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Regional Impacts of Bioenergy Policies in the Southeastern United States

Permanent Link: http://ufdc.ufl.edu/UFE0041091/00001

Material Information

Title: Regional Impacts of Bioenergy Policies in the Southeastern United States A Computable General Equilibrium Analysis
Physical Description: 1 online resource (134 p.)
Language: english
Creator: Huang, Ming-Yuan
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: bioenergy, biofuel, cellulosic, cge, cluster, ethanol, policy, renewable, southeastern, subsidy, us
Forest Resources and Conservation -- Dissertations, Academic -- UF
Genre: Forest Resources and Conservation thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: REGIONAL IMPACTS OF BIOENERGY POLICIES IN THE SOUTHEASTERN UNITED STATES: A COMPUTABLE GENERAL EQUILIBRIUM ANALYSIS Private forests in the southeastern U.S. have a high potential to produce forest biomass that can be utilized to produce cellulosic ethanol or generate electricity. This study analyzes economy-wide impacts of emerging bioenergy policies in the southeastern U.S. A computable general equilibrium approach is used to achieve the task. There are three major components in this dissertation. First, the study classifies southeastern states into homogenous groups based on potential determinants using a cluster analysis. The study then identifies key factors that influence the formulation or adoption of forest bioenergy policies in the southeastern region. Results suggests that Alabama, Arkansas, Louisiana, Mississippi, and South Carolina are likely to adopt regulatory mechanisms for bioenergy; Florida, Georgia, Kentucky, North Carolina, and Tennessee are found to be more amenable for incentive-based bioenergy policies; and, Oklahoma, Texas, and Virginia are shown to be more suitable for support-based bioenergy policies. Second, this research examines the impacts of a series of potential forest bioenergy policy scenarios in the Southeastern U.S. The study applies a static computable general equilibrium (CGE) model to evaluate the impacts on welfare and on all sectors of the economy. The policy scenarios include: displacing 1% of conventional electric power production with forest biomass based electric power; displacing 1% of conventional liquid fuel with cellulosic ethanol; a $1.01 per gallon subsidy for cellulosic ethanol production; and reducing 10% of intermediate inputs for forest bioenergy sectors due to technological progress. Results show that social welfare and gross regional product drop when a portion of conventional energy was substituted with forest biomass; however, these kinds of policies can reduce dependency on fossil fuels. Contrarily, providing incentives and improving technologies for forest bioenergy lead to an increase in welfare, gross regional product, and total labor demand. Furthermore, in response to all policy scenarios, land demand shifts from agricultural production to forest-based activities. Third, the static CGE model was extended to a recursive dynamic model to forecast the impacts of policy scenarios until 2025. This dynamic model can shed light on the resulting economic transition path through time. Results indicate that the logging sector expands but other conventional forest products sectors contract in response to all policy scenarios since the bioenergy market creates an additional demand for forest biomass. The results also show that conventional energy sectors contract in all scenarios. The land for logging increases in the first couple years, leading to a contraction in agricultural land. Labor demand, welfare, and gross regional product decrease when a portion of conventional energy is substituted with forest biomass. Nevertheless, providing incentives and technological progress for forest bioenergy may generate new market opportunities for forest biomass and increase the demand for forest bioenergy resulting in overall positive outcomes for the economy.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Ming-Yuan Huang.
Thesis: Thesis (Ph.D.)--University of Florida, 2010.
Local: Adviser: Alavalapati, Janaki R.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0041091:00001

