et al. [3] estimate uses a bottom-up
approach to estimate GHG emissions
in major cities. In Table 4-2, it canbe seen that the ratio of emissions
from road traffic is 13% to 56%, which
varies greatly depending on the city,
but the ratio of road traffic to the total
emissions is large.

New Delhi
Kolkata
MumbaiHyderabad
ChennaiBangalore
Pune
Ahmadabad
Malapuram
JaipurChandigarh
Lucknow
NagpurIndoreCoimbatore
Vadodara
KanpurAsansol
Ludhiana
RaipurBhopalKavaratti
Varanasi
SuratCochin

Footprint/cap(t CO2)2.6 ± 0.6
1.7 ± 0.3
1.5 ± 0.3
3.2 ± 0.7
2.3 ± 0.6
2.0 ± 0.6
2.8 ± 0.9
1.8 ± 0.7
2.0 ± 0.5
2.5 ± 0.9
3.9 ± 1.3
1.8 ± 0.7
2.8 ± 1.0
2.6 ± 1.0
3.2 ± 1.4
3.5 ± 1.3
1.7 ± 0.7
1.9 ± 0.7
2.9 ± 1.3
2.1 ± 0.7
2.4 ± 0.9
1.7 ± 0.5
1.1 ± 0.5
1.2 ± 0.5
2.2 ± 0.6Population
27,230,000
24,792,000
21,450,000
8,396,000
9,483,000
10,591,000
6,697,000
6,635,000
4,812,000
3,819,000
2,247,000
4,590,000
2,982,000
2,844,000
2,213,000
1,987,000
3,776,000
3,245,000
2,081,000
2,684,000
2,277,000
3,106,000
4,165,000
3,698,000
2,013,000

Footprint Global Domestic(Mt CO2) ranking ranking
69.6 ± 16.0
301
42.9 ± 8.2
492
32.1 ± 6.5
703
26.5 ± 6.2
904
21.8 ± 5.6
1085
21.6 ± 6.0
110618.9 ± 6.1
1307
12.2 ± 4.5
18589.5 ± 2.52349
9.4 ± 3.323810
8.8 ± 2.9257
118.5 ± 3.226912
8.3 ± 2.927313
7.5 ± 2.9291
147.2 ± 3.2303
15
6.9 ± 2.7321
16
6.5 ± 2.5342
17
6.1 ± 2.235618
6.0 ± 2.7365
19
5.7 ± 1.9390
20
5.5 ± 2.0404
21
5.1 ± 1.6436
22
4.6 ± 1.9485
23
4.6 ± 2.0487
24
4.5 ± 1.3491
25

Table 4-2 GHG emission in Major India Cities [3]CityDelhi
Greater Mumbai
Kolkata
Chennai in 2009-10
Greater BangaloreHyderabad
Ahmedabad

Emission ratio by ﬁeld (%)
Total
Waste &Road
Domestic
Agriculture
Electricity
emissions
Industry
Waste
Transport Sector& Livestock
consumption(Mton)
water
38.648
32.08
30.26
7.89
2.49
5.78
19.28
22.797
17.41
37.20
7.89
0.12
8.46
23.44
14.812
13.30
42.78
17.66
0.22
7.18
17.04
22.086
19.50
39.01
20.25
0.05
3.72
15.77
19.799
43.48
21.59
12.31
1.31
5.73
15.46
13.734
56.86
17.05
11.38
0.46
6.70
7.54
9.123
24.92
27.88
22.41
1.52
7.17
11.80

Auxilirayconsumption &Transmission lose
2.22
5.48
1.82
1.70
0.13
0.00
4.31

References[ 1 ] National Institute of Corrections: 2018 Revision of World Urbanization Prospects. https://info.nicic.gov/ces/2018/global/
population-demographics/2018-revision-world-urbanization-prospects-0, 2018.
[ 2 ] Moran D., K. Kanemoto, M. Jiborn, R. Wood, J. Többen, and K. C. Seto: Carbon Footprints of 13 000 Cities. Environmental
Research Letters, Vo.13, No.6, 064041, 2018.
[ 3 ] Ramachandra T.V., B.H. Aithal and K. Sreejith: GHG Footprint of Major Cities in India. Renewable and Sustainable Energy
Reviews, Vol.44, pp.473–495, 2015.

26