How to Visualize Key Climate Change Question in 5 Interactive Emissions Charts

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If you ever tried search for easy answers for climate change questions, based on data, you will surely found that it’s often complicated.
My background in both climate change, programming and data visualizations makes it easy for me to get quick answer, but I noticed most people get easily lost in the data.
I created this series of visualizations below to provide you some inspiration how you can combine data and interactive charts techniques to provide engaging answers. I agree with visualization guru Mike Bostock that examples are a great way to show the use of technology.

This blog however focuses less on the technical details, but illustrates how you 1) can answer a common or interesting question 2) with an engaging, intuitive and interactive visual 3) and explain why this visual works and 4) how you can build your own.
Most visuals in this blog rely on data of the Climate Watch project, which I manage at the World Resurces Institute. Disclaimer: This blog is written in a personal capacity and does represent only my personal views.

If you have suggestions or questions about those visuals, please leave comments below.

1) How has the climate changed since you were born?

Climate change has not just appeared recently – we have been putting carbon dioxide emissions into the atmosphere for centuries now. The causal chain is pretty simple: We emit greenhouse gases (mostly carbon dioxide, CO2), those increased the concentrations of carbon dioxide in the atmosphere, which in turn has a warming effect on the climate.
To illustrate the point, the visual allows you to hover over your birthday below to see the how emissions, concentrations and global temperatures have changed since that year.

Why this visual?
I hope that making a personal connection will help you understand that climate change is already happening now. The interactivity allows you to select your birth year quickly with just your cursor. Admittedly, the up and down movement of the chart is not necessary, but makes the chart a bit more interesting than just having a standard line chart. Sometimes its a about showing folks something they have not seen before to draw them in.

How is it done?
The visual is build in ObservableHQ and you can fork it or download it.
Note that the chart shows three different data types. That is why it does not have one labeled y-axis. If you hover over a year if will show you the increase for these three variable at the top right. For temperature it is relative to the 1950-1980 average, meaning that that the 1900 we were below the 1950-1980 average and the increase from the 1950-1980 average was 0.92degC, but was 1.1degC compared to the 1900 temperature.

2) Who are the largest global emitters?

The top 10 emitters of greenhouse gases are responsible for about 63% of global emissions. Click on the chart below to zoom into a country and see what the main emitting sector are.


Why this visual?
This Sunburst chart is basically a hierarchical pie chart. A normal pie chart could also do it, but wouldn’t that be kind of boring? The advantage of using this chart is that you can more clearly highlight the 10 emitters as well as provide one more level of granularity – the emitting sectors.
Also note in this chart that these numbers can vary depending on what you group the European Union as one country (like done in this chart) and what sectors you include. For the chart above I am excluding land-use change and forestry because those values can be negative (a carbon sink) and cause problems if you want to visualize percentages. It also excludes bunker fuels, which are international aviation & international shipping and are usually not included in country totals.
Sunburst charts work with hierarchical data that add up to a percentage total. This is particularly useful for emissions, but also also work with other information, like trade products categories or causes of death. Another advantage is that you can even flip the categories, so show the data first by sectors, then by gas, see 4th question.

How is it done?
This sunburst visual is created in and can be cloned there. You can also see a D3 version of this chart here.
You will just need the most granular type of data. So in the chart above we are using a table that contains all sectoral emissions by country and totals are calculated automatically.

3) How have the top emitters changed?

The charts below shows you the trends for the top emitters over the last 50 years. Note that the x-axis has a different scale on each chart, so only shows relative change.


Interestingly, while the top 10 emitters have changed their emissions a lot in the last 100 years, the share of top 10 emitters has stayed mostly the same. This is because while some top 10 emitters have increased their emissions a lot, others have stabilized and at the same time the rest of the world has also increased its share. The message stays the same, the top emitters need all pull together to reduce their emissions. Press play on the chart go back in time from 2016 up to 1900.


Why these visuals?
The first chart is commonly called ‘Small Multiples’ and can show comparative trends of different entities next to each other. The advantage is that you can show data that has different scales without overloading one single chart. Alternatively you could also use an index chart, similarly to the first one in this blog.
The second chart is a Treemap and is actually using the same hierarchical approach as the Sunburst, but also includes a timeline. You can actually use them interchangeably and I just introduced a Treemap here to show an alternative to the Sunburst.

How is it done?
Both charts work on The the small-multiples chart uses time series of the largest emitters and the Treemap combines hierarchical data with time series.
An challenge you will face regularly is to have this data in the right table format, with years also being a column. Manipulating tables by turning rows into columns and vice versa is called “pivot” (I also heard the term “normalize” in the past, but this actually would refer to getting data into a range like 0 to 1). A quick way to pivot data is using Microsoft Excel and its Power Query functions. Of course there is also other, more open software that can do it, just try a Google search.

4) So, who is responsible then?

This question is more difficult than it seems. Not only total emissions matter, but many different factors can be brought into this question. Lets look at this from a couple more perspectives:

First, how their total emissions compare on the same scale. Second, how their per capita (per person) emissions compare. Third, how much commutative greenhouse gases they have emitted since 1850 (adding up each years emissions).


Why these visuals?
This is a common question and you should be careful if someone shows you a visual that tries to cast blame on just one country. The reality is that there are many indicators that can be used to show responsibility or whats ‘fair’. There are whole tools that just aim to visualize all different indicators related to ‘equity in climate change. Its best for you to know that there is not just one simple answer and sometimes highlighting how different perspectives emerge depending data and visualizations can be helpful to provide a nuanced view.

How is it done?
They are three separate visualizations based on and Climate Watch data: Total emissions numbers, per capita numbers, and calculated cumulative numbers.

5) What is driving emissions?

A few key sectors are responsible for an increase in emissions. The energy sector is still the singe largest contributor, with nearly 75% of emissions. Within the energy sector the power sector and transportation make up most of the emission with both still on the rise. Outside of energy, industry and agriculture are also significant sources. Industry has doubled since 2000 to become the second largest emitting sector and agriculture was slowly rising with an 13% since 2000.


Why this visual?
You might notice that the Grouped-Circle visual uses the same hierarchical data we have used before in question 2 and 3. This is to illustrate that data can be used to answer different questions depending how you structure and visualize it. Instead of having a country perspective this chart is using sectors as the first level and gases as second level to show which economic sectors are the main drivers.

How is it done?
If you have prepared your data once you can easily use it for Sunburst, Treemap and Grouped-Circle visualizations. You can clone the the visual on and also change the chart into a Treemap or a Sunburst

Bonus question: Can you show me all this data in one chart?

Sure, we can also utilize Hans Roslings famous bubble chart to show you the different dimensions in one visualization. You can also explore the data further with the Google Public Data Explorer.


I hope this will give you some inspiration to enhance communication of climate change within your organizations. If you have any questions or suggestions, feel free to leave a comment or write me an email.

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