136 Energy Consumption and CO 2 Emissions in Urban Counties in the United States with a Case Study of the New York Metropolitan Area Lily Parshall,* Stephen A. Hammer, Kevin Robert Gurney CHAPTER 4 Summary Urban areas are setting quantitative, time-bound targets for emissions reduc- tions within their territories; designing local policies to encourage shiſts toward cleaner energy supply, higher energy efficiency, and transit-oriented develop- ment; and exploring ways to participate in local carbon markets. ese efforts require systematic estimates of energy consumption and emissions presented in a format and at a spatial resolution relevant for local governance. e Vulcan data product offers the type of high-resolution, spatial data on energy consumption and CO 2 emissions needed to create a consistent inventory for all localities in the continental United States. We use Vulcan to analyze patterns of direct fuel consumption for on-road transportation and in buildings and industry in urban counties. We include a case study of the New York Metropolitan Area. *Corresponding author: [email protected]
Transcript
136 �
Energy Consumption and CO2 Emissions in Urban Counties
in the United States with a Case Study of the New York Metropolitan Area
Lily Parshall,* Stephen A. Hammer, Kevin Robert Gurney
CHAPTER4
Summary
Urban areas are setting quantitative, time-bound targets for emissions reduc-tions within their territories; designing local policies to encourage shifts toward cleaner energy supply, higher energy efficiency, and transit-oriented develop-ment; and exploring ways to participate in local carbon markets. These efforts require systematic estimates of energy consumption and emissions presented in a format and at a spatial resolution relevant for local governance. The Vulcan data product offers the type of high-resolution, spatial data on energy consumption and CO2 emissions needed to create a consistent inventory for all localities in the continental United States. We use Vulcan to analyze patterns of direct fuel consumption for on-road transportation and in buildings and industry in urban counties. We include a case study of the New York Metropolitan Area.
Over the past decade, research studies on urban energy consumption and green-house gas (GHG) emissions have multiplied in recognition of the central role that cities play in shaping global energy demand as well as increasing urban leader-ship on climate change mitigation. Urban areas are setting quantitative, time-bound targets for emissions reductions within their territories; designing local policies intended to encourage shifts toward cleaner energy supply, higher energy efficiency, and transit-oriented development; and exploring ways to participate in local carbon markets. These efforts require systematic estimates of energy consumption and emissions presented in a format and at a spatial resolution relevant for local governance. Consistent estimates are also needed to understand how and why localities differ in their consumption profiles, develop appropriate benchmarks, analyze how different aspects of the urban environment interact with socio-demographic and market factors to shape patterns of energy use, and explore the distributional implications of different emissions-reduction policies. The Vulcan data product offers the type of high-resolution, spatial data on energy consumption and CO2 emissions needed to create a consistent inventory for all localities in the continental United States (Gurney et al. 2008; Gurney et al. 2009). In Parshall et al. (2009), we describe our methodology for designing a national inventory at the local scale using Vulcan as the data source. In this paper, we present our results for urban counties in the continental United States and a case study of the New York Metropolitan Area (NY Metro Area). Our analysis focuses on direct final consumption of fossil fuels and associated CO2 emissions, the portion of total emissions for which we could establish consistent, consumption-based estimates for both energy and emissions. We cover heating fuels (natural gas and LPG, distillate and residual fuel oil) and transport fuels (gasoline and diesel); the exclusion of electricity means that energy consumption and CO2 emissions are highly correlated. We show that there is substantial variation in consumption patterns, both across urban areas, as well as within them.
II. PREVIOUS STUDIES
A range of different methodological approaches, accounting systems, urban bound-aries, and datasets have been employed to study urban consumption patterns, with continuing disagreement on what portion of global emissions should be attrib-uted to cities and/or urban households and how best to support urban energy and climate initiatives through local inventories. In the United States, many cities, towns, and counties have followed protocols established by ICLEI–Local Govern-ments for Sustainability (ICLEI 2008). An advantage of ICLEI’s approach is that
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local authorities complete the inventory for their own jurisdictional area using a consistent, consumption-based approach; on the other hand, it can be difficult to compare participating localities because they have some latitude in their choice of data, baseline year, and level of detail, and are free to decide whether and how to disseminate inventory data and results. Partially in response to this limitation, Brown et al. (2008) developed a consistent methodology for computing partial carbon footprints for the 100 largest US metropolitan areas, one of the most comprehensive consumption-based inventories available. The study, which covered residential consumption of electricity and heating fuels as well as highway trans-portation, found that the 100 largest metropolitan areas account for 65% of the US population, 76% of GDP, and 56% of carbon emissions (Brown et al. 2008).1 The addition of commercial and industrial emissions might increase the urban share since the majority of these activities occur within metropolitan area boundaries.
