Economic impacts of expanded woody biomass utilization on the bioenergy and forest products industries in Florida

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Economic impacts of expanded woody biomass utilization on the bioenergy and forest products industries in Florida
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Economic Impacts of Expanded Woody Biomass Utilization on the
Bioenergy and Forest Products Industries in Florida



Sponsored Project Final Report to Florida Department of Agriculture and Consumer Services--
Division of Forestry


By Alan W. Hodges, Thomas J. Stevens and Mohammad Rahmani


University of Florida, Institute of Food and Agricultural Sciences
Food and Resource Economics Department
Gainesville, FL


Revised February 23, 2010








Executive Summary


This study evaluated the economic impacts in the state of Florida from expanded use of biofuels
under selected policies and incentives, as mandated by the Florida legislature in 2008 (HB 7135). The
study focused on use of woody biomass fuels for electric power generation, since this is a mature
technology that is poised to rapidly expand under enabling legislation.

The analysis was conducted using Input-Output analysis and Social Accounting Matrices (I-O/SAM)
for Florida, together with a Computable General Equilibrium (CGE) model of the state's economy. The
Impact Analysis for Planning (IMPLAN) Professional software and associated databases (MIG, Inc.)
provided regional information on industry output, value added, employment, personal income,
commodity supply and demand, state-local and federal government taxes and spending, capital
investment, business inventories, and domestic and foreign trade. The I-O/SAM model was used to
generate a snapshot of the Florida economy that served as the starting point for implementation of
the CGE model, which finds a solution where all markets are in equilibrium, i.e. supply equals
demand. The model was customized to reflect the makeup of the forestry sector (timber production,
logging and support services), wood products manufacturing (sawmills, pulp and paper, etc.), and use
of biomass fuels as a substitute to fossil fuels (coal, natural gas, oil) for electric power generation. It
was assumed that biomass fuels could be provided from domestic and international imports as well
as Florida resources, since commodity trade is a feature of the CGE model. Forestry sector
production is assumed to include sources such as urban wood waste, short rotation energy crops,
and logging residues, as well as merchantable timber resources.

The impact of increasing biomass fuel supply for electric power generation was simulated over a
range of 1 to 80 million green tons annually, at an average price of $30 per ton. The upper end of this
range represents approximately 26 percent of current electricity production in Florida, and about 21
percent of projected generation in the year 2025. These levels can compared to a proposed
Renewable Electricity Standard, which would mandate a certain minimum percentage of renewable
fuels for electric power sales to final consumers by a given date. Simulations were also conducted to
test the effect of a $0.010 or $0.011 per kilowatt-hour state or federal renewable electricity production
tax credit, and a 100 percent federal subsidy for biomass fuel producers under the Biomass Crop
Assistance Program (BCAP). Assumptions about mobility of capital to meet changes in industry
output and intermediate commodity demand were tested with different model settings.

It was estimated that increasing biomass use for electric power generation would bring about a
relatively small increase in Gross Domestic Product (GDP) of Florida, overall employment, and state
government revenues, while modestly decreasing imports of fossil fuels. At the biomass supply level
of 40 million tons, with capital assumed to be mobile, GDP would increase by 0.32 percent above the








base level, representing $2.2 billion. Output or sales of the forestry sector would be increased
dramatically, about 69 percent above current levels, to meet new demand for woody biomass fuels.
Output of the electric power sector would decrease by up to 0.33 percent as a result of marginally
higher costs for biomass fuels. Output of the forest products manufacturing sector would decrease by
6.7 percent due to competition for the forest resource. Imports of fossil fuels would decrease by 2.5
percent, representing a savings in import purchases of $1.14 billion, while imports of forestry
commodities would increase. Employee income would increase by $1.61 billion. Tax revenues to
state government would increase by 0.06 percent ($108 million).

Under the same conditions, i.e. 40 million tons biomass supply, prices for forest commodities may
increase by up to 18 percent in the short run (with fixed capital) due to resource competition, but
would likely be much lower in the long run as capital resources are reallocated to biofuel production.
When the CGE model was modified to disaggregate timber production and logging/forestry support
services, much larger price effects were observed, with composite prices for timber increasing by 42
percent, prices for logging/support services increasing by 143 percent, and prices for manufactured
wood products increasing by 2.4 percent. When the model was further modified to restrict imports of
timber and logging/support services, prices for forestry products increased by 150 percent, prices for
logging/support services increased by 280 percent, and prices for manufactured wood products
increased by 4.6 percent.

Incentives such as a renewable energy production tax credit for electricity generated from biomass,
and a subsidy to forestry biomass producers, would further increase forest sector output and state
GDP and employment, and reduce imports of fossil fuels. In particular, an electricity production tax
credit equivalent to $0.010-$0.011 per kilowatt-hour would substantially increase output of the electric
power sector, and decrease imports of fossil fuels, while reducing the negative impact of higher
electricity prices on all other sectors. However, assuming that the tax credit is unlimited, the state-
sponsored incentive would significantly reduce state government revenues by nearly $200 million at
the 40 million ton biomass supply level. The 100 percent biomass feedstock federal subsidy to
forestry producers would dramatically increase both electric power and forestry commodity output, but
would not appreciably affect state government revenues.

The models used in this analysis represent a "snapshot" in time, and do not incorporate a time
dimension, however, it is assumed that the estimated economic impacts would occur within a
relatively short period of a year or less. It may be expected that the results for the mobile capital
scenario would hold in the long run, say 10 years or more, while fixed capital would prevail in the
short run, subject to limitations on capital movement, especially for highly fixed assets such as forest
inventories. The I-O/ SAM and CGE models with mobile capital do not explicitly incorporate any
physical capacity limitations on production of a commodity such as biomass fuels. This stands in








contrast to bioeconomic models such as the Southern Region Timber Supply (SRTS) model used in a
companion study, which dynamically represents timber inventories, forest growth and harvest
removals. The relatively modest effects on forest commodity prices observed in the fixed capital CGE
analysis, even in the face of a threefold increase in demand, may be attributed to the moderating
effect of increased imports, substitution effects, the diverse mix of different biomass resources
available, and the fact that commercial timber production in the CGE model represents less than 25
percent of the total forestry sector.

Based on these findings, it is concluded that the various policies and incentives for bioenergy
development would have an overall positive impact on the economy of Florida in terms of increased
GDP, employment and state government revenues, and decreased imports of fossil fuels. The
forestry sector would particularly benefit from increased demand and prices. However, the forest
product manufacturing sector would be adversely affected by competition for wood resources and
higher prices for material inputs.








Introduction


Interest in development of renewable energy resources has been motivated by economic,
environmental, and national security concerns. Reliable and cost-effective supplies of fuels for
transportation and electric power generation are a key driver of economic development, and are in
large part responsible for the mobility and high standard of living enjoyed in the United States.
Replacement of fossil fuels with renewable energy sources such as wind, solar and biomass is an
important strategy for reducing greenhouse gas emissions, mitigating effects of global climate
change, reducing expenditures on imports, and reducing dependence on petroleum from politically
unstable regions. Costs for natural gas and petroleum (gasoline, diesel) have dramatically increased
in recent years, motivating development of alternatives to these fuels. Although coal remains an
abundant, low-cost and domestically available fuel, its high carbon emissions have raised concerns
about its dominant use for electric power generation.

Biofuels are a primary candidate for renewable energy in Florida, due to the year-round growing
conditions and relatively abundant forest and water resources, while potential wind and hydropower
resources are considered relatively small (Navigant Consulting, 2008). Woody biomass fuels may be
used directly for electric power generation by utilities, for combined heat and power systems in
industrial facilities, or as a feedstock for production of ethanol biofuel via cellulosic conversion
technology. Solid biomass fuels are currently used for electric power generation in Florida at 23
facilities. The types of biofuels in use include agricultural crop byproducts, wood and wood waste,
biogenic municipal solid waste and landfill gas. Total electric power generation from biomass fuels in
Florida was 2.98 terawatt-hours in 2008, or about 1.4 percent of total generation (USDOE-EIA). In
2006, there were 380 megawatts of installed electric generating capacity in Florida fueled with woody
biomass, and the technical potential for additional electricity generation from woody biomass and
short rotation woody crops was estimated at 2.1 to 4.4 Gigawatts, or 3.9 to 8.3 percent of total
capacity in 2006 (Navigant Consulting, 2008). Although there is considerable research and
development effort ongoing for use of wood and biogenic waste materials for production of liquid
transportation fuels (ethanol, biodiesel) via cellulosic conversion technology, major barriers remain for
its full scale commercialization (USDOE, 2006).

It is anticipated that the need for bioenergy sources will lead to rapid exploitation of forests and other
biomass resources. This has raised concerns about the potential for ecosystem degradation and
adverse impacts on their sustainability. Also, greater use of biomass will inevitably lead to more
competition for forest resources between traditional users of forest products and the emerging
bioenergy sector, with the result that prices may increase significantly. The forest products industry in
Florida generated approximately $16.7 billion in output (revenue) impacts, $7.0 billion in value added








(income) impacts and employment impacts of 89,000 jobs in 2006, and is a leading economic sector
in many rural counties in the northern part of the state (Hodges et al, 2008).

Based on these concerns, the 2008 Florida Legislature mandated an evaluation of the economic and
market impacts of increased utilization of woody biomass resources for bioenergy (HB7135, section
113, page 236), with the Florida Department of Agriculture and Consumer Services (FDACS)
designated as the agency responsible for this mandate. The intent of the legislation is to assure that
future supplies of forest resources and other biomass materials are sufficient to support expanded
bioenergy production, as well as traditional forest products, without undue market disruption.

Federal and state incentive policies are used to encourage electric utility industry to use resources
that have less pollution to the environment. These incentives include investment and production tax
credits, biofuel production subsidies, and a quota system known as a Renewable Portfolio Standard
(RPS). Some incentives reimburse users for part or all of the cost of woody biomass feedstock
delivered to users, while other incentives provide a credit for fuels or electricity generated from
biomass resources. Any type of monetary incentive would have an impact on the cost of biomass
feedstock in comparison to other fuels. Although there may be some non-monetary incentives such
as Healthy Forest Restoration Act of 2003, which recommends forest thinning programs for reducing
the risk of wildfire, only those incentives were taken into account which may have direct monetary
effects on using woody biomass for electricity generation.

Perhaps the most important incentive for electric power generators is the Renewable Portfolio
Standard (RPS), also known as a Renewable Electricity Standard (RES), which consists of a
schedule of targets that prescribe a minimum share of electric power to be generated from renewable
energy sources by certain dates in the future. Under this policy, similar to cap-and-trade programs,
electric utilities may chose to develop and operate biofuel facilities or purchase credits from other
generators with a surplus of credits. The RES has been widely used to evaluate the potential costs
and benefits of increasing renewable energy and controlling greenhouse gas emissions. For example,
a recent study estimated that a 25 percent federal RES in Florida would generate $11.2 billion in new
industry output and create 42,800 jobs from operations of renewable energy facilities by the year
2025 (English et al, 2009). The study considered a mix of dedicated energy crops, solid wastes,
biogas, solar, and cofiring of wood with coal. Although the study determined that electric power rates
would be increased as a result of a RES, raising costs to utility customers by $2.96 billion, the net
impacts on the economy were still overwhelmingly positive. However, this analysis was conducted
with a simple regional input-output model (Implan) that does not consider substitution effects for
capital and labor resources.








Among several other federal and state incentives, the most relevant to biomass resources is the
Renewable Energy Production Tax Credit in Florida (N.C. Solar Center). The program in Florida,
enacted in July 2006, provides a $0.01 per kilowatt-hour credit to cogeneration or combined heat-and-
power (CHP) facilities that use eligible renewable sources such as biomass. The tax credit may be
claimed for electricity produced and sold between January 2007 and June 2010, however, the unused
credit may be carried forward for up to 5 years. A similar federal program provides a $0.011 per
kilowatt-hour tax credit for electricity generation from renewable sources.

A recent incentive introduced by the USDA Farm Service Agency is the Biomass Crop Assistance
Program (BCAP) which allows matching payments for collection, harvest, storage, and transportation
of certain eligible materials to be used by qualified biomass conversion facilities (USDA-FSA, 2009).
The agency began accepting applications for BCAP in July 2009. Under this program, owners of
qualified biomass materials can receive financial assistance for delivering it to conversion facilities
that use biomass fuels for heat, power, biobased products or advanced biofuels. Matching payments
are made at a rate of 100 percent of the price of biomass delivered to a qualified conversion facility,
up to $45 per dry ton equivalent. Biomass owners are eligible to receive payments for two years.
Qualified biomass conversion facilities must be located in the U.S. or U.S. territories, must be a
separate legal entity from owners of biomass materials purchased, and must conduct the purchase in
arms-length transactions.

The purpose of this study was to estimate the potential economic impacts in Florida, both positive and
negative, from expanded use of biofuels under selected federal and state policies, including a
Renewable Electricity Standard, a renewable electricity production tax credit, and a biomass
feedstock subsidy. The study focused on use of woody biomass fuels for electric power generation,
since this is a mature technology that is poised to rapidly expand under enabling legislation.
Estimates of economic impacts were developed for the forestry sector, forest product manufacturing,
electric power, and other major industry sectors in Florida.








Methodology


The economic impacts of changes in demand for woody biomass due to expanded renewable energy
production in Florida were assessed using a regional Input-Output model and Social Accounting
Matrix (1-O/SAM) coupled with a Computable General Equilibrium (CGE) model. The Impact Analysis
for Planning (IMPLAN) Professional software and associated databases for Florida (MIG, Inc. 2008)
were used to construct the I-O/SAM, and the General Algebraic Modeling System software (GAMS
Development Corporation) was used to build and run the CGE model. The I-O/SAM generated by
IMPLAN includes information on industry output, value added, employment, personal income,
commodity supply and demand, state-local and federal government taxes and spending, capital
investment, business inventories, and domestic and foreign trade. Information is detailed for 440
individual industry sectors, nine household income classes, and six state-local or federal government
institutions. The I-O/SAM represents a snapshot of the Florida economy in the base year of 2007 that
serves as a starting point for the implementation of the CGE model, which finds an optimal solution
where all markets are in equilibrium, i.e. supply equals demand. The particular CGE model used in
this analysis was originally developed for national economies (Lofgren et al., 2002), and was later
adapted for use on regional economies and analysis of biofuel policies (Holland, Stodick and
Devadoss, 2009).

Significant components of the IMPLAN databases for industry and institutional transactions are based
on national averages, including the industry production functions that represent the proportion of
industry expenditures on intermediate inputs and value-added components. The IMPLAN production
function coefficients for the Electric Power Generation sector were adjusted to match data available
from the Department of Energy (DOE-EIA) and the Federal Energy Regulatory Commission for
Florida for the year 2007, as shown in Tables 1 and 2. In particular, Florida's electric power industry
uses a much larger proportion of natural gas than the nation on average. Also, like many eastern
states, Florida has no hydro-electric or geo-thermal based generation. The same EIA data also
indicated that the proportion of total expenditures on fuel by Florida's power generators was much
larger than that specified in the IMPLAN databases. Adjusting the total output, production function
coefficients and value added components for this industry to match published data enabled the
I-O/SAM model to more accurately represent the economy of Florida and the activity of the electric
power sector. Once the IMPLAN production function and study-area data for Electric Power
Generation and Transmission were updated, unaggregated I-O/SAM matrix files were produced with
the IMPLAN Professional software using procedures described in the IMPLAN Users Guide (MIG,
2004).








Table 1. Modification of IMPLAN fuel-related production function coefficients for the electric power
generation sector in Florida, 2007.
IMPLAN Original Modified
Sector IMPLAN Sector Name Coefficient Coefficient1
Number
9 Sugarcane Farming 0.000000 0.001660
15 Forestry 0.000000 0.000830
16 Logging 0.000000 0.000830
20 Oil and Gas Extraction 0.087734 0.056140
21 Coal Mining 0.042305 0.073960
32 Natural Gas Distribution 0.000001 0.000010
115 Petroleum Refining 0.013523 0.008650
125 Nuclear Fuel Manufacturing 0.000000 0.006570
337 Pipeline Transportation 0.022228 0.302650
Total 0.165791 0.451300
Derived from Department of Energy (DOE-EIA) and the Federal Energy Regulatory Commission published data.


Table 2. Modifications to electric power sector study area data for Florida.
Original IMPLAN Revised Study
Study Area Data Area Data1
(Million $) (Million $)
Industry Output 12,734.520 23,878.430


Value-added components
Employee Comp
Proprietary income
Other Prop. Income
Indirect Business Taxes
Total Value Added


1,919.534
707.714
5,021.370
1,523.313
9,171.931


2,571.558
936.606
6,645.406
2,015.990
12,138.362


Expenditure Shares
Value Added 0.720242 0.508340
Intermediate Inputs 0.279758 0.491660
Total 1.000000 1.000000

Employment (jobs) 19,250 36,096
1. Department of Energy (DOE-EIA) and the Federal Energy Regulatory Commission (FERC).


Although IMPLAN databases contain data on over 460 industry and institutional sectors, it is
impractical to include all these sectors in a CGE model because of the computational requirements,
so it was necessary to aggregate many of these sectors. For the biofuels CGE model this aggregation
was designed to keep industry sectors of interest relatively disaggregated while combining sectors of
lesser interest into broad general categories. In Table 3, the overall aggregation scheme for the CGE
model is presented in which the 460 IMPLAN industry and institutional sectors are consolidated into
40 aggregate sectors. Industry sectors such as Infrastructure, Construction, and Wholesale Trade








that are unique in their role in the economy, as well as Federal and State government sectors were
left unaggregated because they did not fit well into other aggregate industry classifications. The
sectors for Proprietary Income and Other Property Income were combined, and sectors for
Corporations and Capital were aggregated into a single sector called Capital (Table 3).

