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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
Article

Abstract

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.

Keywords

Residential Onroad STIRPAT Urban carbon Hestia Bottom-up approach 

Notes

Acknowledgements

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 (#/sq.km); 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 (#/sq.km); 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 (#/sq.km); 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 (#/sq.km); 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 (#/sq.km); 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)
High Resolution (TIF 618 kb)
11252_2016_553_MOESM6_ESM.tif (652 kb)
High Resolution (TIF 652 kb)
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)

References

  1. AirNav (2014) Airport information. http://www.airnav.com/. Accessed 9 Jan 2014
  2. Arcaute E, Hatna E, Ferguson P et al (2015) Constructing cities, deconstructing scaling laws. J R Soc Interface 12:20140745. doi: 10.1098/rsif.2014.0745 CrossRefPubMedPubMedCentralGoogle Scholar
  3. Asefi-Najafabady S, Rayner PJ, Gurney KR, et al (2014) A multiyear, global gridded fossil fuel CO2 emission data product: Evaluation and analysis of results. J Geophys Res Atmos 119:2013JD021296. doi:  10.1002/2013JD021296
  4. Asensio OI, Delmas MA (2015) Nonprice incentives and energy conservation. Proc Natl Acad Sci 112:E510–E515. doi: 10.1073/pnas.1401880112 CrossRefPubMedPubMedCentralGoogle Scholar
  5. Barth M, Boriboonsomsin K (2009) Traffic congestion and greenhouse gases. ACCESS Mag 1:1–9Google Scholar
  6. Bettencourt LMA, Lobo J, Helbing D et al (2007) Growth, innovation, scaling, and the pace of life in cities. Proc Natl Acad Sci 104:7301–7306. doi: 10.1073/pnas.0610172104 CrossRefPubMedPubMedCentralGoogle Scholar
  7. Bin S, Dowlatabadi H (2005) Consumer lifestyle approach to US energy use and the related CO2 emissions. Energ Policy 33:197–208. doi: 10.1016/S0301-4215(03)00210-6 CrossRefGoogle Scholar
  8. Bréon FM, Broquet G, Puygrenier V et al (2015) An attempt at estimating Paris area CO2 emissions from atmospheric concentration measurements. Atmos Chem Phys 15:1707–1724. doi: 10.5194/acp-15-1707-2015 CrossRefGoogle Scholar
  9. US Census Bureau (2015) State & County QuickFacts. http://www.census.gov/quickfacts/table/IPE120213/49035,00. Accessed 7 Sep 2015
  10. Ciais P, Sabine C, Bala G et al (2013) Carbon and other biogeochemical cycles. In: Stocker TF, Qin D, Plattner G-K et al (eds) Climate change 2013: the physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, pp 465–570Google Scholar
  11. Cole MA, Neumayer E (2004) Examining the impact of demographic factors on air pollution. Popul Environ 26:5–21. doi: 10.1023/B:POEN.0000039950.85422.eb CrossRefGoogle Scholar
  12. Collins M, Knutti R, Arblaster J et al (2013) Long-term climate change: projections, commitments and irreversibility. In: Stocker TF, Qin D, Plattner GK et al (eds) Climate change 2013: the physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, pp 1029–1136Google Scholar
  13. Cottineau C, Hatna E, Arcaute E, Batty M (2015) Paradoxical interpretations of urban scaling laws. ArXiv E-Prints 1507:7878Google Scholar
  14. Cox PM, Betts RA, Jones CD et al (2000) Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model. Nature 408:184–187. doi: 10.1038/35041539 CrossRefPubMedGoogle Scholar
  15. Dai A (2013) Increasing drought under global warming in observations and models. Nat Clim Chang 3:52–58. doi: 10.1038/nclimate1633 CrossRefGoogle Scholar
  16. Dietz T, Rosa EA (1997) Effects of population and affluence on CO2 emissions. Proc Natl Acad Sci 94:175–179CrossRefPubMedPubMedCentralGoogle Scholar
