This research developed a regional economic model to estimate the ex-ante impacts of biofuel production on the economy of the southeastern United States. The analysis focuses on biofuels produced using biochemical and pyrolysis technologies. The primary feedstocks considered include switchgrass ( Panicum virgatum) and poplar ( Populus spp.). The economic analysis modifies the Impact Analysis for Planning (IMPLAN) input-output model to determine the macroeconomic impacts of a mature industry producing biofuels using these technologies and feedstocks. Optimal facility locations are determined using a site locator model that minimizes the costs of procuring feedstock. Given a change in the land use caused by industry demand for feedstock, shocks to the farm economy are forward-linked to sectors supporting biofuel production. Key economic indicators analyzed include changes in employment and value added to the economy. System output is analyzed using a nonparametric bootstrap procedure to simulate the distributions of the impacts. The null hypothesis is that the economic impacts following the introduction of the industries are not different from baseline economic activity. Findings suggest that the net changes in employment and value added to the regional economy are positive, but modest. For example, job increases attributed to the advancement of the industries analyzed range between 0.18% and 0.95%. Total value added to the regional economy ranged between 0.15% and 0.83%.
Prior research finds that the southeastern (SE) US will be an important supply region for cellulosic biomass feedstocks from dedicated energy crops and from forestry and wood waste sources [
This research estimates the change in regional economic indicators, including total value added to the economy and employment, based on annual facility operating, feedstock transport, storage, labor, renewable identification number credits, and the opportunity costs of replacing conventional crops with the feedstocks of switchgrass and/or short rotation woody crops (e.g., poplar). The analysis is ex-ante; in other words, we ask “what if” questions given assumptions pertaining to 1) the economics and technology of converting cellulosic materials to biofuels using biochemical or pyrolysis pathways; 2) the growing conditions for woody biomass and switchgrass in the southeastern US; 3) the distribution of current agricultural land use in the region; and 4) the economic conditions before the placement of drop-in fuel or cellulosic ethanol production facilities. Attracting biofuel production could provide communities off-farm work opportunities and increase household income through linkages to local agricultural production and supporting businesses [
Research analyzing the effects of biofuel production on local economies is generally characterized by studies applying on input/output (IO) models or econometric studies using primary survey data or secondary “macro” county data. The economic impacts of bioenergy and biofuels development have been analyzed in studies using the economic IO model IMPLAN (the Impact Analysis and Planning system, [
Lazarus and Tiffany [
This analysis develops a system of interrelated modules depicting the linkages between primary feedstock producers, biofuel production, and supporting industries with economy-wide impacts in the SE United States. The core economic model is the IMPLAN IO model, but the system is modified to reflect the opportunity costs of production incurred by landowners and agricultural producers and credits facilities could receive for producing biofuel. The modules are driven by a facility locator model, BioFLAME [
The technology focus of this analysis is on the production of ethanol and “drop-in” fuels, such as biobutanol. The analysis assumes ethanol is produced through biochemical processes. Drop-in fuels are produced using pyrolysis. Either technology can use switchgrass (Panicum virgatum) or the short rotation woody crop (SRWC), poplar (Populus spp.) as energy feedstocks. Switchgrass is a perennial grass native to the region, and is considered favorable for producing cellulosic ethanol and other advanced biofuel products, depending on the technological pathway [
In cases where an industry must be added to IMPLAN (either the industry does not exist in IMPLAN or it does not exist in the region), the projected value of output and expenditures on inputs and services from the “new” renewable industry are constructed by creating production functions for the industry. When this cost and production data about a type of facility is unavailable, representative facilities must be assumed and custom built into the IO model. Investment and operating costs estimates and production coefficients for conversion facilities are based upon prior studies [
Conversion technology specifications for pyrolysis and cellulosic ethanol are based on Wright et al. [
For pyrolytic conversion of biomass to drop-in fuels, the Btu/gallon ratio is 118,300, with a conversion rate of 52 gallons/t of biomass material (
Feedstock/technology | Biochemical | Pyrolysis |
---|---|---|
Switchgrass | X | X |
SRWC (poplar) | X | X |
Fuel type | Ethanol | Drop-in |
BTU fuel value | 74,500 | 118,300 |
Gallons produced (billions) | 10.5 | 6.795 |
Conversion rate (fuel product/ton) | 85 | 52 |
Production a) | 10.5 | 6.795 |
Feedstock (tons/facility) | 884,506 | 701,329 |
Base plant capacity (millions gal.) | 80 | 55.7 |
Plants required meeting target | 147 | 122 |
Notes: a) assumes 100% of the 10.5 billion∙gallon∙year−1 target for the southeastern US is met.
