An integrated approach to improving fossil fuel emissions scenarios with urban ecosystem studies

ecological complexity 6 (2009) 1–14

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An integrated approach to improving fossil fuel emissions scenarios with urban ecosystem studies D.E. Pataki a,*, P.C. Emmi b, C.B. Forster b, J.I. Mills b, E.R. Pardyjak c, T.R. Peterson d, J.D. Thompson e, E. Dudley-Murphy f a

Dept. of Earth System Science and Dept. of Ecology & Evolutionary Biology, University of California, Irvine, CA 92697-3100, United States College of Architecture + Planning, 375 S 1530 E, University of Utah, Salt Lake City, UT 84112, United States c Dept. of Mechanical Engineering, 50 Central Campus Dr, University of Utah, Salt Lake City, UT 84112, United States d Dept. of Wildlife & Fisheries Sciences, Texas A&M University, College Station, TX 77842-2258, United States e Dept. of Human Dimensions of Natural Resources, Colorado State University, Fort Collins, CO 80523-1401, United States f Energy and Geosciences Institute, 423 Wakara Way, University of Utah, Salt Lake City, UT 84112, United States b

article info

abstract

Article history:

The future trajectory of fossil fuel emissions is one of the largest uncertainties in predicting

Received 15 September 2007

climate change. While global emissions scenarios are ultimately of interest for climate

Received in revised form

modeling, many of the factors that influence energy and fuel consumption operate on a local

12 May 2008

rather than global level. However, there have been relatively few comprehensive studies of

Accepted 19 September 2008

the ecological and socioeconomic processes that will determine the future trajectory of net

Published on line 13 November 2008

carbon dioxide (CO2) emissions at local and regional scales. We conducted an interdisciplinary, whole ecosystem study of the role of climate, urban expansion, urban form,

Keywords:

transportation, and the urban forest in influencing net CO2 emissions in the Salt Lake

Urban ecology

Valley, Utah, a rapidly urbanizing region in the western U.S. Our approach involved a

Biocomplexity

detailed emissions inventory validated with atmospheric measurements, as well as a

Carbon dioxide

system dynamics model of future CO2 emissions developed in collaboration with local

CO2 emissions

stakeholders. The model highlighted the importance of a positive feedback between urban

Mediated modeling

land development and transportation investments that may strongly affect emissions by amplifying declines in developmental densities and increases in vehicular traffic. Simulations suggested that while doubling the density of tree planting would have a negligible effect on total urban CO2 emissions, land use and transportation policies that dampen the intensity of the urban sprawl feedback could result in a 22% reduction in CO2 emissions by 2030 relative to a business as usual scenario. We suggest that by advancing our mechanistic understanding of energy and fuel consumption regionally, this urban ecosystem approach has great potential for improving emissions scenario studies if replicated in other cities and urbanizing regions. # 2008 Elsevier B.V. All rights reserved.

1.

Introduction

One of the most uncertain terms in the trajectory of the global carbon cycle is the future magnitude of fossil fuel emissions

(Nakicenovic et al., 2000; IPCC, 2001). Various approaches have been applied to generating global emissions scenarios using assumptions about regional to global economic growth and demography (Edmonds et al., 2000; Nakicenovic et al., 2000). A

* Corresponding author. Tel.: +1 949 824 9411; fax: +1 949 824 3874. E-mail address: [email protected] (D.E. Pataki). 1476-945X/$ – see front matter # 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.ecocom.2008.09.003

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limitation to this approach is the mismatch in scale between local social, environmental, and economic factors that influence patterns of energy use and the impact on global emissions (Kates and Wilbanks, 2003; Lebel, 2004). However, resolving mismatches between local and global processes has been successfully addressed in other aspects of carbon cycle science. For example, natural sources and sinks of carbon in terrestrial ecosystems were highly uncertain terms in the carbon budget, constituting a ‘‘missing’’ sink of carbon that was difficult to measure directly (Siegenthaler and Sarmiento, 1993; Gifford, 1994; Schimel, 1995). While uncertainty in this term remains, an enormous amount of progress has been made in recent years in understanding how local ecological processes affect the terrestrial carbon sink at the global scale (McGuire et al., 2001; Schimel et al., 2001). This has been accomplished, in part, by conducting mechanistic studies of the factors controlling CO2 exchange in a variety of ecosystems, and applying this understanding to large-scale models (Heimann et al., 1998; Canadell et al., 2000; Schimel et al., 2000; Cramer et al., 2001; McGuire et al., 2001). We propose a similar approach for reducing uncertainty in the fossil fuel emissions term of the carbon cycle. A variety of local factors including climate, affluence, access to technology, and regional infrastructure affect the magnitude of fossil fuel emissions in a given region (MacKellar et al., 1995; Kates et al., 2003; Lebel, 2004; Romero Lankao, 2004). In order to understand the basis of underlying local patterns of net greenhouse gas emissions, detailed inventories of energy and fuel use must be conducted at relatively small spatial and temporal scales. Natural sources and sinks of carbon may also be important at the local level to the extent that they influence local policy and emissions reductions programs. Hence, socioeconomic, climatic, and ecological factors must all be considered in conjunction with local fossil fuel emissions inventories. Here we describe the components of an interdisciplinary, whole ecosystem framework to quantify critical determinants of net greenhouse gas (GHG) emissions in the Salt Lake-Ogden metropolitan area. This region in and surrounding Salt Lake City, UT is characterized by good historical records, strong seasonality of temperature and precipitation, and a rapid rate of both population growth and urban expansion, allowing us to examine several key factors that may affect the trajectory of fossil fuel emissions both historically and in the coming decades. We conducted a detailed emissions inventory for the region using fuel and energy consumption data as well as atmospheric measurements of net CO2 flux and the isotopic composition of CO2. Because inventories alone do not provide a mechanistic understanding of drivers of emissions, we related electricity and fuel consumption to climatic and demographic, economic, land use, travel and traffic variables and developed an urban forest growth model to estimate biological sinks. We used mediated modeling to develop a system dynamics model interface with which to explore net CO2 emissions in collaboration with local stakeholders from the government, private, and non-profit sectors. This approach allowed us to develop a model tuned to local perceptions of plausible fossil fuel emissions scenarios for the study region. We hope that this overall, process-level approach will be useful in other urban ecosystems and can

ultimately be used to compare and contrast regional studies and improve the mechanistic basis of global fossil fuel emissions models.

