A challenge in the development of renewable energy is the ability to spatially assess the risk of feedstock supply to conversion facilities. Policy makers and investors need improved methods to identify the interactions associated with landscape features, socioeconomic conditions, and ownership patterns, and the influence these variables have on the geographic location of potential conversion facilities. This study estimated opportunity zones for woody cellulosic feedstocks based on landscape suitability and market competition for the resource. The study covered 13 Southern States which was a segment of a broader study that covered 33 Eastern United States which also included agricultural biomass. All spatial data were organized at the 5-digit zip code tabulation area (ZCTA). A landscape index was developed using factors such as forest land cover area, net forest growth, ownership type, population density, median family income, and farm income. A competition index was developed based on the annual growth-to-removal ratio and capacities of existing woody cellulosic conversion facilities. Combining the indices resulted in the identification of 592 ZCTAs that were considered highly desirable zones for woody cellulosic conversion facilities. These highly desirable zones were located in Central Mississippi, Northern Arkansas, South central Alabama, Southwest Georgia, Southeast Oklahoma, Southwest Kentucky, and Northwest Tennessee.
Energy, its availability and use, is fundamental to a sustainable economy. The 20th century was marked by rapid growth and increased prosperity in the world. By 2020, the world’s energy consumption is predicted to be 40% higher than it is today [
Renewable energy is projected to be one of the fastest growing industries in the US agricultural and forest sectors. As Elbehri [
A plethora of literature exists on the economic availability of biomass [
A major difficulty addressed in this study that biomass production and access to this biomass in the field are not always directly related in a spatial context to decision-makers interested in mill siting. Improved information and methods for biomass markets that display and visualize the costs of supply and logistics from farm to forest gate to collection or conversion facilities may improve knowledge essential for market formation. The supply of biomass may be more constrained when relying on a supply network that is independent of the production facility for the raw material, for example,
This study identifies opportunity zones in a spatial context for woody cellulosic feedstocks available to potential conversion industries, for example,
Specific objectives of the study were (1) compile data on the physical and socioeconomic characteristics of the landscape and display this data in a spatial context at the 5-digit zip code tabulation resolution for 33 eastern United States; (2) develop an index from the spatial data that would discriminate the landscape to identify opportunity zones for biomass-using facilities; and (3) integrate objectives (1) and (2) with the Biomass Site Assessment Tool (BioSAT),
This study involved organizing large volumes of data collected from various sources, including the US Census Bureau [
Another resource that was used to illustrate how this data could be helpful to possible users was the integration of the BioSAT model with the study [
All records were organized at the US Census Bureau 5-digit ZIP Code Tabulation Area (ZCTA) level [
Geographical landscape and socio-economic factors used in study.
Variable | Original data resolution | Unit | Data sources |
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Population density | 5-digit ZCTA | People/mile2 | U.S. Census Bureau (2010) population density in each 5-digit ZCTA. |
Farm net income | County | Dollar | USDA NASS Census Agriculture (2007) farm net income in each county. |
Road density | 5-digit ZCTA | km/km2 | U.S. Census Bureau (2010) road length |
Crop cultivated land area ratio | |||
Forest land area ratio | 5-digit ZCTA | Percent | U.S. National land Cover Database (2006) |
Urban Land area ratio | |||
Water area ratio | |||
Slope | 5-digit ZCTA | Percent | U.S. National Elevation Dataset (1999) NED 1 arc second |
Ecoregions Level III | Ecoregions | — | U.S. EPA (2011) |
Timberland annual growth-to-removal ratio | County | — | Forest Inventory and Analysis—The Timber Products Tools (TPO) (2009) |
Lands in public preserves | 5-digit ZCTA | — | U.S. Forest Service (2009) |
Primary wood-using mill locations | 5-digit ZCTA | — | U.S. Forest Service (2009) and state mill directories |
The research methodology used in this study has four main components: (1) estimation of forest biomass availability; (2) measurement of landscape suitability of forest biomass access; (3) analysis of a spatial market competition for forest biomass resources; and (4) visualization of biomass opportunity zones. Each of these components is described in the following section.
Forest biomass annual growth and removal quantity data were collected at the county level from Forest Inventory and Analysis Database (FIADB) version 3.0 (Figure
Illustration of forest biomass allocation at the level of 5-digit ZCTA.