The analysis compares economic indicators with facility locations and corresponding feedstock demand under different policy scenarios, ranging from 22% fulfillment of the RFS2 mandate to the full, 100% attainment of the goal. The ethanol and drop-in fuel production levels analyzed (22%, 31%, 50%, and 100% of the RFS2 mandate) reflect the possibility that the mandate may be only be partially fulfilled, or, alternatively, the growth of the industry over time.
Changes in the economy are driven by biofuel facility demand for feedstock, which impacts the distribution of land allocated to conventional crop production (
The core components of the IMPLAN model was used to structure these economic impacts. There were 43 industrial sectors identified for the pyrolysis and biochemical-cellulosic fuel technologies using the 2010 IMPLAN data base. There are 55 economic regions according to the Bureau of Economic Analysis (BEA) definitions defining the study area. A separate impact statement is generated for each BEA unit, conditional on the number of facilities locating in a region and the feedstock required by these facilities.
As depicted in
Credits from Renewable Energy Identification Number (RINS) are “pure profit” to facilities and proprietor’s income (+C). They occur as a result of product output. Therefore, it is assumed the RIN value is spread through- out the regional economy in a fashion similar to manner in which proprietor income is spent. The costs of transportation and feedstock storage requirements are considered as operating costs and feed into the economy.
Least-cost facility locations are determined using the site facility locator model, BioFLAME (
BioFLAME’s costs minimization routine integrates GIS functions and database management operations to determine the facility locations minimizing the plant-gate cost of feedstock (
Key location determinants used by BioFLAME include transportation networks and the distribution of biomass from agriculture and forests. The distribution of feedstock materials from cropland was updated using the 2010 USDA NASS [
are estimated in similar fashion using regional crop budget to generate a cost surface [
The economic indicators analyzed are changes in employment, total value added to the economy (TVA), total industry output (TIO), and labor income. Using the economic data of the 440 industries represented in the 2010 IMPLAN data files, we estimated the 2010 baseline TIO to 6.17 trillion dollars, with an aggregate value added from economic activity of 3.59 trillion $USD. Total regional employment was 46.51 million jobs, with a labor income of 2.26 trillion $USD. These indicator levels represent the baseline.
Two measures are used to analyze how these indicators change. The first measure is the percent change from the baseline, aggregate economy of the SE region. The second measure evaluates changes in employment and total value added on a per Btu (energy) equivalent. This metric allows for a relative comparison between biochemical and pyrolysis pathways and their respective impacts on feedstock demand, land use, and the regional economy.
A nonparametric bootstrap procedure is used to simulate the distributions of the economic impact indicators to compare point estimates to the baseline economy. The bootstrap is useful when the moment generating function of a distribution is unknown, or when the variance calculation of a function is intractable [
Let
lower case z an indicator of economic activity in region i. The percent change following a shock to the economy is
1) Resample with replacement indicators
where “*” indicates the indicator is randomly selected from the observed distribution, and assuming each BEA region has an equally likely chance of selection.
2) From the resampled set
therefore
3) Determine the aggregate indicator,
4) Determine the aggregate indicator,
ing the economic shock.