We show in Parshall et al. (2009) that disaggregating US metropolitan areas into counties reveals substantial differences in the urban and rural portion of metropolitan areas. VandeWeghe and Kennedy (2007) disaggregate the Toronto Metropolitan Area into Census tracts and find that per-capita building-related emissions (electricity and fuel in residential, commercial, and industrial buildings) dominate within the urban core and transport emissions are higher outside the urban core. An older study of gasoline consumption found that in 1980 the typical resident of the New York Tristate Region used 335 gallons of gasoline per person, whereas a resident of New York City (5 boroughs) used 153 gallons per person, and a resident of New York County (Manhattan) used 90 gallons per person (Newman and Kenworthy 1989).
Whereas Brown et al. (2008) covered a few sectors for a large number of localities, Kennedy et al. (2009) developed complete emissions inventories for a small number of global cities. The study, which covered 10 major cities, estimates emissions based on several different accounting frameworks and suggests that a lifecycle (or industrial ecology) perspective offers a more complete view of the amount of energy consumed to meet urban demand. Ramaswami et al. (2008) offers a life-cycle perspective on urban-scale GHG emissions accounting. Dodman (2009) reviews existing literature on urban emissions, showing the diffi-1The Brookings study attributed emissions associated with electricity production to the point of demand, an approach consistent with ICLEI’s protocols, but one that can present particular methodological challenges because it requires estimating both total electricity consumption within a locality as well as the local fuel mix. In general, utility service boundaries do not match geopolitical boundaries, and the local fuel mix may differ substantially from the state fuel mix. In the Brookings study, a multi-step procedure was followed to derive consumption in each metropolitan area from data on utility service areas and then a statewide average fuel mix was applied to derive emissions (Brown et al. 2008).
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culty of comparing studies based on differing perspectives and methodologies and from different parts of the world.
Dodman (2009), Newman and Kenworthy (1989), and Kennedy et al. (2009) all focus on major, international cities. In the United States, a growing share of the population lives in smaller and/or less dense urban areas, a trend attributable to the shift toward a more dispersed, service-oriented economy in the latter half of the 20th century (Kim, 2000). These urban areas, many of which rely on personal vehicles as the primary commute mode, are responsible for a growing share of energy demand. Along with Brown et al. (2008), our work is one of the few attempts to compare a large number of urban areas in the United States, rather than focusing on in-depth case studies of a small number of major cities.
Satterthwaite (2008) argues that the goal of urban emissions inventories should be to understand the role of urban lifestyles in different parts of the world, so inventories should include emissions embodied in goods and services. Quanti-fying embodied emissions is difficult, particularly given the globalized nature of the economy; examples of recent studies include Lenzen et al. (2004) and Weber and Matthews (2007). Ramaswami et al. (2008) shows that embodied emissions can be computed for key urban material inputs and that including such emissions helps to reconcile urban GHG inventories with national inventories.
Given the difficulty of measuring embodied energy, most quantitative studies estimate CO2 emissions associated with consumption of energy for electricity, heat and transportation (and, in complete GHG inventories, emissions associated with waste, material production, etc.) within a territorial boundary. Based on this approach, the IEA estimated that 80% of primary energy demand in the United States is attributable to urban areas (IEA, 2008).2 In our previous work, we found that between 37% and 81% of direct final consumption occurs in urban areas, depending on how these areas are defined and bounded in space (Parshall et al., unpublished manuscript 2009). Our focus on energy consumption within a terri-torial boundary is consistent with the perspective taken in most local climate, energy, and sustainability plans in the United States, which tend not to address embodied energy. Since we focus only on direct final consumption, the spatial location of energy consumption is the same as the spatial location of combustion, and thus CO2 emissions. We estimate both energy consumption and emissions because the ability to distinguish between the energy and carbon intensity of different sectors can help local authorities analyze trade-offs between policies to reduce energy consumption and policies to reduce the carbon intensity of fuel use.
2The IEA methodology for computing US urban energy consumption is available on the World Energy Outlook website. The Vulcan data product was used in the US analysis, but the methodology was somewhat different from the methodology employed in Parshall et al. (2009), and the electricity sector was included. See: www.worldenergyoutlook.org/docs/weo2008/WEO_2008_Energy_Use_In_Cities_Modeling.pdf.
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III. QUANTIFYING US URBAN ENERGY CONSUMPTION AND CO2 EMISSIONS
1. Definition of Urban
In Parshall et al. (2009), we evaluated a range of possible definitions of urban for use in local-scale energy and emissions inventories (see Table 1 below). We found that the county-based definition described in Isserman (2005) is most appro-priate because counties are the highest resolution at which non-point source CO2 emissions data are available, and because Isserman’s definition incorporates the US Census thresholds for urban population density and size. Under this defini-tion, the core portions of major cities in the United States tend to be defined as urban, but smaller cities and suburban regions tend to be excluded. For example, in California only the urban cores of San Francisco and Los Angeles are classified as urban, although the majority of California’s counties are in metropolitan areas.
