Policy Analysis pubs.acs.org/est
Regional Allocation of Biomass to U.S. Energy Demands under a Portfolio of Policy Scenarios Kimberley A. Mullins,*,†,‡,∥ Aranya Venkatesh,‡,⊥ Amy L. Nagengast,‡ and Matt Kocoloski§ †
Department of Engineering & Public Policy, ‡Department of Civil & Environmental Engineering, and §Tepper School of Business, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh Pennsylvania 15213, United States S Supporting Information *
ABSTRACT: The potential for widespread use of domestically available energy resources, in conjunction with climate change concerns, suggest that biomass may be an essential component of U.S. energy systems in the near future. Cellulosic biomass in particular is anticipated to be used in increasing quantities because of policy efforts, such as federal renewable fuel standards and state renewable portfolio standards. Unfortunately, these independently designed biomass policies do not account for the fact that cellulosic biomass can equally be used for different, competing energy demands. An integrated assessment of multiple feedstocks, energy demands, and system costs is critical for making optimal decisions about a unified biomass energy strategy. This study develops a spatially explicit, best-use framework to optimally allocate cellulosic biomass feedstocks to energy demands in transportation, electricity, and residential heating sectors, while minimizing total system costs and tracking greenhouse gas emissions. Comparing biomass usage across three climate policy scenarios suggests that biomass used for space heating is a low cost emissions reduction option, while biomass for liquid fuel or for electricity becomes attractive only as emissions reduction targets or carbon prices increase. Regardless of the policy approach, study results make a strong case for national and regional coordination in policy design and compliance pathways.
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INTRODUCTION The potential for widespread use of domestically available energy resources, in conjunction with climate change concerns, suggest that biomass may be an essential component of U.S. energy systems in the near future. Until recently, corn ethanol production for transportation was the focus of biomass energy policy in large part because of crop availability and commercial feasibility, although electricity and heating applications have been historical markets for woody biomass.1 With support from government subsidies, the corn ethanol industry has matured. At this point, corn ethanol subsidies have lapsed, and corn ethanol consumption mandated by the Renewable Fuel Standard updated by the Energy Independence and Security Act (EISA)2 will plateau at 15 billion gallons per year through 2022. In contrast, ethanol from cellulosic sources (e.g., switchgrass or wood) will see continued federal subsidies (e.g., production tax credit) and are candidate feedstocks for the second-generation volume mandate categories, which reach 16 billion gallons in 2022. Cellulosic feedstocks also present an opportunity for biomass use to expand beyond the transportation sector and into electricity generation and residential heating;3−6 it can be converted to ethanol at biorefineries, densified, and cofired with coal for electricity production or pelletized and burned in stoves to heat homes. Currently, parallel, independent efforts to increase the use of cellulosic biomass energy are underway in both the trans© 2014 American Chemical Society
portation and electricity sectors. In addition to the EISA mandates, 30 states with renewable portfolio standards (RPS) include biomass as a renewable energy source or have specific biomass usage mandates for electricity production, though they are small percentages.7 Competing feedstock uses encouraged by independent policy initiatives could result in the suboptimal allocation of these resources. This suboptimality could be exacerbated based on the regional versus national perspective of these policies. Consider a case where cellulosic ethanol is produced in the southeastern U.S. to satisfy federal ethanol mandates. It might be preferable from a cost or greenhouse gas emissions perspective to instead cofire that biomass with coal at a electricity generating station in the region. An integrated assessment of feedstock availability, demand for various end uses, costs, and potential emissions reductions would help policy makers better understand the biomass landscape and perhaps better design a biomass energy strategy that best uses the resources available to achieve policy goals. Previous studies have explored biomass energy, but they tend to focus on single or multiple feedstocks allocated to an individual or a limited subset of end uses. Wahlund et al.,8 Received: Revised: Accepted: Published: 2561
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Wilson et al.,3 Zhang et al.,9,10 Eriksson et al.,11 and Spatari et al.12 each address some choice between feedstocks or end uses, together covering all of the feedstocks and end uses covered in this study. Kocoloski et al.,13 Elia et al.,14 Morrow et al.,15−17 and You et al.18 additionally construct spatially explicit optimization models to choose feedstock type/end use, as well as infrastructure location. However, none of these studies considers as many feedstocks and as many end uses simultaneously in a spatially explicit way, as are modeled in this study. Steubing et al.6 consider the optimal use of several biomass feedstocks to substitute fossil energy technologies in Europe, which is broader than the previously listed studies, but the authors use a ranking method to identify preferred allocation strategies with a nonspatial model. See Table S1 in the Supporting Information (SI) for a summary table of the referenced studies. This study builds on these studies in developing a spatially explicit, best-use framework for model year 2020 that optimally allocates cellulosic biomass feedstocks to competing energy end uses (heating, transportation, electricity) based on minimizing total system costs. Comparison of where biomass is used, what type is used, and for what purpose illuminates what biomass energy policy across different jurisdictions should encourage. While this study goes one step farther than the previously cited studies in considering multiple feedstocks and competing end uses in a spatially explicit model, it does not reach the same level of complexity or scope as comprehensive energyeconomic models such as MARKAL19,20 or the National Energy Modeling System (NEMS).21 Rather than trying to develop an exhaustive description of the energy landscape under various policy initiatives, the primary objective is to assess the relative usage of each feedstock, relative contribution to each end use, and identify spatial trends in biomass usage.