5) The statistic
A similar resampling strategy was used to simulate the distributions of the impact ratios; employment and value added, per Btu produced from biochemical and pyrolytic technologies and the feedstocks analyzed at each policy target. The medians, lower 5%-, and upper 95%-tiles are estimated for each simulated distribution for comparative purposes.
Aggregate economic impacts were modest for all target levels analyzed. Assuming that 100% of the 10.5 bgy mandate was achieved, aggregate economic impacts were typically less than 1% for each of the indicators (
As indicated by the simulated distributions, the shocks are significantly different from the baseline economy (
Impacts are expected to be larger in areas where economic linkages to other sectors of the economy are denser. In addition, areas with stronger ties to conventional agricultural production (typically rural areas away from urban centers) could experience negative impacts on the agricultural sector as traditional crops are displaced by feedstock production. The pyrolysis facilities are, ceteris paribus, smaller in scale compared with the biochemical
100% target | Labor | Total value | |||
---|---|---|---|---|---|
Fuel/technology/feedstock: | TIOa | Jobsb | Incomec | Addedd | |
Ethanol/Biochem./Switchgrass | 0.77% | 0.97% | 0.80% | 0.84% | |
Ethanol/Biochem./SRWC | 0.65% | 0.81% | 0.70% | 0.72% | |
Drop-in (pyrolysis)/SRWC | 0.61% | 0.76% | 0.65% | 0.65% | |
Drop-in (pyrolysis)/Switchgrass | 0.68% | 0.81% | 0.70% | 0.72% | |
50% target | Labor | Total value | |||
Fuel/technology/feedstock: | TIOa | Jobsb | Incomec | Addedd | |
Ethanol/Biochem./Switchgrass | 0.40% | 0.50% | 0.41% | 0.43% | |
Ethanol/Biochem./SRWC | 0.34% | 0.42% | 0.33% | 0.36% | |
Drop-in (pyrolysis)/SRWC | 0.31% | 0.40% | 0.33% | 0.33% | |
Drop-in (pyrolysis)/Switchgrass | 0.34% | 0.41% | 0.35% | 0.36% | |
31% target | Labor | Total value | |||
Fuel/technology/feedstock: | TIOa | Jobsb | Incomec | Addedd | |
Ethanol/Biochem./Switchgrass | 0.24% | 0.31% | 0.25% | 0.26% | |
Ethanol/Biochem./SRWC | 0.20% | 0.25% | 0.20% | 0.21% | |
Drop-in (pyrolysis)/SRWC | 0.20% | 0.25% | 0.20% | 0.20% | |
Drop-in (pyrolysis)/Switchgrass | 0.22% | 0.26% | 0.22% | 0.23% | |
22% target | Labor | Total value | |||
Fuel/technology/feedstock: | TIOa | Jobsb | Incomec | Addedd | |
Ethanol/Biochem./Switchgrass | 0.17% | 0.22% | 0.18% | 0.19% | |
Ethanol/Biochem./SRWC | 0.16% | 0.19% | 0.16% | 0.17% | |
Drop-in (pyrolysis)/SRWC | 0.16% | 0.20% | 0.16% | 0.16% | |
Drop-in (pyrolysis)/Switchgrass | 0.16% | 0.19% | 0.16% | 0.17% |
conversion system, requiring less biomass for conversion. Less biomass demanded translates into compact feedstock sheds, and potentially less economic impact. For the biochemical conversion process using SRWC, the displacement of conventional crops by SRWC is less extensive (compared with switchgrass), translating into lower (in relative magnitude) economic impacts for TVA and employment. However, these differences are indistinguishable at 22% and 31% of the regional target.