Since this analysis is focused on woody biofuels and electric power generation, the aggregation
scheme for these two sectors, and certain other closely related sectors is detailed in Table 4. The
aggregated Forestry sector for the CGE model is comprised of IMPLAN sectors for Forestry,
Commercial Logging, and Support Activities for Agriculture and Forestry. It should be noted that this
industry sector represents forest harvest and transportation activities, as well as forest management
and timber production. Additional runs of the model were also conducted with Forestry and
Logging/Support Activities disaggregated as separate sectors. Industries involved with fossil fuels are
of interest because woody biofuels substitutes for fossil fuels in the overall fuel mix used by electric
power generators. The aggregate fossil fuel sector is comprised of eight IMPLAN sectors that
represent oil, natural gas, and coal extraction, support activities for these sectors, and petroleum
refineries (Table 4). Electric power generation was not aggregated with any other IMPLAN sectors.
This was critical for the simulation of specific scenarios with the CGE model that were used to
estimate the economic impacts of renewable portfolio standards and various other government
incentive programs designed to encourage a shift to this technology. Since the increased use of
Forestry products as biofuel will compete with their use by wood-product manufacturing industries
(such as solid wood and paper products), seventeen wood related manufacturing industries were
aggregated into a wood manufacturing sector, separate from all other types of manufacturing (Table
4).








Table 3. Aggregation scheme for the Florida woody biofuels computable general equilibrium model.

Aggregate Aggregate Sector Name IMPLAN Sector Numbers
Sector
Industry/Enterprise Sectors
1 Agriculture 1 -14
2 Forestry & Related 15,16, 19
3 Fishing And Hunting 17 and 18
4 Fossil Fuels Related 20,21,28, 29, 30, 32, 115, 337
5 Mining 22-27
6 Electric Power Generation 31
7 Infrastructure 33
8 Construction 34 40
9 Manufacturing 41 -318
10 Wood Related Manufacturing 95 112
11 Wholesale Trade 319
12 Retail Trade 320-331
13 Transportation 332- 336 & 338 340
14 Information 341 353
15 Finance 354-360
16 Renting 361 366
17 Services Professional 367-381
18 Services 382 426
19 Government Enterprises & Other 427 440
Institutional Sectors
20 Labor 5001
21 Property Income 6001 7001
22 Indirect Business Taxes 8001
23 Households Less Than $1OK 10001
24 Households $10K To $15K 10002
25 Households $15K To $25K 10003
26 Households $25K To $35K 10004
27 Households $35K To $50K 10005
28 Households $50K To $75K 10006
29 Households $75K To $100K 10007
30 Households $100K To $150K 10008
31 Households Greater Than $150K 10009
32 Federal Government Non-Defense 11001
33 Federal Government Defense 11002
34 Federal Government Investment 11003
35 State Government Non-Education 12001
36 State Government Education 12002
37 State Government Investment 12003
38 Investment 13001, 14001,14002
39 Foreign Trade 25001
40 Domestic Trade 28001
An Excel spreadsheet of the IMPLAN industry sector scheme is available at:
http://implan.com/v3/index.php?option=com docman&task=doc download&qid=148<emid=138








Table 4. Detailed aggregation scheme for selected
computable general equilibrium model.


industry groups in the Florida woody biofuels


Aggregate IMPLAN
Sector Aggregate Sector Name Sector IMPLAN Sector Name
Number Number


Forestry & Related


Fossil Fuels








Electric Power
Wood Product
Manufacturing


Forestry, Forest Products & Timber Tracts
Commercial Logging
Support Activities for Agriculture & Forestry
Oil and Gas Extraction
Coal Mining
Drilling Oil And Gas Wells
Support Activities for Oil & Gas Operations
Support Activities for Other Mining
Natural Gas Distribution
Petroleum Refineries
Pipeline Transportation
Electric Power Generation, Transmission and Distribution

Sawmills and Wood Preservation
Veneer and Plywood Manufacturing
Engineered. Wood Member & Truss Manufacturing
Reconstituted Wood Product Manufacturing
Wood Windows and Doors And Millwork
Wood Container and Pallet Manufacture.
Manu Fact. Home (Mobile Home) Manufacturing
Prefabricated Wood Building Manufacturing
All Other Misc. Wood Product Manufacturing
Pulp Mills
Paper Mills
Paperboard Mills
Paperboard Container Manufacturing
Coated & Laminated Paper & Packaging Paper
All Other Paper Bag, Coated & Treated Paper Manuf.
Stationery Product Manufacturing
Sanitary Paper Product Manufacturing


GAMS routines originally developed by Rutherford and by Stodick, Holland, and Devadoss were used
to aggregate the IMPLAN 1-O/SAM files for use in the CGE model, as shown in Appendix Table 1.
The SAM represents the flows of dollars between the various sectors of the economy. Activities
represent industries, commodities represent goods and services sold or purchased, and institutions
represent income and expenditures for capital, labor, taxes, inventory and trade. Purchases of, or
expenditures on commodities by activities, and revenues derived from the manufacture of
commodities by different Activities are represented by the table columns. Receipts for commodities
and factors, and revenues to activities are represented by table rows. Rows and columns of the
SAM must balance, so there is a complete accounting of all transactions or transfers in the economy.








For the SAM that was derived from the IMPLAN model of Florida, some imbalances occurred due to
the parameter modifications made for the Electric Power Generation sector, however, these
imbalances were subsequently resolved by running a null counterfactual through the CGE model.

The GAMS CGE model used for this analysis is a comparative-static regional CGE model that was
adapted by Holland et al. (2007, 2009) from a national CGE model developed by Lofgren et al.
(2002). Compared to Input-Output models like IMPLAN, where goods and factors are transacted in
fixed proportions, at fixed prices, and without global supply constraints, CGE models include price
changes in response to changes in quantities demanded or supplied, and allow for substitution
between goods and factors based on those relative prices. The demand and supply relationships
specified in this general equilibrium model are derived from neo-classical economic theory where
firms maximize profits, households or consumers maximize a utility function, and all markets clear, i.e.
supply equals demand. In this model, firms maximize a hybrid Leontief/constant-elasticity-of-
substitution (CES) type production function and households or consumers are modeled as
maximizing a Stone-Geary utility function. The Leontief-CES production function uses fixed
proportions for intermediate inputs, but employs a nonlinear CES functional form for the primary
factors of capital and labor for each industry in the model.

The CGE model encompasses both domestic and foreign trade with imperfect substitution, so the
composition of supply depends on the relative prices of foreign, domestic and regional products and
imports. Likewise the mix of domestically marketed and exported goods and services is also
determined by relative prices. The model is constrained by accounting rules or equilibrium conditions
that require production to satisfy all demands. In this case, markets are required to clear for goods
and factors, firms earn zero profits above normal returns to capital, household endowments are fully
employed, and household spending exhausts income.

The biofuels CGE model was constructed in GAMS as a simultaneous system of non-linear equations
and solved using the PATH solver. Initially, consumer prices of domestic goods and imports, the
world price of exports, factor prices, and the currency exchange rate were all set equal to one. The
model was then solved to replicate the IMPLAN SAM, and calibrate many of the model parameters.
However, elasticities of income, substitution or transformation between goods produced and sold in
different markets, and for capital-labor substitution in production, must be specified by the user. For
this application, these elasticities were specified using default values provided in the published CGE
model by Holland et al., and by Bilgic et al (2002). The elasticity parameter values used are shown in
Table 5. Further details on the technical specification of the CGE model and choice of elasticity
parameter values can be found in Holland, Stodick, and Painter (2007).








Table 5. Elasticity parameters for the Florida woody biofuels computable general equilibrium model.
Parameter Value Definition
Xed(C,T) -5 Elasticity of demand for world export function
Esubp(A) 0.99 Elasticity of substitution for production
Esubd(C) 2 Elasticity of substitution (Armington) between regional output and imports
Elasticity of substitution (transformation) between domestic/regional and
Esubs(C) 2 foreign demand
Elasticity of substitution (transformation) for exports between Rest of World
Esube(C) 2 and Rest of U.S.
Elasticity of substitution (Armington) of imports between Rest of World
Esubm(C) 2 imports and Rest of U.S.
Ine(C,H) 1 Income elasticity
Income_Ine 1 Investment on commodities elasticity
Consumption flexibility (determines minimum subsistence level of
Frisch(C) -1 consumption)

Ifrisch(C) -1 Investment demand flexibility (-1 implies no minimum investment level)
Efac(LAB) 4 Demand elasticity for labor
Efac(CAP) 0.5 Demand elasticity for capital


The CGE model includes additional parameters for government taxes and macro-economic closure
settings that can be exogenously specified by the user. Government tax rates can be specified for
sales taxes, consumption taxes paid by households, excise taxes on domestic production, and taxes
on imports and exports. Options for various macro-economic closures are also available for capital,
labor, savings and investment, and current account balances. For the biofuels model, the base run
included the assumption that capital is activity specific and fixed, labor is mobile and unemployment is
possible, savings and investment are not linked, and foreign and rest of U.S. savings are variable
through the export column of the SAM. An alternative set of model runs were made where capital is
mobile and endowment is variable. Over the short-run, capital movement may be a limiting factor for
implementation of a Renewable Electricity Standard or other incentives, however, in the long run, say
ten years or more, it may reasonably be assumed that capital would be mobile and would move to
those areas of highest and best use.

The first set of simulation runs with the CGE model were made for fixed increases in biofuel inputs for
electric power generation at levels of 1, 5, 10, 20, 40, 60 and 80 million tons in a given year. This
range of biomass fuels covers the spectrum of alternative scenarios contemplated for biofuels to meet
a Renewable Electricity Standard in Florida. A supply of 40 million tons of woody biomass
(freshweight basis) for electric power generation would produce approximately 28.2 billion KWhr of
electricity at current technical efficiencies, representing about 13.1 percent of current annual power
generation in Florida, and about 10.6 percent of projected electrical generation in the year 2025, while








the maximum biomass supply level of 80 million tons would account for about 21 percent of projected
electrical generation demand in 2025, as shown in Table 6. The cost of biomass fuels was estimated
at $30 per ton, based on 2007 average delivered prices for timber in Florida (Timber Mart South),
which would represent a total value of $1.20 billion for 40 million tons, and $2.41 billion for 80 million
tons.

Table 6. Biomass supply levels for computable general equilibrium model simulations.

Biomass Gross Electrical Share of Share of Value of
Supply Heat Generation Electrical Electrical Biomass
(million tons, Energy (million Generation Generation Fuel
freshweight Contet kilowatt- in Florida, in Florida, (million $)
basis) BTU) (1)trillion hours) (2) 2007 (3) 2025 (4) (5)
basis) BTU) (1)
1 9.6 706 0.3% 0.3% 30.1
5 48.2 3,529 1.6% 1.3% 150.6
10 96.3 7,057 3.3% 2.7% 301.2
20 192.6 14,115 6.5% 5.3% 602.4
40 385.3 28,230 13.1% 10.6% 1,204.8
60 577.9 42,345 19.6% 15.9% 1,807.2
80 770.6 56,460 26.1% 21.2% 2,409.6
(1) 12.04 million BTU per ton semi-dry woody biomass (USDA, Fuel Value
Calculator, 2004). Semi-dry biomass has 30% moisture content (80% of
freshweight).
(2) Reflects steam-to-electrical energy conversion factor 3,412 BTU/KWh and
25% thermal efficiency factor (USDOE-EIA).
(3) Florida electrical generation in 2007: 216.09 billion kilowatt-hours (USDOE-
EIA, EIA-906-920 report, Monthly generation and fuel stock data at electric
power generating facilities).
(4) Projected Florida electrical generation in 2025: 266.01 billion kilowatt-hours
(USDOE-EIA, Annual Energy Outlook, 2009).
(5) Value of biomass fuel estimated at composite average delivered price for
timber in Florida, 2007: $30.12 per ton (Timber Mart South).


In the parlance of CGE analysis these alternative scenarios are known as counterfactuals. The
counterfactual increases in biofuel inputs were imposed on the CGE model by modifying the Leontief
coefficients for the intermediate inputs, including fuel, in the production function for the Electric Power
Generation sector. Based on 2007 EIA data, it was determined that costs per-kilowatt-hour (KWH) of
generating electricity from woody biofuels were 13.8 percent higher, on average, than the average
cost per KWH for power generated from all types of fossil fuels in the State. Thus, for example, when
biofuel inputs to electric power generation were increased by 10 million tons, or $30 million dollars,
fossil fuel inputs were reduced by 87.8 percent, or $26.4 million. These unequal substitutions in
production function result in a small increase in the sum of the Leontief coefficients for the








intermediate inputs in model, so to keep the production function from over-estimating production, the
shift parameter to the function was calibrated downward to keep output constant. The parameters to
the CES part of the production function for capital and labor are assumed to be independent of
substitutions between types of fuel in the model. The counterfactual Leontief coefficients for the CGE
model are given in Table 7. As would be expected, the largest changes occur in the Leontief
coefficients are for Forestry and Fossil fuels. The reduced shift parameters, shown in the last row,
represent the effect of increases in the cost of electric power generation for biofuels.

Table 7. Leontief coefficients and production function shift parameters for biofuels CGE
counterfactual simulations.
Additional Woody Biofuels For Electric Power Generation
Industry Sector (Million Tons)
Calibrated 1 5 10 1 20 40 60 80
Leontief Coefficients
Agriculture 0.00169 0.00169 0.00169 0.00169 0.00169 0.00168 0.00168 0.00167
Forestry 0.00200 0.00330 0.00852 0.01503 0.02801 0.05387 0.07956 0.10509
Fishing-Hunting 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
Fossil-Fuel 0.44810 0.44688 0.44201 0.43594 0.42382 0.39969 0.37572 0.35190
Mining 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
Electric Power 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
Infrastructure 0.00009 0.00009 0.00009 0.00009 0.00009 0.00009 0.00009 0.00009
Construction 0.00735 0.00734 0.00734 0.00733 0.00732 0.00730 0.00728 0.00725
Manufacturing 0.00971 0.00971 0.00971 0.00970 0.00968 0.00965 0.00962 0.00959
Wood Manufacturing 0.00006 0.00006 0.00006 0.00006 0.00006 0.00006 0.00006 0.00006
Wholesale 0.00079 0.00079 0.00079 0.00079 0.00079 0.00079 0.00078 0.00078
Retail 0.00003 0.00003 0.00003 0.00003 0.00003 0.00003 0.00003 0.00003
Transportation 0.00938 0.00938 0.00937 0.00936 0.00935 0.00932 0.00929 0.00926
Information 0.00062 0.00062 0.00062 0.00062 0.00062 0.00062 0.00062 0.00062
Finance 0.00422 0.00422 0.00422 0.00421 0.00421 0.00419 0.00418 0.00417
Renting 0.00011 0.00011 0.00011 0.00011 0.00011 0.00011 0.00011 0.00011
Services, Professional 0.00926 0.00926 0.00925 0.00925 0.00923 0.00920 0.00917 0.00914
Services, Other 0.00446 0.00446 0.00446 0.00445 0.00444 0.00443 0.00442 0.00440
Government, Other 0.00028 0.00028 0.00028 0.00028 0.00028 0.00028 0.00028 0.00028
Total 0.49816 0.49824 0.49856 0.49896 0.49975 0.50133 0.50290 0.50445
Shift parameters
1.75924 1.75890 1.75756 1.75588 1 1.75254 1.74589 1.73929 1.73272


The model was used to simulate the effect of a $0.011 per kilowatt-hour production federal tax credit
for electric power generated from renewable sources, and a $0.010 per kilowatt-hour state (Florida)
tax credit, corresponding to the existing Renewable Energy Production Tax Credit enacted in 2006
(N.C. Solar Center). The tax credit was modeled as a negative excise tax rate of 11 percent and 10
percent, respectively, on power sales, which is equivalent to $0.011 or $0.010 per KWhr, since the
average cost of power generation in Florida is approximately $0.10 per KWhr, and applied to the
proportion of total fuel expenditures for electrical generation represented by biofuels. Although the
Florida law limits the total value of the tax credit to $5 million annually, and the provision expires in
2010, for this exercise no limitations were considered, in order to illustrate its effect at full scale policy








implementation. A 100 percent subsidy for biomass feedstocks, based upon the federal Biomass
Crop Assistance Program (BCAP), was simulated in the model as a negative sales tax on purchases
of biomass by the electric power sector from the forestry sector.

Additional simulations with the model were done with no domestic or international imports
allowed for Forestry and Logging/Support Services sectors, to determine the effect on prices without
import substitution possibilities, in order to make equivalent comparisons with results from SRTS
bioeconomic model used in a companion study.


Results

Effects on Gross Domestic Product

Gross domestic product (GDP) is the broadest measure of economic activity, representing the net
value of all goods and services produced in the region (value added), or alternatively, the total
personal and business income received. The GDP of Florida in 2007 was about $701 billion.
Estimated changes in GDP of Florida under the scenarios for increased use of biomass for electrical
power are illustrated in Figure 1. In general, changes in output were directly proportional to the
change in amount of biomass supplied to displace fossil fuels. As expected, impacts were somewhat
greater for the scenario where capital was mobile rather than fixed, such that it does not become a
limiting factor. For an increase in biomass supply of 40 million tons, GDP of Florida increased by 0.32
percent or $2.12 billion above the base level (2007) under the mobile capital scenario, and by 0.12
percent or $848 million for the fixed capital scenario. For the maximum biomass supply level of 80
million tons, GDP would increase by 0.24 to 0.62 percent ($1.67 to $4.37 billion), respectively, for
fixed and mobile capital scenarios.








Figure 1. Changes in gross domestic product (GDP) of Florida from increased biomass supply for
electric power generation.

1.8 12.5

1.6 -Mobile Capital--With
Feedstock Subsidy
a 1.4 -

a 1.2 -i -Mobile Capital--With
2 7.5 Federal Tax Credit
1.0 -

.- 0.8 -- --Mobile Capital--With
5.0 a
5. State Tax Credit
w 0.6 o

S0.4 2.5 ,- Mobile Capital--No

0.2 subsidy

0 00 -- Fixed Capital--No
0 20 40 60 80 Subsidy

Biomass Supply to Electric Power (million tons)



When the $0.01 per KWhr renewable energy production Florida (state) tax credit was simulated in the
CGE model, at 40 million tons biomass supply, with capital assumed to be mobile, state GDP
increased by 0.35 percent ($2.42 billion), or by an additional 0.03 percent ($203 million) above the
case without subsidy, as shown in Figure 2. The federal renewable energy production tax credit of
$0.011 per KWhr increased state GDP by 0.38 percent ($2.68 billion) above the base level, and by an
additional 0.07 percent above the no subsidy case, under the 40 million ton biomass supply scenario.