A county-based definition is also appealing because counties are the smallest political unit into which the entire US population can be divided (versus cities and towns which represent only a portion of the US population). Unlike counties, which are a recognized administrative division of state government, metropolitan areas do not play a formal role in governance, reducing the value of metropolitan-scale data to local policy makers. In Kennedy et al. (unpublished manuscript 2009a), the US cities included are defined as either counties (Los Angeles County, Denver County) or as a collection of counties (e.g. the 5 counties of New York City).
Isserman (2005) classifies 157 counties in the continental United States as urban. Although this represents just 5% of counties, it encompasses 45% of the population. By contrast, more than one-third of all counties are in metropolitan areas and more than 50% of the Census-designated rural population lives in metropolitan areas (Isserman, 2005). Also, note that 83% of the US population lives in metropolitan areas and the Census defines 78% of the US population as urban.
Some major US cities are located in counties that do not meet the Isserman (2005) urban criteria. Most notably, Maricopa County in Arizona, which contains the city of Phoenix, is not classified as an urban county. Although this county meets the population density criterion, its sizeable exurban population resulted in a designation of mixed urban, rather than urban. In the western United States, counties are less likely to be classified as urban because cities are often embedded into large counties; in the eastern United States, counties tend to be smaller in spatial extent, so many cities span multiple counties.
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United States Definition
Spatial Units
Used to Construct
Boundaries
Advantages and Disadvantages
of Classification System
Urban area (Census)
Urban cluster or urbanized area
Census block
Most accurate representation of where people live, but spatial boundaries are not aligned with adminis-trative boundaries. Non-urban settlements not separated from one another.
Urbanized area
>50,000 people, 1000/mi2 (386/km2)
Urban cluster >2,500 people, 500/mi2 (193/km2)
Metropolitan area
Core urban center + adjacent counties defined by the U.S. Office of Management and Budget (OMB)
County
Only scale at which GDP data are available, but metropolitan areas include the majority of the rural population and are not irreducible units since they are composed of counties.
Rural-urban continuum
Based on metro/non-metro, adjacent to metro, and population
CountyImproves on metro/non-metro classification of counties, but does not explicitly separate urban and rural population.
Rural-urban density code (Character)
Based on urban-rural density mix and urban agglomeration
County
Classifies counties as urban or rural using density and agglomeration thresholds for Census urban areas as a starting point, so has the advantage of separating urban and rural areas at an administrative level, but counties may include multiple cities or towns.
Commuting area
Based on commuting flow and classification of destination
Census tract
More accurate than metropolitan areas at separating integrated urban regions, but defined at the Census tract level, which is higher resolution than available energy/emissions data.
Urban influence code
Based on metro/micro, core/non-core, existence of town, and population
CountyImproves on urban/rural continuum, but is a somewhat cumbersome classification system and still does not explicitly separate urban and rural counties.
Populated place
Political boundaries for cities, towns, and other incorporated places based on Census (2000)
Census block
Smallest unit for local jurisdictions and appealing from a local governance standpoint, but does not reflect a consistent definition of cities and has a higher resolution than available energy/emissions data.
Urban land cover
Areas of built-up land where large populations exist based on the urban layer of the DCW
None
Based on land use, but spatial boundaries are not aligned with administrative boundaries. Data on population, income, etc. do not match spatial boundaries.
International (selected)
United NationsAccepts urban definition of each country
Non-spatialAuthoritative international source for urban and rural population counts, but does not reflect a standard, international definition of urban.
GRUMP urbanUrban extents identified from night lights, DCW data, and other sources
Urban extents
defined by analysis
Only international, spatial dataset with urban boundaries, but urban extents do not match other types of urban boundaries in the US.
The rural-urban density code (in bold) is the definition of urban used in this paper. Source: Parshall et al. 2009.
TABLE 1 Urban/Rural Definitions in the United States
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2. Vulcan Data Product
We used the Vulcan data product, developed at Purdue University, to estimate energy consumption and related CO2 emissions in each urban county. The Vulcan United States fossil fuel CO2 emissions inventory covers the continental United States and contains hourly data for 2002, although we use aggregate annual data. Vulcan consolidates data from a wide variety of point, non-point, and mobile sources and quantifies these data in their « native » resolution (geocoded points, roads, counties) and on a regular 100-km2 grid over the conterminous United States. Vulcan was originally conceived of as an inventory of fossil-based sources of carbon with scientific applications in carbon cycle modeling, so the dataset does not cover renewable energy or nuclear power, which together comprise approximately 28% of electricity supply and 5% of direct fuel consumption (e.g. for heating, industrial processes). Also, because the dataset attributes electricity emissions to the point of production, rather than the point of consumption, we exclude the electricity sector from our analysis. Since electricity generation comprises 34% of US nationwide GHG emissions, it is important to emphasize that our analysis covers only a portion of total emissions (US EPA 2009). The complete Vulcan methodology and data sources are described in Gurney et al. (2008) and Gurney et al. (2009). Our evaluation of the Vulcan data product and our methodology for extracting and processing relevant data are described in Parshall et al. (2009).
