The Global Carbon Cycle
The global carbon cycle is a crucial component of various nutrient cycles that occur within, above, and below the Earth's surface Carbon, along with other essential nutrients like oxygen, nitrogen, phosphorus, and sulfur, continuously circulates among water, soil, the atmosphere, and living organisms, engaging in complex interactions While the carbon cycle is interconnected with other nutrients—particularly those that chemically bond with carbon or are essential for life—this overview will focus exclusively on carbon.
Earth holds approximately 10^23 grams of carbon, primarily stored in sedimentary rocks The most significant active carbon reservoirs near the surface are the oceans, soils, and atmosphere Additionally, carbon is a crucial element in all living biomass on the planet.
Table A: Earth's largest active C pools [A]
Carbon in biomass experiences constant fluctuations between soils and the atmosphere, with seasonal variations in its levels In contrast, larger carbon sources, such as those found in oceans, exhibit much less variability, having an average residence time of 11 years.
C in the atmosphere has a mean residence time of 5 years Despite the differences in
C (10 15 g) variability between the biomass, ocean and atmospheric pools, annual fluxes between the three are among the largest on Earth [A].
Carbon Cycling at Smaller Scales
Carbon (C) fluxes can be measured on various scales, from global to local, such as regions, countries, ecosystems, states, or watersheds, with net fluxes differing based on local characteristics For instance, in a temperate forest ecosystem, C is sequestered in plants and soil, where plants absorb atmospheric C to create biomass while also releasing some back through respiration Upon the death of plants, decomposition occurs, driven by microbes, which also release C into the atmosphere Additionally, some C may be transported by soil organisms or dissolve in groundwater, moving deeper into the soil Soil C can remain stable for extended periods but may be released when disturbed This carbon cycle has been extensively studied in the Hubbard Brook Experimental Forest in New Hampshire.
The processes outlined in the example interact with significant carbon pools in the ocean and deep soil, albeit without a direct connection The importance of these carbon pools varies depending on the specific region under analysis Refer to Figure A in the First State of the Carbon Cycle Report by the US Climate for further insights.
Change Science Program [20], outlines all the currently understood sources and fluxes of the C cycle
The global carbon cycle is illustrated in a schematic representation that includes three panels: the overall cycle, the ocean cycle, and the land cycle Each panel displays carbon stocks in brackets and fluxes without brackets, with pre-human influence data shown in black and human-induced changes highlighted in red The human-induced carbon stocks represent cumulative totals up to 2003, while the fluxes reflect average values from the 1990s, the latest available data for certain fluxes.
Anthropogenic Influence
Since the Industrial Revolution in the 1800s, human activities have significantly impacted the carbon cycle, primarily through the increased release of carbon dioxide (CO2) into the atmosphere This increase is largely attributed to the extraction and combustion of fossil fuels, as well as land use changes such as deforestation for agriculture and urban development Atmospheric CO2 levels rose from 280 parts per million (ppm) in the pre-industrial era to 380 ppm by 2005, with projections suggesting they could reach 800 ppm by 2100 if current trends continue However, the rate of increase is accelerating, leading to CO2 concentrations that exceed the natural variability observed on Earth over the past 20 million years.
Over the past few centuries, human activities have significantly elevated carbon levels in the Earth's atmosphere, while simultaneously reducing its presence in geological reservoirs.
The Intergovernmental Panel on Climate Change (IPCC) asserts that it is "extremely likely" (over 95% probability) that human activities have significantly contributed to climate warming since 1750 Despite our ongoing research and understanding of element interactions, predicting the precise relationships between atmospheric carbon and climate systems remains challenging Nevertheless, extensive studies, including those by the IPCC, indicate that rising atmospheric carbon levels are likely to result in global climate warming, which could lead to unpredictable yet potentially severe consequences.
Carbon dioxide (CO2), methane (CH4), and carbon monoxide (CO) are natural greenhouse gases that play a significant role in trapping solar energy reflected off the Earth's surface, thereby warming the atmosphere Additionally, other gases such as water vapor (H2O), nitrous oxide (N2O), and sulfur dioxide also contribute to the greenhouse effect.
Anthropogenic compounds such as sulfur dioxide (SO2), ozone (O3), hydrofluorocarbons, and chlorofluorocarbons are significantly contributing to greenhouse gas emissions While the greenhouse effect is essential for sustaining life, the unprecedented rise in these gases is predicted to cause excessive warming This may result in extreme weather events, rising sea levels due to ocean expansion and polar ice melt, and shifts in the natural habitats of various species, potentially leading to extinction Carbon dioxide (CO2) is identified as the primary driver of radiative forcing and climate change currently impacting the Earth.
Anthropogenic emissions lead to various consequences beyond warming, including the release of aerosols—small particles such as organic carbon, soot, and mineral dust—that can cool the atmosphere by reflecting sunlight Additionally, sulphur dioxide (SO2) contributes to this cooling effect However, these cooling influences are insufficient to counteract the overall trend of climate warming Moreover, certain land use changes, like deforestation, can alter the Earth's surface albedo, potentially increasing cooling during winter and spring due to greater exposure of snow cover.
Increased atmospheric CO2 may initially boost plant growth, but the theory of progressive nitrogen limitation suggests that this growth could be hindered by insufficient nitrogen availability Similarly, research indicates that while agricultural crops may thrive with abundant carbon for biomass production, the imbalance of nutrients could lead to plants with low concentrations of essential nutrients This phenomenon may result in a decline in food quality, creating "empty" food sources that contribute to global malnutrition.
Understanding the carbon cycle and the recent human impacts on it is crucial due to the potential negative effects of rising atmospheric carbon levels Additionally, it is essential to find strategies to reduce anthropogenic carbon emissions and enhance carbon sequestration efforts.
Land Use, Energy and Carbon Cycling
Land Use
Previous research has thoroughly examined the effects of various land uses and their changes over time on carbon emissions and sequestration These studies have been conducted across multiple spatial scales and geographic regions, ranging from national assessments to analyses of individual forest stands or urban areas.
Woodbury et al [7] investigated the historical relationship between land use and the carbon (C) cycle in the Southern United States to model future interactions Their findings revealed that tree biomass on forestland serves as a vulnerable carbon stock, with increases observed under afforestation-promoting land uses and rapid declines under deforestation-promoting practices Similarly, forest soil carbon stocks were also influenced by land use changes Notably, the impact of land use on various components of the carbon cycle varied across different topographical areas within the region.
