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Carbon Dioxide Emissions from Fossil Fuel

The previous post reported that total U.S. GHG emissions declined by 1.6% in 2011, the last year for which data has been published. There is no way, however, to compare total GHG emissions among the states. The closest one can come is to compare carbon dioxide emitted by the combustion of fossil fuel. CO2 is the most important greenhouse gas, and the combustion of fossil fuel is the most important emitter of CO2, accounting for 79% of all U.S. GHG emissions.

US 5 Sector ChartThe first chart at right shows United States CO2 emissions from combustion of fossil fuel from 1990 to 2011. This data is calculated from energy consumption data, which I reported in a series of posts starting here. The energy and CO2 series are similar, but not identical, as GHG emissions depend not only on the amount of energy consumed, but also on the type of fuel.

(Click on chart for larger view.)

First, total emissions have grown by about 8.4% since 1990. They peaked in 2007, and have declined about 8.6% since then.

Second, electricity generation accounts for the largest portion of emissions, some 38.4% in 2011.

Third, the Commercial Sector accounts for less than 5% of CO2 emissions in this series, where it often accounts for a larger fraction in other GHG inventories. The emissions attributed to electricity generation in this inventory are for electricity consumed in other sectors, and most, if not all, of the discrepancy would disappear if it were distributed out to its end use.

Fourth, the fraction accounted for by each sector has changed over the years. Electricity generation has risen from 35.6% in 1990 to 38.4% in 2011. Industrial, on the other hand, has declined from 21.9% to 17.6%.

MO 5 Sector ChartThe second chart at right shows Missouri CO2 emissions from combustion of fossil fuel from 1990 to 2011.

(Click on chart for larger view.)

First, Missouri CO2 emissions grew by about 32.1% between 1990 and 2011 (compared to only 8.6% nationally).

Second, electricity generation accounts for an even larger percentage of emissions here than it does nationally – 55.0%, more than all other sources combined.

Third, in 2011 the Commercial Sector accounted for less than 3.0% of emissions, even less than it did nationally. As above, CO2 is emitted when fossil fuel is burned to generate electricity that is consumed in other sectors, and if the emissions were distributed to their end-use sector, the data would look quite different.

Fourth, the fraction accounted for by each sector has changed even more than it has nationally. Commercial, industrial, residential, and transportation emissions have all declined, while emissions from the generation of electric power have increased by 63%!

MO GHG Inventory Total Emissions.xlsxThe very first post in this blog reported on the 1990 Missouri GHG inventory, which reported data on CO2 emissions from fossil fuel combustion. Those data align with the data in this report pretty well. From the Missouri inventory I was able to construct a chart that distributed GHG emissions to their end-use sector, and I’ve reproduced the chart here for comparison purposes.

(Click on chart for larger view.)

With emissions distributed to their end use, transportation emerged as the largest source of GHG emissions in the state.

The data suggest that nationally in 2011 CO2 emissions came primarily from the combustion of fossil fuel. They have grown significantly since 1990. Economic downturns cause CO2 emissions to decline, but policy initiatives are probably also causing them to decline. In Missouri, our emissions have grown at a rate almost 4 times the national rate.

Sources:

EPA. 2013. Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2011. EPA 430-R-13-001. EPA » Learn the Issues/Climate Change » Greenhouse Gas Emissions » National Data. http://www.epa.gov/climatechange/ghgemissions/usinventoryreport.html.

EPA. 2013. CO2 Emissions from Fossil Fuel Combustion. EPA Home » State and Local Climate And Energy Program » Resources » State Energy CO2 Emissions. http://epa.gov/statelocalclimate/resources/state_energyco2inv.html.

Missouri Department of Natural Resources. 2015. Greenhouse Gas Emission Trends and Projections for Missouri, 1990-2015. http://www.dnr.mo.gov/energy/cc/ghg.htm.

U.S. GHG Emissions Decline in 2011

Greenhouse gas emissions in the United States declined by 1.6% in 2011, the most recent year for which data is available, according to the Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2011, issued by the EPA in April, 2013.