2.

Methods

2.1.

Study area

This study focused on the urbanized parts of the Salt Lake City-Ogden metropolitan statistical area as defined by the U.S. Census of 1990 and 2000. The statistical area encompasses Salt Lake, Davis, and Weber counties with a total population of 1.3 million in 2000. The region has a strongly seasonal, continental climate with average winter temperatures of 0.4 8C and average summer temperatures of 23.3 8C from 1971 to 2000. The mean annual temperature is 11 8C and the mean annual precipitation is 42 cm (http://www.wrh.noaa.gov/slc/ climate/slcclimate/SLC/index.php). The metropolitan area is located in a mountain basin bordered by the Wasatch Mountains to the east, the Oquirrh Mountains and the Great Salt Lake to the west, open-valley agriculture to the north, and low mountains and a riverine pass to the south.

2.1.1.

Greenhouse gas emissions inventory

GHG emissions inventories for the residential, commercial, and industrial sectors are available at the state level in the United States (DOE EIA, 2006); however, inventories are generally unavailable at the county or municipal level (Decker et al., 2000; Sahely et al., 2003; Pataki et al., 2006a). This is a major limitation in linking local processes to patterns of fossil fuel emissions. In the Salt Lake-Odgen area, energy consumption is associated with local burning of natural gas for wintertime heating, commercial/industrial processes and seasonal electricity generation (for both wintertime heating and summertime cooling). In addition, emissions are generated elsewhere in Utah by coal-fired power plants that provide the bulk of the electricity used for lighting, heating, air conditioning, commercial/industrial processes and water supply/treatment. We were able to obtain consumption data from all of these sources from the local utilities. Because the energy required for heating and cooling reflects seasonal and longer term climate variations, we evaluated the relationships between energy use, population growth, and local climate conditions (Quayle and Diaz, 1980; Le Comte and Warren, 1981; Suckling and Stackhouse, 1983; Kirshen et al., 2004). We obtained natural gas consumption data from Questar Gas. The smallest spatial scale at which we were able to obtain daily consumption data was at the control gate scale: five control gates provide natural gas fuel to an area that encompasses most, but not all, of Salt Lake County. For electrical energy consumption, we assumed that the proportional contributions of each energy source used within the study area equaled those estimated for the state as a whole. CO2 emissions were estimated using power plant fuel use data provided by local power generators to state and federal government agencies (Utah Geological Survey, 2007). Total monthly electricity consumption data for the entire three county study area were provided for 1999–2005 by Rocky Mountain Power (formerly Utah Power). For the transportation

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sector, we obtained data on vehicle kilometers traveled in the study region from 1980 to 2000 from the U.S. Department of Transportation (1995, 1999a,b, 2000) and used these estimates to calculate CO2 emissions from the transportation sector. We also utilized data on the extent of urban land from the USDA National Resources Inventory (http://www.nrcs.usda.gov/ TECHNICAL/NRI/; R. Grow, pers. commun.) for comparison with population density statistics from the U.S. Census.

2.1.2.

Urban forest growth modeling

We quantified rates of urban forest CO2 sequestration using a simple forest growth model that tracks tree age classes, mortality, and rates of re-planting. Age cohort ranges were chosen to minimize biomass calculation errors associated with using the arithmetic mean of tree age and diameter at breast height (DBH) for a specific age cohort. The model was initiated with a standing stock of deciduous and coniferous trees and their average DBH, using extrapolated data from the Salt Lake City Department of Forestry. Equations for deciduous trees were taken from Wiant (1977) and for coniferous trees from Snowdon et al. (2002). The model tracks age classes over time with prescribed mortality rates and cohort-specific replacement planting rates. A random distribution around a species-specific mean lifespan was assigned to senescent trees. Simulations of the period 1980–2005 corresponded well to existing forest inventory data and to the spatial extent of the urban forest estimated from Landsat TM-derived normalized difference vegetation index (NDVI). The study area was classified with the ISODATA clustering algorithm and groundtruthed with high resolution (1 m) aerial photography to distinguish among plant cover types, e.g. lawns vs. trees. The classification is shown in Fig. 1. The resulting satellite-imagery derived estimate of the extent urban forest cover was 19,593 ha in Salt Lake County vs. 19,534 ha in the urban growth model. The model estimated 2 Mt C stored in the Salt Lake-Ogden urban forest in 2002, which is proportionally smaller than the estimate made by Nowak and Crane (2002) of 3.3 Mt for all urban trees in Utah. The forest growth model can be used to evaluate sequestration at varying forest planting densities and tree replacement rates to evaluate the consequences of urban forest management and landscaping options in new and existing urban land cover.

2.1.3.