Permanent Link: http://ufdc.ufl.edu/UFE0041091/00001

Material Information

Title: Regional Impacts of Bioenergy Policies in the Southeastern United States A Computable General Equilibrium Analysis
Physical Description: 1 online resource (134 p.)
Language: english
Creator: Huang, Ming-Yuan
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: bioenergy, biofuel, cellulosic, cge, cluster, ethanol, policy, renewable, southeastern, subsidy, us
Forest Resources and Conservation -- Dissertations, Academic -- UF
Genre: Forest Resources and Conservation thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: REGIONAL IMPACTS OF BIOENERGY POLICIES IN THE SOUTHEASTERN UNITED STATES: A COMPUTABLE GENERAL EQUILIBRIUM ANALYSIS Private forests in the southeastern U.S. have a high potential to produce forest biomass that can be utilized to produce cellulosic ethanol or generate electricity. This study analyzes economy-wide impacts of emerging bioenergy policies in the southeastern U.S. A computable general equilibrium approach is used to achieve the task. There are three major components in this dissertation. First, the study classifies southeastern states into homogenous groups based on potential determinants using a cluster analysis. The study then identifies key factors that influence the formulation or adoption of forest bioenergy policies in the southeastern region. Results suggests that Alabama, Arkansas, Louisiana, Mississippi, and South Carolina are likely to adopt regulatory mechanisms for bioenergy; Florida, Georgia, Kentucky, North Carolina, and Tennessee are found to be more amenable for incentive-based bioenergy policies; and, Oklahoma, Texas, and Virginia are shown to be more suitable for support-based bioenergy policies. Second, this research examines the impacts of a series of potential forest bioenergy policy scenarios in the Southeastern U.S. The study applies a static computable general equilibrium (CGE) model to evaluate the impacts on welfare and on all sectors of the economy. The policy scenarios include: displacing 1% of conventional electric power production with forest biomass based electric power; displacing 1% of conventional liquid fuel with cellulosic ethanol; a $1.01 per gallon subsidy for cellulosic ethanol production; and reducing 10% of intermediate inputs for forest bioenergy sectors due to technological progress. Results show that social welfare and gross regional product drop when a portion of conventional energy was substituted with forest biomass; however, these kinds of policies can reduce dependency on fossil fuels. Contrarily, providing incentives and improving technologies for forest bioenergy lead to an increase in welfare, gross regional product, and total labor demand. Furthermore, in response to all policy scenarios, land demand shifts from agricultural production to forest-based activities. Third, the static CGE model was extended to a recursive dynamic model to forecast the impacts of policy scenarios until 2025. This dynamic model can shed light on the resulting economic transition path through time. Results indicate that the logging sector expands but other conventional forest products sectors contract in response to all policy scenarios since the bioenergy market creates an additional demand for forest biomass. The results also show that conventional energy sectors contract in all scenarios. The land for logging increases in the first couple years, leading to a contraction in agricultural land. Labor demand, welfare, and gross regional product decrease when a portion of conventional energy is substituted with forest biomass. Nevertheless, providing incentives and technological progress for forest bioenergy may generate new market opportunities for forest biomass and increase the demand for forest bioenergy resulting in overall positive outcomes for the economy.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Ming-Yuan Huang.
Thesis: Thesis (Ph.D.)--University of Florida, 2010.
Local: Adviser: Alavalapati, Janaki R.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0041091:00001


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1 REGIONAL IMPACT S OF BIOENERGY POLICIES IN THE SOUTHEAST ERN UNITED STATES : A COMPUTABLE GENERAL EQUILIBRIUM ANALYSIS By MING YUAN HUANG A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2010