3. Energy and Emissions Accounting
In our analysis, we focus on direct fuel consumption and associated CO2 emissions in urban areas. From an energy accounting perspective, we cover direct final consumption (i.e. delivered energy) within the local territory. From a greenhouse gas emissions accounting perspective, we cover IPCC Scope 1 CO2 emissions associated with stationary combustion in the energy sector, but exclude utility-consumed fuel for electricity/heat generation. This approach is consistent with ICLEI’s community-scale Scope 1, which covers « all direct emissions sources located within the geopolitical boundary of the local government » (in this case, each county) (ICLEI, 2008).
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4. Sector/fuel Categories
We classified fuel consumption into the following categories: 1) gasoline consump-tion for on-road transportation, 2) diesel consumption for on-road transporta-tion, 3) direct consumption of natural gas and LPG in buildings and industry, 4) direct consumption of distillate and residual fuel oil in buildings and industry. We excluded direct consumption of coal because its contribution to total direct fuel consumption is ~1%. Direct fuel consumption in the buildings and industry sector is primarily for heating and hot water, though natural gas also is used for cooking. In industrial buildings, fuel also is consumed in industrial production, processing, and assembly of goods. Emissions from Vulcan can be reported by sectoral (residential, commercial, industrial, transportation) and/or fuel divisions (coal, oil, natural gas). Rather than employ a dataset with sectoral categories, we used a dataset that covered all non-electricity emissions and where emissions were disaggregated by sub-fuel. This allowed us to convert between CO2 emissions and energy consumption with reasonable accuracy, but it did not allow us to separate the residential sector from the commercial and industrial sectors.
5. Urban Energy and CO2 Index
We compare urban counties on the basis of per-capita direct fuel consumption and CO2 emissions, a subset of total urban CO2 emissions. To facilitate rapid comparisons of groups of counties, we created an index that assigns a value of 100 to average per-capita consumption. The index value for each urban county is the percent of the average. For example, a county with twice the average per-capita consumption is assigned an index value of 200. Index values were computed for each sector/fuel combination based on both energy consumption (in GJ per capita) and CO2 emissions (in metric tons per capita).
IV. PATTERNS OF US URBAN ENERGY CONSUMPTION AND CO2 EMISSIONS
1. Total Direct Fuel Consumption
In the United States, 45% of the population lives in urban counties, which account for 37% of direct fuel consumption for on-road transportation, building heat, and heat used in industrial processes. Average per-capita CO2 emissions were estimated to be 5.0 tons per capita for on-road transportation and 4.7 tons per capita for consumption of natural gas and heating oil in buildings and industry (Table 2).
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In Figure 1, we compare urban counties across the United States. These results reveal that, on a per-capita basis, southern and urban outskirt counties (i.e. urban counties that are economically dependent on another county’s commercial center) tend to have the highest gasoline consumption, and southern and midwestern counties tend to have the highest diesel consumption. Midwestern counties tend to have the highest natural gas consumption, and northeastern counties tend to have the highest fuel oil consumption.
In the Appendix, we list index ranges for each urban county for selected sector/fuel categories.
Urban CountiesEnergy Consumption
(GJ/capita)Emissions
(Mt CO2/capita)
Emissions (% of per-capital
total)
Natural gas and LPG
72.2 3.7 38.3%
Fuel oil 14.3 1.0 10.3%
Buildings and Industry
86.5 4.7 48.6%
Gasoline 61.6 4.1 42.5%
Diesel 12.4 0.9 8.9%
Transportation 74.0 5.0 51.4%
Direct Fuel Consumption
160.9 9.7 100%
TABLE 2 Average Per-Capita Fuel Consumption and CO2 Emissions in Urban Counties
*Note that the total population in urban counties in 2000 was 125,254,025 people. Data sources: Vulcan data product for energy and emissions data, US Census 2000 for population data.
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FIGURE 1 CO2 Emissions Associated with Direct Fuel Consumption in US Urban Counties
Only counties classified as urban by Isserman (2005) are shown. Note that each dot represents a single county. Some urban areas encompass several counties. Data source: Vulcan data product.
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2. Transportation
In general, transportation results are easier to interpret than results for build-ings and industry because they are not affected by regional differences in climate. Per-capita gasoline consumption is 12% lower in urban counties than the national average and, unlike some of the other sector/fuel categories, has a normal distri-bution and a small variance (Figure 2). In other words, urban areas differ less in their gasoline consumption compared with other fuels.
