Robbie Esposito
Carson Wolf
The Relationship between Energy and Greenhouse Gas Emissions
Hypothesis
Our main objective in this experiment was to establish connections between greenhouse
gas emissions and energy prices and production in the US. We believed that as energy prices
decrease in a given state, more energy would be produced, which would then cause
greenhouse gas emissions in that state to rise. The parts of this hypothesis are summed up as
follows:
1. Energy price has a linear negative correlation to energy production per capita.
2. Energy production per capita has a linear positive correlation to greenhouse gas
emissions per capita.
3. Energy price has a linear negative correlation to greenhouse gas emissions per capita.
In addition to our main hypothesis, we also wanted to determine the connection between
different energy sources and greenhouse gas emissions per capita. We believed that if a high
percent of energy produced in a state comes from fossil fuels, that state will produce more
greenhouse gas emissions per capita. Conversely, if a high percent of energy comes from
renewable sources, emissions will decrease. The parts of this hypothesis are summed up as
follows:
1. Percentage of energy production from coal has a positive linear correlation to
greenhouse gas emissions per capita.
2. Percentage of energy production from renewable energy sources has a negative linear
correlation to greenhouse gas emissions per capita.
Methodology
In our experiment, we collected several kinds of data in order to find relationships
between variables. Our greenhouse gas emissions data is from the World Resources Institute,
and our energy data is from the Energy Information Administration. We believe these sources to
be reliable and unbiased. The World Resources Institute is a global research organization that
specializes in natural resource management. [4] The Energy Information Administration is a US
federal agency that collects and analyzes data related to energy. [5] Both of these organizations
have been releasing public data for many years. We decided to only look at data from 2009, the
most recent year that appeared in all of our data sets, because we did not believe time to be a
relevant variable in our experiment. Our data sources are listed in the Works Cited section at
the end of this report.
For each variable we considered, we extracted the 2009 data value from every state in
the US, as well as Washington, D.C. This resulted in a set of fifty-one values for each variable.
To to search for relationships between two variables, we plotted all fifty-one points with one
variable on each axis. We then applied a linear fit to the plot. This gave us a correlation
coefficient R, which indicates how well the two variables correlate to one another, and a
coefficient of determination R2, which indicates how well the data fits the statistical linear model.
These coefficients effectively allowed us to determine whether any two variables have a linear
correlation.
The three main variables that we considered for our experiment are greenhouse gas
emissions, energy production, and energy price. We measured emissions as per capita
emissions from the energy sector, measured in metric tons of CO2 equivalent. We decided to
measure emissions per capita to account for the vast differences in population sizes between
states, since states with more people will naturally produce more emissions on average. Our
data allowed us to filter emissions by sector (energy, industrial processes, agriculture, and
waste), so we filtered by energy in order to focus on the relationship between energy and
emissions.
The second main variable that we considered is energy production. We measured
energy production as the amount of energy produced per capita, measured in Btu. Our data
allowed us to filter by energy type, and we used the energy production data for coal and
renewable sources to test our secondary hypothesis. More specifically, we used this data to
calculate for each state a percentage of energy production derived from both coal and
renewable sources. This was achieved by dividing the production for each energy source by the
total production. Finally, the third main variable that we considered is energy price, measured in
US dollars per million Btu.
Results
The table below compares our results to our original hypotheses:
Emissions
per capita
Energy price
Energy
production per
capita
Energy price
% Energy from
Coal
% Energy
Renewable
R = 0.8741
R2 = 0.7641
strong positive
R = -0.4929
R2 = 0.243
moderate negative
R = 0.5061
R2 = 0.2561
moderate positive
R = -0.3991
R2 = 0.1593
weak negative
hypothesis:
positive
hypothesis:
negative
hypothesis:
positive
hypothesis:
negative
R = -0.2496
R2 = 0.0623
weak negative
hypothesis:
negative
Conclusions
From this data we can make a conclusion for each of our hypotheses:
1. Energy price has a linear negative correlation to energy production per capita.
There is a weak correlation between energy price and the amount of energy produced per
capita so we can conclude that there is little to no relationship between energy production per
capita and energy price.
2. Energy production per capita has a linear positive correlation to greenhouse gas
emissions per capita.
There is a strong correlation between energy produced per capita and greenhouse gas
emissions. We can conclude that there is a positive relationship between energy production and
greenhouse gas emissions. for every million Btu produced, 7.4 kg of CO2 equivalent is emitted.
3. Energy price has a linear negative correlation to greenhouse gas emissions per
capita.
There is a moderate correlation between greenhouse gas emission per capita and energy price.
we can conclude that energy price seems to have some impact on greenhouse gas emissions
per capita but not for our initial guess since energy price does not impact energy production per
capita.
4. Percentage of energy production from coal has a positive linear correlation to
greenhouse gas emissions per capita.
There is a moderate correlation between the percentage of coal as an energy source and the
amount of greenhouse gas emission per capita. We can conclude from this that there is some
positive relationship. This is to be as expected since there are still other energy sources that
emit varying amount of greenhouse gases.
5. Percentage of energy production from renewable energy sources has a negative
linear correlation to greenhouse gas emissions per capita.
There is a moderate correlation between the percentage of renewable resources as an energy
source and the amount of greenhouse gas emission per capita. We can conclude from this that
there is some negative relationship. This is to be as expected since there are a large amount of
renewable energy source some of which still emit greenhouse gases.
Discussion
Our results proved three of our hypotheses and provided no conclusive results for the
other two. Our experiment, however, was not perfect, and there are several assumptions that
we had to make. First, we assumed that using data from only one year would be sufficient
because the data set provided a large sample size through the inclusion of data from every
state in the US. Collecting data from more years would have improved the overall reliability of
our results slightly because our results would not be influenced by any factors specific to 2009.
We believe that our decision is unlikely to make a significant difference in the results, but this is
an assumption we cannot prove.
One of the most significant flaws with our experiment is that we operated under the
assumption that energy produced in a state would often be consumed within that same state.
Ideally, instead of using energy production per capita as a variable, we would have liked
to use energy production per consumer of that state's energy. The flaw with “per capita in a
state” is that some states like Wyoming fulfill the energy needs of not just the people in the state
but a large portion of the state around it. Wyoming seemed to be the biggest outlier in our data
since it produces 12% of the United State’s energy but contains only .2% of it’s population.
The topic of our project has social significance because it is important to limit
greenhouse gas emissions to prevent the harmful effects of climate change. We believe that
any factors that contribute to greenhouse gas emissions, including energy prices, energy
production, and energy sources, should be monitored and understood. Our results indicate that
greenhouse gas emissions correlate with low energy prices, high energy production, and a high
percentage of energy created from coal. More surprisingly, our results do not indicate that the
reason that low energy prices result in more emissions is because they lead to an increase in
energy production. If our results are accurate, the causes behind the correlation between low
energy prices and emissions warrants further study.
Works Cited
Data Sources
[1] Per capita greenhouse gas emissions - Energy. World Resources Institute.
http://bit.ly/1RjiTKA.
[2] Energy prices. Energy Information Administration. http://bit.ly/220tlu0.
[3] Energy production. Energy Information Administration. http://bit.ly/1RMmtMr.
Other Information Sources
[4] World Resources Institute. http://www.wri.org/our-work.
[5] Energy Information Administration. https://www.eia.gov/.
[6] US Census Bureau. https://www.census.gov/popest/data/historical/2000s/vintage_2009/.