A 100 percent federal biomass feedstock subsidy paid to biomass producers in the forestry sector,
modeled after the Biomass Crop Assistance Program (BCAP), increased state GDP by 0.81 percent
($5.68 billion) compared to the base case, and by 0.49 percent ($3.46 billion) compared to no subsidy
at the 40 million tons biomass supply level (Figure 2). The effects of all subsidies on GDP were
smaller under the fixed capital scenario than for the mobile capital scenario.








Figure 2. Changes in gross domestic product (GDP) of Florida due to subsidies for 40 million tons
biomass supply to electric power generation.

Percent Change from Base
0.0 0.2 0.4 0.6 0.8 1.0


0.1t
No subsidy 0.1

Fixed
Florida Renewable 0.3 Capital
Electricity Production
Tax Credit 4 Mobile
capital

Federal Renewable
Electricity Production
10.38
Tax Credit 0


Biomass Feedstock 0.48
Subsidy (Federal) 0.81






Effects on Industry Output

Changes in output or sales of major sectors of the Florida economy are summarized in Table 8 and
Figures 3 and 4. Of course, the largest impacts, in percentage terms, were to the forestry, electric
power and fossil fuels sectors, which were directly affected by the change in fuel sources, and also to
the mining sector, which reflects derived demand for fossil fuels (Figure 3). For forestry, the presumed
source of new biomass supply, commodity output increased by 36 percent ($1.47 billion) from the
current base level to supply 40 million tons under the fixed capital scenario and by 69 percent ($2.81
billion) under the mobile capital scenario (Figure 4). Wood products manufacturing decreased in
output by 7.5 percent ($587 million) under the fixed capital scenario at the maximum biomass volume,
but by only 0.5 percent under the mobile capital scenario. This greater decrease for the fixed capital
scenario was because of an increase in prices for forest commodities (see below). The electric power
sector experienced decreased output of 0.2 to 0.7 percent at the 40 million ton biomass level, due to
marginally higher prices resulting from the greater cost of biomass fuels compared to fossil fuels.
Output of fossil fuels decreased by up to 0.8 to 2.4 percent at the maximum biomass level because of
decreased demand from the electric power sector as fossil fuels were replaced with biomass. Output
of the mining sector also decreased by 2.9 percent under the mobile capital scenario, as derived
demand for fossil fuels, but not at all under the fixed capital scenario. Output of the agriculture sector

19








decreased by 1.4 percent under the fixed capital scenario, but very little (0.1%) under the mobile
capital scenario. All other sectors had very small changes in output value of less than 0.2 percent
(Table 8).

The state production tax credit for renewable energy generation would increase the value of output of
the electric power sector by 0.33 percent ($76 million) compared to the base level, and by 0.58
percent ($133 million) compared to without the tax credit at the 40 million ton biomass supply level
with capital mobile. The federal production tax credit for renewable energy generation would increase
the value of output of the electric power sector by 0.11 percent ($27 million) compared to the base
level, and by 0.45 percent ($103 million) compared to no tax credit. The 100 percent biomass
feedstock subsidy increased output of the forestry sector by 79 percent ($3.21 billion), the electric
power sector by 5.8 percent ($1.33 billion), and the wood products manufacturing sector by 0.61
percent ($48 million) compared to the base level. It would also increase the output of these sectors
compared to without the subsidy at the maximum biomass supply, by 9.9 percent ($404 million), 6.0
percent ($1.39 billion), and 1.1 percent ($84 million), respectively.

Figure 3. Changes in industry output value, by sector, for 40 million tons biomass supply to electric
power in Florida under the mobile capital scenario.

Percent Change from Base
-10 0 10 20 30 40 50 60 70 80

Agriculture I I I I I
Forestry i
Fishing, hunting
Fossil Fuels
Mining i
Electric power
Infrastructure
Construction
Manufacturing, general
Wood products manufacturing
Wholesale trade
Retail trade
Transportation
Information
Finance
Rental
Professional Services
Services, other
Government









Figure 4. Changes in output value of forestry, wood manufacturing and electric power sectors in
Florida from increased biomass supply (mobile capital scenario).

160 ----------------------------------------------------------

140 ---Forestry.------------------------- 136.2
---Electric power


100

0
S80

. 60
u
w 40
a. 7


Wood products manufacturing .9

---------------------------------------------------------------------------------------- -------------------------------


--------------------------------------------------- ---------------------------------------------------------------------

34.8
1--------------------------------- ---------------------------------------------------------------------------------------


0.0 -0.1 -0.1 -0.2 -0.5 -0.7 -0.9
-20 -----------------------------------------------------------------------------------------------------------------------------

0 10 20 30 40 50 60 70 80
Biomass Supply to Electric Power (million tons)




Effects on Commodity Prices

Changes in commodity prices resulting from increases in biomass supplied by forestry for electric

power generation are shown in Table 9. These values represent a composite of domestic (Florida)

and imported commodity prices. Prices for all commodities in the base year were normalized to a

value of one. As with GDP and commodity output changes discussed already, the price changes were

linear and proportional to biomass supply levels. The largest price change was an increase of nearly

18 percent for forestry commodities at the 40 million ton biomass supply level under the fixed capital

scenario (Figure 5). However, prices for forestry commodities increased by only 0.07 percent under

the mobile capital scenario, when additional capital investment is allowed to increase industry

capacity in response to greater demand. At the maximum biomass supply level of 80 million tons, with

fixed capital, prices for forestry commodities would increase by 30.9 percent. At the 40 million ton

biomass supply level, prices for electric power increased by about 0.5 percent, while prices for

manufactured wood products increased by 0.40 percent under fixed capital and by 0.03 percent when

capital is mobile.

When the CGE model was modified to disaggregate timber production and logging/forestry support

services, much larger price effects were observed, with composite prices for timber increasing by 42

percent, prices for logging/support services increasing by 143 percent, and prices for manufactured


I l








wood products increasing by 2.4 percent, under the scenario with 40 million tons biomass supply and
fixed capital. The price response was greater for logging/support services than for timber production
in this case because logging is the direct supplier to the electric power sector and timber production
becomes an indirect input. When the model was further modified to restrict imports of timber and
logging/support services, prices for forestry products increased by 150 percent, prices for
logging/support services increased by 280 percent, and prices for manufactured wood products
increased by 4.6 percent.



Figure 5. Changes in composite price for forest commodities from increased biomass supply for
electric power.


30.9


--Fixed capital 24.9

Mobile capital
- ---------------------------------1_ --- ----------------------aL-^i ---------------------------



9.8

5.2


-1 1 1i


0 10 20 30 40 50 60
Biomass Supply to Electric Power (million tons)


70 80


The state renewable energy production tax credit for electric power would reduce electricity prices by
0.64 percent compared to the base level, and by 1.18 percent compared to without the subsidy for 40
million tons of biomass supplied, with mobile capital, while the federal renewable energy production
tax credit would reduce electricity prices by 0.75 percent compared to the base level, and by 1.29
percent compared to without the subsidy. The 100 percent biomass feedstock subsidy would reduce
increase forestry commodity prices by 0.26 percent and reduce electricity prices by 7.4 percent
compared to the base level. When compared to the situation without this subsidy at the maximum
biomass supply level, the subsidy would increase prices for forestry commodities by 0.19 percent and
decrease electricity prices by 7.97 percent.








Effects on Commodity Imports


Changes in the quantity of imported commodities resulting from increased use of biomass for electric
power generation are shown in Table 10. To meet a supply of 40 million tons of woody biomass,
imports of forestry commodities increased by about 119 percent ($104 million) under the fixed capital
scenario and by 69 percent ($61 million) under the mobile capital scenario. Presumably, these
imports would mainly come from the adjoining states of Georgia and Alabama. Importantly, imports of
fossil fuels would decrease by up to 2.5 percent ($1.14 billion), and foreign imports of fossil fuels
would be reduced by 2.3 percent ($138 million). These changes represent a significant reduction of
leakage from the state economy.

The state and federal renewable energy production tax credit would slightly lessen the change in
imports of fossil fuels, by 0.12 percent ($55 million) and 0.16 percent ($73 million), respectively,
compared to without the subsidy at the 40 million ton biomass supply level. The 100 percent biomass
feedstock subsidy would actually increase imports of fossil fuels by 0.26 ($122 million) percent
compared to the base level, and by 2.6 percent ($1.21 billion) compared to no subsidy at the 40
million ton biomass supply level.


Effects on Labor Demand

Changes in labor demands resulting from increased use of woody biomass for electric power in
Florida are shown in Table 11. This information can be understood as representing the total value of
wages, salaries and benefits paid to employees, and is a proxy for employment demand or number of
jobs. For the 40 million ton biomass supply level with mobile capital, employment demand would
increase by 72.5 percent ($1.43 billion) in the forestry sector, decrease by 0.47 percent in wood
products manufacturing, and decrease by 0.58 percent for the electric power sector. Payments to all
employees would be increase by $1.61 billion, but this represents just a 0.29 percent increase from
the base level of $406 billion.



Effects on State Government Revenues

Changes in state government revenues from sales, property and excise taxes are shown in Figure 6.
At the 40 million ton biomass supply level, state government revenues would increase by 0.06
percent, or $108 million with mobile capital, and by 0.04 percent or $66 million with fixed capital. At
the maximum biomass supply level of 80 million tons, state government revenues would increase by
0.12 percent ($212 million) or 0.07 percent ($131 million), respectively.








For 40 million tons of biomass supplied, the state renewable energy production tax credit for electric
power would reduce state government revenues by 0.08 to 0.05 percent ($142 to $89 million), for
fixed or mobile capital, respectively, compared to the base level (Figure 7). In contrast, the federal
renewable energy production tax credit would increase state government revenues by 0.05 to 0.08
percent ($86 to $140 million). The federal tax credit would also increase state government revenues
by 0.01 to 0.02 percent ($21 to $32 million) above that for 40 million tons of biomass without the tax
credit. The federal biomass feedstock subsidy for 100 percent of delivered fuel costs would increase
state revenues by 0.10 to 0.18 percent ($174 to $330 million) compared to the base level, and by 0.06
to 0.12 percent ($222 million) compared to the situation without the subsidy.


Figure 6. Changes in Florida (state) government revenues from increased biomass supply for electric
power.

0.4 -
650
S---Mobile Capital--With
0.3 Feedstock Subsidy
450
E 0.2 Mobile Capital--With
0 Federal Tax Credit
-250
0 .1 0
K, -lobile Capital--No
U 50 r subsidy


-0'''.1' I. -150 Fixed Capital--No
SSubsidy

-0.2 -350
-dl-Mobile Capital--With
0 20 40 60 80 State Tax Credit
Biomass Supply to Electric Power (million tons)








Figure 7. Changes in Florida (state) government revenues due to subsidies for 40 million tons
biomass supply to electric power generation.


Fixed Capital

Mobile capital

No subsid)



Florida Renewable Electricity Productior
Tax Credit


Federal Renewable Electricity Productior
Tax Credit



Biomass Feedstock Subsidy (Federal


Percent Change from Base
-0.10 -0.05 0.00 0.05 0.10 0.15


-0.08

-0.05


().04
0.06






0.05



10.10


0.20


0118










Table 8. Changes in value of output for major economic sectors from increased use of woody biomass for electric power generation in
Florida.


Sector


Agriculture
Forestry
Fishing, Hunting
Fossil Fuels
Mining
Electric Power
Infrastructure
Construction
Manufacturing, General
Wood Products Manufacturing
Wholesale Trade
Retail Trade
Transportation
Information
Finance
Rental
Professional Services
Services, Other
Government


Base
(Million $)


7,967.8
4,066.8
455.9
6,717.5
1,364.1
23,027.4
3,139.4
107,325.9
117,454.1
7,825.0
65,266.3
78,805.1
43,824.9
44,176.7
170,182.9
77,368.4
113,200.1
277,352.2
102,266.1


Capital Fixed


1 5


Capital Mobile


Change In Biomass Supply To Electric Power Sector (Million Tons)
10 20 40 60 80 1 5 10
Percentage Chanqe from Base


-0.04 -0.18 -0.34 -0.65 -1.20 -1.66 -2.06
1.21 6.09 12.32 25.11 51.69 79.12 106.96
0.00 0.00 0.00 0.00 0.00 0.01 0.01
-0.03 -0.16 -0.32 -0.66 -1.34 -2.05 -2.78
0.00 0.00 0.00 -0.01 -0.02 -0.04 -0.06
0.00 -0.02 -0.05 -0.12 -0.33 -0.62 -0.96
0.00 0.01 0.01 0.02 0.05 0.08 0.11
0.00 0.00 0.00 0.01 0.01 0.02 0.03
0.00 -0.01 -0.02 -0.05 -0.09 -0.13 -0.18
-0.21 -1.02 -1.98 -3.74 -6.73 -9.20 -11.29
0.00 0.00 -0.01 -0.01 -0.01 0.00 0.00
0.00 0.01 0.02 0.03 0.07 0.10 0.14
0.00 -0.01 -0.01 -0.02 -0.03 -0.04 -0.05
0.00 0.00 0.00 0.00 0.01 0.01 0.01
0.00 0.00 0.00 0.01 0.02 0.03 0.03
0.00 0.01 0.01 0.03 0.06 0.09 0.12
0.00 0.00 -0.01 -0.01 -0.02 -0.02 -0.03
0.00 0.01 0.01 0.02 0.05 0.07 0.09
0.00 0.00 0.01 0.01 0.03 0.04 0.06


20 40 60 80


0.00 -0.02 -0.03 -0.07 -0.13 -0.20 -0.26
1.75 8.74 17.44 34.76 69.05 102.87 136.23
-0.01 -0.03 -0.05 -0.10 -0.20 -0.29 -0.39
-0.06 -0.31 -0.61 -1.22 -2.43 -3.62 -4.80
-0.08 -0.38 -0.75 -1.49 -2.93 -4.31 -5.65
-0.01 -0.03 -0.06 -0.12 -0.25 -0.37 -0.49
0.00 0.02 0.04 0.08 0.15 0.22 0.29
0.00 0.00 0.01 0.02 0.04 0.05 0.07
0.00 -0.01 -0.03 -0.06 -0.11 -0.17 -0.23
-0.01 -0.06 -0.12 -0.23 -0.46 -0.69 -0.91
0.00 0.02 0.03 0.06 0.12 0.19 0.25
0.00 0.02 0.04 0.08 0.16 0.23 0.31
0.00 0.00 0.00 0.00 0.01 0.01 0.01
0.00 -0.02 -0.03 -0.06 -0.13 -0.19 -0.25
0.00 0.00 -0.01 -0.02 -0.03 -0.05 -0.06
0.00 0.02 0.04 0.07 0.14 0.21 0.28
0.00 0.00 0.00 0.01 0.01 0.02 0.02
0.00 0.01 0.03 0.05 0.11 0.16 0.21
0.00 0.01 0.01 0.03 0.06 0.09 0.12










Table 9. Changes in composite commodity prices from increased use of woody biomass for electric power generation in Florida.
Capital Fixed Capital Mobile


Sector


Agriculture
Forestry
Fishing, Hunting
Fossil Fuels
Mining
Electric Power
Infrastructure
Construction
Manufacturing, General
Wood Products Manufacturing
Wholesale Trade
Retail Trade
Transportation
Information
Finance
Rental
Professional Services
Services, Other
Government


Change in Biomass Supply to Electric Power Sector (million tons)


1 5 10 20 40


0.04 0.08
2.65 5.17
0.00 0.00
-0.01 -0.02
0.00 0.00
0.02 0.06
0.00 0.01
0.00 0.01
0.00 0.00
0.06 0.12
0.00 0.00
0.00 0.00
0.00 0.00
0.00 0.00
0.00 0.00
0.01 0.01
0.00 0.00
0.00 0.00
0.00 0.00


60 80 1 5
Percentage Change from Base


0.15 0.28 0.39 0.49
9.84 17.99 24.92 30.92
0.00 0.00 0.01 0.01
-0.03 -0.07 -0.10 -0.14
0.00 0.00 0.00 0.00
0.16 0.51 0.99 1.60
0.02 0.06 0.10 0.16
0.01 0.02 0.03 0.04
0.01 0.01 0.02 0.02
0.22 0.40 0.56 0.69
0.00 0.01 0.01 0.02
0.01 0.02 0.04 0.05
0.00 -0.01 -0.01 -0.01
0.00 0.01 0.01 0.02
0.01 0.01 0.02 0.03
0.03 0.06 0.09 0.12
0.00 0.00 0.00 0.01
0.01 0.02 0.03 0.05
0.00 0.00 0.00 0.00


0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00


10 20 40 60 80


0.05 0.09
0.04 0.07
0.03 0.05
0.00 0.01
0.04 0.08
0.27 0.54
0.03 0.06
0.02 0.05
0.01 0.02
0.01 0.03
0.03 0.06
0.03 0.05
0.03 0.05
0.03 0.06
0.04 0.08
0.06 0.11
0.03 0.05
0.03 0.05
0.01 0.02









Table 10. Changes in quantity of imports due to increased use of woody biomass for electric power generation in Florida.
Capital Fixed Capital Mobile


Base
(Million $)


Agriculture 3,912.7
Forestry 87.9
Fishing, Hunting 596.4
Fossil Fuels 46,582.0
Mining 1,601.5
Electric Power 1,890.1
Infrastructure 670.3
Construction 0.0
Manufacturing, General 182,669.4
Wood Products Manufacturing 12,511.4
Wholesale Trade 4,846.9
Retail Trade 5,639.2
Transportation 11,428.7
Information 26,725.4
Finance 56,777.2
Rental 1,975.6
Professional Services 21,305.1
Services, Other 38,357.7
Government 14,988.6


1 5


Change In Biomass Supply to Electric Power Sector (Million Tons)
10 20 40 60 80 1 1 5 10
Percentage Change from Base


