Carbon Dioxide Emissions Are the Primary Cause of Climate Change. Who’s Responsible?
Scientists agree that the primary cause of climate change is the release of carbon dioxide into the atmosphere is a result of human activity. According to (NASA, 2021), the concentration of CO2 in the atmosphere has increased by 47% since the beginning of the industrial revolution. Our goal is to find out which countries are the biggest emitters of CO2, and explore some related factors that can help us to make sense of this big and very important challenge that everyone in the world will be facing in the coming decades. One potentially effective way to convey this information is through data visualization. In (Kosara & Mackinlay, 2013), authors Kosara and Mackinlay discussed how the New York Times was able to walk people through complex issues related to climate change, eventually helping to inform policy decisions during the Copenhagen climate conference through their use of a combination of narrative structure and information visualization. Through this format, we will attempt to answer these questions and provide additional details exploring how CO2 emissions are related to GDP, the average energy usage of a country, and CO2 emissions across a span of many years.
First, we took a look at which countries are the biggest emitters of CO2 overall. We did this with a scatterplot that compares CO2 emissions and energy usage, by country. Energy usage is a useful factor to consider for this first look at this problem because most of the CO2 emissions are the result of burning fossil fuels to produce energy, so we should expect to see a strong association between energy usage and CO2 output as the trend line has a positive correlation coefficient.
Fig.1. Linear correlation between the Average Energy Usage vs. Average CO2 Emissions, largest CO2 Emissions and Energy Usage countries are indicated
As expected, CO2 emissions are strongly associated and positively correlated with energy usage, and all countries are close to the trend line without extreme outliers from Fig.1. What is most striking about this first look at this figure is that China and the United States are far ahead of all other countries in terms of both carbon emissions and CO2 output. While the rest of the countries are clumped together at the bottom left of the graph indicating low, average energy usage and low, average CO2 Emissions. So what proportion of all CO2 emissions are they responsible for? It is very hard to answer this question from the scatterplot, so we put just the CO2 numbers in a pie chart. The data set has close to 200 countries and we do not want to see all of them because that would make the chart hard to interpret, so we found just the five top CO2 producing countries, and then added all of the other countries into their own group (seen in the purple area in Fig.2).
Fig.2. CO2 Emissions distributions in the world, grouped by countries
As seen in the pie chart (Fig.2), the top five CO2 emitting countries are in this order; China, the U.S., Russia, India, and Japan who emit more than half of the world’s CO2. The category of “All other countries” includes the emissions of around 190 other countries (some were omitted because of null values but they were all low-income countries so it is likely that their CO2 emissions were low anyways).
Next, let’s see if these countries are emitting so much CO2 because they really are using up a lot of energy per capita, or if it is simply an effect of population size. To see this, we made a new scatterplot that’s similar to the first one, except that both the CO2 emissions and energy usage are on a per capita basis, instead of being an overall value for the country (By doing this, we simply add two columns: Per Capita CO2 Emissions and Per Capita Energy Usage). Before doing further information visualization, we should first make sure that Per Capita CO2 Emission is not collinear to CO2 Emission so that the visualization would be meaningful.
Fig.3. No Linear Correlation between the Per Capita CO2 Emissions vs. CO2 Emissions
After ensuring that CO2 Emissions Per Capita is not collinear with the CO2 Emissions by coincidence, we can explore more on CO2 Emissions Per Capita with the Energy Usage Per Capita.
Fig.4. A positive linear relationship between the Per Capita Energy Usage and the Per Capita CO2 Emissions
Similarly, there is a positive linear correlation in Fig. 4 between the Per Capita Energy Usage and Per Capita CO2 Emissions. From this figure, we can see that there are two outliers that seem to deviate a lot from the predictive trendline: Iceland and Qatar while most countries stick with the trend line at the left lower corner.
Qatar has the highest per capita CO2 production overall, and it is even higher than one would expect given their level of energy usage.