  17. Dodman D (2011) Forces driving urban greenhouse gas emissions. Curr Opin Environ Sustain 3:121–125. doi: 10.1016/j.cosust.2010.12.013 CrossRefGoogle Scholar
  18. DOE (2012) 2011 buildings energy data book. Office of Energy Efficiency and Renewable Energy, Department of Energy, WashingtonGoogle Scholar
  19. Druckman A, Jackson T (2008) Household energy consumption in the UK: a highly geographically and socio-economically disaggregated model. Energ Policy 36:3177–3192. doi: 10.1016/j.enpol.2008.03.021 CrossRefGoogle Scholar
  20. Ehleringer JR, Schauer AJ, Lai C et al (2008) Long-term carbon dioxide monitoring in Salt Lake City. AGU Fall Meet Abstr 43:0466Google Scholar
  21. Ehleringer J, Pataki DE, Lai C, Schauer A (2009) Long-term results from an urban CO2 monitoring network. AGU Fall Meet Abstr 33:0414Google Scholar
  22. Ehrlich PR, Holdren JP (1971) Impact of population growthGoogle Scholar
  23. EIA (2002) Distillate fuel oil sales for railroad use. US Energy Information Administration, Department of Energy. www.eia.gov/dnav/pet/pet_cons_821use_a_epd0_vrr_mgal_a.htm. Accessed 5 Jan 2002
  24. EIA (2013a) Fuel oil and kerosene sales. http://www.eia.gov/petroleum/fueloilkerosene/. Accessed 8 Jul 2013
  25. EIA (2013b) Refiner petroleum product prices by sales type. http://www.eia.gov/dnav/pet/pet_pri_refoth_a_EPJK_PTG_dpgal_a.htm. Accessed 8 Jul 2013
  26. EPA (2015) Social Cost of Carbon. https://www3.epa.gov/climatechange/EPAactivities/economics/scc.html. Accessed 30 Sep 2015
  27. EPA (2016) The 2011 National Emissions Inventory. http://www.epa.gov/ttnchie1/net/2011inventory.html. Accessed 3 Mar 2016
  28. Ewing R, Rong F (2008) The impact of urban form on U.S. residential energy use. Hous Policy Debate 19:1–30. doi: 10.1080/10511482.2008.9521624 CrossRefGoogle Scholar
  29. Ewing R, Pendall R, Chen D (2003) Measuring sprawl and its transportation impacts. Transp Res Rec J Transp Res Board 1831:175–183. doi: 10.3141/1831-20 CrossRefGoogle Scholar
  30. Ewing R, Tian G, Spain A, Goates J (2014) Effects of light-rail transit on traffic in a travel corridor. J Public Transp. doi: 10.5038/2375-0901.17.4.6 Google Scholar
  31. Fan Y, Liu L-C, Wu G, Wei Y-M (2006) Analyzing impact factors of CO2 emissions using the STIRPAT model. Environ Impact Assess Rev 26:377–395. doi: 10.1016/j.eiar.2005.11.007 CrossRefGoogle Scholar
  32. Federal Highway Administration (2014) Field manual. https://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/chapter1.cfm. Accessed 11 Jul 2014
  33. Federal Highway Administration (2015) Flexibility in highway design chapter 3: functional classification. http://www.fhwa.dot.gov/environment/publications/flexibility/ch03.cfm. Accessed 20 Sep 2015
  34. Feng K, Hubacek K, Guan D (2009) Lifestyles, technology and CO2 emissions in China: a regional comparative analysis. Ecol Econ 69:145–154. doi: 10.1016/j.ecolecon.2009.08.007 CrossRefGoogle Scholar
  35. Fragkias M, Lobo J, Strumsky D, Seto KC (2013) Does size matter? Scaling of CO2 emissions and U.S. urban areas. PLoS ONE 8:e64727. doi: 10.1371/journal.pone.0064727 CrossRefPubMedPubMedCentralGoogle Scholar
  36. Frey HC, Rouphail NM, Unal A, Colyar JD (2001) Emissions reduction through better traffic management: an empirical evaluation based upon on-road measurements. CTE/NCDOT Joint Environmental Research Program, RaleighGoogle Scholar
  37. Gately CK, Hutyra LR, Wing IS (2015) Cities, traffic, and CO2: a multidecadal assessment of trends, drivers, and scaling relationships. Proc Natl Acad Sci 112:4999–5004. doi: 10.1073/pnas.1421723112 CrossRefPubMedGoogle Scholar
  38. Glaeser EL, Kahn ME (2010) The greenness of cities: carbon dioxide emissions and urban development. J Urban Econ 67:404–418. doi: 10.1016/j.jue.2009.11.006 CrossRefGoogle Scholar
