SMART CITY: A PROTOTYPE FOR CARBON FOOTPRINT MOBILE …751876/FULLTEXT01.pdf · of one’s carbon...

Degree project in

SMART CITY:

A PROTOTYPE FOR CARBON

FOOTPRINT MOBILE APP

Seyed Mohammad Fazeli

Stockholm, Sweden 2014

XR-EE-ICS 2014:007

ICS

Master thesis

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Abstract. Global warming has increased significantly over the past decades and at its center, there are human factors which have the greatest impacts on productions of carbon dioxide which is considered as a primary greenhouse gas in development of global warming. Greenhouse gas emissions and, in particular, carbon dioxide emissions are growing significantly to the extent that if no initiatives are taken, it can have dramatic consequences for our future generations and in general for human’s life on Earth, therefore we need means by which we can control and maintain the levels of greenhouse gas emissions and in particular carbon dioxide emissions.

One of the efficient solutions that can significantly decrease the levels of carbon dioxide emissions is the construction and development of smart cities. In this context (smart city), individuals can play an important role in reducing the CO2 emissions.

By considering the new opportunities that can result from development of Smart Cities and the essential role of information and communication technology (ICT) in such cities, this thesis work tries to introduce the idea of a self-tracking Carbon Footprint mobile application which enables users to keep track of their individual’s carbon dioxide emissions occurred as a result of their daily activities such as eating, transportation, shopping, energy consumption, and etc. in real time. Being able to measure the generated carbon footprint with respect to each of the user’s activities, users will be able to monitor and control it. This monitoring and controlling of one’s carbon footprint can have significant influences in reducing those human factors which result in production of more carbon dioxide gases and consequently more global warming effects.

Keywords. Smart Cities, Global Warming, Carbon Footprint, CO2, Incentive Systems, Quantified Self, CO2 Tracking, Self-Tracking, Telerik AppBuilder.

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Abstrakt. Global uppvärmning har ökat betydligt under de senaste decennierna och som i en central roll finns mänskliga faktorer vilka har den största påverkan på framställning av koldioxid. Utsläppen av växthusgaser och i synnerhet utsläppen av koldioxid har ökat avsevärt och om inga åtgärder vidtas kan det få dramatiska konsekvenser för våra kommande generationer och i allmänhet för människors liv på jorden. Vi därför behöver medel genom vilka vi kan styra och kontrollera utsläppen av växthusgaser, särskilt utsläpp av koldioxid.

En lösning som avsevärt kan minska koldioxidutsläpp är utveckling och konstruktion av så kallad ’Smart Cities’. I detta sammanhang (smart city) spelar individen en viktig roll när det gäller att minska koldioxidutsläppen.

Med de nya möjligheter som kan uppstå till följd av utvecklingen av Smart Cities och den viktiga roll som informations- och kommunikationsteknik (IKT) i dessa städer har, försöker detta examensarbete introducera idén om en mobilapp som mäter användarens personliga koldioxidutsläpp som uppstår vid dagliga aktiviteter, exempelvis äta, transport, shopping och energiförbrukning. Mobilappen mäter detta i realtid.

Genom att mobilappen mäter de genererade koldioxidutsläppen för varje användare kommer användaren kunna kontrollera och övervaka sitt personliga koldioxidavtryck i samhället. Möjligheten att kontrollera sina personliga koldioxidutsläpp kan ha stor påverkan för att minska de faktorer som leder till produktion av mer koldioxid gaser och därmed ökad global uppvärmning.

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Acknowledgements First I would like to express my sincere gratitude to Professor Pontus Johnson, Head of the Department of Industrial Information and Control Systems at the Royal Institute of Technology (KTH) for allowing me to conduct this thesis work under his auspices.

As a thesis supervisor, Professor Pontus Johnson supported me in all stages of this work. He is the initiator of this project and he always gave me constant encouragement and advice, despite his busy agenda.

Without the support of all members of my family, I would never finish this thesis and I would never find the courage to overcome all the difficulties during this work. My thanks go to my parents for their constant support and their unconditional love. I would especially like to express my gratitude to my wife, Paniz H., who has always supported me and helped me throughout this work.

I would like to acknowledge the assistance of Davood Babazadeh, PhD candidate in the dep. of Industrial Information and Control Systems at KTH - Royal Institute of Technology (KTH) who offered me valuable suggestions for writing this thesis. With his recommendation, I first met Professor Pontus Johnson and I had the honor of conducting this thesis work under his auspices.

I extend my sincere thanks to all my friends and colleagues who helped me conduct the evaluation of this thesis work.

To my wife

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Table of Contents

LIST OF FIGURES V LIST OF SCREENSHOTS VI LIST OF GRAPHS VII 1 INTRODUCTION 1

1.1 Background 1 1.2 Problem 1 1.3 Purpose and goal 1 1.4 Scope and limitations 1

2 METHODOLOGY 2 2.1 Introduction 2 2.2 Research framework 2 2.3 Research process 2

3 EXTENDED BACKGROUND 4 3.1 Global warming 4 3.1.1. Impacts of global warming 5 3.1.2. What is the cure? 6 3.2 Smart Cities 6 3.2.1. Importance of smart cities 9 3.3 Incentive Systems 9 3.4 Quantified Self 10 3.4.1. Quantified Self in practice 10

4 DESIGN AND DEVELOPMENT WORK 13 4.1 Development environment 13 4.2 Development methodology 16 4.3 Requirements definition 16 4.3.1. Functional requirements 17 4.3.2. Non-Functional requirements 19 4.4 Solution architecture 19 4.4.1. Mock-up architecture 19 4.4.2. Required architecture 21 4.5 The Carbon Footprint App prototype 24 4.5.1. Login and registration 24 4.5.2. Home view 26 4.5.3. CO2 usage view 27 4.5.4. Achievements view 32 4.5.5. Statistics and reports view 34 4.5.6. Navigational menu panel 36 4.5.7. User betting 37 4.5.7.1 Betting case discussion 37 4.5.7.2 Betting views in practice 39

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4.5.7.2.1. Bet start view 39 4.5.7.2.2. Bet personalization views 40 4.5.7.2.2.1. Housing settings view 41 4.5.7.2.2.2. Vehicle settings view 42 4.5.7.2.3. Bet target settings views 43

5 EVALUATIONS 45 5.1 Evaluation process 45 5.2 Data analysis 46 5.2.1. General demography 46 5.2.2. Area understanding/Awareness 47 5.2.2.1 Global warming 47 5.2.2.2 Self-Tracking 51 5.2.3. Carbon Footprint Application user experience 52 5.2.3.1 Usefulness and Ease of use 52 5.2.3.2 Underlying motivations for using the App 55 5.3 Conclusion 56

6 DEMONSTRATION AND COMMUNICATION 58 6.1 Demonstration 58 6.2 Communication 58

7 CONCLUSION 59 8 DISCUSSION AND FUTURE WORK 60 9 REFERENCES 61 10 APPENDIX A: Evaluation questionnaire 64 11 APPENDIX B: Telerik AppBuilder environments 68 12 APPENDIX C: Stockholm Royal Sea Port Project 70

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LIST OF FIGURES

Figure 1 – Earth’s annual global mean energy balance 4 Figure 2 – Sea level rise due to global warming 5 Figure 3 – Potential climate change impacts 6 Figure 4 – Characteristics and factors of a smart city 8 Figure 5 – Smart cities taxonomy 8 Figure 6 – Global population increase trend based on Forrester Research 9 Figure 7 – Classification of the Objects of Tracking based on … 11 Figure 8 – Telerik platform overview 13 Figure 9 – Telerik Development Environment tools and services 14 Figure 10 – Telerik’s holistic application development lifecycle 16 Figure 11 – Carbon Footprint App mock-up architecture 21 Figure 12 – Smart City Marketplace required architecture 23 Figure 13 – Steps to Start a Bet 40

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LIST OF SCREENSHOTS

Screenshot 1 – Login View 24 Screenshot 2 – Sign up View 25 Screenshot 3 – Sign up Verification Email 25 Screenshot 4 – Sign up Welcome Email 26 Screenshot 5 – Home view (User tapped on bubble with $10 data object) 27 Screenshot 6 – Home View (After user tapped on $10 data object) 27 Screenshot 7 – CO2 Usage View 28 Screenshot 8 – CO2 Usage View (tapping on CO2 status) 29 Screenshot 9 – CO2 Usage View (swiping left) 29 Screenshot 10 – Bus Transportation Activity Detail View 30 Screenshot 11 – Clothing Purchase Activity Detail View 31 Screenshot 12 – Food Activity Details View 32 Screenshot 13 – Achievements View 33 Screenshot 14 – Detail View of a gained bet 33 Screenshot 15 – Detail View of a lost bet 34 Screenshot 16 – Statistical View Landing Page 35 Screenshot 17 – Statistical Views (swiping to left) 36 Screenshot 18 – Navigation Menu Panel 37 Screenshot 19 – Bet Start Views 40 Screenshot 20 – Bet Personalization Landing View 41 Screenshot 21 – Housing Settings Views 42 Screenshot 22 – Vehicle Settings Views 43 Screenshot 23 – Bet Target Settings Views 44 Screenshot 24 – Bet Initialization Closure Views 44 Screenshot 25 – AppBuilder coding environment 68 Screenshot 26 – AppBuilder iPhone simulator 68 Screenshot 27 – Telerik Backend tools and services 69

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LIST OF GRAPHS

Graph 1 – Age distribution by gender 46 Graph 2 – Participants by gender 46 Graph 3 – Participants by occupation category 47 Graph 4 – Participants' Awareness of Global Warming 48 Graph 5 – General average responses with respect to Global Warming 49 Graph 6 – Participants’ opinion about importance of Climate Change 49 Graph 7 – Participants’ opinions about importance of reduction of CO2 … 50 Graph 8 – Participants’ opinions regarding who should consider reducing … 50 Graph 9 – Participant’s responses with regard to their knowledge about … 51 Graph 10 – Participant’s responses with regard to if they are keeping records … 51 Graph 11 – Participant’s responses with regard to how they keep track … 52 Graph 12 – Participant’s opinions with regard to App’s user-friendliness 53 Graph 13 – Participant’s opinions with regard to difficulty and ease of use … 53 Graph 14 – Participant’s opinions with regard to App’s overall GUI 54 Graph 15 – Participant’s opinions with regard to App’s features usefulness 54 Graph 16 – Overall features usefulness 55 Graph 17 – Participant’s willingness to use this App for tacking their CO2 … 55 Graph 18 – Participants’ motivational reasons to use Carbon Footprint App 56

Dept. of Industrial Information and Control Systems

KTH, Royal Institute of Technology, Stockholm, Sweden

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1 INTRODUCTION

1.1 Background

Global warming has increased significantly over the past decades and at its center, there are human factors which have the greatest impacts on productions of carbon dioxide which is considered as a primary greenhouse gas in development of global warming.

According to (Maslin, 2004), “The Earth’s atmosphere is composed of 78% nitrogen, 21% oxygen, and 1% other gases. It is these other gases that we are interested in, as they include the so-called greenhouse gases.” He further explains that the two most main greenhouse gases are carbon dioxide and water vapour, which “carbon dioxide accounts for 0.03-0.04 % of the atmosphere” (Maslin, 2004). As Maslin states in his book, the rise in atmospheric carbon dioxide has started primarily since the beginning of industrial revolution where the first measurement of CO2 concentration in atmosphere started in 1958 and since then the level of CO2 concentrations have increased every single year.

Proper initiatives to reduce emissions of greenhouse gases and in particular the emissions of carbon dioxide have to be taken to reduce the impacts of global warming, otherwise there would be dramatic consequences which can endanger human’s life on Earth. One of the greatest initiatives that can help in reduction of carbon dioxide is the idea of development of Smart Cities with the aim of creating an ecofriendly environment where not only greenhouse gas emissions are reduced but also there is better management and planning of global energy resources.

1.2 Problem

Greenhouse gas emissions and, in particular, carbon dioxide emissions are growing significantly to the extent that if no initiatives are taken, it can have dramatic consequences for our future generations and in general for human’s life on Earth, therefore we need means by which we can control and maintain the levels of greenhouse gas emissions and in particular carbon dioxide emissions.

