estimating the reduction of carbon dioxide (co2) emission from ...

ESTIMATING THE REDUCTION OF CARBON

DIOXIDE (CO2) EMISSION FROM PRIVATE

VEHICLES IN PENANG ISLAND

MOHAMMAD ZAHIN BIN MOHAMMAD RAZIF

SCHOOL OF CIVIL ENGINEERING

UNIVERSITI SAINS MALAYSIA

2019

ESTIMATING THE REDUCTION OF CARBON DIOXIDE (CO2)

EMISSION FROM PRIVATE VEHICLES IN PENANG ISLAND

By

MOHAMMAD ZAHIN BIN MOHAMMAD RAZIF

This dissertation is submitted to

UNIVERSITI SAINS MALAYSIA

As partial fulfilment of requirement for the degree of

BACHELOR OF ENGINEERING (HONS.)

(CIVIL ENGINEERING)

School of Civil Engineering,

Universiti Sains Malaysia

June 2019

SCHOOL OF CIVIL ENGINEERING

ACADEMIC SESSION 2017/2018

FINAL YEAR PROJECT EAA492/6

DISSERTATION ENDORSEMENT FORM

Title:

Name of Student:

I hereby declare that all corrections and comments made by the supervisor(s)and

examiner have been taken into consideration and rectified accordingly.

Signature: Approved by:

_____________________ _____________________

(Signature of Supervisor)

Date : Name of Supervisor :

Date :

Approved by:

_____________________

(Signature of Examiner)

Name of Examiner :

Date :

I

ACKNOWLEDGEMENT

First and foremost, I would like to express my deepest appreciation to my Final

Year Project (FYP) supervisor, Dr. Nur Sabahiah Abdul Sukor, for her patience,

insightful comments, helpful information, practical advice and unceasing ideas that

have always helped me tremendously. I am grateful to my supervisor, who guided me

and provided me with the necessary information about my project. It would never have

been possible for me to complete this project without her incredible support and

encouragement.

In addition, my utmost gratitude to my Final Year Project course manager,

Assoc. Prof. Dr. Noor Faizah Fitri Md. Yusof and other lecturers for giving me a lot of

guidance in preparing this dissertation. I am also grateful to the lecturers and staff of

PPKA for their kindness, hospitality and technical support.

Finally, I am truly grateful to my parents for their unconditional love and care

throughout my degree life. I would also like to expand my gratitude to all those who

have directly and indirectly guided me in writing this dissertation. A paper is not

enough for me to express the support and guidance that I have received.

II

ABSTRAK

Pertambahan bilangan kenderaan yang semakin banyak saban hari di jalan raya

di Pulau Pinang mempunyai keburukannya tersendiri. Ruang terhad di pulau bersejarah

ini hanya memburukkan lagi keadaan apabila jalan mengalami kesesakan lalu lintas

yang teruk terutama pada waktu puncak. Kesesakan lalu lintas menyebabkan pelepasan

asap kenderaan yang lebih ketara. Gas yang dikeluarkan oleh kenderaan terdiri

daripada karbon dioksida (CO2), sulfur dioksida (SO2) dan hidrokarbon (HC) di

samping gas-gas yang lain. Gas-gas ini amat berbahaya kepada alam sekitar dan dalam

jangka masa panjang, ia boleh menjejaskan kesejahteraan makhluk di muka bumi. Ia

juga membuktikan ketidaklestarian. Kajian ini mengkaji kesan jumlah pelepasan gas

CO2 jika kenderaan persendirian di atas jalan raya telah berkurang. Berdasarkan

cadangan pembangunan Light Rail Transit (LRT) Pulau Pinang, tujuh lokasi di

sepanjang penjajaran LRT yang juga terletak di sebelah timur Pulau Pinang telah

dipilih sebagai kawasan kajian. Tujuh lokasi itu ialah Zon Bandar Sri Pinang (BSPZ),

Zon Skycab (SKYZ), Zon East Jelutong (EJZ), Zon Batu Uban (BUSZ), Zon Sungai

Nibong (STZ), Zon Bukit Jambul (BJZ) dan Zon Jalan Tengah (JTZ). Dalam kajian ini,

data telah dikumpul melalui kaedah tinjauan lalu lintas menggunakan rakaman video

GoPro di semua tujuh lokasi. Data yang telah dianalisis menunjukkan jumlah pelepasan

karbon dioksida (CO2) semasa berada pada kadar 1803 kgCO2/PCU. Pengurangan

kenderaan persendirian dalam bentuk PCU sebanyak 40%, 50% dan 60% menunjukkan

pengurangan langsung sejajar kepada jumlah pelepasan CO2 iaitu 1443 kgCO2/PCU,

1331 kgCO2/PCU dan 1254 kgCO2/PCU. Penemuan dalam kajian ini boleh digunakan

sebagai rujukan bagi kerajaan negeri dalam membantu dasar kerajaan negeri terhadap

pertumbuhan yang mampan.

III

ABSTRACT

The staggering number of vehicles on the road in Penang Island that keeps

increasing as the day goes by has its drawbacks. The limited space of the historical

island only worsens the situation as the roads suffer from massive traffic jam especially

during peak hours. Traffic jam causes greater vehicle emissions to be released on the

road. The gases released by vehicles comprise of carbon dioxide (CO2), sulphur dioxide

(SO2) and hydrocarbons (HC) among many others. These gases are harmful to the

environment and in the long run, it may affect the well-being of the creatures on earth.

It also proves to be unsustainable. This study investigates the effect of CO2 emission

when private vehicles travelling on the road are reduced. Based on the proposed future

Penang LRT alignment, seven locations along the Light Rail Transit (LRT) alignment

which are also located on the eastern side of Penang Island were chosen for the study.

The seven locations are Bandar Sri Pinang zone (BSPZ), Skycab zone (SKYZ), East

Jelutong zone (EJZ), Batu Uban zone (BUSZ), Sungai Nibong zone (STZ), Bukit

Jambul zone (BJZ) and Jalan Tengah zone (JTZ). In this study, data were collected by

means of traffic count survey using GoPro video recording at all seven locations. The

data extracted were analysed and total current carbon dioxide (CO2) emission stood at

1803 kgCO2/PCU. Reduction of private vehicles in PCU by 40%, 50% and 60% shows

a directly proportional reduction of total CO2 emission which was 1443 kgCO2/PCU,

1331 kgCO2/PCU and 1254 kgCO2/PCU. The findings in this study could be used as a

reference for state government in facilitating state government’s policy towards

sustainable growth.

IV

TABLE OF CONTENTS

ACKNOWLEDGEMENT .............................................................................................. I

ABSTRAK ..................................................................................................................... II

ABSTRACT ................................................................................................................. III

TABLE OF CONTENTS ............................................................................................ IV

LIST OF FIGURES ..................................................................................................... VI

LIST OF TABLES .................................................................................................... VIII

LIST OF ABBREVIATIONS ...................................................................................... X

NOMENCLATURES .................................................................................................. XI

CHAPTER 1 INTRODUCTION .................................................................................. 1

1.1 Background ....................................................................................................... 1

1.2 Problem Statement ............................................................................................ 6

1.3 Objectives ........................................................................................................ 10

1.4 Scope of Work ................................................................................................. 10

CHAPTER 2 LITERATURE REVIEW .................................................................... 12

2.1 Overview ......................................................................................................... 12

2.2 Greenhouse Gases ........................................................................................... 12

2.3 Carbon Dioxide (CO2) Emission ..................................................................... 17

2.4 Drawbacks of Private Vehicles ....................................................................... 19

2.5 Contribution of Private Vehicles to CO2 Emission ......................................... 20

2.6 Measurement of Vehicle Emission ................................................................. 21

CHAPTER 3 METHODOLOGY ............................................................................... 25

3.1 Introduction ..................................................................................................... 25

3.2 Area of study ................................................................................................... 28

3.3 Traffic Count Survey ....................................................................................... 32

3.4 Calculation of Carbon Emission ..................................................................... 34

3.4.1 Calculation for PCU/hr ............................................................................ 34

3.4.2 Estimation of Fuel Consumption Rate ..................................................... 36

V

3.4.3 Carbon Emission Coefficient ................................................................... 37

3.4.4 Distance.................................................................................................... 37

3.4.5 Estimating carbon dioxide CO2 emission based on private vehicles

reduction ............................................................................................................. 38

CHAPTER 4 RESULTS AND DISCUSSION ........................................................... 44

4.1 Introduction ..................................................................................................... 44

4.2 Private vehicles in PCU/hr .............................................................................. 44

4.3 Fuel Consumption (FE) ................................................................................... 47

4.4 Distance ........................................................................................................... 48

4.5 CO2 emission (current, 40%, 50% & 60%) ..................................................... 49

4.6 Relationship between traffic volume and CO2 emission ................................ 57

4.7 Relationship between distances travelled by private vehicles and CO2

emissions .................................................................................................................... 59

CHAPTER 5 CONCLUSIONS ................................................................................... 60

REFERENCES ............................................................................................................. 62

APPENDIX A: LIST OF CAR MODELS AND ITS RESPECTIVE FUEL

CONSUMPTION ......................................................................................................... 68

APPENDIX B: LIST OF MOTORCYCLE MODELS AND ITS RESPECTIVE

FUEL CONSUMPTION ............................................................................................. 78

APPENDIX C: PICTURE OF AN ACTUAL FOOTAGE TAKEN DURING

TRAFFIC COUNT SURVEY FOR ALL LOCATIONS ......................................... 84

VI

LIST OF FIGURES

Page

Figure 1.1 Global CO2 emission by sector (Ritchie and Roser, 2017) 2

Figure 1.2 Emission of CO2 by sector in Malaysia (Safaai et al., 2011) 3

Figure 1.3 An Inclusive Transport System for Penang (Source: Penang

Transport Master Plan (PTMP, 2016)

7

Figure 1.4 Proposed LRT alignment in Penang 8

Figure 2.1 Global Greenhouse Gas Emission by Gas from 2010

(Edenhofer, 2015)

13

Figure 2.2 Global Carbon Emissions from Fossil Fuel from 1900 to

2014 (Boden et al., 2009)

14

Figure 2.3 Percentage of greenhouse gases emission in the United

States in 2017 (Environmental Protection Agency of United

States (2016)

15

Figure 2.4 Relationship between the traffic volume at the morning peak

and evening peak (Chang and Lin, 2018)

24

Figure 3.1 Flow chart of this study 27

Figure 3.2 Areas of study located on the eastern side in Penang Island 29

Figure 3.3 Land use map for the area of study (JPBD Geoportal, 2018;

GoogleMaps, 2018; Penang Master Plan, 2013)

30

Figure 3.4 GoPro used to record traffic for traffic count survey 33

Figure 3.5 Position of video camera recording actual footage during

traffic count survey at Bandar Sri Pinang

33

VII

Figure 3.6 Example of 631.05 m distance measurement of Bukit

Jambul by using Google Map

38

Figure 4.1 CO2 emission of existing condition and after reduction of

traffic volume by 40%, 50% and 60%

57

Figure 4.2 Relationship between traffic volumes (PCU) against the

carbon dioxide (CO2) emission (kgCO2/PCU)

58

Figure 4.3 Relationship between distance travelled by private vehicles

(m) and carbon dioxide (CO2) emission (kgCO2/PCU)

59

VIII

LIST OF TABLES

Page

Table 1.1 Malaysian vehicle registration data up to 30th June 2017

(Malaysia Automotive Association, 2017)

4

Table 2.1 Global Greenhouse Gas Emissions by Economic Sector from

2010 (Edenhofer, 2015)

13

Table 2.2 Total CO2 emissions from transportation sector in Malaysia

(Indati and Bekhet, 2014)

19

Table 2.3 Ownership, average distance travelled and CO2 emissions

for the entire fleet, gasoline and diesel cars (Papagiannaki

and Diakoulaki, 2009)

21

Table 3.1 Land use classification for each area of study (JPBD

Geoportal, 2018 and GoogleMaps, 2018)

31

Table 3.2 Survey time 32

Table 3.3 Example of determination of the highest one hour traffic

volume at Batu Uban

35

Table 3.4 Example of calculation from PCU/vehicle to PCU/hour 36

Table 3.5 Example of fuel consumption rate of Batu Uban in one hour

highest traffic volume

36

Table 3.6 An example of 40%, 50% and 60% of one hour highest

traffic volume reduction at Batu Uban in the morning

40

Table 3.7 An example of 40%, 50% and 60% of one hour highest

traffic volume reduction at Batu Uban in the evening

41

Table 3.8 An example of current total CO2 emission in Batu Uban 42

Table 3.9 An example of the total CO2 emission after 40% traffic

reduction in Batu Uban

42



43



43

IX

Table 4.1 Number of private vehicles in the morning in PCU/hr from

each location

45

Table 4.2 Number of private vehicles in the evening in PCU/hr from

each location

46

Table 4.3 Fuel consumption rate of private vehicles for one hour

highest traffic volume for all locations

47

Table 4.4 Travelled distance of private vehicles measured at every

location

48

Table 4.5 Total current CO2 emission for every location 50

Table 4.6 Total CO2 emission for 40% traffic volume reduction for

every location

52


every location

54


every location

56

X

LIST OF ABBREVIATIONS

PTMP Penang Transport Master Plan

MRT Mass Rapid Transit

LRT Light Rail Transit

BRT Bus Rapid Transit

OECD Organisation for Economic Cooperation and Development

DT Distance Travel

PCE Passenger Car Emission

XI

NOMENCLATURES

CO2 Carbon Dioxide

Ni Passenger Car Units (PCU)

FE Fuel Consumption Rate

EC CO2 Emissions Coefficient

Di Length of The Vehicle Travelling in The Block (m)

1

CHAPTER 1

INTRODUCTION

1.1 Background

According to Ma (1998) carbon dioxide (CO2) is referred as a greenhouse gas

(GHG) that absorbs and emits heat radiation, causing a greenhouse effect. In addition to

other greenhouse gases such as nitrous oxide and methane, CO2 is essential in

maintaining the planet's ideal temperature which is liveable for most living creatures:

our planet would simply be freezing cold if there were no GHGs at all.

