Carbon Emissions by Country

Inspiration

What it does

Our project looks at which countries have the highest and lowest aviation emissions, per OCED data, and puts insights into the context of emissions trading frameworks and other significant policies.

How we built it

We used R and RStudio for data analysis. We used essential packages like tidyverse, ggplot2 and janitor. In our code, we designated where we used ChatGPT to write certain lines more efficiently.

Challenges we ran into

• We do not have access to data on countries’ designated carbon credits, so we cannot understand if there’s a ratio between population or economy and carbon credits
• Data was not continuous or incomplete for several smaller countries like Somalia, North Macedonia or Mali, which could render “averages” employed to make easier comparisons useless
• We opted to use “all flights” (passenger and freight) and not select for emissions type, but we are not experts with environmental data and that may have inadvertently shaped our analysis
• Choices to select data by “quarterly” or “annually” were subjective and not based on expert reasoning
• With the heatmap, when we joined our data with the "world" sf, we struggled with matching up the differing names for "China"

Accomplishments that we're proud of

First datathon! We had no clue what we were doing.

What we learned

That you should never solely take what the data says at face value. Probably think about it a little bit.

What's next for Carbon Emissions by Country

No clue :)

Built With

• ggplot
• r