Atmospheric measurements

Eddy covariance of CO2 fluxes from urban land cover can be used to measure net CO2 emissions including both anthropogenic sources and biogenic sources and sinks (Grimmond et al., 2002, 2004; Nemitz et al., 2002; Moriwaki and Kanda, 2004; Vogt et al., 2006). In this study we used eddy covariance methods in a residential neighborhood in Murray, UT, a suburban community south of Salt Lake City, to directly measure CO2 fluxes from common land cover. Details of the measurements and study sites are given in Ramamurthy and Pardyjak (in review). Briefly, flux measurements were made on a cell phone tower at a height of 36 m in the inertial sublayer using a sonic anemometer (CSAT3, Campbell Scientific, Logan, UT) and a closed path infrared gas analyzer (LI 7000, Licor Inc., Lincoln, NE). During the experiments, manual calibrations for zero and span were carried out every 7–14 days and standard

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corrections (Webb et al., 1980; Aubinet et al., 2000) were made to minimize flux error. The site was predominately surrounded by single story residences with irrigated landscapes; the average building height in the footprint surrounding the measurement tower was 4.4 m, while the average tree height was 6.5 m. Analysis of Quickbird imagery within the 80% flux footprint showed that land cover consisted of 56% vegetative cover, 8% bare soil, and 36% impervious surfaces including roofs, roads, and concrete. We also utilized the isotopic composition of atmospheric CO2 to distinguish among CO2 emissions from transportation fuel combustion, natural gas combustion, and biogenic respiration of plants and soils. We have reported that biogenic CO2 can constitute more than 50% of locally derived atmospheric CO2 concentration at night in the Salt Lake Valley (Pataki et al., 2003, 2007). The basis of this method is the unique combination of stable carbon and oxygen isotopes in each CO2 source. Detailed methodology for CO2 sampling, stable isotope measurements, and source apportionment has been described previously (Pataki et al., 2003, 2005a,b, 2006b, 2007). Here, we compared CO2 source apportionment derived from stable isotope measurements to the inventory results for validation.

2.1.4.

Stakeholder workshops

Previous studies have pointed out that in conflicts over environmental pollution, local residents may have access to critical facts and values that are unknown and unavailable to technical experts (Yearley et al., 2003). To access this information we used mediated modeling, which provides a structured framework for enabling diverse stakeholders to use models to understand the multidimensional, dynamic, and interactive aspects of environmental problems (Peterson et al., 2004). Rather than experts dispensing answers or discussing the perceptions of a group of stakeholders, mediated modeling aims for a collaborative team learning experience to raise the shared level of understanding in a group as well as fostering a broad and deep consensus regarding public policy responses to seemingly intractable environmental problems (Peterson et al., 2005). Elite stakeholders, including decision makers, political advisors, and gatekeepers for various politically and economically influential social groups were recruited. Participants in this project agreed to attend monthly workshops that convened for approximately 30 h over 6 months. The group included representatives from government, non-profit, and private industry sectors, as well as individual community residents. First, participants were informed about the project and received training in system dynamics and collaborative discussion. Second, presentations by technical experts, question/answer sessions and informal small group discussions contributed to a common knowledge base about fossil fuel emissions, climate change, and related issues. Participants drew conceptual maps of the urban system and generated specific suggestions for emissions reductions that could be evaluated with a simple, system dynamics model developed jointly by researchers and participants. Modeling was conducted with STELLA1 software (v. 9, http://www.iseesystems. com/) in order to develop a non-technical, user-friendly interface.

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ecological complexity 6 (2009) 1–14

Fig. 1 – Classification of urban forest cover (in green) in the study region using a Landsat TM 2002 satellite image. The classification was based on the Normalized Difference Vegetation Index (NDVI) at 30 m resolution, and validated with high spatial resolution (1 m) aerial photography. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)

As the workshops continued, a series of active learning exercises helped participants think systemically about the local airshed and enabled them to identify key issues, concerns, and interrelationships between variables affecting pollutant emissions. To facilitate this, we developed an initial simulation model that incorporated the relationships between energy use, transportation emissions, climate, population

growth, and land development described in the previous sections. To evaluate the impact of workshop participation, participants were invited to complete questionnaires at the first workshop, and 6 months after the final workshop. The first questionnaire asked participants to provide basic demographic information, and to report on why they were

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Fig. 2 – Daily natural gas use in Salt Lake County and vicinity for 1996–2003: (a) natural gas use and daily heating degree days (HDD) vs. time, and (b) natural gas use vs. daily HDD. Natural gas use data were provided by Questar Gas. HDD is computed as the mean daily temperature subtracted from 18 8C; data were obtained from the National Climate Data Center.

participating, what they believed were major influences on the airshed, and their individual knowledge and skills related to the airshed. Because we hoped that the workshops would contribute to a more systemic view of the relationship between humans and nature, the questionnaire also included the New Ecological Paradigm Scale (Dunlap et al., 2000). The questionnaire completed after the conclusion of the workshops asked for similar information. It also asked if attending the workshops had enabled participants to better understand system concepts related to the airshed.

3.

Results and discussion

3.1.

Fossil fuel emissions inventory for the study region

3.1.1.

Natural gas use

An important element of urban CO2 emissions are those produced by burning natural gas for space/water heating (residential, commercial and industrial), electricity generation, and commercial/industrial processes. Annual and interannual variations reflect changes in weather, population, gas combustion technology, residential heating needs, electric power generation strategies and industrial processes. Daily data from 1999 to 2004 illustrate that natural gas consumption was strongly influenced by local climate, particularly heating degree days (HDD, Fig. 2). A similarly strong correlation was obtained between consumption and HDD using monthly billing data for Salt Lake County customers. The fivefold seasonal variation in natural gas use yields estimated CO2 emissions that range between about 25,000 metric tonnes (Mt) per month in summer to 125,000 Mt per month in winter. This implies a close correlation between HDD and CO2 emissions. Notably, although the population grew at about 2.3% per year in the study area, seasonal climate variations masked the smaller effect of population change over the 7-year period. In fact, climate variability explained 87% of natural gas consumption at this scale.