On the other hand, excessive GHG emission can cause global warming and

fluctuating climate have a range of potential impacts on the environment, physical and

health. Some of these include extreme weather events such as floods, droughts, storms

and heatwaves. It also causes rise of sea-level, crop growth altered and water systems

disrupted (Field et al., 2017).

From the data published by Ritchie and Roser (2017), electricity and heat

production in 2014 resulted in around half of global emissions worldwide. Meanwhile,

the transportation and manufacturing industries attributed about 20 percent; residential,

commercial and public services accounted for around 9 percent, while other sectors

contributed 1 to 2 percent. Figure 1.1 shows a chart of carbon dioxide (CO2) emission

by sectors from 1960 to date.

2

Figure 1.1: Global CO2 emission by sector (Ritchie and Roser, 2017)

In addition, Babatundea et al. (2018) stated that transport systems, electricity

generation, industrial sectors and residual were noted as the main contributors to CO2

emissions in Malaysia. The viewpoint duration for carbon emissions from energy

consumers is projected in Malaysia to grow by approximately 4.2% annually to 414

million tons of dioxide carbon in 2030.

The need for research into reducing GHGs in different countries is highly drawn

attention to when statistics are showing upward trend of CO2 emission over the years

(Hosseini et al., 2013). The agricultural activities, disposal of waste materials as well as

water treatment are also some of Malaysia's other sources of GHG generation besides

fuel combustion. Figure 1.2 shows the fraction of CO2 emissions from different sources

in Malaysia (Safaai et al., 2011).

3

Figure 1.2: Emission of CO2 by sector in Malaysia (Safaai et al., 2011)

A study by Briggs and Leong (2016) have found that Malaysia's transport sector

accounts for approximately 35 percent of total national energy consumption and

produces nearly 50 million metric tons (Mt) of CO2 per year in 2015, second only to the

generation of electricity. The vast majority of emissions which comes from

transportation, 85.2 percent are contributed from road transport. Due to the high rate of

private vehicle ownership, private cars account for approximately 59 percent of total

transport emissions, while freight accounts for 27 percent. Although the number of cars

and motorcycles on the roads is roughly equal, motorcycles account for only 11% of

the CO2 emissions from the transport sector. As the economy continues to expand, the

rate of energy consumption increases and the corresponding emissions of greenhouse

gas (GHG) are also increasing. This ultimately leading to an almost linear rate of CO2

emissions per gross domestic product (GDP).

4

As of June 30, 2017, the Malaysian Automotive Association (MAA) released

Malaysian vehicle registration data with the total number of vehicles on our roads

standing at 28,181,203 units. That is 0.88 vehicles for every person in the country. The

majority of vehicles was registered in the Federal Territories–including Kuala Lumpur,

Putrajaya and Labuan–were 6,320,329. Standing at second is Johor which had

3,611,611 units, while Selangor is in the 3rd place with 2,904,476 units. Close behind

are Penang (2,655,679 units) and Perak (2,260,242 units). The data for on-the-road

vehicles in respective states in Malaysia was presented in Table 1.1. Therefore, the

increasing number of vehicles in Malaysia leads to the unsolved problem of traffic

congestion.

Table 1.1: Malaysian vehicle registration data up to 30th

June 2017 (Lee J., 2017)

State

Private Vehicles Public

Service

Vehicles

(PSV)

Goods

Vehicles Others Total

Cars Motorcycles

Perlis 26,510 84,500 385 2,007 1,365 114,767

Kedah 341,197 954,751 7,273 40,710 20,104 1,364,035

Penang 1,130,601 1,408,528 9,586 80,254 26,710 2,655,679

Perak 772,591 1,359,771 9,534 75,638 42,708 2,260,242

Selangor 1,157,268 1,423,821 24,273 194,390 104,724 2,904,476

Federal

Territories 3,987,468 1,863,260 78,752 268,340 122,509 6,320,329

Negeri

Sembilan 343,007 557,482 4,635 50,160 7,845 963,129

Melaka 344,459 472,701 3,425 28,486 8,830 857,901

Johor 1,498,587 1,873,005 20,365 153,471 66,183 3,611,611

Pahang 392,200 600,470 4,310 45,640 14,663 1,057,283

Terengganu 211,124 393,228 2,159 22,172 6,015 634,698

Kelantan 309,663 549,363 3,928 29,689 7,264 899,907

Sabah 697,541 402,237 9,574 116,292 65,807 1,291,451

Sarawak 813,569 798,227 5,834 95,373 71,782 1,784,785

Business

Partners

Portals

1,263,012 191,698 1,002 3,122 2,076 1,460,910

Total 13,288,797 12,933,042 185,035 1,205,744 568,585 28,181,203

5

Meanwhile, Zhang and Batterman (2013) observed that congestion of the traffic

increases vehicle emissions and deteriorates ambient air quality. Drivers, commuters

and those living near major roads also have excess morbidity and mortality. Besides,

according to Chee and Fernandez (2013), traffic congestion resulting from wider use of

private transport has not only led to a loss of efficiency but has also led to a

deterioration of the environment, especially the deterioration in the air quality caused

by automotive pollution. In order to reduce such congestion, the promotion of public

transport would be crucial.

Actions to encourage the shift of private transport to public transport should be

taken. In order to address the current state of public transport, accessibility, ease and

convenience of travelling can be improved. Moreover, reliability and safety should be

enhanced (Almselati et al., 2011).

Therefore, based on Ma et al. (2019) study, in building new urbanization,

sustainable urban transport plays a vital role. The degree of effectiveness in the

infrastructure of the traffic network determines the mode of travel chosen by urban

residents. The more responsive urban public transport, the better chances that public

transport will become the main mode of travel and the easier it will be to establish a

sustainable urban transport system.

In conclusion, in order to combat the effects of excessive GHGs emission, a

fundamental step should be taken, that is to reduce the GHGs emission from source.

Thus, this study was needed to be conducted to forecast the reduction of CO2 emission

if private vehicles usage is reduced as well as to show how this study could be mean of

support for future public transportation strategy.

6

1.2 Problem Statement

The Penang State Government has identified three main concerns that need to

be addressed which are crime, cleanliness and traffic congestion (Penang Transport

Master Plan PTMP, 2016). Momentous progress has been made in cutting down crime,

enhancing public safety and maintaining a clean, comfortable environment through

continuous efforts. Nonetheless, traffic congestion remains a major concern; worsen by

the progressive of economic growth and tourist inflow to this lively heritage city.

According to Shariff (2012), the population of Penang Island was 575,498 in

2000 and 740,200 in 2010 with 29 percent increase over the last 10 years. This led to

111,882 new registered vehicles in Penang Island alone in 2010. Since ownership of

private vehicles was also linked to external factors such as traffic congestion, accidents,

inadequate parking spaces and pollution, local and regional transport policy was part of

an important component.

Realizing the challenges arose, a Transport Master Plan Strategy Report known

as Penang Transport Master Plan (PTMP) was commissioned by the Penang state with

the aim of improving the current state transport system by introducing a holistic

approach to public transport and highway improvement in 2020. The Penang

Transportation Master Plan (PTMP) represents an all-encompassing, efficient, and

well-connected transport approach and provides the Penangites with an integrated,

modern land-and sea-based transportation system.

This transport plan includes various transport systems and services including

elevated Light Rails Transit (LRT), Monorail, Bus Rapid Transit (BRT), tram, taxi, E-

hailing, ferry and water taxi. Besides, PTMP is aimed at achieving a 40:60 share modal

split of public: private transport by 2030. Figure 1.3 shows the interlink between KTM

Komuter, Bayan Lepas LRT, Georgetown-Butterworth LRT, Tg. Tokong Monorail,

7

Ayer Itam Monorail, Raja Uda – Bukit Mertajam Monorail and P/Tinggi – Batu Kawan

BRT which are a part of PTMP plan.

Figure 1.3: An Inclusive Transport System for Penang (Source: Penang Transport

Master Plan (PTMP, 2016)

Penang government has also come up with future LRT network in Penang

Island. The LRT alignment is shown in Figure 1.4.

8

Figure 1.4: Proposed LRT alignment in Penang

Source: Penang Transport Master Plan (2016)

On a different note, according to Ministry of International Trade and Industry,

2017 (MITI), Penang is obliged to go along with the proposed adaptation of Malaysia

to the United Nations Framework Convention on Climate Change (UNFCCC) as stated

in Malaysia's Second National Communication (NC2). It is also reported that Malaysia

is as firmly on track to achieve its GHG reduction target by 2030 with the following

programs:

9

1) Green Technology Master Plan (2017-2030)

- To make Malaysia a low-carbon, resource-efficient economy by implementing

Green Catalyst projects to reduce carbon intensity by 40% by 2020.

2) Energy Efficiency Action Plan

- The goal is to reduce CO2 emissions equivalent to 13,113 million tons by

2030.

3) Transportation Sector

- The launching of the Mass Rapid Transit (MRT) phase one has successfully

removed 9.9 million cars in 2017 and estimated to remove additional 62-89

million cars in between year 2020 to 2030.

4) Low Carbon Cities Framework

- To implement a carbon reduction plan for decision - making on greener

solutions by local authorities and developers.

As traffic congestion increases, CO2 emissions and fuel consumption in parallel

are also known to increase. Therefore, the growing in numbers of private vehicles in

Penang along with its traffic congestion will increase the CO2 emission and it is needed

for Penang government to fulfil its aspiration in reducing the state’s GHG emission.

10

1.3 Objectives

The objectives of this study are listed as below:

1. To determine the existing traffic volume at selected locations in Penang

Island.

2. To calculate CO2 emissions based on current traffic volume at the selected

locations in Penang Island.

3. To estimate the reduction of CO2 emissions with 40%, 50% and 60%

reduction of private vehicles at selected locations in Penang Island.

1.4 Scope of Work

This research study was done to estimate the current carbon dioxide (CO2)

emission of private vehicles at seven selected locations on the eastern side of Penang

Island. The locations were chosen as they are along the alignment of future Penang’s

Light Rail Transit (LRT) as proposed in Penang Transport Master Plan (PTMP).

Comparison of current carbon dioxide (CO2) emission and future reduction of

carbon dioxide (CO2) emission when people shift to public transport was done. As the

selected locations are along the LRT alignment, assumption of shift mode of private

vehicles to public transport by 40%, 50% and 60% was made. Private vehicles in this

study include cars and motorcycles only.

11

The traffic count was done during the weekdays except for Monday and Friday.

For every location, six hours of traffic count were done. Morning traffic count started

from 6.30 a.m. to 9.30 a.m. and evening traffic count started from 4.30 p.m. to 7.30

p.m. The traffic count procedures were in accordance to the guideline of Highway

Capacity Manual (Highway Planning Unit, Ministry of Works, Malaysian Government,

2015).