The other outlier is Iceland, but for the opposite reason: their level of CO2 output is much lower than one would expect given their level of energy usage. There is a good reason for this: Iceland powers its entire electric grid from hydro and geothermal power, which are fully renewable sources that do not involve burning fossil fuels. (Logadóttir, n.d.). As seen and according to (Raineri & Molinari, 2021), policymakers can and should use data visualization like these to support their decision-making. Since they are able to do further exploratory analysis to find closer answers to the issues of climate change. And, policymakers can also effectively use visualizations to present, communicate, and educate their audience.
Because Fig.4 has so many countries to distract us, we also include a second version of it that shows the five biggest CO2 emitters only in Fig.5, so that they stand out from the crowd. Interestingly, when you look at CO2 emissions and energy usage on a per capita basis, the five biggest CO2 emitters do not look very different from many other countries. This shows that there is nothing especially unusual about these five countries, other than that they are both relatively large consumers of energy and producers of CO2 on a per capita basis, and also have large populations.
Fig.5 Correlations between Per Capita Energy Usage vs. Per Capita CO2 Emissions of top Countries of CO2 Emissions
Fig.5 is another visualization that shows just those top 5 CO2 emitting countries from the pie chart since, on the visualization above, these countries overlap too much with other countries to be seen and are not labeled for this reason.
One interesting country to note is the United States. We created visualizations of the total CO2 emissions of each country by the country’s population to illustrate this.
Fig.6. Scatterplot with trendline showing Average Population Total by Average CO2 Emissions
Note that interestingly, the population of the United States is not that much larger than other large countries such as Indonesia and Brazil. However, for example, the U.S. population is 31% larger than the population of Indonesia, yet the United States releases 16 times the CO2 in the air.
Another interesting outlier is India. For example, India has 93% of the CO2 output of Russia, yet India’s population is 8 times larger.
Accordingly, the large energy usage is always correlated with the high CO2 Per Capita Emissions. Despite these two factors, we wonder if there are other factors that might be a candidate for correlation with Energy Usage.
Considering the factors rather than the CO2 Emissions, “is there anything rather than it would be a good predictor of the Energy Usage” would be vital to know as well. Hence, we take “Internet Usage” to be the candidate for that. Fig. 7, we found out that there is no linear correlation between Internet usage and Energy Usage in total. Specifically, we could find two clusters, hence internet usage is not a good indicator of energy usage.
Hence as aforementioned, CO2 emissions would be the optimal indicator of the energy used considering all the factors this dataset includes.
Fig.7. Scatterplot of Internet Usage vs. Energy Usage; 2 clusters were shown above
Given all the visualizations that we have come up with, it would be convincing to conclude that CO2 emissions are highly correlated with the Energy used. As demonstrated in Fig.7, Internet Usage cannot predict the usage of Energy Usage. For further exploration, we could see that the Fig.8 indicates the trend over time, such that the energy usage and CO2 Emissions Per Capita are increasing over time. Hence, we could use this correlation that we found to predict the future usage of energy or CO2 Emissions, which primarily lead to climate change. Thus it is the responsibility of all us humans to predict and take actions against further climate change issues and decrease the emissions of CO2, which lowers energy usage in the meantime. It is also important to understand that while we all have a part in doing what we can to prevent climate change from becoming a significant issue this planet faces, the distribution of who is responsible is not equal; some countries emit disproportionately higher amounts of CO2 than others. Seeing which countries are contributing the most to climate change through their energy usage and CO2 emissions can help with the process of determining who should be held accountable, which can inform policy decisions.
Fig.8. Time-series visualization to show the Energy Usage and CO2 Emission Per Capita trend overtime
References
Kosara, R., & Mackinlay, J. (2013). Storytelling: The Next Step for Visualization. Computer,
46(5), 44–50. https://doi.org/10.1109/mc.2013.36
NASA. (2021, May 10). The Causes of Climate Change. NASA.
https://climate.nasa.gov/causes/.
Logadóttir, Halla Hrund. (n.d.). Iceland’s Sustainable Energy Story: A Model for the World?
United Nations.
https://www.un.org/en/chronicle/article/icelands-sustainable-energy-story-model-world.
Raineri, P., & Molinari, F. (2021). Innovation in Data Visualisation for Public Policy Making.
The Data Shake, 47–59. https://doi.org/10.1007/978-3-030-63693-7_4