sum of which equals the total system cost. System-wide costs include biomass production, biomass transport, conversion at processing facility (for example, an ethanol plant), product distribution and equipment for end use, if any (such as biomass pellet stoves for heating). Figure S1 in the SI depicts the life cycle stages used to calculate total system costs. The spatial relationship between biomass feedstock supplies and energy demands is essential to this supply chain model. As suggested by Kocoloski et al.,13 optimally locating biomass facilities is important in minimizing infrastructure costs and product costs. Biomass feedstock availability and energy demand vary geographically across the U.S. and are not always colocated (see biomass supply maps in SI Figure S7 and spatially distributed demand maps in Figures S2−S5 in the SI). Therefore, using national level aggregated supply and demand characteristics cannot capture regional trade-offs in the system. This study uses the spatially explicit framework developed in Kocoloski et al.,13 similar to the framework used by Elia et al.14 Relevant biomass supply and energy demand data were collated and used at various geographic levels, as listed below. Further explanation on the definitions and spatial scales of these categories can be found in the SI: Biomass feedstock supply, agricultural statistical district (ASD); coal plant location, absolute locations or county; gasoline demand, metropolitan statistical area (MSA); heating demand, census region, state and county level data aggregated to ASD level. Energy Demand Data. Energy demands for transportation fuel, electricity and heating are fixed inputs to the model; they must be satisfied though some set of fuels chosen by the model. Projected gasoline demand by MSA13 represents transportation energy demand. Cellulosic ethanol can meet some fraction of this gasoline demand in any MSA limited by cost, available biomass supply, or the ethanol blend wall (assumed to be 15% in 2020). In the electricity sector, biomass has the potential to displace coal. A fraction of the coal used as fuel in existing steam turbine plants can be substituted by either cofiring biomass with coal or by using 100% biomass, by retrofitting the boiler to allow biomass input.17 Retrofitting results in a small (assumed 10%) loss of efficiency. The costs of retrofitting are discussed in the section that follows. The locations of existing coal plants by county (see in SI Figure S2) are reported in the Environmental Protection Agency’s Emissions and Generation Resource Integrated Database (eGRID),23 along with nameplate capacity and annual fuel use. A number of different heating fuels such as natural gas, fuel oil, electricity, liquefied petroleum gas (LPG), kerosene, and wood are used in homes across the country for purposes that include space heating and water heating. Using data from the Energy Information Administration (EIA) State Energy Data System,24 the 2005 Residential Energy Consumption Survey (RECS),25 and the American Community Survey,26 heating demands were estimated for various ASD regions. Further details regarding energy demands, including a summary of data sources, level of aggregation, and geographic regions, are presented in the SI. In addition, simplified calculations that illustrate the heating energy demand estimation method are also presented. Biomass Supply and Costs. Feedstock supplies and costs were estimated using a National Academies (NAS) report on biomass energy27 and the spatial distribution of biomass was taken from the Policy Analysis System (POLYSYS) model (described further the SI).28 The POLYSYS data set used in
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DATA AND METHODS Three cellulosic feedstocks are included in this study: an herbaceous energy crop (switchgrass), a crop residue (corn stover), and a wood cellulosic source (forest thinnings and residues). Each feedstock varies in geographic availability and processing costs. Each of these opportunity fuels can replace incumbent fossil fuels to meet energy demands in three sectors: ethanol for gasoline in the transportation sector, densified biomass (briquettes) for coal in the electricity sector, and pelletized biomass for various fuels in home heating. The types of home heating fuels used are regionally dependent but are predominantly natural gas, electricity, and fuel oil. These three sectors are particularly important since they collectively account for 75% of total U.S. primary energy use.22 The energy demands are fixed and must be met by some combination of fuel types (biobased or otherwise). The model chooses the quantity, source, and type of cellulosic feedstock that should be processed and delivered to satisfy these energy demands to minimize total system costs. Changes in greenhouse gas (GHG) emissions associated with fossil fuel replacement are tracked by the model and are used to calculate emissions reductions for the policy scenarios. This study focuses on potential GHG reductions because of policy focus on that impact; however, there can be additional benefits to switching from fossil to biomass energy, including positive impacts on water quality, biodiversity, and criteria air pollutants from power generation. System Boundary. Each of the life cycle stages involved in the biomass energy system modeled has associated costs, the 2562