12

CHAPTER 2

LITERATURE REVIEW

2.1 Overview

In this chapter, preceding studies related to forecasting private vehicular

emissions are reviewed. This is to ensure a better comprehension in order to perform a

thorough research dissertation. The topics covered in this chapter include greenhouse

gases (GHGs), private vehicles, public transport as well as sustainable transport.

2.2 Greenhouse Gases

Environmental Protection Agency of United States (2016) states that

greenhouse gases (GHGs) are essentially known as gases which trap heat in the

atmosphere. Generally, greenhouse gases consist of carbon dioxide (CO2), methane

(CH4), nitrous oxide (N2O) and fluorinated gases. Figure 2.1 shows the percentage of

global greenhouse gas emission by type of gas in 2010. The figure also shows that

carbon dioxide (CO2) is the biggest type of gas emitted onto the atmosphere at 76%

followed by methane (CH4), nitrous oxide (N2O) and fluorinated gases at 16%, 6% and

2% respectively.

Meanwhile, highest greenhouse gases emission by sector was dominated by

electricity and heat production sector followed by agriculture and forestry, industry,

transportation, other energy as well as buildings. This can be referred in Table 2.1.

13

Figure 2.1: Global Greenhouse Gas Emission by Gas from 2010 (Edenhofer, 2015)

Table 2.1: Global Greenhouse Gas Emissions by Economic Sector from 2010

(Edenhofer, 2015)

Sector Gas Emissions Percentage

Electricity and Heat Production 25%

Agriculture, Forestry and Other Land Use 24%

Industry 21%

Transportation 14%

Other energy 10%

Buildings 6%

On the other hand, Boden et al. (2009) has found that global carbon emissions

from fossil fuels have significantly increased since 1900. Since 1970, CO2 emissions

have increased by around 90%. Contributions of 78% of total greenhouse gas emissions

increase from 1970 to 2011 are emissions from fossil fuels and industrial processes.

The second-largest contributors were agriculture, deforestation and other land-use

changes. The pattern of global carbon emissions from fossil fuels can be seen in Figure

2.2.

65% 11%

16%

6%

2% Carbon dioxide (fossil

fuel and industrial

processes)

Carbon dioxide (forestry

and other land use)

Methane

Nitrous Oxide

F-gases

14

Figure 2.2: Global Carbon Emissions from Fossil Fuel from 1900 to 2014 (Boden et al.,

2009)

Greenhouse gas absorbs heat and warms the planet. Over the last 150 years,

human activities are liable for almost all of the growth in atmospheric greenhouse gas

(Change, 2007). Meanwhile, in the United States the total emission of GHGs in 2017 is

equal to 6,456.7 million metric tons of CO2 equivalent. As shown in Figure 2.3, in 2017

carbon dioxide (CO2) was the major gas emitted into the atmosphere at 82% from the

total greenhouse gases followed by methane, nitrous oxide and fluorinated gases.

https://www.epa.gov/ghgemissions/overview-greenhouse-gases#colorbox-hidden

15

Figure 2.3: Percentage of greenhouse gases emission in the United States in 2017

(Environmental Protection Agency of United States (2016)

The significant effects of increase in greenhouse gases (GHGs) are seen by the

increase of dryness as well as frequent rainfall and flood. Besides, increase of earth

temperature and tendency for forest to catch fire, rising of sea water level, occurrence

of severe storm and damage to water resources, farming and the ecosystems are part of

the effects. In addition to the threat to human health, greenhouse gas in various

countries could also be detrimental to national safety (Samimi and Zarinabadi, 2012).

Malaysia has signed numerous international greenhouse gas emissions

agreements, including Montreal's 1987 Protocol, the 1992 Kyoto Protocol, the 2009

Copenhagen Agreement and the 2010 Cancun Agreement (Shahid et al., 2014).

Furthermore, Malaysia has also acknowledged that its greenhouse gas emissions will be

cut down by up to 40 percent by 2020, which is comparable to 2005 levels in order to

implement the Cancun Agreements and the Bali Declaration on the joint efforts of both

developed and developing countries to reduce emissions.

82%

10%

6% 3%

Carbon Dioxide

Methane

Nitrous Oxide

Fluorinated Gases

16

According to Salahudin et al. (2013), in order to mitigate emissions, Malaysia's

government is actively engaged in several international agreements including Montreal

1987, Kyoto protocol in 1997 and the climate summit in Copenhagen Denmark in

2009. On 24th

July 2009, Malaysia's government recently introduced the National

Green Technology policy (NGTP), developing five strategic trusts, including public

awareness in Malaysia's tenth plan. Furthermore, National Green Technology Policy

(NGTP) also has the initiative to implement green technology which can produce zero

or low emissions of greenhouse gas (GHG). The five strategic trusts are as follows:

1. Development on a sustainable Path – Integrate response of climate change

into national development plans in order to accomplish the country's desire for

sustainable development.

2. Conservation of environmental and natural resources – Enhanced

implementation of actions on climate change that contribute to the conservation

of the environment and sustainable use of natural resources.

3. Coordinated Implementation – Include climate change considerations into

implementation of climate change responses.

4. Effective Participation – For effective implementation of climate change

responses, participation of stakeholders and major groups has to be revised.

5. Common but differentiated Responsibilities and Respective Capabilities –

International climate change engagement will be based on the principle of

shared but differentiated responsibility and capabilities.

17

2.3 Carbon Dioxide (CO2) Emission

As stated by Schmalensee et al. (2001), majority of the scientists believe that if

the concentrations of carbon dioxide (CO2) and other so-called greenhouse gasses

continue to increase in the atmosphere, the climate of the earth will become warmer.

Robertson (2006) has studied that the probability if the concentration of carbon

dioxide (CO2) in the atmosphere reaches 426 ppm in less than two generations from

today, the health of at least some sections of the world's population, including those of

developed nations, will deteriorate. It is also clear that the ecosystem and humanity are

seriously threatened if the extremes of conditions described above eventuate.

The severity of the harmful climate change caused by humans is not only on the

extent of the change, but also on the likeliness for irreversibility. Solomon et al. (2009)

stated that the climate change resulting from an increase in the concentration of carbon

dioxide (CO2) is largely irreversible for 1,000 years following the end of emissions.

On the other hand, Ahmad and Wyckoff (2003) claimed that most of carbon

dioxide (CO2) is emitted during the burning of fossil fuels and the organisation for

economic cooperation and development (OECD) countries account for more than half

of the world's total carbon dioxide emissions, while some other four countries (Brazil,

China, India and Russia) account for another quarter of the global total. They also

reported that these policies which aimed at reducing these emissions set emission

reduction targets were based on some previous levels. For example, for many countries

the 1990 Kyoto Protocol was used as a benchmark for success and compliance with the

Protocol.

18

Elhadi et al. (2015) noted that the rising demand for energy and strong

dependence on fossil fuels in transportation will increase the level of carbon dioxide

(CO2) emissions. Carbon dioxide (CO2) is the main emission of road transports. The

amount of carbon dioxide (CO2) emissions is directly associated to the amount of fuel

consumed. Besides, other gas emissions also depend on the amount of fuel used and

they are affected by the vehicle type, the fuel consumption rate and the emission factor

of each fuel.

Likewise, Barth and Boriboonsomsin (2010) claimed that road transport plays a

vital role in carbon dioxide (CO2) emissions, accounting for roughly a third of the

inventory of the United States. Therefore, transport policymakers seek to make vehicles

more efficient and increase the use of carbon-neutral alternative fuels in order to reduce

CO2 emissions in the future. For example, CO2 emissions can be improved by reducing

traffic congestion.

In addition, Papagiannaki and Diakoulaki (2009) stated that the steady increase

in energy use and CO2 emissions from private vehicles lead to more study of

fundamental drivers’ behaviours influencing the change in emissions. At the same time,

the growing demands for energy and highly dependent on fossil fuels in transport will

also increase Malaysian CO2 emission levels (Indati and Bekhet, 2014). Table 2.2

shows total CO2 emissions from transportation sector in Malaysia.

19

Table 2.2: Total CO2 emissions from transportation sector in Malaysia (Indati and

Bekhet, 2014)

Year CO2 Emissions (Tons)

1995 23,923,654

1996 27,362,020

1997 31,362,250

1998 29,911,387

1999 34,856,822

2000 36,954,241

2001 40,214,007

2002 41,137,864

2003 43,677,614

2004 47,082,204

2005 46,746,590

2006 45,294,132

2007 47,976,559

2008 50,085,110

2009 49,187,895

2010 51,338,726

2011 53,060,646

2.4 Drawbacks of Private Vehicles

Read (2005) in his innovation proved that private vehicles have changed the

urban life in term of offering the opportunity and accessibility to travel all over the

place. Besides, private vehicle has a reliability and availability rate near 100%.

However, the cost of owning a private vehicle was reported higher than the average

income. In addition, the increase in private vehicle ownership has created congestion in

urban areas.

Meanwhile, studies by Mohamad and Kiggundu (2007) and Borhan et al. (2014)

have found the same finding which shows that private car is now an essential and has

become dominant means of transportation in many cities today. The rising in people's

choice of private car usage as a transport mode is due to its clear advantages. One of

20

the important reasons many people opt to own a car is the unregulated freedom that car

users enjoy. While public transport modes require services to be shared with strangers,

the private car offers its user with privacy and comfort. It is also suggested that as

more and more active car usage has led to more accessibility problems in and around

industrialized countries because of traffic jams and parking problems. Private vehicles

also cause serious problems in addition to road congestions including CO2, global

warming and noise.

Motor vehicles produce particles matter <2.5 μm (PM2.5), so PM2.5 levels tend

to be higher in proximity of busy streets or in another words urban area (Buckeridge et

al., 2002). McCubbin (1999) says the health costs of motor vehicles are much higher

than reported in the past. Particulates are the most detrimental pollutant when compared

to ozone and other pollutants which have lesser consequences. Due to higher particulate

emissions, diesel vehicles cause more damage per mile than gasoline vehicles.

According to Marshall et al. (2005), motor vehicles are a primary source of

criteria pollutants and harmful air pollutants that are ever-present in urban areas of US

as well as worldwide. Meanwhile, a study by Afroz et al. (2003) have found that over

the last five years the major source of air pollution has been emissions from mobile

sources (i.e. private vehicles), accounting for 70% to 75% of total air pollution in

Malaysia.

2.5 Contribution of Private Vehicles to CO2 Emission

The fuel efficiency of passenger vehicles is frequently highlighted as one of the

most important areas of action to reduce CO2 emissions in the transport sector. This can

be made possible either through automotive technological development, or through

21

demand-based measures such as influencing the choice of first time car buyers Jordan-

Joergensen, J (Cowi, 2002).

Papagiannaki and Diakoulaki (2009) stated that in the cases of Greece and

Denmark, private vehicles are responsible for half the emissions from road transport

including their upward pattern, which causes a decomposition analysis to be carried out

focused precisely on this road transport section. The factors evaluated in the current

analysis of decomposition are associated with ownership of vehicles, fuel mix, motor

power, car technology, and the annual miles. Results comparison showed the difference

in transport profile in both countries and the effects on the CO2 emission trend

were demonstrated in Table 2.3.

Table 2.3: Ownership, average distance travelled and CO2 emissions for the entire fleet,

gasoline and diesel cars (Papagiannaki and Diakoulaki, 2009)

2.6 Measurement of Vehicle Emission

Ragab et al. (2017) has conducted a study to investigate methods to reduce air

emissions which are well known for its harmful effects to the mankind. Road traffic has

been mainly associated with air emissions, particularly road air emissions. In order to

Denmark

1990 4,919 319 16,656 4,434 303 15,834 485 16.2 31,981

1995 5,853 332 19,113 5,334 315 18,360 520 16.8 33,184

2000 6,286 360 18,829 5,577 337 17,884 709 23 32,694

2005 6,423 372 18,262 5,076 329 16,385 1347 43.4 32,484

Greece

1990 4,573 171 14,688 4,112 168 13,490 462 3.0 80,940

1995 5,381 209 13,977 4,823 205 12,829 558 3.6 78,937

2000 7,014 292 13,005 6,411 288 12,200 603 3.6 76,983

2005 8,985 391 12,276 8,267 387 11,602 718 4.1 75,078

CO2Vehicles/

1000cap

Distance

(km)CO2

Vehicles/

1000cap

Distance

(km)CO2

Vehicles/

1000cap

Distance

(km)

Fleet Gasoline Cars Diesel Cars

22

reduce road air emissions, traffic management is very essential. Traffic management

has been used solely to improve traffic flow efficiency. However, with the rising of

environmental concerns, traffic management can also be used to reduce the negative

impacts of traffic on the environment. Improvement of road traffic flow can reduce

vehicle emissions and travel time whereas promotion of public transport can reduce air

emission but cannot reduce travel time. Ragab et al. (2017) studied three situations to

lessen the effect of traffic on air emissions. The situations were:

1) Scenario 0: Original scenario (Real traffic volumes and speeds were used to

analyse the chosen network);

2) Scenario 1: Road traffic improvement; and

3) Scenario 2: Public transportation promotion.

