The country that emits the most CO2 depends on how it is measured. Our World in Data has a graph of annual share of CO2 emissions by country. By this measurement, a graph with the top 5 countries (China, U.S., India, Russia, & Germany) in 2016 was downloaded from Our World in Data. In this case, China has been the largest contributor of CO2 since 2005. In fact, in 2016 China emitted 10,295 million metric tons of CO2 compared to 5,240 million metric tons by the U.S. On the other hand, from EIA data, in 2016 each person in China emitted 7.3 tons of CO2 compared to a person in the U.S. at 16.2 tons. The EIA data dates back to 1980, and from 1980 to 2106 China emitted 177,547 million metric tones of CO2 compared to 197,176 for the U.S. Which is more important, per person, current, or total historical emissions? How does this create challenges in climate talks? Further analysis with other countries can be done with EIA data. Data can also be downloaded from the Our World in Data post. The Calculus Projects page has an example of using this data in a calculus class.
The Nature article Nonlinear rise in Greenland runoff in response to post-industrial Arctic warming by Luke Trusel et. el. (12/5/18) reports on Greenland ice sheet runoff. Referring to fig 4a (copied here) in their paper
We show that an exceptional rise in runoff has occurred over the last two decades, equating to an approximately 50% increase in GrIS-integrated runoff compared to pre-industrial runoff, and a 33% increase over the twentieth century alone.
The Woods Hole Oceanographic Institution (WHOI) provides a less technical summary of the paper in their post Greenland Ice Sheet Melt ‘Off the Charts’ Compared With Past Four Centuries (12/5/18).
Ice loss from Greenland is one of the key drivers of global sea level rise. Icebergs calving into the ocean from the edge of glaciers represent one component of water re-entering the ocean and raising sea levels. But more than half of the ice-sheet water entering the ocean comes from runoff from melted snow and glacial ice atop the ice sheet. The study suggests that if Greenland ice sheet melting continues at “unprecedented rates”—which the researchers attribute to warmer summers—it could accelerate the already fast pace of sea level rise.
“Rather than increasing steadily as climate warms, Greenland will melt increasingly more and more for every degree of warming. The melting and sea level rise we’ve observed already will be dwarfed by what may be expected in the future as climate continues to warm,” said Trusel.
The WHOI post includes a short video with a graph similar to the one copied here and a summary of the science. The Nature article has data available.
As an aside, while we are talking about Greenland, in NASA news International team – NASA make unexpected discovery under Greenland ice (11/15/18)
An international team of researchers, including a NASA glaciologist, has discovered a large meteorite impact crater hiding beneath more than a half-mile of ice in northwest Greenland. The crater — the first of any size found under the Greenland ice sheet — is one of the 25 largest impact craters on Earth, measuring roughly 1,000 feet deep and more than 19 miles in diameter, an area slightly larger than that inside Washington’s Capital Beltway.
The NASA article includes a short video.
The answer is the title of the Census Bureau post More Than Half of U.S. Population in 4.6 Percent of Counties by Haya el Nasser (10/24/18). The map copied here shows the counties.
At the county level, the geographic distribution of the estimated 325.2 million people in the United States clearly distinguishes two main areas where people live: “big” counties and “small” counties.
More than half of all residents live in just 143 big counties (in terms of the number of residents), according to an analysis of U.S. Census Bureau county estimates. That means less than half of the population is spread out across the remaining 2,999 small counties.
The post has a short video with more information. For instance the average population density of big counties is 926 people per square mile and only 48 people per square mile for small counties. Small counties are almost 75% non-Hispanic white, while big counties are under 50% non-Hispanic white.
There is also a notable difference in the rate of growth. “Big-county America is growing nearly twice as fast as small-county America,” Sink said. “They’re not only getting bigger but increasingly more diverse.” Thus, if current trends continue, it’s likely that the divide between big and small will continue to become more pronounced in the future.
The post has another map and some useful tables which include the distribution of small and large counties.