  39. Gomez-Ibanez DJ, Boarnet MG, Brake DR, et al (2009) Driving and the built environment: the effects of compact development on motorized travel, energy use, and CO2 emissions. Oak Ridge National Laboratory (ORNL)Google Scholar
  40. Gurney KR, Law RM, Denning AS et al (2002) Towards robust regional estimates of CO2 sources and sinks using atmospheric transport models. Nature 415:626–630. doi: 10.1038/415626a CrossRefPubMedGoogle Scholar
  41. Gurney K, Ansley W, Mendoza D et al (2007) Research needs for finely resolved fossil carbon emissions. EOS Trans Am Geophys Union 88:542–543. doi: 10.1029/2007EO490008 CrossRefGoogle Scholar
  42. Gurney K, Mendoza D, Zhou Y et al (2009) High resolution fossil fuel combustion CO2 emission fluxes for the United States. Environ Sci Technol 43:5535–5541CrossRefPubMedGoogle Scholar
  43. Gurney KR, Razlivanov I, Song Y et al (2012) Quantification of fossil fuel CO2 emissions on the building/street scale for a large U.S. City. Environ Sci Technol 46:12194–12202. doi: 10.1021/es3011282 CrossRefPubMedGoogle Scholar
  44. Haas R, Auer H, Biermayr P (1998) The impact of consumer behavior on residential energy demand for space heating. Energy Build 27:195–205. doi: 10.1016/S0378-7788(97)00034-0 CrossRefGoogle Scholar
  45. Heiple S, Sailor DJ (2008) Using building energy simulation and geospatial modeling techniques to determine high resolution building sector energy consumption profiles. Energy Build 40:1426–1436. doi: 10.1016/j.enbuild.2008.01.005 CrossRefGoogle Scholar
  46. Hojjati B, Wade SH (2012) U.S. household energy consumption and intensity trends: a decomposition approach. Energ Policy 48:304–314. doi: 10.1016/j.enpol.2012.05.024 CrossRefGoogle Scholar
  47. Holden E, Norland IT (2005) Three challenges for the compact city as a sustainable urban form: household consumption of energy and transport in eight residential areas in the greater Oslo region. Urban Stud 42:2145–2166. doi: 10.1080/00420980500332064 CrossRefGoogle Scholar
  48. Hsu A, Moffat AS, Weinfurter AJ, Schwartz JD (2015) Towards a new climate diplomacy. Nat Clim Chang 5:501–503. doi: 10.1038/nclimate2594 CrossRefGoogle Scholar
  49. Huang J, Akbari H, Rainer L, Ritschard R (1991) 481 prototypical commercial buildings for 20 urban market areas. Lawrence Berkeley Laboratory, BerkeleyGoogle Scholar
  50. Hubacek K, Guan D, Barua A (2007) Changing lifestyles and consumption patterns in developing countries: a scenario analysis for China and India. Futures 39:1084–1096. doi: 10.1016/j.futures.2007.03.010 CrossRefGoogle Scholar
  51. Hunt DRG, Gidman MI (1982) A national field survey of house temperatures. Build Environ 17:107–124. doi: 10.1016/0360-1323(82)90048-8 CrossRefGoogle Scholar
  52. IEA (2008) World energy outlook. Head of communication and information. Office International Energy Agency (EIA), ParisGoogle Scholar
  53. IEA (2009) Cities, towns & renewable energy: yes in my front yard. International Energy Agency (IEA), ParisGoogle Scholar
  54. Jenks M, Burton E, Williams K (1996) Compact cities and sustainability: an introduction. In: Jenks M, Burton E, Williams K (eds) The compact city: a sustainable urban form? E & FN Spon. Chapman & Hall, LondonCrossRefGoogle Scholar
  55. Kennedy C, Steinberger J, Gasson B et al (2009) Greenhouse gas emissions from global cities. Environ Sci Technol 43:7297–7302. doi: 10.1021/es900213p CrossRefPubMedGoogle Scholar
  56. Kinnee EJ, Touma JS, Mason R et al (2004) Allocation of onroad mobile emissions to road segments for air toxics modeling in an urban area. Transp Res Part Transp Environ 9:139–150. doi: 10.1016/j.trd.2003.09.003 CrossRefGoogle Scholar
