Second State of the Carbon Cycle Report

USGCRP

Key Finding 1

Urban areas in North America are the primary source of anthropogenic carbon emissions, with cities responsible for a large proportion of direct emissions. These areas are also indirect sources of carbon through the emissions embedded in goods and services produced outside city boundaries for consumption by urban dwellers (medium confidence, likely).

Key Finding 2

Many societal factors drive urban carbon emissions, but the urban built environment and the regulations and policies shaping urban form (e.g., land use) and technology (e.g., modes of transportation) play crucial roles. Such societal drivers can lock in dependence on fossil fuels in the absence of major technological, institutional, and behavioral change. Some fossil fuel–related infrastructure can have lifetimes of up to 50 years (high confidence).

Key Finding 3

Key challenges for urban carbon flux studies are observational design, integration, uncertainty quantification, and reconciliation of the multiple carbon flux approaches to detect trends and inform emissions mitigation efforts (medium confidence, likely).

Key Finding 4

Improvements in air quality and human health and the reduction of the urban heat island are important co-benefits of urban carbon emissions mitigation (high confidence, very likely).

Key Finding 5

Urban methane (CH₄) emissions have been poorly characterized, but the combination of improved instrumentation, modeling tools, and heightened interest in the problem is defining the range of emissions rates and source composition as well as highlighting infrastructure characteristics that affect CH₄ emissions (high confidence).

Key Finding 6

Urban areas are important sites for policymaking and decision making that shape carbon fluxes and mitigation. However, cities also are constrained by other levels of government, variations in their sources of authority and autonomy, capacity, competing local priorities, and available fiscal resources (high confidence).

Likelihood

Very High	Likely	As Likely As Not	Unlikely	Very Unlikely
≥ 9 in 10	≥ 2 in 3	≈ 1 in 2	≤ 1 in 3	≤ 1 in 10

Confidence Level

Very High Strong evidence (established theory, multiple sources, consistent results, well documented and accepted methods, etc.), high consensus<

High Moderate evidence (several sources, some consistency, methods vary and/or documentation limited, etc.), medium consensus

Medium Suggestive evidence (a few sources, limited consistency, models incomplete, methods emerging, etc.), competing schools of thought

Low Inconclusive evidence (limited sources, extrapolations, inconsistent findings, poor documentation and/or methods not tested, etc.), disagreement or lack of opinions among experts

Note: Confidence levels are provided as appropriate for quantitative, but not qualitative, Key Findings and statements. See Guide to this Report for more on uncertainty of numerical estimates.

Lead Authors:: Kevin Robert Gurney, Northern Arizona University; Paty Romero-Lankao, National Center for Atmospheric Research (currently at National Renewable Energy Laboratory); Stephanie Pincetl, University of California, Los Angeles
Contributing Authors:: Michele Betsill, Colorado State University; Mikhail Chester, Arizona State University; Felix Creutzig, Mercator Research Institute on Global Commons and Climate Change; Kenneth Davis, The Pennsylvania State University; Riley Duren, NASA Jet Propulsion Laboratory; Guido Franco, California Climate Change Research Center; Sara Hughes, University of Toronto; Lucy R. Hutyra, Boston University; Chris Kennedy, University of Victoria; Rob Krueger, Worcester Polytechnic Institute; Peter J. Marcotullio, Hunter College, City University of New York; Diane Pataki, University of Utah; David Sailor, Arizona State University; Karina V. R. Schäfer, Rutgers University

Science Lead:

Paty Romero-Lankao, National Center for Atmospheric Research (currently at National Renewable Energy Laboratory)

Review Editor:

Nathaniel A. Brunsell, University of Kansas

Federal Liaisons:

Elisabeth Larson, North American Carbon Program and NASA Goddard Space Flight Center, Science Systems and Applications, Inc.

Karina V. R. Schäfer (former), National Science Foundation

Gurney, K. R., P. Romero-Lankao, S. Pincetl, M. Betsill, M. Chester, F. Creutzig, K. Davis, R. Duren, G. Franco, S. Hughes, L. R. Hutyra, C. Kennedy, R. Krueger, P. J. Marcotullio, D. Pataki, D. Sailor, and K. V. R. Schäfer, 2018: Chapter 4: Understanding urban carbon fluxes. In Second State of the Carbon Cycle Report (SOCCR2): A Sustained Assessment Report [Cavallaro, N., G. Shrestha, R. Birdsey, M. A. Mayes, R. G. Najjar, S. C. Reed, P. Romero-Lankao, and Z. Zhu (eds.)]. U.S. Global Change Research Program, Washington, DC, USA, pp. 189-228, https://doi.org/10.7930/SOCCR2.2018.Ch4.

Urban areas are concentrated domains of carbon fluxes because of the sheer magnitude of 1) urban populations; 2) economic activities; and 3) the fossil fuel–based energy, goods, and services on which these areas currently depend. Though sensitive to the urban boundary definition chosen and the accounting framework adopted (production versus consumption), carbon fluxes resulting from urban activities are estimated to be responsible for up to 80% of the total North American anthropogenic flux of carbon dioxide (CO₂) to the atmosphere (Jones and Kammen 2014; Seto et al., 2014). Per capita energy consumption in U.S. urban areas is estimated to be 13% to 16% less than the national average, and consumption varies more widely across cities than in rural areas (Parshall et al., 2010; see Figure 4.1). This concentrated source of carbon emissions is dominated by the combustion of fossil fuels (see Ch. 3: Energy Systems for a detailed treatment of carbon emissions associated with energy systems). However, other direct fluxes include carbon exchanged by the urban biosphere, methane (CH₄) emissions from leaking infrastructure, anaerobic decomposition (e.g., landfills and wastewater treatment), and human respiration. Cities are also responsible for large indirect fluxes via the demand for goods and services that are produced elsewhere. Understanding urban carbon fluxes is essential to understanding the spatiotemporal distribution of global anthropogenic carbon flux, the forces driving fossil fuel–based consumption, and the policy options available to cities in their role as innovators in emissions mitigation. This chapter aims to assess this understanding.

Figure 4.1: Per Capita Energy Consumption Versus Total Energy Consumption in Rural to Urban U.S. Counties

Figure 4.1: (a) Direct energy consumption measured in petajoules (PJ) and gigajoules (GJ) in building and industry and (b) direct energy consumption for transportation. [Figure source: Reprinted from Parshall et al., 2010, copyright Elsevier, used with permission.]

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The current understanding of carbon fluxes from urban areas has improved considerably since the First State of the Carbon Cycle Report (SOCCR1; CCSP 2007). Numerous urban carbon flux studies have been completed, and long-term research aimed at understanding aspects of urban carbon flows, drivers, and policy dimensions continues in some cities. Though often challenging to integrate, the growing number of studies within the North American urban domain are helping to improve understanding and establish new scientific knowledge and application to policymaking (Chester et al., 2014; Gurney et al., 2015; Hutyra et al., 2014; Marcotullio et al., 2014; Romero-Lankao et al., 2014).

Carbon flux differences within and across urban areas are more complex than the sum of populations, reflecting complex relationships among consumption, technology, infrastructure, economics, and behavior and lifestyle (see Figure 4.2; Lenzen and Peters 2009; Lenzen et al., 2008; Seto et al., 2014). A key component of urban carbon emissions, and a driver of future trends, is the interaction between human activity and the built environment, which includes large infrastructural systems such as buildings, roads, and factories. One need is to explore how urban infrastructure and morphology will influence current and future energy consumption and development (Creutzig et al., 2016; Müller et al., 2013; Salat and Bourdic 2012; Schiller 2007; Tanikawa and Hashimoto 2009).

Figure 4.2: Key Components of Urban Carbon Cycling

Figure 4.2: Major reservoirs and processes (colored boxes) are depicted, along with carbon (C) emission and removal fluxes (blue block arrows), major drivers (oval boxes), and examples of process linkages (colored thin arrows). Outer boxes depict the relationships among local, regional, and global carbon through transboundary (lateral) carbon fluxes as well as interconnected drivers (e.g., socioeconomic, geographical, and built systems). [Figure source: Redrawn from Hutyra et al., 2014, used with permission under a Creative Commons license (CC-BY-NC-ND 3.0).]

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The emerging role of subnational and transnational organizations and stakeholders within international policymaking, combined with the dominance of urban carbon emissions, has brought mitigation of carbon emissions from cities into consideration (Hsu et al., 2015; Rosenzweig et al., 2010, 2016; Wang 2012). Carbon mitigation approaches in North American cities vary widely due to a number of factors such as the urban economic profile, local policy initiatives, climate, and interactions with other governance levels (Homsy and Warner 2014; Krause 2012; Markolf et al., 2017; Sharp et al., 2010; Zahran et al., 2008). The impact of local policies on carbon emissions often is not monitored or assessed (Bulkeley 2010; Portney 2013), nor are the drivers for carbon mitigation policies systematically understood. Thus, causal links between policy and atmospheric effects are not always well known and may be unique to the city (Hughes 2017). Critically, urban emissions mitigation opportunities are often dependent upon or limited by interaction with governance at county, state, or provincial scales, emphasizing a need to better understand these relationships within the context of climate policy. For a better understanding of the societal drivers, further research is necessary on the interrelated environmental costs, benefits, constraints, and opportunities of different approaches within North American cities.

