Greenhouse gas (CO2) emissions and oil refining | by Ranganath Venkataraman | Towards Data Science

My previous posts have used Python visualizations to explore oil refining capacity across the United States. Further, we analyzed behavior of crude and fuel prices over the years.
I’m going to explore something else in this article: carbon dioxide (CO2) emissions from oil refining. Source data for all work below is from the EPA’s Greenhouse Gas Reporting Program, whose data is publicly available here. This data is made up of self-reported emissions of CO2-equivalent in metric tons from 5,441 companies in 1,210 industries from 2011 through 2018.
A point of education: CO2 equivalency is a way to measure emissions of any pollutant. For example, 1 ton CO2 equivalent means that amount of actual gas released would warm the earth by same amount as by release of 1 ton CO2.
TL/DR: Power generation generates little more than 62% of the total emitted carbon dioxide to the atmosphere, with refining and chemicals comprising a distant second at 9.6%. Texas, Louisiana and California are key sources — and Illinois features as a key emitter for certain industries as well.
Oil refining brings to the mind’s eye massive structures that belch clouds of smoke and steam. But — just how much CO2 do oil refineries emit compared to other major industries in the United States? And how do these numbers vary across states? Which industry offers the biggest reduction target in terms of current emissions?
Let me preface by stating the well-known point that CO2 emissions are important to understand since CO2 in the atmosphere absorbs radiation and traps heat in the Earth’s atmosphere. The higher the CO2, the more heat is stored in the atmosphere, and the greater the increase in temperatures and general weather disruptions — also known as global warming or climate change.
First, let’s use the code below to generate a bar plot to visualize the top CO2equiv-producing industries.
fig = px.bar(ByIndustry, x='Latest Reported Industry Type (sectors)', y='%')fig.update_xaxes(tickangle=45, tickfont=dict(family='Rockwell', size=14))fig.update_layout(width=1000, height=500, title='% of emissions from various Industries')fig.add_annotation(x=31, y=62.38, font=dict(family="Courier New, monospace", size=18, color="black",),            text="62.38% of emissions from Power Plants")fig.show()
Interesting, power plants are responsible for little more than 62% of total emissions as measured from 2011 through 2018. Refining and chemicals, by contrast, are responsible for a combined 9.1% of emissions — 85% less!
It’s fascinating to observe the role played by various industries in emitting CO2-equiv. How does this vary across states? Take a look at the dynamic choropleth below examining the contributions of different states in 2018 emissions for the industries specified in the list above:
CO2-equiv from power plants was peak in Texas at 185 million tons. Florida and Indiana were distant second place-holders at 84 million and 81 million tons respectively.
How about refineries and chemicals? Texas again leads the way with 38 million tons of CO2eq, with Louisiana running second at 28 million tons of CO2eq — this matches the message conveyed in the choropleth showing January 2019 refinery capacity in my Refinery capacity article.
The industrial gases and minerals businesses provide food for thought. For context, minerals are unearthed as part of the mining business while industrial gases cover the manufacture of commonly-used gases for other businesses e.g. nitrogen, CO2, argon, hydrogen, and helium.
Kentucky emits roughly 1 million tons annually creating industrial gases, while Louisiana and Indiana are close behind emitting slightly more than 700,000 tons per year. Meanwhile, Texas, Missouri, and Illinois are leading emitters in the minerals industry
Bottom-line here is that many states and many industries play a role in emitting CO2 to the atmosphere, with power generation being the industry holding the greatest potential for emissions reduction.
This conclusion holds steady across the years in which greenhouse gas data was collected by the EPA, 2011–2018. As observed in Figure 4 below, no particular year in that time frame changes the narrative above. Indeed, there simply isn’t a significant change in emissions profiles over those 7 years across any industry.
Let’s start shifting tack to consider emissions reductions in refining.
I had previously discussed the economics of oil refining that converts crude oil to gasoline, jet fuel, diesel etc. How exactly does a refinery accomplish this? It does so, through the use of various refining process units, chief among which are: Hydrocrackers, Fluid Catalytic Crackers (FCCs), Reformers, and Delayed Cokers.
That’s a lot of new terminology and we’ll spend the next few articles diving into how machine learning algorithms can help optimize the balance between profitability and emissions for these different units.
As always, I welcome any feedback or questions on this or other articles. The repo to support analysis for this article is here.