Across all technologies, there were 46 to 58 jobs generated per 100 bil. Btu under the different target levels (
Ratios of total value added to the regional economy and Btu’s (100,000 s, 100 K) were evaluated for each the technology/feedstock combinations at 22%, 31%, 50%, and 100% achievement of the 10.5 bgy RFS2 target (
--------------------------Percent of 10.5 bgy mandate achieved------------------------ | ||||
---|---|---|---|---|
22% | 31% | 50% | 100% | |
Employment/100 bil. Btu | ||||
Fuel/technology/feedstock: | ||||
Ethanol/Biochem./Switchgrass | 57.74 | 57.8 | 58.36 | 55.95 |
Ethanol/Biochem./SRWC | 48.54 | 47.46 | 48.41 | 46.82 |
Drop-in (pyrolysis)/SRWC | 53.36 | 46.31 | 46.27 | 44.24 |
Drop-in (pyrolysis)/Switchgrass | 49.41 | 48.98 | 46.87 | 46.66 |
Total value added/100,000 Btu | ||||
Fuel/technology/feedstock: | ||||
Ethanol/Biochem./Switchgrass | 3.77 | 3.76 | 3.84 | 3.75 |
Ethanol/Biochem./SRWC | 3.19 | 3.09 | 3.2 | 3.13 |
Drop-in (pyrolysis)/SRWC | 3.31 | 2.91 | 2.92 | 2.89 |
Drop-in (pyrolysis)/Switchgrass | 3.36 | 3.33 | 3.18 | 3.23 |
the distribution of facilities. The TVA 100 K∙Btu−1 generated by the biochemical technology option (switchgrass feedstock) dominated the other technologies, ranging between $3.75 100 K∙Btu−1 (at 100% of the mandate) to $3.84 100 K∙Btu−1 (at 50% of the 10.5 bgy target).
As the variability of the simulated employment Btu−1 and TVA 100 K∙Btu−1 distributions suggest, it is difficult to discern a clear advantage with respect to jobs or total value added to the economy generated by each technology/feedstock combinations (
From the perspective of investors, the decision to select a location is influenced by the expected cost savings arising from the external economies that emerge from the up- and downstream linkages which define the bioenergy sectors. From the perspective of rural communities, local planners require timely information about which community attributes they can leverage to attract and retain businesses and the jobs they support as bioenergy sectors develop. This research analyzed 1) the economic linkages between the agriculture sector and
two emerging biomass-bioenergy sectors-biochemical production of ethanol, and pyrolysis-using switchgrass and the short rotation woody crop, poplar, and 2) the potential impact of these activities on rural economies given federal mandates encouraging the production of advanced biofuels.
Information about suitability of potential locations for conversion facilities will be helpful for siting facilities and determining feedstock sheds. Factors influencing the location of conversion facilities include assessment of potential feedstock availability and logistics costs as investors seek least-cost sites. Local comparative advantage will be driven by skilled work force availability, access to capital, and infrastructure. But the socioeconomic and environmental impacts of an expanding second generation fuels complex remain unclear as new demands on agriculture and forestlands arise, and as primary and secondary road traffic increases.
While the net effects of the technologies and feedstock analyzed in this paper are positive for the regional economy, the effects of spatial competition for limited feedstock resources have on job, income, and business establishment growth will likely vary, depending on the competiveness of a county and its ability to leverage local resources or its connection to wider economies. The findings of this research are driven by primary data sources, but also key assumptions about the capacity, scale, and efficiency of the technologies evaluated. As these technologies mature, the relative costs of the technologies may change. These changes would impact the economic indicators analyzed, and potentially rankings of the regional impacts the technologies have on local economies.
Research was supported by the United States Department of Agriculture-National Institute of Food and Agriculture grant awards #11025775 and #2011-68005-30410, which supports the Integrate Biomass Supply System research. Funding was also made available by the United States Geological Survey and the University of Tennessee Water Resources Research Institute Program. The authors thank anonymous reviewers for comments and suggestions. The views are those of the authors.
Dayton M. Lambert,Burton C. English,R. Jamey Menard,B. Wilson, (2016) Regional Economic Impacts of Biochemical and Pyrolysis Biofuel Production in the Southeastern US: A Systems Modeling Approach. Agricultural Sciences,07,407-419. doi: 10.4236/as.2016.76042