4.2.1 Accounting Framework and Methods

Many urban researchers, using a spectrum of methodological frameworks and measurement approaches, have quantified urban carbon flows and stocks in North American cities. The accounting framework determines the meaning and application of urban carbon flux information. Broadly speaking, two frameworks have been used: accounting for direct fluxes only or accounting that also includes indirect fluxes occurring outside the chosen urban area but driven by activities within it (Gurney 2014; Ibrahim et al., 2012; Wright et al., 2011). The former, also variously referred to as “production-based” or “in-boundary” accounting, quantifies all direct carbon flux between the Earth’s surface and the atmosphere within the geographic boundaries of the urban area of study (Chavez and Ramaswami 2011; Ramaswami and Chavez 2013; Wright et al., 2011). In-boundary accounting also is aligned with “scope 1” flux, a term emanating from carbon footprinting of manufacturing supply chains (WRI/WBCSD 2004). This framework will include within-city combustion of fossil fuels, exchange of carbon with vegetation and soils, absorption by concrete, human respiration, anaerobic decomposition, and CH₄ leaks. An in-boundary accounting framework often is favored for integration with atmospheric measurements, which also can be used to estimate surface-to-atmosphere fluxes within the chosen geographical domain (Lauvaux et al., 2016).

Indirect fluxes include those associated with energy used to create or deliver electricity, products, or services consumed in a given urban area or the carbon flux associated with waste decay or removal of material to the waste stream (Minx et al., 2009; Mohareb and Kennedy 2012). These fluxes include consumption-based flow of products manufactured outside the consuming city (see Figure 4.3). A study of eight cities found that the urban carbon footprint increased by an average of 47% when indirect fluxes were included (Hillman and Ramaswami 2010). Quantification of indirect fluxes typically employs a life cycle assessment framework and also can quantify the carbon stock residing in urban infrastructure or materials (Churkina et al., 2010; Fraser and Chester 2016; Hammond and Jones 2008; Lenzen 2014; Reyna and Chester 2015).

Figure 4.3: Relationships Between Carbon Inventory Approaches.

Figure 4.3: Interactions are depicted between in-boundary or production-based urban carbon inventories and those that incorporate embedded or embodied carbon emissions. [Figure source: Adapted from Wright et al., 2011, used with permission.]

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In practice, urban carbon flux studies have used hybrids of the two frameworks, and the mixture reflects academic disciplinary interest, practical policy needs, and differing notions of responsibility or environmental justice (Blackhurst et al., 2011; Lin et al., 2015). There have been important attempts at standardizing urban carbon flux accounting frameworks via protocols or Intergovernmental Panel on Climate Change (IPCC)–approved methods (Carney and Shackley 2009; Ewing-Thiel and Manarolla 2011; Fong et al., 2014; WRI/WBCSD 2004). However, comparing urban carbon fluxes remains challenging without careful consideration of the accounting framework, city boundaries, and flux categories (Bader and Bleischwitz 2009; Hsu et al., 2016; Kennedy et al., 2009; Lamb et al., 2016; Parshall et al., 2010).

Distinct from the accounting framework used to conceptualize an urban carbon budget, the methods used to quantify urban carbon fluxes can be classified into two measurement approaches. “Top-down” approaches infer fluxes by using atmospheric measurements of CO₂ and CH₄ (and associated tracers) and either measured or simulated atmospheric transport (Cambaliza et al., 2014; Lamb et al., 2016; Lauvaux et al., 2013, 2016; McKain et al., 2015; Miles et al., 2017; Turnbull et al., 2015; Wong et al., 2015). (See Ch. 8: Observations of Atmospheric Carbon Dioxide and Methane for more information on top-down approaches.) Multiple carbon sampling strategies have been used, including in situ stationary sampling from the ground (Djuricin et al., 2010; Miles et al., 2017; Turnbull et al., 2015), mobile ground-based sampling, aircraft measurements (Cambaliza et al., 2014, 2015), and remote sensing (Kort et al., 2012; Wong et al., 2015; Wunch et al., 2009). In addition, eddy covariance measurements have been employed on towers, buildings, and aircraft (Christen 2014; Crawford and Christen 2014; Grimmond et al., 2002; Menzer et al., 2015; Velasco and Roth 2010; Velasco et al., 2005). Recent aircraft and satellite remote-sensing studies have demonstrated the ability to map and estimate regional anthropogenic CO₂ (Hakkarainen et al., 2016) and facility-scale sources of CH₄ fluxes within cities and other complex areas (Frankenberg et al., 2016; Thompson et al., 2016).

“Bottom-up” approaches, by contrast, include a mixture of direct flux measurement, indirect estimation, and modeling. For example, a common estimation method uses a combination of economic activity data (e.g., population, number of vehicles, and building floor area) and associated emissions factors (e.g., amount of CO₂ emitted per activity), socioeconomic regression modeling, or scaling from aggregate fuel consumption (Gurney et al., 2012; Jones and Kammen 2014; Pincetl et al., 2014; Porse et al., 2016; Ramaswami and Chavez 2013). Direct end-of-pipe flux monitoring often is used for large facility-scale emitters such as power plants (Gurney et al., 2016). Indirect fluxes can be estimated through either direct atmospheric measurement (and apportioned to the domain of interest) or modeled through process-based (Clark and Chester 2017) or economic input-output (Ramaswami et al., 2008) models.

A key advance in quantifying urban carbon flux over the past decade has been the emergence of space and time bottom-up flux estimation to subcity scales (Brondfield et al., 2012; Gately et al., 2013; Gurney et al., 2009, 2012; Parshall et al., 2010; Patarasuk et al., 2016; Pincetl et al., 2014; Shu and Lam 2011; VandeWeghe and Kennedy 2007; Zhou and Gurney 2011). These approaches enable the interpretation of top-down approaches in addition to informing policy at the local scale for many cities globally (Duren and Miller 2012; Gurney et al., 2015). Despite recent attempts to integrate and reconcile various approaches to estimating urban carbon fluxes (Davis et al., 2017; Gurney et al., 2017; Lamb et al., 2016; Lauvaux et al., 2016; McKain et al., 2015), much research clearly remains to be done.

Table 4.1 provides a sample of published research on urban carbon fluxes in North American cities, including key information about the studies, such as the accounting framework, flux measurement and estimation techniques, and references.

Table 4.1. Scientifically Based Urban Carbon Estimation Studies in North American Cities

Domain	Framework, Scope, Boundary^a	Estimation Technique^b	Sectors Estimated^c	References	Notes^d
Indianapolis, IN	In-boundary	Direct flux, activity-EF, and fuel statistics; airborne eddy flux measurement; isotopic atmospheric measurement; atmospheric inversion	All FF	Cambaliza et al. (2014); Gurney et al. (2012, 2017); Lauvaux et al. (2016); Turnbull et al. (2015)	Much of the work is space and time explicit; atmospheric monitoring includes 14CO₂, CO, and CH₄
Toronto, Canada	Life cycle (scopes 1, 2)	Activity-EF	Residential Kennedy et al. (2009); VandeWeghe and Kennedy (2007)	Annual and census tract
Los Angeles, CA	In-boundary; embedded in buildings	Atmospheric measurement; activity-EF	All FF; on-road transportation; buildings	Feng et al. (2016); Kort et al. (2012); Newman et al. (2016); Pincetl et al. (2014); Porse et al. (2016); Reyna and Chester (2015); Wong et al. (2016); Wunch et al. (2009)	Some work is space and time explicit; atmospheric monitoring includes 14CO₂, CO, and CH₄
Salt Lake City, UT	In-boundary; consumption	Atmospheric measurement; direct flux, activity-EF, and fuel statistics; forest growth modeling and eddy flux measurement	All FF; biosphere	Kennedy et al. (2009); McKain et al. (2012); Pataki et al. (2006, 2009); Patarasuk et al. (2016)	Some work is space and time explicit
Baltimore, MD	In-boundary	Eddy flux measurement	All FF; biosphere	Crawford et al. (2011)
Denver, Boulder, Fort Collins, and Arvada, CO; Portland, OR; Seattle, WA; Minneapolis, MN; Austin, TX	Hybrid life cycle (scopes 1, 2, 3)	Activity-EF	All FF	Hillman and Ramaswami(2010)	Addition of scope 3 emissions increased total footprint by 47%
New York City, NY; Denver; Los Angeles; Toronto; Chicago, IL	Scopes 1, 2, 3	Activity-EF, fuel statistics, and downscaling	Excludes some scope 3 emissions	Kennedy et al. (2009, 2010, 2014)
Boston, MA; Seattle; New York City; Toronto	Scopes 1, 2 (some scope 3 included); scope 1 in lowland area	Activity-EF, fuel statistics and downscaling; flux chambers and remote sensing	Excludes some sectors; biosphere carbon stock change	Hutyra et al. (2011); Kennedy et al. (2012)
Boston	In-boundary	Activity-EF; atmospheric monitoring; atmospheric monitoring and inversion	Onroad; pipeline leak; biosphere respiration	Brondfield et al. (2012); Decina et al. (2016); McKain et al. (2015); Phillips et al. (2013)	Some work is space and time explicit; includes some CH₄
Washington, D.C.; New York City; Toronto	Scope 1 Activity-EF and fuel statistics	All greenhouse gases	Dodman (2009) Mixture of methods from multiple sources	Chicago Grimmond et al. (2002)
Mexico City, Mexico	In-boundary	Eddy flux measurement; activity-EF	All FF, biosphere; onroad	Chavez-Baeza and Sheinbaum-Pardo (2014); Velasco and Roth (2010); Velasco et al. (2005, 2009)	Footprint of single monitoring location; whole- city inventory
Halifax, Canada	Scopes 1, 2	Activity-EF Buildings, transportation	Wilson et al. (2013)	Spatially explicit
Pittsburgh, PA	Scopes 1, 2	Activity-EF, fuel statistics, and downscaling	Residential, commercial, industrial, and transportation	Hoesly et al. (2012)
Phoenix, AZ	In-boundary	Activity-EF and soil chamber	Onroad, electricity production, airport and aircraft	Koerner and Klopatek (2002)
Vancouver, Canada	In-boundary	Eddy flux measurement	All FF, biosphere	Crawford and Christen (2014)
Vancouver, Edmonton, Winnipeg, Toronto, Montreal, and Halifax, Canada	Scopes 1, 2	Activity-EF	Residential building stock	Mohareb and Mohareb (2014)
20 U.S. cities	In-boundary; consumption; hybrid	Activity-EF	All energy related	Ramaswami and Chavez (2013)