US GHGs Sector Electricity Separate 1990-2011The first chart at right shows U.S. greenhouse gas (GHG) emissions for 1990, 2005, and for each of the past 5 years (2007-2011). The 50 states are included, plus the District of Columbia, but U.S. Territories have been omitted. The emissions are shown by sector, with the generation of electrical power included as a separate sector. Land Use & Forestry absorbs more GHG from the atmosphere than it emits, so its contribution is shown as a negative emission. The result is that net GHG emissions (the black line) are reduced from the total emitted by the other sectors.

(Click on chart for larger view.)

Viewed this way, the generation of electrical power is the largest emitter of GHGs, with transportation second. However, electricity is generated for use in other sectors. If you distribute the GHGs from electricity to the sectors that consume it, the data looks much different.

US GHGs by Sector Electricity DistributedThe second chart at right shows U.S. GHG emissions with electricity-related GHGs distributed to their end-use sectors. Again, the District of Columbia has been included, but U.S. Territories have been omitted. Viewed this way, industry is the largest GHG emitter, followed closely by transportation. Land Use & Forestry are still a GHG sink, not a GHG emitter.

(Click on chart for larger view.)

Between 1990 and 2011, total GHG emissions grew from 6,183.2 to 6,702.3 million metric tons of CO2 equivalent (MMTCO2e). They declined, however, in 1991, 2001, 2006, 2008, 2009, and 2011.

Source: EPA, 2013, Inventory of U.S. Greenhous Gas Emissions and Sinks.

Source: EPA, 2013, Inventory of U.S. Greenhous Gas Emissions and Sinks: 1990-2011.

(See the third chart at right. Unlike the previous two charts, this one includes U.S. Territories, and it concerns total emissions, not net emissions). The pattern suggests that economic downturns reduce GHG emissions, but that they are probably not the whole story. U.S. GDP grew by about 1.8% in 2011, but total emissions declined by 1.6%, suggesting that GHG reduction policies likely played a role.

(Click on chart for larger view.)

GHG emission growth rates for the sectors varied. In 2011 Industry emitted less GHG and Land Use & Forestry absorbed more GHG from the atmosphere, but every other sector increased emissions. Distributing electricity-related emissions to their end-use sectors, between 1990 and 2011 the growth was as follows: Commercial +23%, Residential +20%, Agriculture +18%, Transportation +18%, Industry -13%, and Land Use & Forestry -14%.

The Inventory does not break out emissions by state, and the last year for which Missouri conducted a greenhouse gas inventory was 1996. However, carbon dioxide is the most important GHG, and combustion of fossil fuel to create energy is the most important source of CO2, accounting for about 79% of all emissions in 2011. Because the Department of Energy collects extensive data on the generation and consumption of energy in the various states, one can calculate the amount of GHG emitted by the combustion of fossil fuel for each state. The result leaves out the effects of Agriculture and Land Use & Forestry, but it nevertheless captures the lions share of GHG emissions. The following post will look at that data.

Sources:

EPA. 2013. Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2011. EPA 430-R-13-001. EPA » Learn the Issues/Climate Change » Greenhouse Gas Emissions » National Data. http://www.epa.gov/climatechange/ghgemissions/usinventoryreport.html.

Missouri Department of Natural Resources. 1999. Greenhouse Gas Emission Trends and Projections for Missouri, 1990-2015. http://www.dnr.mo.gov/energy/cc/ghg.htm.

University City Reports GHG Emissions

UCity Comm by SectorUniversity City, a suburb of St. Louis, published a greenhouse gas inventory in 2009. University City studied community emissions and emissions from government operations for the baseline year of 2005. The latter are a subset of the former, but municipalities break them out separately because they have direct control over their own emissions, while they can only influence community emissions indirectly. Many municipalities also want to demonstrate leadership on the issue.

The report’s headline total does not include ghg emissions from the energy used to purify and supply University City with potable water. The report, however, does provide an estimate of those ghg emissions, and I see no valid reason to exclude them, so I have included them in my totals and in the graphic at left.

Total University City community emissions were 520,816 metric tons of CO2e. According to the report, this represented about 15.84 metric tons of CO2e per capita. This is below average compared to other municipalities.

UCity Comm by SourceThe first graph shows community emissions by sector. The residential sector was largest, accounting for almost half, followed by the transportation and commercial sectors.

The report does not have an overall summary of community emissions by source, but it provides information that can be used to construct one. The second chart at right shows the results. As we have found in many municipalities, consumption of electricity was the largest source of ghg emissions.