FiveThirtyEight has an excellent article on the 2018 senate race and the possible implications for future elections. The article, What Really Happened In Texas by Kirk Goldsberry (11/14/18) analyzes voting patterns by county and compares 2014 to 2018. Their graph copied here is the fourth in a series of maps and mostly summarizes the previous maps.
Cruz won by 220,000 votes last week. But in Harris County alone, 500,000 more people voted in the 2018 midterms than had voted in 2014. In Dallas County, 300,000 more people voted than in the last midterms, and in Travis, Bexar and Tarrant counties, 200,000 more people voted.
Indeed, aside from some noteworthy increase in voter numbers in suburban Dallas, the biggest white circles on the map above tend to hover over Beto country. Meanwhile, the darkest red counties — the places that carried Cruz back to Washington — have exhibited very little, if any, change in the number of votes cast compared to 2014. Those areas may be staunchly red, but they’re also staunchly stagnant too. O’Rourke almost won in 2018 by taking roughly 60 percent of the vote in the big five counties. This map suggests that if Democrats can repeat that feat as these places continue to grow, that may be all they need to turn Texas blue.
The data for their analysis comes from the Texas Secretary of State election results. The 2014 data is available by following the Historical Election Results (1992-current) link. The 2018 data is available through a link along the top. This is a stats project in the making (do this for you home state). The article can also be used in a QL course.
The Pew Research Center article Nearly one-in-five teens can’t always finish their homework because of the digital divide by Monica Anderson and Andrew Perrin (10/26/18) provides insights on how lacking access to the internet impacts the ability to complete homework. Their chart (copied here) gives the percent of school-age children by race and income without high-speed internet. A second chart provides the results of survey about how this impacts homework. In particular,
One-quarter of black teens say they are at least sometimes unable to complete their homework due to a lack of digital access, including 13% who say this happens to them often. Just 4% of white teens and 6% of Hispanic teens say this often happens to them. (There were not enough Asian respondents in this survey sample to be broken out into a separate analysis.)
The article includes a link at the bottom for results and methodology. This includes sample sizes making this article particularly useful for statistics courses.
The recent EIA report Carbon dioxide emissions from the U.S. power sector have declined 28% since 2005 (10/29/18) provides the graphic (copied here) showing the changes of the source of electricity generation and corresponding changes in CO2 emissions from 2005 to 2017.
Electricity related CO2 emissions declined but not all sectors decreased. The EIA report U.S. Energy-Related Carbon Dioxide Emissions, 2017 (9/25/18) provides a detailed analysis of U.S. CO2 emissions. Figure 4 (copied here) from the report shows that transportation related CO2 emissions have grown, although they haven’t reached pre 2008 levels. This report contains 11 graph and 2 tables with downloadable data.
Overall U.S. CO2 emissions have declined in the last three years (see figure 1 in the second report), but unfortunately according to the IEA after little change from 2014-2016:
Global energy-related CO2 emissions grew by 1.4% in 2017, reaching a historic high of 32.5 gigatonnes, a resumption of growth after three years of global emissions remaining flat.
Further, according to the Quartz article Instead of falling, global emissions are set to rise in 2018 by Akshat Rathi (10/8/18)
“When I look at the first nine months of data, I expect in 2018 carbon emissions will increase once again. This is definitely worrying news for our climate goals,” Fatih Birol, executive director of the IEA, told the Guardian. “We need to see a steep decline in emissions. We are not seeing even flat emissions.”
Glen Peters of the Center for International Climate Research says he agrees with Birol’s assessment. Emissions from both China and the US, the world’s two largest emitters, are up in the first nine months of the year. The reason is likely tied to strong economic growth, according to Peters.
The Pew Research Center article U.S. trails most developed countries in voter turnout by Drew Desilver (5/21/18) provides a summary of voting percentages by country in the chart copied here (data available). In terms of the percent of eligible voters, the U.S. is near the bottom with 56% voting n 2016, although once registered the turnout is 87%. This is the second largest spread of the percent voting between eligible voters and registered voters. In the U.S., if you want to keep someone from voting, keep them from registering.