Illustration of county forest biomass quantity
Land cover map and county boundary
Land cover for 5-digit ZCTA boundary
Forest biomass allocation by 5-digit ZCTA
Due to the mismatch of county boundary and 5-digit ZCTA (i.e.,
The availability of forest biomass, as well as other forest resources, is physically constrained by a set of factors from the natural and socialeconomic environment [
The final suitability index value was organized into ordinal levels based on expert judgment to estimate the amount and accessibility of forest biomass given the aforementioned possible constraints (Table
Definitions of landscape suitability index.
Level | Description |
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High suitability | Lands suitable for forest production only, for example, forests of northern Minnesota |
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Moderate suitability | Lands that have moderate capability for being only in forest production |
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Low suitability | Lands may be easily converted to agricultural production from forestland |
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Unsuitable | Land areas as defined by ecoregion classification that are not suitable for forest or agricultural production, for example, desert in western Texas, mountain tops of Smoky mountains |
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Exclusion | Land areas that will not support forest or agricultural production given socioeconomic and/or legal constraints, for example, national parks, military bases, urban areas with population density >58 people/km2 [ |
5-digit ZCTAs excluded in the 13-state study region.
Potential forest biomass availability is also strongly influenced by the level of competition for the resource. Resource competition is usually negatively correlated with forest biomass availability unless the potential supply is a byproduct of existing harvesting operations such as forest residues [
A “zone-of-influence” model was developed in this study. The zone-of-influence model assumes the procurement zones associated with existing demand points or mills may not be concentric and that neighboring mills have procurement zones that occupy the same space and overlap [
Primary wood-using facilities in the 13-state study region.
Six ordinal levels were developed defining the intensity of competition based on expert judgment (Table
Definitions of competition index on resource utilization.
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GIS methods are highly effective for visualization mapping of spatial opportunity zones for biomass availability and accessibility when a study area consists of more than 10,000 location units (5-digit ZCTAs), as was the case in this study. Two sets of maps were produced in this study. The first set was spatial opportunity zones for woody cellulosic conversion facilities using the aforementioned landscape suitability and competition indices. The second set of maps illustrates the spatial pattern of the competition intensity for the resource assuming a fixed haul distance from each existing wood-using facility.
The four indicators of “federal lands,” “population density,” “slope,” and “unsuitable ecoregions” were used to “exclude” ZCTAS from the study region. “Federal lands” included lands in ownership by the Bureau of Indian Affairs, Department of Defense, Fish and Wildlife Service, Forest Service, National Aeronautics and Space Administration, National Park Service, Tennessee Valley Authority, and US Department of Agriculture Research Center. ZCTAs with “population density” (>58 people/km2) were excluded given the results of previous research [
The criteria to assess the other four levels of landscape suitability are given in Table
Criteria for four levels of landscape suitability.
Level | Criteria |
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High suitability | Forest area ratio greater than 30%; and timberland annual growth-to-removal ratio greater than 1.5; and ecoregions defined as mostly forestland; and slope lower than 30%; |
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Moderate suitability | Forest area ratio greater than 10%; and timberland annual growth-to-removal ratio greater than 1; and ecoregions defined suitable for forestland; and slope equal or lower than 30% |
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Low suitability | Forest area ratio greater than 10%; and timberland annual growth-to-removal ratio equal or less than 1; and ecoregions defined suitable for forestland or cropland; and slope equal or lower than 30% and population density equal or less than 58 people/km2 |
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Unsuitable for forests | Forest area ratio equal or less than 10%; or timberland annual growth-to-removal ratio less than 0; or ecoregions defined as mostly cropland; or negative farm net income but median family income greater than $49,445 or road density higher than 5 km/km2 |
Regions that had forest area ratio greater than 30%, timberland annual growth-to-removal ratios greater than 1.5, ecoregions defined as mostly forestland, slopes less than 30%, and less than 39 people/km2 were considered areas that were highly suitable for forest productions. Based on these criteria, high suitable opportunity zones for facilities relying on woody cellulosic feedstocks were located along the Central Mississippi, northwest and southeast Alabama, north Arkansas, west Georgia, east Oklahoma, and areas in Kentucky, Tennessee and Virginia close to Smokey Mountains (Figure
Opportunity zones for woody biomass-using facilities identified by landscape suitability index in the 13-state study region.