Woomer et al highlighted that between 1965 and 2000, deforestation and vegetation disturbance led to over 95% of terrestrial carbon stock loss in Senegal They suggested that revegetation programs could mitigate this decline Similarly, Houghton and Hackler found that forest area reduction contributed to 90% of carbon flux in the United States An Austrian study by Erb revealed that converting forestland to agricultural and urban uses caused 77% of aboveground carbon stock loss, with the remaining 23% linked to forest management practices involving young stands with lower biomass and unnatural species These findings illustrate that despite variations in climate and environment, human management significantly influences carbon stocks and flux through changes in vegetative composition, soil disturbance, and biomass reduction.
Jandl et al [6] investigated the capacity of forestland to sequester carbon (C) through effective management practices Their research highlighted that integrating fast-growing tree species into forest ecosystems can enhance the biomass carbon capture rate Additionally, they concluded that optimizing forest management for maximum productivity significantly contributes to carbon sequestration efforts.
Implementing techniques such as thinning and fertilization enhances biomass sequestration and carbon retention in soils Additionally, minimizing disturbance to the land surface during management practices ensures that existing soil carbon remains preserved indefinitely.
Undisturbed old-growth forests in temperate regions are significant carbon dioxide (CO2) sinks due to their high biomass in live and dead vegetation, as noted by Harmon et al [3] While actively harvested younger forests sequester carbon at a faster rate, they ultimately contribute to a net carbon flux to the atmosphere because of their reduced storage capacity Additionally, harvested wood is often transformed into durable products that sequester carbon during their lifespan and in landfills However, inefficiencies in wood processing and production-related emissions can diminish the overall benefits of carbon sequestration from wood products [40].
While forests are the primary carbon (C) sinks, agricultural lands also contribute to C sequestration, albeit to a lesser extent due to annual crop harvesting and soil disturbance A study by Houghton and Hackler revealed that agricultural soil cultivation was responsible for 25% of total soil C loss in the United States from 1700 to 1990 Additionally, certain ruminant animals, like cows, hinder C sequestration efforts by emitting substantial amounts of methane during digestion.
Urban areas present both challenges and opportunities for carbon sequestration and emissions management While increasing population densities can lead to higher CO2 emissions from fossil fuels, cities often benefit from more efficient public transit systems, which can reduce transportation emissions For example, New York City, with its extensive public transport network, has a significantly lower rate of car ownership and per capita energy use for heating, cooling, and lighting compared to other cities Additionally, urban trees contribute to carbon sequestration and provide energy savings by reducing heating and cooling needs when strategically planted around buildings However, these environmental benefits may be compromised if urban vegetation is heavily managed with fertilizers and fossil-fuel-powered equipment.
Urban soils should not be overlooked, as research by Pouyat et al [14] indicates that residential lawns can exhibit high carbon densities, often surpassing those found in forest soils across various regions of the United States Moreover, cities frequently contain remnants of native vegetation, which can cover up to 10% of land area in certain instances, enhancing carbon sequestration potential Additionally, soil located beneath impervious surfaces serves as a significant carbon sink.
Pouyat et al [14] highlighted that the potential vegetation and soil properties of urban regions can significantly influence the carbon (C) cycle For instance, the conversion of forestland to urban areas across the United States generally results in a reduced capacity for carbon sequestration However, the impact can vary significantly from one city to another In regions like the Southwest, where native soils have low carbon sequestration capabilities, urbanization may enhance soil carbon storage Conversely, in the Northeast, where soils are naturally rich in carbon, urban development could lead to a decline in sequestration potential.
Chittenden County, Vermont, situated in the Northeastern United States, historically featured forested landscapes Since European settlement in the 1600s, these forests have faced significant deforestation due to timber harvesting and agricultural expansion However, in recent decades, there has been a notable increase in forested areas within the region.
Northeast has been increasing as agricultural use and timber harvesting decrease Albani et al [4] examined the relationship between land use change and C fluxes in the
A recent study in the Northeastern US reveals that the majority of carbon storage increase in the region is primarily due to reforestation efforts However, it is important to note that there will come a point when forest area expansion will plateau, with further growth only possible following events like timber harvests, forest fires, or other disturbances This highlights the necessity for diverse mitigation strategies to effectively address carbon storage and environmental sustainability in the area.
Energy Use and Transportation
Fossil fuels, including petroleum, coal, and natural gas, are extracted from the Earth's crust and primarily consist of carbon and hydrogen compounds The combustion of these fuels results in the release of water vapor and carbon dioxide (CO2) into the atmosphere As the extraction of fossil fuels increases, so does the emission of CO2, contributing to rising atmospheric carbon concentrations This escalating reliance on fossil fuels raises significant concerns about their impact on climate change and global warming.
Over the past century, fossil fuel consumption in the United States has surged dramatically and is expected to keep rising Petroleum remains the dominant fuel across all sectors, followed by coal and natural gas While nuclear, hydroelectric, and various renewable energy sources are employed, their usage is significantly lower These fossil fuels are essential for heating buildings, generating electricity, manufacturing goods, and powering vehicles.
Land use significantly impacts fossil fuel consumption, as the way land is utilized influences its energy requirements For example, standing forests primarily release carbon through natural processes like decomposition and occasional fires, while agricultural lands contribute to higher carbon emissions due to ruminant digestion, soil disturbance, and the use of fossil fuels in machinery Urban areas exhibit the highest energy-related emissions, driven by the need for heating and lighting in buildings, as well as the energy demands of vehicles and infrastructure In North America, residential and transportation sectors account for approximately 40% of total fossil fuel emissions.
Urban growth significantly influences carbon emissions, with factors such as population growth and a rise in the number of households contributing to increased emissions in North American urban and suburban areas As more individuals and families acquire their own homes or second homes, the trend toward smaller households leads to a greater demand for developed land and an increase in the number of dwellings per capita This additional living space necessitates higher energy consumption for maintenance, further exacerbating carbon emissions.
Urban growth patterns in developing countries can differ significantly, as seen in Mexico, where population growth outpaces household growth, resulting in fewer dwellings per capita and higher population density Interestingly, high population density can correlate with lower per capita emissions, a trend exemplified by New York City In contrast, Vermont, with a low population density of approximately 270 people per square mile, aligns more closely with traditional development patterns.
Transportation infrastructure significantly influences carbon emissions Urban sprawl, characterized by decreased development density and the separation of residential and commercial areas, fosters auto-dependency and requires extensive transportation networks to connect distant buildings This sprawling network contributes to the loss of forests and agricultural land, increases daily commuting distances, exacerbates traffic congestion, and raises the total vehicle miles traveled by cars, trucks, and buses As vehicles typically rely on refined fossil fuels, this leads to higher energy consumption in transportation and greater carbon emissions In contrast, higher density development on smaller land areas can reduce carbon emissions and lessen reliance on fossil fuels.