FIGURE 2 Distribution of CO2 Emissions Index Values
Mean for each variable is 100. Gasoline standard deviation is 66 versus 98 for diesel, 307 for natural gas & LPG, 863 for fuel oil, and 248 for the total (all fuels). Note that index values above 300 are not shown on the graph, although the distributions do include outliers with values > 300. Data source: Vulcan data product
Comparing the 157 urban counties reveals no relationship between population density and per-capita gasoline consumption for counties with fewer than 1000 people per square kilometer, but a relationship does emerge for higher-density areas (Figure 3). Note that there are just 33 counties with population density above 1000 people per square kilometer, and that 24 of these have an index value of less than 100. Among these 24 counties are many of the oldest and largest East Coast cities including New York City (5 boroughs), Philadelphia, Washington D.C. plus Alexandria and Arlington counties, Suffolk County (Boston), Baltimore City, and the counties containing Jersey City and Newark, NJ. The list, notably, does not include any counties in the south, southwest, or west (besides California). This supports the suggestion that current population density and access to public transit are an expression of when and why different urban economies developed
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(Kim 2000), and that cities’ historical economic development and timing of expansion have had a substantial impact on current consumption patterns. The lack of a relationship between population density and per-capita consumption and emissions at lower densities also suggests that the threshold above which there is a relationship between urban form and energy consumption may be relatively high in the United States, but additional, quantitative research is needed to confirm this preliminary finding.
FIGURE 3 Gasoline CO2 Emissions Index Versus Population Density
a) Urban counties with population density less than 1000 people/km2. b) Urban counties with population density between 1000 and 4000 people/km2. c) Urban counties with population density greater than 4000 people/km2. Note that the urban county with the lowest population density has 147 people/km2. Data Sources: Vulcan data product and US Census 2000.
3. Buildings and Industry
Energy demand for space heating and cooling is strongly related to climate, and the United States spans desert to sub-arctic climate zones. In most cases, electricity is the primary source of energy for space cooling, but natural gas and fuel oil are the primary sources of energy for space heating. Since we do not cover electricity, urban counties in warmer climate zones tend to have lower consumption in build-ings and industry compared with counties in cooler climates. Also, since we do not separate residential, commercial, and industrial consumption, counties with major commercial centers tend to consume more energy per capita than counties that are primarily residential. For example, New York County (Manhattan) has higher per-capita consumption than the outer boroughs of New York City.
Natural gas, which has lower CO2 emissions per unit of energy compared with fuel oil, is the dominant source of heat in the United States. In some urban areas in the Northeast (e.g. New York, Boston, New Haven), fuel oil consumption is
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more than twice the per-capita average across all urban counties. In some cases, urban areas with high fuel oil consumption have low natural gas consumption (e.g. areas in Connecticut such as Hartford, Stamford, and New Haven). In other cases, per-capita natural gas consumption may be higher than per-capita fuel oil consumption even though the index value for fuel oil is higher (e.g. in the urban counties surrounding Boston). This is related to the differing distributions for natural gas and fuel oil (see Figure 2). In urban areas in the North-Midwest (e.g. Chicago, Minneapolis, Detroit), the great majority of heating demand is met with natural gas. With the exception of Minneapolis, these urban areas have high index values (>125) for natural gas and low index values (<75) for fuel oil. These results must be viewed with caution, as electricity is not included in the analysis and can be used in some states in place of natural gas for water heating and space heating. Ideally building energy-use from both electricity and natural gas is summed to assess efficiency.
V. ENERGY CONSUMPTION AND CO2 EMISSIONS IN THE NEW YORK METROPOLITAN AREA
The NY Metro Area, which spans 23 counties in New York, New Jersey, and Pennsylvania, is one of the oldest, most densely-populated urban areas in the United States (Figure 4). The area’s large population (18.8 million people) and economy ($1.1 trillion or about 8% of total US GDP in 2006), translate into higher-than-average total energy consumption and CO2 emissions relative to other urbanized regions. On the other hand, per-capita emissions in the NY Metro Area’s 19 urban counties are more than 25% lower, on average, than per-capita emissions in all US urban counties (Figure 5). In the 5 counties of New York City, per-capita emissions are less than 50% of the US urban average.
The New York Metropolitan Area is highly integrated with several other adjacent metropolitan areas. Together, this “Combined Statistical Area,” which covers the portions of New York, New Jersey, Connecticut, and Pennsylvania within commuting distance of New York City, encompasses a total population of 21.9 million and is sometimes referred to as the “Tri-State Region.” To avoid confusion between these two designations, we refer to the New York Metro-politan area as the “NY Metro Area” and the Combined Statistical Area as the “Tri-State Region.” Within the Metro Area, 19 out of the 23 counties are classified as urban; within the Tri-State Region, 22 out of 29 counties are classified as urban.
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1. Total Direct Fuel Consumption
We estimated total direct fuel consumption of 125.4 GJ/capita in the NY Metro Area and 78.0 GJ/capita in New York City (Table 3). Associated CO2 emissions were estimated at 7.8 tons/capita and 4.8 tons per capita respectively. The differ-ence is mainly driven by much lower per-capita gasoline consumption in New York City compared with the NY Metro Area. Our estimates agree well with results presented in City of New York (2008), Brown et al. (2008), and Kennedy et al. (unpublished manuscripts, 2009). In Appendix C, we present detailed comparisons of our results with these other studies.