Notes
^a In-boundary refers to fluxes exchanged within a geographic boundary of a city (equivalent to scope 1); scope 2 refers to fluxes from power production facilities allocated to the electricity consumption within the boundary of a city; scope 3 refers to fluxes from the production of goods and services consumed within the boundary of a city.
^b Estimation Technique refers to the measurement or modeling approach taken to estimate or report emissions. “Activity-EF” refers to the combination of activity data (i.e., proxies of fuel consumption) and emissions factors to estimate fluxes. “Fuel statistics” refers to methods that use estimated fuel consumption and carbon content to estimate fluxes. “Downscaling” refers to the use of estimates at larger scales downscaled to the urban scale via spatial proxies or scaling factors. “Direct flux” refers to in situ flux measurement distinct from eddy flux approaches, such as measurement of stack flue gases.
^c Sectors Estimated refers to the categories of emissions included in the study. They can be broadly referred to as residential, commercial, industrial, transportation (includes onroad, nonroad, airport and aircraft, waterborne, and rail), electricity production, and biosphere (includes photosynthesis and respiration). “All FF” refers to all emissions related to fossil fuel combustion (all sectors).
^d ¹⁴CO₂, radioisotopic carbon dioxide; CO, carbon monoxide; CH₄, methane.

4.2.2 Human Activity and the Built Environment

The dominant source of carbon flux to the atmosphere from cities is associated with human activities and behaviors within the built landscape—energy use in buildings, fuel consumed in transportation (e.g., cars, airplanes, and rail), energy for manufacturing in factories, production of electricity, and energy used to build and rebuild urban infrastructure. (See Ch. 3: Energy Systems for more information on energy system carbon emissions and Ch. 6: Social Science Perspectives on Carbon for an analysis of the social and institutional practices and behaviors shaping carbon fluxes.) In addition to the combustion of fossil fuels (within and outside the urban domain), human activity within the built environment generates fluxes from 1) waste streams associated with the decomposition of materials containing carbon, 2) infrastructure leaking natural gas (composed primarily of CH₄), and 3) industrial processes that emit carbon without fuel combustion. Urban carbon fluxes associated with human activity and the built landscape often are categorized into economic sectors such as “residential,” “commercial,” “industrial,” and “transportation,” but the descriptions vary. Similarly, the distribution of fluxes among these sector divisions varies across urban areas, depending on the many intersecting drivers of carbon fluxes including history, geography, climate, technology, energy supply, urban form, and socioeconomics.

Among these economic sectors, activities within buildings and vehicle transportation are often the largest emitters and thus have garnered the greatest amount of study. For example, depending on the urban definition adopted, recent research found that up to 77% of onroad gasoline and diesel consumption occurs in urban areas within the United States and that urban areas accounted for 80% of the onroad emissions growth since 1980 (Gately et al., 2015; Parshall et al., 2010). In Mexico City, onroad vehicles account for 44% of metropolitan emissions of greenhouse gases (GHGs) such as CO₂, CH₄, and nitrous oxide (N₂O; Chavez-Baeza and Sheinbaum-Pardo 2014), while all of the country’s transportation accounts for 31% of total emissions (INECC 2012).¹ Similarly, between 37% and 86% (varying with the definition of “urban”) of direct fuel consumption in buildings and industry occurs in urban areas (Parshall et al., 2010).

While urban CO₂ emissions are dominated by fossil fuel combustion (see Figure 4.4), a large portion of urban CH₄ emissions arise from leaking natural gas infrastructure serving cities (Alvarez et al., 2012; Cambaliza et al., 2015; Jackson et al., 2014; Lamb et al., 2016; McKain et al., 2015; Phillips et al., 2013; Wennberg et al., 2012). (See Ch. 3: Energy Systems for details of leaked CH₄ emissions at the regional scale.) A study of CH₄ emissions from 13 urban distribution systems showed that emissions were roughly a factor of two smaller than U.S. Environmental Protection Agency (EPA) estimates, suggesting possible improvements in leak detection and maintenance work. However, the different methodologies between the two approaches would make assessing changes in leakage rates difficult (Lamb et al., 2015). At the same time, CH₄ emissions downstream from natural gas consumption meters on homes, buildings, and industrial facilities seem to be much higher than expected. A study in the San Francisco region suggests that emissions from the natural gas system can be equivalent to 0.3% to 0.5% of the region’s natural gas consumption (Jeong et al., 2017). A similar study for the Los Angeles region estimates emissions at about 1.6% of consumption (Wunch et al., 2016). Los Angeles emissions may be higher because this region produces crude oil and natural gas. Aircraft mass balance and tower-based atmospheric inversions in Indianapolis differed by a factor of two and also exceeded the emissions estimated from a bottom-up inventory (Lamb et al., 2016). This difference suggested that the aircraft estimate and the inventory did not account for widespread distribution of relatively small diffuse sources. These comparisons are complicated by the fact that they do not overlap in time and that emissions may be quite episodic and vary temporally. Long-term trend studies with sufficient precision to detect changes over time do not yet exist in the literature.

Figure 4.4: U.S. Fossil Fuel Carbon Emissions, Highlighting Four Urban Areas

Figure 4.4: [Data source: Gurney et al., 2009; units in log 10 tons of carbon (t C) per year.]

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Methane also is produced by municipal waste facilities. In Toronto, these facilities account for as much as 10% of urban emissions (City of Toronto 2013); in Indianapolis, about 35% of emissions are attributed to one landfill (Cambaliza et al., 2015; Lamb et al., 2016).

4.2.3 Land and Ecosystems

Urban development directly and indirectly alters above- and belowground vegetation carbon pools and fluxes through land clearing, removal of vegetation, and disruption of soils (Raciti et al., 2012). Estimates of urban vegetation carbon densities vary substantially among cities or states and are based on extrapolation of limited, nonrandom sampling. Using extensive remote sensors and field observations, case studies in both Maryland and Massachusetts found that developed areas hold about 25% of the biomass per unit area of nearby forests (Huang et al., 2015; Raciti et al., 2014). Trees in urban areas in the United States and Canada store an estimated 643 teragrams of carbon (Tg C) and 34 Tg C, respectively (Nowak et al., 2013). In contrast, studies in xeric ecosystems show relative enhancement in urban biomass densities that result from landscaping preferences and addition of non-native vegetation (McHale et al., 2017).

Growing conditions for vegetation in urban areas typically differ from nonurban ecosystems, potentially accelerating the cycling of carbon and nutrients (Briber et al., 2015; Reinmann and Hutyra 2017; Zhao et al., 2016). For example, urban areas experience elevated ambient air temperatures (i.e., the “urban heat island” [UHI] effect; Oke 1982). These elevated temperatures cause seasonally dependent changes in carbon fluxes from urban vegetation and soils (Decina et al., 2016; Pataki et al., 2006; Zhang et al., 2004; Zhao et al., 2016), altering the length of the urban growing season (Melaas et al., 2016; Zhang et al., 2006). Urban respiration and growth patterns also may differ due to human additions of water and fertilizers, removal or addition of labile carbon sources (e.g., leaf litter and mulch), and planting preferences (Templer et al., 2015). Urban vegetation also can influence local climate and energy use (Abdollahi et al., 2000; Gill et al., 2007; Lal and Augustin 2012; Nowak and Greenfield 2010; Wilby and Perry 2006). For example, urban trees may affect building energy consumption and associated carbon emissions directly through shading of building surfaces and altered use of cooling equipment (Raji et al., 2015) and indirectly through local reductions in air temperature (Nowak 1993; Sailor 1998). These effects require accounting for water and energy penalties associated with irrigation of managed urban vegetation (Litvak et al., 2017). In addition, fertilization of urban landscapes and management practices such as lawn mowing can carry a high energy cost that must be assessed when determining the net effect of urban vegetation on the carbon cycle (McPherson et al., 2005; Townsend-Small and Czimczik 2010).

Investigations across a variety of research disciplines (e.g., urban economics, urban planning, urban geography, and urban physics) have tried to discern the driving factors of per capita urban carbon fluxes. International comparisons have demonstrated that economic factors such as available income and energy price levels play crucial roles, but so do urban density profiles, building age and construction, climate, and technology (Creutzig et al., 2015a).

4.3.1 Consumption

Manufacturing of goods such as clothing emits carbon if energy consumption is satisfied by fossil fuels, but consumption of goods and services, production systems, and supply chains are the fundamental drivers of emissions. As mentioned in Section 4.2.1 accounting frameworks that reflect a consumption perspective will allocate to the importing consumer the carbon fluxes associated with the production of goods and services. In particular, urban populations in wealthier nations that are nominally decarbonizing or stabilizing their carbon emissions often have total emissions that are increasing once traded carbon is considered in this way (Baiocchi and Minx 2010; Peters et al., 2011). Movement of goods among nations often is a result of trade policy, labor, and land costs that drive production location choices (Hertwich and Peters 2009). In U.K. cities, for example, a large carbon footprint is embedded in trade with large import partners such as China (Baiocchi and Minx 2010; Minx et al., 2013). Trade agreements, such as the North American Free Trade Agreement, have shifted automobile production and clothing manufacturing, along with their associated carbon emissions, from the United States to Canada and Mexico (Shui and Harriss 2005).