Energy providers and development patterns vary across regions, influenced by factors such as land use history, climate, topography, and geographic location In Vermont, electricity is generated from diverse sources, with two-thirds already derived from non-fossil fuels, including the Vermont Yankee nuclear plant.
HydroQuebec supplies a significant portion of Vermont's power, while the remaining third is sourced from various independent providers, including the McNeil biomass plant in Burlington, small wind energy projects, and minor hydroelectric operations Additionally, any unmet electricity requirements are fulfilled through purchases from the wholesale energy market in New England.
In 2005, Vermont's energy supply comprised various sources, including 'System A,' which represents energy acquired from New England's wholesale market, and 'System B,' which denotes renewable energy sold to other utilities as renewable energy credits Additionally, small hydro projects are categorized as 'Hydro,' while other renewable resources, such as the McNeil biomass plant, fall under the 'Renewable' designation.
HydroQuebec 'Nuclear' refers to Vermont Yankee
Vermont primarily meets its heating needs—residential, commercial, and industrial—through distillate fuel oil, with natural gas and propane also serving as common heat sources While kerosene, wood, coal, and other fuels are used, their prevalence is significantly lower All these fuels contribute to carbon emissions during production and consumption For a detailed analysis, refer to Figure C, which illustrates Vermont's fuel usage by type, and Figure D, which categorizes that usage by sector.
Figure C illustrates the percentage of Vermont's total energy market share by fuel types in 1973 and 2003 Notably, the 'electricity' segment highlights electricity's role in overall energy consumption, although it does not specify the individual fuel sources utilized for electricity generation.
Figure D: Percent of Vermont's total energy market share by sector over time [H]
Vermont's transportation system consists of roadways, railways, ferries, and airports, all contributing to carbon emissions, with road vehicles being the primary source In 2005, the state had nearly 14,500 miles of roadways, which saw over 7 billion vehicle miles traveled.
By 2025, vehicle miles traveled in Chittenden County are expected to rise by 60%, with traffic volume projected to surge by 360% This significant increase can be attributed to Vermont's rural, low-density population distribution, along with a notable rise in development surrounding urban areas in recent years.
Enhancing energy efficiency and adopting renewable energy sources, along with improving transportation infrastructure, can significantly reduce carbon emissions at the county level A study by Vadas et al highlighted that Tompkins County, New York, could achieve substantial emission reductions at low costs by utilizing renewable energy options like wood and biomass heating, altering driving behaviors, upgrading to energy-efficient lights and appliances, and improving building insulation These strategies represent just a fraction of the available emissions mitigation methods, with many more yet to be explored Vermont will need a tailored set of mitigation strategies, as outlined by the Vermont Governor's Commission on Climate Change.
Carbon Accounting Considerations
In recent years, there has been a growing emphasis on carbon (C) accounting to quantify the complete carbon cycle across various scales, driven by the increasing focus of governments, businesses, and individuals on regulating carbon emissions Some carbon budgets concentrate solely on natural systems, ranging from local forest stands to national and global levels, while others explicitly incorporate anthropogenic emissions and land use changes Most anthropogenic-focused budgets are presented as greenhouse gas inventories, typically aligned with political boundaries and applicable at multiple scales, from towns to cities.
[18] to the state [19] to the country [20] to the world [1]
This thesis examines carbon emissions and sequestration at the county level, categorizing them by land use It highlights the importance of understanding both anthropogenic emissions and land-based carbon sinks, which are often viewed separately, for effective mitigation strategies Notably, emissions from energy used in the production of imported goods across county borders are excluded from this analysis.
Carbon budgets differ significantly based on the sources and sinks considered, the reporting units used, and the associated error levels While efforts have been made to standardize inventory methods at the national level since the Kyoto Protocol, inconsistencies remain It is essential to clearly and transparently document all data sources, calculations, and assumptions involved in creating each carbon budget.
A key aspect of carbon budgeting is determining responsibility for CO2 emissions, which can be approached through either the production or consumption accounting principles The production accounting principle assigns responsibility to entities that produce energy or products leading to emissions, while the consumption accounting principle holds accountable those who consume these energy sources or products Clarifying this responsibility early in the budgeting process is crucial to avoid double counting emissions across sectors Additionally, the differing outcomes from these two budgeting methods can significantly influence policy decisions regarding the implementation of mitigation strategies.
The Chittenden County carbon budget employs a consumption-based methodology, although exceptions exist for scenarios where calculations are impractical or not pertinent to policy and decision-making Detailed documentation of this methodology is available in the article below.
A land-use-based county-level carbon budget for
Erin E Quigley 1 , Jennifer C Jenkins 1§ , Steven P Hamburg 2 , Timothy J Fahey 3
1Rubenstein School of Environment and Natural Resources, University of Vermont, Aiken Center, 81 Carrigan Drive, Burlington, VT, USA 05405
2Center for Environmental Studies, Brown University, Box 1943, 135 Angell Street, Providence, RI, USA 02912
3Department of Natural Resources, Cornell University, 12 Fernow Hall, Ithaca, NY, USA 14853 §Corresponding author
EEQ: erin.quigley@uvm.edu
JCJ: jennifer.c.jenkins@uvm.edu
SPH: steven_hamburg@brown.edu
This project integrates natural and human-induced sources and sinks of atmospheric carbon dioxide (CO2) at the county level In partnership with the Hubbard Brook Research Foundation's Sciencelinks Carbon Group, we developed a land-use-based carbon budget specifically for Chittenden County, Vermont.
The budget's main objective is to deliver current and precise decision-making information to county planners and policymakers, maximizing the benefits of mitigation efforts Additionally, this project develops and tests a replicable methodology applicable in any U.S county, enabling the creation of county-level carbon balance data beyond Vermont and the Northeast.
This work contributes to a larger ongoing study by the HBRF which compares C emissions and sequestration among seven counties representing different patterns of land use.
A recent study indicates that Chittenden County serves as a net carbon sink, with an annual accumulation of 1.12 teragrams (Tg) of carbon in its biomass and soils In contrast, the county emits approximately 0.418 Tg of carbon each year due to human activities It is important to note that this carbon budget does not factor in the energy consumed during the manufacturing and transportation of products brought into the area.
Chittenden County acts as a net sink for carbon (C), yet urbanized areas contribute substantially higher C emissions per hectare compared to the carbon sequestered by forestlands As urban development expands into forested and agricultural regions, the carbon balance in the county could be adversely affected Although urban vegetation and soils can help mitigate some annual C emissions, their impact remains limited.
