Worldwide Carbon Emission Data Trends

Posted on Dec 9, 2021

Worldwide Carbon Emission Data Trends

The skills the author demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.

Background & Inspiration

As the COP26 climate summit kicked off in Glasgow, news spread worldwide on how nations were gathering together to combat climate change. I enjoyed following the updates on this conference and learning of the ambitious goals that the participating nations laid out. In order to better understand climate change and how each nation is contributing to it, I decided to perform a deeper analysis and examine the current data and landscape of Carbon emissions, how emissions are evolving over time, which solutions are available, and which countries should be looked at as role models.

Data Collection

In order to achieve the above learning objectives, I utilized data from the world bank measuring many different metrics.

First, I started with CO2 emissions, which the world bank provides by country from 1960 through 2018. I then looked for other factors that might affect C02 emissions, which led me to extract GDP data and Population data from 1960 through 2018 from the world bank.

Ultimately, these sources would give me enough to understand the landscape and trends of emissions worldwide. Lastly, I decided to use a final dataset from the world bank that measured the amount of renewable energy each country utilized to analyze how that affected the countryโ€™s overall carbon output.



I started with a very simple graph to understand how emissions are changing over time. This graph measure total CO2 emissions (Kt) globally from 1960 through 2018:

Worldwide Carbon Emission Data Trends

As is clear from the graph above and no surprise to many, CO2 emissions have grown consistently and significantly in each time period from 1960 through 2018. Also no surprise, the graphs for worldwide population and GDP also show similar trends, undoubtedly contributing to the trend of Carbon emissions.

Data in Countries

For this project, I wanted to look more granularly and understand which countries are producing most of the emissions. In order to this, I examined the distribution of countries by their emission output over different time periods:

Worldwide Carbon Emission Data Trends

Starting in 1990, it is clear that most countries are not contributing significantly to emission output. The graph shows that there are a few countries that are disproportionally producing Carbon emissions compared to others. If we change the timeframe to the most recent 2018, we will notice this trend continues:

With the most current timeframe (2018) selected, it is clear that the insights from 1990 still holds true in 2018 - most emissions are coming from only a few countries. However, if you look at the x-axis on both graphs, you will notice that the amount of CO2 emissions increased significantly. This shows us that not only are a minority of the countries producing a majority of CO2 emissions, but the amount of emissions produced by those countries is growing significantly.

In order to better understand the vast difference in emissions by country, I grouped every country into one of 10 segments based on their emission output in 2018. Those in group 10 were of the highest CO2 producing countries while those in group 1 were the least.


As shown above, the top 10% of emission producing countries account for close to 80% of Co2 emissions globally while the next 10% account for nearly 15% of all emissions.

From this chart, it is clear that any solutions to fight combat change must have a focus on the countries that lie within the 9th and 10th buckets. In addition to overall output, we can look at the increase in emissions over the last 20 years (1998-2018):

The graph above shows that not only are the top two buckets producing the most carbon emissions, they are also the countries that are experiencing the largest increase in emission output over the last 20 years.

Ultimately, from the exploratory phase, it is clear that Co2 Emissions show no signs of slowing and are highly concentrated within about 20% of countries globally.

Tools for a Solution

In order to make this project more useful, I have created several visuals to allow users to better understand which countries should be used as models in the fight against climate change and which should focus on improvements. To start, I looked at the use of renewable energy over time globally.

Renewable Energy and CO2 Emission Data

The promising news from this chart is that renewable energy is growing significantly since 1960, specifically from 2010 to 2015. I then decided to look if renewable energy was being pinpointed to the countries that need it the most:

In the chart above, it is clear that the biggest culprits of Co2 Emissions are the same countries that are adopting renewable energy at higher amounts. This is a very promising sign as we are starting to see some positive changes in the right segments of countries.

The last objective of my project was to allow countries to identify a country that they can use as a model in order to reduce their own carbon emissions and set realistic goals.

Of course, it doesn't make sense to compare countries that differ greatly in terms of population and GDP, so I decided to utilize K-Means clustering to create clusters of similar countries. The inputs for the clustering consisted of population, population growth, GDP, GDP growth, and overall Co2 emissions. An example of the output is below:

The above chart shows an example of the clustering output. All countries (or geographical regions) in the output above have been grouped by similar GDP, GDP growth, population, population growth, and CO2 emissions.

After clustering, they are split by whether they have increased CO2 emissions in the last 20 years or have decreased CO2 emissions in the last 20 years.

The ones on the left have decreased CO2 emissions, and since they are similar to other countries within this cluster, the countries on the right (the ones who are increasing CO2 emissions over the last 20 years) should look to others in their cluster as an example.

My hope is that this technique can help facilitate knowledge sharing and brainstorming as countries can look to similar counterparts that are successful in reducing emission output.


Overall, it is exciting to see how many countries are adopting green energy sources and starting to reduce their emission output vs. 20 years ago. That being said, emissions are still growing significantly and are highly concentrated within 20% of countries globally. I am excited to see how the COP26 conference will influence emission output and am looking forward to a greener future.

About Author

Jack Copeland

After graduating from the University of Virginia in 2019 with a degree in Computer Science, I went on to join Anheuser-Busch as a Global Management Trainee. I received cross functional training in sales, marketing, supply and more before...
View all posts by Jack Copeland >

Leave a Comment

No comments found.

View Posts by Categories

Our Recent Popular Posts

View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI