How Clean is Your State's Energy?

This project follows the Scientific Method. Review the steps before you begin.

Setting Up the Google Colab Environment

1. You will need a Google account. If you do not have one, make one when prompted. 

2. Download the emissions.ipynb file from Science Buddies. This is the code you will need to process your data.

3. Download the plant_data_combined.csv file. This file contains EPA eGRID power plant data for the years 2018-2023. To use data from more years, visit the EPA eGRID website and download the data files that you want to include. 

4. Within your Google Drive, click on ‘MyDrive,’ then create a new folder and rename it to egrid_emissions. In this new folder, upload the emissions.ipynb and plant_data_combined.csv files. 

   1. Read the Troubleshooting Tips and How to Use This Notebook sections. Follow the instructions you find in that section. 

   2. Run the block under Importing Libraries to ensure you have access to all the functions we will use for this project. 

Loading the Data into a Pandas DataFrame

1. (Code Block 1A) This code block creates a DataFrame, which is essentially a data table, to load and manipulate data in the notebook. The data from your plant_data_combined.csv file will populate a table below the code block. Run this code block. 

Visualizing the Data

1. (Code Block 2A) This code block creates an interactive map of the United States showing the locations of power plants that use various energy sources, including oil, hydro, coal, natural gas, biomass, nuclear, geothermal, wind, and solar. You can zoom in on the map and hover over each dot to see the name of the power plant. Under the #TODO comment, you can change the year for which you are viewing the data. 

   1. As you explore the map, look for patterns across the country. 

      1. Do some regions appear to have more renewable energy sources, such as wind, solar, or hydro?

      2. Do other regions seem to rely more on fossil fuels, such as coal, oil, or natural gas? 

      3. How do you think geography, natural resources, and local climate affect where different types of power plants are built?

   2. Next, zoom in on your state, or another state that interests you. Based on the map, what appears to be the most common type of power plant in that state? Why do you think that is?

2. (Code Block 2B) This code block creates an interactive map of the United States showing power plant emissions for one pollutant at a time: CO₂, SO₂, or NOₓ. Under the #TODO comment, change the emission_type variable to choose which emissions map you want to view. Type the pollutant name exactly as shown: “CO2”, “SO2”, or “NOx”. You can also change the year for which you want to view the data. 

   1. Explore the map for each pollutant. Where are the most emissions located? Are they spread evenly across the country, or are there certain “hot spots” with higher emissions?

   2. Compare the maps for CO₂, SO₂, or NOₓ. Do the same power plants or regions have high emissions for all three pollutants, or do the patterns look different? What might explain these similarities or differences? Think about the types of energy sources used by different power plants and how each fuel type affects the amount and type of pollution released. 

   3. Finally, zoom in on your state or another state of interest. Which areas or power plants appear to have the highest emissions? How does this compare with the fuel types you observed in Code Block 2A?

3. (Code Block 2C) This code block creates a heat map comparing average annual emissions for power plants grouped by primary fuel type. 

   1. Which energy source has the highest average emissions for CO₂, SO₂, and NOₓ?

   2. What patterns do you notice when comparing emissions from different fuel categories?

   3. Are there any results that surprise you? What might explain those results?

Comparing State to National Data

1. (Code Block 3A) This code block defines a helper function that compares the main fuel sources used by power plants in a U.S. state to those used nationwide. Under the #TODO comment, enter the two-letter state abbreviation of the state you are interested in, and the function will print a detailed comparison.

   1. What are the top three fuel sources used by power plants for the state you chose?

   2. How is your state’s fuel mix different from the nationwide fuel mix?

   3. Which fuel source in your state is much higher than the nationwide average? Which one is much lower?

   4. Based on your state’s fuel sources, would you expect its emissions to be higher or lower than the national average per capita? Why?

   5. What might explain why your state uses certain fuel sources more than others? 

2. (Code Block 3B) This code block defines a helper function that calculates the contribution of each state to total U.S. power plant emissions for each pollutant. Under the #TODO comment, enter the two-letter abbreviation for the state you are interested in, as well as the year for which you want to view the data. The function will print your state’s annual emissions for CO₂, SO₂, and NOₓ emissions, the nationwide annual emissions for each pollutant, and the percentage of the nationwide total that comes from your state for the year you have chosen. This percentage is calculated using total emissions, not emissions per person, per power plant, or per megawatt-hour of electricity generated. 

   1. For example, if your state’s CO₂ emissions are listed as 5% of the nationwide total, that means power plants in your state produced 5% of all U.S. power plant CO₂ emissions in that year. A small-population state may still have a small percentage of nationwide emissions even if most of its electricity comes from fossil fuels, because the percentage depends on the state’s total emissions compared to the whole country. 

   2. After you run the code, answer these questions:

      1. What percentage of the nationwide total does your state contribute to CO₂, SO₂, and NOₓ emissions?

      2. For your state, which pollutant has the highest annual emissions in tons?

      3. Is your state’s percentage of the nationwide total about the same for CO₂, SO₂, and NOₓ, or does your state contribute to a larger share of some pollutants than others? Use evidence from the output to support your answer. 

      4. Why might this percentage not tell the full story about how “clean” or “dirty” a state’s electricity is? In your answer, consider factors such as state population, total electricity generated, emissions per megawatt-hour, and whether your state import or exports energy to other states. 

