The Impact of Urban Form and Spatial Structure on per Capita Carbon Footprint in U.S. Larger Metropolitan Areas
Abstract
:1. Introduction
2. Methodology and Data
2.1. Methodology
2.2. Dataset
2.2.1. Carbon Footprint Indicators
2.2.2. Explanatory Variables
3. Results
3.1. Location Patterns of Cities with Higher and Lower Carbon Footprint
3.2. Location Pattern of Cities According to Their Form and Spatial Structure
3.3. Urban Form and Spatial Structure as Determinants of Carbon Footprints
4. Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
Appendix A
Data and Sources | Original Indicator | Primary Conversion Factors | Secondary Conversion Factors | Summary | |
---|---|---|---|---|---|
Mobility. Gas consumption. | National Household Transportation survey (NHT) 2001 | Miles of travel | NHT estimates gasoline consumption based on the type of vehicle. From the consumption of gasoline, CO2 emissions are estimated. Gallons of gasoline: lbs. CO2 *19,564 | To incorporate indirectly emitted CO2, 20% is added to the previous values. | Data miles traveled. NHT converts them into gasoline consumption. General model where gasoline consumption is explained by socio-economic variables and characteristics of the ZIP code (population density and distance to the CBD). Consumption of gasoline by a standard family ($62,500 rent and 2.62 members) is estimated for each census tract. Finally, census tracts aggregate metropolitan areas are formed. |
Housing. Heating. Fuel consumption. | Census 2000 IPUM (sample 5%). | House owners and tenant expenditure | Fuel oil 22.39 lbs. of CO2 per gallon. Natural gas 120.59 CO2 per 100 cubic feet. | To incorporate indirectly emitted CO2, 20% is added to the previous values. | With a sub-sample of IPUM for owners who live in the 66 metropolitan areas studied, a regression is carried out where the consumption of fuel oil and natural gas depends on individual characteristics for each metropolitan area. They use the coefficients to estimate the consumption of gas and fuel oil for a family of 2.62 members with an income of $62,500, controlling for individual characteristics and temperature but not for the type of building. CO2 is estimated by applying the conversion factors. |
Housing. Electricity. | Census 2000 IPUM (sample 5%) North American Electricity Reliability Corporation (NERC). | House owners and tenant expenditure. | Conversion of electricity expenditure to electricity consumption. | Conversion of electricity consumption into CO2 emissions. | With a sub-sample of IPUM for owners who live in the 66 metropolitan areas studied, a regression is carried out where electricity consumption depends on individual characteristics for each metropolitan area. They use the coefficients to estimate the consumption of gas and fuel oil for a family of 2.62 members with an income of $62,500, controlling for individual characteristics and temperature but not for the type of building. Consumption spending is converted by considering regional electricity markets. Primary energy sources are controlled for. CO2 is estimated by applying the conversion factors |
Data and Sources | Original Indicator | Transformed Indicators | Primary Conversion Factors | Secondary Conversion Factors | Summary | |
---|---|---|---|---|---|---|