In Figure 6, we present total direct fuel consumption (in TJ of energy) in each county in the NY Metro Area. The counties are organized along the x-axis by population density, with higher-density counties on the left and lower-density counties on the right. In higher-density core counties, buildings and industry account for well over 50% of direct fuel consumption. In lower-density counties, gasoline consumption accounts for the majority of direct fuel consumption. Figure 6b shows the ratio between fuel consumption for buildings and fuel consumption for transportation against population density. In counties where the ratio is above 100%, building consumption dominates; in other counties, trans-portation consumption dominates. This effect would be dampened if electricity consumption were to be included in buildings and industry.
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Figure 4 NY Metro Area and Tri-State Region
Data sources: Spatial boundaries from Tele Atlas North America, Inc. and ESRI; county character clas-sification from Isserman (2005); population density from US Census 2000.
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Figure 5 CO2 Emissions Associated with Direct Fuel Consumption in Urban Counties in the NY Tri-State Region
Data sources: Vulcan data product.
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Figure 6a Direct Fuel Consumption in the NY Metro Area
FIGURE 6b Direct Fuel Consumption in the NY Metro Area
a) Values given are for total fuel consumption. Counties are organized in order of decreasing population density. All counties in the NY Metro Area are classified as urban with the exception of Putnam, Hunter-don, Sussex, and Pike. b) Values along the y-axis are per-capita buildings and industry fuel consumption (natural gas + LPG, fuel oil, coal) divided by per-capita transportation fuel consumption (gasoline, diesel). A value of 100% indicates that per-capita buildings and industry fuel consumption is equal to per-capita transportation fuel consumption. Data sources: Vulcan data product and US Census 2000.
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2. Transportation
New York City has the largest public transit system in the United States and is one of the few American cities where the majority of the population relies on public transportation to travel to work (City of New York 2007). Whereas nationally, 90% of households own one or more private vehicles, in New York City only 44% own a car (City of New York 2007). Reliance on public transit within the city is reflected in low per-capita gasoline consumption; outside the city, regional commuter railroads and bus systems serve densely-populated suburbs, but personal vehicles are the primary mode of transportation for most people. This is reflected in higher per-capita gasoline consumption outside the core, for example in Nassau and Suffolk counties on Long Island. Throughout the NY Metro Area, per-capita diesel consumption is lower than the national average, perhaps as a result of higher overall population density and thus fewer freight kilometers driven per person.
As Figure 7 confirms, only a small number of core urban counties are transit-
TABLE 3 Average Per-Capita Direct Fuel Consumption and CO2 Emissions for Subsets of Counties in the NY Metro Area Compared to the United States
Category (# of counties)
Buildings and Industry Transportation
Energy Consumption
(GJ/capita)
Emissions(Mt CO2/capita)
Energy Consumption
(GJ/capita)
Emissions(Mt CO2/capita)
Rural counties (1) 35.1 2.2 121.2 8.2
Mixed rural counties (2) 50.3 3.0 98.5 6.6
Mixed urban counties (1) 32.9 2.2 238.7 15.9
Urban counties (19) 57.3 3.2 56.2 3.8
NY Metro Area (23) 54.7 3.1 70.7 4.7
Rural counties (1767) 136.7 7.5 147.3 9.9
Mixed rural counties (1013) 143.8 8.0 103.4 7.0
Mixed urban counties (145) 89.4 4.8 86.2 5.8
Urban counties (157) 86.5 4.7 74.0 5.0
United States (3108) 133.8 7.4 125.8 8.5
New York State (62) 56.9 3.3 91.5 6.1
Tri-State Area (29) 53.2 3.0 73.1 4.9
New York City (5) 54.1 3.2 23.9 1.6
Data sources: Vulcan data product for energy and emissions data, US Census 2000 for population data. Note that the buildings and industry category covers only direct fuel consumption, so electricity is excluded.
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oriented, and it is these counties that drive down the metropolitan area’s average per-capita consumption. All counties, both urban and rural, in New York State, and the Tri-State Region (expanded metropolitan area) are shown on this plot to emphasize the distinction of a small sub-set of urban counties.
FIGURE 7 Population Density And Per-Capita CO2 Emissions for Transportation All counties in New York State and the Tri-State Region are Shown
Data sources: Vulcan data product and US Census 2000.
FIGURE 8 Population Density and Per-Capita CO2 Emissions for Buildings and Industry. All Counties in New York State and the Tri-State Region are Shown
Data sources: Vulcan data product and US Census 2000.
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3. Buildings and Industry
A number of factors shape the region’s energy consumption and emissions for buildings and industry. These include the region’s temperate climate and service-based economy as well as high energy prices. Among the 50 largest metropolitan areas in the United States, New York ranks 20th in terms of heating-degree days (Siviak 2008) and has the largest regional economy, both of which have an upward effect on per-capita emissions from heating fuel. Nonetheless, the area has lower-than-average per-capita natural gas and fuel oil consumption in all (natural gas) and most (fuel oil) urban counties. With the exception of New York County (Manhattan), counties within the urban core (New York City plus Newark and Jersey City) have high population density and low per-capita consumption (Figure 8), indicating the possible importance of multi-family housing stock and smaller-than-average residential housing units. High energy prices and reduced demand for space heating associated with the urban heat island effect may also have a downward effect on per-capita heating fuel consumption, whereas small household size and high per-capita income may have an upward effect on per-capita consump-tion. Further research is needed to analyze interactions between these factors.