3.1.2.

Emissions from electrical energy generation

Electricity consumption in the study area is generated within the area, elsewhere in Utah, and in adjacent states. Data obtained from the Utah Geological Survey (2007) show that about 95% of the power generated in Utah (36,664 Gigawatthours (GWh) or 131,990 Terajoules in 2000) is derived from coal burning and about 3% from natural gas-fired power plants. The remaining 2% is derived from hydropower and renewable energy (Utah Geological Survey, 2007). Electricity consumption accounted for 33% of the energy consumed in Utah; about 10% of the state’s energy consumption is exported as electricity. In 2000, residential consumption was about 30% of the total electricity consumption while commercial and industrial use accounted for the bulk of consumption (66%) with 4% for public (streets, roads and lights) and other uses. Because electricity is used to power heating and cooling systems in both residential and non-residential applications, a strong bimodal seasonal pattern was observed with peaks in use during both winter and summer (Fig. 3) and minimums in spring and fall. Over the 6.5-year period, average annual consumption in the three county study area grew 5% from 9.25 to 9.75 GWh, reflecting economic and population growth in the region. Population growth explained 89% of the increase in baseline springtime (May) electricity consumption, while temperature expressed as cooling degree days (CDD) and HDD were closely correlated with summer and winter maxima (Fig. 3).

3.1.3.

Transportation emissions

In the electricity and residential energy sectors, climatic variability was closely correlated with trends in energy consumption. However, in the transportation sector, patterns of road building and urban form may be the best predictors of emissions. In general, there have been large increases in vehicle kilometers traveled (VKT) per capita in the study region during the last two decades that have greatly exceeded the rate of population growth. At the same time, population

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Fig. 3 – Electricity use in the three-county study area for 2000–2005: (a) monthly residential and non-residential consumption in relation to monthly cooling degree days (CDD) and heating degree days (HDD); (b) monthly residential use in May (shown as circles in (a)) vs. population, and (c) monthly winter and summer seasonal per capita residential use vs. monthly HDD and CDD, respectively. Electricity use data were provided by Rocky Mountain Power Company. HDD was computed as in Fig. 2, while CDD was computed as 18 8C – mean daily temperature. Temperature data were obtained from the National Climate Data Center.

densities have decreased. During the period 1980–2000, as the area of developed urban land increased from 65,000 to 114,000 ha, traffic generation increased from 26.7 to 34.8 daily vehicle kilometers traveled per capita (Fig. 4). This pattern

appears to be related to declines in gross population density. As urban road kilometers expanded from 4300 to about 7400, gross urban developmental densities declined from over 14.3 to under 12.6 persons per hectare (ha). These patterns are

Fig. 4 – Key relationships between urban land development and transportation in the Salt Lake-Ogden metropolitan area: (a) population density as a function of urban road building and (b) daily vehicle kilometers traveled (VKT) per capita as a function of urban land development. Data sources: U.S. Census Bureau, U.S. Dept. of Transportation (http:// www.fhwa.dot.gov/ohim/1995/, http://www.fhwa.dot.gov/policy/) and the USDA National Resources Inventory (http:// www.nrcs.usda.gov/TECHNICAL/NRI/).

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similar to other regions in the United States where population density has been declining. Between 1950 and 1990, urbanized lands in the United States quintupled while urban populations merely doubled (Diamond and Noonan, 1996). In New York, Chicago and San Francisco, growth in urban land over this period outpaced population growth by 250 to 500% while in the Cleveland area, urban land expanded by 33% even as its population declined by 11% (Diamond and Noonan, 1996). As described by Newman and Kenworthy (1999), road building, traffic emissions, urban land development, and population density declines may be closely related. While the cause and effect are not always clear, extensive road building often spreads out urban land development. Newly developed land generates traffic, and the extra traffic and congestion calls for further road building, which further spreads out urban land in regions where undeveloped land is available for growth. This may result in a positive feedback of road building, land development, traffic, congestion and more road building. In system dynamics terms, Newman and Kenworthy describe a positive feedback loop whereby urban sprawl and traffic congestion are generated by the reciprocal relationship between urban road building and urban land development, with consequent implications for developmental densities and traffic generation. The characteristic behavior of such a system is to require an increasingly greater effort in roadbuilding in pursuit of the ever-receding goal of alleviating traffic congestion. We suggest that in our study region, increases in transportation emissions per capita have been and are still associated with this self-reinforcing feedback mechanism and its effects on developmental densities and traffic congestion.

3.2.

Inventory validation with atmospheric monitoring

We compared direct measurements of CO2 fluxes with estimates of CO2 emissions per ha derived from the inventory of emissions from electricity generation, residential natural gas consumption, and the transportation sector. Fig. 5 shows the monthly ensemble average of half-hourly fluxes measured in a residential neighborhood in Murray, UT from June to

