CarbonViz is a web-based data visualization for monitoring CO2 Emissions, Emissions Per Capita and Emissions Per Dollar of GDP from year 1990 to 2011 and to provide its user with a comprehensive understanding of the change occurred across the globe.

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CarbonViz – A Web-Based Data Visualization for Carbon Dioxide Emission
ABSTRACT
Carbon Dioxide (CO2
), among other greenhouse gases [1], is responsible for keeping the
Earth’s surface warm [2] by allowing sunlight to pass through the atmosphere freely. Various
human activities are responsible for increasing the concentration of CO2
in the atmosphere
upsetting the natural balance. Therefore, monitoring carbon dioxide emission is crucial to reduce
emission level and to slow climate change. This paper aims to discuss benefits of visualizing
carbon dioxide emission and propose CarbonViz, a web-based data visualization for monitoring
CO2
Emissions, Emissions Per Capita and Emissions Per Dollar of GDP from year 1990 to 2011
and to provide its user with a comprehensive understanding of the change occurred across the
globe.
1. INTRODUCTION
In the last two decades, we saw a significant increase in greenhouse gas emissions. More
specifically, the carbon dioxide emissions by human-produced sources are increasing. Carbon
dioxide emission sources are both natural and human-made, and the contribution of sources
produced by humans is far less than the natural emissions. However, human-produced sources
disrupt the natural balance of the climate. To make it worse, the rate of emissions is growing along
with world economy. Therefore, visualizing data of carbon dioxide emissions is crucial as it will
provide a clear picture of which country and continent have the higher CO2
emission rate. It will
also help in studying the rate of increment and factors affecting it.
Studying just the trends of a country’s total carbon emissions does not give an idea of their
role in global warming. For that, we need to resort to more detailed measurement. Carbon

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emissions per capita (per person) serves that purpose. For example, while looking at the trend of
total carbon emission of China and US, it seems that China overtook the US in 2007. However, it
gives the idea that China is fast-growing country. On the other hand, while looking at trend of
carbon emission per capita, one can see that an average person in the US is responsible for 19
metric tonnes per capita compared to China’s five metric tonnes per capita. Examining carbon
dioxide emission per capita gives an idea of the difference between responsibility for climate
change of developed countries and that of developing countries [3]. CarbonViz includes
visualization of both total carbon dioxide emission and carbon dioxide emission per capita for
providing comparison between the responsibilities of countries in climate change.
From the time of the Industrial Revolution, the number of human-made sources of carbon
dioxide emissions is increasingly rapidly. Of all these sources, 87 percent of emissions come from
burning coal, natural gas and oil[3].
Fig. 1 The global carbon budget 1959-2011 [4].
Both energy consumption and carbon dioxide emissions are rising as fast as GDP [5]. Therefore,
it is interesting to see the trend of carbon dioxide emissions per dollar of GDP across the globe.
For instance, one of the biggest industrial economies in Asia, Japan, showed lowest carbon dioxide
emissions per dollar of GDP. It is because Japanese industries are highly regulated and
environmentally conscious. On the other hand, countries with less or regulation on environmental

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laws for industry shows higher output. CarbonViz includes the visualization of carbon dioxide
emissions per dollar of GDP by plotting the data on the map and providing a definite trend of
country’s data by year.
The first objective of this paper is to discuss the importance of visualization for monitoring
CO2 Emissions, Emissions Per Capita and Emissions Per Dollar of GDP and propose a web-based
data visualization (CarbonViz). Section two presents the system design and methodology used for
creating CarbonViz and also discuss some of the challenges faced during its development. Section
three of this paper will provide instructions for users to efficiently load and operate CarbonViz
using its control panel. Finally, this paper will conclude in the fourth section.
2. SYSTEM DESIGN & METHODOLOGY
Fig. 2 Map View of CarbonViz.
2.1 Technical Specifications:
CarbonViz is developed entirely using JavaScript that adopts HyperText Markup Language
(HTML5) for handling rendering behavior of graphical elements. The styling of graphical elements
is done using Cascaded Style Sheet (CSS3). D3.js [6], a JavaScript library, is used for manipulating

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Document Object Model (DOM) [7] based on data. A third-party jQuery plugin – One Page Scroll
[8] - is used for smooth navigation between different graphs.
2.2 Datasets:
For implementing CarbonViz, datasets were retrieved from Millennium Development
Goals Database [9]. These datasets are provided by Carbon Dioxide Information Analysis Center
[10]. The datasets used are as follows:
• Carbon dioxide emissions (CO2), thousand metric tons of CO2 (CDIAC) [11]
• Carbon dioxide emissions (CO2), metric tons of CO2 per capita (CDIAC) [12]
• Carbon dioxide emissions (CO2), kg CO2 per $1 GDP (PPP) (CDIAC) [13]
The data available in these datasets are from year 1990 to 2011. Furthermore, the data for some of
the small countries are unavailable. However, the available data still provides the user with a
comprehensive understanding of the changes occurred across the globe in 21 years.
For better optimization, the data from these datasets were extracted, and is compiled into a single
comma-separated values (CSV) file.
Fig. 3 A sample of Compiled Dataset.
2.3 Choropleth Map