1.3 Purpose and goal

One of the efficient solutions that can significantly decrease the levels of carbon dioxide emissions is the construction and development of Smart Cities. In this context (smart city), individuals can play an important role in reducing the CO2 emissions.

From this perspective, the purpose of this thesis is to study the smart city idea as a strategy which can help in reduction of global warming and introduce means by which users can influence their Carbon Footprint emissions.

The goal of the work is to develop a mobile application prototype that could be used by users to keep track and measure their carbon dioxide emissions and help them take actions to reduce/control their Carbon Footprints.

1.4 Scope and limitations

This thesis work aims to develop a prototype for a Carbon Footprint mobile application that can be used by users in a smart city context. Besides this, the thesis will try to study topics such as smart city, global warming, incentive systems, and quantified self in order to provide context for such a self-tracker application.

The time limitations of the master thesis work will not make it possible to develop a fully functional application and therefore some of the back end systems and services that will support the final application developed by this thesis work will be mocked systems/data. In addition, the entire



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development work conducted for accomplishment of this thesis work undergoes with the assumption that the developed App is a visioned application designed with Smart City Marketplace idea in mind.

2 METHODOLOGY

2.1 Introduction

This section intends to provide justifications of the thesis work. The section starts with a description of the research framework followed by the processes for which the research is carried out.

2.2 Research framework

A research framework can provide useful guidelines for initiating a research, conducting it and evaluating the end result. The design science research methodology (DSRM) was introduced by Peffers et al. for “[…] production and presentation of DS research in IS” (Hevner & Chatterjee, 2010).

This thesis work follows the steps introduced by DSRM framework. The DSRM framework consists of six steps. According to (Hevner & Chatterjee), these steps are named and described as follow:

1. Problem identification and motivation. In this step the research problem will be specified and proper justifications will be made towards a possible solution.

2. Define the objectives for a solution. From the problem definition in step one, the objectives of the solution will be identified. These objectives can be of type quantitative or qualitative. For example, in a quantitative manner, the preferred solution could be better that the current ones. Or for example in a qualitative manner, a new artifact is necessary to be built to address problems that have not been solved previously.

3. Design and development. At this step the actual artifact is supposed to be developed. According to Hevner (Hevner, et al., 2004), the artifact could be in the form of a construct, a model, a method, or an instantiation.

4. Demonstration. This step involves the demonstration of the developed artifact to solve the problem. “This could involve its use in experimentation, simulation, a case study, proof, or other appropriate activity.” (Peffers, et al., 2006)

5. Evaluation. This step involves the evaluation and the measurements of the usefulness of the solution to solve the identified problem.

6. Communication. The last step involves the communication of the problem, the artifact and the solution to the community and relevant audiences.

2.3 Research process

As described earlier, this thesis is using design science research methodology (DSRM) as a research framework. The six steps explained in DSRM framework will be used to conduct this thesis.

In research there are two broad approaches of reasoning, known as inductive and deductive approaches (Taylor, et al., 2008). In an inductive approach, reasoning starts from specific observations to broader generalizations and theories, while in a deductive approach reasoning works from the opposite direction which is from a more general to more specific one. (Burney, 2008)

In inductive approach, the process will begin by identifying that an artifact is needed, therefore empirical studies will be carried out “[…] to find out, what the requirements should be/what functionality should exist/which algorithms are useful/which data is needed/which information should the system produce/which other systems are in use, etc. etc. The result of this type of approach is that the result is the system design.” (Brash, 2010, p. 23). In this



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approach, the data collection will be “qualitative through interviews/case study/ethnographical study/observations/literature study etc.” (Brash, 2010).

Conversely, in a deductive approach, we claim that a specific hypothesis will solve a problem. In this approach, the data collection is normally of type quantitative where the aim is to investigate if the hypothesis is valid or invalid. (Brash, 2010, p. 23).

The line of reasoning in this thesis work is of type inductive, where the reasoning starts by identifying that there is a need for an artifact (Carbon Footprint App) that can help users measure their daily carbon dioxide emissions in a smart city context, therefore the initiatives for identifying requirements, possible functionalities and features are carried out by studying literatures, observations of similar systems and discussions with stakeholder.

In the following, the six steps to carry out the thesis work based on DSRM will be explained in details:

Problem identification and motivation Carbon dioxide emissions are increasing significantly and we need to take initiatives towards CO2 reductions. Smart city ideas are one of the solutions that can have significant influences in reduction of greenhouse gas emissions and in particular carbon dioxide emissions. In such cities, managing and controlling the amount of CO2 is highly important to keep the CO2 levels low. Having said this, the need for a mobile application that can keep track of individual’s carbon footprint is necessary. Knowing the daily carbon footprint emissions, an individual can take initiatives to either reduce it, if it is more than average, or maintain it and keep it low.

Define the objectives for a solution This thesis aims to develop a prototype for a carbon footprint mobile application that can help users to manage and maintain their daily carbon footprint emissions by measuring their daily CO2 emissions created from their daily activities where this application acts as a self-tracker device providing real-time data regarding individual’s daily carbon emissions.

Design and development The final output of this thesis work will be a prototype for a mobile application. The development of the application will be done using App Builder platform by Telerik. In addition, two architecture solutions will be proposed known as mock-up architecture and a required architecture.

The development process will be done using agile development methodology, where development work will be done in small iterations and new features and functionalities will be implemented as the development process continues.

Demonstration The final App prototype which is developed as a result of this thesis work along with the two proposed architectures (a mock-up architecture which supports this prototype and a required architecture which shall support a fully functional App) will be demonstrated in a presentation held by the master thesis work, where a demo will be played during the presentation for the audiences and the thesis inspectors.

Evaluation The mobile application will be put into testing by random people to express their opinions about the application usefulness and its features. Further improvements or modifications to the application can be considered from users’ experiences during the evaluation session.

Communication In addition to the work being presented at the end of the thesis, the thesis work will be accomplished in a complete written report which will further be published in the KTH university library and it will be publically available for the IS community to access it.



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3 EXTENDED BACKGROUND

This chapter intends to provide a deeper description and discussion of topics such as smart cities, global warming, incentive system/theory, lifelogging, quantified self, and incentive-centered design.

3.1 Global warming

Global warming has increased significantly over the past decades and at its center, there are human factors which have the greatest impacts on productions of carbon dioxide which is considered as a primary greenhouse gas in development of global warming. In fact, one of the biggest problems that put our today’s planet into great danger is global warming and perhaps, the best way to better understand global warming is to refer to its definition.

In general, the term global warming refers to a gradual increase in Earth’s average temperature. As explained by Maslin, Earth’s temperature “is controlled by the balance between the input from energy of the sun and the loss of this back into space.” (Maslin, 2004, p. 4) He further explains that around one-third of the energy that is received from the sun reflects back into space and the remaining is absorbed by the atmosphere and the Erath’s surface including both lands and oceans. This makes the Erath’s surface warm and it will cause the Earth to project long-wave infrared radiation into space. Greenhouse gases including water vapour, carbon dioxide, ozone, methane, and nitrous oxide in the upper layer re-emit the projected long-wave infrared radiation from the Earth and warm the atmosphere resulting in a blanket effect warming the Earth by 35 degrees Celsius. (Maslin, 2004)

Figure 1 illustrates this energy transition in more details,

Figure 1 – Earth’s annual global mean energy balance adapted from (Maslin, p. 5)

According to Maslin, “The Earth’s atmosphere is composed of 78% nitrogen, 21% oxygen, and 1% other gases. It is these other gases that we are interested in, as they include the so-called greenhouse gases”. He further explains that the two most main greenhouse gases are carbon dioxide and water vapour, which “carbon dioxide accounts for 0.03-0.04 % of the atmosphere” (Maslin, 2004). As Maslin states in his book, the rise in atmospheric carbon dioxide has started primarily since the beginning of industrial revolution where the first measurement



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of CO2 concentration in atmosphere started in 1958 and since then the level of CO2 concentrations have increased every single year. (Maslin, 2004)

3.1.1. Impacts of global warming

Global warming has a direct influence in Earth’s climate changes to the extent which it can change the climate permanently and consequently, this climate change will affect weather, oceans, agriculture, forests and in general human’s life. One of the major problems of the global warming is the gradual rise of sea levels. Greenhouse gases emitted by human, results in a temperature increase and it will consequently cause melting Earth’s icecaps and eventually a rise of sea level and flooding.

According to Bjørke, sea level has risen around 10 to 25 cm over the past decade and if the rise continues the same pattern, sea level can rise between 20 to 88 cm in the next 100 years. Figure 2, which is adapted from his report, illustrates this more precisely.

Figure 2 – Sea level rise due to global warming (Åke Bjørke, et al., 2001)

The rise of sea level can result in the flooding of coastal areas and cities near the sea shores. This can have catastrophic outcomes, for example for the case of small island countries such as Maldives in the Indian Ocean or the Marshall Islands in the Pacific, a one meter rise in the sea level would flood up to 75% of the land (Maslin, 2004). The sea level rise can even result in disappearance of some countries.

Global warming can also affect weather changes causing extreme temperature fluctuations, droughts, severe rain, severe hurricanes and earthquakes and as a result all these effects have direct influences on human’s life. Extreme temperature fluctuations can be considered as a dangerous factor for human’s life in different parts of the Earth, an extreme low temperature in tropical countries can not only be killer factor for human but the entire ecosystem (e.g. animals, environment, etc.) of that country and in a same manner, an extreme hot summer in the North hemisphere can put many human’s lives into great risks and result in a big scale ecosystem change.

Figure 3 illustrates some of the impacts of climate changes as a result of global warming.



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Figure 3 – Potential climate change impacts (Baker, et al., 2005)

3.1.2. What is the cure?

As a matter of fact, there are many ways that could be taken to omit or reduce the impacts of global warming. At its center, human factors play a great role in production and development of global warming. Factors such as emissions of greenhouse gases generated from factories, manufacturing plants, and in general from burning of fossil fuels or deforestation factor which can harm the carbon cycle significantly as trees play a crucial role in the global carbon cycle. Therefore, one important step to overcome all these problems is to control the human factors in an effective way so that the impacts of global warming are omitted or reduced.

In a bigger scale, there are governments who are responsible to set proper action points and initiatives to control the carbon dioxide emissions into atmosphere and in fact an international cooperation and efforts are needed in such a plan to save the Earth. In addition to the big scale governments’ efforts, it is also necessary for every individual to contribute to efforts leading to development of less carbon dioxide emissions.

3.2 Smart Cities

With the current challenges in urbanization, pollution, resource scarcity, and concentration of population within cities, the needs for more efficient and smarter solutions to overcome these issues are highly demanded. The term - Smart City - acts as an umbrella concept which promises to contribute towards a better and smarter resource and infrastructure management, a more eco-friendlier environment, and in general, a higher living standards in different areas such as environment, city administration, education, health, transportation, etc.



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In fact, there are many different definitions of the term Smart City among academicians and practitioners and the term is still used as a buzzword referring to various aspects in a smart city context but most of these definitions have a similar point of view and that is the focus in efficiency to increase capacity whereby the city itself has the ability to interact and respond to the needs of citizens.

In the following, to provide a common understanding about the term Smart City, we will refer to some of these definitions.

As referred by Giffinger, a smart city is defined as a ‘[…] city well performing in a forward-looking way in six characteristics, built on the ‘smart’ combination of endowments and activities of self-decisive, independent and aware citizens’ (Giffinger, et al., 2007). These six characteristics are as follow:

1. Smart economy

2. Smart mobility

3. Smart environment

4. Smart people

5. Smart living

6. Smart governance

This definition will further list 33 factors that are used to describe each of the six characteristics. Figure 4 illustrates these factors.



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Figure 4 – Characteristics and factors of a smart city (Giffinger, et al., 2007)

Another definition by Caragliu claims that ‘a city to be smart when investments in human and social capital and traditional (transport) and modern (ICT) communication infrastructure fuel sustainable economic growth and a high quality of life, with a wise management of natural resources, through participatory governance.’ (Caragliu, et al., 2009)

From these definitions and many other similar explanations of smart cities, we can group them based on objectives and elements according to Figure 5.