4.3.2 Economics—Wealth and Energy Prices

Economic development and urbanization reinforce each other through co-location of activities and investments (Fujita et al., 1999). In a global typology of cities, per capita gross domestic product (GDP) is identified as the most relevant sorting variable; transportation fuel prices also are relevant, distinguishing emissions among richer cities (Creutzig et al., 2015a). Urban development theories suggest that factors such as the clustering of investment and production, land development and transportation policies, and fuel prices shape urban form over the long run. For instance, incentives for dense urbanization exist when fuel prices are high and for sprawled suburbanization when prices are low, though legacy land uses—initiated during low fuel prices—continue to drive private automobile transportation use (Creutzig 2014; Fujita 1989). More recent urbanization patterns in mature cities have trended toward rehabitation or gentrification of urban cores. However, more time is needed to know the long-term impact of these patterns and whether they represent a shift toward lower GHG emissions due to less reliance on automobiles (Florida 2010). Cities also create new public transportation systems to reduce automobile dependence, but carbon fluxes from infrastructure creation remain significant in the short term (Chester et al., 2013). In an international comparison, the United States belongs to a grouping of countries with high incomes but low fuel prices. A nationwide study estimating U.S. household flux at the zip code level found that the number of vehicles per household and annual household income were the most relevant variables explaining estimated household carbon emissions (Jones and Kammen 2014). This finding illustrates the difficulties of meeting multiple policy objectives in most North American cities; when priority is given to development and urbanization, there are implications for the carbon cycle (Romero-Lankao et al., 2015, 2017).

4.3.3 Behavior—Lifestyles and Norms

Urban mobility in North America is dominated by personal automobile use, shaping and reconfiguring daily urban life (Sheller and Urry 2000). Lifestyles and norms clearly play a powerful role in explaining everyday decisions about urban mobility and energy use, but their importance as drivers for carbon emissions generally has not been studied quantitatively (Axsen and Kurani 2012; Mattauch et al., 2016; Wilson and Dowlatabadi 2007). In the United Kingdom, lifestyle changes could contribute as much to climate mitigation in the transport sector as technological changes (Anable et al., 2012). A typology of residential carbon emissions reveals that infrastructure patterns are mirrored in lifestyle classes. For example, low-emitting households in the dense urban cores of London and some U.S. cities typically are either “young professionals” or “multicultural inner city” communities of young people seeking inner-city living with downsizing or elimination of personal automobiles. Households in peri-urban London having higher emissions mostly identify as “affluent urban commuters” living in relatively inefficient houses (Baiocchi et al., 2015). However, whether these patterns are indicative of a long-term shift or merely a short-term adjustment is unclear. Another example from the Los Angeles Energy Atlas finds that wealthy neighborhoods have higher per capita energy consumption than low-income residents who have higher consumption per unit area (Porse et al., 2016). In Salt Lake City, Utah, increments of wealth among high-income residents were found to lead to greater residential CO₂ emissions than those of low-income residents (Patarasuk et al., 2016). A systematic investigation of lifestyles, especially in interaction with urban infrastructures, has been identified as a major priority for further research (Creutzig et al., 2016). Social norms and behavior patterns in terms of energy use and consumption also exhibit carbon “lock-in,” whereby norms act in isolation and in concert with institutional and technological constraints to add inertia to existing patterns of consumption and carbon emissions (see further details in Section 4.3.5).

4.3.4 Urban Form and Density

Research has identified urban form and the density of cities as key drivers of urban carbon emissions (Baiocchi et al., 2015; Creutzig et al., 2015a; Karathodorou et al., 2010; Mindali et al., 2004; Newman and Kenworthy 1989, 1999). In theory, dense settlement affords energy efficiencies by encouraging multidwelling living, reduced travel distances, public transit use, and walking and cycling (Boyko and Cooper 2011; Oleson et al., 2008). In the United States, analysis has shown declines in per capita carbon emissions with increasing population density at densities greater than 1,158 persons per km² (Jones and Kammen 2014). At lower densities, typical of suburban areas, carbon emissions rise with increases in density (Glaeser and Kahn 2010; Jones and Kammen 2014). These results are supported by recent research on transportation energy consumption (Liddle 2014), electricity consumption in buildings (Lariviere and Lafrance 1999), and overall urban carbon emissions (Marcotullio et al., 2013). A recent study found that the high correlation between per capita electricity use and urbanized area per person can be explained by the higher per capita building floor area in less-dense cities (Kennedy et al., 2015).

Urban form and density are determined by local plans, existing infrastructure, land costs, and public attitudes (Ewing and Rong 2008). These factors often are determined by local actions and constrained by national, state, or other regulations, such as the Federal Emergency Management Agency’s 100-Year Flood Maps, insurance policies, and perceived costs of existing infrastructure and land. Change in land-use patterns, as well as services such as public transportation, require long-term commitment, public support, and funding. Once a pattern has been set, it tends toward obduracy, making change difficult (Unruh 2000). Zoning codes that segregate land uses contribute to urban sprawl and a car-dependent road infrastructure that, in turn, influences carbon emissions (Fischel 2015; Hamin and Gurran 2009). These rules vary across states, provinces, and cities because of different relationships of autonomy between cities and other governmental scales. Policy drivers may be generated at the different scales, including national (e.g., transportation infrastructure investments), state, provincial (e.g., requirements for cities to create general plans or set building codes), or city (e.g., specific zoning codes; Knaap et al., 2015). These rules, codes, and standards establish frameworks for cities, including facilitating sprawled urban form through road subsidies or land regulation or encouraging density and efficient building through strict building codes and tax policy that discourages automobile use and ownership (Grazi and van den Bergh 2008). Stricter land-use regulation can induce sprawl development in nearby suburban and peri-urban areas, an occurrence that may increase overall carbon emissions. That is, cities with stricter land-use regulations externalize development to adjacent communities with more lenient regulations, engendering higher rates of suburbanization in the region (Glaeser and Kahn 2010). Harmonization of land-use regulation or higher fuel taxes can reduce the likelihood of this outcome.

4.3.5 Technology

Technological attributes, such as power generation (see Ch. 3: Energy Systems), urban design, and waste processing, partly determine city profiles for carbon emissions (Kennedy et al., 2009). Availability of low-carbon technologies reduces urban per capita carbon emissions. For example, cities with carbon intensity of electricity below approximately 600 metric tons (t) CO₂ equivalent² (CO₂e) per gigawatt hour (GWh), such as Los Angeles, New York City, and Toronto, can reduce life cycle carbon emissions through electrification of transportation and heating systems (Kennedy 2015; Kennedy et al., 2014). However, because of the relative permanence of large technological and infrastructural systems in urban areas, the notion of infrastructure lock-in is critical and often makes shifts to low-carbon technologies and systems costly or not feasible (Unruh 2000). Lock-in results from the high cost of the infrastructure; the expended energy in the infrastructure; and the social systems of regulation, codes, and conventions that reinforce existing systems (Pincetl et al., 2016; Reyna and Chester 2015; Seto et al., 2016). However, technology is influenced by institutions, individual behavior, and policy actions (Chester et al., 2014), and technology has replacement or turnover cost implications with fossil fuel–burning infrastructure having lifetimes of up to 50 years (Erickson et al., 2015; see Figure 4.5). The issue of carbon lock-in is another example of the interactions, constraints, and opportunities that involve multiple scales of governance beyond urban domains.

Figure 4.5: Assessments of Lock-In Related to Different Types of Infrastructure Emitting Carbon Dioxide (CO₂)

Figure 4.5: Different fossil fuel–burning infrastructures are plotted according to their historical lifetime (x-axis) and the carbon price (in dollars per ton of CO₂) required to equalize the marginal cost of existing infrastructure (mainly fuel) with the total levelized cost (i.e., including capital and operating expenses) of a low-carbon replacement (y-axis). Circle sizes reflect the cumulative future emissions of each type of infrastructure that are in excess of what that infrastructure can emit under a 2°C climate scenario. Colors are qualitative indicators of the techno-institutional resistance of that type of infrastructure to unlocking (e.g., stocks of very specific intellectual capital, established subsidies, entrenched social norms, large supporting infrastructures, and political influence). Key: ICE, internal combustion engine; BF, blast furnace; BOF, basic oxygen furnace; Gt, gigaton. [Figure source: Redrawn from Seto et al., 2016 (originally adapted from Erickson et al., 2015), used with permission.]

SHRINK

In 16 U.S. states and Washington, D.C., regulatory changes, such as Incentives for Renewables and Efficiency, are both facilitating and requiring decarbonization of energy (URL). U.S. public utilities commissions (PUCs) regulate the large investor-owned utilities, and PUCs of states such as New York and California are creating new regulatory frameworks for increased renewable energy generation, purchase, and storage to decrease reliance on fossil fuel–generated energy. In 2015, California established a 50% renewable portfolio standard for the electricity system that is to be accomplished by 2030 (Senate Bill 350). The state also adopted a new legal mandate in September 2016 requiring statewide reductions of GHG emissions by 40% from 1990 levels by 2030 (Senate Bill 32).

4.3.6 Climate

Local climate is also a modifier of urban carbon emissions in conjunction with socioeconomic and urbanization characteristics (Baiocchi et al., 2015; Creutzig et al., 2015a; Glaeser and Kahn 2010; Kennedy et al., 2015). Global climate change typically modifies local energy use by reducing heating and increasing air conditioning demands (Huang and Gurney 2016). Local climate also can be partly influenced by human activity via the UHI effect (Boehme et al., 2015; Georgescu et al., 2014; Oke 1982), which, in turn, drives changes in energy consumption and carbon emissions (Lin et al., 2015; Wang et al., 2010).

A quantitative understanding of contemporary urban carbon trends continues to face limitations related to data availability across the North American domain. Some understanding can be gleaned from statistics on urban growth in general, along with several case studies of urban carbon fluxes over particular time spans or locations. For example, Mexico’s annual urban population grew at a rate of 1.9% between 1995 and 2015, while both Canada and the United States had urban growth rates of 1.2% (UN DESA 2015). Future projections at the global level and for North America suggest increases in urban land use. For example, there is a greater than 75% probability that global urban land will increase from 652,825 km² in 2000 to 1,863,300 km² in 2030 (Seto et al., 2012). Other studies have projected a near tripling in the percentage of land devoted to urban cover by midcentury (Nowak and Walton 2005).