Mobility. Gas consumption Reference [40] | 1. Daily Vehicle Miles of travel (DVMT) (Highway Performance Monitoring System (HPMS); Federal Highway national Administration (FHWA); Highway Statistics (FHWA). 2. Conversion into gallons of fuel consumed (Oack Ridge National Laboratory (ORLN); Transportation Energy data Book; FHWA Highway Statistics Publications Tracks: US Census Bureau 2002 Vehicle Inventory and US Survey (VIVS); FHWA’s Highway Statistics. | Daily Vehicle Miles of travel (DVMT). | Gas consumption by cars and small trucks | Caloric content of fuels (Btu/gallon) | Conversion of caloric content into CO2 (TgCO2/QBtu). | 1. DVMT calculation at urban area scale 2. Rescale at the metropolitan area scale 3. Conversion of fuel consumption into CO2. |
Heating Fuel Reference [39] | EIA (Fuel consumption per household. Census, 2000. Environmental Protection Agency (EPA) 2007 conversion factors. | Households’ fuel consumption at state level | Fuel consumption considering differences in housing typologies. | EPA (2007) CO2/fuel type. | 1. Fuel consumption per family at state level. 2. Fuel consumption according to type of housing nationwide. 3. Number of households for each metropolitan area according to housing type. 4. Assign fuel consumption at the metropolitan scale according to the weight of each type of housing in the metropolitan area 5. Fuel consumption at metropolitan scale 6. Conversion of fuel consumption into CO2 | |
Electricity Reference [39] | Platts Analytics Census 2000 Brooking Institution EIA (Annual Energy Outlook) EIA (state electricity profiles). | Utilities $ and MWh | Direct payments Household and consumption estimation of households that pay for their electricity consumption in the rental of the property. | Conversion MWh/Btu (10776). | Tones CO2/MWh (0.62). | 1. MWh for each utility in 100-m areas (Platts Analytics). 2. Estimate the number of households with the scope map of the different utilities. 3. Total consumption ZIP code = average consumption per number of households. 4. Aggregation at county level. 5. Adjust consumption included in rentals. 6. Add to metropolitan scale. 7. Convert MWh into CO2 emissions. |
Variable | Obs. | Mean | Std. Dev. | Min. | Max. | 10% |
---|---|---|---|---|---|---|
cbd | 75 | 10.76 | 4.93 | 2.8 | 28.2 | 1.08 |
centrali | 74 | 34.98 | 14.27 | 6.18 | 85.84 | 3.50 |
subcenters | 75 | 7.78 | 6.47 | 0 | 28.8 | 0.78 |
policentr | 74 | 26.69 | 17.76 | 0 | 59.27 | 2.67 |
dispers | 75 | 81.47 | 6.03 | 62.9 | 95.6 | 8.15 |
Descriptive Statistics | 10% Increase in x Variable | ||||||||
---|---|---|---|---|---|---|---|---|---|
Variable | Obs. | Mean | Std. Dev. | Min | Max | % Change | Var. Exp. | ||
Elec. 2 | 58 | 3.164 | 1.236 | 1.107 | 5.035 | −0.08 | 3.09 | −2.43 | centrali |
Auto 3 | 58 | 4.745 | 0.602 | 3.191 | 5.860 | −0.02 | 4.73 | −0.36 | subcenters |
Auto 4 | 58 | 4.745 | 0.602 | 3.191 | 5.860 | 0.22 | 4.97 | 4.64 | dispers |
Descriptive Statistics | 10% Increase in x Variable | ||||||||
---|---|---|---|---|---|---|---|---|---|
Variable | Obs | Mean | Std. Dev. | Min | Max | % Change | Var. Exp. | ||
Elec 1 | 75 | 0.708 | 0.298 | 0.16 | 1.3 | −0.025 | 0.683 | −3.51 | centrali |
Fuel 2 | 75 | 0.333 | 0.194 | 0.022 | 0.71 | −0.006 | 0.326 | −1.94 | CBD |
Fuel 3 | 75 | 0.333 | 0.194 | 0.022 | 0.71 | −0.002 | 0.330 | −0.70 | subcenters |
Fuel 4 | 75 | 0.333 | 0.194 | 0.022 | 0.71 | 0.041 | 0.373 | 12.25 | dispers |
Auto 3 | 75 | 1.078 | 0.176 | 0.664 | 1.435 | −0.008 | 1.070 | −0.74 | policentr |
Total 2 | 75 | 2.118 | 0.401 | 1.245 | 2.804 | −0.017 | 2.101 | −0.83 | centrali |