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GHG emissions for electricity used in each state were estimated using data reported by the EIA,39 and the results are presented as a range in SI Table S5. For biomass pellet/briquette production, electricity is the primary energy source and therefore emissions from pellets and briquettes are subject to the localized GHG emission factors. Optimization Model Formulation. A mixed integer linear programming (MILP) model was developed to minimize total system costs of using biomass for different energy demands. The objective function that represents the total system cost is the sum of biomass feedstock costs, feedstock transport costs, feedstock processing costs (such as conversion to ethanol), product transport costs, end-use capital costs of using the biomass (for example, a pellet stove, or retrofitting a coal plant to allow cofiring), and costs of incumbent fuels used to supplement biomass. The primary constraint ensures that all energy demands (transportation, heating, and electricity) are met using biomass or incumbent fuels and technologies. Supply constraints ensure that the quantity of biomass utilized in a particular region does not exceed total biomass availability in that region. Binary variables for each ASD are used to represent whether or not an ethanol refinery is built in that ASD; the construction decision is also chosen as part of the optimization routine. Additional information on the optimization formulation is presented in the SI. Scenario Definitions. Using the aforementioned problem formulation, two reference cases and three policy scenarios are modeled to explore how biomass may be utilized under commonly proposed policy mechanisms, while minimizing total system costs. For comparisons among scenarios, metrics for total system costs and GHG emissions are reported. Scenario 0: All Fossil Fuels. This scenario uses only incumbent fossil fuels, such as coal, gasoline, natural gas, etc., to meet energy demands. No cellulosic biomass was introduced in this scenario. Results were used to compare potential GHG emission reductions through the use of biomass-based alternatives. Scenario 1: Base Case. In the base case, all energy demands were met using some combination of incumbent and biomassbased fuels, while minimizing total system costs. No biomass supportive policies are implemented in this scenario. Some of the critical base case parameters are presented in Table 1. Results are used as a benchmark to compare potential GHG emissions reduction strategies through the use of various policy mechanisms to increase biomass usage. Scenario 2: Carbon Price. Under this policy scenario, carbon emissions have an economic value. As a consequence, this policy penalizes carbon intensive fuels such as coal and gasoline, providing an advantage to the less GHG-intensive biomass-based fuels. A range of carbon prices ($0−100 per ton CO2e) was included in the model, which affect both fossil and biofuel costs based on their respective life-cycle GHG emissions (see SI Table S5). This range was selected to accommodate the wide range of estimated social costs of carbon emissions, presented by Tol,41 though the present market value of carbon in the European Union, for example, is at the low end of this range.42 Functionally, these carbon prices are modeled as additional costs in the objective function. Results for $100 per ton CO2e are discussed in detail in further sections and are referred to as scenario 2. Scenario 3: Greenhouse Gas Emissions Cap. Under this policy scenario, the total greenhouse gas emissions from