The future trajectory of urban carbon fluxes is unambiguously tied to increases in aggregate urban energy demand and the proportion met by fossil fuels (Hoornweg et al., 2011; Jones and Kammen 2014; Marcotullio et al., 2013). Theoretically, these increases are the cumulative result of concentrated population and economic activity, which today are predicated on the more energy intensive processes in agriculture, transportation, buildings, industry, and waste management (Liddle 2014). However, despite consensus about the positive correlation between population and energy demand or carbon emissions, there is debate about the magnitude of the effect and the implications of future urbanization. The effect of population size on carbon emissions or energy demand may be contingent on other factors, including, for example, a city’s starting population size (Bettencourt et al., 2007). Some evidence for this scaling relationship suggests that urban areas with larger population sizes have proportionally smaller energy infrastructures than smaller cities (Bettencourt et al., 2007; Fragkias et al., 2013). Other evidence suggests that carbon emissions may increase at a rate greater than population growth rates, so that larger cities exhibit proportionally higher energy demand as they grow than do smaller cities (Marcotullio et al., 2013). Theoretically, such an outcome is possibly due to diminishing returns, threshold effects, negative synergisms, and the disproportionate escalation of cost for maintaining environmental quality with population growth (Ehrlich and Holdren 1971). Finally, the difficulty occurs with predicting not only trends in policymaking, but also the impact of policy change on energy sources (Tuckett et al., 2015). For instance, in some U.S. states, policy is shifting some of the energy generation toward renewables (Lutsey and Sperling 2008). However, cost drivers for energy sources evolve over time and influence the choice of energy supply (Gan et al., 2007).

The generation of waste heat, coincident with carbon emissions from the combustion of fossil fuels, has the potential to initiate feedbacks with the urban carbon cycle through the UHI effect—a phenomenon whereby urban areas are warmer than their unbuilt surroundings (Boehme et al., 2015; Oke 1982). Averaged at the city scale, the magnitude of this waste heat can be up to 100 watts per m² (Sailor et al., 2015), potentially increasing urban warming by 2 to 3^oC in winter and 0.5 to 2^oC in summer (Fan and Sailor 2005). As urban areas warm due to both large-scale changes in climate and localized UHI, the energy consumed for space cooling in summer increases while the energy used for heating in winter decreases, “spilling over” into other seasons (Li et al., 2015; Wang et al., 2010). For example, recent research found that summer electricity demand may increase up to 50% in some U.S. states at the end of this century due to increased cooling needs under climate change alone (Huang and Gurney 2016). In fact, a recent modeling study by Georgescu et al. (2014) found that for U.S. cities, the effects of urban expansion on urban air temperatures by 2100 will be on the same order of magnitude as GHG-induced climate change. The UHI effect, in addition to changes in heatwave event frequency and magnitude, would further exacerbate this feedback (Li and Bou-Zeid 2013).

4.5.1 Global Urban Carbon

Of the nearly 1,000 urban agglomerations with more than 500,000 people across the world, three-quarters are in developing countries (UN DESA 2015). The share of energy-related urban CO₂ emissions worldwide is 71%, somewhat less than the share in North America (IEA 2008). Given the greater levels of current urbanization in North America and recent trends across the world, most future urban growth and associated urban carbon emissions likely will be dominated by low- and middle-income countries. In smaller urban areas within the United States and Europe, de-urbanization is occurring (Martinez-Fernandez et al., 2012), and its implications for carbon emissions are still poorly understood.

Within the global context, North America (particularly Canada and the United States) has smaller urban population densities but greater per capita built-up area (Seto et al., 2014). Due to extensive urbanization levels and fossil fuel consumption associated with transportation and urban infrastructure, North America has the largest percent of total carbon emissions emanating from urban areas (Marcotullio et al., 2013).

4.5.2 United States, Canada, and Mexico—Urban Carbon in Context

Cities in the United States and Canada generally have recorded amongst the highest per capita carbon emissions when compared to global cities (Dodman 2009; Hoornweg et al., 2011; Kennedy et al., 2009; Sovacool and Brown 2010). In cities for which there are repeat carbon inventories (e.g., Boston, New York City, Toronto, and Seattle, from 2004 to 2009), per capita emissions are declining at the same rate as national inventories (Kennedy et al., 2012). But when indirect emissions are included in city inventories, urban per capita emissions are about the same as national per capita emissions (Ramaswami et al., 2008). This measurement further highlights the importance of understanding indirect carbon fluxes and the increase in the export of emissions outside the North American urban domain. Core aspects of per capita energy and material consumption have been found to be inversely correlated to urban population density (Kennedy et al., 2015).

Since the mid-1990s, cities around the world have increasingly engaged in carbon management efforts, reflecting a growing recognition that cities are both locations where emissions-producing activities occur and political jurisdictions with authority over some of those activities (Castan Broto and Bulkeley 2013). The number of cities that have committed to some form of carbon reduction has increased exponentially, from fewer than 50 in the early 1990s, several hundred by the early 2000s (Bulkeley and Betsill 2003), and several thousand a decade later (Krause 2011; Pitt 2010). North American cities have played a particularly important leadership role, emerging as key sites for experimentation and innovation with different types of policies, technologies, and programs (Burch 2010; Castan Broto and Bulkeley 2013; Hoffmann 2011; Hughes and Romero-Lankao 2014, 2015).

4.6.1 Importance of Governance and Multilevel Networks

Key factors in the ability of city governments to manage carbon emissions are the mandates and competencies of municipal governments, financial resources, presence of political champions, multilevel networks, an open political opportunity structure, and the ability to capitalize on co-benefits valued by local residents (Betsill and Bulkeley 2007; Ryan 2015). Local authorities in North America also encounter a number of barriers, including the lack of coordination across different parts of city government, sunk investments in infrastructure, and resistance to change of the local political economy (Romero-Lankao et al., 2013, 2015; Sharp et al., 2010; Tang et al., 2010; Tozer 2013). A recent study found that U.S. city membership in the International Council for Local Environmental Initiatives (ICLEI) declined 22% between 2010 and 2012 and that large numbers of cities had abandoned their climate policy efforts altogether (Krause 2015).

Local carbon mitigation efforts also are limited by infrastructure lock-in and “path dependencies” created from previous policy decisions and investments, which can make changing direction politically difficult and expensive (Unruh 2000). Path dependency is a function of infrastructure cost and life cycle and is influenced by the way that decisions are made (Romero-Lankao et al., 2017). For instance, the low-density urban form of North American cities such as Los Angeles has been largely the result of freeway construction programs of the California Division of Highways (Wachs 1993). These decisions have created a path-dependent use of private vehicles, associated with more energy use and more carbon emissions (Kenworthy 2006).

There is one important difference in the policy contexts of cities in the United States, Canada, and Mexico. Cities occupy different jurisdictional space and face different economic, institutional, and political contexts. Decision making in the United States is generally more decentralized than that in Canada and Mexico, potentially giving city governments more autonomy (Bulkeley and Betsill 2013). Notwithstanding these across-country differences, the challenges and opportunities cities face, such as economic development, air pollution, and transit access, vary as much within countries as between them. For example, policy aimed at mitigation of local air pollution has resulted in climate policy co-benefits in most large North American cities, including Mexico City, but results typically are not as salient for smaller cities (Romero-Lankao 2007).

While municipal governments have some control over carbon emissions, urban carbon management ultimately takes place in a multilevel governance context, whereby climate policy efforts have the potential to be spread across different levels of political jurisdiction and pursued through diverse forms of governance instruments (see Ch. 3: Energy Systems; Ch. 6: Social Science Perspectives on Carbon; and Ch. 18: Carbon Cycle Science in Support of Decision Making). For example, utilities can be governed by federal, regional, and state institutions and by public, private, and nonprofit partnerships that each make decisions on policy, infrastructure, and the mix of power generation in the electricity grid (Bulkeley 2010; Pincetl et al., 2016; Schreurs 2008). Municipal priorities and outcomes are shaped not only locally, but also by international agreements; national policies, legislation, and regulation; and state- and provincial-level efforts such as the adoption of renewable portfolio standards and the initiation of emissions trading markets (Bulkeley 2010; Bulkeley and Betsill 2013; Burch 2010; Romero-Lankao et al., 2017). National and state or provincial policies shape urban management efforts by creating a permissive or restrictive institutional setting for local action (Bulkeley and Betsill 2013; Burch 2010; Homsy and Warner 2014; Romero-Lankao et al., 2013, 2015, 2017). For example, federal and state agencies (e.g., public utility commissions) independently shape a number of energy-supply characteristics through rules, regulations, and standards. In California, state-level regulations are playing a significant role in spurring local action, such as calling for Zero Net Energy residential buildings by 2020, doubling energy efficiency for the existing building stock by 2030, and meeting renewable portfolio standards. In many North American cities, there is relatively little explicit interaction or coordination among these different levels of government (Betsill and Rabe 2009; Jacoby et al., 2014).

Thousands of North American cities and towns have joined municipal networks such as the C40 Cities Climate Leadership Group and the ICLEI (Kern and Bulkeley 2009; Robinson and Gore 2011), though participation is declining, as noted. Municipal climate change networks play a role in generating norms and standards for setting targets and monitoring and measuring progress (Betsill and Bulkeley 2004). These networks also provide opportunities for information sharing and capacity building. Cities join such networks to demonstrate leadership and secure recognition. However, the impact of network membership on local implementation or broader-scale policy change has yet to be demonstrated (Gore 2010; Krause 2012).