September of 2005. Two positive peaks occurred in the morning and evening each day that are likely associated with daily commuting. The morning peak is quite pronounced, while the evening peak is more drawn out. During non-rush hour, daylight hours, the site showed net photosynthetic uptake, similar to data reported for Chicago (Grimmond et al., 2002). However, the site was a net source of CO2 on a daily basis due to fossil fuel emissions, which are dominated by traffic emissions during the growing season (Pataki et al., 2003). The average daily flux was 282 kg m2 d1 which corresponds well to the average local CO2 emissions (excluding remote electricity generation) estimated by our emissions inventory-based model estimates of 275 kg m2 d1. We also compared inventory-based estimates of emissions sources with measurements of the stable isotope composition of local atmospheric CO2. Using stable isotope measurements, we calculated the proportion of locally derived (non-background) CO2 originating from natural gas combustion. This proportion was closely related to HDD at a monthly scale over a 1-year period and at the daily scale during an intensive measurement campaign in January (Fig. 6). The average annual proportion of local CO2 emissions (excluding remote electricity production) derived from natural gas combustion in the fossil fuel emissions inventory was 35%. The annual proportion of local atmospheric CO2 attributed to natural gas combustion as derived from atmospheric measurements was also 35%; however, if biogenic CO2 was excluded from the atmospherically based estimate, natural gas then contributed 51% to local atmospheric CO2. Hence, there is not complete agreement between the natural gas fraction of the fossil fuel inventory (35%) and the atmospheric measurements (51%). This may be due to the high degree of spatial variability in urban environments, as the atmospherically based estimates were derived from measurements at one location. However, previous studies in urban areas have also found a greater proportion of natural gas combustion using atmospheric measurements than estimated from inventory-based methods (Kuc and Zimnoch, 1998). While there are a number of spatial and temporal scaling issues that may causes these discrepancies, conducting validations in additional urban areas may be very informative in revealing systematic errors or biases.

3.3.

Fig. 5 – Half hourly average CO2 fluxes measured by eddy covariance in a residential neighborhood in the Salt Lake Valley in 2005. Each curve represents an ensemble of at least 9 days per month.

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Stakeholder involvement

We wished to include local stakeholders in our assessments of the largest sources of greenhouse gas emissions in the study region, the likely future trajectory of emissions, and possible mitigation policies. One of the first outcomes of the workshops was recognition of the importance of air quality co-benefits in GHG emissions reductions. While few participants expected immediate direct benefits of reducing or offsetting CO2 emissions, air quality is a serious local concern in the Salt Lake-Odgen region as in many others, and participants were interested in exploring potential consequences of urbanization and emissions management policies for concentrations of pollutants of concern to human health. This is not altogether surprising as air quality co-benefits are one of the most common reasons that municipalities in the U.S. participate in voluntary GHG reduction programs (Betsill, 2001; Kousky and

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Fig. 6 – The proportion of locally emitted, urban CO2 derived from natural gas combustion in relation heating degree days (HDD) as estimated from measurements of the isotopic composition of CO2 described in (a) Pataki et al. (2003) and (b) Pataki et al. (2006a,b).

Schneider, 2003). Hence, we responded to stakeholder concerns by explicitly incorporating key pollution variables in addition to generating CO2 scenarios. As participants developed an increasing desire and competence to engage in quantitatively grounded environmental management decisions, they began to integrate their own needs into what they perceived to be the needs of fellow stakeholders. At this point, they drew situation maps, or conceptual models, of their systems. Working in small groups of four to six people, participants were encouraged to visually represent all significant components, activities, processes, and relationships. They used their maps to develop the essential elements for building a quantitative model. The teams then reviewed each other’s maps and commented on how they might make minor additions or changes. For example: [It] looks like a lot of people started differently, but ended up with the same sources. They have population, urban form, and emissions. . . It seems that how it is structured is the only difference. It is just a matter of presentation, not a matter of substance. In our [map], I think we looked at it as, ‘the enemy is us and we already know it.’ Another group commented: Well, I’m kind of surprised, I really am. The one thing that is a little bit different. . .is that, they don’t explicitly go from the sources through an emissions inventory to get to air quality, . . .which, you know, probably isn’t a big deal, but it is a point worth thinking about.. . .I think the differences. . .are really just presentation, methodologies, how people look at it, you know, focus.’’ The mapping exercise not only facilitated the quantitative modeling process, but also provided a common ground for participants to compare and explore their understandings of the system. This exercise provided the basis for developing a

model that responded directly to stakeholder concerns. From this point, participants spent an increasing portion of each meeting developing and refining the simulation model that would enable them to explain the recommendations they would eventually present. They also began discussing how to encourage implementation of their recommendations. Thus, the simulation model evolved into a communication interface that provided a sense of joint ownership between scientists, decision makers, and other stakeholders. Beyond its contributions to a specific model, the mutual learning resulting in a sense of group identity that may contribute directly to broad support for emissions reductions policies. Although >70 stakeholders participated in at least one workshop, less than 30 remained involved from the beginning through the end, and only 19 participants completed both pre- and post-workshop questionnaires. Although the small number of paired respondents limits what we can learn from the questionnaire, responses generally indicated that the workshops provided participants with the experiences and information they had sought. There was a trend toward participant scores on the NEP scale that indicated stronger endorsement of the New Ecological Paradigm after participation in the workshops, although this result was not statistically significant. Responses from 26 post-workshop questionnaires indicated that attending the workshops enabled participants to better understand time related issues, interconnections, feedback loops, and complexity. Of the 26, none disagreed with this statement for any of these four system concepts. They reported that attending the workshops enabled them to better understand time related issues (25 agreed and 1 uncertain), interconnections (25 agreed and 1 uncertain), feedback loops (22 agreed and 4 uncertain), and complexity (25 agreed and 1 uncertain).

3.4.