The difference between annual C sequestration and annual C emissions in
Chittenden County is much larger than that calculated in a comparable Tompkins County,
NY study Tompkins County was found to be a source rather than a sink for CO2
Mitigating transportation and residential petroleum emissions presents a cost-effective opportunity for significant environmental benefits Additionally, enhancing carbon sequestration can be achieved through strategies such as forest preservation, the production of durable wood products, the creation of urban greenspaces, and the promotion of no-till agriculture.
Since the late 1800s, human activities have significantly raised atmospheric carbon dioxide (CO2) levels from 280 parts per million in the pre-industrial era to 380 parts per million by 2005 This increase is largely due to the intensified burning of fossil fuels for energy, alongside land use changes such as deforestation and urban development The rate of CO2 increase is accelerating, leading to atmospheric conditions not seen on Earth for the past 20 million years If CO2 concentrations are not stabilized at approximately double pre-industrial levels, we risk facing irreversible and unpredictable climate changes Since 2000, emissions have consistently exceeded the necessary levels for stabilization.
Research shows that land use patterns significantly influence CO2 emissions and sequestration For instance, forests that are allowed to mature without human interference serve as substantial carbon sinks.
Deforestation significantly contributes to carbon (C) emissions, while practices such as afforestation, reforestation, and effective forest management enhance carbon sequestration and increase carbon storage in forest ecosystems Agricultural activities typically result in a net release of carbon due to deforestation for farmland and certain crop management techniques However, agricultural soils have the potential to sequester carbon by reducing tillage and increasing cropping intensity Urban development also leads to carbon emissions through deforestation, earth movement, and heightened fossil fuel consumption, yet urban vegetation and soils can play a crucial role in carbon storage.
Research on the carbon (C) cycle has primarily concentrated on either sequestration patterns or anthropogenic emissions, with few studies addressing both aspects simultaneously Understanding the interplay between these two areas is essential for effective mitigation strategies Accurately quantifying the complexities of the C cycle, particularly the interactions between human emissions and land-based sinks, is critical for reducing anthropogenic C emissions While certain land uses may enhance C sequestration, they can also contribute to increased emissions, negating potential benefits Urban areas with low C emissions might seem advantageous, yet their development often displaces natural landscapes that are more effective at sequestering carbon Therefore, comprehending the impact of land use on the C cycle can inform better policy-making Accessible data on emissions and sequestration can serve as a valuable resource for decision-makers aiming to maximize the effectiveness of their mitigation efforts and policies.
This study analyzes anthropogenic CO2 sources and natural sinks at the county level, specifically focusing on Chittenden County, Vermont A comprehensive carbon budget was developed, categorizing key carbon sinks and emissions by land use The primary objective was to present the findings in a clear and accessible manner for future land management and development planning Additionally, the results serve as a foundation for selecting from various carbon mitigation strategies, enabling policymakers and planners to identify cost-effective options for reducing emissions.
Study Area
The study area for this project is Chittenden County, located in the northwestern corner of Vermont, USA (Figure 1) Lake Champlain forms the county's western border
Chittenden County, home to the Burlington-South Burlington Metro Area, has a population of approximately 150,000 and covers a land area of 139,610 hectares, resulting in a low population density of about 1 person per hectare While the county's borders extend into Lake Champlain, only the land area is considered in this analysis The elevation in Chittenden County ranges from 29 meters above sea level at Lake Champlain's shores to 1,339 meters at the summit of Mount Mansfield, the highest peak in Vermont.
Figure 1: Vermont, USA with Chittenden County highlighted
Chittenden County experiences a temperate climate, with average temperatures reaching 21°C in July and dropping to -8°C in January The region receives approximately 97 cm of annual precipitation, which varies significantly due to its diverse topography A key feature of Chittenden County's weather is its notable changeability, a trait common throughout New England, resulting in frequent fluctuations around these average temperatures.
Abnormal weather events (i.e the ice storm of 1998, the drought of 2001-02 and the extreme temperature variations of spring 2001 [24]) can affect both C emissions and sequestration.
Land Use History
Chittenden County has experienced significant changes in land use over the past century, with much of its former agricultural land reverting to forest However, this natural landscape has also faced challenges from urban development, leading to a dramatic transformation of the region's land use patterns.
Figure 2: Land use change over time in Chittenden County, Vermont
Figure 2 illustrates land use in 1870, derived from historical documents, specifically the handwritten United States Census of Agriculture available on microfilm This data provides estimates of the land area dedicated to forest and agricultural purposes, highlighting the extent of cultivated acres during that period.
Forest Agriculture/Cleared Dev eloped
The total area for all farms in the county was calculated, but the data contained unavoidable errors due to reliance on estimations and surveys from individual landowners, which were recorded manually by census-takers Additionally, any land not included in the census was assumed to be forested, overlooking towns and emerging urban areas that were relatively small compared to the extensive forest and agricultural land For example, the city of Burlington was considered insignificant in this analysis, as it was a minor population center at the time, with development primarily along the waterfront.
Land area in forest and agricultural use in 1948 was calculated using ArcMap to georeference and digitize topographic maps of the county from within five years of 1948
Historic topographic maps, meticulously created by early mapmakers using aerial photographs, indicated green areas that offered a rough estimate of forested regions Additionally, other land was presumed to be agricultural or designated as cultivated open spaces.
During this period, urban areas like Burlington represented a modest portion of the overall land use, accounting for only a small percentage of the total county land area Additionally, the classification of original aerial photographs and the manual georeferencing and digitizing of topographic maps introduced unavoidable errors.
In 2000, land use across forest, urban, and agricultural areas was assessed using the National Land Cover Database (NLCD), which classifies remote sensing imagery The analysis identified three main land cover categories: forest, agriculture, and developed areas The forest category encompassed evergreen, deciduous, and mixed forests, along with shrubland and woody wetlands Agriculture was represented by pasture/hay, row crops, small grains, orchards, and grassland/herbaceous classes The developed area included low, medium, and high-density classifications, as well as developed open spaces The accuracy of these NLCD land use classifications is further elaborated in Section 7.
Currently, Chittenden County is about 70% forested, 15% urban, and 15% agricultural land (Figure 3).
Figure 3: Current land use in Chittenden County, Vermont Data (from Tables 11 and 12) represents range of years from 1997-2002.