4.6.2 Sectoral Mitigation Approaches

Three urban sectors have been identified as key for mitigating urban carbon emissions: the built environment, transportation, and energy systems (see Section 4.2.2). Carbon emissions from energy use in buildings can contribute as much as 80% of a city’s total and primarily are controlled by private building owners (Rosenzweig et al., 2010). As a result, states and local authorities in many North American cities have begun to partner with private actors—the owners of these buildings—to integrate carbon mitigation and transition to low-carbon development within broader urban agendas (Bulkeley and Betsill 2013; Bulkeley and Castán Broto 2013; Hodson and Marvin 2010; While et al., 2010). Reducing energy consumption through energy-efficient building design and construction is an ongoing effort at the state and local levels in North America (Griego et al., 2012; Koski 2010; Larsson 1999). Mexico hosts the seventh largest green building market in the world,³ and Canada is the largest green building market outside the United States. Cities also can incentivize or require energy conservation more directly. Energy-use benchmarking policies for the private sector are being promoted for North American cities, several of which have adopted these policies including New York City, Philadelphia, San Francisco, and Seattle (Cox et al., 2013). New York City’s Greener, Greater Buildings program benchmarks energy use in private buildings and mandates energy efficiency and conservation measures (Block and Semel 2010). Similarly, California’s Senate Bill 802 may make benchmarking mandatory for commercial buildings.⁴ These examples have informed the National Resources Defense Council’s City Energy Project, which is helping cities introduce benchmarking and conservation efforts of their own. The actual performance of buildings also depends on correct equipment installation, occupant behavior, and attitudes toward energy conservation (Mills and Schleich 2012; Virote and Neves-Silva 2012). Additionally, local authorities in Toronto are piloting a carbon credit trading program, and many cities have placed energy use and efficiency at the center of their climate change mitigation efforts (IEA 2015; Sun et al., 2015). California’s Title 24 building codes, first established in 1978, have required increasingly stringent energy conservation for buildings, including insulation, window glazing, and more. These codes are credited for much of the state’s energy savings (CEC 2015), but there also is evidence for a rebound effect as buildings, though more efficient, are bigger overall (Porse et al., 2016). Finally, the energy embodied in building construction can be incorporated into green building policy (Biswas 2014; Hammond and Jones 2008; Reyna and Chester 2015). Accounting and labeling systems, for example, measure and inform consumers about the environmental impacts of a structure (Dixit et al., 2010; Monahan and Powell 2011).

Transportation mitigation options include facilitating the transition to lower-emission vehicles and expanding the availability and use of public transit (Creutzig et al., 2015b). Cities are building electric vehicle charging stations, requiring low-emission vehicles in their own fleets, and encouraging biking and walking. Transit-oriented developments are designed to reduce the carbon emissions correlated with low-density suburban sprawl (Glaeser and Kahn 2010), though high capital costs and fragmented decision making continue to pose challenges. Additional challenges include long-term tradeoffs regarding the carbon impacts of different transit and fuel-mix options that continue to be evaluated (Chester et al., 2013).

Because cities consume about 75% of power generation worldwide (Dodman 2009), a common mitigation focus for cities is energy production itself. Many cities do not have formal authority to dictate the fuel sources for their energy supply and thus must rely on action from other levels of government and the private sector (Kern and Alber 2009). Reliance and cooperation require indirect action on the part of city governments, such as facilitating or incentivizing the expansion of renewable energy sources and lobbying relevant decision-making bodies. Examples include Toronto and Halifax’s use of deep lake water to cool buildings, though there are barriers to scaling up such technologies (Newman and Herbert 2009). At the same time, there is increasing understanding of the need to couple solar generation with storage. Currently, “excess solar” generated in the middle of the day is not stored, requiring other electricity generation sources for peak load times and in the evening. Often this energy is provided by natural gas “peaker” power plants that constantly are powered, emitting CO₂ (St. John 2014).

Cities often have more direct control in areas such as waste-to-energy schemes and local distributed solar generation. For example, CH₄ capture at two of Toronto’s largest landfills is responsible for just over 10 million tons of GHG reductions since 2004 (City of Toronto 2007, 2015). In California, local governments have begun to create Community Choice Aggregation alternative utilities that offer customers greater proportions of renewable energy (Roberts 2015). Key to ensuring the success of these programs is maintaining the subsidies and incentives to overcome behavioral and technological challenges (Kammen and Sunter 2016).

Two additional urban carbon cycle components deserve mention when considering sectoral mitigation approaches: CH₄ leakage (referred to as “fugitive” emissions) and urban vegetation. As mentioned in Section 4.2.2, several studies have identified CH₄ emissions from leaking natural gas infrastructure serving cities (Jackson et al., 2014; Lamb et al., 2016; McKain et al., 2015; Phillips et al., 2013). Methane emissions also can occur downstream of building meters, for example, from leaky gas pipes in buildings, stoves, hot water heaters, and other appliances (Jeong et al., 2017; Lavoie et al., 2017; Wunch et al., 2016). The quantity of CH₄ emissions from the natural gas system is not well constrained (Brandt et al., 2014; Hendrick et al., 2016; Lamb et al., 2016; McKain et al., 2015), but there are specific thresholds for CH₄ loss from natural gas, which, if exceeded, would negate the climate benefit of switching to natural gas. According to Alvarez et al. (2012),⁵ realizing an immediate net climate benefit from the use of natural gas would require CH₄ emissions from the natural gas system to be lower than 0.8%, 1.4%, and 2.7% of production to justify a transition from heavy-duty diesel vehicles, gasoline cars, and coal-burning power plants, respectively.

At the municipal scale, reports indicate that biological carbon uptake within urban boundaries constitutes 0.2% to 3% of total emissions, depending on the locality (Escobedo et al., 2010; Liu and Li 2012; Tang et al., 2016; Velasco et al., 2016). However, biological carbon respiration rates are sensitive to management practices (e.g., Decina et al., 2016), and urban vegetation possibly can constitute a net source of carbon to the atmosphere. The role of urban vegetation dynamics may be much more significant in affecting emissions through indirect impacts on the urban carbon cycle, such as shading of buildings that reduces energy consumption, evaporative cooling of urban vegetation, and wind sheltering (Akbari et al., 2001; Shashua-Bar et al., 2009; Susca et al., 2011). These indirect carbon reductions—a result of urban vegetation on energy consumption rather than direct carbon emissions–reducing technologies, for example—must be weighed against the energy and water penalty of increasing vegetation cover in locales with little or no historic vegetation canopy, such as the southwestern United States (Middel et al., 2014, 2015).

4.6.3 Co-Benefits and Tradeoffs—Links to Air Quality, Health, and UHI

Studies have identified co-benefits between carbon mitigation in urban areas and improvements in human health and other urban environmental issues (Harlan and Ruddell 2011; Milner et al., 2012; Viguié and Hallegatte 2012; see Ch. 6: Social Science Perspectives on Carbon). For example, reducing fossil fuel consumption or CH₄ emissions also decreases emissions of traditional air pollutants such as carbon monoxide (CO), sulfur oxides (SO_x), volatile organic compounds (VOC), particulates, and oxides of nitrogen (NO_x). Three of these—NO_x, VOCs, and CO—are associated with the production of ground-level ozone, which is linked to respiratory diseases such as emphysema, bronchitis, and asthma (Kim et al., 2011). Various studies have linked fine particulate exposure to significant health problems including aggravated asthma, chronic respiratory disease in children, and premature death in people with heart or lung disease (Valavanidis et al., 2013). However, carbon mitigation practices also have tradeoffs. For instance, renewable energy systems that lower carbon emissions and reduce health impacts of traditional air pollutants are not completely free from environmental and health impacts (Miller et al., 2013).

Carbon emissions often are associated with waste heat production, which plays a role in the UHI effect. Strategies that reduce fossil fuel carbon emissions may contribute to reduced waste heat and, subsequently, a decrease in both summer and winter urban air temperatures. The magnitude of urban cooling may be modest and dependent on the location and timing of reduced energy consumption (Huang et al., 2013; Ostro et al., 2011; Sarofim et al., 2016) and the fuel mix used for electricity production and building heating systems (Jacobson and Ten Hoeve 2012).

Dozens of completed or underway studies on urban carbon flux are now reported in the peer-reviewed literature (see Table 4.1). Among these are intensive efforts testing different methods and approaches to understanding flux magnitudes, trends, driving activity, emissions mitigation guidance, and reduction performance tracking. Despite these efforts, consistent and comparable data on carbon fluxes in cities are still lacking, particularly at spatial resolutions below the whole-city level (Kennedy et al., 2015). Greater integration of these studies and greater exploration of whether and how this information can be used by stakeholders are needed. This will require continued efforts in interdisciplinary integration of existing subcommunities engaged in urban carbon research. For example, the use of sometimes singular reliance on atmospheric concentration observations common in inversion studies could move toward an assimilation framework in which all available observational constraints are incorporated with their accompanying uncertainties to arrive at optimized carbon fluxes, further integrating bottom-up and top-down approaches. Equally important are 1) the integration of information on CO₂, CH₄, and relevant local air pollution and 2) the continued trend toward data with higher space and time resolutions, particularly relevant to urban stakeholders. Finally, integration across ongoing urban studies will provide more insight into which research methods and approaches are successful under differing urban morphologies and social and physical constraints (e.g., urban density, data transparency, and topography). These advances could be achieved in part by integrating existing approaches with remote sensing of urban CO₂ and other attributes relevant to the urban carbon cycle.

Urban carbon trends remain difficult to assess because of a lack of compatible and comparable data and limited historical information. Results from a number of intensive studies underway should begin to inform trend information in North America. Improvement to trend detection is critical to the assessment and prognostic capabilities important to urban stakeholders. Integration of urban trend detection with trend activity at larger scales could advance the ability of observing systems to systematically assess urban trends.