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2 2010 Ming Y uan H uang

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3 To my family

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4 ACKNOWLEDGMENTS I am very g rateful to my committee members: Drs. Sherry Larkin, Richard Kilmer, Douglas Carter and Donald Rockwood. My s pecial thanks go to my advisor Dr. Janaki Alavalapati for his encouragement guidance and trust. I am also thankful to all of my lab mates in Fores t Resource Economics and Policy : Tyler Nesbit, Puneet Dwivedi, Andres Susaeta, Sidhanand Kurkrety, and Pankaj Lal. I would like to express my gratitude to Dr. Onil Banerjee, who has helped me with CGE model ing issues Finally, my thanks go to my husband, Ai Hsuan Chiang for his p atience and having confidence in me Funding support from the United States Department of Agriculture and United States Department of Energy through Biomass Research and Development Initiative is greatly appreciated.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ...............................................................................................................4 LIST OF FIGURES .........................................................................................................................8 ABSTRACT .....................................................................................................................................9 CHAPTER 1 INTRODUCTION ..................................................................................................................11 Background .............................................................................................................................11 Research Questions .................................................................................................................14 Research Framework ..............................................................................................................15 2 FOREST BIOENERGY DEVELOPMENT IN THE SOUTHEASTERN UNITED STATES : A CLUSTER ANALYSIS .....................................................................................18 Introduction .............................................................................................................................18 Bioenergy Technology and Infrastructure and Cellulosic Bioenergy Feedstocks ..................21 Bioenergy Technology and Infrastructure .......................................................................21 Cellulosic Bioenergy Feedstocks ....................................................................................23 Forest biomass resources ..........................................................................................23 Bioenergy plantations ...............................................................................................24 Waste products .........................................................................................................25 Key Factors Affecting Forest Bioenergy Development .........................................................26 Economic Factors ............................................................................................................26 Increasing demand for bioenergy .............................................................................26 Bioenergy technology ..............................................................................................27 Food prices effect .....................................................................................................27 Environmental Factors .....................................................................................................28 Sustainable development ..........................................................................................28 Land use change .......................................................................................................29 Social Factors ..................................................................................................................29 Public/Landowners preference and awareness .........................................................29 Policies and programs ..............................................................................................30 Bioenergy Policies in the Southeastern States ........................................................................30 Cluster Analysis: Data Sources and Techniques ....................................................................33 Data Sources for Variables ..............................................................................................34 Techniques of Cluster Analysis .......................................................................................36 Results and Discussion ...........................................................................................................38 Conclusions .............................................................................................................................39 3 A STATIC COMPUTABLE GENERAL EQUILIBRIUM ANALYSIS O F FOREST BIOENERGY POLICY IN THE SOUTHEASTERN UNITED STATES ............................44

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6 Introduction .............................................................................................................................44 CGE Applications in Bioenergy .............................................................................................45 Modeling Framework .............................................................................................................48 Database ..........................................................................................................................51 The Policy Scenarios .......................................................................................................53 Simulation Results ..................................................................................................................57 Supply Price and Quantity ...............................................................................................57 Primary Factor Demand and the Government .................................................................59 Household and Welfare Impacts .....................................................................................60 Conc lusions .............................................................................................................................61 4 A RECURSIVE DYNAMIC COMPUTABLE GENERAL EQUILIBRIUM ANALYSIS OF FOREST BIOENERGY POLICY IN THE SOUTHEASTERN UNITED STATES ..................................................................................................................69 Introduction .............................................................................................................................69 Dynamic Computable General Equilibrium Models for Policy Analysis ..............................70 The Dynamic CGE Model for the Southeastern U .S. Region ................................................73 Data and Policy Scenarios ......................................................................................................76 Database ..........................................................................................................................76 The Policy Scenarios .......................................................................................................76 Simulation Results ..................................................................................................................77 Supply Price and Quantity ...............................................................................................77 Factor Markets .................................................................................................................80 Government, Household, and Welfare Impacts ..............................................................81 Conclusions .............................................................................................................................83 5 SUMMARY AND CONCLUSIONS .....................................................................................95 APPENDIX A SOCIAL ACCOUNTING MATRIX FOR THE SOUTHEASTERN U.S. ..........................104 The Southeastern Social Accounting Matrix Accounts ........................................................104 Social Accounting Matri x for the Southeastern U.S. ...........................................................106 B COMPLETE MODEL SETS, PARAMETERS, VARIABLES, AND EQUATION LISTING ...............................................................................................................................115 Variables in the Model ..........................................................................................................118 Model Equations ...................................................................................................................120 LIST OF REFERENC ES .............................................................................................................127 BIOGRAPHICAL SKETCH .......................................................................................................134

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7 LIST OF TABLES Table page 21 Descriptive statistics for variables used in the analysis .....................................................41 22 Cluster means for classification variables from Wards clustering ...................................41 31 Percent change in producer commodity prices ..................................................................65 32 Percent change in quantity of commodity supply ..............................................................65 33 Change in the level of comm odity supply ........................................................................66 34 Percent change in demand for land ....................................................................................66 35 Change in the level of government revenue and expenditure ............................................66 36 Change in the level of social welfare .................................................................................67 41 Average annual growth rate in quantity of commodity supply during 2006 to 2025 .......86 42 Average annual growth rate in producer commodity prices during 2006 to 2025 ............86 43 Average annual growth rate in quantity demand of labor by activity during 2006 to 2025....................................................................................................................................87 44 Average annual growth rate in quanti ty demand of land by activity during 2006 to 2025....................................................................................................................................87 45 Average annual growth rate in rental of capital by activity during 2006 to 2025 .............88 46 Average annual growth rate in macroeconomic in dicators during 2006 to 2025 .............89