A more detailed look at voting by county is available in the Washington Post article The geography of voting — and not voting by
According to the Climate Central post, Fall Nights Are Warming in Our Changing Climate (10/17/18), of 244 cities in the U.S., 83 percent have average fall low temperatures on the rise. For example, the graph here is for NYC. Why does this matter:
Warming fall nights mean more than just a delay in pulling out those comfortable sweaters and drinking hot apple cider. The lack of cool nights effectively lengthens the summer, as the first frost of the year also comes later. While warm-weather fans may celebrate, this also means that disease-carrying pests like mosquitoes and ticks will persist longer before dying off in the winter. Nationally, the long-term warming trend has lengthened the growing season by two weeks compared to the beginning of the 20th century. The allergy season is also getting longer, with ragweed pollens not disappearing until the first freeze of the fall.
The article has a drop down menu to select cities across the U.S. to see a graph similar to the one copied here for the selected city. They don’t post the data that was used to create the graphs, but they do explain their data sources under methodology.
A statistics project could have students create this graph for their hometown. One way to obtain the data was noted in our post, What do we know about nighttime minimum temperatures?: Go to NOAA’s Local Climatological Data Map. Click on the wrench under Layers. Use the rectangle tool to select your local weather station. Check off the station and Add to Cart. Follow the direction from their being sure to select csv file. You will get an email link for the data within a day. Note: You are limited in the size of the data to ten year periods. You will need to do this more than once to get the full data set available for your station.
Propublica’s article, Miseducation – Is There Racial Inequality at Your School? by Lena V. Groeger, Annie Waldman and David Eads, (10/16/18), provides data by state on the percent of nonwhite students, the percent of students who get free/reduced-price lunch, high school graduation rate, the number of times White students are likely to be in an AP class as compared to Black students, and the number of times Black students are likely to be suspended as compared to White students. The comparison is also available for Hispanic students.
The graph here was created with their data and compares the percent of students on free and reduced lunch with the number of times Black students are likely to be suspended compared to White students (state data isn’t available for HI, ID, MT, NH, NM, OR, UT, or WY). The red lines uses all the data where as the blue line removes the outliers of DC and ND. The blue regression line has a p-value of 0.012 and R-squared of 0.15. This suggests that wealthier states, as measured by free and reduced lunch programs, have a greater disparity is suspensions between black and white students. The impact of outliers is instructive here and there are other scatter plots worth graphing from the article. There are also statistics projects waiting to be created with this data.
The article also has an interactive map or racial disparities by districts, but the map can be misleading based on missing data from districts. Can you see how? This makes the map itself useful for QL courses. R Script that created this graph. Companion csv file.
The EPI article, Student absenteeism – Who misses school and how missing school matters for performance by Emma García and Elaine Weiss (9/25/18) provides a detailed account of absenteeism based on race and gender. For example, their chart here is the percent of students that missed three or more days in the month prior to the 2015 NAEP mathematics assessment. There are noticeable differences. For instance, the percentage of Black, White, and Asian (non ELL) that missed three or more days in the month is 23%, 18.3%, and 8.8% respectively.
Why does this matter?
In general, the more frequently children missed school, the worse their performance. Relative to students who didn’t miss any school, those who missed some school (1–2 school days) accrued, on average, an educationally small, though statistically significant, disadvantage of about 0.10 standard deviations (SD) in math scores (Figure D and Appendix Table 1, first row). Students who missed more school experienced much larger declines in performance. Those who missed 3–4 days or 5–10 days scored, respectively, 0.29 and 0.39 standard deviations below students who missed no school. As expected, the harm to performance was much greater for students who were absent half or more of the month. Students who missed more than 10 days of school scored nearly two-thirds (0.64) of a standard deviation below students who did not miss any school. All of the gaps are statistically significant, and together they identify a structural source of academic disadvantage.
These results “… identify the distinct association between absenteeism and performance, net of other factors that are known to influence performance?” The article has 12 graphs or charts, with data available for each, including one that reports p-values.