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BOX 1
Comparison of New York City and NY Metro Area Estimates with Other Studies
Using the Vulcan data product, we estimated on-road transport emissions to be 1.6 tons per capita in New York City (5 boroughs) and building and industry emissions to be 3.2 tons per capita in 2002. These numbers agree well with the results of New York City’s greenhouse gas emissions inventory (City of New York, 2008). Based on data in this report, New York City’s on-road transport emissions were estimated to be 1.4 tons per capita. Building and industry emissions were estimated to be 5.9 tons per capita, including electricity (City of New York, 2008). The report indicates that 41% of city-wide emissions were associated with electricity versus 37% for heating fuels (including steam production); based on these figures, heating fuels account for approximately 2.8 tons of CO2 emissions per capita. This estimate is 15% lower than our estimate, a reasonable discrepancy given the differing methodologies and baseline years. Our numbers also agree well with fuel consumption data received from the City of New York (personal communication, 2008), and the New York City estimates in Kennedy et al. (unpublished manuscripts, 2009), which were also derived from City of New York data. Using data provided by the City of New York (personal communication, 2008), we estimated on-ground transportation consumption to be 22.1 GJ/capita; using the Vulcan data product, we estimated emissions to be 23.9 GJ/capita. For buildings and industry, we estimated heating fuel consumption of 50.2 GJ/capita and 54.1 GJ/capita using City of New York and Vulcan data, respectively.
Brown et al. (2008) estimated CO2 emissions associated with residential heating fuel to be 0.4 tons of carbon per capita, which is equivalent to 1.6 tons of CO2 per capita. In New York City, the residential sector accounts for approximately 52% of heating fuel consumption. Assuming a similar ratio for the NY Metro Area, total heating fuel emissions might be approximately 3.2 tons/capita, an estimate that agrees well with our estimate of 3.1 tons/capita based on Vulcan data. Transportation estimates are not as close: Brown et al. (2008) estimate 3.0 tons/capita, and we estimate 4.7 tons/capita. A large part of this discrepancy is likely explained by the exclusion of non-highway transportation in Brown et al. (2008).
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VI. CONCLUSIONS
Urban energy consumption is shaped by local geography and economic develop-ment, and by the history and culture of individual cities. A better understanding of consumption patterns can help individual urban areas move forward with locally-tailored energy efficiency and climate mitigation policy. A national inven-tory could also aid efforts to establish a formal role for local authorities in US energy and climate policy, which is currently dominated by federal and state governments.
Many cities have ambitious goals. For example, through PlaNYC, New York City has set a target of a 30% reduction in greenhouse gas emissions by 2030 (City of New York 2007). The city is pursuing a variety of technology shifts (e.g. promotion of combined heat and power and renewables) and policy changes (e.g. adaptation of the city’s building code). The city is also attempting to strengthen local energy governance (e.g. arguing for more direct control over energy efficiency funding traditionally controlled by the state). Policies currently under debate at the federal and state level focus most heavily on the power sector and on vehicle fuel efficiency standards. Therefore, there is an opening for urban areas to target consumption of heating fuels, which are the largest source of energy demand in many cities in the northern half of the United States, and to promote building-level energy efficiency measures as a complement to national and state measures targeting power generation and/or distribution utilities.
A complete and regularly updated inventory could help to quantify the potential contribution of these and other urban initiatives. The Vulcan data product could become the basis of an effort to develop and institutionalize a national inventory at the local scale, although further research is needed to address some the limitations of Vulcan. For example, since Vulcan was originally intended to be a production-based inventory of carbon emissions, currently it is not possible to estimate urban electricity consumption, just electricity production. Because electricity use in buildings and in the urban rail transport sector is not incorporated, the Vulcan data product presents an incomplete picture of these sectors. A more wide-reaching eff ort would involve the development of interna-A more wide-reaching effort would involve the development of interna-tionally recognized standards for local-scale energy and emissions inventories, which could help to identify low-energy and low-carbon pathways in a range of different local settings.
158 � CITIES AND CLIMATE CHANGE
References
Brown, M., Southworth F., and A. Sarzynski. 2008. Shrinking the Carbon Footprint of Metropolitan America. Brookings Institute Metropolitan Policy Program. http://www.brookings.edu/reports/2008/05_carbon_footprint_sarzynski.aspx. [Accessed February 2009].
City of New York. 2007. PlaNYC: A Greener, Greater New York. Mayor’s Office of Long Term Planning and Sustainability. www.nyc.gov/html/planyc2030/html/downloads/download.shtml. [Accessed March, 2009].
______. 2008. Inventory of New York City Greenhouse Gas Emissions, September 17, 2008. http://www.nyc.gov/html/planyc2030/html/downloads/download.shtml. [Accessed May 2009].