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The choropleth map (Fig. 2) is created using d3-geo and TopoJSON [14] for geospatial
mapping data. TopoJSON removes redundant geometries and efficiently stores them in a single
file. For this project, world atlas TopoJSON is used [15]. It contains geometries of countries and
continents. TopoJSON file is loaded on the using d3.json which will add topology object that
represents boundaries of the countries and continents in the world. Although TopoJSON stores
topological data efficiently, for displaying the map we need to convert to GeoJSON. In world atlas
TopoJSON, the Admin-0 contains countries boundaries as feature collection [14]. This feature
array is extracted to create path element for each feature. An on-click method is applied to each
path element for selecting country. It is used for interaction with the bar graph for visualizing the
trend. Constituent countries are colored after scaling the data and then mapping it to a range of
colors. These colors are dependent on the selected dataset, selected year and emission data
available. As soon as the webpage is loaded, the map is drawn, and colors are assigned based on
the selected dataset. The map is redrawn every time the user changes the dataset or the current year
from the control panel. One of the significant challenges faced while drawing the map was scaling
the data for different datasets and assigning colors. The color change is not very significant for
some countries as there wasn’t a significant change in data from 1990 to 2011. However, this was
rectified to some extent by choosing the approximate values for scaling.
2.4 Bar Graph
The bar graph (Fig. 4) interacts closely with the map. Initially, no data is loaded in the bar
graph as the country parameter is empty on page load. Once the country is selected on the map, it
checks the current country and passes the name of the country to the bar graph function. The bar
is redrawn every time the country selected. Furthermore, the corresponding bar is highlighted when
the user changes year using timeline slider but x-axis of the bar graph stays static. The y-axis

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changes based on the dataset and selected country. Bars are drawn using an on click event handler
inside the map function which allows the user to click on the map.
Fig. 4 Bar Graph View of CarbonViz.
2.5 Pie Chart
The pie chart (Fig. 5) only shows data of total carbon dioxide emissions. It is drawn using
d3’s arc and pie generators. The path for drawing arc is sorted by continent first and then by
emissions. The data on the pie chart changes with year using the time line slider. The pie chart is
colored based on the five continents available in the dataset.
2.6 Summary Views
The summary views (Fig. 6) placed on the right of the active graph is a duplicate of two
other graph’s SVG container. All the interactions associated with either of the three main graphs
work in their respective summary view. The tooltips are disabled on summary views because the
sole purpose of a summary view is to provide the user with the brief idea of changes in trends.
However, because of disabling tooltip, an error occurs in the console if the user tries to hover over
the summary view. It does not affect the usability of the system.

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Fig 5. Pie Chart View of CarbonViz.
Fig. 6 Summary View of CarbonViz.
3. USER INSTRUCTIONS
CarbonViz must be loaded on HTTP server or local web server to run it properly. It is
mainly because a web server is needed to skirt security restrictions loading data out of the local
file system [16]. After CarbonViz is loaded, the user will see a webpage as shown in Fig. 1. A
control panel (Fig. 7) is provided on the left side of the graph which consists of dataset selector
and timeline slider. Dataset selector can be used to switch between CO2 Emission, CO2
Emissions Per Capita and CO2 Emissions Per Dollar of GDP. The timeline slider can be used to
traverse from year 1990 to 2011. There are two indicators on the control panel for showing
current year and selected country. The user can click on the map to select the country and study

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its trends on a bar graph. The selected country’s boundary is highlighted on the map. If the
country is selected and the dataset is changed, the user will need to click on the country again to
see the trend on the bar graph.
Fig. 7 Control Panels and Indicators.
To change the primary graph user can either use UP – DOWN arrows keys on their keyboard or
the scroll UP – DOWN mouse button. There is a page indicator on the right of the summary view
which can also be used to change the graph. The user can hover over any of the three graphs to see
the detailed information via the tooltip. Additionally, the tooltip on pie chart also includes the
percentage of total emission of countries.
4. CONCLUSION
In the last two decades, the number of human-produced sources of carbon emissions has
increased significantly. It makes every human being for global warming and sudden climate
change. Therefore, it is necessary to visualize the current and past data. It will help people in
understanding the trend of emissions. Furthermore, it will also help governments of various
countries to establish regulations based on the visualized data. The data visualization proposed in
this paper is one of the examples of how carbon dioxide emission data can be visualized. It provides
the user with significant information that can be used to reduce carbon emissions.

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