Figure 5 – Smart cities taxonomy ( Lee & Hancock, 2012)



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3.2.1. Importance of smart cities

Why do we need to build smart cities? A shot answer to this question would be to save global energy consumption and consequently reducing amount of world’s greenhouse gas emissions. As stated by Webb, cities are considered to be the main global energy consumer, responsible for nearly 80% of the global energy consumption resulting in half of the world’s greenhouse gas emissions and with an increasing rise in the trend. (Webb, 2010)

Urbanization and the city populations are growing quickly where population forecasts show a 3.1 billion increase in urban population by 2050 resulting in a total of 6.4 billion people. Studies also show that by 2050, nearly 70% of the world’s population will be living in cities. Figure 6, based on Forrester Research, illustrates the population increase trend and the forecast by 2050.

Figure 6 – Global population increase trend based on Forrester Research adapted from (Pardo, 2012)

As a matter of fact, with the current trend in population growth and the rapid increase of greenhouse gases accumulation in the atmosphere, moving towards smart solutions is inevitable. Having said this, smart cities can be considered as a great initiative to address many of the obstacles with regard to urbanization growth including population growth, climate changes, natural resource scarcity, healthcare, education, etc. by making use of ICT to maximize efficiency and effectiveness towards more sustainable ecofriendly smart cities.

3.3 Incentive Systems

Incentive system/theory is considered as one of the important topics in human psychology and it plays a crucial role in the way people act and do things. According to (Franzoi), ‘incentive theory states that any stimulus that you think has either positive or negative outcomes for you will become an incentive for your behavior. An incentive is a positive or negative stimulus in the environment that attracts or repels you’. (Franzoi, 2011)

In other words, incentive theory tries to emphasize the fact that human’s behavioral change pattern occurs as a result of foreseen rewards and incentives that an individual believes to gain from his/her intended action in a way that all his/her ’[…] actions are directed towards gaining rewards’ (Cherry, 2013). In fact, human mind under the influence of a reward gaining philosophy constantly produces positive reactions and pulses and it helps human in realization of his/her motivational desires concerning the actions he/she wishes to do in a given situation.



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Perhaps we have all seen and experienced examples when we have been promised to be given a reward by our parents, teachers, friends, bosses, etc. if we could successfully achieve a given goal and this reward motivated us to work really hard to achieve that goal. In a normal situation, achieving that goal which could be anything such as getting good grades at school, or getting a certain certificate of recognition at work, etc. might not have been so important to us but things turn entirely differently when a reward is introduced. In fact, the introduction of this reward provides strong motivational factors which influence one’s actions towards performing activities that could result in gaining the reward.

Having said these, the incentive theory as a known and proven physiological approach to stimulate human’s motivations, is extremely used in many various applications, systems and design solutions such as applications within gaming and entertainment sector, educational sector, advertising sector, government and industrial sector, and etc.

One of the design approaches that can be considered in designing a system to incorporate incentive theory is Incentive-Centered Design (ICD) introduced by MacKie. According to (MacKie & Jian), Incentive-Centered Design (ICD) ‘is the science of designing a system or institution according to the alignment of individual and user incentives with the goals of the system. Using incentive-centered design, system designers can observe systematic and predictable tendencies in users in response to motivators to provide or manage incentives to induce a greater amount and more valuable participation’ (MacKie & Jian, 2012).

The design and implementation of the prototype presented by this thesis work also performed by considering an Incentive-Centered Design (ICD) approach in mind, whereby the proposed presented prototype tries to conceptualize this approach by incorporating a betting game scenario into the Carbon Footprint App prototype with the aim of motivating the potential App’s users towards lowering their daily CO2 emissions by rewarding them in a betting context.

In fact, the idea of the betting game presented in this thesis work acts as a way for introducing the incentive theory in form of a so called betting system. This betting system introduced to not only act as a motivation stimulator but also to result in a behavioral change in users actions concerning their individual’s daily carbon footprint production when they are engaged more and more in this betting game.

3.4 Quantified Self

Quantified Self is a new idea that was first introduced by Gary Wolf and his colleague Kevin Kelly on 2007, which can be considered as a type of lifelogging technique used to gather and track data about human’s actions and behaviors. According to the article presented in IADIS International Conference, lifelogging is defined as ‘[…] the process of tracking personal data generated by our own behavioral activities like data about sleep, exercise, food, mood, location, alertness, productivity, or even spiritual well-being’ (Rivera-Pelayo, et al., 2012).

The term lifelogging first coined by Gorden Bell in the late 1990s, who believed that tracking data about human’s behavioral activities and analyzing these data provide a significant source of information for optimizing his/her behavior. Since then, the idea of lifelogging evolved rapidly and it resulted in creation of great initiatives and movements, from both big IT corporations and also IT practitioner communities, in terms of development of new tools, services, technologies, applications, frameworks, guidelines, and etc. all with the purpose of tracking humans’ behaviors and actions.

One of the great movements within lifelogging context is the formation of Quantified Self community group, which is referred as a global movement of international collaboration users and makers of self-tracking tools who are brought together as a community founded by Gary Wolf and Kevin Kelly with a single motto of ‘self knowledge through numbers’. (QuantifiedSelf.com)

3.4.1. Quantified Self in practice

Using the collection of tools, apps, services, and in general self-tracking technologies, users are able to collect data about their daily actions and behaviors and turn these data into useful information and they



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also provide means for users to measure and improve their behaviors in a positive way. In fact, the application of self-tracking technology enabled devices and services can be really wide and they can provide potential opportunities in many different areas such as health sectors, educational sectors, lifestyle, and etc.

Figure 7 illustrates the classification of the objects of tracking based on the categorization of self-tracking possibilities with relation to the currently self-tracking tools and services used within different sector areas.

Figure 7 – Classification of the Objects of Tracking based on the categorization of self-tracking possibilities (Nißen, 2013)

Table 1, adapted from (Choe, et al., 2014), contains statistical data resulted from a qualitative and quantities data analysis performed by Eun K. Choe, Nicole B. Lee, Bongshin Lee, Wanda Pratt, Julie A. Kientz as part of a report paper in collaboration with Microsoft. The authors of this research article conducted a qualitative and quantitative analysis of 52 video recordings of Quantified Self Meetup talks to understand what the users did, how they did it, and what they learned from it. These data are then analyzed and presented as findings resulted from this paper work.

The table below is one of the findings presented by (Choe, et al.) and it shows the motivational factors inspiring users to use Quantified Self enabled devices and service for self-tracking. The data presented by Table 1 categorizes these motivational factors into three different categories such as (1) to improve health, (2)to improve other aspects of life, and (3)to find new life experiences based on the 52 individuals using a type of self-tracking device. This table also presents some sample Apps related to each sub category.



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Motivations

Categories

Tracking example

To improve health

To cure or manage a condition

Track blood glucose to hit the target range

To achieve a goal Track weight to get back to the ideal weight of 135 pounds

To find triggers Log triggers that cause atrial fibrillation

To answer a specific question

Track niacin intake dosage and sleep to identify how much niacin to take for treating symptoms

To identify relationships

Track exercise, weight, muscle mass, and body fat to see the relationships among the factors

To execute a treatment plan

Log food, exercise, and panic as a recovery plan for panic attack

To make better health decisions

Record ideas of things that thought were healthy and unhealthy to make better decisions

To find balance

Log sleep, exercise, and time to get back from erratic lifestyle

To improve other aspects of life

To maximize work performance

Track time to know the current use of time and ways to be more efficient

To be mindful Take a self-portrait shot everyday for 365 days to capture each day’s state of mind.

To find new life

experiences

To satisfy curiosity and have fun

Log the frequency of “puns” to see how often these puns happened and what triggered them

To explore new things

Track every street walked in Manhattan to explore as much of the city as possible

To learn something interesting

Track heart rate for as long as possible and see what can be learned from it

Table 1 – Quantified-Selfers’ tracking motivations with example. Reprinted from (Choe, et al., 2014)



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4 DESIGN AND DEVELOPMENT WORK

4.1 Development environment

The development work to setup and create this prototype is initiated using Telerik platform. Telerik platform provides a complete development environment for developing cross-platform mobile applications where a comprehensive set of tools for application design, development, test, deployment and publishing are integrated seamlessly on the cloud.

Figure 8 – Telerik platform overview (Telerik, 2014)

Figure 8, adapted from Telerik, depicts a holistic overview of the entire list of available integrated functionalities in the platform which support a cross-platform application development lifecycle. At its center there is Telerik IDE also known as AppBuilder acts as a development tool for developing and deploying applications. In addition, Kendo UI Mobile framework defines the means to structure the application and it provides a set of common user interface components. Moreover, the backend services in the Telerik platform are also provided by Telerik Backend Services which support the management and administration of simple CRUD data operations, databases and storage operations and etc.



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Figure 9 – Telerik Development Environment tools and services, edited from (Pelovski, 2013)

As illustrated by Figure 9, the platform consists of two major layers, known as frontend and backend layers, where a middle API layer which is formed by a set of RESTful services links them together. The entire group of tools and services reside on cloud and are accessible through AppBuilder IDE. In the following, some of these tools and services that are used in this project are elaborated in more details.

PhoneGap It is a framework used for development of cross-platform mobile applications using standards-based Web technologies such as HTML, JavaScript, and Cascading Style Sheets (CSS). PhoneGap was produced by Nitobi in 2008 and later in October 2011, it was donated to Apache Software Foundations under the name Apache Cordova. In fact, PhoneGap is now an open source distribution of Cordova.

Integrated support for Apache Cordova into Telerik AppBuilder leverages the Cordova framework capabilities and enables the development of mobile applications that run natively on IOS, Android, and Windows phones by taking advantage of device capabilities, using nothing more than HTML5, CSS and JavaScript. (Cowart, 2013)

Kendo UI Kendo UI is a comprehensive HTML5, jQuery-based framework used to develop GUI and layout on the frontend layer and it provides a seamless integration between the model data and the view. It offers a native look and feel and provides plenty of UI widgets, a rich data visualization framework, auto-adaptive mobile framework, data source, templating, Model View ViewModel (MVVM) architectural pattern, drag-and-drop API, and etc. for modern web and mobile app development (Telerik, 2013).

Kendo UI Mobile is a sub library under Kendo UI which provides UI widgets for Android, iOS and Windows Phone. It also offers an application to handle app navigation, views, layout templates, and other features ( Prasad, 2013) .

Phone simulator AppBuilder provides the ability to simulate the look and feel of the app directly during development work by using the simulators for Android, iOS, and Windows Phone. In addition, simulators also can be used for debugging and testing without the need for deploying and provisioning to multiple devices.



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jQuery Mobile jQuery Mobile is described as an open source, touch-optimized web framework and a HTML5-based user interface system which is built on solid jQuery and jQuery UI foundation (Schmitz, et al., 2010). jQuery Mobile and Kendo UI have many similarities and they both follow same goal and that is the fact that they both offer frameworks for developing mobile web sites and applications.

In fact, both jQuery UI and Kendo UI are JavaScript frameworks and they are built on top of jQuery which is one of the most popular JavaScript libraries (Bristowe, 2012). jQuery mobile and Kendo UI can be used as substitute, since they have many similar features and functionalities and they both support Model View ViewModel (MVVM) design pattern approach.

In this thesis work, Kendo UI framework is in use.

JavaScript SDK JavaScript SDK in Telerik AppBuilder is used to provide an abstraction layer over Telerik Backend Services REST API by offering APIs for CRUD operations for plain objects and integration with Kendo UI framework.

Source control system AppBuilder manages the code based version control by Telerik version control cloud services, which is integrated in AppBuilder windows client and in-browser clients. Collaboration within the project is also possible by adapting and setting up a GitHub repository, where the repository can be set to be available to public or it can be set to only enable invited collaborators to participate in a project team.

RESTfull services These services in Telerik AppBuilder act as an intermediate layer between frontend and backend layers enabling the exposure of resources using XML or JSON. Representational State Transfer (REST) ‘‘[…] specifies a collection of architecture principles defining how data resources are represented and addressed […] systems that follow the REST principles are often called RESTful’’ ( Su & Chiang, 2012).