Urban carbon fluxes are dominated, directly and indirectly, by the human activities within the built environment that includes large infrastructural systems such as buildings, roads, and factories, along with their co-evolution with fossil fuel energy sources. The carbon fluxes associated with this co-evolved technological system are modulated by underlying climate and socioeconomic dynamics such as consumption, wealth, lifestyles, social norms, governance, and energy prices. A quantitative understanding of these drivers and flux outcomes remains difficult to generalize. This challenge is due to both the emergent properties of urban carbon fluxes and the idiosyncratic nature of cities and the studies performed thus far, which tend to focus on single urban domains. Particularly in Mexico, for example, little work has been accomplished outside the Mexico City metropolitan area. More research is needed that systematically explores multiple urban domains to better understand the relationships between emissions and the physical, social, and technological dynamics in cities.

The urban domain is a source of significant carbon mitigation potential evidenced by the rapid rise in individual urban-scale climate policy efforts. This mitigation, combined with the dominant role that cities play in total anthropogenic carbon emissions, implies that proposed emissions mitigation measures must be tested against documented success in urban areas. The ability of cities to manage carbon fluxes is determined by what control cities can exert over flux sources or their drivers. Cities and their carbon management efforts exist within a larger multilevel governance matrix that can both enable and hinder carbon mitigation efforts. For example, without control over energy supply systems, some cities have limited capability to mitigate emissions.

More targeted research evaluating how specific reductions in emissions are linked to specific policies would enhance the ability to design and implement effective policies in the future. There is limited evidence on the effects of urban climate policy on reducing community-wide emissions, advancing other urban policy goals, or contributing to a transition to low-carbon development. Attributing changes in urban carbon emissions to the actions of city governments also can be challenging, partly because of the complex networks of authority at play. Moreover, there has been little effort to study other effects of urban climate policy, such as cost-effectiveness, co-alignment with other goals and processes, and distributional effects on marginalized populations. Without common frameworks and comparable case studies, the extent to which local or distant political and economic factors shape these outcomes is unclear.

Given the increasing role that urban areas play in the total carbon fluxes within the three North American countries, there is a critical need to improve urban carbon flux projection capabilities in North American cities. Better information on fluxes and their drivers, combined with improved understanding of successful mitigation, would offer researchers and urban decision makers the means to bend urban flux trajectories toward low-carbon pathways. Continued work on the co-benefits and tradeoffs associated with carbon mitigation practices will further enrich carbon emissions planning to account for the important related issues of the UHI, urban air quality, and human health.

SUPPORTING EVIDENCE

KEY FINDINGS

Key Finding 1

Urban areas in North America are the primary source of anthropogenic carbon emissions, with cities responsible for a large proportion of direct emissions. These areas are also indirect sources of carbon through the emissions embedded in goods and services produced outside city boundaries for consumption by urban dwellers (medium confidence, likely).

Description of evidence base

Key Finding 1 is supported by empirical evidence and modeling studies aimed at quantifying and understanding urban extent, energy, carbon, and material flows (Jones and Kammen 2014; Hoornweg et al. 2011; Seto et al., 2014). Research has highlighted the importance of direct versus indirect carbon fluxes in addition to the relative importance of urban carbon flows within the national landscape (Lin et al., 2015).

Major uncertainties

Very few studies have attempted a comprehensive assessment of the urban portion of North American carbon emissions. Only two have attempted estimates for the North American domain (Marcotullio et al., 2013; Grubler et al., 2012). Both contain unquantified uncertainties acknowledged to include not only the underlying data, but also the definition of “urban” and objective methods to spatially enclose urban areas (Parshall et al., 2010). Uncertainty also exists in the exact quantification of urban versus nonurban carbon emissions because of limited data and methodological inconsistencies in defining direct and indirect carbon fluxes.

Assessment of confidence based on evidence and agreement, including short description of nature of evidence and level of agreement

Key Finding 1 is supported by a growing number of urban carbon footprint studies in North America. Much of this work is in the United States, with some work in Canada and very few studies in Mexico. There is general agreement that urban areas constitute the majority of anthropogenic carbon emissions in North America. However, a more precise assessment remains uncertain because of a lack of comprehensive data. Recent formalization of methods now defines direct versus indirect anthropogenic carbon emissions, but these methods are applied inconsistently in studies of urban carbon emissions, challenging attempts to compare emissions among cities.

Summary sentence or paragraph that integrates the above information

For Key Finding 1, anthropogenic carbon fluxes associated with North American cities represent the majority of total anthropogenic carbon emissions from North America, though uncertainty remains on the precise share. These emissions consist of both direct and indirect emissions, the latter of which are recognized as important, but often poorly characterized, components of total urban anthropogenic carbon flux.

Key Finding 2

Many societal factors drive urban carbon emissions, but the urban built environment and the regulations and policies shaping urban form (e.g., land use) and technology (e.g., modes of transportation) play crucial roles. Such societal drivers can lock in dependence on fossil fuels in the absence of major technological, institutional, and behavioral change. Some fossil fuel–related infrastructure can have lifetimes of up to 50 years (high confidence).

Description of evidence base

Key Finding 2 involves societal factors that drive urban carbon emissions, including consumption and supply chains (Baiocchi and Minx 2010; Peters et al., 2011), wealth (Creutzig et al., 2015a), fuel prices (Creutzig 2014), lifestyle and norms (Patarasuk et al., 2016; Porse et al., 2016), urban form and density (Baiocchi et al., 2015; Creutzig et al., 2015a; Karathodorou et al., 2010; Mindali et al., 2004; Newman and Kenworthy 1989, 1999), technology (Kennedy et al., 2009, 2014, 2015), and climate (Baiocchi et al., 2015; Creutzig et al., 2015a; Glaeser and Kahn 2010; Kennedy et al., 2015). Research continues to establish the relative permanence of large technological and infrastructural systems in urban areas. For example, fossil fuel–burning infrastructures have lifetimes up to 50 years, leading to systemic dependence (i.e., “lock-in”) on fossil fuel–based technology (Unruh 2000; Seto et al., 2016; Erickson et al., 2015).

Major uncertainties

Increasing numbers of studies examine relationships between urban density and 1) atmospheric emissions and 2) building energy use. Uncertainty exists relative to the ability of cities to change their infrastructure because of cost considerations and municipal regulations, as well as state and national regulations that affect city form and infrastructure. Relationships among the core elements of carbon lock-in (i.e., technological, institutional, and behavioral) are poorly understood and involve interactions among scales of governance larger than urban areas. All these aspects vary widely across cities and North American countries.

Assessment of confidence based on evidence and agreement, including short description of nature of evidence and level of agreement

Studies are emerging that investigate these relationships, but more research is needed to understand the processes.

Summary sentence or paragraph that integrates the above information

For Key Finding 2, cities are complex systems with a mix of societal factors driving carbon emissions. Uncertainties remain regarding a complete typology of driving factors and the extent to which these factors lead to path dependencies and the ability of urban areas to alter infrastructure and technological trajectories.

Key Finding 3

Key challenges for urban carbon flux studies are observational design, integration, uncertainty quantification, and reconciliation of the multiple carbon flux approaches to detect trends and inform emissions mitigation efforts (medium confidence, likely).

Description of evidence base

Key Finding 3 is supported by recent research that begins to integrate and reconcile carbon flux information from intensive urban study sites in North America. Key supporting references include Gurney et al. (2017), Lamb et al. (2016), Lauvaux et al. (2016), and McKain et al. (2012, 2015).

Major uncertainties

The major uncertainties related to integrating and reconciling urban carbon budget studies are those intrinsic to the different methodologies used. For trend detection and mitigation guidance, major uncertainties arise from the differences in scientific goals versus policy application.

Assessment of confidence based on evidence and agreement, including short description of nature of evidence and level of agreement

There is broad agreement that integration and reconciliation remain challenging. However, the various disciplines that pursue different methodological approaches to urban carbon flux assessment have different 1) definitions of uncertainty, 2) needs for attribution, and 3) criteria for successful mitigation guidance. Hence, some disagreement exists over specific policy application and utility.

Estimated likelihood of impact or consequence, including short description of basis of estimate

Continued integration and reconciliation of urban carbon fluxes are likely to achieve methodologically consistent and agreed-on approaches, results of which will be useful for trend detection and mitigation guidance. Assessment of enacted policy has received limited study, and thus the ability to independently assess atmospheric trends and use that information to inform mitigation progress and potential is highly important and relevant to urban carbon mitigation and climate policy.

Summary sentence or paragraph that integrates the above information

For Key Finding 3, the research community recently has begun to integrate and reconcile multiple approaches to urban carbon flux assessments for intensive study sites of urban carbon in North America. These efforts are ongoing but remain challenging due to methodological differences, methodological uncertainties, and differing disciplinary perspectives and criteria. The relevance and importance of these efforts are high because there remains limited independent assessment of urban carbon mitigation efforts or progress.

Key Finding 4

Improvements in air quality and human health and the reduction of the urban heat island are important co-benefits of urban carbon emissions mitigation (high confidence, very likely).

Description of evidence base

Numerous studies contribute to Key Finding 4, including research on the impacts of carbon emissions reductions on local air pollution, related human health benefits, and reduction of waste heat discharge (Harlan and Ruddell 2011; Huang et al., 2013; Jacobson and Ten Hoeve 2012; Milner et al., 2012; Ostro et al., 2011; Sarofim et al., 2016; Viguié and Hallegatte 2012).

Major uncertainties

Uncertainties include the precise magnitude of health and environmental benefits associated with reductions of carbon emissions. Benefits will vary with a number of factors such as urban population sociodemographics, urban meteorology, composition of emissions sources, and energy fuel mix. Tradeoffs require further research and remain uncertain.