CO2 emissions scenarios

The final model product simulates population density, urban land area, vehicular traffic, fuel use, forest biomass, and CO2

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Fig. 7 – Modeled total CO2 emitted from the three-county study area over the period 1980–2030. Three alternative future scenarios are shown beginning in 2005 relative to a ‘business as usual’ reference case as described in the text.

emissions from 1980 to the present, and in future scenarios extending to 2030 (Fig. 7). Details of the model structure are given in Appendix A. The model uses a system dynamics approach that aggregates urban ecosystem processes over the study area without taking into account spatial interactions. We adopted this approach to investigate a core set of urban temporal dynamics based on available data and validated for the study region. Because a major goal was to develop CO2 emissions inventories and scenarios in collaboration with a broad array of stakeholders, a simplified, interactive but quantitative tool was an asset. Stakeholders in this project participated directly in the development of the model, which provides a more effective communication interface than an externally developed and more complex modeling tool. As experience increases knowledge of the model’s range and limitations, spatially explicit components can be added to identify critical spatial interactions associated with policy choices. Model scenarios feature a business as usual reference case in which population densities decline over the period from 1980 to 2030 with associated increases in fuel consumption for urban development and vehicular traffic. A climate-warming scenario was developed that increased daily average temperature by 3 8C over a 25-year period in both summer and winter. A ‘‘technology’’ scenario imposed a 20% increase in fuel and energy use efficiency in all sectors for comparison with a ‘‘dampened feedback scenario’’ in which a combination of urban transportation and land use policies are used to reduce the rates of urban density decline and traffic increase. In this latter scenario, gross population density is stabilized with policies assumed to increase the density of new development by 20% to an average of 13.6 people per ha. In addition, in lieu of building new roads, existing road capacity is presumed to increase by 10% through intelligent traffic systems improvement. Furthermore, the proportion of nonsingle-occupancy vehicle traffic is presumed to increase from its current rate of 3–20%. In this regard, the dampened feedback scenario represents the result of coordinated local land use policies and transportation investment decisions that limit the tendency for new roads to decrease developmental densities and for new development to increase traffic volumes. Finally, an urban forest scenario presumed doubling the density of tree planting on newly developed urban land in

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order to explore the urban forest’s effects on rates of carbon sequestration. The influence of these scenarios on modeled CO2 emissions is shown in Fig. 7. The dampened feedback scenario (initiated in 2005) shows the largest reduction in emissions relative to the reference case, with a 22% decrease in emissions by 2030 compared to a 14% decrease in emissions in the technology scenario. In this particular region, we suggest that developmental density coupled with investments in transportation systems have a large impact on CO2 emissions, and a great potential for mitigating future CO2 emissions with policies that promote efficient urban form. In contrast, the climatewarming scenario showed a decline of 3% in emissions by 2030, despite the close correlation between energy use and temperature (Figs. 2, 3 and 6). In this case, emissions from increased electricity use for summer air conditioning more than offset reduced wintertime declines in emissions from natural gas used for space heating. We also wished to evaluate the influence of urban forest planting policies on the potential for CO2 emissions mitigation. The Salt Lake Valley is located in the Great Basin Desert, where seasonal precipitation is too low to support natural forests. However, since the colonization of the valley in 1847, an extensive urban forest supported by irrigation has been planted. Generally, losses in net primary production have been estimated as a consequence of conversion from agricultural to urban cover (Imhoff et al., 2004). However, ‘‘afforestation’’ of semi-arid land cover has been proposed as a means of enhancing CO2 sequestration, albeit with potential consequences for water resources (Farley et al., 2005; Jackson et al., 2005). Nowak and Crane (2002) estimated that urban trees in Utah store more than 3 Mt of C and sequester approximately 108 kT C/yr. While these estimates are small in comparison to total fossil fuel emissions from Utah, which total about 17 Mt C/year (DOE EIA), they may be significant from a policy perspective. Salt Lake City is one of many municipalities that have committed to GHG emissions reductions under the Cities for Climate Protection Program of ICLEI (http://www.iclei.org). Because the emissions targets for such programs are relatively modest, the contribution of urban trees to CO2 reduction programs is currently of interest. In the reference case, planting densities on new urban land were specified as 2.5 trees per ha for conifers and 5 trees per ha for deciduous trees. Replacement planting in response to tree mortality was specified at a 1:1 ratio. Doubling the planting density in 2005 increased the number of trees in the study region from 4.0 to 5.2 million and tree biomass from 6.1 to 6.3 Mt by 2030. The computed impacts on total net CO2 emissions were negligible, with only a 0.2% reduction in emissions by 2030. This is not surprising given that planting on new urban land represents a relatively small amount of tree biomass, particularly in the first 30 years of growth. The influence of soil carbon, and perhaps more significantly, the role of trees in urban energy balance were not included in this analysis because empirical datasets on these parameters are largely absent, particularly for semi-arid, irrigated cities (Grimmond et al., 1996). We suggest that the largest benefits of urban tree planting in terms of climate change mitigation are not to be found in direct C sequestration, but rather in the effects of afforestation on surface energy balance, which

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ecological complexity 6 (2009) 1–14

varies widely with planting density, location, species, and other local parameters (Huang et al., 1987; Taha et al., 1991; Akbari and Taha, 1992; Akbari et al., 1997; Spronken-Smith et al., 2000; Akbari, 2002; Akbari and Konopacki, 2005; Mueller and Day, 2005). In future studies, we wish to include the influence of altered albedo, shading, and latent heat fluxes on air temperature resulting from tree planting in this irrigated urban environment.

4.

rently, a major limitation to conducting integrated, local studies of fossil fuel emissions is the lack of fossil fuel inventory data at neighborhood, municipal and county scales. We suggest that these data are needed, along with concurrent validation from atmospheric measurements, in order to conduct process-level studies of the major drivers of net CO2 emissions in urban areas. These datasets can facilitate development of plausible scenarios regarding the impacts of rapid urbanization on the magnitude and trajectory of CO2 emissions in the 21st century.