Dodman, D. 2009. “Blaming Cities for Climate Change? An analysis of Urban Greenhouse Gas Emissions Inventories.” Environment and Urbanization 21 (1): 185–201.
Gurney, K.R., Seib B., Ansley W., Mendoz D., Miller C., and M. Fischer. 2008. Project Vulcan, Purdue University. Vulcan Science Methods Documentation. http://www.purdue.edu/eas/carbon/vulcan/research.html. [Accessed October 2008].
Gurney, K.R., Mendoza D.L., Zhou Y., Fischer M.L., Miller C.C., Geethakumar S., and S. De La Rue Du Can. 2009. “High Resolution Fossil Fuel Combustion CO2 Emission Fluxes for the United States.” Environmental Science and Technology (Accepted May, 2009).
ICLEI — Local Governments for Sustainability (ICLEI). 2008. International Local Government GHG Emissions Analysis Protocol Release Version 1.0. http://www.iclei.org/index.php?id=8154. [Accessed February 2009].
International Energy Agency (IEA). 2008. World Energy Outlook 2008. OECD/IEA. Isserman, A.M. 2005. “In the National Interest: Defining Rural and Urban Correctly in
Research and Public Policy.” International Regional Science Review 28: 465–99.Kennedy, C., Steinberger J., Gasson B., Hansen Y., Hillman T., Havranek M., Pataki D.,
Phdungsilp A., Ramaswami A., and G.V. Mendez. 2009. “Greenhouse Gas Emissions from Global Cities.” Environmental Science and Technology 43 (19): 7297–302.
Kim, S. 2000. “Urban Development in the United States, 1690-1990.” Southern Economic Journal, 66 (4): 855–80.
Lenzen, M., Pade L., and J. Munksgaard. 2004. “CO2 Multipliers in Multi-region Input-Output Models.” Economic Systems Research 16 (4): 391–412.
Newman, P.W.G. and J.R. Kenworthy. 1989. “Gasoline Consumption and Cities: A Comparison of U.S. Cities with a Global Survey.” Journal of the American Planning Association 55 (1): 24–37.
Parshall, L, Gurney, K., Hammer, S.A., Mendoza, D., Zhou, Y., and S. Geethakumar. 2009. “Modeling Energy Consumption and CO2 Emissions at the Urban Scale: Method-ological Challenges and Insights from the United States.” Energy Policy (in press, corrected proof), doi:10.1016/j.enpol.2009.07.006.
Ramaswami, A., Hillman T., Janson B., Reiner M., and G. Thomas. 2008. “A Demand-Centered, Hybrid Life-Cycle Methodology for City-Scale Greenhouse Gas Inven-tories.” Environmental Science and Technology 42 (17): 6455–61.
Satterthwaite, D. 2008. “Cities’ Contribution to Global Warming: Notes on the Allocation of Greenhouse Gas Emissions.” Environment and Urbanization 20 (2): 539–49.
CHAPTER 4 � 159
Siviak, M. 2008. “Where to Live in the United States: Combined Energy Demand for Heating and Cooling in the 50 Largest Metropolitan Areas.” Cities (in press).
United States Department of Commerce, BEA (Bureau of Economic Analysis). 2008. http://www.bea.gov/newsreleases/regiona;/gdp_metro/2008/gdp_metro0908.htm. [Accessed March 2009].
USEPA (United States Environmental Protection Agency). 2009. Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990 to 2008. Executive Summary. http://epa.gov/climatechange/emissions/downloads10/US-GHG-Inventory-2010-Chapter-Executive-Summary.pdf. [Accessed March 2009].
VandeWeghe, J., and C. Kennedy. 2007. “A Spatial Analysis of Residential Greenhouse Gas Emissions in the Toronto Census Metropolitan Area.” Journal of Industrial Ecology 11 (2): 133–44.
Weber, C.L., and H.S. Matthews. 2007. “Quantifying the Global and Distributional Aspects of American Household Carbon Footprint.” Ecological Economics 66: 379–91.
160 � CITIES AND CLIMATE CHANGE
Urban County StateTotal
Bldgs & Industry (CO2)
Transportation (CO2) Population (2000)
CO2 EnergyNatural
GasFuel Oil Gasoline Diesel Density Total
Kings NY <50 <50 <50 76-100 <50 <50 13,922 2,465,326
Bronx NY <50 <50 <50 126-150 <50 <50 11,991 1,332,650
Queens NY <50 <50 <50 101-125 <50 <50 8,056 2,229,379
Davis UT >150 >150 >150 <50 101-125 76-100 147 238,994
Bold counties are in the NY Metro Area. Bold, italic counties are in the Tri-State Region but not the NY Metro Area. With the exception of total energy, all index values were computed on the basis of per-capita emissions of CO2 associated with direct fuel consumption. Total energy was computed on the basis of per-capita direct fuel consumption (measured in GJ). Data sources: Vulcan data product and US Census 2000.
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