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8 LIST OF FIGURES Figure page 11 Study area of the research ..................................................................................................17 12 Flow chart of the research ..................................................................................................17 21 Impacts of different levels of economic, environmental, and social factors on the three types of bioenergy policies .......................................................................................42 22 Dendrogram for cluster analysis by Wards method in the Southeastern states ................42 31 Structure of sector mapping. ..............................................................................................68 41 Economy wide output impacts of displacing 1% of conventional electric power with forest biomass based electric power. .................................................................................90 42 Economy wide supply price impacts of displacing 1 % of conventional electric power with forest biomass based electric power. .........................................................................90 43 Economy wide output impacts of displacing 1% of conventional liquid fuels with second generation biofuels .................................................................................................91 44 Economy wide supply price impacts of displacing 1% of conventional liquid fuels with second generation biofuels .........................................................................................91 45 Economy wide output impacts of a $1.01 per gallon subsidy for cellulosic ethanol production ..........................................................................................................................92 46 Economy wide supply price impacts of a $1.01 per gallon subsidy for cellulosic ethanol production .............................................................................................................92 47 Economy wide output impacts of technology progress by reducing 10% of intermediate inputs for second generation bioenergy ........................................................93 48 Economy wide supply price impacts of technology progress by reducing 10% of intermediate inputs for second generation bioene rgy ........................................................93 49 Policy impacts on agricultural land in the Southeastern U.S. ............................................94 410 Policy impacts on forestland demand in the Southeastern U.S. ........................................94

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9 Abstract of Dissertation Presented to the Graduate School of the University of Florida i n Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy REGIONAL IMPACTS OF BIOENERGY POLICIES IN THE SOUTHEAST ERN UNITED STATES : A COMPUT ABLE GENERAL EQUILIBRIUM ANALYSIS By Ming Yuan Huang May 2010 Chair: Janaki Alavalapati Major: Forest Resources and Conservation Private forests in the southeastern U.S. have a high potential to produce forest biomass that can be utilized to produce cel lulosic ethanol or generate electricity. This study analyzes economy wide impacts of emerging bioenergy policies in the southeast ern U.S. A computable general equilibrium approach is used to achieve the task. There are three major components in this dissertation. First, the study classifies s outheastern states i nto homogenous groups based on potential determinants using a cluster analysis. The study then identif ies key factors that influence the formulation or adoption of forest bioenergy polic ies in the s outheastern region Results suggests that Alabama, Arkansas, Louisiana, Mississippi, and South Carolina are like ly to adopt regulatory mechanisms for bioenergy; Florida, Georgia, Kentucky, North Carolina, and T ennessee are found to be more amenable for incentivebased bioenergy policies; and, Oklahoma, Texas, and Virginia are shown to be more suitable for support based bioenergy polic ies Second, this research examine s the impacts of a series of pot ential fores t bioenergy policy scenarios in the S outheastern U.S. The study applies a static computable general equilibrium (CGE) model to evaluate the impacts on welfare and on all sectors of the economy. The policy scenarios include : displacing 1% of conventional electric power production w ith forest biomass

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10 based electric power ; displacing 1 % of conventional liquid fuel with cellulosic ethanol; a $1.01 per gallon subsidy for cellulosic ethanol production; and reducing 10% of intermediate inputs for forest bioenergy sector s due to technological progress. Results show that social welfare and gross regional product drop when a portion of conventional energy was substituted with forest biomass; however these kinds of policies can reduce dependency on fossil fuels Contrarily, providing incentives and improving technolog ies for forest bioenergy lead to an increase in welfare, gross regional product, and total labor demand. Furthermore, in response to all policy scenarios, land demand shi f ts from agri cultural production to forest based activities. Third, the static CGE model was extended to a recursive dynamic model to forecast the impacts of policy scenarios until 2025. T his dynamic model can shed light on the resulting economic transition path through time Results indicate that the logging sector expands but other conventiona l forest products sectors contract in respons e to all policy scenarios since the bioenergy market creates an additional demand for forest biomass. The results also show tha t conventional energy sectors contract in all scenarios T he land for logging increases in the first couple year s leading to a contraction in agricultural land. L abor demand, welfare, and gross regional product decrease when a portion of conventional ener gy is substituted with forest biomass. Nevertheless, providing incentives and technological progress for forest bioenergy may generate new market opportunities for forest biomass and increase the demand for forest bioenergy resulting in overall positive outcomes for the economy.

