Sustainability Math A Quantitative Literacy and Mathematics Resource for Instructors

The chart here comes from using FRED (Federal Reserve Economic Data). Since at least the 1970s Hispanic or Latino (using FRED terms) unemployment was consistently between Black or African American and White and more recently slightly closer to White unemployment. For possibly the first time since the 1970s Hispanic or Latino unemployment (18.9%) exceeded Black or African American (16.7%) in April 2020. The Feb 2020 to April 2020 increase in unemployment for the four groups in the chart are (in order from smallest to largest) Black or African American (10.9%), White (11.1%), Asian* (11.8%), Hispanic or Latino (14.5%). It would seem that by both the total increase and the magnitude of unemployment that the Hispanic or Latino population was hit hardest by Covid-19. Moving to May, while Hispanic or Latino unemployment has decreased, along with White unemployment while Black or African American is stable and Asian increasing, they still exceed the other three groups.

The link here to FRED is only for the graph of Black or African American unemployment. Use the Edit Graph button (top right) and then Add Line (middle top tab). Search for the other groups and add them to the chart. The chart will provide data starting in 1972. The graph is interactive and the data is available.

*Asian Unemployment numbers are not seasonally adjusted while the other three are – FRED didn’t have seasonally adjusted for Asians or I couldn’t find it.

The U.S. Bureau of Labor Statistics post People born in early 1980s held an average of 8.2 jobs from ages 18 through 32 (6/3/2020) includes the graph copied here. They note

Women with higher levels of education held more jobs than women with lower levels. Women with a bachelor’s degree held 8.8 jobs from ages 18 through 32, compared with 6.5 jobs for female high school dropouts. Men held a similar number of jobs regardless of their level of education.

People held an average of 4.5 jobs from ages 18 to 22. The average number of jobs dropped to 3.3 from ages 23 to 27, and then dropped more, to 2.3 jobs, from ages 28 to 32. The pattern of people holding fewer jobs as they aged was similar among women and men and across racial and ethnic groups and levels of education.

The chart data is available and there are links to the original survey.

From the climate.gov article Brief periods of dangerous humid heat arrive decades early  by Alison Stevens (5/12/2020):

The paper authors used an index called “wet-bulb temperature” based on weather station temperature and humidity data. The reading, from a thermometer when covered in a wet cloth, is related to how muggy conditions feel. This map shows locations that experienced extreme heat and humidity levels briefly (hottest 0.1 percent of daily maximum wet-bulb temperatures) from 1979–2017. Darker colors show more severe combinations of heat and humidity. Some areas have already experienced conditions at or near humans’ survivability limit of 35°C (95°F).

Who’s close to the 95°F?

The authors identified over 7,000 past occurrences of wet-bulb temperatures above 88°F (31°C), over 250 above 91°F (33°C) around the world, and two stations that reported multiple daily-maximum wet-bulb temperatures above 95°F. These extremes occurred for 1–2 hours in parts of coastal southwest North America, South Asia, and the coastal Middle East.

The southeastern United States, especially along the Gulf of Mexico, had multiple incidences of wet-bulb temperatures at or above 88°F; specifically, in east Texas, Louisiana, Mississippi, Alabama, the Florida Panhandle, Arkansas, and North Carolina. Parts of India, Pakistan, northwestern Australia, the coast of the Red Sea, and areas along the Gulf of California in Mexico saw even higher extremes.

The article links to the original paper.

The EIA article U.S. renewable energy consumption surpasses coal for the first time in over 130 years by Mickey Francis (5/28/2020) has the data.

In 2019, U.S. annual energy consumption from renewable sources exceeded coal consumption for the first time since before 1885, according to the U.S. Energy Information Administration’s (EIA) Monthly Energy Review. This outcome mainly reflects the continued decline in the amount of coal used for electricity generation over the past decade as well as growth in renewable energy, mostly from wind and solar. Compared with 2018, coal consumption in the United States decreased nearly 15%, and total renewable energy consumption grew by 1%.

The obvious question is are we using less electricity and if not what other source is picking up the slack? The eia Electricity explained page has an interactive time series of electricity generation.  Natural gas has picked up the slack.

The first article has two other charts and links to data.

The Climate.gov article 2010-2019: A landmark decade of U.S. billion-dollar weather and climate disasters by Adam B. Smith (1/8/2020) reports:

During 2019, the U.S. experienced a very active year of weather and climate disasters. In total, the U.S. was impacted by 14 separate billion-dollar disasters including: 3 major inland floods, 8 severe storms, 2 tropical cyclones (Dorian and Imelda), and 1 wildfire event. 2019 also marks the fifth consecutive year (2015-19) in which 10 or more separate billion-dollar disaster events have impacted the U.S.

Historical context:

In broader context, the total cost of U.S. billion-dollar disasters over the last 5 years (2015-2019) exceeds $525 billion, with a 5-year annual cost average of $106.3 billion (CPI-adjusted), both of which are records. The U.S. billion-dollar disaster damage costs over the last decade (2010-2019) were also historically large, exceeding $800 billion from 119 separate billion-dollar events. Moreover, the losses over the most recent 15 years (2005-2019) are $1.16 trillion in damage from 156 separate billion-dollar disaster events.