Mustapa and Bekhet (2015) examined the key factors of CO2 emissions in the

road transport sector using multiple regressions analyse by using data from 1990 to

2013. The variables used in the analysis were fuel consumption (FC), fuel efficiency

(FE), fuel price (FP) and distance travel (DT). The results indicated that the primary

factors causing the hike of CO2 emissions were fuel efficiency (FE), fuel price (FP) and

distance travel (DT). For the reduction of CO2 emissions, the authors proposed some

policy recommendations which were:

Since most passenger cars were running on petrol (93 %), by increasing use of

efficient vehicle technology, like hybrid and electric vehicles, can reduce CO2

emissions in this sector. The government should therefore amplify the

promotion of these vehicles and proceed to provide imperative fiscal

encouragement to speed up their use.

23

Distance travel (DT) was considered the key factor for diesel vehicles and

therefore the options for fuel switching can be introduced to reduce FC while

satisfying mobility needs. This can be achieved in order to achieve reductions in

CO2 emissions by stepping up the use of alternative fuels, including biofuels

that contain less CO2.

Now that FP is also shown to have major effect on CO2 emission reductions, the

Government has decided to withdraw the FP subsidies in 2014 for both gasoline

and diesel vehicles. Therefore, additional requirements management measures

can be implemented to both reducing FC and CO2 emissions, such as higher

vehicle taxes, carbon tax, and congestion charges in city areas.

According to Franco et al. (2013), the development of Emission Factor (EFs) in

the formula helped to achieve a more accurate outcome. The method of testing the

chassis and engine dynamometer was found to be not adequate, as it cannot depict the

actual circumstances of road traffic. Nevertheless, the testing of chassis and engine

dynamometers is still an important method for gauging emissions from a vehicle.

For the case of Taichung City, in order for Chang and Lin (2018) to analyse

energy consumption and its relevance, the study had calculated the mutual relationship

between the emission of carbon dioxide from traffic and building development scale.

The duo had used multiplication of the type of vehicles (such as passenger cars, lorries

and cars) by fuel type (diesel, petrol, etc.) and then by unit fuel emissions factor or unit

mileage emission coefficients to calculate for total traffic CO2 emissions.

Based on Chang and Lin (2018) analysis, following a count of the total number

of vehicles in 24 hours at the road crossing, the linear regression analysis shall be

carried out according to the morning and evening high data concerning the forecast

24

model for the total traffic volume at each road crossing. As shown in Equation (2.1)

below the prediction of total traffic volume in road crossing is obtained. Figure 2.4

shows the relationship between total traffic volume and hour.

y = 2261.52 + 2.36y1 + 10.18y2 R2 = 0.99 (2.1)

Figure 2.4: Relationship between the traffic volume at the morning peak and evening

peak (Chang and Lin, 2018)

In terms of driving behaviour, Tong et al. (2000) had found that fluctuating

driving behaviours (i.e., acceleration and deceleration) were more polluting than the

constant-speed driving behaviours (i.e., cruising and idling) in terms of g/km and g/sec

produced. These results showed that measuring emissions on the road is viable in the

derivation of emissions from vehicles and fuel consumption factors in urban driving

conditions.

The transport sector currently accounts for 13.5% of global warming. The amount

of carbon dioxide (CO2) emitted from the distance travelled is directly proportional to

the fuel economy, with approximately 2.4 kg of CO2 released from each litre of

gasoline burnt (Ong et al., 2011).

y1: Morning peak hours

y2: Evening peak hours

25

CHAPTER 3

METHODOLOGY

3.1 Introduction

Upon commencement of this research study, a methodology was laid out so that

the whole process can be executed in conformity with the objectives of the project. It

was essential to have a deep understanding before planning a methodology. It was

important to have fundamental insight and narrowing the knowledge gaps from past

studies as well as eventually creating structure for a new study.

This study was meant to forecast the reduction of carbon dioxide (CO2)

emissions from private vehicles in Penang Island in the case where private vehicles

usage is reduced by 40%, 50% and 60%. For start-up, a total of seven locations have

been selected in Penang Island for this study namely Bandar Sri Penang zone (BSPZ),

Skycab zone (SKYZ), East Jelutong zone (EJZ), Batu Uban zone (BUSZ), Sungai

Nibong zone (STZ), Bukit Jambul zone (BJZ) and lastly Jalan Tengah zone (JTZ). The

chosen locations are along the future LRT alignment in Penang.

Next, for the traffic survey purpose, video cameras and GoPros were set up at

all seven locations to record the traffic twice a day (6.30a.m.-9.30a.m. for morning

peak hour and 4.30p.m.-7.30p.m. for evening peak hour). Traffic counting was then

conducted by means of observation from the videos recorded. Number of private

vehicles was calculated and converted into passenger car unit (PCU). Statistical data

was presented in Microsoft Excel.

26

Calculation of carbon dioxide (CO2) emission estimation from private vehicles

was done manually by using an equation. All the data were gathered in accordance to

the brands and models of the private vehicles. The reduction of carbon dioxide (CO2)

emission will be forecasted with 40%, 50% and 60% reduction in private vehicles

usage. Figure 3.1 shows a flow chart which contains the processes involved for this

research study. For a clearer view, Figure 3.2 shows the exact locations on the map of

Penang Island.

27

LITERATURE REVIEW

It further enhanced knowledge in

Type of vehicle emissions

Calculation of vehicle emission

Suitable method for data collection

was able to be determined

Method of result presentation was also

figured

DATA COLLECTION

Area of study: Eastern region of Penang Island.

Time of data collection: Morning Peak hour (6.30a.m. to 9.30a.m.) & Evening Peak

hour (4.30p.m. to 7.30p.m.) to forecast the emissions during traffic congestion.

Research areas comprised of 7 locations.

Traffic volume was determined

By video camera recording

By counting the vehicles from the

videos

DATA ANALYSIS AND PARAMETER GENERATION

Statistical data was analysed using

Microsoft Excel

Traffic flow and peak hour was generated

from Microsoft Excel. Calculation of

vehicle emission was done manually using

an equation.

EQUATION n

∑ PCEi = Ni × FE × EC × Di (Chang and Lin, 2018)

t=1

PRESENTATION OF DATA AND RESULT

Data, results from statistical analysis and comparisons in emission were presented.

CONCLUSION AND RECOMMENDATION

Figure 3.1: Flow chart of this study

28

3.2 Area of study

Areas of study are located on the eastern side of Penang Island. Seven locations

were selected for this study which includes:

1) Bandar Sri Pinang zone (BSPZ);

2) Skycab zone (SKYZ);

3) East Jelutong zone (EJZ);

4) Batu Uban zone (BUSZ);

5) Sungai Nibong zone (STZ);

6) Bukit Jambul zone (BJZ); and

7) Jalan Tengah zone (JTZ).

The locations of the study are shown in Figure 3.2. These locations were chosen

because they exhibited high traffic volume based on the traffic study. Besides, the

presence of academic institutions, residential areas, mixed-developments as well as

commercial areas contributed to the high traffic volume. Figure 3.3 shows the type of

land use at each selected zone. On the other hand, Table 3.1 shows the summary of the

land use that each zone has. The seven locations are actually on the alignment of the

proposed future Penang’s first Light Rail Transit (LRT). This will aid in comparisons

between existing (CO2) emission and forecasted (CO2) emission in the future as the

tendency for people to shift from private vehicles to public transport (LRT) is higher as

the facilities were made available.

29

1

2

3

4 6

7

5

Legend.

1) Bandar Sri Pinang Zone

(BSPZ)

2) Skycab Zone (SKYZ)

3) East Jelutong Zone (EJZ)

4) Batu Uban Zone (BUSZ)

5) Sungai Nibong Zone

(STZ)

6) Bukit Jambul Zone (BJZ)

7) Jalan Tengah Zone (JTZ)

Figure 3.2: Areas of study located on the eastern side in Penang Island

30

Figure 3.3: Land use map for the area of study (JPBD Geoportal, 2018; GoogleMaps,

2018; Penang Master Plan, 2013)

Zone 1

Zone 2

Zone 3 Zone 4

Zone 7

Zone 6

Zone 5

31

Table 3.1: Land use classification for each area of study (JPBD Geoportal, 2018 and

GoogleMaps, 2018)

Zone Location Land use

1 Bandar Sri Pinang

Commercial

Public facilities

Government offices

Place of worship

Residential

Industry

2 Skycab

Commercial

Public facilities

Residential

3 East Jelutong Commercial

Residential

4 Batu Uban

Commercial

Academic institution

Government offices

Residential

5 Sungai Nibong

Commercial

Public facilities

Residential


6 Bukit Jambul

Commercial

Public facilities

Place of worship

Residential


7 Jalan Tengah

Commercial

Public facilities


Place of worship

Residential

Industry

32

3.3 Traffic Count Survey

Traffic count survey was conducted in a period of four weeks. During the first

week, two locations were covered which were Bandar Sri Pinang zone and Skycab

zone. The rest of the locations were covered in the following week. The survey was

conducted on weekdays from Tuesday to Thursday only and during morning and

evening peak hours. The peak hour times were shown in Table 3.2.

Table 3.2: Survey time

Peak hour Time

Morning 6.30 a.m. – 9.30 a.m.

Evening 4.30 p.m. – 7.30 p.m.

A GoPro was used to record the traffic during peak hours. Figure 3.4 shows

image of the GoPro. The GoPro was set up near a bus stop or on the pedestrian bridge

so that it was able to record a clearer view which also helped in the traffic counting

process by means of video observation later on. Bus stop was also chosen as a place for

recording because it would depict the future traffic scene whereby less private vehicles

on the road and more public transport usage (LRT and buses). The reason for using a

recording camera was due to limitation of manpower. Besides, the traffic scenario was

quite packed it would be difficult to do traffic count survey manually. The position of

the video camera was shown in Figure 3.5.

33

Figure 3.4: GoPro used to record traffic for traffic count survey

(Source: Google Image, 2019)

Figure 3.5: Position of video camera recording actual footage during traffic count

survey at Bandar Sri Pinang

34

3.4 Calculation of Carbon Emission

In this study, the calculation of carbon emission will be based on equation (3.1)

from Chang and Lin (2018).

∑ 𝑃𝐶𝐸𝑖 = 𝑁𝑖 × 𝐹𝐸 × 𝐸𝐶 × 𝐷𝑖

𝑛

𝑡=1

(3.1)

where,

PCEi is the CO2 emissions generated by each passenger car unit (kgCO2/PCU);

Ni is the passenger car units (PCU) for each of the road,

FE is the fuel consumption rate (L/m),

EC is the CO2 emissions coefficient (kgCO2/L),

Di is the length of the vehicle driving in the block (m).

3.4.1 Calculation for PCU/hr

Current condition of traffic volume was analysed based on the traffic count

survey done earlier. From the data gathered, the one hour highest traffic volume was

obtained and the percentage of cars and motorcycle in one hour highest traffic volume

was recorded for every location. To obtain PCU/vehicle, the percentage of cars was

multiplied with 1.00 for vehicle class one which is car and multiplied with 0.33 for

vehicle class five which is motorcycle based on Malaysia’s Highway Capacity Manual.

Then, the traffic volume in PCU/vehicle was multiplied with the highest one hour

traffic volume to obtain PCU/hr. An example is shown in Table 3.3 and Table 3.4.