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11 CHAPTER 1 INTRODUCTION Background Greater dependence on foreig n oil, continuous rise in crude oil prices, and greenhouse gas emissions associated w ith fossil fuels have prompted the United States ( U.S. ) government to explore alternative energy sources. Fo r example, greenhouse gases ( GHGs) emissions in the United State s were about 7,282 million metric tons of carbon dioxide equivalents in 2007; fossil fuel s combustion alone accounts for 82.3% of these emissions ( E nergy Information Administration ( EIA ) 2008a ) In the recent past, bioenergy has been receiving more attent ion because of its potential advantages over fossil fuels. Unlike fossi l fuels, bioenergy is thought to be environmentally benign, socially desirable, and even economically competitive (Alavalapati et al ., 2009) and its production is expected to rise in the future For example, EIA (2008b) indicated that liquid biofuel production would grow by 3.3% per year until 2030 in the U.S. although fossil fuel s would still account for 79% of total energy use in 2030. The U.S. produced around 10.7 billion gallons of biofuels in 2009 and corn is the major feedstock of this production ( EIA, 2010). Some studies have shown that the energy content of grain based bioenergy also known as first generation biofuels is lowe r than that of conventional energ y, and production of such energy may compete with food and feed crops for land, water and other inputs (Childs and Bradley, 2007; Fargione et al ., 2008). These findings have driven research to explore second generation biofuels using cellulosic feedstocks such as switch grass, corn stover, and forest biomass Recent research has identified a number of advantages of second generation bioenergy over its predecessor. Second generation bioenergy is found to reduce competition between crops destined for food and those designated for fuel production, have a greater net energy balance, and leads to greater reductions in GHG emissions (Hill et al ., 2006;

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12 Marshall and Greenhalgh, 2006) Furthermore, t he use of logging residues to produce electricity can be highly co st effective (Gan and Smith, 2006) The removal of small diameter forest biomass (which may be used to produce fuel) can also improve forest health, enhance biodiversity, and reduce wildfire risk (Polagye et al ., 2007). The U.S. government promotes the ethanol industry on both demand and supply sides through various incentive policies and mandates On the demand side, blending mandates and consumption tax incentives promote the uptake of biofuels such as the Renewable Fuel Standard and Renewable Electricity Standard The first farm bill Farm Security and Rural Development Act of 2002 incorporated various programs such as the Federal Biobased Products Preferred Procurement Program (FB4P), Biodiesel Fuel Education Program, Renewable Energy Systems a nd Energy Efficiency Improvements Program, and Value Added Grant Program to promote bioenergy production and utilization. The American Jobs Creation Act of 2004 changed provisions regarding the energy taxation of ethanol blends. It extended the Volumetric Ethanol Ex cise Tax Credit (VEETC) that provided oil companies with an incentive to blend ethanol with gasoline. As of January 1, 2009, the original tax credit totaling 51 cents per gallon on pure ethanol was reduced to 45 cents per gallon. The VEETC is cur rently authorized through December 31, 2010. In 2005, the first major energy bill, t he Energy Policy Act of 2005, develop ed a market for ethanol b y accelerating deployment of necessary supporting infrastructure. It established a Renewable Fuel Standard (RF S) that set 7.5 billion gallons of renewable fuels to be sold or dispensed by 2012. It also stipulated that one gallon of ethanol produced from crop residues and tree crops can be counted as 2.5 gallons to satisfy the RFS. By 2010, the RFS will require at least 250 million gallons of cellulosic ethanol to be contributed to the fuel mix.

