It has been more than a year since I summarized GHG emissions by Missouri communities, and since then I have reported on 2 additional communities that have completed GHG inventories, University City and Brentwood. In addition, Columbia has updated its inventory. That bring the total to 14, including the State of Missouri itself.
The chart at right shows per capita emissions. Each column stands for a different community. Within each column, the colors stand for sectors of the community. In all jurisdictions, buildings account for the largest portion of emissions. The built environment is best represented by combining the residential (blue) and commercial (red) sectors. Across the 14 communities, it accounted for between 43% and 87% of per capita emissions.
Several communities have inventoried emissions for more than one year: Missouri, Kansas City, and the City of St Louis all have inventoried emissions in two years, and Columbia has inventoried emissions in three. In my first summary analysis I used the first inventory completed by a community. In this analysis, I have used the most recent one.
The most important factor determining the total amount of GHG emitted is the population of the community. Large communities with lots of people emit more GHG than do small communities with fewer people. I used per capita emissions to adjust for population differences, but I still found significant differences between communities. What accounts for the difference?
The primary answer is change in daytime population. Some communities are primarily residential. They lose population during the day as their residents go off to work and shop in other communities. Other communities have significant commercial sectors. They gain population during the day, as people from other communities come to work and shop. One can compare the daytime population gain (loss) to the residential population to give an indication of the extent to which a community serves as a bedroom community or a commercial center.
The second chart at right shows per capita GHG emissions plotted against daytime population change. In this graph, if a community has a daytime population gain of, say, 187, that means that its population increases by 187% during the day. If its daytime population loss is -35.3, that means its population decreases by 35.3% during the day. The correlation between per capita emissions and daytime population change is 0.83. Correlation is a statistic used to measure the relationship between two variables. It runs from 1.0, which indicates a perfect relationship, through 0.0, which indicates no relationship, to -1.0, which indicates a perfect inverse relationship. A correlation of 0.83 indicates a strong positive relationship. One caution here: the data for GHG emissions derive from various years between 1990 and 2010, while the percentage change in daytime population is for 2005. (Missouri has been omitted from the chart because it is an entire state. We don’t ordinarily think of large midwestern states serving as bedroom communities for other states.)
Multiple regression is a statistical technique that provides an estimate of the degree to which a set of variables “account” for the variation in a target variable. Using the StatPlus Mac computer program, I constructed a multiple regression of total community GHG emissions on residential population and percent change in daytime population. It showed that residential population and percent change in daytime population accounted for almost all of the variation between these 13 communities in total GHG emissions (r-squared > .99, irrespective of whether I used the data from my original analysis or this one).
Thus, the difference in per capita emissions between communities is mostly a function of their size and the degree to which they serve as a commercial center vs. a bedroom community. With that said, there are clearly some communities in which the opportunity to reduce per capita emissions are greater, simply because there are more per capita emissions.
I have individually reported on each greenhouse gas inventory summarized in this post. See each individual post to find links to the respective GHG inventories. You can find the individual posts by clicking on the “Climate Change” category at the top of the home page.
County populations are from Table 1. Annual Estimates of the Resident Population for Counties of Missouri: April 1, 2000 to July 1, 2009, http://www.census.gov/popest/data/counties/totals/2009/tables/CO-EST2009-01-01.xls.
Municipal populations through 2009 are from Table 4. Annual Estimates of the Resident Population for Incorporated Places in Missouri: April 1, 2000 to July 1, 2009, https://www.census.gov/popest/data/cities/totals/2009/SUB-EST2009-4.html.
Municipal populations in 2010 are from: U.S. Census Bureau. 2012. Missouri: 2020. Population and Housing Unit Counts (CPH 2-27). http://www.census.gov/prod/cen2010/cph-2-27.pdf.
Daytime population changes are from Daytime Population Changes in Missouri Counties and Selected Cities, Missouri Economic Research & Information Center, December, 2005, http://www.missourieconomy.org/pdfs/daytime_population.pdf.