  57. Koa Corporation (2011) Traffic signal management and synchronization project city of Salt Lake City. Koa Corporation, Orange, CAGoogle Scholar
  58. Lankao PR, Tribbia JL, Nychka D (2009) Testing theories to explore the drivers of cities’ atmospheric emissions. Ambio 38:236–244CrossRefPubMedGoogle Scholar
  59. Lauvaux T, Pannekoucke O, Sarrat C et al (2009) Structure of the transport uncertainty in mesoscale inversions of CO2sources and sinks using ensemble model simulations. Biogeosciences 6:1089–1102CrossRefGoogle Scholar
  60. Lauvaux T, Miles NL, Deng A, et al. (2016) High resolution atmospheric inversion of urban CO2 emissions during the dormant season of the Indianapolis Flux Experiment (INFLUX). (minor revision in Atmospheric Chemistry and Physics)Google Scholar
  61. Lin T, Yu Y, Bai X et al (2013) Greenhouse gas emissions accounting of urban residential consumption: a household survey based approach. PLoS ONE 8, e55642. doi: 10.1371/journal.pone.0055642 CrossRefPubMedPubMedCentralGoogle Scholar
  62. Lochner (2013) UTA network study: next tier program final report. Lochner, Salt Lake CityGoogle Scholar
  63. Madireddy M, De Coensel B, Can A et al (2011) Assessment of the impact of speed limit reduction and traffic signal coordination on vehicle emissions using an integrated approach. Transp Res Part Transp Environ 16:504–508. doi: 10.1016/j.trd.2011.06.001 CrossRefGoogle Scholar
  64. McKain K, Wofsy SC, Nehrkorn T et al (2012) Assessment of ground-based atmospheric observations for verification of greenhouse gas emissions from an urban region. Proc Natl Acad Sci 109:8423–8428. doi: 10.1073/pnas.1116645109 CrossRefPubMedPubMedCentralGoogle Scholar
  65. Meehl GA, Washington WM, Collins WD et al (2005) How much more global warming and sea level rise? Science 307:1769–1772. doi: 10.1126/science.1106663 CrossRefPubMedGoogle Scholar
  66. Mendoza D, Gurney KR, Geethakumar S et al (2013) Implications of uncertainty on regional CO2 mitigation policies for the U.S. onroad sector based on a high-resolution emissions estimate. Energ Policy 55:386–395. doi: 10.1016/j.enpol.2012.12.027 CrossRefGoogle Scholar
  67. Newman DP, Kenworthy JR (1989) Cities and automobile dependence: a sourcebook. Gower Publishing, BrookfieldGoogle Scholar
  68. Norman J, MacLean H, Kennedy C (2006) Comparing high and low residential density: life-cycle analysis of energy use and greenhouse gas emissions. J Urban Plan Dev 132:10–21. doi: 10.1061/(ASCE)0733-9488(2006)132:1(10) CrossRefGoogle Scholar
  69. NRC (2010) Verifying greenhouse gas emissions: methods to support international climate agreements. National Research Council (NRC). The National Academies Press, WashingtonGoogle Scholar
  70. O’Toole R (2008) Does rail transit save energy or reduce greenhouse gas emissions? Cato Policy Anal 615:1–24Google Scholar
  71. Oliveira EA, Andrade JS, Makse HA (2014) Large cities are less green. Sci Rep. doi: 10.1038/srep04235 PubMedCentralGoogle Scholar
  72. Olivier JG, Janssens-Maenhout G, Muntean M, Peters J (2014) Trends in global CO2 emissions: 2014 report. JBL/JRC, The HagueGoogle Scholar
  73. Opower (2015) OPOWER. https://opower.com/. Accessed 17 Oct 2015
  74. Pachauri S (2004) An analysis of cross-sectional variations in total household energy requirements in India using micro survey data. Energy Policy 32:1723–1735. doi: 10.1016/S0301-4215(03)00162-9 CrossRefGoogle Scholar
  75. Pataki DE, Bowling DR, Ehleringer JR, Zobitz JM (2006) High resolution atmospheric monitoring of urban carbon dioxide sources. Geophys Res Lett 33, L03813. doi: 10.1029/2005GL024822 CrossRefGoogle Scholar
  76. Pataki DE, Xu T, Luo YQ, Ehleringer JR (2007) Inferring biogenic and anthropogenic carbon dioxide sources across an urban to rural gradient. Oecologia 152:307–322. doi: 10.1007/s00442-006-0656-0 CrossRefPubMedGoogle Scholar
