The Hertzsprung-Russel (HR) Diagram is a chart that maps star types based on temperature (x-axis; also can be labeled as color/ spectral type) and luminosity (y-axis). It actually accurately charts stars based on stellar evolution and mass. The main descending linear line is called the “main sequence.” These stars are all in the hydrogen-fusing portion of their evolution, which constitutes most of their lifespan. To the top right, you find red giants and supergiants, the next phase in stellar development. To the lower left you also find white dwarfs: essentially “dead” low mass stars. It is a very important astronomical tool used to identify characteristics of stars and their current phase of life.
Photo Credit: Brinkworth and Thomas, University of Leicester
Our professor has challenged us to make our own version of the HR Diagram (let’s call it an MS Diagram for my initials). The only requirement is that it represents some part of the human experience. I must admit, when trying to come up with an idea for my diagram, I was at a loss. I then thought that there might be a correlation between a person’s income and their carbon footprint. This led me to a study conducted by a high school in the Philippines. Using their data, I was able to construct my own plot on their findings. They are as follows:
As you can tell, individuals with higher incomes use a lot more energy and emit more carbon emissions. This is most likely because of their access to private vehicles and technological luxuries those with lower incomes do without. The income a person is born into is their “main sequence.” Throughout one’s life, they may or may not move up or down a financial bracket, though they most likely will stay in the same place. Movement throughout economic classes would be their “evolution” throughout life.
Though not quite as predictable as the HR Diagram, the MS Diagram provides a good basis for estimating a person’s carbon footprint. Of course, many other factors can affect this as well, like upbringing, environmental awareness, etc. There are always exceptions, but in general it is a generally accurate way to think of the correlation based on the data I have found.