this study was provided by the U.S. EIA (as used in Kocoloski et al.13), and contains biomass supply data for prices ranging from $20 to $100/dry ton. In this study, feedstock supply under average production levels at a midrange $50/dry ton in 2020 is used to define the amount of each type of biomass available in each ASD, and the market price is taken from the NAS report. The POLYSYS prices were not used because they are less realistic than the more recently estimated NAS prices. A total 140 million tons of switchgrass, 110 million tons of corn stover and 120 million tons of wood are available, as suggested by the NAS report. Regional supplies of switchgrass, corn stover, and wood at this price are presented in Figure S7 in the SI. Transportation activities are modeled after methods used in Kocoloski et al.13 Biomass is shipped using truck and rail infrastructure (discussed further in the SI) from the harvesting location to the production facility (assumed at the destination ASD centroid), and finally to the end use location (i.e., MSAs for transportation, coal power plants for electricity and ASD for home heating). The costs of biomass feedstock transport, capital, operations and maintenance expenditures and product transportation costs are based on literature sources summarized and used in Kocoloski et al.13 When biomass supplies are insufficient to satisfy transportation demand, gasoline is used to make up the difference. Gasoline costs were assumed to be $3.4 per gallon based on EIA Annual Energy Outlook (AEO) projections29 for 2020. For electricity generation, biomass supply chain costs include transportation from the ASD centroid to a briquette production facility, followed by transport to the closest coal plant. At the coal plants, biomass can be cofired at 2%, 10%, or 20% by energy.15 Alternatively, biomass can be the sole feedstock if the coal facility is completely retrofitted. Each degree of modification has an associated capital cost, identified by Morrow et al.16 and summarized in Table S3 the SI. In scenarios where biomass fails to meet the fixed demand, or the boiler is not chosen to be retrofitted, coal is the secondary supply at $2.2 per MMBtu (based on AEO projections).29 For heating, biomass is harvested and pelletized before being transported to end-users. Pelletization costs vary widely in literature, from $20 per ton to $160 per ton (including feedstock, shipping and handling costs).9,30−32 In this study, a pelletization cost of $90 per dry ton was used3 (with sensitivity to pelletization costs presented in SI Figure 9). The final item included in the biomass supply chain is the cost of biomass stoves used in households. The necessary heating stove capacity/size based on energy demand was estimated using a method suggested by researchers at the University of Maine33 and is presented in detail in the SI. Boiler costs of 300 $/kW obtained from Azar et al.34 were used to estimate capital costs, which were then annualized. Annual operations and maintenance costs were added, assumed to be 4% of capital costs. Pellet stove efficiency was assumed to be 90%, as reported by the U.S. Department of Energy (DOE) Energy Savers program.35 In scenarios where biomass failed to meet heating demand, incumbent fuels such as natural gas and fuel oil are used, at costs based on AEO projections29 (see SI Table S2). Greenhouse Gas Emissions. Although there is uncertainty in GHG emissions from both biomass- and fossil-based fuels36−38 only the mean values of emissions specific to feedstock and intermediate products are used in the model to maintain computational tractability. In terms of fossil fuels, there also is notable regional variability in electricity emissions. 2563
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provide emissions reductions beyond 11%. The emissions cap is modeled as an additional constraint in the optimization formulation. Results for 11% reduction are discussed in detail in subsequent sections. Scenario 4: Ethanol Volume Mandate. This policy mechanism, like the Renewable Fuel Standard (RFS2), requires ethanol be used in the transportation sector. Currently, ethanol has a 10% share of gasoline consumption (by volume) in the U.S.,43 a consequence of an EPA rule that existing gasoline engines can run on a fuel blend with no more than 10% ethanol (termed E10). The EPA concluded that gasoline vehicles of model year 2001 and onward can run on a blend up to 15% (E15). In this model, up to 5% additional ethanol capacity added by the transition from E10 to E15 vehicles can be filled by cellulosic ethanol. In the optimization model formulation, the volume mandate is included as an additional constraint. Perhaps the key model limitation is that no competing renewable energy technologies are choices in the model. For example, were electric vehicles to play a large role in the 2020 transportation sector, and wind and photovoltaic power be electricity alternatives, the market for bioelectricity might increase or decrease, the market for cellulosic ethanol would likely decrease. Biomass usage patterns would certainly change. Shifts would also result if geothermal heat in the residential sector were an option, thereby affecting a robust result of this study. The results presented here, then, provide insight as to how biomass mandates across sectors and feedstocks interact, but not how biomass might be used in a market that includes these alternative, nonbiomass technologies. Model limitations are discussed further in the SI.