“Applications that want to use these web services access a particular representation by transferring application content using a small globally defined set of methods that describe the action to be performed on the resource. This basic REST design principle establishes a one-to-one mapping between CRUD operations and HTTP methods.” (Telerik, 2014)

Backend services As defined by Telerik, backend services in Telerik platform is a set of cloud services used to provide backend support for the application so that the need to set up servers and infrastructure separately to serve the applications is eliminated (Telerik, 2014). The following functionalities are offered by Telerik backend services:

- User management

- Database and file storage

- Email, SMS and push notifications

- Backend module for asset management

Telerik backend services fall under the category of Backend as a Service (BaaS), or also known as Mobile Backend as a Service (mBaaS), which speeds up application development lifecycle by offering a managed and integrated environment for activities such as data storage, user management, push notifications and cloud-code execution.

Figure 10 illustrates a holistic view of application development lifecycle using Telerik AppBuilder.



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Figure 10 – Telerik’s holistic application development lifecycle

4.2 Development methodology

The nature of this thesis work required the work to be done in an agile way. Since the idea was to design and propose a prototype for an application that can be used as part of Smart City Marketplace platform in the Stockholm Royal Seaport project, then it was essential to perform this task using agile approach for the following reasons:

- Identifying and defining requirements simultaneously along with the development work.

- Working closely with stakeholders to achieve best performance and reduce possible ambiguities.

- Starting with development work in early stages of the project lifecycle.

- Being able to define new features and adapt new changes as they occur during the development phase.

- Being able to work with stakeholders throughout development and acquire feedbacks more often to revise or add new functionalities.

- Being able to complete the task within the given timeframe.

Therefore, the development work in this thesis performed using agile software development approach for which the project work, from initial idea creation and design to the finished job, small iterations were in use until the prototype was ready for the final delivery. Each iteration usually lasted one week where by the end of every week the work was presented to stakeholder for review and feedback. The feedback from stakeholder was then used to either improve the presented work or add new functionalities to it.

4.3 Requirements definition

This section intends to present and define the requirements for design and development of prototype for Carbon Footprint application. These requirements are extracted from meetings and discussions held with stakeholder/s during the project lifecycle with Smart City Marketplace idea in mind.

The requirements are defined into two different categories known as functional requirements and non-functional requirements. Functional requirements are defined as requirements which describe what the system must do while non-functional requirements describe how the system works and they define qualities for the resulting system.

Table 2 and Table 3 will present functional and non-functional requirements as follow:



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4.3.1. Functional requirements

Functional requirements are defined as requirements which describe what the system must do. These requirements for development of prototype for Carbon Footprint application are presented by Table 2 as follow:

Req#

Requirement

Brief description

1 The App shall enable users to register/signup. A registration view shall be available for signup.

2 The App shall enable users to login. A login view shall be available in order to authenticate users against the App.

3 The App shall enable users to logout.

Users shall have the ability to logout from the app.

4 The App shall be able to capture users’ current housing conditions.

This view is needed to capture user’s current living place and its details. (E.g. apartment or house, house’s size, number of households, main energy source for heating, electricity, etc.). These details are used for personalizing a bet.

5 The App shall be able to capture users’ vehicle type.

This view is needed to capture user’s transportation vehicle. (E.g. car, motor cycle, bike, etc.) These details are used later to for bet calculations. These details are used for personalizing a bet.

6 The App shall present users’ activities during a day.

This view is needed to let users keep track of their current CO2 emissions based on their daily activities which could generate CO2.

7 The App shall enable users to view their current amount of generated carbon emissions.

This information will enable users to keep track of generated carbon emissions as they occur.

8 The App shall enable users to view their bet’s status.

This information will enable users to keep track of their bet’s status.

9 The App shall enable users to view details of each recorded activities.

This view is needed to enable users to see details of a record. (e.g. How many kilometers they drove a car, what they have eaten and how much each of these activities generated carbon emissions)

10 The App shall enable users to view amount of carbon emissions that was captured based on each individual activity.

This information is needed to enable users to see how much CO2 it is with response to each recorded activity. (e.g. How much CO2 is emitted during a car drive )

11 The App shall implement a betting system idea. Betting system in this context tries to encourage users to get more involved in the App. This betting system is used



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conceptualize an incentive design approach.

12 The App shall enable users to initiate a bet.

Users can start a bet. The idea of betting in this context is to encourage users to produce less CO2 in their daily activities by earning through bets while trying to decrease their possible CO2 emissions. (E.g. Transportation, shopping, eating, etc.)

13 The App shall enable users to choose number of days for a given bet.

This view enables users to specify number of days for a bet. For each day a bet is missed users are charged based on their selection.

14 The App shall enable users to personalize their bets according to their conditions.

This enables users to customize their bets.

15 The App shall enable users to choose amount of stake for a bet.

This enables users to specify the bet’s amount of stake.

16 The App shall allow a bet to be made for 1 day up to 7 days.

This enables users to specify number of days for a bet

17 The App shall allow a bet to be made for 5 $, 10 $, or 15 $.

This enables users to specify the bet amounts.

18 The App shall present the distance traveled on a map.

This feature allows users to see the start point, end point, and the distance traveled while using a transportation vehicle.

19 The App shall present rewards that have been achieved by the user.

This view enables users to track their achievements and rewards gained by each bet.

20 The App shall present statistical reports for users achievements, carbon usage, etc.

This view enables users to have an overall picture of their earnings and CO2 emissions in a graphical representation.

21 The App shall present a counter displaying the remaining time of a started bet. Both number of remaining days and remaining time.

This view allows users to track amount of time remaining from a possible bet so they can take actions towards winning a bet by reducing their CO2 emissions during this period.

22 The design shall propose a mock-up architecture Refer to Figure 11

23 The design shall propose a required architecture with respect to Smart City Marketplace

Refer to Figure 12

Table 2 – Functional requirements of Carbon Footprint App



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4.3.2. Non-Functional requirements

Non-Functional requirements describe how the system works and they define qualities for the resulting system. These requirements for development of prototype for Carbon Footprint application are presented by Table 3 as follow:

Req#

Requirement

Brief description

1 The App must be easy to use. App should be simple to use.

2 The App must support IOS 7 for iPhone 5 series. App must be compatible with iOS7 on iPhone 5 series.

3 The app must have nice GUI for end users App must provide satisfying user experience through nice GUI.

Table 3 – Non-Functional requirements of Carbon Footprint App

4.4 Solution architecture

This section intends to describe and discuss the application architecture. The section is divided into two parts namely mock-up architecture and required architecture. Since one of the main goals of this thesis work is to perform a preliminary study about implementation of a prototype for individual’s carbon footprint reduction, the final developed prototype acts as a proof of concept application with certain limited functionalities and features.

This application is supposed to be used as one of the applications of Smart City Marketplace platform in the Stockholm Royal Seaport project to provide users with means to track their individuals’ carbon footprint and give them the ability to take actions for keeping their carbon footprint low. The idea of betting in this context is realized to provide incentives for users to actively participate in activities which can result in less carbon emissions.

Having said this, the mock-up architecture in this section presents a general overview of how the prototype has been constructed based on limitations and assumptions that exist in the project. These limitations and assumptions are discussed in more details in mock-up architecture’s section.

The required architecture on the other hand, aims to demonstrate an ideal architecture which can be used to support the development of a fully operational application as part of Smart City Marketplace platform.

4.4.1. Mock-up architecture

As discussed earlier, the main goal of this thesis work is to design and develop a proof of concept mobile application for Smart City Marketplace platform to be used by users for tracking their carbon footprints with the intention of providing users means to manage and control their carbon emissions by keeping it low. This proof of concept application tries to help the realization of the idea and demonstrate its feasibility with the aim of providing grounds for future developments and conceptualizations. Therefore, there exist assumptions and limitations in this proof of concept prototype which will be explained here.

The actual contents and database entries used in this prototype are only mock-up data and it is assumed to be provided by data providers such as the smart city itself, energy providers (e.g. Electricity and heating companies), real estate companies (e.g. information about house, neighborhood), transport data providers (e.g. public transportation companies and road tax companies), and other possible data providers such as retail stores. These are considered as third party companies which can provide different sorts of data according to users’ daily activities.



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The mock-up architecture is mainly grounded on a three-layer architecture built on top of Telerik platform. As Figure 11 illustrates, these three layers are Presentation layer, Business Logic layer, and Data layer respectively from top to bottom.

The presentation layer encapsulates a set of components and functions that are used to provide a comprehensible user interface for the end users of the application. The application is written in pure HTML5 and CSS. ‘HTML (the Hypertext Markup Language) and CSS (Cascading Style Sheets) are two of the core technologies for building Web pages. HTML provides the structure of the page, CSS the (visual and aural) layout, for a variety of devices. Along with graphics and scripting, HTML and CSS are the basis of building Web pages and Web Applications’ (W3C, 2014).

The graphical user interface (GUI) implementation follows a Model View ViewModel (MVVM) architectural approach with help of Kendo UI framework. The Model View ViewModel (MVVM) pattern is a known UI architectural pattern introduced by Microsoft as a variation of another presentation model design known as Model View Presenter (MVP). In MVVM, ‘[…] Model contains the data and does not know about the View or the ViewModel and the ViewModel is an abstraction of the View, which contains all of its data and state.’ (Jarnjak & Croatia, 2007) MVVM UI architecture provides the capability of separating the frontend layer from the backend layer and consequently reducing complexity in the development lifecycle.

The frontend layer in Figure 11 communicates with the other two layers namely Business Logic and Data layers through two components known as Java SDK and RESTfull services. JavaScript SDK in Telerik AppBuilder is used to provide an abstraction layer over Telerik Backend Services REST API by offering APIs for CRUD operations for plain objects and integration with Kendo UI framework.

The Business Logic layer in Figure 11 is assumed to provide a business rule engine containing a set of rules and logics that are required for the Carbon Footprint app for operations such as calculation of emitted carbon usage for a user, calculation of rewards, calculation of users’ bets, and other business rules that may be introduced for using the application. The Business Logic layer has not been implemented in this prototype but it was taken into assumption that such a rule engine is needed for a fully functional application. The back and forth communication between frontend and backend layers are assumed to be checked against the rules and logics in Business Logic layer in every occasion and both Business Logic and Data layers are assumed to be layers that belong to Backend layer.

The Data layer defines components which support the database management system (DBMS) operations. This layer contains the main database and a set of tools and services introduced by Telerik backend services for operations such as read, write, update, delete, and in general database management operations. This layer also includes a component for sending push notifications which provides the ability to send notification messages to users for certain operations such as when a user registers and so on.

Integrated support for Apache Cordova into Telerik AppBuilder provides the ability to deploy and publish the final application on iOS, Android, and Windows devices. Figure 11 illustrates the Apache Cordova component which is used as a JavaScript-to-Native bridge technology for the Carbon Footprint application publication for end user devices including iOS, Android, and Windows devices. In fact, this developed prototype can be published on these three mobile operating systems but it was mainly optimized for iOS 7 on both iPhone 4 and iPhone 5 series.



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Figure 11 – Carbon Footprint App mock-up architecture

4.4.2. Required architecture

The required architecture which will be explained in this section aims to propose and conceptualize an integrated architecture solution for which the Smart City Marketplace can be built upon and eventually provide a comprehensive framework for a variety of applications to serve end users’ devices such as smart phones, tablets, PCs, and even Smart TVs. This proposed architecture is then assumed to provide the foundational architecture for which the Carbon Footprint application can be grounded upon.

Figure 12 illustrates this proposed architecture with all the different main players and components around it. In general, this architecture consists of two outer layers and one inner layer. Components in the outer layer, on the left side, act as data and content providers for the middle inner layer in terms of raw data accumulated from third party data providers, new projects and applications from developers, and management and administration operations imposed from administrator agents. The outer layer, on the right side, however consists of a series of user agent devices which can consume the outcome artifacts produced from the inner layer in terms of new applications and data offered by the App store.

The inner layer in this proposed architecture contains the main building blocks of the Smart City Marketplace providing a comprehensive framework for the entire system so that useful and powerful artifacts can be built upon to be used by the end user agents.

The left building block in the inner layer can be considered as an entry point of the platform where multiple entry gateways are defined in terms of portal points providing various types of content and data into the platform. As illustrated by Figure 12, the three components in this block are Partner Portal, Developer Portal, and Admin Portal.



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The Partner Portal acts as a standalone supplier gateway which enables third party companies and partners to provide different kinds of data to be processed and consumed for generating useful information by the platform. In a similar manner, the Developer Portal in this block aims to provide a comprehensive development environment for developer communities to define and implement various types of projects in terms of new applications using the contents and data provided by the data providers. Moreover, the Admin Portal aims to provide administration modules enabling system administrators to control and manage the platform.