0.01 0.06 0.12 0.24 0.45 0.65 0.82
2.52 12.87 26.37 55.10 118.73 189.18 264.94
0.00 0.00 0.01 0.01 0.03 0.04 0.06
-0.06 -0.30 -0.60 -1.22 -2.46 -3.71 -4.95
0.00 -0.01 -0.02 -0.04 -0.07 -0.09 -0.12
0.00 0.03 0.07 0.23 0.74 1.49 2.42
0.00 0.01 0.02 0.05 0.12 0.21 0.30
0.00 0.00 0.00 0.00 0.00 0.00 0.00
0.00 0.00 0.01 0.02 0.04 0.06 0.09
-0.01 -0.03 -0.06 -0.11 -0.19 -0.25 -0.29
0.00 0.00 -0.01 -0.01 0.00 0.02 0.05
0.00 0.01 0.02 0.05 0.10 0.16 0.22
0.00 -0.01 -0.02 -0.03 -0.05 -0.06 -0.07
0.00 0.01 0.01 0.02 0.05 0.07 0.10
0.00 0.01 0.01 0.02 0.05 0.08 0.11
0.00 0.02 0.03 0.06 0.13 0.20 0.28
0.00 0.00 -0.01 -0.01 -0.02 -0.02 -0.02
0.00 0.01 0.02 0.04 0.09 0.14 0.19
0.00 0.00 0.01 0.02 0.03 0.05 0.06


20 40 60 80


0.01 0.05 0.10 0.21 0.41 0.62 0.82
1.75 8.76 17.50 34.89 69.36 103.41 137.07
0.00 0.02 0.04 0.08 0.15 0.23 0.30
-0.06 -0.30 -0.59 -1.18 -2.34 -3.49 -4.62
0.00 0.00 0.00 0.00 -0.01 -0.01 -0.01
0.02 0.11 0.22 0.45 0.90 1.34 1.79
0.01 0.03 0.06 0.11 0.23 0.34 0.44
0.00 0.00 0.00 0.00 0.00 0.00 0.00
0.00 0.02 0.04 0.08 0.16 0.24 0.32
0.00 0.00 0.00 0.00 0.00 -0.01 -0.01
0.01 0.03 0.06 0.12 0.24 0.36 0.48
0.01 0.03 0.06 0.12 0.23 0.35 0.46
0.00 0.02 0.04 0.08 0.16 0.23 0.31
0.00 0.02 0.04 0.07 0.14 0.21 0.28
0.01 0.03 0.05 0.10 0.20 0.30 0.39
0.01 0.04 0.07 0.14 0.29 0.43 0.57
0.00 0.01 0.03 0.05 0.11 0.16 0.21
0.01 0.03 0.05 0.11 0.21 0.32 0.42
0.00 0.01 0.02 0.04 0.09 0.13 0.17


Sector










Table 11. Changes in quantity of labor demanded (factor payments) due to increased use of woody biomass for electric power generation
in Florida.


Sector


Agriculture
Forestry
Fishing, Hunting
Fossil Fuels
Mining
Electric Power
Infrastructure
Construction
Manufacturing, General
Wood Products Manufacturing
Wholesale Trade
Retail Trade
Transportation
Information
Finance
Rental
Professional Services
Services, Other
Government


Base
(Million $)


1,280.6
1,973.6
27.6
196.3
298.7
2,454.8
186.1
30,469.4
21,234.8
1,306.9
23,512.9
32,178.8
11,899.5
11,355.6
35,320.3
2,292.6
43,200.0
111,126.1
75.497.7


CaDital Fixed


Capital Mobile


Change In Biomass Supply to Electric Power Sector (Million Tons)
1 5 10 20 40 60 80 1 5 10
Percentage Chanae from Base


-0.13 -0.65 -1.27 -2.41 -4.39 -6.06 -7.49
1.26 6.35 12.85 26.27 54.34 83.51 113.29
0.00 0.00 -0.01 -0.02 -0.03 -0.04 -0.05
-0.10 -0.49 -0.99 -1.97 -3.93 -5.87 -7.78
0.00 0.00 -0.01 -0.02 -0.05 -0.10 -0.16
-0.02 -0.15 -0.37 -1.01 -2.95 -5.57 -8.70
0.00 0.01 0.02 0.06 0.15 0.28 0.42
0.00 0.00 0.00 0.00 -0.01 -0.01 -0.02
0.00 -0.02 -0.04 -0.08 -0.16 -0.24 -0.32
-0.36 -1.76 -3.39 -6.37 -11.35 -15.39 -18.75
0.00 0.00 -0.01 -0.01 -0.02 -0.02 -0.02
0.00 0.01 0.02 0.03 0.06 0.09 0.11
0.00 -0.01 -0.01 -0.02 -0.04 -0.05 -0.07
0.00 0.00 0.00 0.00 -0.01 -0.01 -0.02
0.00 0.00 0.00 0.01 0.01 0.01 0.01
0.00 0.01 0.02 0.04 0.08 0.13 0.17
0.00 0.00 -0.01 -0.02 -0.03 -0.04 -0.06
0.00 0.01 0.01 0.02 0.04 0.06 0.07
0.00 0.00 0.01 0.02 0.04 0.06 0.08


20 40 60 80


0.00 -0.02 -0.03 -0.06 -0.13 -0.19 -0.25
1.84 9.17 18.31 36.49 72.48 107.97 142.98
0.00 -0.02 -0.05 -0.09 -0.18 -0.27 -0.36
-0.07 -0.37 -0.74 -1.48 -2.94 -4.39 -5.81
-0.08 -0.40 -0.79 -1.57 -3.08 -4.54 -5.94
-0.01 -0.07 -0.15 -0.29 -0.58 -0.87 -1.16
0.02 0.08 0.15 0.30 0.60 0.89 1.18
0.00 0.00 0.01 0.02 0.04 0.05 0.07
0.00 -0.01 -0.03 -0.05 -0.11 -0.16 -0.21
-0.01 -0.06 -0.12 -0.24 -0.47 -0.70 -0.93
0.00 0.01 0.03 0.06 0.12 0.17 0.23
0.00 0.02 0.04 0.07 0.14 0.21 0.28
0.00 0.00 0.00 0.00 0.00 0.00 0.00
0.00 -0.02 -0.03 -0.06 -0.12 -0.18 -0.24
0.00 0.00 -0.01 -0.01 -0.03 -0.04 -0.06
0.00 0.02 0.04 0.09 0.18 0.26 0.35
0.00 0.00 0.00 0.01 0.02 0.03 0.04
0.00 0.01 0.03 0.05 0.10 0.15 0.20
0.00 0.01 0.02 0.04 0.07 0.11 0.14








Conclusions


This study evaluated the potential impacts on the Florida economy resulting from substitution of
woody biomass biofuels for fossil fuels used for electric power generation, under the mandates of a
Renewable Electricity Standard that would require a minimum percentage of renewable energy
sources, state and federal production tax credits, and biomass feedstock subsidies. The analysis was
conducted using a computable general equilibrium model coupled to an Input-Output/Social
Accounting Matrix representing the structure of the Florida economy in 2007.

The study found that increased biomass use for electric power generation would bring about a
modest increase in the Gross Domestic Product of Florida, employment, and state government
revenues, while decreasing total imports, particularly for fossil fuels. For a biomass supply level of 40
million tons, with mobile capital assumed, GDP would be increased by 0.32 percent, representing a
$2.2 billion addition to Florida's economy. Output of the forestry sector would be increased
dramatically, by 69 percent above current levels, to meet new demand for woody biomass fuels, while
output of the electric power sector would decrease by up to 0.33 percent as a result of higher costs
for biomass replacing fossil fuels. The largest adverse impact of these policies would be a decrease
in output of the forest products manufacturing sector by up to 6.7 percent, because of competition and
increased prices for forest resources. Prices for forest commodities may increase as much as 18
percent in the short-run due to this resource competition, but would likely be much lower in the long-
run if capital is allowed to move freely. The much greater price increases observed when Forestry and
Logging/Support Services sectors were disaggregated, and when imports of these commodities were
prohibited are more comparable to results from bioeconomic models such as the Southern Region
Timber Supply (SRTS) model used in a companion study (Rossi, Carter and Abt).

Imports of fossil fuels would be decreased by up to 2.5 percent, representing a savings in import
purchases of $1.14 billion annually. Employee income would increase by up to $1.61 billion. State
government tax revenues would increase by 0.06 percent ($108 million).

The analysis also showed that incentives, such as a state and federal renewable energy production
tax credits for electricity generated from biomass equivalent to $0.010 and $0.011 per KWhr
respectively, and a 100 percent subsidy to forestry biomass producers, would marginally further
increase state GDP and employment. The electricity production tax credit would substantially
increase output of the electric power sector, and decrease imports of fossil fuels, while reducing the
negative impact of higher electricity prices on all other sectors. However, assuming that the tax credit
is unlimited, this state-sponsored incentive would significantly reduce state government revenues by
nearly $200 million at the 40 million ton biomass supply level. The federally sponsored renewable
production tax credit would not adversely affect state government revenues. The biomass feedstock








federal subsidy to forestry producers would dramatically increase both electric-power and forestry
commodity output, but would not appreciably affect fossil fuel imports or state government revenues.

In summary, it is concluded that the various policies and incentives for bioenergy development that
were examined would have an overall positive impact on the economy of Florida in terms of increased
GDP, employment and state government revenues, and decreased imports of fossil fuels. The
forestry sector would particularly benefit from increased demand and prices. However, the forest
product manufacturing sector would be adversely affected by competition for wood resources and
higher prices for material inputs.

Of course, all economic analyses are based on certain assumptions that are integral to the economic
models and data used, and this study is no exception. Firstly, 1-O/SAM models assume a fixed
relationship between production volume (output) and intermediate inputs estimated based on national
averages, however, the CGE modeling approach overcomes some of the limitations of standard
Input-Output analysis by allowing substitution of labor and capital resources and changes in
commodity prices. Secondly, the I-O/SAM and CGE models used in this study do not explicitly have a
time dimension; the impacts are assumed to occur within a relatively short period of a year of less. It
is expected that the results under the mobile capital scenarios would hold in the long run, say 10
years or more, while fixed capital would prevail in the short run. Also, these models do not recognize
physical or biological capacity constraints on commodity production, such as forest growth. Changes
in commodity demand are assumed to be fulfilled from either local or imported sources, in order for
the market to reach equilibrium. This is in contrast to bioeconomic models such as the SRTS model
which represents forest inventories, growth and harvest removals dynamically over time.

Future studies on the economic impacts of bioenergy development policies may more fully explore
other types of incentives, such as investment tax credits, as well as possible trade policy provisions
that could mitigate the adverse effects on certain sectors, or the effects of model parameters and
closure rules that may better reflect the characteristics of specific industry sectors or commodities









Literature and Information Sources Cited


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Commodity Trade Elasticities. The Journal of Regional Analysis and Policy, 32(2), 2002.
English, Burton, Kim Jensen, Jamey Menard, and Daniel De La Torre Ugarte. Projected impacts of
proposed federal renewable portfolio standards on the Florida economy. Final report to the
Bipartisan Policy Center. University of Tennessee, Department of Agricultural Economics,
Knoxville, TN. 97 pages. August, 2009.
Federal Energy Regulatory Commission (FERC). Financial Report, Form 1: Annual Report of Major
Electric Utilities, Licensees and Others and Supplemental Form 3-Q: Quarterly Financial
Report, 2007. http://www.psc.state.fl.us/utilities/annualreports/default.aspx.
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Washington, DC 2007. http://www.gams.com/default.htm
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Accounting Software and Data for Florida Counties. Stillwater, MN, 2007.
http://www.implan.com
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National Laboratory, 311 pages, Dec. 30, 2008, Burlington, MA.
www.psc.state.fl.us/utilities/electricqas/RenewableEnergy/Full Report 2008 11 24.pdf
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http://www.dsireusa.orq/, accessed 6/17/2009.
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Model: GAMS programming documentation Washington State University, School of Economic
Sciences.
http://www.agribusiness-mgmt.wsu.edu/Holland model/documentation.htm
U.S. Department of Agriculture--Farm Service Agency (USDA-FSA), News Release Number 0348.09,
2009.








U.S. Department of Agriculture--Farm Service Agency (USDA-FSA). Implementing the Biomass Crop
Assistance Program's Collection, Harvest, Storage, and Transportation Matching Payment
Program, Notice BCAP-2.
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generation and fuel consumption database, file EIA-923 and EIA-860.
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Energy Expenditure Estimates by Source, Table S6b, 2006.
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Major U.S. Investor-Owned Electric Utilities, 2007.
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Dec. 7-9, 2005, Rockville, MD. DOE/SC-0095, 216 pages, June 2006.









Appendix Table 1. IMPLAN Social Accounting Matrix for Florida, 2007.

Activities
Agricul- Fossi- Infra- Const- Manu- Wbod Whole- Transpor- info na- Services-
ture Forestry Fish-hunt fuel Mining Electric structure ruction facture manuf sale Retal tation tion Finance Renting prof
Agriculture
Forestry
Fish-hunt
Fossl-fuel
Mining
Electric
Infrastructure
Construction
Manufacture
_" WHodmanut
S Wholesale
< Retail
Transportation
hfornation
Finance
Renting
Services-prof
Services
Gov-Other


567 21
682 417

333 12
6 0
130 2
31 0
44 3
929 202
59 2
172 42
5 0
111 14
9 1
492 20
34 4
190 38
50 29
2 1


0 1
1

1 2277
1
0 78
0 0
1 412
15 449
1 14
4 72
0 17
4 35
0 17
3 150
0 300
65 549
12 92
0 17


3 40
40

1 10,.540
59
62 0
0 2
0 173
78 228
2 1
B1 19
1 1
64 220
3 is
43 99
26 3
96 21B
9 105
2 7


265 2,563 1 1 76 1 0 84 12 12
63 684 0 3
223 15 1 0
7 3,204 3599 223 676 3V 4.363 262 185 74 305
1 829 61D 6 0 0 1 15 9 42 6
1 446 1.378 159 382 1071 17 99 1352 155 447
47 59 6 17 35 43 26 83 9 13
59 15 537 57 128 324 8B 451 1.077 2,405 271
7 25,094 43,81) 1062 1,969 1.811 2808 2,756 791 1,599 2.79
0 3,949 2,684 2,00 354 220 91 482 12 323 198
1 3.503 6.0-B 546 2.404 885 453 451 168 845 51B
0 5,354 3B9 7 78 208 11 Bs 46 354 45
2 2.042 3,454 363 2.202 1969 4.455 853 667 376 1376
3 923 1,13 41 556 754 339 9,330 2,111 202 2,843
1B 2.677 2.080 109 3.212 7,075 3.149 2.498 38,065 15.436 8,384
1 1327 884 47 479 421 654 670 433 352 1043
51 9.345 11023 454 6.281 5,077 1'547 6,362 9.430 2,654 12.322
1 3,421 23,30 219 3,012 3,089 2,681 3,656 7,497 2,961 7,824
2 20 470 64 .131 75 2.252 641 1,414 95 758


Labor 1,328 1421 28 213 298 2,572 193 30,477 21219 1,444 23,514 32,158 11895 1f,352 35,304 2292 43,202
Capital 2,726 490 265 715 575 7=582 205 B.587 10,929 768 9,528 10,210 5,294 12273 61.729 36,472 13.380
Ind-Taxes 173 83 23 120 44 2,016 27 731 1559 58 9,355 12,094 1203 2,31B 11276 7266 1,97
E House-holds
I- Fed-Gov-Non-
4 Fed-Gov-Def
c Fed-Gov-ln
* St-Gov-non-
St-Gov-edu
i St-Gov-nv
- Inventory
Foreign-T rade
Domestic-
Total 8.075 2.783 440 5.529 1,491 23jB78 591 1D7.356 117.415 8.337 65281 78,522 41,725 54.613 171.904 74J022 10865

All values are in millions of U.S. dollars 2 Household sectors were consolidated to conserve space.

34


Agriculture
Forestry
Fish-hunt
Fossil-fuel
Mining
Electric
infrastructure
Construction
Manufacture
Wood-manuf
Wholesale
Retail
Transportation
Information
Fhiance
Renting
Services-prof
Services
Gov-Other









Appendix Table 1 (continued). IMPLAN Social Accounting Matrix for Florida, 2007.

Activities Commodities
Gov- Agricul- Fossi- Infra- Construc- Manufac- Wood Whole- Transpor- hforma-
Services Other ture Forestry Fish-hunt fuel Mining Electric structure tion ture manuf. sale Retal station tion Finance
Agriculture 7,978 71
Forestry 2.783
Fish-hunt 440
Fossi-fuel 5.414 0 77 38
Mining 7 1U298 5
Electric 914 22.741 224
Infrastructure 5V
Construction 107,356
, Manufacture 38 64 1A,405 86
5 Woodmanuf 126 8203
SWholesale 65281
R< etal 78.522
Transportation 41725
Information 44,040
Finance 168,628
Renthag
Services-prof 32
Services 704
Gov-Other 51 866 2,404 266 2,096 17 827
Agriculture 204 0
Forestry 0
Fish-hunt 228 0
Fossil-fuel 3,129 1638
M inng 45 33
Electric 2.909 114
a Infrastructure 340 92
I Construction 859 61
0 Manufacture 21L081 .152
U Wood-manuf 1,529 31
Wholesale 3.435 244
Retail 672 1
Transportation 3.028 361
Information 4,448 W9
Finance 22.371 974
Renting 1,548 5
Services-prof 19.714 900
Services 19,237 661
Gov-Other 2.342 145
Labor 1tl050 75 557
Capital 33.787 12.347
Ind-Taxes 8,398
M House-holds
I- Fed-Gov-Non- 37 4
d Fed-Gov-Def
C Fed-Gov-lnv
St-Gov-non-edu 19 103 15 58 75
= St-Gov-edu
U St-Gov-nv
Inventory 12 26 0 659 2 6 8
Foreign-Trade 478 0 314 6.208 123 8 46,520 1467 570 154 598
Domestic- 3.4S 41 282 42.872 1.479 2.025 695 136.147 1069 4.850 5640 10,869 26,569 56,175
Total 260.356 95,022 11944 3,002 1052 55.987 2.965 25.717 3JB97 107.356 300,081 20.828 70.132 84.428 55.266 70.894 226.932
All values are in millions of U.S. dollars 2 Household sectors were consolidated to conserve space.