Table 1. Base Case Parameters Used in the Optimization Model parameter switchgrass supply corn stover supply woody biomass supply co-firing capital cost ethanol capital cost
value
units
source
feedstock production 164 million tons 112 million tons 124 million tons
NAS report27 NAS report27 NAS report27
100− 2000a 9
technologies $/kW biomass
ethanol yield pelletization cost biomass boiler capital cost
73−79b 90 300
switchgrass supply corn stover supply woody biomass supply gasoline
118 86 72
$/gallon EtOH capacity gallons/dry ton $/dry ton $/kW-thermal biomass prices $/dry ton $/dry ton $/dry ton
3.38
$/gallon
coal
2.16
$/MMBtu
natural gas
11.25
$/MMBtu
carbon tax ethanol blend wall
0 15%
policies $/Mg CO2e by volume
Morrow16 Hamelinck et al.40 Hamelinck et al.40 Wilson et al3 Azar et al.34
NAS report27 NAS report27 NAS report27 AEO 2011,29 year 2020 AEO 2011,29 year 2020 AEO 2011,29 year 2020 (assumption) (assumption)
a Range represents costs for different levels of cofiring, detailed in SI Table S3. bRange represents ethanol yield for different biomass feedstock types.
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RESULTS For the base case, biomass was used as fuel to meet heating demand predominantly in the northeastern and western United States, as presented in Figure 1A (note that the results in Figure 1 represent biomass used, rather than biomass sourced in a particular region). Much of available biomass remains unused, particularly in the center of the country, as biomass is not cost-
transportation, electricity, and heating sectors cannot surpass some specified total, or a cap, defined in terms of a reduction from a benchmark value. The required emissions reductions across the three end use sectors were assessed from 0 to 11% of the base case. There is insufficient biomass resource available to
Figure 1. Scenario results of regional biomass use to meet different energy demands. Results represent biomass used rather than sourced in a particular region. Color indicates which energy demand (if any) uses the most biomass, which does not necessarily mean the other end uses see zero biomass utilization. 2564
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competitive with coal or other incumbent fuels such as natural gas or gasoline. To achieve significant reductions in GHG emissions from the base case, a more expensive suite of fuels is needed; cheap coal or gasoline are replaced with more expensive biomass. With a 2% increase in total system costs ($581 to $591 billion) from the base case, a $100/ton carbon price (scenario 2) would curb GHG emissions by 3%, but will result in higher total expenditure due to carbon price payments (SI Table S5). An even larger investment of 10% over the base case total system cost ($581 to $639 billion) would result in an 11% decrease in GHG emissions with GHG cap policy. By implementing different climate policies, regional biomass utilization changes both geographically and by end use when compared to the base case (Figure 1). In general, GHG emissions from gasoline, coal, and heating fuels (such as fuel oil and natural gas) are higher than their biomass-derived alternatives. Therefore, when a carbon price is introduced, these incumbent fuels pay a higher relative penalty for their emissions, thereby making biomass cost-competitive. For example, at a carbon price of $100/ton (Figure 1B), biomass previously unutilized in the Northwest U.S. is used to meet heating demand, and previously unused biomass in the Midwest U.S. is used for ethanol production. At this carbon price, all available wood and 75% of available corn stover is utilized (see Figure 2A and SI Table S5). Wood is the least expensive feedstock, and the most cost-effective way to reduce GHG emissions, followed by corn stover and then switchgrass. Switchgrass is only utilized to meet the emissions reduction target when both woody biomass and corn stover have been used completely (see Figure 2A), and is never cost competitive in other scenarios. The cost effectiveness of wood is due, in part, to wide availability (see SI Figure S7) and high energy density. These results follow from prices and emissions in Table 1 and SI Table S5. The 11% emissions reductions target represents an aggressive scenario where all biomass supply is utilized (see Figures 1C and 2A), mainly for electricity production. The target of 11% was selected because emissions reductions greater than 11% were not possible in this model due to insufficient supply of biomass and limited amounts of coal to cheaply displace at power plants. Some biomass is utilized for home heating in this scenario, mostly in the Northeast (Figure 1C). This result suggests that biomass transport distances from ASDs in this region to the nearest coal plants are prohibitively far. For the ethanol volume mandate scenario, cellulosic ethanol cannot be used to meet more than 5% of gasoline energy demand because of the E15 blend wall constraint. Note that existing corn ethanol occupies 10% of the allowed ethanol volume (E15 blend wall), so at most 5% is available to be filled by cellulosic ethanol. Under this modeled policy mandate, cellulosic ethanol is used in regions of the Midwest, Southeast, and Northwest (Figure 1D). Throughout the modeled scenarios, all three cellulosic feedstocks are used for home heating. Moreover, biomass meets up to 29% of total heating energy demand, 16% of coal energy demand for electricity generation and cellulosic ethanol meets 5% of transportation at most, as shown in Figure 2B (and summarized in SI Table S5). Sensitivity Analysis. This section explores how the biomass allocation between the three end uses is affected by parametric changes in four key input parameters: carbon price, emissions reduction target (as a percent decrease from the base
Figure 2. Summary of results (A) biomass utilization by mass and (B) percentage of energy demand met by biomass and percentage reductions in CO2e emissions for all scenarios. Percent emissions reductions are from the base case (scenario 1). Results are also tabulated in SI Table S5.