A strength of the data analyses was that socioeconomic data which is collected at the 5-digit ZCTA resolution was incorporated in the data overlays and therefore were not aggregated which maintained data integrity for these variables. A potential weakness of the study was the de-aggregation of forest inventory data which implies that the opportunity zones have improved validity as the procurement area for a potential site location increase in area.
The spatial pattern of competition intensity within a 128.8 km one-way haul distance zone is displayed in Figure
Criteria for four levels of the combined landscape suitability and competition indices.
Level | Landscape suitability criteria | Competition criteria |
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High suitability | Forest area ratio greater than 30%; and timberland annual growth-to-removal ratio greater than 1.5; and ecoregions defined as mostly forestland; and slope lower than 30%; and population density less than 39 people/km2 | Competition index ≤ 4 |
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Moderate suitability | Forest area ratio greater than 10%; and timberland annual growth-to-removal ratio greater than 1; and ecoregions defined suitable for forestland; and slope equal or lower than 30% and population density equal or less than 58 people/km2 | Competition index ≤ 4 |
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Low suitability | Forest area ratio greater than 10%; and timberland annual growth-to-removal ratio equal or less than 1; and ecoregions defined suitable for forestland or cropland; and slope equal or lower than 30% and population density equal or less than 58 people/km2 | Competition index ≤ 5 |
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Unsuitable for forests | Forest area ratio equal or less than 10%; or timberland annual growth-to-removal ratio less than 0; or ecoregions defined as mostly cropland; or negative farm net income but median family income greater than $49,445 or road density higher than 5 km/km2 | Competition index ≤ 6 |
Competition index on resource utilization within a 128.8 km one way travel distance.
“High” suitability areas from the landscape suitability index when combined with high competition intensity (>5) resulted in a reduction of 395 ZCTAs, most of which were located in northwest Alabama. Given the aforementioned criteria, a total of 592 ZCTAs were considered highly desirable opportunity zones for forest biomass availability. These preferred zones were located in Central Mississippi, Northern Arkansas, South central Alabama, Southwest Georgia, Southeast Oklahoma, Southwest Kentucky, and Northwest Tennessee (Figure
Opportunity zones identified by the combined landscape suitability and competition indices in the 13-state study region.
One potential value to the practitioner from the aforementioned analyses is in the siting of biomass-using facilities. An example of use for practitioners involved in plant siting would be to combine the analyses with the BioSAT model. As cited earlier [
In this study the BioSAT model was used to derive more detailed economic information for one of the high suitability opportunity zones located in central Mississippi (Figure
Opportunity zone in central Mississippi (highlighted in red) for mill location at ZCTA 39090 (Kosciusko MS).
Spatial representation of biobasin for ZCTA 39090 (Kosciusko MS) and associated marginal cost curve for pine pulpwood (
Renewable energy is projected to be one of the fastest growing industries in the US agricultural and forest sectors. However, replacing petroleum products with renewable energy presents technical, economic, and research challenges, one of which is the availability of biomass feedstock. This study directly addresses this problem by developing spatial geographic information for potential users of the woody cellulosic feedstocks for a 13-state study region in the Southern United States. The spatial geographic data accounts for landscape features, socioeconomic factors, and competition for the resource, organized at the 5-digit zip code resolution. Landscape and competition indices were developed in the study and combining these indices in a spatial geographic context derives a classification of “opportunity zones” for potential users of woody cellulosic feedstocks. A total of 592, 5-digit ZCTAs were considered highly desirable opportunity zones for woody cellulosic feedstocks. These preferred zones were located in Central Mississippi, Northern Arkansas, South central Alabama, Southwest Georgia, Southeast Oklahoma, Southwest Kentucky, and Northwest Tennessee.
Project work is ongoing in developing short rotation woody crop (SRWC) data layers. The SRWC data layers will incorporate soils data, climatology data, growth modeling, and economic cost analyses. The SRWC data layers will provide dedicated energy crop analyses as a feedstock source for practitioners interested in siting scenarios using SRWC.
This research was funded by US Forest Service Southern Research Station under contract agreement 07-CR-11330115-087, Southeastern Sun Grant Center, and University of Tennessee Agricultural Experiment Station.