Conclusions

We developed a local fossil fuel emissions inventory in the Salt Lake City-Ogden urbanizing region and utilized this information to develop a mediated model of net CO2 emissions. We found that projections of future emissions were sensitive to the magnitude of a positive feedback between urban road building and urban land development. Land use and transportation policies that dampen the strength of this feedback relationship may substantially reduce future emissions. A simulated 3 8C increase in average annual temperature led to a small decline in fossil fuel emissions when applied equally in winter and summer because increases in energy consumption for summer air conditioning were offset by decreases in winter heating. The direct effect of urban forests on carbon sequestration was very small in relation to total CO2 urban emissions. Scenario experiments showed that technical improvements in energy efficiency were important and could be used to complement land use and transportation policy improvements. We also found that using mediated modeling to involve stakeholders contributed to their understanding of the system, which may increase their ability to justify, develop, and implement appropriate land use and transportation policies. This process-based approach is necessarily local in its structure and calibration, but the model development framework provides a template for integrated studies that measure and model the net CO2 emissions from urban regions. In order to extrapolate to global fossil fuel emissions scenarios, a diversity of localized studies in both developed and developing regions are necessary to determine the major similarities and differences across geographic and political boundaries. Cur-

Acknowledgements This research was funded by U.S. National Science Foundation grant ATM 02157658. We thank our community partners and stakeholder participants.

Appendix A. The systems dynamics model The model developed in this study computes annualized, aggregate estimates of dynamic changes in stocks and flows associated with the coupled natural–human system found along the urbanized Wasatch Front of northern Utah. Changes in the stocks of households, jobs, trees, urbanized land, road kilometers and other features of the system are computed from 1980 to 2030. Flows include the usage rates of natural gas, electricity, and transportation fuel that, in turn, cause emissions of carbon dioxide and associated air pollutants. Historical demographic, energy use, transportation, and land use data for three key counties (Salt Lake, Davis, and Weber) in the Salt Lake City-Ogden Metropolitan Statistical Area provide a historical foundation for the model and provide fundamental, quantitative relationships between the various parameters. A high-level view of the interactions between principal sectors is shown in Fig. A.1. At the core of the model is a selfreinforcing feedback loop (shown in Fig. A.1 with a circular arrow) that represents how growth in employment and population yields a demand for urban land development that, in the default case, leads to growth in traffic that, in turn, causes

Fig. A.1 – Schematic of the interaction between sectors represented in the systems dynamics model.

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additional roads to be built according to schedules and policies defined by the user. In the default case, the added roads encourage declines in developmental densities so that additional population and jobs are accommodated at slightly lower densities, generate slightly more traffic and require slightly more road kilometers. The time-dynamic structure of this feedback relationship causes urban developmental densities to decline and traffic volumes to increase at rates disproportionate to the underlying change in people and jobs. User-defined land use and transportation policies alter the strength of this feedback and result in different aggregate urban forms as represented by varying developmental densities, road densities, traffic volumes and traffic congestion levels. Growth in total employment ET(t) is driven by growth in basic jobs EB(t) that are, in turn, driven by user-specified values of the rate of formation of basic jobs JF(t). The dependency of non-basic jobs ENB(t) on basic jobs is captured by FNB(t), the portion of total employment that is non-basic. Changes in population growth in the urbanized area of the three counties, P(t), reflect the changes in job growth, net natural growth (births minus deaths) and migration that are captured by the value of employment ratio ER(t). Based on stakeholder feedback, an air quality feedback can be explored in the model. The possibility that people migrate from the region as population increases due to poor air quality is represented by Ploss(t) which, in turn, is assumed to be driven by the rate of air pollutant emissions that are directly related to the local urban carbon emissions rate Clocal. Because residential natural gas use is best represented on a per-household, rather than a percapita basis, the number of households H(t)is computed using the estimated population P(t) and the value of household size SH(t). Note that default values of ER(t), FNB(t), JF(t) and SH(t) are provided for the historical period 1980–2000. User-specified values for these parameters are accepted, as a function of time, for the projection period 2001–2030. dEB ðtÞ ¼ JFB ðtÞ dt

ET ðtÞ ¼ EB ðtÞ þ ENB ðtÞ ¼

(1)

EB ðtÞ 1  FNB ðtÞ

dPðtÞ dfER ðtÞ  ET ðtÞg ¼  Ploss ðtÞ dt dt

HðtÞ ¼

PðtÞ SH ðtÞ

(2)

(3)

(4)

where EB(t) is the employment in basic jobs [jobs], ET(t) the total employment [jobs], ENB(t) the employment in non-basic jobs [jobs], ER(t) the specified, employment ratio – used to represent change in demographics [people/job]; ER(hist)1980–2000; ER( proj)2001–2030, FNB(t) the specified, portion of employment that is non-basic; FNB(hist)1980–2000; FNB( proj)2001–2030, H(t) the number of urban households [households], JF(t) the specified, basic job formation rate – used to represent economic future [jobs/year]; JF(hist)1980–2000; JF( proj)2001–2030, P(t) the urban population [people], Ploss(t) = outmigration caused by poor air quality [people/ year]; f(Clocal) = the carbon emitted by fossil fuel burning within

the three-county study area, SH(t) is the specified, urban household size [people/household]; SH(hist)1980–2000; SH( proj)2001–2030. The rate of growth in urbanized area UL(t) reflects growth in urban population P(t) multiplied by population density PDen(t). In the period 1980–2000, a strong linear correlation (R2 = 0.99) between population density and constructed road kilometers RM(t) is used to estimate projected values of PDen(t) with coefficients A and B (Eq. (6)). dULðtÞ dfPðtÞ  PDen ðtÞg dPDen ðtÞ dPðtÞ ¼ ¼ PðtÞ þ PDen dt dt dt dt

(5)

PDen ðtÞ ¼ A  RM ðtÞ þ B

(6)