Assessment of confidence based on evidence and agreement, including short description of nature of evidence and level of agreement

There is broad agreement about the benefits of reducing carbon emissions. Major uncertainties are related to assessing quantitatively the impacts and precise relationships between carbon emissions reductions and urban health and environmental benefits.

Estimated likelihood of impact or consequence, including short description of basis of estimate

Key Finding 4 is of high impact. The quantitative relationship between carbon emissions reductions and urban health and environmental impacts has direct and important implications for stakeholder decision making associated with urban air quality, urban climate policy, and general urban planning.

Summary sentence or paragraph that integrates the above information

For Key Finding 4, fossil fuel energy systems emit carbon dioxide (CO₂) and methane (CH₄). These systems also result in emissions of local air pollution and heat discharge in urban environments. Hence, reducing fossil fuel dependence can provide co-benefits to human health and environmental impacts associated with urban heat. The net benefit of these related outcomes remains uncertain because of potential tradeoffs and unforeseen outcomes.

Key Finding 5

Urban methane (CH₄) emissions have been poorly characterized, but the combination of improved instrumentation, modeling tools, and heightened interest in the problem is defining the range of emissions rates and source composition as well as highlighting infrastructure characteristics that affect CH₄ emissions (high confidence).

Description of evidence base

For Key Finding 5, consistent and persistent evidence of under-reported CH₄ emissions was found in Los Angeles, Boston, and Indianapolis (Lamb et al., 2016; McKain et al., 2015; Wong et al., 2016). Other studies report inverted distributions of CH₄ emissions in Los Angeles (75% thermogenic, 20% biogenic; Hopkins et al., 2016) compared with San Francisco (17% thermogenic, 82% biogenic; Jeong et al., 2017). Intensive field surveys of urban natural gas systems in seven cities indicate large variations in CH₄ leakage rates from urban gas distribution infrastructure attributed to differences in pipeline material and age (Hopkins et al., 2016; Jackson et al., 2014; Phillips et al., 2013; von Fischer et al., 2017).

Major uncertainties

The uncertainties in urban-scale CH₄ emissions estimates are not well established because the number of cities where these emissions have been studied is small and the temporal duration of the studies is very limited. While Key Finding 5 is of high confidence for the limited times and numbers of cities represented in the literature, this finding cannot yet be generalized across other North American cities.

Assessment of confidence based on evidence and agreement, including short description of nature of evidence and level of agreement

The assessment of confidence is based on a small number of cities where emissions have been studied over a short period of time. The confidence level is based on the results of these studies, which are robust and agreed upon, but this confidence does not necessarily apply across the continent due to the limited number of studies conducted to date.

Summary sentence or paragraph that integrates the above information

For Key Finding 5, urban CH₄ emissions estimates exist for several North American cities. Yet there are discrepancies between these estimates and governmental inventories. As such, further research is needed to gain a complete understanding of uncertainties and assess the representativeness of these studies.

Key Finding 6

Urban areas are important sites for policymaking and decision making that shape carbon fluxes and mitigation. However, cities also are constrained by other levels of government, variations in their sources of authority and autonomy, capacity, competing local priorities, and available fiscal resources (high confidence).

Description of evidence base

Thousands of North American cities have joined municipal networks to pursue co-benefits from climate mitigation measures, including benchmarking initiatives. However, many cities do not have authority to dictate fuel sources for their energy supply or for vehicles, nor they do control carbon inputs into products that come into cities. Evidence for Key Finding 6 indicates that municipal carbon emissions mitigation initiatives in the United States vary significantly among states. This variation suggests that state-level policies and characteristics may influence the propensity of cities in their borders (Krause 2011). Jurisdictional barriers that restrict decision making by municipalities may impede change because of a lack of authority over decision making (Tozer 2013).

Major uncertainties

Cities vary in extent and type of innovation, though the precise motivation lacks sufficient evidence to provide a clear understanding of the factors involved. In addition, each country has different governmental arrangements that affect city autonomy; even within states in the same country, these arrangements may vary.

Assessment of confidence based on evidence and agreement, including short description of nature of evidence and level of agreement

Evidence of the importance of cities is supported by the large proportion of North American anthropogenic carbon emissions (see Key Finding 1). The evidence for the moderated influence over carbon emissions is supported by the mixture of political, economic, and social authority of cities over direct and indirect emissions sources.

Summary sentence or paragraph that integrates the above information

For Key Finding 6, cities are making policies to reduce their carbon emissions, but they also are constrained by many factors that can limit their authority. Moreover, cities vary widely among themselves. An understanding of the limitations in the ability of cities to mitigate their carbon emissions and why certain cities are more proactive than others is still to be developed.

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Footnotes

Also see unfccc.int/national_reports/annex_i_ghg_inventories/national_inventories_submissions/items/9492.php.↩
Carbon dioxide equivalent (CO₂e): Amount of CO₂ that would produce the same effect on the radiative balance of Earth’s climate system as another greenhouse gas, such as methane (CH₄) or nitrous oxide (N₂O), on a 100-year timescale. For comparison to units of carbon, each kg CO₂e is equivalent to 0.273 kg C (0.273 = 1/3.67). See Preface for details.↩
www.gbes.com/blog/mexico-is-a-leed-leader/↩
www.energy.ca.gov/sb350/↩
These numbers were modified from the Alvarez et al. (2012) study by the Environmental Defense Fund to account for new data (see www.energy.ca.gov/2014_energypolicy/documents/2014-06-23_workshop/presentations/13_O_Connor_EDF_IEPR-Presentation.pdf).↩

[fn1] Also see unfccc.int/national_reports/annex_i_ghg_inventories/national_inventories_submissions/items/9492.php.↩

[fn2] Carbon dioxide equivalent (CO₂e): Amount of CO₂ that would produce the same effect on the radiative balance of Earth’s climate system as another greenhouse gas, such as methane (CH₄) or nitrous oxide (N₂O), on a 100-year timescale. For comparison to units of carbon, each kg CO₂e is equivalent to 0.273 kg C (0.273 = 1/3.67). See Preface for details.↩

[fn3] www.gbes.com/blog/mexico-is-a-leed-leader/↩

[fn4] www.energy.ca.gov/sb350/↩

[fn5] These numbers were modified from the Alvarez et al. (2012) study by the Environmental Defense Fund to account for new data (see www.energy.ca.gov/2014_energypolicy/documents/2014-06-23_workshop/presentations/13_O_Connor_EDF_IEPR-Presentation.pdf).↩

Front Matter

Synthesis

Human Dimensions of the Carbon Cycle

State of Air, Land and Water

Consequences and Ways Forward

Appendices

Key Findings

Key Finding 1

Key Finding 2

Key Finding 3

Key Finding 4

Key Finding 5

Key Finding 6

Key Finding 1

Key Finding 2

Key Finding 3

Key Finding 4

Key Finding 5

Key Finding 6

Likelihood

Confidence Level

CHAPTER 4 Understanding Urban Carbon Fluxes

4.1 Introduction

Authors

Acknowledgments

Recommended Citation

Figure 4.1: Per Capita Energy Consumption Versus Total Energy Consumption in Rural to Urban U.S. Counties

Figure 4.2: Key Components of Urban Carbon Cycling

4.2 Current Understanding of Carbon Fluxes and Stocks

4.2.1 Accounting Framework and Methods

Figure 4.3: Relationships Between Carbon Inventory Approaches.

Table 4.1. Scientifically Based Urban Carbon Estimation Studies in North American Cities

4.2.2 Human Activity and the Built Environment

Figure 4.4: U.S. Fossil Fuel Carbon Emissions, Highlighting Four Urban Areas

4.2.3 Land and Ecosystems

4.3 Societal Drivers

4.3.1 Consumption

4.3.2 Economics—Wealth and Energy Prices

4.3.3 Behavior—Lifestyles and Norms

4.3.4 Urban Form and Density

4.3.5 Technology

Figure 4.5: Assessments of Lock-In Related to Different Types of Infrastructure Emitting Carbon Dioxide (CO2)

4.3.6 Climate

4.4 Trends and Feedbacks

4.5 Global, North American, and Regional Context

4.5.1 Global Urban Carbon

4.5.2 United States, Canada, and Mexico—Urban Carbon in Context

4.6 Carbon Management Decisions

4.6.1 Importance of Governance and Multilevel Networks

4.6.2 Sectoral Mitigation Approaches

4.6.3 Co-Benefits and Tradeoffs—Links to Air Quality, Health, and UHI

4.7 Synthesis, Knowledge Gaps, and Outlook

SUPPORTING EVIDENCE

KEY FINDINGS

Key Finding 1

Description of evidence base

Major uncertainties

Assessment of confidence based on evidence and agreement, including short description of nature of evidence and level of agreement

Summary sentence or paragraph that integrates the above information

Key Finding 2

Description of evidence base

Major uncertainties

Assessment of confidence based on evidence and agreement, including short description of nature of evidence and level of agreement

Summary sentence or paragraph that integrates the above information

Key Finding 3

Description of evidence base

Major uncertainties

Assessment of confidence based on evidence and agreement, including short description of nature of evidence and level of agreement

Estimated likelihood of impact or consequence, including short description of basis of estimate

Summary sentence or paragraph that integrates the above information

Key Finding 4

Description of evidence base

Major uncertainties

Assessment of confidence based on evidence and agreement, including short description of nature of evidence and level of agreement

Estimated likelihood of impact or consequence, including short description of basis of estimate

Summary sentence or paragraph that integrates the above information

Key Finding 5

Description of evidence base

Major uncertainties

Assessment of confidence based on evidence and agreement, including short description of nature of evidence and level of agreement

CHAPTER 4
Understanding Urban Carbon Fluxes

Figure 4.5: Assessments of Lock-In Related to Different Types of Infrastructure Emitting Carbon Dioxide (CO₂)