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13 In 2007, t he U.S. government established the Energy Independence and Security Act with a goal to produce 36 billion gallons of biofuels by 2022. Of that, corn ethanol produc tion is capped at 15 billion gallons per year starting in 2015, and t he remainder is anticipated to be met by cellulosic based biofuels. This policy is expected to stimulate new market opportunities for forest biomass. In addition, the Farm Bill included $ 1.6 billion for new renewable energy and energy efficiency projects in 2007, including supporting loan guarantees for cellulosic ethanol projects. The 2008 Farm Bill, titled the Food, Conservation, and Energy Act of 2008 provided $1 billion in mandatory funding for related renewable energy activities. These activities could accelerate the commercialization of cellulosic biofuels encourage the production of biomass crops and expand the current Renewable Energy and Energy Efficiency Program. The 2008 Farm Bill also provid es a $1.01 subsidy on every gallon of cellulosic ethanol produced. In 2009, the U.S. Congress passed a comprehensive climate and energy bill, the American Clean Energy and Security Act (ACES). This act included efficiency energy standards and regulations and authorized programs aimed at modern izing the countrys electricity infrastructure and paving the way for plug in hybrid and electric vehicles. Moreover, the bill also set a renewabl e electricity requirement with combined Energy Efficiency and Renewable Electricity Standards (RES) for 20% of the nations power to come from renewable sources, such as wind, solar, biomass, and geothermal energy, by 2020. There is significant interest in biofuel promotion at the state level as well. Some states (such as Florida, Hawaii, Louisiana, Minnesota, Mis souri, Montana, New Mexico, Oregon, South Carolina, and Washington) have enacted a blending mandate requiring that all marketed fuel contain a specific percentage of biofuels, mainly E10 (10% ethanol and 90% gasoline) or B2 (a 2% blend of biodiesel). Alava lapati et al (2009) organized related renewable energy polices

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14 of the s outheastern states into three categories: regulatory mechanisms, incentive based policies, and support based policies. These s tate policies will be discussed in detail in later chapter s In this study, the S outheastern U.S. includes 13 states : Alabama, Arkansas, Florida, Georgia, Kentucky, Louisiana, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, and Virginia (Figure 1 1). The Southeastern region contains approximately 214 million acres of forest lands About 60% of this forestland is owned by nonindustrial private forest (NIPF) landowners. In the past, forest landowners in the Southeastern states were encouraged to create high density forest plantations for pul pwood by availing themselves of federal policies, such as the Conservation Reserve Program and Forest Incentive Program. These plantations are currently near maturity; however, the landowners are not willing to undertake prescribed forest practices because the practices require large expenditures with little economic return as the prices of timber products (sawtimber, chipnsaw, and pulpwood) are low This situation may lead to an increase in the risk of wildfire and diseases and thereby threaten forest he alth and environment. Therefore, physical removal of small diameter timber ( thinning) is considered a good way to improve forest health and prevent wildfires. These thinnings would result in potential raw material for the production of bioenergy as well. T his chapter has so far addresse d the background of bioenergy development in the S outheast ern region and the U.S T he remainder of this chapter outlines the purpose of this study and the research questions to be addressed. The final section provides the research framework of the dissertation. Research Questions The objective of this study is to evaluate the macroeconomic effects of biomass related policy instruments and market change expectations from government policy programs.

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15 Regarding forest bioenergy development and policy in the Southeastern U.S. Chapter 2 addresses the following questions: What are the key factors that affect the formulation or adoption of forest bioenergy development? Given three categories of policies (regulatory mechanism, incentive based, and support base policies), which states will prefer which category of policies based on their economic, environmental, and social characteristics? Following the second question, what are the characteristics of t he states in a group? Chapter 3 develops a static computable general equilibrium (CGE) model to address the following questions: How are the out puts and prices of forestry sectors (logging, sawmill, pulpmill, and other wood products) and related sectors affected by the implementation of a set of policies relating to forest bioenergy ? The policy scenarios include: displacing 1 % of conventional electri c power production w ith forest biomass based electric power ; displacing 1% of conventional liquid fuel with cellulosic ethanol; a $1.01 per gallon subsidy for cellulosic ethanol production; and reducing 10% of intermediate inputs for forest bioenergy secto r s due to technological progress. How are primary factors, such as labor, capital, and land, and government institutions affected by the proposed policy scenarios? Finally, how do the policy scenarios impact the distribution of different levels of househol d (i.e., low, medium, and high income households), social welfare, and GDP? Chapter 4 develops a recursive dynamic CGE model to addresses the same questions listed in Chapter 3 More specifically the following question is answered : What are the long term impacts of forest bioenergy policies implement ed in the S outheastern region? The final chapter provides a summary and discussion of key results, limitations of the analysis, and suggestions for fruitful future research directions. Research Framework The or ganization of this dissertation is explained in the next few paragraphs. The second chapter classif ies the 13 Southeastern states into homogenous groups based on potential