case), gasoline price, and biomass stove price. These specific variables were chosen because the carbon price and emissions reduction targets represent the weight given to GHG emissions within the decision framework, gasoline price is highly influential in bioethanol use, and stove price is highly influential in home heating fuel choice. A further sensitivity analysis of the pelletization cost is included in the SI (Figure S9). The use of biomass for home heating is consistent across most sensitivity cases tested because it is preferred over fuel oil even without a carbon price or an emissions reduction target; increasing these variables makes other uses of biomass cost competitive, or necessary for emissions reduction, but have little effect on the localized use of wood for heating. Carbon Price Sensitivity. At low carbon prices, biomass is predominantly used for home heating. Up to a price of $60/ ton, no biomass is used for ethanol or electricity (Figure 3A). As the carbon price is increased to $100/ton, biomass-derived alternatives become increasingly cost-competitive with the incumbent technologies due to their lower emissions. The quantity of biomass used at this price is almost twice the amount used in the base case. All the available woody biomass and 75% of available corn stover is used for electricity, transportation and heating (Figure 2, scenario 2). Gasoline price. The optimal allocation of biomass is highly sensitive to gasoline price, and gasoline prices can be highly volatile. Only when the gasoline price increases above about 2565
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Figure 3. Sensitivity of biomass utilization to (A) carbon price, (B) GHG emissions reduction, (C) gasoline price, and (D) biomass stove price.
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DISCUSSION The projected distribution of cellulosic biomass energy across these three sectors by the AEO in year 2020 is 25% in the form of ethanol, 40% in electricity production, and 35% home heating (although this only considers wood as a heating fuel).29 Crane et al.44 explore a national RPS requirement of 25%, and conclude that 37% of renewable electricity generation comes from biomass, or roughly 9% of national production. Including competing end uses instead of focusing exclusively on one sector, results from this study find roughly 2% of all electricity is supplied by biomass (taking AEO 2020 projection that about one-third of generation comes from coal) with an 11% emissions reduction target. With most of the available biomass used for heating in the modeled scenarios, less is available for electricity production. The result that home heating is costeffective in the Northeast echoes findings by Wilson et al.,3 who conclude that replacing fuel oil with biomass can be very costeffective as a greenhouse gas mitigation strategy. They, and others,45 also find that using biomass in place of coal offers greater GHG reductions but results in greater costs than other biomass applications. Policy design is a multicriteria, multiobjective decisionmaking process whose scope is much wider than the system presented here. Discriminative metrics, such as those reported in this study (emissions and costs), can at least be useful in identifying comparative advantages of different policy alternatives. These metrics help inform policy preferences, to underline those mechanisms that may be aligned with the decision-makers’ objectives. For greater emissions reductions, more expensive policies are required (e.g., GHG emissions cap). Going from the base case to a $100/ton carbon price yields a 3% decrease in emissions but requires 2% higher total system costs (see SI Table S5). Depending on whether the
$4/gal is biomass utilized for ethanol production (see Figure 3C). This is $0.6/gal more expensive than the base case price assumption. At a gasoline price of $5 per gallon, about 200 million tons of biomass are utilized for ethanol production, beyond which the blend wall is hit. In the model framework, electricity and transportation fuels are not substitutable, so increasing gasoline prices do not induce a shift to increased electricity generation from biomass. If biomass electricity were economically competitive (i.e., present) in the base case results, increasing gasoline prices would have affected the distribution of biomass use between the demands. Greenhouse Gas Emissions Cap Sensitivity. At low GHG emissions reduction targets, biomass is predominantly used for home heating. At a GHG emissions reductions target of 1%, some biomass is used for ethanol production: ethanol production from biomass peaks at a 5% emissions reduction target (Figure 3B). At 2% emissions