  77. Pataki DE, Emmi PC, Forster CB et al (2009) An integrated approach to improving fossil fuel emissions scenarios with urban ecosystem studies. Ecol Complex 6:1–14. doi: 10.1016/j.ecocom.2008.09.003 CrossRefGoogle Scholar
  78. Petit JR, Jouzel J, Raynaud D et al (1999) Climate and atmospheric history of the past 420,000 years from the Vostok ice core, Antarctica. Nature 399:429–436. doi: 10.1038/20859 CrossRefGoogle Scholar
  79. Polyakov IV, Timokhov LA, Alexeev VA et al (2010) Arctic Ocean warming contributes to reduced polar ice cap. J Phys Oceanogr 40:2743–2756. doi: 10.1175/2010JPO4339.1 CrossRefGoogle Scholar
  80. Poumanyvong P, Kaneko S (2010) Does urbanization lead to less energy use and lower CO2 emissions? A cross-country analysis. Ecol Econ 70:434–444. doi: 10.1016/j.ecolecon.2010.09.029 CrossRefGoogle Scholar
  81. Rahmstorf S (2007) A semi-empirical approach to projecting future sea-level rise. Science 315:368–370. doi: 10.1126/science.1135456 CrossRefPubMedGoogle Scholar
  82. Rao P, Gurney K, Patarasuk R, et al. (2016) Spatio-temporal variations in onroad vehicle fossil fuel CO2 emissions in the Los Angeles Megacity (submitting to Environmental Polllution).Google Scholar
  83. Rayner PJ, Raupach MR, Paget M et al (2010) A new global gridded data set of CO2 emissions from fossil fuel combustion: methodology and evaluation. J Geophys Res Atmos 115, D19306. doi: 10.1029/2009JD013439 CrossRefGoogle Scholar
  84. Rignot E, Velicogna I, van den Broeke MR et al (2011) Acceleration of the contribution of the Greenland and Antarctic ice sheets to sea level rise. Geophys Res Lett 38, L05503. doi: 10.1029/2011GL046583 CrossRefGoogle Scholar
  85. RITA (2012) National transportation atlas database. Bureau of Transportation Statistics, US Department of Transportation.Research and Innovative Technology Administration (RITA). http://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/national_transportation_atlas_database/index.html. Accessed 11 Jul 2012
  86. Salt Lake City (2010) Salt Lake City: community carbon footprint. Salt Lake City, UTGoogle Scholar
  87. Salt Lake City (2011) Salt Lake City green: energy and transportation sustainability plan. Salt Lake City, UTGoogle Scholar
  88. Salt Lake City (2014) Plan Salt Lake: Existing conditions report. Salt Lake City, UTGoogle Scholar
  89. Salt Lake City (2015) Sustainable Salt Lake 2015. Salt Lake City, UTGoogle Scholar
  90. Salt Lake City Transportation Division (2013) Salt Lake City Traffic Studies (ESRI Geodatabase). Data provided by Salt Lake City Department of Information Management Services on 22 July 2013Google Scholar
  91. Salt Lake County Assessor’s Office (2013) Salt Lake County parcel data (ESRI Shapefile). Salt Lake County, UTGoogle Scholar
  92. Santamouris M, Kapsis K, Korres D et al (2007) On the relation between the energy and social characteristics of the residential sector. Energy Build 39:893–905. doi: 10.1016/j.enbuild.2006.11.001 CrossRefGoogle Scholar
  93. Schuur EAG, Bockheim J, Canadell JG et al (2008) Vulnerability of permafrost carbon to climate change: implications for the global carbon cycle. Bioscience 58:701–714. doi: 10.1641/B580807 CrossRefGoogle Scholar
  94. Seto KC, Fragkias M, Güneralp B, Reilly MK (2011) A meta-analysis of global urban land expansion. PLoS ONE 6, e23777. doi: 10.1371/journal.pone.0023777 CrossRefPubMedPubMedCentralGoogle Scholar
  95. Seto KC, Güneralp B, Hutyra LR (2012) Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proc Natl Acad Sci 109:16083–16088. doi: 10.1073/pnas.1211658109 CrossRefPubMedPubMedCentralGoogle Scholar