3. (Code Block 3C) This code block creates two stacked bar graphs showing the annual fuel mix by nameplate capacity from 2018 to 2023. Under the #TODO comment, enter the two-letter abbreviation for the state you are interested in. 

   1. The first graph shows the percentage of your state’s total power plant capacity that comes from each fuel category each year. The second graph shows the same information for the entire United States. Each bar adds up to 100%, so the graph shows the relative mix of fuel sources, not the total amount of electricity generated. 

   2. Nameplate capacity is the maximum amount of electricity a power plant could produce under ideal conditions. This is different from actual electricity generation, because some power plants do not run all the time. For example, wind and solar power plants depend on weather and daylight, while fossil fuel or nuclear plants may be able to operate more consistently. 

   3. After you run the code, answer these questions:

      1. How has your state’s fuel mix changed from 2018 to 2023? Identify at least one fuel category that increased and one that decreased. 

      2. How has the nationwide fuel mix changed from 2018 to 2023?

      3. Why might nameplate capacity not tell the full story about how much electricity each fuel source actually produces? Consider that some energy sources, such as wind and solar, may not operate at full capacity at all times. 

Predict Future Emissions and Fuel Mix

1. (Code Block 4A) This code block uses a linear regression model to estimate future emission trends for CO₂, SO₂, and NOₓ. Under the #TODO comment, enter the two-letter abbreviation for the state you are interested in. 

   1. The first graph shows historical emissions data for your state from 2018 to 2023, followed by predicted emissions for the next five years. The second graph shows the same information for the entire United States. The solid lines show the historical data, and the dashed line shows the model’s predictions. 

   2. Linear regression is a model that finds a general trend in past data and extends it into the future. These predictions are only estimates of what could happen if emissions continue changing at about the same rate as they did from 2018 to 2023. The model does not account for future changes, such as new power plants, power plant closures, changes in electricity demand, new laws, or major shifts toward renewable energy. 

   3. These graphs show total annual emissions in tons. They are not normalized by population, number of power plants, or amount of electricity generated. 

   4. After you run the code, answer these questions:

      1. For your state, which pollutant is predicted to have the highest emissions in the next five years? 

      2. Are your state’s CO₂, SO₂, and NOₓ emissions predicted to increase, decrease, or stay about the same? Use evidence from the dashed prediction to support your answer. 

      3. How do your state’s predicted trends compare to the nationwide predicted trends? Are the same pollutants increasing or decreasing in both graphs? 

      4. Which pollutant shows the biggest change over time in your state? Is that a change, an increase, or a decrease?

      5. Why should you be careful about treating these predictions as definite future outcomes? Remember that the linear regression model only estimates what might happen if emissions continue to change at the same rate as in the historical data. 

2. (Code Block 4B) This code block also uses a linear regression model to estimate future trends in fuel mix by nameplate capacity. Under the #TODO comment, enter the two-letter abbreviation for the state you are interested in. 

   1. The first graph shows the historical fuel mix data for your state from 2018 to 2023, followed by predicted fuel mix percentages for the next five years. The second graph shows the same information for the entire United States. The solid lines show the historical data, and the dashed lines show the model’s predictions. 

   2. Again, remember that linear regression examines the general trend in historical data and extends it into the future. These predictions are only estimates of what could happen if each fuel category continues to change at roughly the same rate as it did from 2018 to 2023. The model does not know about future changes such as new power plants, power plant retirements, changes in electricity demand, energy policies, fuel prices, or major shifts toward renewable energy. 

   3. These graphs show each fuel category as a percentage of total nameplate capacity, not actual electricity generation. This means the graph shows the mix of installed power plant capacity, not how much electricity each fuel source actually produced. 

   4. Because each fuel category is predicted separately, the percentages may not add up to exactly 100%. The predictions should be used to identify general trends, not exact future values. 

   5. After you run the code, answer these questions:

      1. For your state, which fuel category is predicted to make up the largest percentage of nameplate capacity five years in the future?

      2. Which fuel categories in your state are predicted to increase? Which are predicted to decrease?

      3. How do your state’s predicted fuel mix trends compare to the nationwide predicted trends? Are the same fuel categories increasing or decreasing?

3. (Code Block 4C) This code block combines individual fuel categories into three broader groups: fossil fuels, renewable energy, and nuclear energy. It then uses a linear regression model to estimate how these three groups may change over the next five years. Under the #TODO comment, enter the two-letter abbreviation for the state you are interested in. 

   1. The first graph shows your state’s historical and predicted energy mix by nameplate capacity. The second graph shows the same comparison for the entire United States. Solid lines show the historical data from 2018 to 2023, and dashed lines show the predicted trend from 2024 to 2028. 

   2. After you run the code, answer these questions:

      1. In your state, which group makes up the largest percentage of nameplate capacity in the most recent historical year shown: fossil fuels, renewable energy, or nuclear energy?

      2. According to the prediction, is the fossil fuel percentage in your state expected to increase, decrease, or stay about the same over the next five years? What about renewable energy?

      3. How does your state’s trend compare with the nationwide trend? Are fossil fuels and renewable energy changing in the same direction in both graphs?