35

Table 3.3: Example of determination of the highest one hour traffic volume at Batu

Uban in the morning

Date: 9/10/2018 Time: 6.30a.m. – 9.30a.m. Location: Batu Uban

Batu Uban

Time No. of Car No. of

Motorcycle

Total

Vehicle

One-hour

highest traffic

volume

6.30 - 6.45 1138 621 1759

6.45 - 7.00 1468 809 2277

7.00 - 7.15 1509 941 2450

7.15 - 7.30 1500 1005 2505 8991

7.30 - 7.45 1309 1008 2317 9549

7.45 - 8.00 1524 865 2389 9661

8.00 - 8.15 1405 881 2286 9497

8.15 - 8.30 1523 770 2293 9285

8.30 - 8.45 1323 661 1984 8952

8.45 - 9.00 1086 540 1626 8189

9.00 - 9.15 392 221 613 6516

9.15 - 9.30 0 0 0 0

Peak hour time recorded: 7.00 a.m. – 8.00 a.m.

Total number of car in 1-hour highest traffic volume = 1509+1500+1309+1524

= 5842

Calculation for percentage of car = 5842

9661× 100%

= 60%

Total number of motorcycle in 1 hour highest traffic volume = 941+1005+1008+865

= 3819

Calculation for percentage of motorcycle = 3819

9661× 100%

= 40%

36

Table 3.4: Example of calculation from PCU/vehicle to PCU/hour

BATU UBAN

Percentage of

car

Car

PCU

Percentage of

motorcycle

Motorcycle

PCU PCU/Veh

0.60 1.00 0.40 0.33 0.73

Calculation of PCU/vehicle = (0.60 × 1.00) + (0.40 × 0.33)

= 0.73 PCU/vehicle

Calculation of PCU/hour = 0.73 × 1 hour highest traffic volume

= 0.73 × 9661

= 7053 PCU/hour

3.4.2 Estimation of Fuel Consumption Rate

Fuel consumption rate was estimated by collecting information of the fuel

consumption of every private vehicle which can be observed through the recorded

videos. Every private vehicle’s fuel consumption information was collected from

websites search in km/l. From the observation of the recorded videos, private vehicles’

brands and models were determined. Then, the fuel consumption rate in one-hour

highest traffic volume was identified and converted into L/m. Table 3.5 shows an

example of Batu Uban’s fuel consumption rate in one hour highest traffic volume from

km/l to L/m.

Table 3.5: Example of fuel consumption rate of Batu Uban in one hour highest

traffic volume

Location

Average fuel consumption

(km/l) for cars and motorcycle

for one hour highest traffic

volume

Average fuel consumption

(km/l) for cars and motorcycle

for one hour highest traffic

volume in (L/m)

Batu Uban 26.9 0.000037

37

The calculation for conversion of fuel consumption rate

= 26.9 𝑘𝑚

𝐿 ×

1000 𝑚

1 𝑘𝑚 = 26900

𝑚

𝐿 =

1

26900 ×

(1)𝐿

(1)𝑚

= 0.000037 L/m

3.4.3 Carbon Emission Coefficient

The carbon emission coefficient in this study is based on Ong et al. (2011) study

which was conducted in Malaysia. He found out approximately 2.4kg of CO2 are

released into the atmosphere for one litre of gasoline (petroleum) burnt for private

vehicle. Thus, 2.4kgCO2/L is used as the carbon emission coefficient for this study.

3.4.4 Distance

Distance is the length travelled by the private vehicles in the area. In this study,

the length was recorded in between two major junctions in the vicinity of the bus stop.

The measurement of distance was done using Google Maps. Figure 3.6 shows an

example of distance measured at Batu Uban using Google Maps.

38

Figure 3.6: Example of 535.06m distance measurement of Batu Uban by using Google

Map

3.4.5 Estimating carbon dioxide CO2 emission based on private vehicles

reduction

In order to calculate carbon emission produced from the private vehicles,

several attributes need to be calculated. The attributes are distance travelled by the

private vehicles (Di), the traffic volume in PCU (Ni), the vehicle estimated fuel

consumption rate (FE) and carbon emission coefficient (EC).

Traffic volume which was converted into PCU/hr was multiplied with distance,

carbon emission coefficient as well as the fuel consumption of the vehicle in order to

obtain the current CO2 emission. Summation of all CO2 emission by all the vehicles

will give total CO2 emission from private vehicles in that area. The process continued

with the rest of area of study to collect the data of CO2 emission at each location.

39

Next, traffic volume in PCU/hr was the only parameter to be reduced by 40%,

50% and 60% for every location. The rest of the parameters were neglected. This is

because traffic volume was the most significant factor that would affect carbon

emission. The process of calculating CO2 emissions was redone with the new traffic

volumes that have been reduced in order to forecast the reduction of CO2 emissions.

For 40% reduction of private vehicles, it was done by reducing 20% of the one hour

highest traffic volume in PCU/hr of cars and 20% reductions of one hour highest traffic

volume in PCU/hr of motorcycle.

The calculation was then repeated again with 50% traffic volume reduction

which saw 30% reduction from cars and 20% reduction from motorcycles. Lastly, for

60% overall traffic volume reduction will see 30% reduction of one hour highest traffic

volume in PCU/hr of cars and 30% reductions of one hour highest traffic volume in

PCU/hr of motorcycle. The calculation was done for all seven locations. Table 3.6

shows an example of one hour highest traffic volume reduction by 40%, 50% and 60%

at Batu Uban in the morning and Table 3.7 shows an example of one hour highest

traffic volume reduction by 40%, 50% and 60% at Batu Uban in the evening.

40

Table 3.6: An example of 40%, 50% and 60% of one hour highest traffic volume

reduction at Batu Uban in the morning

Batu Uban = 7053 PCU/hr

40% traffic volume reduction Total

4502 PCU/hr 2551 PCU/hr 7053 PCU/hr

20% reduction of cars from

one hour highest traffic

volume

20% reduction of motorcycle

from one hour highest traffic

volume






volume



volume






volume



volume


41

Table 3.7: An example of 40%, 50% and 60% of one hour highest traffic

volume reduction at Batu Uban in the evening

Batu Uban = 5366 PCU/hr

40% traffic volume reduction Result


20% reduction of cars

from one hour highest

traffic volume

20% reduction of

motorcycle from one hour







traffic volume

20% reduction of








traffic volume

30% reduction of




For total CO2 emission calculation, the formula of

∑ 𝑃𝐶𝐸𝑖 = 𝑁𝑖 × 𝐹𝐸 × 𝐸𝐶 × 𝐷𝑖

𝑛

𝑡=1

was incorporated. An example of current total CO2 emission in Batu Uban is shown in

Table 3.8.

42

Table 3.8: An example of current total CO2 emission in Batu Uban

Current total CO2 emission (AM)

No. Location

Traffic

volume in

PCU (Ni)

Fuel

Consumption

Rate (L/m)

(FE)

Emission

Coefficient

(kgCO2/L)

(EC)

Distance

(m) (Di)

Total CO2

Emission

(kgCO2)

1 Batu

Uban 7,053 0.000037 2.4 535.06 335

Current total CO2 emission (PM)

No. Location

Traffic

volume in

PCU (Ni)

Fuel

Consumption

Rate (L/m)

(FE)

Emission

Coefficient

(kgCO2/L)

(EC)

Distance

(m) (Di)

Current CO2

Emission

(kgCO2)

1 Batu

Uban 5,366 0.000037 2.4 535.06 255

For 40% traffic reduction, the total CO2 emission is shown in Table 3.9.

Table 3.9: An example of the total CO2 emission after 40% traffic reduction in Batu

Uban

CO2 Emission After 40% Traffic Reduction (AM)

No. Location

Traffic

volume in

PCU (Ni)

Fuel

Consumption

Rate (L/m)

(FE)

Emission

Coefficient

(kgCO2/L)

(EC)

Distance

(m) (Di)

Total CO2

Emission

(kgCO2)

1 Batu

Uban 5,642 0.000037 2.4 535.06 268

CO2 Emission After 40% Traffic Reduction (PM)

No. Location

Traffic

volume in

PCU (Ni)

Fuel

Consumption

Rate (L/m)

(FE)

Emission

Coefficient

(kgCO2/L)

(EC)

Distance

(m) (Di)

Total CO2

Emission

(kgCO2)

1 Batu

Uban 4,293 0.000037 2.4 535.06 204

43



Uban


No. Location

Traffic

volume in

PCU (Ni)

Fuel

Consumption

Rate (L/m)

(FE)

Emission

Coefficient

(kgCO2/L)

(EC)

Distance

(m) (Di)

Total CO2

Emission

(kgCO2)

1 Batu

Uban 5,192 0.000037 2.4 535.06 247


No. Location

Traffic

volume in

PCU (Ni)

Fuel

Consumption

Rate (L/m)

(FE)

Emission

Coefficient

(kgCO2/L)

(EC)

Distance

(m) (Di)

Total CO2

Emission

(kgCO2)

1 Batu

Uban 3,955 0.000037 2.4 535.06 188



Uban


No. Location

Traffic

volume

in PCU

(Ni)

Fuel

Consumption

Rate (L/m)

(FE)

Emission

Coefficient

(kgCO2/L)

(EC)

Distance

(m) (Di)

Total CO2

Emission

(kgCO2)

1 Batu

Uban 4,937 0.000037 2.4 535.06 235


No. Location

Traffic

volume

in PCU

(Ni)

Fuel

Consumption

Rate (FE)

Emission

Coefficient

(EC)

Distance

(Di)

Total CO2

Emission

(kgCO2)

1 Batu

Uban 3,756 0.000037 2.4 535.06 178

44

CHAPTER 4

RESULTS AND DISCUSSION

4.1 Introduction

In this chapter, results were analysed and a few statistical data were conferred.

Discussion was also performed to clarify the findings. Data analysis included the

volume of private vehicles recorded at each location, the calculated CO2 emissions at

existing level as well as the forecasted CO2 emissions when 40%, 50% and 60% of

traffic have been reduced.

4.2 Private vehicles in PCU/hr

The traffic volume for the study at seven locations only comprises of private

vehicles which are car and motorcycle. A total of 43,585 private vehicles in PCU/hr

which consist of cars and motorcycles were recorded during the traffic count. Lorries

and buses were not counted as they are regarded as commercial vehicles. Table 4.1

shows the number of private vehicles in PCU/hr in the morning from the seven

locations of area of study.

Batu Uban recorded the highest one hour traffic volume at 7,053 PCU/hr as the

traffic count was conducted on the main road called Jalan Sultan Azlan Shah. This

main road links commercial area, public facilities, residential area as well as academic

institution. Meanwhile, East Jelutong had the least volume of traffic at 936 PCU/hr as

the secondary road named as Jalan Perak only serves the residential and commercial

area in its vicinity.

45

Table 4.1: Number of private vehicles in the morning in PCU/hr from each location

Number of private vehicles in PCU/hr in the morning (AM)

Location

Peak

Hours

(AM)

Number of

cars and its

percentage

(%)

Number of

motorcycle

and its

percentage

(%)

Total

private

vehicle in 1

hour highest

traffic

volume

PCU/vehicle PCU/hr

Bandar

Sri

Penang

7.30 -

8.30

2334

(55%)

1920

(45%) 4254 0.7 2978

Skycab 7.30 -

8.30

1205

(49%)

1241

(51%) 2446 0.66 1614

East

Jelutong

7.30 -

8.30

609

(38%)

1004

(62%) 1613 0.58 936

Batu

Uban

7.00 -

8.00

5842

(60%)

3819

(40%) 9661 0.73 7053

Sungai

Nibong

6.30 -

7.30

2277

(64%)

1279

(36%) 3556 0.76 2703

Bukit

Jambul

7.40 -

8.40

4446

(66%)

2260

(34%) 6706 0.77 5164

Jalan

Tengah

6.40 -

7.40

1611

(54%)

1368

(46%) 2979 0.69 2056

Total

18324 12891

22502

The same situation applies with the number of private vehicles in PCU/hr in the

evening where Batu Uban had the highest number of private vehicles at 5,366 PCU/hr

and East Jelutong had the least number of private vehicles at 893 PCU/hr. The data for

number of private vehicles in PCU/hr in the evening for all locations is shown in Table

4.2.

