Urban Ecosystems

, Volume 19, Issue 3, pp 1013–1039 | Cite as

Urban high-resolution fossil fuel CO2 emissions quantification and exploration of emission drivers for potential policy applications

  • Risa PatarasukEmail author
  • Kevin Robert Gurney
  • Darragh O’Keeffe
  • Yang Song
  • Jianhua Huang
  • Preeti Rao
  • Martin Buchert
  • John C. Lin
  • Daniel Mendoza
  • James R. Ehleringer


Fossil fuel carbon dioxide (FFCO2) emissions are the largest driver of anthropogenic climate change. Approximately three-quarters of the world’s fossil fuels carbon dioxide emissions are generated in urban areas. We used the Hestia high resolution approach to quantify FFCO2 for Salt Lake County, Utah, USA and demonstrate the importance of high resolution quantification to urban emissions mitigation policymaking. We focus on the residential and onroad sectors across both urbanized and urbanizing parts of the valley. Stochastic Impact by Regression on Population, Affluence, and Technology (STIRPAT) regression models using sociodemographic data at the census block group level shows that population, per capita income, and building age exhibit positive relationships while household size shows a negative relationship with FFCO2 emissions. Compact development shows little effect on FFCO2 emissions in this domain. FFCO2 emissions in high income block groups is twice as sensitive to income than low income block groups. Emissions are four times as sensitive to household size in low-income versus high-income block groups. These results suggest that policy options targeting personal responsibility or knowledge feedback loops may be the most effective strategies. Examples include utility bill performance comparison or publicly available energy maps identifying high-emitting areas. Within the onroad sector, high emissions density (FFCO2/km) is associated with primary roads, while high emissions intensity (FFCO2/VMT) is associated with secondary roads. Opportunities exist for alignment of public transportation extension with remaining high emission road segments, offering a prioritization of new onroad transportation policy in Salt Lake County.


Residential Onroad STIRPAT Urban carbon Hestia Bottom-up approach 



This research was supported by grants from the Department of Energy DE-SC-001-0624, the National Science Foundation grant EF-01241286, National Institute of Standards and Technology grant 70NANB14H321, and National Oceanic and Atmospheric Administration Climate Program Office’s Atmospheric Chemistry, Carbon Cycle, and Climate Program grant NA14OAR4310178. We also would like to thank Jerome Zenger, Kevin Bell, and Semih Yildiz for assisting with the data collection and inquiry.

Supplementary material

11252_2016_553_Fig8_ESM.gif (77 kb)
Fig. S1

US Census block groups used in the regression analysis, a) low vs. high income block groups, b) block groups designated as within and outside Salt Lake City. The two outlier block groups are also noted. (GIF 77 kb)

11252_2016_553_Fig9_ESM.gif (110 kb)
Fig. S1

US Census block groups used in the regression analysis, a) low vs. high income block groups, b) block groups designated as within and outside Salt Lake City. The two outlier block groups are also noted. (GIF 77 kb)

11252_2016_553_MOESM1_ESM.tif (505 kb)
High Resolution (TIF 504 kb)
11252_2016_553_MOESM2_ESM.tif (589 kb)
High Resolution (TIF 588 kb)
11252_2016_553_Fig10_ESM.gif (101 kb)
Fig. S2

Spatial distribution at the block group level for the independent variables used in the residential regression analysis. a) population (persons); b) housing units per capita (#/persons); c) housing units per area (#/; d) building age (years); e) income per capita (US$). Total FFCO2 emissions (dependent variable) are shown in Fig 2. (GIF 100 kb)

11252_2016_553_Fig11_ESM.gif (100 kb)
Fig. S2

Spatial distribution at the block group level for the independent variables used in the residential regression analysis. a) population (persons); b) housing units per capita (#/persons); c) housing units per area (#/; d) building age (years); e) income per capita (US$). Total FFCO2 emissions (dependent variable) are shown in Fig 2. (GIF 100 kb)

11252_2016_553_Fig12_ESM.gif (95 kb)
Fig. S2

Spatial distribution at the block group level for the independent variables used in the residential regression analysis. a) population (persons); b) housing units per capita (#/persons); c) housing units per area (#/; d) building age (years); e) income per capita (US$). Total FFCO2 emissions (dependent variable) are shown in Fig 2. (GIF 100 kb)

11252_2016_553_Fig13_ESM.gif (93 kb)
Fig. S2

Spatial distribution at the block group level for the independent variables used in the residential regression analysis. a) population (persons); b) housing units per capita (#/persons); c) housing units per area (#/; d) building age (years); e) income per capita (US$). Total FFCO2 emissions (dependent variable) are shown in Fig 2. (GIF 100 kb)

11252_2016_553_Fig14_ESM.gif (104 kb)
Fig. S2

Spatial distribution at the block group level for the independent variables used in the residential regression analysis. a) population (persons); b) housing units per capita (#/persons); c) housing units per area (#/; d) building age (years); e) income per capita (US$). Total FFCO2 emissions (dependent variable) are shown in Fig 2. (GIF 100 kb)

11252_2016_553_MOESM3_ESM.tif (673 kb)
High Resolution (TIF 673 kb)
11252_2016_553_MOESM4_ESM.tif (697 kb)
High Resolution (TIF 697 kb)
11252_2016_553_MOESM5_ESM.tif (619 kb)
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11252_2016_553_MOESM6_ESM.tif (652 kb)
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11252_2016_553_MOESM7_ESM.tif (695 kb)
High Resolution (TIF 695 kb)
11252_2016_553_Fig15_ESM.gif (68 kb)
Fig. S3

Probability density function for onroad FFCO2 emissions on primary roads in Salt Lake County disaggregated by the number of lanes. (GIF 67 kb)

11252_2016_553_MOESM8_ESM.tif (313 kb)
High Resolution (TIF 312 kb)
11252_2016_553_MOESM9_ESM.docx (49 kb)
Table S1 (DOCX 49 kb)
11252_2016_553_MOESM10_ESM.docx (46 kb)
Table S2 (DOCX 46 kb)
11252_2016_553_MOESM11_ESM.docx (46 kb)
Table S3 (DOCX 45 kb)
11252_2016_553_MOESM12_ESM.docx (46 kb)
Table S4 (DOCX 45 kb)
11252_2016_553_MOESM13_ESM.docx (46 kb)
Table S5 (DOCX 45 kb)
11252_2016_553_MOESM14_ESM.docx (46 kb)
Table S6 (DOCX 45 kb)


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© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Risa Patarasuk
    • 1
    Email author
  • Kevin Robert Gurney
    • 1
    • 2
  • Darragh O’Keeffe
    • 1
    • 2
  • Yang Song
    • 1
  • Jianhua Huang
    • 1
  • Preeti Rao
    • 3
  • Martin Buchert
    • 4
  • John C. Lin
    • 5
  • Daniel Mendoza
    • 5
  • James R. Ehleringer
    • 6
  1. 1.School of Life SciencesArizona State UniversityTempeUSA
  2. 2.Global Institute of SustainabilityArizona State UniversityTempeUSA
  3. 3.Jet Propulsion LaboratoryPasadenaUSA
  4. 4.Global Change and Sustainability CenterUniversity of UtahSalt Lake CityUSA
  5. 5.Department of Atmospheric SciencesUniversity of UtahSalt Lake CityUSA
  6. 6.Department of BiologyUniversity of UtahSalt Lake CityUSA

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