The middle block in the inner layer consists of various components which are formed into three building blocks known as integration layer block, database management system block and a group of standalone modules on the upper block. The Integration layer in this block acts as a backbone layer for the platform and it aims to unify all data, contents, processes and operations that are imposed by partner, developer, and admin portals and provide a unified data structure to be used by the database layer.

The upper layer consists of components such as content, billing and payment modules, broker services, push notification module, reporting services, and analytics modules which can offer different kinds of services within the platform to be used either directly by partner portals or indirectly as a resource for the applications in App store.

As illustrated form Figure 12, the third block introduces the App Store. The App Store within Smart City Marketplace aims to provide a group of useful applications that are developed by the developer communities to be used by end user device agents within the smart city context. The App Store intends to provide a platform which can host different sorts of multipurpose applications that can be installed as standalone applications on smart phones, tablets, PCs, or Smart TVs offering a range of handy and useful features and functionalities for the citizens of smart city.

The applications in the App Store use the data and resources provided by the Smart City Marketplace platform to serve their intentions and they are in fact considered the end artifacts of the Smart City Marketplace. The Carbon Footprint application is also assumed to be one of the applications that can be developed based on the Smart City Marketplace platform to serve its users with useful information about their daily carbon emissions with the aim of reducing it.



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Figure 12 – Smart City Marketplace required architecture



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4.5 The Carbon Footprint App prototype

This section intends to present the idea of the Carbon Footprint App and introduce the implemented features and functionalities with the help of application’s screenshots and mock-up data and contents. In general, the application consists of a group of views which are built using Kendo UI framework on top of Telerik platform supporting the presentation layer. The application is written in pure HTML 5, CSS 3, and JavaScript where a set of backend services and APIs provided by Telerik platform enables the back and forth data communication between presentation and backend layers. The backend layer consists of the main database, database tables with mock up data, and a group of backend services and components provided by Telerik platform to perform database management operations.

The overall architecture has been illustrated by Figure 11 in more details.

In the following all the different views and features will be presented in more details.

4.5.1. Login and registration

Screenshot 1 illustrates the App’s landing view which is the Sign in view. The App requires all users to login with valid credentials to be able to use the features and functionalities offered by the App. In this view users have the possibility to either login into the Carbon Footprint App or attempt to register as a new user by tapping on the Signup button.

Screenshot 1 – Login View

Screenshot 2 illustrates the Sign up and user registration view. Signup View requires users to provide a valid name, email address, username, and password. The other fields are optional. Upon successful registration a verification email along with a welcome email will be sent to the user.

Screenshot 3 and Screenshot 4 illustrate the emails sent to user upon successful signup.



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Screenshot 2 – Sign up View

Screenshot 3 – Sign up Verification Email



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Screenshot 4 – Sign up Welcome Email

4.5.2. Home view

The home view intends to provide a general overview of the user’s current status with regard to the amount of CO2 he/she has generated so far, status of his/her earnings so far, and a countdown day/time component displaying the remaining time for a bet accomplishment.

This view provides an elegant user interface by incorporating an animated bubble view where a group of floating bubbles aim to represent different statuses about amount of carbon emissions so far and the user’s bet earnings. Each of the bubbles that hold a user’s data object can be tapped by the user to show what data they currently hold.

Screenshot 5 illustrates the Home view and it shows that the bubble with ‘$10’ data object is tapped by the user and Screenshot 6 illustrates the change of data in the tapped bubble to show the data that the bubble was holding. The bubble which is holding ‘120 gr’ data object has the same characteristic, whereby tapping on the bubble holding this data object will show the bubble’s title ‘CO2’, indicating this bubble holds data about current CO2 generated by the user in grams. These bubbles float on a randomized manner and they intend to show updated data as they occur in real time.

The countdown timer on the Home view is another useful component, which displays the remaining time of the user’s bet until he/she wins or loses a bet. This counter resets every time user makes a new bet and it counts down the time in number of days followed by hours, minutes, and seconds.

Screenshot 5 and Screenshot 6 illustrate the home view with mock-up data for a user who has generated ‘120 grams’ of CO2, and earned ‘$10’ for his/her bets so far, and has 13 days, 9 hours, 24 seconds, and 14 seconds until the end of his/her bet.



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Screenshot 5 – Home view (User tapped on bubble with $10 data object)

Screenshot 6 – Home View (After user tapped on $10 data object)

4.5.3. CO2 usage view

This view offers one of the important features of the Carbon Footprint App where all of the user’s daily activities that can result in generation of CO2 are presented in a calendar representation, where each day will then be divided into a series of events resembling a timeline view.

All the data regarding user’s activities will be provided by data provider vendors and these different types of data will then be analyzed in the integration layer and the structured data will be pushed towards the main database for data storage. Upon users’ request to access these data, they will then be passed into business logic layer where additional data processing will take place to apply required business rules and required data categorizations and also possible calculation of CO2 emissions by each of these activities. The final processed data will then be pushed into the CO2 usage view where each activity will be presented in a daily timeline list view.

Screenshot 7 illustrates the Carbon usage View for a user on 8th of December. The first screenshot (1) displays the first view of the list view when the user taps on the My CO2 tab option on the main tab bar. The second screenshot (2) shows that the list is about to be swiped up so that the rest of the user’s activities can be seen. The third screenshot (3) shows the remaining user’s activates after swiping up the list view.

As illustrated by Screenshot 7 series of images, the user’s activities in this particular case, which are mock-up data, are divided into four main categories such as bus transportation, car transportation, purchase of cloths, and food which are presented in a timeline view according to their time of occurrences.



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(1)

(2)

(3)

Screenshot 7 – CO2 Usage View

The CO2 emissions generated by all of the activities in the list view in Screenshot 7 are presented in the big green circle on the top of the view. Tapping this circle will change the data object it holds from current CO2 in grams to the bet’s status on the given date and vice versa. Screenshot 8 illustrates this view and the tapping event triggered by the user.



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(1)

(2)

Screenshot 8 – CO2 Usage View (tapping on CO2 status)

Screenshot 9 illustrates the CO2 usage View when user swipes the list view to the left to be able to view the list of activities on every day from 8th of December to 11th of December.

(1)

(2)

(3)

(4)

Screenshot 9 – CO2 Usage View (swiping left)



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Every activity presented in the CO2 usage View has a detail content view where it provides more details about that activity and also how much CO2 it has generated. Screenshot 10 illustrates the fact that user has tapped on the Bus transportation activity on the timeline (Figure (1)) to view more details about this activity. Figure (2) presents the detail view of the Bus transportation activity and it shows the user is trying to tap on the Distance green circle to be able to see amount of CO2 this activity generated. Figure (3) illustrates that the user is swiping up the view to see the route map of the traveled distance related to this activity. Figure (4) illustrates this route map with start (A) and end (B) points marked on the map with help of Google Map APIs.

(1)

(2)

(3)

(4)

Screenshot 10 – Bus Transportation Activity Detail View

In a same way, Screenshot 11 depicts a case where user is trying to see the details view of a clothing purchase and as illustrated from figure (1) user taps on the clothes purchase activity on the timeline. Figure (2) is the detail view of the clothing purchase activity and this view further illustrates the case where user is trying to see the time of the activity by tapping on the emissions green circle which makes this green circle to display the time for which the activity took place.



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(1)

(2)

(3)

Screenshot 11 – Clothing Purchase Activity Detail View

In a similar manner, Screenshot 12 illustrates the detail view of an activity of type food. Figure (1) depicts the case when user taps on the food activity on the timeline where he/she lands on the details view illustrated on figure (2). Figure (2) also shows the user’s action when the emission green circle is being tapped to change its data from current emission amount to the time for which the activity took place.



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(1)

(2)

(3)

Screenshot 12 – Food Activity Details View

4.5.4. Achievements view

Achievements view is designed to enable users to track their gain and loss statuses with respect to a bet made by the user. Screenshot 13 illustrates this view with mock-up data where those bets that are successfully met by the user are presented by green circle with the amount of achievement inside it and those bets that the user failed to achieve are presented by red circle with the amount of loss inside the circle. The date besides each circle indicates the starting date of a respected bet.



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Screenshot 13 – Achievements View

Screenshot 14 and Screenshot 15 illustrate the details view of a bet’s achievement which was successfully won by the user and details view of a bet’s lost that the user failed to achieve respectively.

(1)

(2)

Screenshot 14 – Detail View of a gained bet



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(1)

(2)

Screenshot 15 – Detail View of a lost bet

4.5.5. Statistics and reports view

Statistical views are designed to provide users with a general overview of their activities within the application in a graphical manner. These views enable users with helpful reports about the CO2 emissions generated based on each activity group, a monthly view of CO2 emissions within a year, and an overall graphical view of user’s current betting activities. The view illustrated by Screenshot 16 represents the first landing page of the Statistics View when the user taps on the Statistics tab on the main tabs bar.



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Screenshot 16 – Statistical View Landing Page

Screenshot 17 illustrates the swiping feature in the Statistics View where each view contains a different graphical representation of user’s activities based on mock-up data. Figure (1) displays the CO2 emission percentages produced by each of the different activity categories such as transportation, shopping, food, and etc. on a doughnut chart. Figure (2) represents CO2 monthly emissions of the user throughout a year on a line chart. Figure (3) presents a general overview of the user’s bets achievements progress on a doughnut chart.



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(1)

(2)

(3)

Screenshot 17 – Statistical Views (swiping to left)

4.5.6. Navigational menu panel

The navigational menu panel provides a useful sliding panel which can be accessed throughout the application and it offers users quick access to different views such as Help, About, and Bet views. In addition the menu panel also contains a log out tab which enables users to log out while anywhere within the App.

Screenshot 18 illustrates the navigational menu panel in use by a user while the user is on the Home view.



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(1)

(2)

Screenshot 18 – Navigation Menu Panel

4.5.7. User betting

This section intends to present the idea of the betting case incorporated into the development of this prototype. A bet, in this App, is defined as a commitment that a user admits to fulfil in order to achieve a certain target goal which is the reduction of his/her carbon footprint in accordance with his/her normal daily activities. This thesis work believes that a betting game in this context can create potential opportunities for more user engagements, whereby winning a potential bet is seen as a reward achievement form the end user’s point of view. These reward achievements can be considered as a strong motivation factor which can create more user engagements resulting in a lower carbon emission production with respect to user’s daily activities.

4.5.7.1 Betting case discussion

One of the characteristics of the Carbon Footprint App is the ability of users to participate in a competition/bet with the intention of motivating them to produce less CO2 in a betting context.

A potential better in this betting system can start a bet by using the App’s betting feature, where he/she can set a bet to be achieved between 1 day up to 7 days and he/she can also choose how much he/she is willing to invest (e.g. $5, $10, or $15) on each day he/she meets the winning point. The amount of stake agreed by user to start the bet is then considered to be the actual money that the user receives in case he/she wins or otherwise in case he/she does not meet the bet then he/she is obliged to pay back that amount as a result of his/her failure.



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The design of the idea of this betting system is made under the assumption that the betting system integrated into this prototype is capable of generating the payable rewards from its potential active players (App’s users) who are using the betting feature incorporated into the App. To put this concept into simple words, the collected money received by the App from those users who did not successfully accomplish their bet will be used to reward those who have won their bet.

Since an ideal betting system can be quite complex from this perspective that it should provide a fare betting system that can benefit a large group of target users, this section also provides a discussion of various betting scenarios and it tries to argue about cons and pros in each approach and eventually propose a good enough approach for implementation of a fare betting system to be incorporated into the Carbon Footprint App’s business rule engine.

All the required rules and guidelines for implementation of this betting system shall then be incorporated into the App’s business rule engine which is located in the backend layer.

This prototype does not provide any implementation of the business rule engine layer, since it is not within the scope of this thesis work. This thesis work only aims to present the idea of Carbon Footprint App with the help of this prototype and it can be used as a conceptualized model for future development, design, implementation, and enhancement works.

In the following, three approaches will be discussed and argued.