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16 determinants using cluster analysis. A set of factors affect ing the formulation or a doption of forest bioenergy polic ies in the Southeastern region will be identified as well. Classification of similar states into homogenous groups can help customize forest bioenergy strategi es and policies, since states in a group would likely have simil ar outcomes resulting from a given policy intervention or incentive. Figure 12 represents the overall research framework used for this dissertation. The third chapter develops a static CGE model to assess socio economic and environmental impacts of forest bioenergy development. The fourth chapter builds on the third chapter by introducing recursive dynamics and evaluates the long run socioeconomic impacts of a set of bioenergy policies in the Southeastern region. Finally, the fifth chapter provides a summ ary of the research findings documents the limitations of the study, and discusses directions for future research.

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17 Figure 1 1. Study area of the research (Source: http://gacc.nifc.gov/sacc/index.htm accessed February 10, 2010) Figure 1 2. Flow chart of the research

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18 CHAPTER 2 FOREST BIOENERGY DEVELOPMENT IN THE SOUT HEASTERN UNITED STAT ES : A CLUSTER ANALYSIS Introduction Biomass based energy is one of the key renewable energy source s in the S outheastern United S tates which include s Alabama, Arkansas, Florida, Georgia, Kentucky, Louisiana, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas and Virginia Generally, bioenergy production can be divided into two major categories: liquid biofuels (ethanol and biodiesel used mainly for transportation) and nonliquid bioenergy for production of electricity or combined heat and power (CHP). Both c ategories of bioenergy in the region account for about 19% of the total U.S. production (EIA, 2007). The S outheastern region consists of approximately 214 million acres of forestland and can contribute significantly towards forest bioenergy development F orest biomass removed during ecological restoration and conventional silvicultural activities can become a source of renewable energy ; at the same time, these activities can improve forest health, enhance biodiversity, and reduce wildfire risk (Polagye et al ., 2007). Furthermore, use of forest bioenergy is thought to decrease greenhouse gas (GHG) emissions over fossil fuels and also reduce competition between agricultural crops destined for food and those for fuel production (Hill e t al ., 2006). Thus, fores t bioenergy development has gained considerable attention and support from policymakers. T he first major energy bill, t he Energy Policy Act of 2005, established a Renewable Fuel Standard (RFS) that set 7.5 billion gallons of renewable fuels to be sold or dispensed by 2012. It stipulated that one gallon of ethanol produced from crop residues and tree crops can be counted as 2.5 gallons to satisfy the RFS. By 2010, the RFS will require at least 250 million gallons of cellulosic ethanol to be contributed to t he fuel mix. In 2007, t he U.S. government established the Energy Independence and Security Act (EISA), setting a goal to produce 36 billion gallons of

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19 biofuels by 2022. The goal also stipulates that of those 36 billion gallons, cellulosic biofuels and othe r advanced biofuels must account for 21 billion gallons In addition, the 2007 Farm Bill included $1.6 billion for new renewable energy and energy efficiency, including supporting loan guarantees for cellulosic ethanol projects. The 2008 Farm Bill, titled the Food, Conservation, and Energy Act of 2008, allocated about $1 billion in mandatory funding for related renewable energy activities. These activities could accelerate the commercialization of cellulosic biofuels and encourage the production of biomass crops The 2008 Farm Bill also provided a $1.01 subsidy on every gallon of cellulosic ethanol produced. In 2009, Congress passed a comprehensive climate and energy bill, the American Clean Energy and Security Act (ACES). This bill included efficiency energy standards and regulations, authorization for programs aime d at modernizing the countrys electricity infrastructure and paving the way for plugin hybrid and electric vehicles. Moreover, the bill set a renewable electricity requirement with a combined Energy Efficiency and Renewable Electricity Standards (RES) fo r 20% of the nations power to come from renewable sources, such as wind, solar, biomass, and geothermal energy, by 2020. There is significant interest in bioenergy promotion at the state level as well. Many states have developed and integrated comprehens ive state energy plans to maximize resources within their states. As of May 2009, 7 of the 13 Southeastern states had passed state energy plans, including Florida, Georgia, Kentucky, North Carolina, South Carolina, Virginia, and Texas. Furthermore, the Southeastern states have implemented a suite of bioenergy policies, such as set blending requirements and renewable energy standards Some other policy instruments, including tax incentives, subsidies, loans, technical assistance, and public educational outre ach, are implemented in the states as well. For example, Florida initiated the Renewable Energy