Appendix Table 1 (continued). IMPLAN Social Accounting Matrix for Florida, 2007.
Commodities Institutions and Trade
Services- Gov- Indirect- House- Fed-gov- Fed-gov- Fed-gov- St-gov- St-gov- St-gov- Foreign- Dom-
Renting prof Services Other Labor Capital Taxes holds NonDef Def Inv NonEdu Edu Inv Inventory Trade Trade Total
Agriculture 25 8,075
Forestry 2.783
Fish-hunt 440
Fossi-fuel 5,529
Mining 1491
Electric 23.878
Infrastructure 75 591
Construction D107356
.! Manufacture 735 25 62 117,415
V* Wbodmanuf 8 8.337
S Wholesale 65,281
R< etal 78.522
Transpo rotation 41725
Information 10.508 65 54.613
Finance 3,276 171904
Renting 74,022 74.022
Services-prof 101774 59 101865
Services 66 155 259.396 35 260.356
Gov-Other 629 87.408 95,022
Agriculture 3.306 8 0 63 11 10 751 3.855 11,946
Forestry 31 42 1 71 1,728 3,629
Fish-hunt 409 4 1 0 22 149 1,052
Fossil-fuel 18246 61 327 1563 345 239 95 1374 53,515
Mining 8 2 48 51 149 1.045 2.965
SElectric 8,424 22 142 417 91 42 6,880 24,980
Shtrastruclure 2606 13 33 297 63 4 0 3jB25
E Construction 0 120 358 819 1649 143 15236 73.284 5 7.998 07.356
0 Manufacture 96.1B5 305 4,525 3.381 4.937 985 2.15 1B.550 17274 48.249 300.086
C Wood-manuf 1,754 46 5 0 765 222 342 735 4,328 20,828
Wholesale 26.117 63 309 127 1160 252 330 4.625 5.338 12J021 70.1B2
Retail 66,590 0 0 1 10 1895 8,705 84,428
Transportation 14.581 98 689 47 871 417 71 750 5,704 D0.452 55266
Information 23,029 385 779 17 2,282 760 19 1697 784 18,147 70,895
Finance 62,297 223 20 2.336 59 4.838 1683 48,627 226.932
Renting 64,659 10 26 270 40 2,262 ,831 79,340
Services-prof 8.15 199 4,921 109B 3,677 901 355 6.704 1.620 19.006 134.530
Services 182,414 426 1791 6,109 1.029 346 66.173 315635
Gov-Other ___ ___127 6.136 B.049 1911 36.276 B7 165 13.935 1888 117256
Labor 405.5B
Capital 237364
Ind-Taxes 57,920
House-holds 8.557 358994 1Z6.714 24.759 11.147 1B.346 202.522 0 5.593 830,632
I-Fed-Gov-Non- 365 46,086 1,704 5.443 77,858 48,644 0 0 10, 40
Fed-Gov-Def 31,983 0 31983
Fed-Gov-Inv 9 5,481 0 5,500
St-Gov-non-edu 44 17J072 394 764 -231 52.478 4318 19.599 25.068 0 0 19.776
3 St-Gov-edu 244 41359 41603
St-Gov-inv 1B.420 0 1B.420
SInventory 5.1fl -325 161tB9 2.137 0 336 17.597 23.178 64,657 404,636
Foreign-Trade 1 84 29 11,956 344 3,556 1,678 0 0 0 0 0 0 3.354 77.442
Somestic- 1.974 21230 38.335 3.022 -31,855 0 0 0 0 0 0 0 0 334.837
Total 79.340 B14.530 315.635 117.256 405,51B 237364 57,920 830,632 150.140 31.983 5500 19.776 41603 1B.420 401989 77.442 334.837 5,650,074
Note: all values are in millions of U.S. dollars; household sectors were consolidated to conserve space.




Full Text

PAGE 1

1 Economic Impact s of Expanded Woody Biomass Utilization on the Bioenergy and Forest Products Industries in Florida Sponsored Project Final Report to Florida Department of Agriculture and Consumer Services -Division of Forestry By Alan W. Hodges, Thomas J. Stevens and Mohammad Rahmani University of Florida, Institute of Food and Agricultural Sciences Food and Resource Economics Department Gainesville, FL Revised February 23 2010

PAGE 2

2 Executive Summary This study evaluated the economic impacts in the state of Florida from expanded use of biofuels under sele cted policies and incentives as mandated by the Florida legislature in 2008 (HB 7135) The study focused on use of woody biomass fuels for electric p ower generation, since this is a mature technology that is poised to rapidly expand under enabling legislation. The analysis was conducted using Input Output analysis and Social Accounting Matri ces (I O/SAM ) for Florida together with a Computable General Equilibrium (CGE) model of the s he Impact Analysis for Planning ( IMPLAN ) Professional software and associated databases (MIG, Inc.) provide d regional information on industry output, value added employment person al income, commodity supply and demand state local and federal government taxes and spending cap ital investment, business inventories, and domestic and foreign trade T he I O/SAM model w as use d to generate a snapshot of the Florida economy that serve d as the starting point for implementation of the CGE model which finds a solution where all markets are in equilibrium, i.e. supply equals demand The model was customized to reflect the makeup of the forestry sector (timber production, logging and support services), wood products manufacturing (sawmills, pulp and paper, etc.) and use of biomass fuels as a substitute to fossil fuels (coal, natural gas, oil) for electric power generation It was assumed that biomass fuels could be provided from domestic and international imports as well as Florida resources, since commodity trade is a feature of the CGE model. Forestry sector production is assumed to include sources such as urban wood waste, short rotation energy crops, and logging residues, as well as mercha ntable timber resources. The impact of increasing biomass fuel supply for electric power generation was simulated over a range of 1 to 80 million green tons annually at an average price of $30 per ton. The upper end of this range represents approximately 26 percent of current electricity production in Florida, and about 21 percent of projec ted generation in the year 2025 These levels can compared to a proposed Renewable Electricity Standard, which would mandate a certain minimum percentage of renewable fu els for electric power sales to final consumers by a given date Simulations were also conducted to test the effect of a $0.010 or $0.01 1 per kilowatt hour state or federal renewable electricity production tax credit, and a 100 percent federal subsidy for biomass fuel producers under the Biomass Crop Assistance Program (BCAP) A ssumptions about mobility of capital to meet changes in industry output an d intermediate commodity demand were tested with different model settings. It was estimated that increasing biomass use for electric power generation would bring about a relatively small increase in Gross Domestic Product (GDP) of Florida, overall employment, and state government revenues, while modestly decreasing imports of fossil fuels. A t the biomass supply level of 40 million tons, with capital assumed to be mobile, GDP would increase by 0.32 percent above the

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3 base level representing $2.2 billion Output or sales of the forestry sector would be increased dramatically, about 69 percent above current levels, to meet new demand for woody biomass fuels O utput of the electric power sector would decrease by up to 0.33 percent as a result of marginally higher costs for biomass fuels. Output of the forest products manufacturing sector w ould decrease by 6.7 percent due to competition for the forest resource Imports of fossil fuels would decrease b y 2.5 percent, representing a savings in import purchases of $1.14 billion while imports of forestry commodities would increase Employee income would increase by $1.61 bi llion T ax revenues to state government would incre ase by 0.06 percent ($108 million). Under the same conditions, i.e. 40 million tons biomass supply, prices for forest commodities may increase by up to 18 percent in the short run (with fixed capital) due to resource competition, but would likely be much lower in the long run as capital resources are reallocated to biofuel production. When the CGE model was modified to disaggregate timber production and logging/forestry support services, much larger price effects were observed, with composite prices for timber increasing by 42 percent, prices for logging/support services increasing by 143 percent, and prices for manufactured wood products increasing by 2.4 percent When the model was further modified to res trict imports of timber and logging/support services, prices for forestry products increased by 150 percent, prices for logging/support se rvices increased by 280 percent, and prices for manufactured wood products increased by 4.6 percent. In centives such as a renewable energy production tax credit for electricity generated from biomass, and a subsidy to forestry biomass producers, would further increase forest sector output and state GDP and employment, and reduce imports of fossil fuels In particular, an electricity production tax credit equivalent to $0.010 $0. 011 per kilowatt hour would substantially increase output of the electric power sector, and decrease imports of fossil fuels, while reducing the negative impact of higher electricity prices on all other sectors. However, assuming that the tax credit is unlimited, the state sponsored incentive would significantly reduce state government revenues by nearly $200 million at the 40 million ton biomass supply level. The 100 percent biomass f eedstock federal subsidy to forestry producers would dramatically increase both electric power and forestry commodity output, but would not appreciably affect state government revenues. T he models used in this analysis d o not incorporate a time dimension however, it is assume d that the estimated economic impacts would occur within a relatively short period of a year or less. It may be expected that the result s for the mobile capital scenario would hold in the long run, s ay 10 years or more, while fixed capital would prevail in the short run subject to limitations on capital movement, especially for highly fixed assets such as forest inventories. The I O/ SAM and CGE models with mobile capital do not explicitly incorporat e any physical capacity limitations on production of a commodity such as biomass fuels. This stands in

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4 contrast to bioeconomic models such as the Southern Region Timber Supply (SRTS) model used in a companion study which dynamically represents timber inventories, forest growth and harvest removals The relatively modest effects on forest commodity prices observed in th e fixed capital CGE analysis, even in the face of a threefold increase in demand, may be attributed to the moderating effect of increase d imports, substitution effects, the diverse mix of different biomass resources available, and the fact that commercial timber production in the CGE model represent s less than 25 percent of the total forestry sector. Based on these findings, it is conclude d that the various policies and incentives for bioenergy development would have an overall positive impact on the economy of Florida in terms of increased GDP, employment and state government revenues, and decreased imports of fossil fuels. The forestry se ctor would particularly benefit from increased demand and prices. However, the forest product manufacturing sector would be adversely affected by competition for wood resources and higher prices for material inputs.

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5 Introduction I nterest in development o f renewable energy resources ha s been motivated by economic, environmental, a nd national security concerns. Reliable and cost effective supplies of fuels for transportation and electric power ge neration are a key driver of economic development, and are in large part responsible for the mobility and high standard of living enjoyed in the United States. Replacement of fossil fuels with renewable energy sources such as wind, solar and biomass is an important strategy for reducing greenhouse gas emissions, mitigating effects of global climate change, reducing expenditures on imports, and reducing dependence on petroleum from politically unstable regions. Costs for natural gas and petroleum (gasoline, diesel) have dramatically increased in recent years, motivating development of alternatives to these fuels. Although coal remains an abundant, low cost and domestically available fuel, its high carbon emissions have raised concerns about its dominant use for electric power generation. Biofuels are a primary candidate for renewable energy in Florida, due to the year round growing conditions and relatively abundant forest and water resources, while potential wind and hydropower resources are considered relat ively small (Navigant Consulting, 2008 ). Woody biomass fuels may be used directly for electric power generation by utilities, for combined heat and power systems in industrial facilities, or as a feedstock for production of ethanol biofuel via cellulosic c onversion technology. Solid biomass fuels are currently used for electric power generation in Florida at 2 3 facilities The types of biofuels in use includ e agricultural crop byproducts, wood and wood waste, biogenic municipal solid waste and landfill gas Total electric power generation from biomass fuels in Florida was 2.98 terawatt hours in 2008, or about 1.4 percent of total generation (USDOE EIA). In 200 6 there were 380 megawatts of installed electric generating capacity in Florida fueled with woody biomass and the t echnical potential for additional electricity generation from woody biomass and short rotation woody crops was estimated at 2 .1 to 4 4 Gigawatts, or 3.9 to 8.3 percent of total capacity in 2006 (Navigant Consulting, 2008). Although there is considerable research and development effort ongoing for use of wood and biogenic waste materials for production of liquid transportation fuels (ethanol, biodiesel) via cellulosic conversion technology major barriers remain for its full scale commercialization (USDOE, 2006). It is anticipated that the need for bioenergy sources will lead to rapid exploitation of forests and other biomass resources. This has raised concerns about the potential for ecos ystem degradation and adverse impacts on their sustainability. Also, greater use of biomass will inevitably lead to more competition for forest resources between traditional users of forest products and the emerging bioenergy sector, with the result that p rices may increase significantly. The forest products industry in Florida generate d approximately $16.7 billion in output (revenue) impacts, $ 7.0 billion in value added

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6 (income) impacts and employment impacts of 89,000 jobs in 2006 and is a leading econom ic sector in many rural counties in the northern part of the state (Hodges et al, 2008). Based on these concerns, t he 2008 Florida Legislature mandated an evaluation of the economic and market impacts of increased utilization of woody biomass resources fo r bioenergy (HB7135, section 113, page 236) with the Florida Department of Agriculture and Consumer Services (FDACS) designated as the agency responsible for this mandate. T he intent of th e legislation is to assure that f uture supplies of forest resource s and other biomass materials are sufficient to support expanded b ioenergy production as well as traditional forest product s without undue market disruption. Federal and state incentive policies are used to encourage electric utility industry to use resources that have less pollution to the environment. These incentives include investment and production tax credits, biofuel production subsidies, and a quota sys tem known as a Renewable Portfolio Standard (RPS) Some incentives reimburse users for part or all of the cost of woody biomass feedstock delivered to users while other incentives provide a credit for fuels or electricity generated from b iomass resources. Any type of monetary incentive would have an impact on the cost of biomass feedstock in comparison to other fuel s Although there may be some non monetary incentives such as Healthy Forest Restoration Act of 2003 which recommends forest thinning programs for reducing the risk of wildfire, only those incentives were taken into account which may have direct monetary effects on using woody biomass for electricity generation. Perhaps the most important incentive for electric power generators is the Re newable Portfolio Standard (RPS), also known as a Renewable Electricity Standard (RES), which consists of a schedule of targets that prescribe a minimum share of electric power to be generated from renewable energy sources by certain dates in the future. Under this policy, similar to cap and trade programs, e lectric utilities may chose to develop and operate biofuel facilities or purchase credits from other generators with a surplus of credits The RES has been widely used to evaluate the potential costs a nd benefits of increasing renewable energy and controlling greenhouse gas emissions. For example, a recent study estimated that a 25 percent federal RES in Florida would generate $11.2 billion in new industry output and create 42,800 jobs from operations o f renewable energy facilities by the year 2025 (English et al, 2009). The study considered a mix of dedicated energy crops, solid wastes, biogas, solar, and cofiring of wood with coal. Although the study determined that electric power rates would be increa sed as a result of a RES, raising costs to utility customers by $2.96 billion, the net impacts on the economy were still overwhelmingly positive. However, this analysis was conducted with a simple regional input output model ( Implan ) that does not consider substitution effects for capital and labor resources.

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7 Among several other federal and state incentives, the most relevant to biomass resources is the Renewable Energy Production Tax Credit in Florida ( N.C. Solar Center ). Th e program in Florida enacted i n July 2006 provides a $0.01 per kilowatt hour credit to cogeneration or combined heat and power (CHP) facilities t hat use eligible renewable sources such as biomass. T he tax credit may be claimed for ele ctricity produced and sold between January 2007 and June 2010, however, the unused credit may be carried forward for up to 5 years. A similar federal program provides a $0. 011 per kilowatt hour tax credit for electricity generation from renewable sources. A recent incentive introduced by the USDA Farm Serv ice Agency is the Biomass Crop Assistance Program (BCAP) which allows matching payments for collection, harvest, storag e, and transportation of certain eligible materials to be used by qualified biomass conversion facilities ( USDA FSA, 2009 ). The agency b egan accept ing application s for BCAP in July 2009. Under this program, o wners of qualified biomass material s can receive financial ass istance for delivering it to conversion facilities that use biomass fuels for heat, power, biobased products or advanced biofuels. M atching payments are made at a rate of 100 percent of the price of biomass delivered to a qualified conversion facility, up to $ 45 per dry ton equivalent. B iomass owner s are eligible to receive payments for two years. Q ualified biomass conversi on facilities must be located in the U.S. or U.S. territories must be a separate legal entity from owners of biomass material s purchased, a nd must conduct the purchase in arms length transactions The purpose of this study wa s to estimate the potential ec onomic impacts in Florida both positive and negative, from expanded use of biofuels under sele cted federal and state policies, including a Renewable Electricity Standard, a renewable electricity production tax credit, and a biomass feedstock subsidy. The study focuse d on use of woody biomass fuels for electric power generation, since this is a mature technology that is poised to rapidly expand under enabling legislation. E stimates of economic impacts were developed for the forest ry sector, forest product manufacturing, electric power, and other major industry sectors in Florida.

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8 Methodology The economic impacts of changes in demand for woody biomass due to expanded renewable energy production in Florida were assessed using a regional Input Output model and Social Accounting Matrix (I O/SAM) coupled with a Computable General Equilibrium (CGE) model. The Impact Analysis for Planning ( IMPLAN ) Professional software and associated databases for Florida (MIG, Inc. 2008) were used to construct the I O/SAM, and the General Algebraic Modeling System software (GAMS Development Corporation) was used to build and run the CGE model The I O/SAM generated by IMPLAN includes information on industry output, value added, employment personal income, commodity supply and demand state local and federal government taxes and spending, capital investment, business inventories, and domestic and foreign trade. Information is detailed for 440 individual industry sectors, nine household income classes, and six state local or fede ral government institutions T he I O/SAM represents a snapshot of the Florida economy in the base year of 2007 that serves as a starting point for the implementation of the CGE model which finds an optimal solution where all markets are in equilibrium, i. e. supply equals demand The particular CGE model used in this analysis was originally developed for national economies (Lofgren et al., 2002), and was later adapted for use on regional economies and analysis of biofuel policies ( Ho lland Stodick and Devadoss 2009). Significant components of the IMPLAN databases for industry and institutional transactions are based on national averages, including the industry production functions that represent the proportion of industry expenditures on intermediate inputs and value added components. The IMPLAN production function coefficients for the Electric Power Generation sector were adjusted to match data available from the Department of Energy (DOE EIA ) and the Feder al Energy Regulatory Commissio n for Florida for the year 2 007, as shown in Tables 1 and 2 uses a much larger proportion of natural gas than the nation on average. Also, like many eastern states, Florida has no hydro electric or geo the rmal based generation. The same EIA data also larger than that specified in the IMPLAN databases. Adjusting the total output, production function coefficien ts and value added components for this industry to match published data enabled the I O/SAM model to more accurately represent the economy of Florida and the activity of the electric power sector. Once the IMPLAN production function and study area data fo r Electric Power Generation and Transmission were updated, unaggregated I O/ SAM matrix files were produce d with the IMPLAN Professional software using procedures described in the IMPLAN Users Guide (MIG, 2004).