Study Context
The ongoing HBRF Sciencelinks project involves in-depth case studies of C sequestration and emissions for five counties across the Northeastern US (Figure 4)
Forest Active Agriculture Inactive/Abandoned Agriculture
Figure 4: United States counties involved in the Hubbard Brook Research Foundation's Sciencelinks
Table 1: United States counties involved in the Hubbard Brook Research Foundation's Sciencelinks
C project All population and land area information from the US Census [22]
The completed case study in Tompkins County, New York, has laid the groundwork for potential future additions, including Plum Island and Harvard Forest in Massachusetts, although these have yet to be confirmed While several methods utilized in the Tompkins County study were innovative, many techniques were specifically tailored for Chittenden County.
The project aimed to create a methodology that simplifies the carbon budgeting process for counties and similar political entities To achieve this, the data and methods for calculating anthropogenic carbon emissions and natural carbon sinks in Chittenden County were selected based on three key criteria: accuracy, comprehensive coverage, and the availability of comparable data for other counties The following subsections detail the sources of useful data and the extent of its availability.
The emissions data analyzed spans multiple years, with a primary focus on the year 2000, which serves as a representative baseline for typical energy consumption in Vermont and is crucial for future projections.
County State Population Dominant Land Use
Baltimore County Maryland 790,000 682 1300 large urban center
Tompkins County New York 100,000 476 200 rural; mixed forest and farmland Grafton County New Hampshire 85,000 1750 50 rural forested; primarily recreational use
The county features a small but rapidly growing population, characterized by a forested rural landscape primarily utilized for industrial timber To assess carbon flux, forest inventory data and various other sources were employed, leading to a budget that serves as a general estimation rather than a representation of a specific year, particularly around the turn of the 21st century.
Several known greenhouse gases incorporate C, including CO2, CH4 and CO
Various gases contribute to the greenhouse effect, including water vapor, nitrous oxide, sulfur dioxide, ozone, and anthropogenic compounds like hydrofluorocarbons and chlorofluorocarbons However, this report focuses solely on carbon dioxide (CO2) due to its significant impact on climate change When data was presented in CO2 equivalents, it was disaggregated to report only CO2 emissions and sequestration, which were calculated in megagrams (Mg) of carbon.
Land Area
Forest Land Area
The forest types in Chittenden County and the land area of each (Tables 3 and 4) were determined using Forest Inventory Mapmaker version 3.0 [29] Forest Inventory
Mapmaker utilizes the USDA Forest Service Forest Inventory and Analysis database to generate tailored tables that reflect the user's geographic interests and specific attributes For this study, Mapmaker was directed to analyze the latest comprehensive FIA dataset from 1997 to determine the area of forest land in Chittenden County, classified by forest type.
When comparing forest area estimates from different datasets, Mapmaker's calculation of 85,891 ha falls between the National Land Cover Database (NLCD) estimate of 92,189 ha and the University of Vermont's Spatial Analysis Laboratory (SAL) estimate of 75,307 ha Despite potential errors in remotely sensed data, Mapmaker demonstrates reliable estimates of forest land in Chittenden County.
Area of wood product harvest was assumed to be the same as area of forestland
In actuality, some forest areas might not be harvested due to topography, tree health, or species composition.
Agricultural Land Area
The 2002 US Census of Agriculture [32] was used to determine agricultural land area Table 2 outlines each 2002 Census of Agriculture land use category and its associated area in Chittenden County
Table 2: Land in agricultural uses in 2002 in Chittenden County, Vermont [32]
The land area engaged in active agriculture is determined by summing the county-level land-use categories of 'harvested cropland' and 'cropland in cultivated summer fallow.' It is assumed that the area of active agricultural vegetation corresponds directly with the soil In contrast, inactive cropland and soils enrolled in the Conservation Reserve Program are categorized under 'cropland used only for pasture and grazing' at the county level.
The analysis categorizes land use into three main types: 'cropland idle,' 'pastureland and rangeland,' and land enrolled in Conservation Reserve or Wetland Reserve programs It is important to note that cropland classified as idle in one year may become active the following year; however, the study assumes a consistent conversion rate of active land to idle status annually to maintain balanced area calculations Additionally, abandoned agricultural land is defined as 'cropland on which all crops failed or were abandoned,' with the area of abandoned agricultural vegetation and soil assumed to be equivalent.
Cropland used only for pasture and grazing
Cropland on which all crops failed or were abandoned
Cropland in cultivated summer fallow
Land enrolled in Conservation Reserve or Wetlands Reserve programs data not disclosed – only 1 farm enrolled
The analysis of urban vegetation and soil areas for carbon sequestration utilized the National Land Cover Database (NLCD), which provided more accurate urban land classifications compared to regional land use classifications derived from similar satellite imagery The NLCD dataset's incorporation of impervious surface data was crucial for this analysis, categorizing urban areas based on the percentage of impervious cover These classifications included developed open space (less than 20% impervious cover), developed low intensity (20-49% impervious cover), developed medium intensity (50-79% impervious cover), and developed high intensity (80-100% impervious cover).
To assess urban vegetation, it is essential to differentiate between pervious and impervious surfaces, as plants thrive only on pervious soil Each urban category's pixels were converted into hectares, and the average percentage of impervious surfaces was calculated and multiplied by the total area of that category to estimate the total impervious area By summing the impervious areas across all categories and subtracting this from the total urban land area, we can determine the extent of urban vegetation, pervious urban soil, and soil located beneath impervious surfaces.
4.4 Residential, Commercial and Industrial Land Area
For the analysis of carbon emissions, the land area allocated to residential, commercial, industrial, and transportation uses in the state was determined using pixel data from Landsat TM satellite imagery from 2002, as classified by the University of Vermont's Spatial Analysis Laboratory (SAL) Although the National Land Cover Database (NLCD) could also serve this purpose, the SAL dataset's specific classifications of urban land proved more beneficial for emissions analysis than the broader urban categories in the NLCD, which emphasizes impervious surfaces that were not relevant for this study.
A third data source for Chittenden County, developed by UVM SAL, enhanced the 2001 NLCD data for the Lake Champlain Basin by incorporating information from various sources and conducting manual error assessment While this dataset offered improved accuracy over previous land use classifications, it was not utilized in this study due to its overly general classifications, which only identified a broad 'urban' category This limitation hindered the analysis of carbon emissions and sequestration by specific urban land use types.
Total land area in Chittenden County, according to the US Census, is 139,610 ha
The total land area calculated using the specified methods amounts to approximately 119,000 hectares; however, this figure excludes several land use categories such as water bodies, barren land, bare rock, non-forested wetlands, and land designated for sand and gravel extraction Additionally, discrepancies in the datasets may contribute to this variation.