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Ranking | Population Density pop/ha | Monocentrism (CBD) | Polycentrism (Subcenters) | Dispersion (Disperse) |
---|---|---|---|---|
1 | New York 2028.7 | Las Vegas 28.2 | L.A. 28.8 | Allentown 95.6 |
2 | Chicago 1322 | Birmingham 22.8 | San Francisco 24.2 | Orlando 92.1 |
3 | Miami 1230 | Bakersfield 21.7 | San Diego 22.7 | Springfield 91.2 |
4 | Philadelphia 1042.7 | Austin 21.5 | Detroit 22.2 | Tucson 91 |
5 | Providence 1041.5 | Dayton 20.1 | Houston 20.8 | Harrisburg 88.4 |
6 | Boston 1034.1 | Syracuse 19.3 | Omaha 20.8 | Greenville 88.3 |
7 | San Francisco 955 | Charleston 16.8 | Dallas 15.8 | El Paso 88 |
8 | Milwaukee 942.3 | New Orleans 16.7 | San Antonio 15.6 | Albuquerque 87 |
9 | Tampa 938.1 | Omaha 16.4 | Miami 15 | Cleveland 87 |
10 | Detroit 831.1 | Columbia 16.3 | Norfolk 14.3 | Buffalo 86.9 |
Urban Model Variables | Elec 1 | Elec 2 | Fuel 1 | Fuel 2 | Auto 1 | Auto 2 | Auto 3 | Auto 4 | Total |
---|---|---|---|---|---|---|---|---|---|
Population | 0.0003 (3.0) | −0.00005 (−3.0) | |||||||
Density | 0.0009 (2.3) | 0.0003 (1.93) | −0.0002 (−4.8) | 0.0015 (2.3) | |||||
CBD | |||||||||
Centrali | −0.022 (−2.14) | ||||||||
Subcentre | −0.022 (−2.6) | ||||||||
Policentri | |||||||||
Dispers | 0.027 (2.6) | ||||||||
Control Variables | |||||||||
Income | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Temp | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Coast | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Regulation | Yes | Yes | |||||||
Historic population growth | Yes | Yes | Yes | Yes | |||||
Pop 1900 | Yes | ||||||||
No. obs. | 58 | 57 | 45 | 45 | 58 | 45 | 45 | 58 | 58 |
Adj. R2 | 0.45 | 0.27 | 0.70 | 0.69 | 0.54 | 0.56 | 0.51 | 0.44 | 0.31 |
Urban Model Variables | Elect 1 | Fuel 1 | Fuel 2 | Fuel 3 | Fuel 4 | Auto 1 | Auto 2 | Auto 3 | Total 1 | Total 2 |
---|---|---|---|---|---|---|---|---|---|---|
Population | −0.00002 (−4.47) | −0.00003 (−1.98) | ||||||||
Density | 0.00009 (2.18) | −0.0003 (−5.42) | ||||||||
CBD | −0.006 (−2.05) | |||||||||
Centrali | −0.0071 (−2.6) | −0.005 (−2.29) | ||||||||
subcentres | −0.003 (−2.16) | |||||||||
policentri | −0.003 (−2.13) | |||||||||
dispers | 0.005 (2.43) | |||||||||
Control Variables | ||||||||||
GDP | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Temp | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Coast | Yes | |||||||||
Regulation | Yes | Yes | Yes | |||||||
Historic population growth | Yes | Yes | Yes | Yes | ||||||
Pop 1900 | Yes | Yes | ||||||||
No. obs. | 53 | 54 | 75 | 75 | 75 | 75 | 54 | 74 | 54 | 74 |
Adj. R2 | 0.48 | 0.71 | 0.56 | 0.64 | 0.65 | 0.23 | 0.36 | 0.31 | 0.48 | 0.44 |
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Muñiz, I.; Dominguez, A. The Impact of Urban Form and Spatial Structure on per Capita Carbon Footprint in U.S. Larger Metropolitan Areas. Sustainability 2020, 12, 389. https://doi.org/10.3390/su12010389
Muñiz I, Dominguez A. The Impact of Urban Form and Spatial Structure on per Capita Carbon Footprint in U.S. Larger Metropolitan Areas. Sustainability. 2020; 12(1):389. https://doi.org/10.3390/su12010389
Chicago/Turabian StyleMuñiz, Ivan, and Andrés Dominguez. 2020. "The Impact of Urban Form and Spatial Structure on per Capita Carbon Footprint in U.S. Larger Metropolitan Areas" Sustainability 12, no. 1: 389. https://doi.org/10.3390/su12010389