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9 Table 1. Modification of IMPLAN f uel r ela ted p roduction f unction c oefficients for the e lectric p ower g eneration s ector in Florida 2007. IMPLAN Sector Number IMPLAN Sector Name Original Coefficient Modified Coefficient 1 9 Sugarcane Farming 0.000000 0.001660 15 Forestry 0.000000 0.000830 16 Logging 0.000000 0.000830 20 Oil a nd Gas Extr action 0.087734 0.056140 21 Coal Mining 0.042305 0.073960 32 Nat ural Gas Distribution 0.000001 0.000010 115 Petroleum Refining 0.013523 0.008650 125 Nuclear Fuel Manufacturing 0.000000 0.006570 337 Pipeline Transp ortation 0.022228 0.302650 Total 0.165791 0.451300 1. Derived from Department of Energy (DOE EIA) and the Federal Energ y Regulatory Commission published data. Table 2. Modifications to electric power sector s tudy a rea d ata for Florida. Original IMPLAN Study Area Data (Million $) Revised Study Area Data 1 (Million $) Industry Output 12,734.520 23,878.430 Value added components Employee Comp 1,919.534 2,571.558 Proprietary income 707.714 936.606 Other Prop. Income 5,021.370 6,645.406 Indirect Business Taxes 1,523.313 2,015.990 Total Value Added 9,171.931 12,138.362 Expenditure Shares Value Added 0.720242 0.508340 Intermediate Inputs 0.279758 0.491660 Total 1.000000 1.000000 Employment (jobs) 19,250 36,096 1. Department of Energy (DOE EIA) and the Federal Energ y Regulatory Commission (FERC). Although IMPLAN d atabases contain data on over 460 industry and institutional sectors, it is impractical to include all these sectors in a CGE model because of the computational requirements, so it was necessary to aggregate many of these sectors. For the biofuels CGE model this aggregation was designed to keep industry sectors of interest relatively disaggregated while combining sectors of lesser interest into broad general categories. In Table 3, the overall aggregation scheme for the CGE model is presented in which the 460 IMPLAN industry and institutional sectors are consolidated into 40 aggregate sectors. Industry sectors such as Infrastructure, Construction, an d Wholesale Trade

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10 that are unique in their role in the economy, as well as Federal and State government sectors were l e f t unaggregated because they did not fit well into other agg regate industry classifications The s ectors for Proprietary Income and Other Property Income w ere combined, and sectors for Corporations and Capital were aggregated into a single sector called Capital (Table 3 ). Since this analysis is focused on woody biofuels and electric power generation, the aggregation scheme for these two s ectors, and certain other closely related sectors is detailed in Table 4. The aggregated Forestry sector for the CGE model is comprised of IMPLAN sectors for Forestry, Commercial Logging, and Support Activities for Agriculture and Forestry. It should be no ted that this industry sector represents forest harvest and transportation activities, as well as forest management and timber production Additional runs of the model were also conducted with Forestry and Logging/Support Activities disaggregated as separa te sectors. Industries involved with fossil fuels are of int erest because woody biofuels substitute s for fossil fuels in the overall fuel mix used by electric power generators. The aggregate fossil fuel sector is comprised of eight IMPLAN sectors that represent oil, natural gas, and coal extraction, support activities for these sectors, and petroleum refineries (Table 4). Electric power generation was not aggregated with any other IMPLAN sectors. This was critical for the simulation of spe cific scenarios with the CGE model that were used to estimate the economic impacts of renewable portfolio standards and various other government incentive programs designed to encourage a shift to this technology Since the increased use of Forestry produ cts as biofuel will compete with their use by wood product manufacturing industries ( such as solid wood and paper products ) seventeen wood related manufacturing industries were aggregated into a wood manufacturing sector, separate from all other types of manufacturing (Table 4).

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11 Table 3 Ag gregation s cheme for the Florida w oody b io fuels computable g eneral e quilibrium m odel. Aggregate Sector Aggregate Sector Name IMPLAN Sector Numbers Industry/Enterprise Sectors 1 Agriculture 1 14 2 Forestry & Related 15,16, 19 3 Fishing And Hunting 17 and 18 4 Fossil Fuel s Related 20,21,28, 29, 30, 32, 115, 337 5 Mining 22 27 6 Electric Power Generation 31 7 Infrastructure 33 8 Construction 34 40 9 Manufacturing 41 318 10 Wood Related Manufacturing 95 112 11 Wholesale Trade 319 12 Retail Trade 320 331 13 Transportation 332 336 & 338 340 14 Information 341 353 15 Finance 354 360 16 Renting 361 366 17 Services Professional 367 381 18 Services 382 426 19 Government Enterprises & Other 427 440 Institutional Sectors 20 Labor 5001 21 Property Income 6001 7001 22 Indirect Business Taxes 8001 23 Households Less Than $ 10K 10001 24 Households $ 10K To $ 15K 1000 2 25 Households $ 15K To $ 25K 1000 3 26 Households $ 25K To $ 35K 1000 4 27 Households $ 35K To $ 50K 1000 5 28 Households $ 50K To $ 75K 1000 6 29 Households $ 75K To $ 100K 1000 7 30 Households $ 100K To $ 150K 1000 8 31 Households Greater Than $ 150K 1000 9 32 Federal Government Non Defense 11001 33 Federal Government Defense 11002 34 Federal Government Investment 11003 35 State Government Non Education 12001 36 State Government Education 12002 37 State Government Investment 12003 38 Investment 13001, 14001, 14 002 39 Foreign Trade 25001 40 Domestic Trade 28001 An Excel spreadsheet of the IMPLAN industry sector scheme is available at: http://implan.com/v3/index.php?option=com_docman& task=doc_download&gid=148&Itemid=138

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12 Table 4 Detailed a ggregation s cheme for selected industry groups in the Florida w oody b io fuels computable g eneral e quilibrium m odel. Aggregate Sector Num ber Aggregate Sector Name IMPLAN Sector Number IMPLAN Sector Name 2 Forestry & Related 15 Forestry Forest Products & Timber Tracts 16 Commercial L ogging 19 Sup port Activities f or Agriculture & Forestry 4 Fossil Fuel s 20 Oil a nd Gas Extraction 21 Coal Mining 28 Drilling Oil And Gas Wells 29 Support Activities f or Oil & Gas Operations 30 Support Activities f or Other Mining 32 Natural Gas Distribution 115 Petroleum Refineries 337 Pipeline Transportation 6 Electric Power 31 Electric Power Generation, Transmission a nd Distribution 10 Wood Product Manufacturing 95 Sawmills a nd Wood Preservation 96 Veneer a nd Plywood Manufacturing 97 Engineered Wood Member & Truss Manufacturing 98 Reconstitut ed Wood Product Manufacturing 99 Wood Windows a nd Doors And Millwork 100 Wood Con tainer a nd Pallet Manufacture. 101 Manu Fact Home (Mobile Home) Manufacturing 102 Prefabrica ted Wood Building Manufactur ing 103 All Other Misc. Wood Product Manufactur ing 104 Pulp Mills 105 Paper Mills 106 Paperboard Mills 107 Paperboard Container Manufacturing 108 Coated & Laminated Paper & Packaging Paper 109 All Other Paper Bag, Coated & Treated Paper Manuf. 110 Stationery Product Manufacturing 111 Sanitary Paper Product Manufacturing GAMS routines originally developed by Rutherford and by Stodick, Holland, and Devadoss were used to aggregate the IMPLAN I O/ SAM files for use in the CGE model as shown in Appendix Table 1. T he SAM represents the flows of dollars between the various sectors of the economy. Activities represent industries, commodities represent goods and services sold or purchased, and institut ions represent income and expenditures for capital, labor, taxes, inventory and trade. Purchases of, or expenditures on c ommodities by ac tivities, and revenues derived from the manufacture of c ommodities by different Activities are r epresented by the tabl e columns. Receipts for c ommodities and f actors, and revenues to a ctivities are represented by table rows. Rows and columns of the SAM must balance, so there is a complete accounting of all transactions or transfers in the economy.

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13 For the SAM that was derived fro m the IMPLAN model of Florida some imbalances occurred due to the parameter modifications made for the Elec tric Power Generation sector, however, t hese imbalances were subsequently resolved by running a null counterfactual through the CGE mode l. The GAMS CGE model used for this analysis is a comparative static regional CGE model that was adapted by Holland et al. (2007, 2009) from a national CGE model developed by Lofgren et al. (2002). Compared to Input Output models like IMPLAN where goods a nd factors are transacted in fixed proportions, at fixed prices, and without global supply constraints, CGE models include price changes in response to changes in quantities demanded or supplied and allow for substitution between goods and factors based o n those relative prices The demand and supply relationships specified in this general equilibrium model are derived from neo classical economic theory where firms maximize profits, households or consumers maximize a utility function and all markets clea r, i.e. supply equals demand In this model, firms maximize a hybrid Leontief/constant elasticity of substitution (CES) type production function and households or consumers are modeled as maximizing a Stone Geary utility function The Leontief CES producti on function uses fixed proportions for intermediate inputs but employs a nonlinear CES functional form for the primary factors of capital and labor for each industry in the model The CGE model encompasses both domestic and foreign trade with imperfect substitution, so the composition of supply depends on the relative prices of foreign, domestic and regional products and imports. Likewise the mix of domestically marketed and exported goods and services is also determined by relative prices. The model is constrained by accounting rules or equilibrium conditions that require production to satisfy all demands. In this case, markets are required to clear for goods and factors, firms earn zero profits above normal returns to capital household endowments are f ully employed, and household spending exhausts income. T he biofuels CGE model was constructed in GAMS as a simultaneous system of non linear equations and solved using the PATH solver Initially, consumer prices of domestic goods and imports, the world pr ice of exports, factor prices, and the currency exchange rate we re all set equal to one. T he model was then solved to replicate the IMPLAN SAM, and calibrat e many of the model parameters. However, elasticities of income, substitution or transformation betw een goods produced and sold in different markets, and for capital labor substitution in production, must b e specified by the user. For this applicat i on, these elasticities were specified using default values provided in the published CGE model by Holland et al. and by Bilgic et al (2002). The elasticity parameter values used are shown in Table 5 Further details on the technical specification of the CGE model and choice of elasticity parameter values can be found in Holland, Stodick, and Painter ( 2007 )

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14 Table 5 Ela sticit y parameters for the Florida woody biofuels c omputable g eneral e quilibrium m odel. Parameter Value Definition Xed( C T ) 5 Elasticity of demand for world export function Esubp( A ) 0.99 Elasticity of substitution for production Esubd( C ) 2 Elasticity of substitution ( A rmington) between regional output and imports Esubs( C ) 2 Elasticity of substitution (transformation) between domestic/regional and foreign demand Esube( C ) 2 Elasticity of substitution (transformation) for exports between Rest of World and Rest of U S Esubm( C ) 2 Elasticity of substitution (Armington) of imports between Rest of World imports and R est of U S Ine( C H ) 1 Income elasticity Income_Ine 1 Investment on commodities elasticity Frisch( C ) 1 Consumption flexibility ( determines minimum subsistence level of consumption ) Ifrisch( C ) 1 Investment demand flexibility ( 1 implies no minimum investment level) Efac( LAB ) 4 Demand elasticity for labor Efac( CAP ) 0.5 Demand elasticity for capital The CGE model includes additional parameters for government tax es and macro economic closure settings that can be exogenously specified by the user. Government tax rates can be specified for s ales tax es, c onsumption ta xes paid by households, excise taxes on domestic production, and taxes on imports and exports. Options for various macro economic closures are also available for capital, labor, savings and investment, and current account balances. For the biofuels model, the base run included the assumption that c apital is activity specific and fixed, l abor is mobile and u nemployment is possible s avings and investment are not linked and foreign and rest of U.S. savings are variable through the export column of the SAM An alternative set of model runs wer e made where capital is mobile and endowment is variable Over the short run, capital movement may be a limiting factor for implementation of a Renewable Electricity Standard or other incentives, however, in the long run, say ten years or more, it may reas onably be assumed that capital would be mobile and would move to those areas of highest and best use. The first set of simulation runs with the CGE model were made for fixed increases in biofuel inputs for electric power generation at levels of 1, 5, 10, 20 40 60 and 80 million tons in a given year. This range of biomass fuels covers the spectrum of alternative scenarios contemplated for biofuels to meet a Renewable Electricity Standard in Florida. A supply of 40 million tons of woody biomass (freshweigh t basis) for electric power generation would produce approximately 28.2 billion KWhr of electricity at current technical efficiencies, representing about 13.1 percent of current annual power generation in Florida, and about 10.6 percent of projected electr ical generation in the year 2025, while

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15 the maximum biomass supply level of 80 million tons would account for about 21 percent of projected electrical generation demand in 2025, as shown in Table 6. The cost of biomass fuels was estimated at $30 per ton, based on 2007 average delivered prices for timber in Florida (Timber Mart South) which would represent a total value of $1.20 billion f or 40 million tons and $2.41 billion for 80 million tons Table 6. Biomass supply levels for computable general equilibrium model s imulations Biomass Supply (million tons, freshweight basis) Gross Heat Energy Content (trillion BTU) (1) Electrical Generation (million k ilo w att hours) (2) Share of Electrical Generation in Florida, 2007 (3) Share of Electrical Generation in Florida, 2025 (4) Value of Biomass Fuel (million $) (5) 1 9.6 706 0.3% 0.3% 30.1 5 48.2 3,529 1.6% 1.3% 150.6 10 96.3 7,057 3.3% 2.7% 301.2 20 192.6 14,115 6.5% 5.3% 602.4 40 385.3 28,230 13.1% 10.6% 1,204.8 60 577.9 42,345 19.6 % 15.9% 1,807.2 80 770.6 56,460 26.1% 21.2% 2,409.6 (1) 12.04 million BTU per ton semi dry woody biomass (USDA, Fuel Value Calculator, 2004). Semi dry biomass has 30% moisture content (80% of freshweight). (2) Reflects steam to electrical energy conversion factor 3,412 BTU/KWh and 25% thermal efficiency factor (USDOE EIA). (3) Florida electrical gene ration in 2007: 216.09 billion k ilo w att hours (USDOE EIA, EIA 906 920 report, Monthly generation and fuel stock data at electric power generating facilities). (4) Projected Florida electrical generation in 2025: 266.01 billion k ilo w att hours (USDOE EIA, Annual Energy Outlook, 2009). (5) Value of biomass fuel estimated at composite average delivered price for timber in Florida, 2007: $30.12 per ton (Timber Mart South). In the parlance of CGE analysis these alternative scenarios are known as counterfactuals. The counterfactual increases in biofuel inputs were imposed on the CGE model by modifying the Leontief coefficients for the intermediate inputs, including fuel, in the production function for the Electric Power Generation sector. Based on 2007 EIA data, it was determined that costs per kilowatt hour (KWH) of generating electricity from woody biofuels were 13.8 percent higher, on average, than the average cost per KWH for power generated from all types of fossil fuels in the State. Thus, for example, when biofuel inputs to electric power generation were increased by 10 million tons, or $30 million dollars, fossil fuel inputs were reduced by 87.8 percent, or $ 26. 4 mill ion. These unequal substitutions in production function result in a small increase in the sum of the Leontief coefficients for the

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16 intermediate inputs in model, so to keep the production function from over estimating production, the shift parameter to the function was calibrated downward to keep output constant. The parameters to the CES part of the production function for capital and labor are assumed to be independent of substitutions between types of fuel in the model. The counterfactual Leontief coeffi cients for the CGE model are given in Table 7. As would be expected, the largest changes occur in the Leontief coefficients are for Forestry and Fossil fuels. The reduced shift parameters, shown in the last row, represent the effect of increases in the co st of electric power generation for biofuels. Table 7 Leontief coefficients and production function shift parameters for biofuels CGE counterfactual simulations. Industry Sector Additional Woody Biofuels For Electric Power Generation (Million Tons) Calibrated 1 5 10 20 40 60 80 Leontief Coefficients Agriculture 0.00169 0.00169 0.00169 0.00169 0.00169 0.00168 0.00168 0.00167 Forestry 0.00200 0.00330 0.00852 0.01503 0.02801 0.05387 0.07956 0.10509 Fishing Hunting 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 Fossil Fuel 0.44810 0.44688 0.44201 0.43594 0.42382 0.39969 0.37572 0.35190 Mining 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 Electric P ower 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 Infrastructure 0.00009 0.00009 0.00009 0.00009 0.00009 0.00009 0.00009 0.00009 Construction 0.00735 0.00734 0.00734 0.00733 0.00732 0.00730 0.00728 0.00725 Manufacturing 0.00971 0.00971 0.00971 0.00970 0.00968 0.00965 0.00962 0.00959 Wood Manufacturing 0.00006 0.00006 0.00006 0.00006 0.00006 0.00006 0.00006 0.00006 Wholesale 0.00079 0.00079 0.00079 0.00079 0.00079 0.00079 0.00078 0.00078 Retail 0.00003 0.00003 0.00003 0.00003 0.00003 0.00003 0.00003 0.00003 Transportation 0.00938 0.00938 0.00937 0.00936 0.00935 0.00932 0.00929 0.00926 Information 0.00062 0.00062 0.00062 0.00062 0.00062 0.00062 0.00062 0.00062 Finance 0.00422 0.00422 0.00422 0.00421 0.00421 0.00419 0.00418 0.00417 Renting 0.00011 0.00011 0.00011 0.00011 0.00011 0.00011 0.00011 0.00011 Services Professional 0.00926 0.00926 0.00925 0.00925 0.00923 0.00920 0.00917 0.00914 Services Other 0.00446 0.00446 0.00446 0.00445 0.00444 0.00443 0.00442 0.00440 Government Other 0.00028 0.00028 0.00028 0.00028 0.00028 0.00028 0.00028 0.00028 Total 0.49816 0.49824 0.49856 0.49896 0.49975 0.50133 0.50290 0.50445 Shift parameters 1.75924 1.75890 1.75756 1.75588 1.75254 1.74589 1.73929 1.73272 The model was used to simulate the effect of a $0.01 1 per kilowatt hour production federal tax credit for electric power generated from renewable sources, and a $0.01 0 per kilowatt hour state (Florida) tax credit, corresponding to the existing Renewable Energy Production Tax Credit enacted in 2006 (N.C. Sol ar Center) The tax credit was modeled as a negative excise tax rate of 1 1 percent and 10 percent respectively, on power sales, which is equivalent to $0.01 1 or $0.01 0 per KWhr, since the average cost of power generation in Florida is approximately $0.10 per KWhr, and applied to the proportion of total fuel expenditures for electrical generation represented by biofuels. Although the Florida law limits the total value of the tax credit to $5 million annually, and the provision expires in 2010, for this exer cise no limitations were considered, in order to illustrate its effect at full scale policy

