Sustainability Math A Quantitative Literacy and Mathematics Resource for Instructors

A NASA report notes that April 2017 was second-warmest April on record.

April 2017 was the second-warmest April in 137 years of modern record-keeping, according to a monthly analysis of global temperatures by scientists at NASA’s Goddard Institute for Space Studies (GISS) in New York.

Last month (meaning April) was 0.88 degrees Celsius warmer than the mean April temperature from 1951-1980. The two top April temperature anomalies have occurred during the past two years.

If you like the cool map here that was in the NASA report you can make your own for various time period at Global Maps from GHCN v3 Data. This is a great app and can be used for discussions about means vs distributions in stats or QL classes, for example. The article also has links to data sources.

A recent EPI report notes: Straight out of college, women make about $3 less per hour than men.

Right out of college, young men are paid more than their women peers—which is surprising given that these recent graduates have the same amount of education and a limited amount of time to gain differential experience.

What may be worth exploring is the historic difference in starting pay between women and men, which you can do since the data is available and can easily be placed into Excel.

Project MIDAS recently posted Larsen C takes another step towards calving.

In the largest jump since January, the rift in the Larsen C Ice Shelf has grown an additional 17 km (11 miles) between May 25 and May 31 2017. This has moved the rift tip to within 13 km (8 miles) of breaking all the way through to the ice front, producing one of the largest ever recorded icebergs. The rift tip appears also to have turned significantly towards the ice front, indicating that the time of calving is probably very close.

STEM folks are presented with challenging problems modeling and predicting changes in ice and glaciers due to climate change.

A recent Economic Policy Institute report, “Competitive” distractions – Cutting corporate tax rates will not create jobs or boost incomes for the vast majority of American families, provides some useful data and charts. For example, the graph here compares changes in productivity and hourly compensation (Data are for average hourly compensation of production/nonsupervisory workers in the private sector and net productivity of the total economy. “Net productivity” is the growth of output of goods and services minus depreciation per hour worked.) You can download the data which can be used for linear regression.  There are other graphs in the article with data that can also be used.

Beyond that the article is rich with quantitative information that can be used in a QL based course.  For example, there is a lengthy discussion on the statutory and effective tax rates of corporations and how the U.S. compares to the rest of the world. The conclusion:

We find their central argument—that U.S. corporations face high corporate taxes—to be empirically false. While U.S. statutory tax rates are higher, the effective tax rate paid by corporations is in fact roughly equivalent to the effective tax rates of our peer countries, due to loopholes in the U.S. tax code.

If you are looking for data on wealth and income inequality visit the World Wealth and Income Database. You can create graphs and download the data. For example, the graph here is pre-tax share of income for the top 1% (20.2% in 2014) and bottom 50% (12.6% in 2014) of adults in the U.S. The trends since 1980 are roughly linear and so the data, which you can download in a number of formats, can be used for regression. Once you have the lines, they can be used in other places in the curriculum. Other categories exist including wealth instead of income and groups such as the top 10% or middle 40%.

The energy flow diagram here is from the EIA and represents 2016 petroleum use in millions of barrels per day. For example, the U.S. used 13.88 million barrels of petroleum per day for transportation in 2016. The EIA energy flow diagrams (found on the right sidebar) are excellent for use in the classroom and they have recently been updated with 2016 data. They produce flow diagrams for total energy, petroleum, natural gas, coal, and electricity as well as a sources and sectors chart. They keep an archive of their charts dating back to 1996.

Thanks to the folks at the NASA Scientific Visualization Studio for this visualization of Annual Arctic Sea Ice Minimum 1979-2015 with Area Graph (click on the visual to play). Arctic ice data is available in the calculus and statistics sections. A recent Economist article The thawing Arctic threatens an environmental catastrophe adds some context.

The Arctic has been warming at twice the rate of the rest of the world for decades because of feedback loops that have reduced the albedo effect, a measure of the way Earth reflects heat. Unlike the rest of the planet the polar regions release more heat into space than they absorb, in effect cooling the planet, because sunlight is reflected by ice and snow. When it is replaced by water or dark ground, more heat is retained. That is precisely what is happening in the Arctic’s defrosting landscape.

This blog has already noted a the feedback loop from melting permafrost, Methane Bubbles – A Feedback Loop. A recent BBC article, There Are Diseases Hidden In Ice And They Are Waking Up – Long-dormant bacteria and viruses, trapped in ice and permafrost for centuries, are reviving as Earth’s climate warms, is a well referenced article about the possible consequences of melting permafrost (picture from the article with caption: Bacteria have been found dormant in Antarctic ice (Credit: Colin Harris/Era Images/Alamy)).

“Permafrost is a very good preserver of microbes and viruses, because it is cold, there is no oxygen, and it is dark,” says evolutionary biologist Jean-Michel Claverie at Aix-Marseille University in France. “Pathogenic viruses that can infect humans or animals might be preserved in old permafrost layers, including some that have caused global epidemics in the past.”

If you are incorporating climate change issues in the classroom then the article provides for excellent classroom discussions with interesting science. The article concludes:

How much should we be concerned about all this?

One argument is that the risk from permafrost pathogens is inherently unknowable, so they should not overtly concern us. Instead, we should focus on more established threats from climate change. For instance, as Earth warms northern countries will become more susceptible to outbreaks of “southern” diseases like malaria, cholera and dengue fever, as these pathogens thrive at warmer temperatures.

The alternative perspective is that we should not ignore risks just because we cannot quantify them.

“Following our work and that of others, there is now a non-zero probability that pathogenic microbes could be revived, and infect us,” says Claverie. “How likely that is is not known, but it’s a possibility. It could be bacteria that are curable with antibiotics, or resistant bacteria, or a virus. If the pathogen hasn’t been in contact with humans for a long time, then our immune system would not be prepared. So yes, that could be dangerous.”

It would be nice if these articles discussed the populations most at risk. It is often the case that those of lower socioeconomic status are more vulnerable.  Many issues related to climate change have both a social justice component, as well as an ethical question related to the fact that those that contribute little to climate change are often impacted disproportionately.

A few posts ago I posted about Vital Signs of the Planet from NASA as place for graphs and data. I also recently noted the NASA Scientific Visualization Studio. Putting the two together here is a visualization of changes of Greenland Ice (click anywhere to play) and again you can get the data from NASA’s Vital Signs of the Planet.

Stats classes are always looking for interesting data. One place to look in YouGov. For example, they did a poll (Note: it is not clear how the sample was obtained but they do provide a sample size.) asking people if life is fair.  Here are the results by gender.

1. Do you think life is fair or not fair?

You are set for a statistical test comparing Male vs Female perception of life being fair or not. This now allows for a discussion of why women would respond differently than men. One extra bonus on the site is you can look at the same questions broken down by other categories including income. Go to the YouGov Results page to see the data they have.