46

Table 4.2: Number of private vehicles in the evening in PCU/hr from each location

One hour highest traffic volume in the evening (PM)

Location

Peak

Hours

(PM)

Number of

cars and its

percentage

(%)

Number of

motorcycle

and its

percentage

(%)

Total private

vehicle in 1

hour highest

traffic

volume

PCU/vehicle PCU/hr

Bandar

Sri

Penang

5.15 -

6.15

2177

(54%)

1849

(46%) 4026 0.6 2416

Skycab 5.30 -

6.30

1773

(48%)

1893

(52%) 3666 0.65 2383

East

Jelutong

5.30 -

6.30

591

(39%)

923

(61%) 1514 0.59 893

Batu

Uban

5.15 -

6.15

4523

(63%)

2632

(37%) 7155 0.75 5366

Sungai

Nibong

5.15 -

6.15

2538

(63%)

1486

(37%) 4024 0.75 3018

Bukit

Jambul

6.15 -

7.15

3904

(60%)

2581

(40%) 6485 0.73 4734

Jalan

Tengah

5.35 -

6.35

1821

(63%)

1090

(37%) 2911 0.75 2183

Total 17327 12454 20993

47

4.3 Fuel Consumption (FE)

Fuel consumption rate for private vehicle ranges from as low as 26.5 km/l to

32.6 km/l at most. The rate was then converted to Litre/metre. Different vehicle models

will have different fuel consumption because of factors such as different engine sizing,

engine technology, overall weight and size of vehicle. In Table 4.3, it shows the

average fuel consumption of private vehicles in one hour highest traffic volume for

every location. The fuel consumption list for each of the vehicle model is shown in

Appendix A and Appendix B.

Table 4.3: Fuel consumption rate of private vehicles for one hour highest traffic volume

for all locations

Location

Fuel consumption rate (km/l) for

cars and motorcycle for one

hour highest traffic volume

Conversion of fuel

consumption rate from

(km/l) to (L/m)

Bandar Sri Pinang 27.8 0.000035

Skycab 29.2 0.000034

East Jelutong 32.6 0.000031

Batu Uban 26.9 0.000037

Sungai Nibong 26.7 0.000037

Bukit Jambul 26.5 0.000038

Jalan Tengah 28.4 0.000035

48

4.4 Distance

The travelled distance of private vehicles was measured on google map. The

distances recorded for each location is shown in Table 4.4.

Table 4.4: Travelled distance of private vehicles measured at every location

Location Distance (m)

Bandar Sri Penang 459.48

Skycab 220.16

East Jelutong 250.44

Batu Uban 535.06

Sungai Nibong 338.8

Bukit Jambul 631.05

Jalan Tengah 442.55

The longest route measured was 631.05 m at Bukit Jambul. The basis of

measurement was done between two major junctions. The first junction is a signalised

three-legged junction that is adjacent with Sekolah Jenis Kebangsaan (Cina) Min Sin

whilst the second junction is also a signalised three-legged junction with commercial

lots and residential area around its vicinity.

49

As for Skycab, it measured the shortest distance amongst the other six locations

at 220.16m. The basis of measurement is the same as the one conducted in Bukit

Jambul. The measurement was measured along Jalan Jelutong which is the primary

road. The first junction is a four-legged stop-control junction connecting to two

secondary roads namely Jalan Lintang Penawar 1 and Jalan Lintang Bakau. The second

junction is a three-legged stop-control junction located around commercial lots.

4.5 CO2 emission (current, 40%, 50% & 60%)

The sum of existing CO2 emission for all seven locations had amounted to a

whopping 5,831 kgCO2. For the current total CO2 emission in the morning, Batu Uban

had the highest amount of CO2 emission at 335 kgCO2 whilst in the evening Bukit

Jambul took the spot as the having the highest CO2 emission at 272 kgCO2. The CO2

emission for the rest of the location is shown in Table 4.5.

50

Table 4.5: Total current CO2 emission for every location

AM

No. Location

Traffic

volume

in PCU

(Ni)

Fuel

Consumption

Rate (L/m)

(FE)

Emission

Coefficient

(kgCO2/L)

(EC)

Distance

(m) (Di)

Current CO2

Emission

(kgCO2/PCU)

1

Bandar

Sri

Pinang

2,978 0.000035 2.4 459.48 115

2 Skycab 1,614 0.000034 2.4 220.16 29

3 East

Jelutong 936 0.000031 2.4 250.44 17

4 Batu

Uban 7,053 0.000037 2.4 535.06 335

5 Sungai

Nibong 2,703 0.000037 2.4 338.8 81

6 Bukit

Jambul 5,164 0.000038 2.4 631.05 297

7 Jalan

Tengah 2,056 0.000035 2.4 442.55 76

Total = 951

PM

No. Location

Traffic

volume

in PCU

(Ni)

Fuel

Consumption

Rate (L/m)

(FE)

Emission

Coefficient

(kgCO2/L)

(EC)

Distance

(m) (Di)

Current CO2

Emission

(kgCO2/PCU)

1

Bandar

Sri

Pinang

2,414 0.000035 2.4 459.48 93

2 Skycab 2,383 0.000034 2.4 220.16 43

3 East

Jelutong 893 0.000031 2.4 250.44 17

4 Batu

Uban 5,366 0.000037 2.4 535.06 255

5 Sungai

Nibong 3,018 0.000037 2.4 338.8 91

6 Bukit

Jambul 4,734 0.000038 2.4 631.05 272

7 Jalan

Tengah 2,183 0.000035 2.4 442.55 81

Total = 852

51

As for total emission after 40% traffic reduction, Batu Uban had the highest

CO2 emission at 268 kgCO2 in the morning and for evening Bukit Jambul had the

highest CO2 emission at 218 kgCO2. The total emission after 40% traffic reduction for

the rest of locations is shown in Table 4.6.

52

Table 4.6: Total CO2 emission for 40% traffic volume reduction for every location

AM

No. Location

Traffic

volume

in PCU

(Ni)

Fuel

Consumption

Rate (L/m)

(FE)

Emission

Coefficient

(kgCO2/L)

(EC)

Distance

(m) (Di)

CO2

Emission

After 40%

Traffic

Reduction

(kgCO2/PCU)

1 Bandar

Sri Pinang 2,382 0.000035 2.4 459.48 92

2 Skycab 1,291 0.000034 2.4 220.16 23

3 East

Jelutong 749 0.000031 2.4 250.44 14

4 Batu

Uban 5,642 0.000037 2.4 535.06 268

5 Sungai

Nibong 2,162 0.000037 2.4 338.8 65

6 Bukit

Jambul 4,131 0.000038 2.4 631.05 238

7 Jalan

Tengah 1,645 0.000035 2.4 442.55 61

Total = 761

PM

No. Location

Traffic

volume

in PCU

(Ni)

Fuel

Consumption

Rate (L/m)

(FE)

Emission

Coefficient

(kgCO2/L)

(EC)

Distance

(m) (Di)

CO2

Emission

After 40%

Traffic

Reduction

(kgCO2/PCU)

1 Bandar

Sri Pinang 1,931 0.000035 2.4 459.48 75

2 Skycab 1,906 0.000034 2.4 220.16 34

3 East

Jelutong 714 0.000031 2.4 250.44 13

4 Batu

Uban 4,293 0.000037 2.4 535.06 204

5 Sungai

Nibong 2,414 0.000037 2.4 338.8 73

6 Bukit

Jambul 3,787 0.000038 2.4 631.05 218

7 Jalan

Tengah 1,746 0.000035 2.4 442.55 65

Total = 682

53

Meanwhile, for total emission after 50% traffic reduction, Batu Uban had 247 kgCO2

of carbon dioxide emission in the morning which was the highest compared to other places

and the least CO2 emission was from East Jelutong 13 kgCO2. In the evening, Bukit Jambul

had the highest CO2 emission at 202 kgCO2 while the lowest CO2 emission was from East

Jelutong at 13 kgCO2. Table 4.7 shows total CO2 emission for 50% traffic volume reduction

for every location.

54


AM

No. Location

Traffic

volume

in PCU

(Ni)

Fuel

Consumption

Rate (L/m)

(FE)

Emission

Coefficient

(kgCO2/L)

(EC)

Distance

(m) (Di)

CO2

Emission

After 50%

Traffic

Reduction

(kgCO2/PCU)

1 Bandar

Sri Pinang 2,149 0.000035 2.4 459.48 83

2 Skycab 1,212 0.000034 2.4 220.16 22

3 East

Jelutong 713 0.000031 2.4 250.44 13

4 Batu

Uban 5,192 0.000037 2.4 535.06 247

5 Sungai

Nibong 1,989 0.000037 2.4 338.8 60

6 Bukit

Jambul 3,790 0.000038 2.4 631.05 218

7 Jalan

Tengah 1,534 0.000035 2.4 442.55 57

Total = 700

PM

No. Location

Traffic

volume

in PCU

(Ni)

Fuel

Consumption

Rate (L/m)

(FE)

Emission

Coefficient

(kgCO2/L)

(EC)

Distance

(m) (Di)

CO2

Emission

After 50%

Traffic

Reduction

(kgCO2/PCU)

1 Bandar

Sri Pinang 1,815 0.000035 2.4 459.48 70

2 Skycab 1,792 0.000034 2.4 220.16 32

3 East

Jelutong 680 0.000031 2.4 250.44 13

4 Batu

Uban 3,955 0.000037 2.4 535.06 188

5 Sungai

Nibong 2,224 0.000037 2.4 338.8 67

6 Bukit

Jambul 3,503 0.000038 2.4 631.05 202

7 Jalan

Tengah 1,609 0.000035 2.4 442.55 60

Total = 631

55

Lastly, for total emission after 60% traffic reduction, Batu Uban still held the highest

CO2 emission at 235 kgCO2 in the morning and East Jelutong with the least CO2 emission at

12 kgCO2. As for evening, Bukit Jambul also still had the highest CO2 emission at 191

kgCO2. The overall total CO2 emission after 60% traffic volume reduction is shown in Table

4.8

56


AM

No. Location

Traffic

volume

in PCU

(Ni)

Fuel

Consumption

Rate (L/m)

(FE)

Emission

Coefficient

(kgCO2/L)

(EC)

Distance

(m) (Di)

CO2 Emission

After 60%

Traffic

Reduction

(kgCO2/PCU)

1

Bandar

Sri

Pinang

2,085 0.000035 2.4 459.48 80

2 Skycab 1,130 0.000034 2.4 220.16 20

3 East

Jelutong 655 0.000031 2.4 250.44 12

4 Batu

Uban 4,937 0.000037 2.4 535.06 235

5 Sungai

Nibong 1,892 0.000037 2.4 338.8 57

6 Bukit

Jambul 3,615 0.000038 2.4 631.05 208

7 Jalan

Tengah 1,439 0.000035 2.4 442.55 53

Total = 666

PM

No. Location

Traffic

volume

in PCU

(Ni)

Fuel

Consumption

Rate (L/m)

(FE)

Emission

Coefficient

(kgCO2/L)

(EC)

Distance

(m) (Di)

CO2 Emission

After 60%

Traffic

Reduction

(kgCO2/PCU)

1

Bandar

Sri

Pinang

1,690 0.000035 2.4 459.48 65

2 Skycab 1,668 0.000034 2.4 220.16 30

3 East

Jelutong 625 0.000031 2.4 250.44 12

4 Batu

Uban 3,576 0.000037 2.4 535.06 170

5 Sungai

Nibong 2,113 0.000037 2.4 338.8 64

6 Bukit

Jambul 3,314 0.000038 2.4 631.05 191

7 Jalan

Tengah 1,528 0.000035 2.4 442.55 57

Total = 588

57

From Figure 4.1, it was found that the trend of CO2 emission will decrease when there

is a reduction of traffic volume. This is because lesser vehicle on the road will emit lesser

CO2 into the atmosphere.

Figure 4.1: CO2 emission of existing condition and after reduction of traffic volume by 40%,

50% and 60%.

4.6 Relationship between traffic volume and CO2 emission

Figure 4.2 show the relationship between traffic volumes against the carbon dioxide

(CO2) emission in the morning and in the evening. Through the graph, it showed that as the

number of traffic volume increases, the carbon dioxide (CO2) emission also increases. The R2

value obtained in the morning is 0.9569 while in the evening is 0.9028 which mean that

parameter of traffic volume greatly affects CO2 emission.