First approach A simple and general approach is to create a similar condition for all the users to participate in a betting scenario by providing conditions that are set in a same way at the beginning of a so called bet. For example, taking the average carbon footprint emissions per capita in a city can provide us with figures which could be used to initiate a possible betting case by setting this amount as the threshold of a bet.

A use case in this scenario would be:

If we assume the average carbon footprint emissions for each inhabitants in Stockholm city is X grams per day then user makes a bet based on his/her allowed daily CO2 emissions’ threshold and for each day that he/she meets this limit (daily carbon emissions is less than or equals the allowed daily CO2) then he/she is rewarded otherwise he loses the bet.

Although, this approach may provide a simple schema for an ideal betting case which can benefit many user groups (e.g. users with really low daily CO2 emissions) but it may not provide a fair betting system, since we may end up in cases that a specific user group will always lose or win the bets as the starting points for users in such a betting system would be different depending to individual’s living situations and conditions.

Second approach In a second approach, we can consider providing different betting category options for users with different interests where each user has the option to choose the best option suitable to his/her condition/interest depending on where he/she sees the most potentials to win a given bet.

In this approach, we can provide betting cases in three different main categories namely Energy, Foods and drinks, and Transportation.

Some of the use cases in this case would be as follow:

A so called bet in the category of Energy would mean the home electricity usage in a monthly basis according to house size in square meter. For example a 70 square-meter-house with 4 person households is allowed to use 700 kilowatts electricity per month to be considered as a normal energy consumer with respect to carbon emissions. In this case if the monthly electricity usage is below this limit then the user can be considered a potential winner in the bet.

In a second category, which belongs to Foods and Drinks category, we can calculate the amount of CO2 emissions that can be produced by using each type of food or drink. For example, red meat and carbohydrate drinks have the most CO2 emissions while vegetables and non-carbohydrate drinks have less CO2 emissions. In a same way, a threshold can be introduced here and based on this threshold, we can verify if a user can win or lose a bet based on their monthly total amount of generated CO2 created by only food and drink consumption.



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The third betting category which belongs to Transportation category can also have a wide variety of target user groups who may wish to participate in a bet within this category. In this category, the type of user’s transportation can be considered as an important factor to make the user win or lose a possible bet. For example, driving a personal car would generate more CO2 emissions than taking public transport, or cycling can significantly reduce user’s total CO2 emissions.

Third approach A third approach is to personalize the betting case based on different criteria for the user and in this way create a fair betting system for all users. In this approach, the aim is to personalize the bet for a user based on criteria such as age, weight, eating habits, living conditions (neighborhood, house location, house size, number of households, etc.), user’s occupation, type of user’s vehicle/transport. In this approach, we first try to study the user’s current situations and conditions then based on the factors that we have identified by studying him/her; we will set the bet’s thresholds. For example if user A is using private car instead of public daily transport then a change in his/her behavior to switch to public transport can give her a good score to win a potential bet.

Some of the benefits in this approach are:

- We can hope to create a same start point for all the individuals who are interested to participate in a bet game.

- Users are able to create personal goals and targets as part of the bet as well. - Users would have more options to influence their bets. - There is a better chance to improve users’ behaviors in this approach.

Conclusion As a matter of fact, setting up an ideal and fair betting system which can suite every users in the system is somewhat impossible as there are always users who may not be able to participate in such a betting system for different reasons. Eventually, the final goal in this section was to discuss the different options that can be taken into considerations about a betting scenario in our prototype model for Carbon Footprint App.

Based on the different betting options that we have discussed above, the third option provides more incentives to conclude that the betting case can be formulated based on a more personalized betting system for which a bet can be customized to a specific user. The data regarding the user’s conditions will then be used to influence his/her bet’s results.

The betting system incorporated into this prototype tries to simulate approach three presented above whereby the app tries to capture data about user’s housing and means of transportation through setting a bet. The provided details by the user will then enable the App to categorize the user into a proper user group and also make proper calculations to identify the winning and losing points of the user’s bet. In this way, users with same conditions are always evaluated within same user category and therefore the App can offer a fair betting environment where all users are treated in a similar way.

4.5.7.2 Betting views in practice

This section intends to present the different views that are designed and implemented to cover the betting case in the Carbon Footprint App prototype.

4.5.7.2.1. Bet start view

A potential betting scenario can be initiated by a user through accessing the bet’s start view from the sliding menu panel which is available throughout the App once the user logged in. This gives the user the opportunity to start a bet anytime he/she is willing to participate in a so called bet/competition.



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Figure 13, illustrates the different steps required to start a potential bet in the Carbon Footprint App prototype.

Figure 13 – Steps to Start a Bet

Screenshot 19 illustrates this case whereby figure (1) presents the view for which user is trying to start a bet by going through the start bet tab located within the App’s sliding panel menu and figure (2) depicts the result of this action whereby the user lands on the bet’s start view to initiate a bet according to first step presented by Figure 13.

(1)

(2)

Screenshot 19 – Bet Start Views

4.5.7.2.2. Bet personalization views

As discussed in the ‘betting case discussion’ section, this prototype implementation considers a personalized approach of handling user betting scenarios with the aim of providing a fare betting environment for all participants. In this approach, each individual is treated separately under a given bet commitment,



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where for example certain criteria such as means of user’s transportation or user’s housing can influence the user’s chance to win or lose a potential bet, whereby these factors can be taken into consideration during the calculations processes in the App’s rule engine layer so that users with same conditions and characteristics will be evaluated in the same user target category group.

In fact, the calculation algorithm in the rule engine first tries to categorize users into similar target groups and then it applies the rules to make the calculations to either recognize a user as a winner or a looser.

Screenshot 20 illustrates the bet’s personalization landing view where figure (1) depicts the view where user attempts to initiate a bet and figure (2) displays the landing view resulted from this action.

This view complies with step two presented by Figure 13 above.

(1)

(2)

Screenshot 20 – Bet Personalization Landing View

4.5.7.2.2.1. Housing settings view

Screenshot 21 illustrates one of the options for personalizing a bet which is provided by Bet Personalization View. This view lets users to specify different factors relating to their housing situation. The contents in this view are presented under three different headlines namely Housing, Electricity, and Address.

The Housing headline, as presented by figure (2), contains factors such as the size of user’s accommodation, number of households living at same accommodation, the accommodation’s means of heating energy, and the accommodation’s type. The Electricity headline as presented by figure (3) contains a dropdown list with a list of electricity providers which the user can choose from. And last but not least, the Address headline provides fields which enable users to specify their address.



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In general, the data captured from Housing Settings View can help the App to identify the user’s housing conditions, sources of heating and electricity powers and the user’s neighborhood. These factors provide significant data for the App in order to place the user into a right user target group for later calculations of a potential bet’s winning and losing points.

(1)

(2)

(3)

Screenshot 21 – Housing Settings Views

4.5.7.2.2.2. Vehicle settings view

In a similar manner as the housing settings view, the vehicle’s settings view provides a view whereby a potential user can specify details about his/her means of private transportations.

Screenshot 22 illustrates this view where two different options, namely Car and Motorcycle, are presented to be filled in by the user. Both options provide a drop down list with a list of possible fuel types for each of the vehicle types and also an additional field about the engine size for the respected vehicle.

In general, the data captured from Vehicle Settings View can help the App to identify the user’s means of private transportation. In a same way, as for the data in Housing Settings View, these factors provide significant data for the App in order to place the user into a right user target group for later calculations of a potential bet’s winning and losing points.



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(1)

(2)

Screenshot 22 – Vehicle Settings Views

4.5.7.2.3. Bet target settings views

Upon the completion of the bet’s personalization step (complying with step two of the betting process), the user continues with the betting process by moving to the next view, where the App requires the user to specify the number of days for which he/she is willing to commit to accomplish a bet and also the amount of money (stake) he/she wishes to gain in case he/she wins or to lose in case he/she fail the bet.

Screenshot 23 illustrates this scenario where figure (1) presents the view where the user is trying to continue the bet process by tapping the continue tab on the bet’s personalization view and this user action results in the view presented by figure (2). Figure (2) is considered as the main view complying with step three of the betting process and in this view, user is attempting to add number of days by tapping on the plus sign. Figure (3) is presenting the same view where the user’s action presented in figure (2) resulted in an increase in number of days by one. This figure is also illustrating the user’s action in increasing the amount of bet by tapping on the plus sign besides the amount indicator. The last view presented by figure (4) illustrates the final result of user’s actions performed on figures (2) and (3), which indicates that the user has chosen the bet to last for 2 days and he/she has committed to make an investment of $10.

To further clarify this scenario, for every day that the user meets his/her goal which is if he/she succeeded to keep his/her CO2 emissions below his/her CO2 emissions’ threshold, then he/she is eligible to receive the amount that he/she specified during the bet and he/she is considered a winner, otherwise he/she is considered a loser and his/her invested stake will be taken by the App to be used to reward other users in the betting system who win their bets.



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(1)

(2)

(3)

(4)

Screenshot 23 – Bet Target Settings Views

Screenshot 24 illustrates the final betting step where the user by tapping on the ‘Start Bet’ tab agrees to start the bet with chosen set of settings specified in the previous three steps (figure (1)). Figure (2) depicts the home view with the bet’s count down component which has resulted from the user’s action in figure (1).

(1)

(2)

Screenshot 24 – Bet Initialization Closure Views



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5 EVALUATIONS

This chapter, which complies with the evaluation step of Design Science Research Methodology (DSRM) will present the findings resulted from the interview sessions held with 12 users in order to evaluate the developed prototype and measure its usefulness based on interviewees opinions experiencing this prototype.

5.1 Evaluation process

The evaluation has been carried out using a set of questions in form of a questionnaire sheet which was passed on to the participants during demo sessions. A total of 12 participants have been used to conduct these demo sessions. Since these 12 participants were not in the same place, 4 separate demo sessions had to be carried out, where in each session there were 2 to 4 participants attending the demo.

During these sessions, at first participants were given some background information about the Smart City idea for which this prototype had assumed to be built on and then a demo of the App was presented to them simply by using a test iPhone device which had the prototype App preinstalled on it. During the demo, participants were also introduced to the assumptions and limitations that were taken into account in this prototype.

After the demo, they had the chance to try the App by themselves under assumption that they are using this App during an ordinary day by impersonating a smart city citizen. At last, when participants have tried the App they were given the questioners to be filled.

The questionnaire has been designed to collect participant’s data within three different dimensions known as:

1. General demography

2. Area understanding and awareness

3. Carbon Footprint Application user experience

Each dimension contains questions to capture different useful data from participants and also to provide them with some insights about terminologies and concepts such as global warming, climate change, Quantified Self, CO2 emissions, self-tracking and etc. The questions in the second dimension provides participants with a better understating for answering the questions regarding their experience using the prototype in the third dimension.

‘General demography’ dimension, as the name says, intends to capture some useful details about participants’ demographic details such as gender, age, and their occupation sectors.

The ‘Area understanding and awareness’ dimension consists of two sub-dimensions known as ‘Global warming’ and ‘Self-Tracking’, which intends to identify participants’ understanding and their degree of awareness with respect to terms and concepts such as global warming, climate change, Quantified Self, CO2 emissions, and self-tracking. The data captured from this section provides important information for identifying the participant’s quality of evaluations using this prototype.

Last but not least, the ‘Carbon Footprint Application user experience’ dimension contains a set of questions designed to identify the prototype’s usefulness, its quality, and underlying motivational factors based on users’ experience while using the App. This dimension also groups the questions into two different sub-dimensions known as ‘Usefulness and Ease of use’ and ‘Underlying motivations for using the App’. In the following, the results and findings identified by these user evaluation sessions will be presented and discussed in more details.



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5.2 Data analysis

5.2.1. General demography

As mentioned earlier, a total of 12 individuals have been participated in this evaluation with an age range between 23 to 50 years old. Graph 1 illustrates the age distribution based on gender, which was extracted from the demography questions set in the questionnaire sheet during the evaluation demo sessions.

Graph 1 – Age distribution by gender

Among these 12 participants, 33% are women while 67% are men with a total of 8 men and 4 women, as illustrated by Graph 2.

Graph 2 – Participants by gender

1

3

2

1 1

2 2

20-25 26-31 32-37 38-43 50-55

Age Destribution by Gender

Men Women

67%

33%

Participants by Gender

Male Female



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Another useful data which was extracted from the questioners is the occupation category type for which each of the participants belongs to, as illustrated by Graph 3 below.