The report delves into the ghg savings resulting from University City’s composting and recycling programs. These programs prevented emissions of 14,926 and 2,428 metric tons of carbon dioxide equivalent, respectively. While significant, combined they represent about 3% of University City’s total community emissions. Composting and recycling programs are not primarily designed as ghg reduction strategies, they provide other benefits.

UCity Govt by SectorThe third graph shows emissions from University City government operations in 2010. Government buildings accounted for the most emissions, followed by streetlights. The large percentage of emissions represented by streetlights is unusual – much more energy was being consumed than in other municipalities. As a result, University City undertook a reevaluation of their streetlighting policies.

Oddly, the University City community ghg emissions look more like Maplewood’s than any other municipality. There is a similar feel to those two communities, and emissions in both are dominated by the residential sector.

Thanks to Lois Sechrist who alerted me to the University City inventory. If anybody else knows of any other inventories I’ve missed, please let me know by commenting on this post.

Source:

Heneberry, Joe. 2010-2011 Baseline Greenhouse Emissions Inventory and Forecast, City of University City, MO. University City Home Page » Boards and Commissions » Green Practices Commission » University City Greenhouse Gas Inventory. http://apps.ucitymo.org/WebLink8/DocView.aspx?id=42630&page=4&dbid=0.

Why Per Capita GHG Emissions Vary Across Missouri Communities

In several previous posts, I summarized the results of 12 GHG inventories that have been conducted in Missouri. In this post I will discuss what I think they mean.

Per Capita Emiss 12 JurisThe first graph shows per capita GHG emissions for each of the 12 jurisdictions. For most jurisdictions, GHG emissions range between 20 and 30 MTCO2e per capita. However, there are some exceptions: Creve Coeur has significantly higher per capita emissions, while Maplewood and Wildwood have significantly lower per capita emissions. These exceptions show that population is not the only driver of GHG emissions.

The profile varies between Missouri communities. The commercial sector is the most important sector driving this variation. Clayton and Creve Coeur have particularly large per capita commercial emissions – more than twice as large as Richmond Heights, the third largest. In fact, their commercial emissions are larger than total emissions in Wildwood, a community with more than twice their population. Clayton and Creve Coeur are the two communities that have the largest increase in daytime population. In fact, their percentage increases in daytime population were the second and third largest in the state in 2005, trailing only Fenton. (Fenton was the home of the Chrysler Assembly Plant, which has since closed.) Wildwood and Lee’s Summit on the other hand, lose a significant fraction of their population during the day.

Creve Coeur and Richmond Heights have the largest per capita transportation emissions. A couple of factors are at work here. Both communities are crossed by significant highways and roads, and this increases emissions. But in addition, both are destination communities with significant business and retail districts. Many people who live in other communities do lots of driving in these two communities, especially compared to the size of their residential population.

Creve Coeur, Columbia and St. Louis County have the largest per capita residential emissions, and the pattern between jurisdictions is somewhat unexpected. Per capita residential emissions are probably a function of the number of people living in each home, building energy efficiency, building size, and the amount of electronic equipment used therein. It would seem that Creve Coeur might be the location of some large residences with lots of electronic equipment, but so might Clayton and Wildwood. Alternatively, one might expect that Creve Coeur, with an aging population, might have relatively few people per residential building. But so might Clayton, and one might expect Columbia to be the opposite, with its housing for Mizzou students. One might expect that Creve Coeur, with its ranch style houses constructed in the 1960s, has an energy inefficient housing stock. But the housing stock in Clayton is probably as big or bigger and significantly older. This is an interesting finding that calls out for further understanding, however it seems to be a less important driver of per capita emissions than are commercial and transportation emissions.

Daytime Pop Per Cap GHG ChartThe second graph shows per capita emissions graphed against daytime change in population. In this graph, the per capita computation is made using residential population, and the daytime population is shown as the percentage change in population during the day. The data points do not form a perfect line, but they show a definite trend from lower left to upper right. This suggests a positive relationship between the two variables.

Population is the primary driver of GHG emissions. If you compute a regression of total emissions on population for the 11 cities and counties (a statistical technique that yields an overall estimate of how much emissions depend on population), you find that population almost entirely predicts emissions (R-squared = .997). But that analysis is distorted by the large-scale differences between communities: Kansas City and St. Louis have 35 times the population of Maplewood and Richmond Heights. You’d be surprised if there wasn’t a large difference in total community emissions. That’s why the per capita analysis is so important.