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17 implementation. A 100 percent subsidy for biomass feedstocks, based upon the federal Biomass Crop Assistance Program (BCAP), was simulated in the model as a negativ e sales tax on purchases of biomass by the electric power sector from the forestry sector. Additional simu lations with the model were done with no domestic or international imports allowed for Forestry and Logging/Support Services sectors, to determine the effect on prices without import substitution possibilities in order to make equivalent comparisons with results from SRTS bioeconomic model used in a companion study. Results Effects on Gross Domestic Product Gross domestic product (GDP) is the broa dest measure of economic activity, representing the net value of all goods and services produced in the region (value added) or alternatively, the total personal and business income received. The GDP of Florida in 2007 was about $701 billion. Estimated c h anges in GDP of Florida under the scenarios for increased use of biomass for electrical power are illustrated in Figure 1. In general, changes in output were directly proportional to the change in amount of biomass supplied to displace fossil fuels. As exp ected, impacts were somewhat greater for the scenario whe re capital was mobile rather than fixed, such that it does not become a limiting factor. For an increase in biomass supply of 40 million tons, GDP of Florida increased by 0.32 percent o r $2.12 billi on above the base level (2007) under the mobile capital scenario, and by 0.12 percent or $848 million for the fixed capital scenario. For the maximum biomass supply level of 80 million tons, GDP would increase by 0.24 to 0.62 percent ($1.67 to $4.37 billio n), respectively, for fixed and mobile capital scenarios

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18 Figure 1 Change s in gross domestic product (GDP) of Florida from increased biomass supply for electric power generation. When the $0.01 per KWhr renewable energy production Florida ( state ) tax credit was simulated in the CGE model, at 40 million tons biomass supply, with capital assumed to be mobile, state GDP increased by 0.35 percent ($2.42 billion), or by an additional 0.0 3 percent ($203 million) above the case without subsidy, as shown in Figure 2. The federal renewable energy production tax credit of $0.01 1 per KWhr increased state GDP by 0.38 percent ($2.68 billion) above the base level and by an additional 0.07 percent above the no subsidy case under the 40 million ton biomass suppl y scenario. A 100 percent federal biomass feedstock subsidy paid to biomass producers in the forestry sector, modeled after the Biomass Crop Assistance Program (BCAP), increased state GDP by 0.81 percent ($5.68 billion) compared to the base case and by 0. 49 percent ($3.46 billion) compared to no subsidy at the 40 million tons b iomass supply level (Figure 2). The effects of all subsidies on GDP were smaller under the fixed capital scenario than for the mobile capital scenario. 0.0 2.5 5.0 7.5 10.0 12.5 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 0 20 40 60 80 Billion Dollars Percent Change from Base Biomass Supply to Electric Power (million tons) Mobile Capital -With Feedstock Subsidy Mobile Capital -With Federal Tax Credit Mobile Capital -With State Tax Credit Mobile Capital -No subsidy Fixed Capital -No Subsidy

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19 Figure 2 Change s in gross domestic product (GDP) of Florida due to subsidies for 40 million ton s biomass supply to electric power generation. Effects on Industry Output C hange s in output or sales of major sectors of the Florida economy are summarized in Table 8 and Figures 3 and 4. Of course, the largest impacts, in percentage terms, were to the forestry, electric power and fossil fuels sectors, which were directly affected by t he change in fuel sources, and also to the mining sector, which reflects derived demand for fossil fuels (Figure 3). For forestry, the presumed source of new biomass supply, commod ity output increased by 36 percent ($1.47 billion) from the curren t base lev el to supply 40 million tons under the fixed capital scenario and by 69 percent ($2.81 billion) under the mobile capital scenario (Figure 4). Wood products manufa cturing decreased in output by 7.5 percent ($587 million) under the fixed capital scenario at the maximum biomass volume, but by only 0.5 percent under the mobile capital scenario. This greater decrease for the fixed capital scenario was because of an increase in prices for forest commodities (see below). The electric power sector experienced decre ased output of 0.2 to 0. 7 percent at the 40 million ton biomass level due to marginally higher prices resulting from the greater cost of biomass fuels compared to fossil fuels. Output of f ossil fuels decreased by up to 0.8 to 2.4 percent at the maximum bi omass level because of decreased demand from the electric power sector as fossil fuels were replaced with biomass. Output of the mining sector also decreased by 2.9 percent under the mobile capital scenario as derived demand for fossil fuels but not at a ll under the fixed capital scenario. O utput of the agr iculture sector 0.12 0.13 0.17 0.48 0.32 0.34 0.38 0.81 0.0 0.2 0.4 0.6 0.8 1.0 No subsidy Florida Renewable Electricity Production Tax Credit Federal Renewable Electricity Production Tax Credit Biomass Feedstock Subsidy (Federal) Percent Change from Base Fixed Capital Mobile capital

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20 decreased by 1.4 percent under the fixed capital scenario, but very little (0.1%) under the mobile capital scenario. All other sectors had very small changes in output value of less than 0.2 percent (Table 8). Th e state production tax credit for renewable energy generation would increase the value of output of the electric power sector by 0.33 percent ($76 million) compared to the base level, and by 0.58 percent ($133 million) compared to without the tax credit at the 40 million ton biomass supply level with capital mobile. The federal production tax credit for renewable energy generation would increase the value of output of the electric power sector by 0.11 percent ($27 million) compared to the base level, and by 0.45 percent ($103 million) compared to no tax credit. The 100 percent biomass feedstock subsidy increased output of the forestry sector by 79 percent ($3.21 billion), the electric power sector by 5.8 percent ($1.33 billion), and the wood products manufacturing sector by 0.61 percent ($48 million) compared to the base level. It would also increase the output of these sectors compared to without the subsidy at the maximum biomass supply, by 9.9 percent ($404 million), 6.0 percent ( $1.39 billion), and 1.1 percent ($84 million), respectively. Figure 3 Change s in industry output value by sector, for 40 million ton s biomass supply to electric power in Florida under the mobile capital scenario. 10 0 10 20 30 40 50 60 70 80 Agriculture Forestry Fishing, hunting Fossil Fuels Mining Electric power Infrastructure Construction Manufacturing, general Wood products manufacturing Wholesale trade Retail trade Transportation Information Finance Rental Professional Services Services, other Government Percent Change from Base

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21 Figure 4 Change s in output value of forestry, wood manufacturing and electric power sectors in Florida from increased biomass supply ( mobile capital scenario ) Effects on Commodity Prices Changes in commodity prices resulting from increases in biomass supplied by forestry for electric power generation are shown in Table 9. These values represent a composite of domestic (Florida) and imported commodity prices. Prices for all commodities in the base year were normalized to a value of one. As with GDP and commodity output changes discussed already, the price changes were linear and proportional to biomass supply levels. The largest price change was an increase of nearly 18 percent for forestry commodities at the 40 million ton biomass s upply level under the fixed capital scenario (Figure 5) However prices for forestry commodities increased by only 0.07 percent under the mobile capital scenario, when additional capital investment is allowed to increase industry capacity in response to g reater demand At the maximum biomass supply level of 80 million tons, with fixed capital, prices for forestry commodities would increase by 30.9 percent. At the 40 million ton b iomass supply level, prices for electric power increased by about 0.5 percent, while prices for manufactured wood products increased by 0.40 percent under fixed capital and by 0.03 percent when capital is mobile When the CGE model was modified to disaggregate timber production and logging/forestry support services, much larger price effects were observed, with composite prices for timber increasing by 42 percent, prices for logging/support services increasing by 143 percent, and prices for manufactured 1.8 8.7 17.4 34.8 69.1 102.9 136.2 0.0 0.1 0.1 0.2 0.5 0.7 0.9 20 0 20 40 60 80 100 120 140 160 0 10 20 30 40 50 60 70 80 Percent Change from Base Biomass Supply to Electric Power (million tons) Forestry Electric power Wood products manufacturing

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22 wood products increasing by 2.4 percent, under the scenario with 40 million t ons biomass supply and fixed capital. The price response was greater for logging/support services than for timber production in this case because logging is the direct supplier to the electric power sector and timber production becomes an indirect input. W hen the model was further modified to restrict imports of timber and logging/support services, prices for forestry products increased by 150 percent, prices for logging/support se rvices increased by 280 percent, and prices for manufactured wood products in creased by 4.6 percent. Figure 5 Change s in composite price for forest commodities from increased biomass supply for electric power. The state renewable energy production tax credit for electric power would reduce electricity prices by 0.64 percent compared to the base level, and by 1.18 percent compared to without the subsidy for 40 million tons of biomass supplied, with mobile capital while the federal renewable energy production tax credit would reduce electricity prices b y 0.75 percent compared to the base level, and by 1.29 percent compared to without the subsidy. The 100 percent biomass feedstock subsidy would reduce increase forestry commodity prices by 0.26 percent and reduce electricity prices by 7.4 percent compared to the base level. When compared to the situation without this subsidy at the maximum biomass supply level, the subsidy would increase prices for forestry commodities by 0.19 percent and decrease electricity prices by 7.97 percent. 0.5 2.7 5.2 9.8 18.0 24.9 30.9 0 5 10 15 20 25 30 35 0 10 20 30 40 50 60 70 80 Percent Change from Base Biomass Supply to Electric Power (million tons) Fixed capital Mobile capital

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23 Effects on Commodity Imports Changes in the quantity of imported commodities resulting from increased use of biomass for electric power generation are shown in Table 10. To meet a supply of 40 million tons of woody biomass, imports of forestry commodities increased by about 11 9 percent ($104 million) under the fixed capital scenario and by 69 percent ($61 million) under the mobile capital scenario. Presumably, these imports would mainly come from the adjoining states of Georgia and Alabama. Importantly, imports of fossil fuels would decrease by up to 2.5 percent ($1.14 billion), and foreign imports of fossil fuels would be reduced by 2.3 percent ($138 million). These changes represent a significant reduction of leakage from the state economy. The state and federal renewable en ergy production tax credit would slightly lessen the change in imports of fossil fuels, by 0.12 percent ($55 million) and 0.16 percent ($73 million), respectively, compared to without the subsidy at the 40 million ton biomass supply level The 100 percent biomass feedstock subsidy would actually increase imports of fossil fuels by 0.26 ($122 million) percent compared to the base level, and by 2.6 percent ($1.21 billion) compared to no subsidy at the 40 million ton biomass supply level. Effects on Labor Dem and Changes in labor demands resulting from increased use of woody biomass for electric power in Florida are shown in Table 11. This information can be understood as representing the total value of wages, salaries and benefits paid to employees, and is a p roxy for employment demand or number of jobs For the 40 million ton b iomass supply level with mobile capital employment demand would increase by 72 .5 percent ($1.43 billion) in the forestry sector, decrease by 0.47 percent in wood products manufacturing, and decrease by 0.58 percent for the electric power sector. P ayments to all employees would be increase by $1.61 billion, but this represents just a 0.29 percent increase from the base level of $40 6 billion. Effects on State Government Revenues Changes in state government revenues from sales, property and excise taxes are shown in Figure 6 At the 40 million ton biomass supply level state government revenues would increase by 0.06 percent, or $108 million with mobile capital and by 0.04 percent or $66 million with fixed capital At the maximum biomass supply level of 80 million tons, state government revenues would increase by 0.12 percent ($212 million) or 0.07 percent ($131 million), respectively.

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24 For 40 million tons of biomass supplied, t he s tate renewable energy production tax credit for electric power would reduce state government revenues by 0.08 to 0.05 percent ( $142 to $89 million) for fixed or mobile capital, respectively, compared to the base level (Figure 7). In contrast, the federal renewable energy production tax credit would increase state government revenues by 0.05 to 0.08 percent ($86 to $140 million) The federal tax credit would also increase state government revenues by 0.01 to 0.02 percent ($21 to $32 million) above that for 40 million tons of biomass without the tax credit The federal biomass feedstock subsidy for 100 percent of delivered fuel costs would increase state revenues by 0.10 to 0.18 percent ($ 174 to $ 330 million) compared to the base level, and by 0.06 to 0.12 pe rcent ($222 million) compared to the situation without the subsidy. Figure 6 Change s in Florida ( state ) government revenues from increased biomass supply for electric power. 350 150 50 250 450 650 0.2 0.1 0.0 0.1 0.2 0.3 0.4 0 20 40 60 80 Million Dollars Percent Change from Base Biomass Supply to Electric Power (million tons) Mobile Capital -With Feedstock Subsidy Mobile Capital -With Federal Tax Credit Mobile Capital -No subsidy Fixed Capital -No Subsidy Mobile Capital -With State Tax Credit

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25 Figure 7 Change s in Florida ( state ) government revenues due to subsidies for 40 million ton s biomass supply to electric power generation 0.04 0.08 0.05 0.10 0.06 0.05 0.08 0.18 0.10 0.05 0.00 0.05 0.10 0.15 0.20 No subsidy Florida Renewable Electricity Production Tax Credit Federal Renewable Electricity Production Tax Credit Biomass Feedstock Subsidy (Federal) Percent Change from Base Fixed Capital Mobile capital

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26 Table 8 Change s in v alue of output for major economic sectors f rom increased use of woody biomass for electric power generation in Florida. Sector Base (Million $) Capital Fixed Capital Mobile Change In Biomass Supply To Electric Power Sector (Million Tons) 1 5 10 20 40 60 80 1 5 10 20 40 60 80 Percentage Change from Base Agriculture 7,967.8 0.04 0.18 0.34 0.65 1.20 1.66 2.06 0.00 0.02 0.03 0.07 0.13 0.20 0.26 Forestry 4,066.8 1.21 6.09 12.32 25.11 51.69 79.12 106.96 1.75 8.74 17.44 34.76 69.05 102.87 136.23 Fishing Hunting 455.9 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.03 0.05 0.10 0.20 0.29 0.39 Fossil Fuels 6,717.5 0.03 0.16 0.32 0.66 1.34 2.05 2.78 0.06 0.31 0.61 1.22 2.43 3.62 4.80 Mining 1,364.1 0.00 0.00 0.00 0.01 0.02 0.04 0.06 0.08 0.38 0.75 1.49 2.93 4.31 5.65 Electric Power 23,027.4 0.00 0.02 0.05 0.12 0.33 0.62 0.96 0.01 0.03 0.06 0.12 0.25 0.37 0.49 Infrastructure 3,139.4 0.00 0.01 0.01 0.02 0.05 0.08 0.11 0.00 0.02 0.04 0.08 0.15 0.22 0.29 Construction 107,325.9 0.00 0.00 0.00 0.01 0.01 0.02 0.03 0.00 0.00 0.01 0.02 0.04 0.05 0.07 Manufacturing General 117,454.1 0.00 0.01 0.02 0.05 0.09 0.13 0.18 0.00 0.01 0.03 0.06 0.11 0.17 0.23 Wood Products Manuf acturing 7,825.0 0.21 1.02 1.98 3.74 6.73 9.20 11.29 0.01 0.06 0.12 0.23 0.46 0.69 0.91 Wholesale Trade 65,266.3 0.00 0.00 0.01 0.01 0.01 0.00 0.00 0.00 0.02 0.03 0.06 0.12 0.19 0.25 Retail Trade 78,805.1 0.00 0.01 0.02 0.03 0.07 0.10 0.14 0.00 0.02 0.04 0.08 0.16 0.23 0.31 Transportation 43,824.9 0.00 0.01 0.01 0.02 0.03 0.04 0.05 0.00 0.00 0.00 0.00 0.01 0.01 0.01 Information 44,176.7 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.00 0.02 0.03 0.06 0.13 0.19 0.25 Finance 170,182.9 0.00 0.00 0.00 0.01 0.02 0.03 0.03 0.00 0.00 0.01 0.02 0.03 0.05 0.06 Rental 77,368.4 0.00 0.01 0.01 0.03 0.06 0.09 0.12 0.00 0.02 0.04 0.07 0.14 0.21 0.28 Professional S ervices 113,200.1 0.00 0.00 0.01 0.01 0.02 0.02 0.03 0.00 0.00 0.00 0.01 0.01 0.02 0.02 Services Other 277,352.2 0.00 0.01 0.01 0.02 0.05 0.07 0.09 0.00 0.01 0.03 0.05 0.11 0.16 0.21 Government 102,266.1 0.00 0.00 0.01 0.01 0.03 0.04 0.06 0.00 0.01 0.01 0.03 0.06 0.09 0.12