Graph 3 – Participants by occupation category

Information captured from the demographic section of the questionnaires reveals that the population under study is young with an average age of 33 years old including a total of 8 men and 4 women for which almost half of them (57%) are working within IT sector as senior managers, managers, IT consultants, and programmers, 7% are working in healthcare sector, 22% are students, and 14% belong to category of ‘housewife/husband’. From these data, it can also be inferred that almost half of the participants (57%) can be considered as advance users as they are engaged in works within IT sector and the other half (43%) can be considered as normal users who are familiar with new technologies (with respect to the average total age) but not as advanced as the other half. This makes a good population for our evaluation.

5.2.2. Area understanding/Awareness

This dimension is further dived into two sub-dimensions known as ‘Global warming’ and ‘Self-Tracking’ which intends to identify participants’ understanding and their degree of awareness with respect to terms and concepts such as global warming, climate change, Quantified Self, CO2 emissions, self-tracking.

5.2.2.1 Global warming

This sub-dimension consists of a total of 4 questions which were presented in the questionnaire sheet for participants to be filled in. These questions are designed to evaluate participant’s understanding and their degree of awareness with respect to global warming concept. Analysis of data extracted from participants’ answers will be presented below. The first question asked in this dimension designed to capture participant’s response to different terminologies which exists within global warming domain such as climate, climate change, global warming, greenhouse effect, greenhouse gases, carbon dioxide, emissions, recycling, reforestation, and deforestation. Participants were asked to choose one of the provided options based on their familiarities with respect to each of the terminologies mentioned earlier. Graph 4, presents all the captured

57%

7%

22%

14%

Participants by Occupation Category

IT sector Healthcare sector Student Housewife/-husband



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responses with each terminology on the vertical axis and the number of responses on the horizontal axis.

Graph 4 – Participants' Awareness of Global Warming

Extracted data captured by Graph 4, reveals that generally participants in this study have a fair degree of awareness with respect to global warming domain. This can be seen from Graph 5, which indicates that a total of 52% understand these terms very well, 46% believe they have heard about these terminologies, and only 2% responded they have never heard about some of these terminologies.

0 2 4 6 8 10 12 14

Climate

Climate Change

Global Warming

Greenhouse Effect

Greenhouse Gases

Carbon Dioxide

Emissions

Recycling

Reforestation

Deforestation

Climat

e

Climat

e

Chang

e

Global

Warm

ing

Green

house

Effect

Green

house

Gases

Carbo

n

Dioxid

e

Emissi

ons

Recycl

ing

Refor

estati

on

Defor

estati

on

I have no idea what this term

means2

I have heard this term but I'm not

very confident in using it4 5 9 8 4 6 1 9 9

I understand this term very well 12 8 7 1 4 8 6 11 3 3

Participants' Awareness of Global Warming



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Graph 5 – General average responses with respect to Global Warming

Graph 6 illustrates the analysis of data captured from participants regarding their opinions about importance of climate change. The extracted data shows that majority of participants believe climate change is a very important issue (64%), while 27% believed that this issue is extremely important, and 9% believed it is moderately important.

Graph 6 – Participants’ opinion about importance of Climate Change

In a similar way, Graph 7 shows the data captured regarding the importance of CO2 emission reduction to fight climate change, where participants were asked to give their opinions in a scale from 1 to 5 (not important to extremely important). The analysis of this question reveals that 58% believe that reducing CO2 emissions is extremely important to fight climate change, 34% considered it to be very important, and 8% believed that it is moderately important.

52%46%

2%

General average responses with respect to Global

Warming

I understand this term

very well

I have heard this term but

am not very confident in

using it

I have no idea what this

term means

9%

64%

27%

Do you consider climate change as an important issue?

Not important

Slightly important

Moderately important

Very important

Extremely important



50

Graph 7 – Participants’ opinions about importance of reduction of CO2 to fight Climate Change

The last question under this dimension was asking participants to express their opinions about who they think should consider reducing their CO2 emissions. According to Graph 8, 29% believed that governments, multinational companies, small and mid-sized companies, and individuals should take initiatives to reduce their CO2 emissions, 24% believed small and mid-sized companies must do that, 24% believed big multinational companies should reduce their emissions, 14% believed that it is individuals who should reduce their CO2 emissions and 9% believed all of these parties together should reduce their CO2 emissions.

Graph 8 – Participants’ opinions regarding who should consider reducing their CO2 emissions

8%

34%

58%

Do you believe reducing CO2 emissions is important

to fight climate change?

Not important

Slightly important

Moderately important

Very important

Extremely important

9%

24%

24%

14%

29%

Who should reduce CO2 emissions in your opinion?

Governments

Multinational companies

Small & medium-sized

companies

Individuals

All of the above

None of the above



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5.2.2.2 Self-Tracking

This sub-dimension contains a total of 3 questions which were presented in the questionnaire sheet for participants to be filled in. These questions are designed to evaluate participant’s understanding and their degree of awareness with respect to Quantified Self/self-tracking concept. Analysis of data extracted from participants’ answers will be presented below. The first question asked from participants was if they have ever heard of terms Quantified Self or self-tracking devices and services. The extracted data that was retrieved from questionnaires shows that 33% of participants believed they know about these concepts, 42% believed that they did not know what are these concepts about prior to this study, and 25% were not sure if they have heard these terms before even though they might have crossed by it somehow before. Graph 9 illustrates these findings.

Graph 9 – Participant’s responses with regard to their knowledge about Quantifies Self/self-tracking devices and services

On a different question, participants were asked if they are keeping records of or tracking anything that occurs in their life, where all of them responded positively and they believed to some degrees they are keeping records of different things during their daily life. (E.g. monthly expenses list, grocery lists, weight control, etc.) These data are presented by Graph 10 below.

Graph 10 – Participant’s responses with regard to if they are keeping records of or tracking anything in their life

33%

42%

25%

Have you ever heard of Quantified Self or self-tracking

devices and services?

Yes No Not sure

12

0

Number of subjects

Are you keeping records of or tracking anything that

occurs in your life?

Yes No



52

Graph 11 illustrates the data extracted from another question in the questionnaire sheet which was asking the participants to specify how they keep track of those things that they usually keep track of. This graph indicates that 31% are using some type of web and desktop applications, 23% were using some kind of mobile Apps, 15% had some kind of self-made desktop tools like spreadsheets, 15% were using an ordinary type of tracking with pen and paper, and 4% were using other means of record keeping.

Graph 11 – Participant’s responses with regard to how they keep track of everything they are tracking

5.2.3. Carbon Footprint Application user experience

This dimension is further dived into two sub-dimensions known as ‘Usefulness and Ease of use’ and ‘Underlying motivations for using the App’, which intends to identify the prototype’s usefulness, its quality, and underlying motivational factors based on the users’ experience while using the App during the evaluation demo sessions.

5.2.3.1 Usefulness and Ease of use

This sub-dimension contains a total of 4 questions which were presented in the questionnaire sheet for participants to be filled in. These questions are designed to evaluate participants’ opinions regarding the prototype’s usefulness and ease of use. Analysis of data extracted from participants’ answers will be presented below.

As the first question, participants were asked to express their opinions about the App’s overall user-friendliness factor where they were given the option to rate this factor on a 1 to 5 scale from ‘Not at all satisfied’ to ‘Completely satisfied’. Graph 12 illustrates these data extracted from questionnaire sheets.

15%

12%

23%31%

15%

4%

How do you keep track of everything you are tracking?

Pen and paper

Self-tracking dedicated

hardware

Mobile software (Apps)

Web and Desktop

Applications

Self-made desktop tools

Other



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Graph 12 – Participant’s opinions with regard to App’s user-friendliness

Participants were then asked to express their opinions about their experiences with regard to the App’s difficulty or ease of use after trying the App. As presented by Graph 13, majority of participants (41%) believed that it was easy to use the App, 25% indicated that it was really easy, 17% thought they had a moderate experience with this regard, and the other 17% believed that it was somewhat difficult using the App.

Graph 13 – Participant’s opinions with regard to difficulty and ease of use factors

On a separate question, participants were asked to rate their experience with regard to App’s GUI with a scale from 1 to 5 from ‘Not at all satisfied’ to ‘Completely satisfied’, where according to Graph 14, the majority (75%) indicated that they were completely satisfied with the App’s overall GUI, 17% were very satisfied, and 8% were moderately satisfied.

8%

25%

67%

How did you find the App in terms of user-friendliness in

general?

Not at all Satisfied

Slightly Satisfied

Moderately Satisfied

Very Satisfied

Completely Satisfied

17%

17%

41%

25%

Overall, how difficult or easy to use did you find this

App?

Very Difficult

Difficult

Somewhat Difficult

Moderate

Easy

Very Easy



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Graph 14 – Participant’s opinions with regard to App’s overall GUI

In another question in this sib-dimension, participants were asked to rate their experience with regard to App’s implemented features in the prototype with a scale from 1 to 5 from ‘Not at all useful’ to ‘Completely useful’. Graph 15 illustrates the analysis of data captured from participants’ questioners graphically where the horizontal axis presents the list of features and the vertical axis presents the number of participants who rated each feature from a scale of 1 to 5.

Graph 15 – Participant’s opinions with regard to App’s features usefulness

8%

17%

75%

How do ou s ore our e perien e a out the App’s overall graphical user interface (GUI)? (e.g. look/layout,

colors, text fonts and size, etc.)

Not at all Satisfied

Slightly Satisfied

Moderately Satisfied

Very Satisfied

Completely Satisfied

4

21 1

21 1 1

8

1011

1211

10

12 1211 11 11

Features usefulness ased parti ipants’ opinions

Not at all useful Slightly useful Moderately useful

Very useful Completely useful



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Further analysis of the data presented on Graph 15 reveals that participants believe 90% of the features they have tried within the App were completely useful while the other 10% of the features are very useful and that none of the features are moderately useful, slightly useful, or not at all useful. This fact is illustrated by Graph 16 below.

Graph 16 – Overall features usefulness

5.2.3.2 Underlying motivations for using the App

This sub-dimension consists of a total of 2 questions and it intends to identify the underlying motivational factors that this App can provide for users in order to encourage them to use this App on a regular basis.

As a first question in sub-dimension, participants were asked if they would consider themselves using this App in the future after they have been presented with the idea of this App. Surprisingly, all the participants believed that they are interested to use this App for tacking their CO2. Graph 17 illustrates this fact.

Graph 17 – Participant’s willingness to use this App for tacking their CO2 emissions

10%

90%

Overall Features Usefulness

Very useful

Completely useful

12

0

Number of subjects

Would you consider yourself using this App for tracking

your CO2 emissions?

Yes No



56

A second question in this sub-dimension focusses on motivational reasons that can encourage users to use Carbon Footprint App. The data presented by Graph 18 illustrates the participants’ responses for this question.

Based on this analysis:

9 out of 12 participants want to use this App to earn money using the betting system in the App.

12 participants want to use this App to measure their CO2 emissions.

9 out of 12 participants want to use this App to experience something new

12 participants want to use this App to help reduce the effects of global warming

7 out of 12 participants want to use this App to track their behavioral changes with regard to their CO2 tracking

8 out of 12 participants want to use this App to learn more about themselves

6 out of 12 participants want to use this App to have more control over their actions with regard to their CO2 tracking

Graph 18 – Participants’ motivational reasons to use Carbon Footprint App

5.3 Conclusion

Analysis of the questions within the demographic section of questionnaires indicate that, the population under study is young with an average age of 33 years old including a total of 8 men and 4 women for which almost half of them (57%) are working within IT sector as senior managers, managers, IT consultants, and programmers, 7% are working in healthcare sector, 22% are students, and 14% belong to category of ‘housewife/husband’. Although, the population under study is not that big but their age, occupation, and gender distributions can be considered as a good sample for our purpose which is to try the prototype and evaluate its usefulness. Most probably, a prototype which is considered as a proof

9

12

9

12

78

6

to earn

money

to measure

my CO2

emissions

to experience

something

new

to help

reducing

global

warming

to track my

behavioral

changes

to learn more

about myself

to have more

control over

my actions

What do you think can motivate you to use this App?

I want to use this App ...

Number of participants



57

of concept system/implementation would not be an easy to understand/use system if our sample group was not young and consequently we would not get correct results from this evaluation.