  96. Seto KC, Dhakal S, Bigio A et al (2014) Human settlements, infrastructure and spatial planning. In: Edenhofer O, Pichs-Madruga R, Sokona Y et al (eds) Climate change 2014: mitigation of climate change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, CambridgeGoogle Scholar
  97. Shakun JD, Clark PU, He F et al (2012) Global warming preceded by increasing carbon dioxide concentrations during the last deglaciation. Nature 484:49–54. doi: 10.1038/nature10915 CrossRefPubMedGoogle Scholar
  98. Smeds J, Wall M (2007) Enhanced energy conservation in houses through high performance design. Energy Build 39:273–278. doi: 10.1016/j.enbuild.2006.07.003 CrossRefGoogle Scholar
  99. Solomon S, Plattner G-K, Knutti R, Friedlingstein P (2009) Irreversible climate change due to carbon dioxide emissions. Proc Natl Acad Sci 106:1704–1709. doi: 10.1073/pnas.0812721106 CrossRefPubMedPubMedCentralGoogle Scholar
  100. Stephens BB, Gurney KR, Tans PP et al (2007) Weak northern and strong tropical land carbon uptake from vertical profiles of atmospheric CO2. Science 316:1732–1735. doi: 10.1126/science.1137004 CrossRefPubMedGoogle Scholar
  101. Strong C, Stwertka C, Bowling DR et al (2011) Urban carbon dioxide cycles within the Salt Lake Valley: a multiple-box model validated by observations. J Geophys Res Atmos 116, D15307. doi: 10.1029/2011JD015693 CrossRefGoogle Scholar
  102. Trenberth (2011) Changes in precipitation with climate change. Clim Res 47:123–138CrossRefGoogle Scholar
  103. Turnbull JC, Sweeney C, Karion A et al (2015) Toward quantification and source sector identification of fossil fuel CO2 emissions from an urban area: results from the INFLUX experiment. J Geophys Res Atmos 120:292–312. doi: 10.1002/2014JD022555 CrossRefGoogle Scholar
  104. UNFCCC (2015) Greenhouse Gas Inventory Data. http://unfccc.int/ghg_data/items/3800.php
  105. Vincent W, Jerram L (2006) The potential for bus rapid transit to reduce transportation-related CO2 emissions. J Public Transp. doi: 10.5038/2375-0901.9.3.12 Google Scholar
  106. Walker IS, Meier AK (2008) Residential thermostats: comfort controls in California Homes. Lawrence Berkeley National Laboratory, BerkeleyGoogle Scholar
  107. Wang R, Tao S, Ciais P et al (2013) High-resolution mapping of combustion processes and implications for CO2 emissions. Atmos Chem Phys 13:5189–5203. doi: 10.5194/acp-13-5189-2013 CrossRefGoogle Scholar
  108. Wheeler SM (2008) State and municipal climate change plans: the first generation. J Am Plan Assoc 74:481–496. doi: 10.1080/01944360802377973 CrossRefGoogle Scholar
  109. WWF, ICLEI (2015) Measuring up 2015: how local leadership can accelerate national climate goals. World Wildlife Fund (WWF), Local Governments for Sustainability (ICLEI) USA, WashingtonGoogle Scholar
  110. York R, Rosa EA, Dietz T (2003) STIRPAT, IPAT and ImPACT: analytic tools for unpacking the driving forces of environmental impacts. Ecol Econ 46:351–365. doi: 10.1016/S0921-8009(03)00188-5 CrossRefGoogle Scholar
  111. Zhao Y, Nielsen CP, McElroy MB (2012) China’s CO2 emissions estimated from the bottom up: recent trends, spatial distributions, and quantification of uncertainties. Atmos Environ 59:214–223. doi: 10.1016/j.atmosenv.2012.05.027 CrossRefGoogle Scholar
  112. Zheng S, Wang R, Glaeser EL, Kahn ME (2010) The greenness of China: household carbon dioxide emissions and urban development. J Econ Geogr lbq031. doi:  10.1093/jeg/lbq031
  113. Zhou Y, Gurney K (2010) A new methodology for quantifying on-site residential and commercial fossil fuel CO2 emissions at the building spatial scale and hourly time scale. Carbon Manag 1:45–56. doi: 10.4155/cmt.10.7 CrossRefGoogle Scholar

Copyright information

© 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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