A ¼ 0:00001435; B ¼ 0:1316

where PDen(t) is the population density [acres/person], RM(t) the road kilometers [km], and UL(t) is the gross area of urban land [acres]. The sprawl feedback process is driven by the interdependency between growth in urban land UL(t) and the corresponding growth in road kilometers RM(t). The rate of construction of road kilometers depends on the computed difference between a target number of road kilometers RTarg(t) and the current constructed road kilometers RM(t). The target road kilometers computed as a function of the total equivalent singleoccupancy vehicle kilometers traveled VMTeq(t), the portion of the total kilometers that are traveled in single-occupancy vehicles MS(t), the number of road kilometers desired to manage traffic congestion RCdes(t), and the impact of congestion reduction technologies and policies RCpol(t) such as intelligent traffic control systems. Ultimately, the rate of road construction depends upon policy decisions that dictate the portion of the road mile gap RBF(t) actually constructed. In the period 1980– 2000, a strong correlation (R2 = 0.99) between desired road capacity RCdes(t) and time was used to estimate projected values of RCdes(t) with coefficients I and K (Eq. (10)). Similarly, a strong correlation (R2 = 0.99) between urban land area and total equivalent vehicle kilometers traveled was used to estimate projected values of VMTeq(t) with coefficients G and D (Eq. (11)). Default values of RBF(t) are provided for the historical period 1980–2000. User-specified values for this parameter is accepted, as a function of time, for the projection period 2001–2030. dRM ðtÞ dfRG ðtÞ  RBF ðtÞg dRBF ðtÞ dRG ðtÞ ¼ ¼ RG ðtÞ þ RBF ðtÞ dt dt dt dt

(7)

RG ðtÞ ¼ RTarg ðtÞ  RM ðtÞ

(8)

RTarg ðtÞ ¼ VMTeq ðtÞ  MSðtÞ  RCdes ðtÞ  RC pol ðtÞ

(9)

RCdes ðtÞ ¼ I  t þ K I ¼ 0:00133; K ¼ 2:858

(10)

VMTeq ðtÞ ¼ G  ULðtÞ  D G ¼ 0:00012214; D ¼ 3:979

(11)

where MS(t) is the modal split offset factor = portion of VKTeq as single occupant automobile traffic, RBF(t) the specified, fraction of road gap constructed; RBF(hist)1980–2000; RBF( proj)2001–2030, RCdes(t) the specified, desired road capacity (desired road kilometers/daily VKT) [d1]; RCpol(t) the specified, factor accounting for impact of various road congestion reduction policies, RG(t) the road kilometers gap [km], RTarg(t) the road lane kilometers

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needed to manage congestion [km], and VKTeq(t) is the total equivalent daily vehicle kilometers travelled [km/d]. Historical estimates of energy use are computed using a variety of demographic data (U.S. Census Bureau), energy use data (from Questar Gas and Rocky Mountain Power) and electricity generation data (Utah Geological Survey, 2007). The impact of past climate variations (from National Climate Data Center data) on both residential and non-residential energy use is estimated using an approach similar to that of Amato et al. (2005) and Ruth and Lin (2006) in their studies of the Baltimore urban area. The resulting relationships, which may be modified by the model user to reflect possible changes in future conditions, are used to estimate emissions rates to 2030. The model provides opportunities for the user to vary future energy use rates, and the corresponding emissions rates, as a consequence of changes in energy efficiency and alternative future climate scenarios. Because 90% of the electrical power used in the study area is generated by coalfired power plants outside the urban area, local and regional emissions rates are differentiated in the calculations. The estimated total carbon emitted by the three-county urban area CTotal(t) is the sum of local Clocal(t) and regional Cregional emissions minus the amount of carbon sequestered in the local urban forest SEQlocal(t). Local carbon emissions rates are estimated as the sum of carbon emitted by the local transportation fleet, the consumption of natural gas in commercial/industrial/service/government sectors CNG,jobs(t), natural gas consumed by households CNG,resident(t) and natural gas burned locally to generate electricity CNG,elec-local(t). Regional carbon emissions Cregional are estimated as the sum of carbon emitted by natural gas CNG,elec-regional and coal CCoal,elecregional burned to generate electricity outside the three-county study area. CTotal ðtÞ ¼ Clocal ðtÞ þ Cregional ðtÞ þ SEQ local ðtÞ

(12)

Clocal ðtÞ ¼ CTrans ðtÞ þ CNG; jobs ðtÞ þ CNG;reident ðtÞ þ CNG;elec-local ðtÞ Cregional ¼ CNG;elec-regional þ CCoal;elec-regional

(13)

(14)

where CTotal (t) is the total urban carbon emissions rate [tonnes/year], Clocal(t) the carbon emitted within the threecounty study area [tonnes/year], Cregional(t) the carbon emitted by electricity production outside the study area [tonnes/year], CTrans(t) the carbon emitted by transportation fleet = f(VKTeq(t)) [tonnes/year], CNG,jobs(t) the carbon emitted by non-residential burning of natural gas = f(ET(t)) [tonnes/year], CNG,residential(t) the carbon emitted by residential burning of natural gas = f(H(t)) [tonnes/year], CNG,elec-local(t) the carbon emitted by burning natural gas for local electricity generation = f(P(t)) [tonnes/ year], CNG,elec-regional(t) the carbon emitted outside the study area by burning natural gas to generate electricity for the study area = f(P(t)) [tonnes/year], CCoal,elec-regional(t) the carbon emitted outside the study area by burning coal to generate electricity for the study area = f(P(t)) [tonnes/year], and SEQlocal(t) is the carbon sequestered by the urban forest within the study area = f(UL(t)) [tonnes/year].

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