Carbon Pools and Sequestration
Forest Vegetation
The carbon stored in forest vegetation was assessed across various biomass categories, including live trees, standing dead trees, understory plants, downed dead wood, and forest floor biomass The live-tree biomass was derived from forest inventory data, while the other biomass pools were estimated using reference tables.
C storage and flux in live tree biomass were assessed using tree-level measurements from the USDA Forest Service Forest Inventory and Analysis (FIA) and aggregated at the plot scale, in accordance with the methods outlined by Hicke et al This study was conducted in Northern Vermont.
(Caledonia, Essex, Franklin, Grand Isle, Lamoille, Orange, Orleans, and Washington counties), 378 plots were measured in the most recent inventory, performed between
Between 1996 and 1998, a total of twenty-seven plots were established in Chittenden County to enhance the estimation of large-scale trends in biomass and net primary production by forest type This analysis utilized plots from both Chittenden County and the broader Northern Vermont region to increase the overall number of plots available for assessment.
In Northern Vermont, biomass and wood net primary production were determined using allometric equations, as outlined in Hicke et al The calculations were averaged for each forest type, leading to the assessment of average carbon density and carbon accumulation, based on the assumption that carbon constitutes half of the biomass.
The carbon pool (Mg) in live tree biomass within Chittenden County was determined by multiplying the land area of each forest type by its average carbon density (Mg C/ha) and summing the results This approach relies on plot-level inventory data, assuming that the carbon density values derived from FIA data accurately represent the average across all forest age and size classes in the county.
Table 3: Live tree C density and total C by forest type in Chittenden County, Vermont
The net annual carbon (C) accumulation in live tree biomass for the county was calculated by multiplying the land area of each forest type by its average carbon accumulation rate, followed by summing these results To determine the total carbon accumulation for the county, the net annual carbon accumulation was then multiplied by the total forest area, as shown in Table 4.
Average Biomass [calculated from all
Land Area in Chittenden (ha) [29]
Birch calculation assumes that the C accumulation value from FIA data is an accurate average of all forest age and size classes present in Chittenden County as described above.
Table 4: Live tree C accumulation by forest type in Chittenden County, Vermont
To assess carbon (C) density and accumulation in various forest components, including standing dead trees, understory, downed dead wood, and forest floor biomass, an average stand age was established for two main forest type categories based on FIA plot-level data The maple-beech-birch category, encompassing all hardwood types, had an average stand age of 45 years, while the pine category, representing all softwood types, averaged 65 years C stock data for reforestation in these forest types can be found in Smith et al [36] By utilizing the C density values corresponding to the average stand ages of each forest type and multiplying them by the respective area of each type in the county, the total C pool was calculated for the specified biomass components.
Average Wood NPP [calculated from N.VT counties] (Mg/ ha/yr) [34]
N VT Counties (Mg/ha/yr)
Land Area in Chittenden (ha) [29]
Total C Accumulation in Chittenden (Mg/year)
Table 5: C density [36], land area [29] and total C for non-live-tree forest biomass components in
This value was added to the value calculated for live trees C density in the county was then determined by dividing county C storage by total forest area
Smith et al [36] indicate that the contribution of standing dead trees, understory, down dead wood, and forest floor biomass to net annual carbon accumulation is minimal Therefore, this report focuses solely on the net annual carbon accumulation in live trees.
Biomass Component standing dead 6.6 69,569 459,158 understory 1.7 69,569 118,268 down dead wood 7.0 69,569 486,986 forest floor 23.0 69,569 1,600,096
Pine Forests: standing dead 5.0 16,321 81,606 understory 1.6 16,321 26,114 down dead wood 5.3 16,321 86,502 forest floor 13.7 16,321 223,600
C Density at Avg Stand Age (Mg C/ha)
Land Area in Chittenden (ha) Total C Pool
Table 6: Net C accumulation in non-live-tree forest biomass components in Chittenden County,
Vermont [36] Negative values indicate emissions to the atmosphere
This analysis implicitly incorporates mortality and harvest through the C accumulation assessment using the stock change method By comparing forest stock at different time periods, the method estimates net change, effectively accounting for mortality and harvest at the county level within each time increment.
Wood Products
The net annual accumulation of wood products, encompassing both the product stream and landfills, was determined using methodologies outlined in a report by the Center for Climate Strategies for the state of Pennsylvania, which references tables from Smith et al.
C Accumulation, Pine Forest (Mg/ha/10 years)
C Accumulation, Maple-Beech- Birch Forest (Mg/ha/10 years)
Avg C Accumulation (Mg/ha/10 years)Avg C Accumulation (Mg/ha/yr)
The 2006 US Census reported the annual hardwood and softwood harvest in Vermont, which was then adjusted to reflect the proportion of forestland in Chittenden County Data from Smith et al indicated the distribution of hardwood and softwood growing stock volume within sawtimber and pulpwood size classes Additionally, the forest biomass analysis utilized percentage data of timber types, specifically maple-beech-birch and pine, based on the area information provided in the referenced tables.
The total harvest of hardwood and softwood sawtimber and pulpwood in the county was quantified in board feet To determine the biomass harvested, each figure was multiplied by the average specific gravity from Smith et al [36] and then divided by 2 to calculate the total carbon (C) harvested Based on calculations by Ingerson [39] using data from Smith et al [36] and Gower et al [40], it was estimated that 35.2% of the total carbon harvested is incorporated into wood products, contributing to the annual wood product carbon pool.
The total carbon pool and density for wood products were not assessed in this study, as they were deemed irrelevant Consequently, the duration that carbon remains in the wood product pool, differentiated by product type such as pulp and lumber, was not evaluated.
In Chittenden County, a portion of harvested wood is utilized for heat energy rather than durable wood products, primarily by individual small landowners, which was not reflected in the harvest data Additionally, Burlington hosts the Joseph C McNeil Generating Station, which converts woody biomass into electricity Although the electricity produced at McNeil contributes to Vermont's electric grid, it does not specifically serve Chittenden County; therefore, its emissions are included in the overall electricity emissions calculations but are not separately addressed in this context.
Other Sequestration Pools
Table 7 shows C density and net C accumulation values found in the literature for the remaining C sequestration pools Literature values were used because quantitative
C data for Chittenden County were not available C pool size and total county-level C accumulation rates for each pool were calculated using area measurements.
The selected papers in the table were chosen for various reasons, including the estimation of forest soil carbon density and active agricultural soil carbon density from Ellert and Gregorich's study, which provided quantitative measurements in Ontario, Canada This location was geographically close to Vermont, making it a suitable reference for soil carbon density data Additionally, the ability of Ellert and Gregorich's research to offer consistent values for both forest and agricultural soils enhanced the reliability of the data.