The article has other graphs and tables.  These events are tracked on NOAA’s Billion-dollar Weather and Climate Disasters: Overview page.

For those of us living in the northeast or a good part of the U.S. we might have felt that April was cold and it was. It is easy to use that as evidence that climate change is “fake news” yet it is good to keep in mind that if it is cold where you are it is likely much warmer somewhere else. The map here is from NASA’s GISS Surface Temperature Analysis page where similar maps can be made for a variety of time periods.  Here we can see that for April 2020 parts of the U.S. and Canada where one of the  few cold spots in the world. The rest of the planet was warmer.

NOAA’s Global Climate Report – April 2020 notes:

Averaged as a whole, the global land and ocean surface temperature for April 2020 was 1.06°C (1.91°F) above the 20th century average of 13.7°C (56.7°F) and the second highest April temperature in the 141-year record. Only April 2016 was warmer at +1.13°C (+2.03°F). The eight warmest Aprils have occurred since 2010. April 2016 and 2020 were the only Aprils that had a global land and ocean surface temperature departure above 1.0°C (1.8°F).

Time series data is available on the NOAA page. Note that NASA uses 1951-1980 as their baseline while NOAA is using the 20th century. This accounts for the slight differences in their calculations on April’s anomaly from the baseline.

The COVID-19 Projections web page contains daily updates of predictions for COVID-19. For example, the graphs copied here provide predictions for deaths per day, total deaths, and the reproduction number. Users can select projections for individual states and countries. The pages provide full model details which can be useful for any course that studies SIR models. In brief:

To quickly summarize how an SEIR model works, at each time period, an individual in a population is in one of four states: susceptible (S), exposed (E), infectious (I), and recovered (R). If an individual is in the susceptible state, we can assume they are healthy but have no immunity. If they are in the exposed state, they have been infected with the virus but are not infectious. If they are infectious, they can actively transmit the disease. An individual who is infected ultimately either recovers or dies. We assume that a recovered individual’s chances of re-infection is low, but not zero. We can model the movement of individuals through these various states at each time period. The model’s exact specifications depend on its parameters, which we describe in the next section.

The model details page includes clear statements on the fixed parameters and variable parameters, as well as how they are estimated.  Along with the projections page there is an infections tracker page. Overall, there are numerous graphs, projections, and details about modeling.

The NASA article Can’t ‘See’ Sea Level Rise? You’r looking in the Wrong Place by Alan Buis (5/13/2020) combines the quantitative facts of sea level rise with stories of places feeling the impact.

“Thanks to satellite and tide gauge data, we know that sea level is rising about 3.3 millimeters (0.13 inches) a year, a rate that grows by another 1 millimeter (0.04 inches) per year every decade or so,” Willis said. “Each year, global warming is currently adding about 750 gigatonnes of water to the ocean – enough to cover my home state of Texas about 1 meter (more than 3 feet) deep. We can’t really eyeball a few millimeters of sea level rise a year just by looking at the ocean because of waves, tides, etc. But we can definitely see the effects of it, both short- and long-term.”

Ocean Isle Beach NC:

I passed a woman walking her dog and asked her about the homes. “There used to be two streets of houses in front of these homes,” she told me. “Now they’re oceanfront.”

Norfolk, VA:

Over the past couple of decades, high tide flooding here has accelerated rapidly, and now occurs about 10 days a year, causing flooding in downtown Norfolk.

Sea Level data from NASA’s Sea Level Page.

The CDC’s new webpage Excess Deaths Associated with COVID-19 provides one method to measure pandemic related deaths:

Estimates of excess deaths presented in this webpage were calculated using Farrington surveillance algorithms (1). For each jurisdiction, a model is used to generate a set of expected counts, and the upper bound of the 95% Confidence Intervals (95% CI) of these expected counts is used as a threshold to estimate excess deaths. Observed counts are compared to these upper bound estimates to determine whether a significant increase in deaths has occurred. Provisional counts are weighted to account for potential underreporting in the most recent weeks. However, data for the most recent week(s) are still likely to be incomplete. Only about 60% of deaths are reported within 10 days of the date of death, and there is considerable variation by jurisdiction.

The interactive graphics allows the user to choose a jurisdiction and different data types. The graph here is for the U.S. and weekly excess deaths. All data can be downloaded as a csv file.

TPM put together a number of graphs comparing the NYC metro area to the rest of the country in their article Distinguishing the NYC Metro Outbreak from the Rest of the Country by Josh Marshall (5/6/2020).

The NYC metro area was hit early and hard by COVID-19, but will it end up a unique hot spot in the U.S. or will the rest of the country be hit similarly? Given the size of the U.S. we really can’t even compare U.S. states to European countries. Time will tell, but this data is worth keeping in mind.

The data for COVID-19 deaths by county in the U.S. from Johns Hopkins is here. The data is updated daily.