Does population predict per capita emissions? If it did, then we would be expecting that as communities grew larger, it would reliably affect the amount of GHG each resident emitted. Some might argue, for instance, that large communities are inherently more (or less) energy efficient. For these 11 communities, the answer is “no.” Population does a very poor job of predicting per capita emissions (R-squared = .003). What, then, does predict per capita emissions?

Daytime population change is a large part of the answer. If one conducts a regression, one finds that daytime population change is a strong predictor of per capita emissions (R-squared = .664). To host a large daytime population increase, a community has to have lots of commercial buildings to house workers and shoppers. These buildings all cause GHG emissions. In addition, people drive to and from these buildings, emitting GHGs inside the community boundaries as they do so.

An R-squared of .664 means that about 2/3 of the variation in per capita emissions can be explained by the daytime population change. The remaining 1/3 is left unexplained. We don’t really know what accounts for that remaining 1/3, although we might suspect it could involve factors such as the energy requirements of the businesses in the community, and the energy efficiency of the building stock. Other factors might include the energy practices of the residents (like driving less or using high mpg vehicles). We have no evidence, however, that large differences in energy practices exist in the communities studied, and until such evidence emerges, I would be skeptical.

It appears that people emit GHG emissions. Where there are lots of people, there are lots of emissions. If they leave their community to work or shop in another community, then they take some of their GHG emissions with them to that other community. It is unlikely that differences in GHG emissions between communities should be attributed to community energy practices. At least, not yet.

Well, I’ve just offered an opinion, not something I do often in this blog. Do you agree? Also, I’ve reported on all the GHG inventories I know about in Missouri. If you know of one that I’ve missed, let me know.

Sources

For links to the GHG inventories from the 12 jurisdictions, see my previous posts on each one. Also, see the 4 previous posts in this summary series. Here’s a link to a list of all previous posts.

Missouri population is from Part 1. Population of the United States and Each State: 1790-1990, http://www.census.gov/population/www/censusdata/Population_Part1.xls.

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 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.

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.

Geographic areas are from the Wikipedia article for each location.

Regressions were computed using StatPlus Mac.

GHG Emissions and Daytime Population

Twelve Missouri communities have conducted GHG inventories. This is the fourth in a series of posts summarizing and analyzing those inventories.

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.

Daytime Pop Per Cap GHG ChartThe graph 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 -14.5, that means its population shrinks by 14.5% during the day. (Missouri has been omitted because it is an entire state. We don’t ordinarily think of entire states serving as bedroom communities.) One caution here: the data for GHG emissions derive from various years between 1990 and 2010, while the percentage change in daytime population are all for 2005.

The correlation between per capita emissions and daytime population is +.79, a very strong relationship. (A correlation coefficient measures the relationship between two variables. It runs from -1.00 to +1.00, with -1.00 indicating a perfect inverse relationship, 0.00 indicating no relationship, and +1.00 indicating a perfect positive relationship.) Where daytime population increases, per captia GHG emissions are high. Where daytime population decreases, they are low.

Sources:

For links to the GHG inventories from the 12 jurisdictions, see my previous posts on each one. Here’s a link to a list of previous posts.

Missouri population is from Part 1. Population of the United States and Each State: 1790-1990, http://www.census.gov/population/www/censusdata/Population_Part1.xls.

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 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.

GHG Emissions Per Square Mile

Twelve Missouri communities have conducted GHG inventories. This is the third in a series of posts summarizing and analyzing them.

Emiss Per Sq Mi 12 JurisThe graph at right shows GHG emissions per square mile of land area for the 12 Missouri communities. Each column represents a community, and the colors within the columns represent community sectors. These findings vary widely across jurisdictions, perhaps suggesting that GHG emissions are only slightly a function of the geographic size of a jurisdiction.

Emissions per square mile for Wildwood and for the State of Missouri were very small compared to the other jurisdictions, an intuitive finding given that they contain large areas of farmland and forest, while most of the smaller jurisdictions were developed urban and suburban locations.

There is no doubt that big places have lots of GHG emissions. But it is not because of the land area, it is because they also tend to contain lots of people and lots of economic activity.

This analysis is unrevealing.

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