Research by Smith et al [36] suggests that the annual carbon accumulation in forest soils is minimal Additionally, carbon stock data for reforestation in maple-beech-birch and pine forests (A2 and A6) reveal that soil organic matter remains stable for at least 125 years following harvesting.
Net annual C accumulation in active agricultural soil was estimated from Table
A comprehensive analysis of 76 long-term agricultural soil carbon experiments across the United States was conducted, focusing on fossil fuel inputs The study reported values for both conventional tillage and no-till practices, although conventional tillage was assumed for the analysis Given that active agricultural biomass is harvested annually, the net carbon accumulation and carbon pool size were considered negligible.
The Conservation Reserve Program (CRP) provides financial compensation to farmers who temporarily withdraw agricultural land from production An analysis of the carbon density in inactive cropland and CRP soils was conducted, referencing data from Paul et al [44], which averaged findings across various afforested plot pools This study focused on inactive agricultural land in Ontario and Ohio, aligning with the geographic scope of Ellert and Gregorich [42] Additionally, net carbon accumulation was assessed based on research by Gebhart et al [45], which specifically examined plots on CRP lands in the United States.
The carbon density of vegetation on abandoned agricultural lands in the Northeastern United States was determined to be half the biomass of the youngest study plot, as referenced in Hooker and Compton [46] This study uniquely focused on forest regeneration following agricultural abandonment in the region Additionally, the net annual carbon accumulation was calculated by summing the values for plant biomass, woody debris, and the forest floor, as detailed in Table 3 of the same study.
The carbon density of abandoned agricultural soil is considered equivalent to that of inactive cropland and Conservation Reserve Program (CRP) soils, as the early stages of regeneration in fields without active agriculture are likely to exhibit similar dynamics This holds true whether the land is classified as temporarily inactive or permanently abandoned Net annual carbon accumulation was estimated based on the findings of Post and Kwon, utilizing average values from their study for the transition from old fields or agricultural land to cool temperate moist forest.
This paper provided a valuable summary of studies measuring soil C accumulation post- agriculture and allowed for the easy aggregation of values from those studies.
The carbon density of urban vegetation was assessed using data from Jo and McPherson, focusing on an average of two plots in Chicago, Illinois This study also estimated the net annual carbon accumulation and explored the impacts of management practices, marking it as one of the first significant analyses of carbon cycling in urban environments.
The carbon density of urban soil was assessed in both pervious and impervious areas, utilizing data from Pouyat et al This research compiled urban soil carbon information from multiple sources to generate specific carbon density estimates for different geographic regions across the United States The values presented for residential and impervious areas, including commercial, industrial, and transportation zones in the Northeast region, were referenced from Table 5.
C accumulation in urban soils was estimated in pervious areas and under impervious surfaces from Jo and McPherson [48] using an average of two plots
Table 7: C density and net C accumulation values from the literature by data type and geographic region
Soil carbon (C) density and net soil C accumulation values, as presented in Table 7, originate from studies employing diverse methodologies with varying soil sampling depths Generally, greater sampling depths result in higher C density and accumulation values, while samples taken nearer to the soil surface tend to exhibit even larger values due to higher carbon density in those layers Therefore, understanding the impact of sampling depth is crucial when evaluating soil C values Table 8 provides details on the specific sampling depths used in these studies.
Data Type Value Data Source Geographic Area
Forest soil C density 107 Mg/ha Ellert and Gregorich 1996 Ontario
Forest soil C accumulation negligible Smith et al 2006 Northeast US negligible n/a n/a negligible n/a n/a
70 Mg/ha Ellert and Gregorich 1996 Ontario, CA
0 Mg/ha/yr West and Marland 2002 United States
82 Mg/ha Paul et al 2003 Eastern North America
1 Mg/ha/yr Gebhart et al 1994 US Great Plains
4 Mg/ha Hooker and Compton 2003 Rhode Island, US
2 Mg/ha/yr Hooker and Compton 2003 Rhode Island, US
82 Mg/ha Paul et al 2003 Eastern North America
0.24 Mg/ha/yr Post and Kwon 2000 Urban biomass C density 41 Mg/ha Jo and McPherson 1995 Chicago, IL, US
4 Mg/ha/yr Jo and McPherson 1995 Chicago, IL, US
Urban soil C density Pouyat et al 2006 Northeast US
Urban soil C accumulation Jo and McPherson 1995 Chicago, IL, US
Inactive cropland and CRP soils C density
Inactive cropland and CRP soils C accumulation
Abandoned agricultural soil C accumulation Worldwide cool temperate moist forests
2 Mg/ha/yr pervious, negligible under impervious each study from which soil C density and C accumulation values were estimated for this paper.
Table 8: Sampling depths from the soil C density and C accumulation literature listed in Table 7.
Carbon Emissions
Transportation
Fossil fuel use for transportation was determined using both the “top-down” and the “bottom-up” methods for the purposes of comparison
The bottom-up method initiated with vehicle miles traveled (VMT) data from the Vermont Agency of Transportation (VTrans) in 2004, categorized by road type In 2006, VTrans provided additional data on vehicle distribution across similar road types, enabling the analysis of the types of vehicles operating on each road.
In 2004, the average fuel efficiencies for various vehicle types were sourced from the Oak Ridge National Laboratory Transportation Data Book By dividing the miles traveled by each vehicle type by its respective fuel efficiency, we calculated the total gallons of fuel consumed in the county for that year by each vehicle category.
In Chittenden County, Vermont, various fuel types are utilized for automobile transportation, with vehicle type codes categorizing trucks based on their axle count and tonnage Fuel efficiency data is sourced from ORNL, while vehicle miles traveled (VMT) and distribution of vehicle types are provided by VTrans.
Vehicle Miles Traveled in Chittenden on All Road
Pickup truck/SUVSchool/transit buses
The US Energy Information Administration (EIA) provided state energy data
The study analyzed the percentage of gasoline and diesel fuel consumption in the state to calculate total fuel usage By applying standard conversion factors of 2332 g C per gallon for gasoline and 2716 g C per gallon for diesel, the carbon emissions for each fuel type were determined These emissions were then divided by the land area allocated for transportation, resulting in the calculation of carbon flux density associated with petroleum use in transportation.
In the top-down approach, the 2004 vehicle miles traveled data for Vermont and Chittenden County were analyzed to calculate the percentage of county miles relative to state miles This percentage was subsequently applied to the state's transportation-related carbon emissions from 2000 to estimate the carbon emissions attributable to transportation in Chittenden County.