951

761 700 666

852

682 631

588

0

100200300

400500600

700800900

1,000

Existing TrafficVolume

40% TrafficVolume Reduction



CO

2 Em

issi

on

(kg

CO

2/P

CU

)

AM

PM

58

a) Morning (AM)

b) Evening (PM)

Figure 4.2: Relationship between traffic volumes (PCU) against the carbon dioxide (CO2)

emission (kgCO2) a) in the morning (AM) and b) in the evening (PM)

0

50

100

150

200

250

300

350

400

0 1000 2000 3000 4000 5000 6000 7000 8000

CO

2 E

mis

sion

(k

gC

O2/P

CU

)

Traffic Volume (PCU)

AM

R2 = 0.9569 R2 = 0.9569

0

50

100

150

200

250

300

0 1,000 2,000 3,000 4,000 5,000 6,000

CO

2 E

mis

sion

(k

gC

O2/P

CU

)

Traffic Volume (PCU)

PM

R2 = 0.9028

59

4.7 Relationship between distances travelled by private vehicles and CO2 emissions

In Figure 4.3, the graph showed that as distance travelled by private vehicle increases,

the CO2 emission also increases. It has R2

value of 0.7677 which means distance has a

slightly lesser significance towards CO2 emission.

Figure 4.3: Relationship between distance travelled by private vehicles (m) and carbon

dioxide (CO2) emission (kgCO2/PCU)

0

50

100

150

200

250

0 100 200 300 400 500 600 700

Co

2 E

mis

sion

(k

gC

O2/P

CU

)

Distance (m)

R2 = 0.7677

60

CHAPTER 5

CONCLUSIONS

5.1 Conclusion

In this dissertation, the primary objectives were to determine the existing traffic

volumes, to obtain the CO2 emissions based on existing traffic volume as well as to forecast

the reduction CO2 emission with 40%, 50% and 60% reduction of private vehicles at selected

locations in Penang Island. The study areas were at Bandar Sri Pinang, Skycab, East Jelutong,

Batu Uban, Sungai Nibong, Bukit Jambul and Jalan Tengah. The existing traffic volume was

determined by means of traffic count survey. The travelled distance by private vehicles,

emission coefficient, private vehicle’s fuel consumptions and total CO2 emission were

analysed and presented in this study.

Following the result and discussion, some conclusions can be deduced from this dissertation:

The travelled distance by private vehicles, private vehicle’s fuel consumptions and

traffic volume affect the CO2 emissions are almost linear. It also means the greater

the distance, fuel consumption or traffic volume in an area the higher CO2

emission produced in that specific location.

The emission coefficient is a fixed parameter that does not have effect on the CO2

emission. The emission coefficient value is relatively determined by the type of

fuel used. As private vehicles in this study’s context is all run by petroleum thus

the value of emission coefficient is fixed for all type of vehicles.

When there is a reduction of private vehicles on the road, it also shows that CO2

emission can be reduced. This is in line with state government’s effort in reducing

Penang’s CO2 emission overall. Besides, the effort by the state government to

61

introduce light rail transit (LRT) in Penang proves to be rewarding. People will

start shifting to public transport to avoid the hassle of traffic jam and this will

enable to lower the CO2 emission as there is less private vehicles on the road.

5.2 Recommendation

Private vehicles which consist of cars and motorcycles were focused in this study. In

order to fully predict the emission of CO2 from transportation, it was suggested that the

conclusion of commercial vehicles (light and heavy) be included in the study. The reason

behind this being these vehicles are usually larger and use diesel as its fuel. This type of fuel

emits larger particulate matter and higher CO2 emission.

The use of Geographic Information System (GIS) could learn the driving pattern of

the vehicles in the area and better predict the CO2 emission overall. Besides, incorporating

software related to forecasting CO2 emission from transportation would ease the process of

the study and give better understanding on the importance of each parameter involved in

forecasting CO2 emission from vehicles.

During the traffic count survey, a better camera position could be set. A location that

is able to record the dual carriageway and direction in a single frame. For example, the

recording can be made in the middle of pedestrian bridge.

62

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68

APPENDIX A

LIST OF CAR MODELS AND ITS RESPECTIVE FUEL

CONSUMPTION

69

a) Car fuel consumption based on car models observed at Bandar Sri Pinang

Date of observation: 21/1/2019

Peak Hour Time: 7.30 – 8.30 (AM) & 5.15 – 6.15 (PM)

Fuel consumption rate for private vehicles in one hour highest traffic volume:

0.000035 L/m

b) Car fuel consumption based on car models observed at Skycab




0.000034 L/m

c) Car fuel consumption based on car models observed at East Jelutong




0.000031 L/m

70

d) Car fuel consumption based on car models observed at Batu Uban




0.000037 L/m

e) Car fuel consumption based on car models observed at Sungai Nibong




0.000037 L/m

f) Car fuel consumption based on car models observed at Bukit Jambul




0.000038 L/m

g) Car fuel consumption based on car models observed at Jalan Tengah




0.000035 L/m

71

20.1

21

18

21.1

16

11

15

11

13.6

13.6

14.5

15.6

12.8

13.6

18.2

16.4

16.4

17.5

14

11.8

18.5

15.5

15.2

13.5

13.8

25.6

25

12.7

15.3

12

12

12

13.9

10.1

13.6

16.3

15.5

14.5

12.9

11.7

13.8

13.6

14.6

16.7

Myvi

Bezza

Viva

Axia

Alza

Persona

Saga

Satria Neo

Suprima S

Preve

Perdana

Iriz

Stream

Accord

City

Civic

HRV

BRV

CRV

Odyssey

Jazz

Vios

Altis

Camry

Avanza

Prius C

Prius

Innova

Sienta

Alphard

Estima

Vellfire

Hilux

Fortuner

Harrier

Almera

Sylphy

Serena

X-trail

Navara

Grand Livina

Teana

Mazda 3

Mazda 2

(a)

14

12.6

13.3

13.5

12.9

13.5

14.8

13.5

16

14.6

13.6

14.2

13.5

11.2

10.7

13.8

14.6

13.7

14.9

12.5

12.5

23

13.9

18

17.1

17.6

16.7

15.5

14.4

13.7

12.9

14.8

15.7

13.2

12.8

14.8

13.5

12.9

12

13

17.4

15.3

13.8

11.9

Mazda 6

CX-5

CX-3

BMW 3 series

BMW 5 series

BMW X5

Merc. C class

Merc. GLC class

A-class

Merc. E class

Kia Forte

Kia Cerato

Kia Optima

Kia Carnival

Kia Sorento

Kia Sportage

Kia Rio

Hyundai Tuscon

Hyundai Elantra

Hyundai Santa Fe

Hyundai Starex

Hyundai Ioniq

Hyundai Sonata

VW Polo

VW Jetta

VW Golf

VW Passat

VW Tiguan

VW Beetle

Subaru XV

Subaru Forester

Peugeot 508

Peugeot 308

Peugeot 3008

Peugeot 5008

Audi A4

Audi A6

Audi Q5

Audi Q7

Mitsubishi Triton

Mitsubishi Lancer

Mitsubishi ASX

Mitsubishi…

Lexus IS250

72

(b)

20.1

21

18

21.1

16

11

15

11

13.6

13.6

14.5

15.6

12.8

13.6

18.2

16.4

16.4

17.5

14

11.8

18.5

15.5

15.2

13.5

13.8

25.6

25

12.7

15.3

12

12

12

13.9

10.1

13.6

16.3

15.5

14.5

12.9

11.7

13.8

13.6

14.6

16.7

Myvi

Bezza

Viva

Axia

Alza

Persona

Saga

Satria Neo

Suprima S

Preve

Perdana

Iriz

Stream

Accord

City

Civic

HRV

BRV

CRV

Odyssey

Jazz

Vios

Altis

Camry

Avanza

Prius C

Prius

Innova

Sienta

Alphard

Estima

Vellfire

Hilux

Fortuner

Harrier

Almera

Sylphy

Serena

X-trail

Navara

Grand Livina

Teana

Mazda 3

Mazda 2

14

12.6

13.3

13.5

12.9

13.5

14.8

13.5

16

14.6

13.6

14.2

13.5

11.2

10.7

13.8

14.6

13.7

14.9

12.5

12.5

23

13.9

18

17.1

17.6

16.7

15.5

14.4

13.7

12.9

14.8

15.7

13.2

12.8

14.8

12.9

12

13

17.4

15.3

13.8

10.5

11.9

Mazda 6

CX-5

CX-3

BMW 3 series

BMW 5 series

BMW X5

Merc. C class

Merc. GLC class

A-class

Merc. E class

Kia Forte

Kia Cerato

Kia Optima

Kia Carnival

Kia Sorento

Kia Sportage

Kia Rio

Hyundai Tuscon

Hyundai Elantra

Hyundai Santa Fe

Hyundai Starex

Hyundai Ioniq

Hyundai Sonata

VW Polo

VW Jetta

VW Golf

VW Passat

VW Tiguan

VW Beetle

Subaru XV

Subaru Forester

Peugeot 508

Peugeot 308

Peugeot 3008

Peugeot 5008

Audi A4

Audi Q5

Audi Q7

Mitsubishi Triton

Mitsubishi Lancer

Mitsubishi ASX

Mitsubishi Outlander

Lexus RX350

Lexus NX 200T

73

(c)

20.1

21

18

21.1

16

11

15

11

13.6

13.6

15.6

12.8

13.6

18.2

16.4

16.4

17.5

14

18.5

15.5

15.2

13.5

13.8

25.6

12.7

15.3

12

12

12

13.9

10.1

13.6

16.3

15.5

14.5

12.9

11.7

13.8

13.6

14.6

16.7

Myvi

Bezza

Viva

Axia

Alza

Persona

Saga

Satria Neo

Suprima S

Preve

Iriz

Stream

Accord

City

Civic

HRV

BRV

CRV

Jazz

Vios

Altis

Camry

Avanza

Prius C

Innova

Sienta

Alphard

Estima

Vellfire

Hilux

Fortuner

Harrier

Almera

Sylphy

Serena

X-Trail

Navara

Grand Livina

Teana

Mazda 3

Mazda 2

14

12.6

13.3

13.5

12.9

13.5

14.8

13.5

16

14.6

13.6

14.2

13.5

14.7

11.2

10.7

13.8

14.6

13.7

14.9

12.5

23

13.9

18

17.1

17.6

16.7

15.5

14.4

13.7

12.9

15.7

13.2

14.8

13.5

12

13

17.4

15.3

13.8

11.9

Mazda 6

CX-5

CX-3

BMW 3 series

BMW 5 series

BMW X5

Merc C class

Merc GLE class

Merc. A-class

Merc E class

Kia Forte

Kia Cerato

Kia Optima

Kia Picanto

Kia Carnival

Kia Sorento

Kia Sportage

Kia Rio

Hyundai Tuscon

Hyundai Elantra

Hyundai Starex

Hyundai Ioniq

Hyundai Sonata

VW Polo

VW Jetta

VW Golf

VW Passat

VW Tiguan

VW Beetle

Subaru XV

Subaru Forester

Peugeot 508

Peugeot 308

Peugeot 3008

Audi A6

Audi Q7

Mitsubishi Triton

Mitsubishi Lancer

Mitsubishi ASX

Mitsubishi Outlander

Lexus IS250

74

(d)

20.1

21

18

21.1

16

11

15

11

13.6

13.6

14.5

15.6

12.8

13.6

18.2

16.4

16.4

17.5

14

18.5

15.5

15.2

13.5

13.8

25.6

25

12.7

15.3

12

12

12

13.9

10.1

13.6

16.3

15.5

14.5

12.9

11.7

13.8

13.6

14.6

16.7

14

12.6

Myvi

Bezza

Viva

Axia

Alza

Persona

Saga

Satria Neo

Suprima S

Preve

Perdana

Iriz

Stream

Accord

City

Civic

HRV

BRV

CRV

Jazz

Vios

Altis

Camry

Avanza

Prius C

Prius

Innova

Sienta

Alphard

Estima

Vellfire

Hilux

Fortuner

Harrier

Almera

Sylphy

Serena

X-trail

Navara

Teana

Mazda 3

Mazda 2

Mazda 6

CX-5

13.3

13.5

12.9

13.5

14.8

13.5

16

14.6

13.6

14.2

13.5

11.2

10.7

13.8

14.6

13.7

14.9

12.5

12.5

23

13.9

18

17.1

17.6

16.7

15.5

14.4

13.7

12.9

14.8

15.7

13.2

12.8

14.8

13.5

12.9

12

13

17.4

15.3

13.8

11.9

10.5

11.5

11.9

CX-3

BMW 3 series

BMW 5 series

BMW X5

Merc. C class

Merc. GLC class

A-class

Merc. E class

Kia Forte

Kia Cerato

Kia Optima

Kia Carnival

Kia Sorento

Kia Sportage

Kia Rio

Hyundai Tuscon

Hyundai Elantra

Hyundai Santa Fe

Hyundai Starex

Hyundai Ioniq

Hyundai Sonata

VW Polo

VW Jetta

VW Golf

VW Passat

VW Tiguan

VW Beetle

Subaru XV

Subaru Forester

Peugeot 508

Peugeot 308

Peugeot 3008

Peugeot 5008

Audi A4

Audi A6

Audi Q5

Audi Q7

Mitsubishi Triton

Mitsubishi Lancer

Mitsubishi ASX

Mitsubishi…

Lexus IS250

Lexus RX350

Lexus GS250

Lexus NX200T

75

(e)