Having said this, based on the analysis of the demographic section, it can also be inferred that almost half of the participants (57%) can be considered as advance users as they are engaged in works within IT sector and the other half (43%) can be considered as normal users who are familiar with new technologies (with respect to the average total age) but not as advanced as the other half.

In addition, data analysis of individuals’ responses in the second dimension, which was about area understanding and awareness with regards to global warming terminologies and self-tracking idea in general, indicates that our sample group of participants had a somewhat good understanding and degree of awareness about general terminologies such as global warming, climate changes, greenhouse gas, greenhouse effects, and carbon dioxide, where 52% believed that they understand these terms very well, 46% believed they had heard about these terminologies but were not confident in using it, and only 2% thought they had no idea about some of these terminologies.

Moreover, concerning the participants’ understanding and degree of awareness about Quantified Self/self-tracking concept in general, the results of the analysis show that 33% of participants believed they know about these concepts, 42% believed that they did not know what are these concepts about prior to this study, and 25% were not sure if they have heard of these terms before even though they might have crossed by it somehow. Even though, the majority of participants believed that they either did not know about self-tracking or they have not even heard of it before but surprisingly they all believed that, to some extent, they actually do record keepings and tracking during their daily life (e.g. monthly expense list, grocery lists, weight control, etc.) using various means such as ordinary pen and paper, mobile software and apps, web desktop applications, etc.

This fact can create a valid hypothesis for which it can be inferred that in fact, all the participants under study were engaged in some sorts of self-tracking activities using different kinds of tools and techniques and the whole idea of these record keeping activities are being able to measure that specific object/s. This finding can be seen as a positive factor influencing the participant’s judgments about App’s usefulness which makes them to view this App as a good tool to keep track of their carbon emissions and to be able to measure it.

Analysis of the participants’ responses with regards to the App’s usefulness and its degrees of difficulty and ease of use indicates that majority of participants (92%) believed that they were completely satisfied or very satisfied with the App in terms of its user-friendliness factor, 66% have found the App either very easy or easy to use which again makes the majority of the participants, and the other 34% found the App either moderate or somewhat difficult to use. In addition, 75% of the participants were completely satisfied with the App’s overall GUI. Apart from this, participants also agreed that 90% of the implemented features in the App were completely useful and they have found the other 10% of the features to be very useful. Moreover, all the participants responded positively to the question that was asking them if they would consider using this App in the future to track their CO2 emissions. Additionally, the specified motivational factors for which participants believed that could encourage them to use this App were at least more than one per each participant where all the 12 participants believed that they want to use this App to measure their CO2 emissions and to help environment for reducing global warming, 9 of them want to earn money through the App’s betting game and also to experience something new, 7 of them want to use the App for tacking their behavioral changes with respect to their CO2 emissions, 8 of them want to learn about themselves by using the App, and 6 of them wanted to have more control over their actions by using the App.

In general, majority of participants have found the idea of this App really interesting and useful and they were satisfied with their experience trying the prototype during the evaluation sessions.



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6 DEMONSTRATION AND COMMUNICATION

This section, which complies with demonstration and communication steps of Design Science Research Methodology (DSRM), will explain how this thesis work will fulfill each of these steps based on DSRM framework.

6.1 Demonstration

This step which involves the demonstration of the developed artifact to solve the problem suggests that in order to be able to prove that a proposed solution can be used to solve a given problem, one should be able to make use of an ‘[…] experimentation, simulation, a case study, proof, or other appropriate activity […]” to verify such a solution. (Peffers, et al., 2006)

Having said this, the final App prototype which has been developed as a result of this thesis work along with two proposed architectures (a mock-up architecture which supports this prototype and a required architecture which shall support a fully functional App), a complete written report, and final user’s evaluations will be demonstrated in a presentation held by the master thesis work, where a demo will be played during the presentation for the audiences and the thesis inspectors.

6.2 Communication

This step, which involves the communication of the problem, the artifact and the solution to the community and relevant audiences will also be fulfilled by this thesis work where in addition to the work being presented at the end of the thesis, the thesis work will be accomplished in a complete written report which will further be published in the KTH university library and it will be publically available for the IS community to access it.



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7 CONCLUSION

This thesis work successfully implemented a prototype for a mobile application that can be used as a self-tracking tool/device which enables users to keep track of their individual’s carbon dioxide emissions occurred as a result of their daily activities such as eating, transportation, shopping, energy consumption, and etc. in real time. Being able to measure the generated carbon footprint with respect to each of the user’s activities, users will be able to monitor and control it and this monitoring and controlling of one’s carbon footprint can have significant influences in reducing those human factors which result in production of more carbon dioxide gases and consequently more global warming effects. Human factors are considered as one of the primary reasons behind global warming, therefore proper initiatives to reduce emissions of greenhouse gases and in particular the emissions of carbon dioxide have to be taken to reduce the impacts of global warming, otherwise there would be dramatic consequences which can endanger human’s life on Earth. Knowing this fact, besides all the other global movements that are currently undergoing to reduce the Earth’s carbon footprint levels (e.g. initiatives that governments are taking or initiatives that big international companies like Google, Microsoft, etc. are taking), it is also important that individuals join this carbon footprint reduction movements and take proper initiatives. The idea that this prototype implementation tries to vision can be used as a potential tool which can help individuals to influence their carbon footprints by measuring it in real time and take proper initiatives to keep it low. The prototype evaluation results show that the population under study (the 12 participants in the demo sessions) were confident that they are interested to use this application in their daily life in order to reduce their CO2 emissions. This can be indicated that there is a strong willingness in users towards taking actions to join this carbon footprint reduction movement but lack of proper tools and services to do so has impacted this willingness factor negatively so far. This prototype has been developed under the assumption that all the supported data and required systems and services are provided by the Smart City Marketplace platform. Smart City Marketplace can be defined as a visioned platform proposed by Royal Institute of Technology to be used as a comprehensive and integrated middleware between end users and data providers providing users with comprehensive applications and online services within a smart city context idea.



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8 DISCUSSION AND FUTURE WORK

This thesis work tried to touch many different areas such as Global Warming, Smart Cities, Incentive Theories, Quantified Self, Self-Tracking Tools and etc. in order to provide contexts that can give meanings to the final developed prototype which was successfully carried out and evaluated during this thesis’s life time, but this prototype and the proposed architecture can open up doors for many other questions which can be defined in terms of future work for a fully functional Carbon Footprint App. Telerik platform, which was used for implementation of this thesis work, is a robust platform with many useful features and services that can come very handy for both developers and also administrators managing and administrating the App but it also has some limitations. Since the platform offers the development work to be done by only HTML5, CSS and JavaScript, this can never result in an application with native user experience. In fact, Telerik AppBuilder platform is a really robust and useful platform for developing prototypes and simple systems. Hence, a future fully functional Carbon Footprint App with this complexity needs to be developed within the native OS for each intended vendor (e.g. OSX for iPhone, Android OS for Android, etc.) The betting system mentioned in this App can be a really complex system to implement and therefore extensive research and study needs to be performed in order to consider all aspects of a successful betting strategy which can result in full users’ involvement in the App. For a future development work of the Carbon Footprint App, there should be implemented the possibilities for users to easily connect other third party Apps or other means of data source to it. In this way, users will be able to easily define their source of data and this will further provide more user engagement and trust to use the App.



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9 REFERENCES

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Baker, E. et al., 2005. Vital Climate Change Graphics, Longum Park: UNEP, UNEP/GRID-Arendal.

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10 APPENDIX A: Evaluation questionnaire

GENERAL DEMOGRAPHY DIMENSION

Question 1. You are… Male

Female

Question 2. How old are you? _________ years old.

Question 3. What describes your occupation category type best? I work within IT sector

I work within healthcare sector

I work within finance sector

I work within manufacturing sector

I’m a student I’m unemployed

Other (please specify): __________________

AREA UNDERSTANDING & AWARENESS

Global warming

Question 4. How familiar are you with these terminologies? (For every term choose your level of understanding)

Awareness Terminology

I understand this term very well

I have heard this term but am not very

confident in using it

I have no idea what this term

means

Climate

Climate Change

Global Warming

Greenhouse Effect

Greenhouse Gases

Carbon Dioxide

Emissions

Recycling

Reforestation

Deforestation



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Question 5. Do you consider climate change as an important issue?

1 2 3 4 5

Not Important at all

Extremely Important

Question 6. Do you believe reducing CO2 emissions is important to fight climate change?

1 2 3 4 5

Not Important at all

Extremely Important

Question 7. Who should reduce CO2 emissions in your opinion?

Governments Multinational companies Small & medium-sized companies Individuals All of the above None of the above

Self-Tracking

Question 8. Have you ever heard of Quantified Self or self-tracking devices and services?

Yes

No

Not sure

Question 9. Are you keeping records of or tracking anything that occurs in your life?

Yes (if yes move to next question) No (if no move to question12)

Question 10. How do you keep track of everything you are tracking?

Pen and paper Self-tracking dedicated hardware Mobile software (Apps) Web and Desktop Applications Self-made desktop tools (spreadsheets, …) Other: __________

CARBON FOOTPRINT APP PROTOTYPE

Usefulness and Ease of use

Question 11. How did you find the App in terms of user-friendliness in general?

1 2 3 4 5

Not at all satisfied

Completely satisfied



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Question 12. Overall, how difficult or easy to use did you find this App?

1 2 3 4 5

Very Difficult

Very Easy

Question 13. How do you score your experience about the App’s overall graphical user interface (GUI)? (e.g. look/layout, colors, text fonts and size, etc.)

1 2 3 4 5

Not at all satisfied

Completely satisfied

Question 14. How did you experience using each of the following views presented by the App in terms of usefulness complying with the App’s intention which is to help users keep track of their CO2 emissions so they can take initiatives to keep their emissions low?

Features/Views

1 2 3 4 5

Not at all useful Completely useful

Signup view

Login/logout Views

Home view

CO2 Tracking Views

Achievements View

Achievement’s detail Views

Statistics Views

Activity detail Views

App’s navigational tabs

Betting steps’ Views

Betting status View

Underlying motivations for using the App

Question 15. Would you consider yourself using this App for tracking your CO2 emissions?

Yes, I would like to use this App frequently. No, I’m not interested



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Question 16. What do you think can motivate you to use this App?

I want to use this App

.. to participate in the App’s betting feature to earn money. .. to be able to measure my CO2 emissions .. to experience a new way of tracking my daily activities .. to help reducing global warming effects .. to track my behavioral changes .. to learn more about myself .. because it gives me more control over my actions Other (please specify)

Thank you for your participation!



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11 APPENDIX B: Telerik AppBuilder environments

Screenshot 25 – AppBuilder coding environment

Screenshot 26 – AppBuilder iPhone simulator



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Screenshot 27 – Telerik Backend tools and services



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12 APPENDIX C: Stockholm Royal Sea Port Project

“ To enable the realization of City of Stockholm´s (Sthlm) vision for the Stockholm Royal Seaport (SRS) and

meet the goals of social, economic and environmental sustainability, new collaborative partnerships, new business models and new ICT solutions are required that enable interactive realtime feedback across the

district´s verticals. To define how the that feedback should be made the City is collaborating with Industrial Ecology (IE) in an ongoing R&D project to develop the SRS-Model. The model identifies metrics and

KPI´s to develop the appropriate feedback processes among respective actors (individuals, businesses, facilities, neighborhood). SRS-Model can only be made possible by a new information platform that integrates,

processes, distributes, and visualizes data among the district´s stakeholders.

This application for the Vinnova B-Project Smart City SRS is an R&D project based on a triple helix collaboration between city, industry, and academia to develop an interactive information platform that allows

for dynamic feedback in the new eco-district. The project is implemented in cooperation with other related projects in the SRS such as Smart ICT and the Active House. The driving organizations are the City of

Stockholm, IBM, KTH and Fortum together with building developers and other community stakeholders. In order to achieve real and needed changes in behavior and awareness among residents and workers the Interactive Institute is leading the effort to develop the necessary innovative visualization solutions.

The result is a smart and sustainable community in which decisions can be made by individuals, businesses and community stakeholders in an effective manner, based on easily accessible, relevant realtime data.

“ Project description summary (VINNOVA, 2012)

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