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27 Table 9 Change s in composite commodity prices from increased use of woody biomass for electric power generation in Florida Sector Capital Fixed Capital Mobile Change in Biomass Supply to Electric Power Sector (million tons) 1 5 10 20 40 60 80 1 5 10 20 40 60 80 Percentage Change from Base Agriculture 0.01 0.04 0.08 0.15 0.28 0.39 0.49 0.00 0.01 0.02 0.05 0.09 0.14 0.18 Forestry 0.54 2.65 5.17 9.84 17.99 24.92 30.92 0.00 0.01 0.02 0.04 0.07 0.11 0.15 Fishing Hunting 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.00 0.01 0.01 0.03 0.05 0.08 0.11 Fossil Fuels 0.00 0.01 0.02 0.03 0.07 0.10 0.14 0.00 0.00 0.00 0.00 0.01 0.01 0.01 Mining 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.02 0.04 0.08 0.12 0.15 Electric Power 0.00 0.02 0.06 0.16 0.51 0.99 1.60 0.01 0.07 0.13 0.27 0.54 0.81 1.08 Infrastructure 0.00 0.00 0.01 0.02 0.06 0.10 0.16 0.00 0.01 0.02 0.03 0.06 0.09 0.12 Construction 0.00 0.00 0.01 0.01 0.02 0.03 0.04 0.00 0.01 0.01 0.02 0.05 0.07 0.09 Manufacturing General 0.00 0.00 0.00 0.01 0.01 0.02 0.02 0.00 0.00 0.01 0.01 0.02 0.03 0.05 Wood Products Manufacturing 0.01 0.06 0.12 0.22 0.40 0.56 0.69 0.00 0.00 0.01 0.01 0.03 0.04 0.05 Wholesale Trade 0.00 0.00 0.00 0.00 0.01 0.01 0.02 0.00 0.01 0.02 0.03 0.06 0.09 0.12 Retail Trade 0.00 0.00 0.00 0.01 0.02 0.04 0.05 0.00 0.01 0.01 0.03 0.05 0.08 0.10 Transportation 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.00 0.01 0.01 0.03 0.05 0.07 0.10 Information 0.00 0.00 0.00 0.00 0.01 0.01 0.02 0.00 0.01 0.01 0.03 0.06 0.09 0.11 Finance 0.00 0.00 0.00 0.01 0.01 0.02 0.03 0.00 0.01 0.02 0.04 0.08 0.12 0.16 Rental 0.00 0.01 0.01 0.03 0.06 0.09 0.12 0.00 0.01 0.03 0.06 0.11 0.17 0.22 Professional Services 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.01 0.01 0.03 0.05 0.07 0.10 Services Other 0.00 0.00 0.00 0.01 0.02 0.03 0.05 0.00 0.01 0.01 0.03 0.05 0.08 0.10 Government 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.02 0.02 0.03

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28 Table 10 Changes in q uantity of imports due to increased use of woody biomass for electric power generation in Florida Sector Base (Million $) Capital Fixed Capital Mobile Change In Biomass Supply t o Electric Power Sector (Million Tons) 1 5 10 20 40 60 80 1 5 10 20 40 60 80 Percentage Change f rom Base Agriculture 3,912.7 0.01 0.06 0.12 0.24 0.45 0.65 0.82 0.01 0.05 0.10 0.21 0.41 0.62 0.82 Forestry 87.9 2.52 12.87 26.37 55.10 118.73 189.18 264.94 1.75 8.76 17.50 34.89 69.36 103.41 137.07 Fishing Hunting 596.4 0.00 0.00 0.01 0.01 0.03 0.04 0.06 0.00 0.02 0.04 0.08 0.15 0.23 0.30 Fossil Fuels 46,582.0 0.06 0.30 0.60 1.22 2.46 3.71 4.95 0.06 0.30 0.59 1.18 2.34 3.49 4.62 Mining 1,601.5 0.00 0.01 0.02 0.04 0.07 0.09 0.12 0.00 0.00 0.00 0.00 0.01 0.01 0.01 Electric Power 1,890.1 0.00 0.03 0.07 0.23 0.74 1.49 2.42 0.02 0.11 0.22 0.45 0.90 1.34 1.79 Infrastructure 670.3 0.00 0.01 0.02 0.05 0.12 0.21 0.30 0.01 0.03 0.06 0.11 0.23 0.34 0.44 Construction 0.0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Manufacturing General 182,669.4 0.00 0.00 0.01 0.02 0.04 0.06 0.09 0.00 0.02 0.04 0.08 0.16 0.24 0.32 Wood Products Manufac turing 12,511.4 0.01 0.03 0.06 0.11 0.19 0.25 0.29 0.00 0.00 0.00 0.00 0.00 0.01 0.01 Wholesale Trade 4,846.9 0.00 0.00 0.01 0.01 0.00 0.02 0.05 0.01 0.03 0.06 0.12 0.24 0.36 0.48 Retail Trade 5,639.2 0.00 0.01 0.02 0.05 0.10 0.16 0.22 0.01 0.03 0.06 0.12 0.23 0.35 0.46 Transportation 11,428.7 0.00 0.01 0.02 0.03 0.05 0.06 0.07 0.00 0.02 0.04 0.08 0.16 0.23 0.31 Information 26,725.4 0.00 0.01 0.01 0.02 0.05 0.07 0.10 0.00 0.02 0.04 0.07 0.14 0.21 0.28 Finance 56,777.2 0.00 0.01 0.01 0.02 0.05 0.08 0.11 0.01 0.03 0.05 0.10 0.20 0.30 0.39 Rental 1,975.6 0.00 0.02 0.03 0.06 0.13 0.20 0.28 0.01 0.04 0.07 0.14 0.29 0.43 0.57 Professional Services 21,305.1 0.00 0.00 0.01 0.01 0.02 0.02 0.02 0.00 0.01 0.03 0.05 0.11 0.16 0.21 Services Other 38,357.7 0.00 0.01 0.02 0.04 0.09 0.14 0.19 0.01 0.03 0.05 0.11 0.21 0.32 0.42 Government 14,988.6 0.00 0.00 0.01 0.02 0.03 0.05 0.06 0.00 0.01 0.02 0.04 0.09 0.13 0.17

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29 Table 11 Changes in q uantity of labor demanded (factor payments) due to increased use of woody biomass for electric power generation in Florida Sector Base (Million $) Capital Fixed Capital Mobile Change In Biomass Supply t o Electric Power Sector (Million Tons) 1 5 10 20 40 60 80 1 5 10 20 40 60 80 Percentage Change f rom Base Agriculture 1,280.6 0.13 0.65 1.27 2.41 4.39 6.06 7.49 0.00 0.02 0.03 0.06 0.13 0.19 0.25 Forestry 1,973.6 1.26 6.35 12.85 26.27 54.34 83.51 113.29 1.84 9.17 18.31 36.49 72.48 107.97 142.98 Fishing Hunting 27.6 0.00 0.00 0.01 0.02 0.03 0.04 0.05 0.00 0.02 0.05 0.09 0.18 0.27 0.36 Fossil Fuels 196.3 0.10 0.49 0.99 1.97 3.93 5.87 7.78 0.07 0.37 0.74 1.48 2.94 4.39 5.81 Mining 298.7 0.00 0.00 0.01 0.02 0.05 0.10 0.16 0.08 0.40 0.79 1.57 3.08 4.54 5.94 Electric Power 2,454.8 0.02 0.15 0.37 1.01 2.95 5.57 8.70 0.01 0.07 0.15 0.29 0.58 0.87 1.16 Infrastructure 186.1 0.00 0.01 0.02 0.06 0.15 0.28 0.42 0.02 0.08 0.15 0.30 0.60 0.89 1.18 Construction 30,469.4 0.00 0.00 0.00 0.00 0.01 0.01 0.02 0.00 0.00 0.01 0.02 0.04 0.05 0.07 Manufacturing General 21,234.8 0.00 0.02 0.04 0.08 0.16 0.24 0.32 0.00 0.01 0.03 0.05 0.11 0.16 0.21 Wood Products Manufac turing 1,306.9 0.36 1.76 3.39 6.37 11.35 15.39 18.75 0.01 0.06 0.12 0.24 0.47 0.70 0.93 Wholesa le Trade 23,512.9 0.00 0.00 0.01 0.01 0.02 0.02 0.02 0.00 0.01 0.03 0.06 0.12 0.17 0.23 Retail Trade 32,178.8 0.00 0.01 0.02 0.03 0.06 0.09 0.11 0.00 0.02 0.04 0.07 0.14 0.21 0.28 Transportation 11,899.5 0.00 0.01 0.01 0.02 0.04 0.05 0.07 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Information 11,355.6 0.00 0.00 0.00 0.00 0.01 0.01 0.02 0.00 0.02 0.03 0.06 0.12 0.18 0.24 Finance 35,320.3 0.00 0.00 0.00 0.01 0.01 0.01 0.01 0.00 0.00 0.01 0.01 0.03 0.04 0.06 Rental 2,292.6 0.00 0.01 0.02 0.04 0.08 0.13 0.17 0.00 0.02 0.04 0.09 0.18 0.26 0.35 Professional Services 43,200.0 0.00 0.00 0.01 0.02 0.03 0.04 0.06 0.00 0.00 0.00 0.01 0.02 0.03 0.04 Services Other 111,126.1 0.00 0.01 0.01 0.02 0.04 0.06 0.07 0.00 0.01 0.03 0.05 0.10 0.15 0.20 Government 75,497.7 0.00 0.00 0.01 0.02 0.04 0.06 0.08 0.00 0.01 0.02 0.04 0.07 0.11 0.14

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30 C onclusions This study evaluated the potential impacts on the Florida economy resulting from substitution of woody biomass biofuels for fossil fuels used for electric power generation, under the mandates of a Renewable Electricity Standard that would require a minimum percentage of renewable energy sources state and federal production tax credits, and biomass feedstock subsidies The analysis was conducted using a computable general equilibrium model coupled to an Input Output/Social Accounting Matrix representing the structure of the Florida economy in 2007. The study found that increas ed biomass use for electric power generation would bring about a modes t increase in the Gross Domestic Product of Florida, employment, and state government revenues, while decreasing total imports, particularly for fossil fuels. For a biomass supply level of 40 million tons, with mobile capital assumed, GDP would be increase d by 0.32 percent, representing a $2.2 billion Output of the forestry sector would be increased dramatically, by 69 percent above current levels, to meet new demand for woody biomass fuels, while output of the electric power sector would decrease by up to 0.33 percent as a result of higher costs for biomass replacing fossil fuels. The largest adverse impact of these policies would be a decrease in o utput of the forest products manufacturing sector by up to 6.7 percent, becaus e of competition and increased prices for forest resource s P rices for forest commodities may increase as much as 18 percent in the short run due to this resource competition, but would likely be much lower in the long run if capital is allowed to move fre ely. The much greater price increases observed when Forestry and Logging/Support Services sectors were disaggregated, and when imports of these commodities were prohibitied are more comparable to results from bioeconomic models such as the Southern Region Timber Supply (SRTS) model used in a companion study (Rossi, Carter and Abt). Imports of fossil fuels would be decreased by up to 2.5 percent, representing a savings in import purchases of $1.14 billion annually Employee income would increase by up to $ 1.61 billion State government t ax revenues would increase by 0.06 percent ($108 million). The analysis also showed that incentives such as a state and federal renewable energy production tax credit s for electricity generated from biomass equivalent to $ 0.01 0 and $0.01 1 per KWhr respectively and a 100 percent subsidy to forestry biomass producers, would marginally further increase state GDP and employment The electricity production tax credit would substantially increase output of the electric power sec tor, and decrease imports of fossil fuels, while reducing the negative impact of higher electricity prices on all other sectors. However, assuming that the tax credit is unlimited, this state sponsored incentive would significantly reduce state government revenues by nearly $200 million at the 40 million ton b iomass supply level. The federally sponsored renewable production tax credit would not adversely affect state government revenues. The biomass feedstock

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31 federal subsidy to forestry producers would dramatically increase both electric power and forestry commodity output, but would not appreciably affect fossil fuel imports or state government revenues. In summary, it is concluded that the various policies and incentives for bioenergy development that were examined would have an overall positive impact on the economy of Florida in terms of increased GDP, employment and state government revenues, and decreased imports of fossil fuels. The forestry sector would particularly benefit from increased dem and and prices. However, the forest product manufacturing sector would be adversely affected by competition for wood resources and higher prices for material inputs. Of course, all economic analyses are based on certain assumptions that are integral to the economic models and data used, and this study is no exception. Firstly, I O/SAM models assume a fixed relationship between production volume (output) and intermediate inputs estimated based on national averages, however, the CGE modeling approach overcome s some of the limitations of standard Input Output analysis by allowing substitution of labor and capital resources and changes in commodity prices. Secondly, the I O/SAM and CGE models used in this study do not explicitly have a time dimension; the impact s are assumed to occur within a relatively short period of a year of less. It is expected that the results under the mobile capital scenarios would hold in the long run, say 10 years or more, while fixed capital would prevail in the short run. Also, these models do not recognize physical or biological capacity constraints on commodity production, such as forest growth. Changes in commodity demand are assumed to be fulfilled from either local or imported sources, in order for the market to reach equilibrium. This is in contrast to bioeco nomic models such as the SRTS model which represents forest inventories, growth and harvest removals dynamically over time. Future studies on the economic impacts of bioenergy development policies may more fully explore other types of incentives, such as investment tax credits, as well as possible trade policy provisions that could mitigate the adverse effects on certain sectors, or the effects of model parameters and closure rules that may better reflect the characteristics o f specific industry sectors or commodities

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32 Literature and Information Sources Cited Bilgic, Abdulbaki, Stephen King, Aaron Lusby and Dean F. Schreiner. Estimates of U.S. Regional Commodity Trade Elasticities. The Journal of Regional Analysis and Po licy 32(2) 2002. English, B urton K im Jensen, J amey Menard, and D aniel De La Torre Ugarte. Projected impacts of proposed federal renewable portfolio standards on the Florida economy. Final report to the Bipartisan Policy Center. University of Tennessee, Department of Agricultural Economics, Knoxville, TN. 97 pages. August, 2009. Federal Energy Regulatory Commission ( FERC ). Financial Report F orm 1: Annual Report of Major Electric Utilities, Licensees and Others and Supplemental Form 3 Q: Quarterly Financial Report 200 7 http://www.psc.state.fl.us/utilities/annualreports/default.aspx GAMS (General Algebraic Modeling System) Development Corporation, 1217 Potomac Street, NW, Washington, DC 2007. http://www.gams.com/default. htm Hodges, A lan W., M ohammad Rahmani and W illiam D avid Mulkey. Economic contributions of Florida agriculture, natural resources, food and kindred product manufacturing, distribution and service industries in 2006. University of Florida/IFAS, electronic do cument FE702, 23 pages, March 2008. Available at http://edis.ifas.ufl.edu/pdffiles/FE/FE70200.pdf Holland, David, Leroy Stodick and Stephen Devadoss, Washington State Regional Computable Genera l Equilibrium (CGE) Modeling System. Washington State University, School of Economic Sciences 2009. http://www.agribusiness mgmt.wsu.edu/Holland_model/ Holland, David, Leroy Stodick, and K athleen Painter Assessing the Economic Impact s of Energy Price Increases on Washington Agriculture and the Washington Economy: A General Equilibrium Approach. Washington State University, School of Economic Sciences, W orking Paper Series, WP 2007 14, 2007 Lofgren, Hans, Rebecca Lee Harris, and Sherman Robinson. A Standard Computable General Equilibrium (CGE) Model in GAMS. International Food Policy Research Institute, Washington, D.C 2002. http://www.ifpri.org/pubs/microcom/micro5.htm Minnesota IMPLAN Group (MIG ) IMPLAN Professional, version 2, Economic Impact and Social Accounting Software and Data for F lorida Counties. Stillwater, MN 2007 http:// www.i mplan.com Navigant Consulting Florida Renewable Energy Potential Assessment. F inal Report p repared for National Laboratory, 311 pages, Dec. 30, 2008, Burlington, M A. www.psc.state.fl.us/utilities/electricgas/RenewableEnergy/Full_Report_2008_11_24.pdf North Carolina Solar Center. Database of State Incentives for Energy Efficiency and Renewable Energy Florida North Carolina State University and U.S. Department of Energy. http://www.dsireusa.org/ a ccessed 6/17/2009. Stodick, Leroy, David Holland and Stephen Devadoss. Documentation for the Idaho Washington CGE Model: GAMS programming documentation Washington State University, School of Economic Sciences. http://www.agribusiness mgmt.wsu.edu/Holland_model/documentation.htm U.S. Department of Agriculture -Farm Service Agency (USDA FSA), News Release N umber 0348.09 2009.

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33 U.S. Department of Agriculture -Farm Service Agency (USDA FSA). Imp lementing the Biomass Crop Program, Notice BCAP 2. U.S. Department of Energy Energy Information Administration (USDOE EIA). Monthly electric power generation and fuel co nsumption data base, file EIA 923 and EIA 860. U.S. Department of Energy Energy Information Administration (USDOE EIA) Electric Power Sector Energy Expenditure Estimates by Source, Table S6b 2006 http://www.eia.doe.gov/emeu/states/hf.jsp?incfile=sep_sum/plain_html/sum_ex_eu.html U.S. Department of Energy En ergy Information Administration. Revenue and Expense Statistics for Major U.S. Investor Owned Electric Utilities, 2 007 http://www.eia.doe.gov/cneaf/electricity/epa/epat8p1.html U.S. Department of Energy Office of Science (USDOE). Breaking the barriers to cellulosic ethanol: a joint research agenda; a research roadmap resulting from the biomass to biofuels workshop, Dec. 7 9, 2005, Rockville, MD. DOE/SC 0095, 216 pages, June 2006.

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34 Appendix Table 1. IMPLAN Social Accounting Matrix for Florida, 2007. 1 All values are in millions of U.S. dollars 2 Household sectors were consolidated to conserve space.

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35 Appendix Table 1 (continued). IMPLAN Social Accounting Matrix for Florida, 2007. 1 All values are in millions of U.S. dollars 2 Household sectors were consolidated to conserve space.

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36 Appendix Table 1 (continued). IMPLAN Social Accounting Matrix for Florida, 2007. Note: all values are in millions of U.S. dollars; household sectors were consolidated to conserve space.