reductions, biomass is used for electricity generation. The allocation of biomass to electricity increases as the emissions reductions target increases, replacing a less expensive but more GHG intense ethanol as the optimal end use of biomass. If emissions reductions are considered a secondary objective of the optimization model, the plot of optimal total system cost versus emissions reductions would represent a Pareto optimal curve (Figure S8 in the SI). Biomass Stove Price. To understand the impact on biomass utilization, biomass stove price was varied between 100 and 1600 $/kW-thermal (Figure 3D). Up to a price of 900 $/kW-thermal (three times more expensive than the value used in this study), utilizing biomass for space heating appears to be cost-competitive with incumbent fossil fuel technologies. 2566
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Environmental Science & Technology policy objective is to meet aggressive emissions reductions targets or not, different policy choices would be made. The use of all three feedstocks for home heating is a robust result across the various scenarios and sensitivity analysis cases (see Figures 2 and 3). This is particularly strong for the Northeast U.S., where fuel oil, which is expected to increase in price over time, is still used in many homes for heating. Overall, biomass is preferentially used in the heating sector, followed by the transportation sector, and finally in the electricity sector as model constraints are introduced which increase the favorability of biomass. Similarly, the biomass feedstocks have a utilization hierarchy (i.e., wood > corn stover > switchgrass) with increasing emissions reductions requirements. Despite high levels of biomass penetration in the energy demand sectors, only 11% reduction in system-wide GHG emissions can be expected at best, given projections of biomass supply. Finally, the total national aggregate GHG emissions reductions and costs results do not call attention to the differences in regional biomass utilization. The biomass utilization maps (see Figure 1) could be of more use to regional policy makers, since some clear trends are observed. For instance, much of the Northeast can benefit from using biomass as heating fuel. Therefore using biomass sourced in this region to produce ethanol consumed elsewhere in the U.S. is likely to be a suboptimal decision. Similarly, in all of the scenarios with more aggressive emissions reductions targets, results consistently suggest that biomass electricity is costcompetitive with coal in the southeastern U.S. This suggests that regional policy efforts are required to complement and supplement federal decision-making. For example, regional greenhouse gas trading schemes, such as the Regional Greenhouse Gas Initiative in northeastern states, and the associated carbon pricing can impact biomass markets substantially since the biomass market is likely to be within the trading jurisdiction. It is therefore important to consider relevant regional decisions to best enable an optimal deployment and use of resources.
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ACKNOWLEDGMENTS
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REFERENCES
The authors thank W. Michael Griffin, Jeremy Michalek, and the Green Design Reading Group at Carnegie Mellon for their valuable feedback. Funding was provided by the center for Climate and Energy Decision Making (SES-0949710), through a cooperative agreement between the National Science Foundation and Carnegie Mellon University, the USDA (NIFA Grant 2013-67009-20377) and the U.S. DOE (EERE Grant DE-EE0004397).
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ASSOCIATED CONTENT
S Supporting Information *
A mathematical formulation of the optimization model, further detail on the data sources and assumptions, sensitivity graphs not presented in the manuscript, data visualizations not included in the manuscript and spatial allocation methodology are included in the Supporting Information. This information is available free of charge via the Internet at http://pubs.acs.org/.
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Policy Analysis
AUTHOR INFORMATION
Corresponding Author
*Phone: 412-999-0916. Fax: 612-624-3005. E-mail: kmullins.
[email protected]. Present Addresses ∥
K.A.M.: Bioproducts and Biosystems Engineering, University of Minnesota, St. Paul, MN 55108. ⊥ A.V.: ExxonMobil Research and Engineering, Annandale, NJ 08801. Author Contributions
K.A.M. and A.V. contributed equally to this work. Notes
The authors declare no competing financial interest. 2567
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Policy Analysis
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