County Transportation Emissions = Total Vermont Transportation Emissions
* % of Total Vermont Vehicle Miles Traveled in the County
The emissions values for the state were initially determined using the US Environmental Protection Agency's State Greenhouse Gas Inventory Tool software, incorporating default values that were substituted with specific data from the Vermont Department.
The Vermont Agency of Transportation focuses on environmental conservation by addressing traffic congestion, a key contributor to increased emissions Given that Chittenden County houses Vermont's largest city, it is essential to recognize that traffic patterns and their environmental impacts may vary significantly across the state.
(1) more congestion occurs in Chittenden County than elsewhere in the state, affecting the accuracy of these calculations
The top-down method of calculating fuel consumption encompasses non-highway sources, including aircraft, boats, and trains, unlike the bottom-up approach In Vermont, Chittenden County hosts the state's only international airport, yet emissions from this significant transportation hub are allocated proportionally across the entire state rather than solely to Chittenden County.
A third method for calculating transportation emissions in Chittenden County, developed by the Chittenden County Metropolitan Planning Organization, involved a detailed transportation model that accurately assessed vehicle miles traveled and accounted for traffic congestion However, this model was limited to peak driving hours and could not estimate conditions at other times of the day While it provided more accurate data than the top-down and bottom-up methods for the periods it covered, it was ultimately not selected due to its inability to represent all daily traffic conditions in the county.
Electricity
Electricity emissions were calculated using a top-down approach due to the unavailability of complete consumption data from local electric utilities CO2 emissions from electricity consumption at the state level were sourced from the VGCCC report, which analyzed electricity sales and fuel mix data provided by the Vermont Department of Public Service To assess the land-use contributions of residential, commercial, and industrial activities to the overall carbon balance, emissions for each sector were divided by the land area designated for each land use type within the county It was assumed that electricity sales in each sector accurately reflected actual electricity usage.
To determine the amount of state electricity use attributed to Chittenden in the residential sector, the following equation was used:
County Residential Electricity Emissions = Total Vermont Electricity Emissions
* % of Total Vermont Households in the County * % of Vermont Electricity
Sales in the Residential Sector
The percentage of state electricity sales in the residential sector was derived from EIA data from 2000 The proportion of Vermont households in the county was determined using the 2000 US Census data For this report, households included both Census-defined households and second-home housing units, calculated by subtracting the total number of households from the total number of housing units and assuming half-time occupancy for second homes This approach assumes that individual households throughout the state consume a relatively consistent amount of electricity annually.
To determine the amount of state electricity use attributed to Chittenden in the commercial sector, the following equation was used:
County Commercial Electricity Emissions = Total Vermont Electricity
Emissions * % of Vermont Employment in the Commercial Sector in the County
* % of Vermont Electricity Sales in the Commercial Sector
The analysis of state electricity sales in the commercial sector utilized EIA data from 2000, while employment percentages in Vermont were derived from the 2002 US Economic Census A fundamental assumption in this analysis is that emissions from commercial and industrial activities correlate with the number of employees in each sector However, the accuracy of this assumption may vary across different industries; for instance, some energy-intensive industries may operate with minimal staff, whereas certain businesses with a larger workforce might not significantly impact energy consumption in the commercial sector.
To determine the amount of state electricity use attributed to Chittenden in the industrial sector, the following equation was used:
County Industrial Electricity Emissions = Total Vermont Electricity Emissions *
% of Vermont Employment in the Industrial Sector in the County * % of
Vermont Electricity Sales in the Industrial Sector
Percentage of state electricity sales in the industrial sector were found using EIA data from 2000 [59] Percentage of Vermont employment in the county and the state were
The calculation of carbon emissions (C emissions) in Chittenden County, based on the 2002 US Economic Census, involves dividing these emissions by land area to determine carbon flux density However, a key assumption in this analysis is that emissions levels are uniform across all sizes and types of industrial production This assumption is problematic, as Chittenden County hosts the largest industrial producers in the state, leading to variations in carbon density that could compromise the accuracy of the method and introduce inconsistencies in scaling.
Petroleum
Petroleum emissions for Chittenden County were assessed exclusively using a top-down approach due to the lack of localized petroleum consumption data The CO2 emissions from petroleum usage for space heating, cooling, process heating, and other non-electric energy applications in the state were sourced from the VGCCC report, which employed the EPA's State Greenhouse Gas Inventory Tool This report updated default data with the latest figures from the Energy Information Administration's state energy data reports For analytical purposes, petroleum encompassed natural gas, oil products, and coal The emissions were categorized into residential, commercial, and industrial sectors, with agricultural energy use included under industrial emissions.
To determine the amount of state petroleum use attributed to Chittenden County in the residential sector, the following equation was used:
County Residential Petroleum Emissions = Total Vermont Residential Petroleum (5)
Percentage of Vermont households in the county were calculated with adjustment for half-time occupancy as described above, with similar assumptions included
To determine the amount of state petroleum use attributed to Chittenden County in the commercial sector, the following equation was used:
County Commercial Petroleum Emissions = Total Vermont Commercial
Petroleum Emissions * % of Vermont Employment in the Commercial Sector in the County
Percentage of Vermont employment in the county and the state were calculated from the
2002 US Economic Census as described above, with similar assumptions included.
To determine the amount of state petroleum use attributed to Chittenden County in the industrial sector (including agriculture), the following equation was used:
County Industrial Petroleum Emissions = Total Vermont Industrial Petroleum
Emissions * % of Vermont Employment in the Industrial Sector in the County
Percentage of Vermont employment in the county and the state were calculated from the
2002 US Economic Census, and area dedicated to residential, commercial and industrial activity in the county was used to calculate C flux density as described above, with similar assumptions included.
The individual emissions calculations can be aggregated and analyzed as distinct point sources that contribute to the total carbon emissions in the county Alternatively, they can be assessed based on emissions per area of land use through the carbon flux density calculation.
Land Use Change
Land use change, particularly deforestation and soil disturbance, significantly contributes to carbon emissions in Chittenden County as forest and agricultural lands are converted to developed uses each year However, quantifying these emissions is challenging due to the lack of detailed data needed to detect significant changes over short periods Current resources, such as the National Land Cover Database and satellite data from UVM's SAL, indicate land use changes from 1992 to 2001-2002, but their resolution is insufficient for accurate measurement Future studies should focus on developing methods to effectively quantify land use change emissions.
Concentrating on smaller areas within counties for land use change analyses may increase the availability of accurate, high-resolution data.