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ESTIMATING THE REDUCTION OF CARBON DIOXIDE (CO

2

) EMISSION FROM PRIVATE

VEHICLES IN PENANG ISLAND

MOHAMMAD ZAHIN BIN MOHAMMAD RAZIF

SCHOOL OF CIVIL ENGINEERING

UNIVERSITI SAINS MALAYSIA

2019

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ESTIMATING THE REDUCTION OF CARBON DIOXIDE (CO

2

) 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

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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 :

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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.

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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.

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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.

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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

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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

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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

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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

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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 Table 3.10 An example of the total CO2 emission after 50% traffic

reduction in Batu Uban

43 Table 3.11 An example of the total CO2 emission after 60% traffic

reduction in Batu Uban

43

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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 Table 4.7 Total CO2 emission for 40% traffic volume reduction for

every location

54 Table 4.8 Total CO2 emission for 40% traffic volume reduction for

every location

56

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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

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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)

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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.

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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).

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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).

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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

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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.

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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,

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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.

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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:

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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.

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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.

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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).

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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.

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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.

11% 65%

16%

6%

2% Carbon dioxide (fossil

fuel and industrial processes)

Carbon dioxide (forestry and other land use) Methane

Nitrous Oxide F-gases

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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 6,456.7million 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.

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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

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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.

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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.

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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.

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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

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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

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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

CO2 Vehicles/

1000cap

Distance CO2 Vehicles/ (km)

1000cap

Distance

(km) CO2 Vehicles/

1000cap

Distance (km)

Fleet Gasoline Cars Diesel Cars

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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.

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 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

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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

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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.

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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.

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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

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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.

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1 2 3

6 4

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

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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

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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

 Academic institution

6 Bukit Jambul

 Commercial

 Public facilities

 Place of worship

 Residential

 Academic institution

7 Jalan Tengah

 Commercial

 Public facilities

 Academic institution

 Place of worship

 Residential

 Industry

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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.

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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

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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).

∑ 𝑃𝐶𝐸

𝑖

= 𝑁

𝑖

× 𝐹𝐸 × 𝐸𝐶 × 𝐷

𝑖

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.

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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%

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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

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The calculation for conversion of fuel consumption rate

= 26.9 𝑘𝑚

𝐿

×

1000 𝑚1 𝑘𝑚

=

26900𝑚𝐿

=

1

26900

×

(1)𝑚(1)𝐿

=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.

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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.

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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.

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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

3602 PCU/hr 2041 PCU/hr 5642 PCU/hr

50% traffic volume reduction Total

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

30% reduction of cars from one hour highest traffic

volume

20% reduction of motorcycle from one hour highest traffic

volume

3151 PCU/hr 2041 PCU/hr 5192 PCU/hr

60% traffic volume reduction Total

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

30% reduction of cars from one hour highest traffic

volume

30% reduction of motorcycle from one hour highest traffic

volume

3151 PCU/hr 1786 PCU/hr 4937 PCU/hr

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Table 3.7: An example of 40%, 50% and 60%

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