20.1

21

18

21.1

16

11

15

11

13.6

13.6

14.5

15.6

12.8

13.6

18.2

16.4

16.4

17.5

14

18.5

15.5

15.2

13.5

13.8

25.6

25

12.7

15.3

12

12

12

13.9

10.1

13.6

16.3

15.5

14.5

12.9

11.7

13.8

13.6

14.6

16.7

Myvi

Bezza

Viva

Axia

Alza

Persona

Saga

Satria Neo

Suprima S

Preve

Perdana

Iriz

Stream

Accord

City

Civic

HRV

BRV

CRV

Jazz

Vios

Altis

Camry

Avanza

Prius C

Prius

Innova

Sienta

Alphard

Estima

Vellfire

Hilux

Fortuner

Harrier

Almera

Sylphy

Serena

X-trail

Navara

Teana

Mazda 3

Mazda 2

14

12.6

13.3

13.5

12.9

13.5

14.8

13.5

16

14.6

13.6

14.2

13.5

10.7

13.8

14.6

13.7

14.9

18.9

12.5

12.5

23

13.9

18

17.1

17.6

16.7

15.5

14.4

13.7

12.9

14.8

15.7

13.2

12.8

14.8

13.5

12.9

12

13

17.4

15.3

13.8

11.9

Mazda 6

CX-5

CX-3

BMW 3 series

BMW 5 series

BMW X5

Merc. C class

Merc. GLC class

A-class

Merc. E class

Kia Forte

Kia Cerato

Kia Optima

Kia Sorento

Kia Sportage

Kia Rio

Hyundai Tuscon

Hyundai Elantra

Hyundai i10

Hyundai Santa Fe

Hyundai Starex

Hyundai Ioniq

Hyundai Sonata

VW Polo

VW Jetta

VW Golf

VW Passat

VW Tiguan

VW Beetle

Subaru XV

Subaru Forester

Peugeot 508

Peugeot 308

Peugeot 3008

Peugeot 5008

Audi A4

Audi A6

Audi Q5

Audi Q7

Mitsubishi Triton

Mitsubishi Lancer

Mitsubishi ASX

Mitsubishi…

Lexus NX200T

76

(f)

20.1 21

18 21.1

16 11

15 11

13.6 13.6 14.5

15.6 12.8 13.6

18.2 16.4 16.4

17.5 14

18.5 15.5 15.2

13.5 13.8

25.6 25

12.7 15.3

12 12 12

13.9 10.1

13.6 16.3

15.5 14.5

12.9 11.7

13.8 13.6 14.6

16.7 14

12.6 13.3 13.5

Myvi

Bezza

Viva

Axia

Alza

Persona

Saga

Satria Neo

Suprima S

Preve

Perdana

Iriz

Stream

Accord

City

Civic

HRV

BRV

CRV

Jazz

Vios

Altis

Camry

Avanza

Prius C

Prius

Innova

Sienta

Alphard

Estima

Vellfire

Hilux

Fortuner

Harrier

Almera

Sylphy

Serena

X-trail

Navara

Grand Livina

Teana

Mazda 3

Mazda 2

Mazda 6

CX-5

CX-3

BMW 3 series

12.9

13.5

14

14.8

12.6

10.6

13.5

13.5

13.7

16

14.6

13.6

14.2

13.5

11.2

10.7

13.8

14.6

13.7

14.9

12.5

12.5

23

13.9

18

17.1

17.6

16.7

15.5

14.4

13.7

12.9

14.8

15.7

13.2

12.8

14.8

13.5

12.9

12

13

17.4

15.3

13.8

11.9

10.5

11.5

11.9

BMW 5 series

BMW X5

BMW X3

Merc. C class

Merc. S class

Merc. GLE class

Merc. GLA…

Merc. GLC…

Merc. CLA…

Merc. A-class

Merc. E class

Kia Forte

Kia Cerato

Kia Optima

Kia Carnival

Kia Sorento

Kia Sportage

Kia Rio

Hyundai Tuscon

Hyundai Elantra

Hyundai…

Hyundai Starex

Hyundai Ioniq

Hyundai Sonata

VW Polo

VW Jetta

VW Golf

VW Passat

VW Tiguan

VW Beetle

Subaru XV

Subaru Forester

Peugeot 508

Peugeot 308

Peugeot 3008

Peugeot 5008

Audi A4

Audi A6

Audi Q5

Audi Q7

Mitsubishi…

Mitsubishi…

Mitsubishi ASX

Mitsubishi…

Lexus IS250

Lexus RX350

Lexus NX200T

Lexus GS250

77

(g)

20.1

21

18

21.1

16

11

15

11

13.6

13.6

14.5

15.6

12.8

13.6

18.2

16.4

16.4

17.5

14

18.5

15.5

15.2

13.5

13.8

25.6

25

12.7

15.3

12

12

12

13.9

10.1

13.6

16.3

15.5

14.5

12.9

11.7

13.8

13.6

14.6

16.7

Myvi

Bezza

Viva

Axia

Alza

Persona

Saga

Satria Neo

Suprima S

Preve

Perdana

Iriz

Stream

Accord

City

Civic

HRV

BRV

CRV

Jazz

Vios

Altis

Camry

Avanza

Prius C

Prius

Innova

Sienta

Alphard

Estima

Vellfire

Hilux

Fortuner

Harrier

Almera

Sylphy

Serena

X-trail

Navara

Teana

Mazda 3

Mazda 2

14

12.6

13.3

13.5

12.9

13.5

14.8

13.7

13.5

16

14.6

13.6

14.2

13.5

11.2

10.7

13.8

14.6

13.7

14.9

12.5

12.5

23

13.9

18

17.1

17.6

16.7

15.5

14.4

13.7

12.9

14.8

15.7

13.2

12.8

14.8

12.9

12

13

17.4

15.3

13.8

Mazda 6

CX-5

CX-3

BMW 3 series

BMW 5 series

BMW X5

Merc. C class

Merc. CLA class

Merc. GLC class

A-class

Merc. E class

Kia Forte

Kia Cerato

Kia Optima

Kia Carnival

Kia Sorento

Kia Sportage

Kia Rio

Hyundai Tuscon

Hyundai Elantra

Hyundai Santa Fe

Hyundai Starex

Hyundai Ioniq

Hyundai Sonata

VW Polo

VW Jetta

VW Golf

VW Passat

VW Tiguan

VW Beetle

Subaru XV

Subaru Forester

Peugeot 508

Peugeot 308

Peugeot 3008

Peugeot 5008

Audi A4

Audi Q5

Audi Q7

Mitsubishi Triton

Mitsubishi Lancer

Mitsubishi ASX

Mitsubishi…

78

APPENDIX B

LIST OF MOTORCYCLE MODELS AND ITS RESPECTIVE FUEL

CONSUMPTION

79

a) Motorcycle fuel consumption based on 17 motorcycle models observe at Bandar

Sri Pinang

b) Motorcycle fuel consumption based on 17 motorcycle models observed at Skycab

c) Motorcycle fuel consumption based on 16 motorcycle models observed at East

Jelutong

d) Motorcycle fuel consumption based on 17 motorcycle models observed at Batu

Uban

e) Motorcycle fuel consumption based on 16 motorcycle models observed at Sungai

Nibong

f) Motorcycle fuel consumption based on 17 motorcycle models observed at Bukit

Jambul

g) Motorcycle fuel consumption based on 16 motorcycle models observed at Jalan

Tengah

80

(a)

(b)

52.8

41.4

55.6

42.5

45.5

40

38.5

35

39.6

45.5

40

50.2

26

41.7

43

48.1

47.6

Honda EX5

Honda RS150R

Honda Wave 110

Honda CBR 150R

Yamaha LC135

Yamaha Y15

Yamaha FZ150i

Yamaha RXZ

Yamaha 125 ZR

Suzuki Shogun

Suzuki Belang

SYM 110

Kawasaki ninja 250

Lagenda 110

Demak DVS 110

Modenas Kriss 110

Modenas GT 128

52.8

41.4

55.6

42.5

45.5

40

38.5

35

39.6

45.5

40

50.2

26

41.7

43

48.1

47.6

Honda EX5

Honda RS150R

Honda Wave 110

Honda CBR 150R

Yamaha LC135

Yamaha Y15

Yamaha FZ150i

Yamaha RXZ

Yamaha 125 ZR

Suzuki Shogun

Suzuki Belang

SYM 110

Kawasaki ninja 250

Lagenda 110

Demak DVS 110

Modenas Kriss 110

Modenas GT128

81

(c)

(d)

52.8

41.4

55.6

42.5

45.5

40

38.5

35

39.6

45.5

40

50.2

26

41.7

43

48.1

Honda EX5

Honda RS150R

Honda Wave 110

Honda CBR 150R

Yamaha LC135

Yamaha Y15

Yamaha FZ150i

Yamaha RXZ

Yamaha 125 ZR

Suzuki Shogun

Suzuki Belang

SYM 110

Kawasaki ninja 250

Lagenda 110

Demak DVS 110

Modenas Kriss 110

52.8

41.4

55.6

42.5

45.5

40

38.5

35

39.6

45.5

40

50.2

26

41.7

43

48.1

47.6

Honda EX5

Honda RS150R

Honda Wave 110

Honda CBR 150R

Yamaha LC135

Yamaha Y15

Yamaha FZ150i

Yamaha RXZ

Yamaha 125 ZR

Suzuki Shogun

Suzuki Belang

SYM 110

Kawasaki ninja 250

Lagenda 110

Demak DVS 110

Modenas Kriss 110

Modenas GT128

82

(e)

(f)

52.8

41.4

55.6

42.5

45.5

40

38.5

35

39.6

45.5

40

50.2

26

41.7

43

48.1

Honda EX5

Honda RS150R

Honda Wave 110

Honda CBR 150R

Yamaha LC135

Yamaha Y15

Yamaha FZ150i

Yamaha RXZ

Yamaha 125 ZR

Suzuki Shogun

Suzuki Belang

SYM 110

Kawasaki ninja 250

Lagenda 110

Demak DVS 110

Modenas Kriss 110

52.8

41.4

55.6

42.5

45.5

40

38.5

35

39.6

45.5

40

50.2

26

41.7

43

48.1

47.6

Honda EX5

Honda RS150R

Honda Wave 110

Honda CBR 150R

Yamaha LC135

Yamaha Y15

Yamaha FZ150i

Yamaha RXZ

Yamaha 125 ZR

Suzuki Shogun

Suzuki Belang

SYM bonus 110

Kawasaki ninja 250

Lagenda 110

Demak DVS 110

Modenas Kriss 110

modenas Gt128

83

(g)

52.8

41.4

55.6

42.5

45.5

40

38.5

35

39.6

45.5

40

50.2

26

41.7

43

48.1

Honda EX5

Honda RS150R

Honda Wave 110

Honda CBR 150R

Yamaha LC135

Yamaha Y15

Yamaha FZ150i

Yamaha RXZ

Yamaha 125 ZR

Suzuki Shogun

Suzuki Belang

SYM 110

Kawasaki ninja 250

Lagenda 110

Demak DVS 110

Modenas Kriss 110

84

APPENDIX C

PICTURE OF AN ACTUAL FOOTAGE TAKEN DURING

TRAFFIC COUNT SURVEY FOR ALL LOCATIONS

85

(a) Bandar Sri Pinang Zone (BSPZ)

86

(b) Skycab Zone (SKYZ)

87

(c) East Jelutong Zone (EJZ)

88

(d) Batu Uban Zone (BUSZ)

89

(e) Sungai Nibong Zone (STZ)

90

(f) Bukit Jambul Zone (BJZ)

91

(g) Jalan Tengah Zone (JTZ)

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