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20 Corporate Tax program, which included a sales tax exemption for the sale or use of specific clean fuels such as biodiesel and ethanol, and an investment tax c redit of 75% for all capital, operational maintenance, and research and development costs for biofuel production. Based on the rational choice theories, it is expected that states would seek to maximize resource benefit by choosing the best set of policies according to their economic, environmental, and social backgrounds (Ostrom, 2005; Feiock, 2007). Furthermore, states with similar socioeconomic and political conditions are expected to adopt certain type s of policies to realize a set of goals ( Roy et al ., 2009). This suggests that it would be useful to classify states in order to shed light on the likelihood of their adoption of certain policies. Huang et al (2006) found that states with Republican Party dominance are less likely to be adopters of renewab le portfolio standards (RPS). In this chapter an attempt is made to classify the 13 Southeastern states i nto homogen e ous groups based on potential determinants using cluster analysis. A cluster analysis approach is used to achieve the task. Meanwhile, a se t of factors that affect the formulation or adoption of forest bioenergy polic ies in the Southeastern region will be identified Classification of similar states into homogeneous groups can help customize forest bioenergy strategi es and policies, since sta tes in a group would likely have similar outcomes resulting from a given policy intervention or incentive. Thus, improved knowledge of state groups (clusters) and their characteristics may potentially incre ase the chances of success for promoting forest bi oenergy policies and strategies This chapter is organized as follows: section 2 provides an overview of bioenergy infrastructure and potential cellulosic feedstocks in the Southeastern U.S. ; section 3 discusses key factors affecting forest bioenergy development; and section 4 gives an overview of bioenergy policies in the Southeastern states and the main hypothese s of this study. Data and

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21 model specification and cluster analysis methodology are described in section 5. In section 6, results and discussion are provided. The final section contains a summary of the key findings. Bioenergy Technology and Infrastructure and Cellulosic Bioenergy Feedstocks Bioenergy Technology and Infrastructure Thermo chemical and biochemical processes are two major conversion t echnologies for bioenergy. Thermochemical conversion breaks down biomass into intermediates using heat and upgrades them to fuels by a combination of heat and pressure in the presence of catalysts. Biochemical conversion breaks down biomass into sugars us ing either enzymatic or chemical processes and then converts them into ethanol via fermentation (Faaij, 2006). A bio refinery can integrate biomass conversion processes and equipment to produce fuels, power, and chemicals from biomass. Most of the biorefi neries are located in the Midwestern states, and the majority of them produce ethanol using corn as feedstock. Nevertheless, there were 18 cellulosic biorefineries completed, under construction, or in the planning stage in the Southeastern region as of Ju ly 2008 (Mattingly et al ., 2008). Range Fuels in Georgia, using the thermochemical conversion process to produce cellulosic bioenergy, will be the first commercialscale bio refinery in the U.S.. It was awarded $76 million in grants in 2007 by the Depart ment of Energy (DOE). This biorefinery will utilize leftover wood residues from timber harvesting and convert them to approximately 10 million gallons of ethanol and other alcohols in the first year. It is expected to produce up to 100 million gallons of ethanol per year at full scale operation1. 1 http://www.rangefuels.com/our first commercial plant. (Accessed on May 25, 2009)

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22 Ecofin, LLC, in Kentucky, Mascoma Corporation in Tennessee, and Verenium Corporation in Louisiana were awarded grants by the DOE for small scale bio refinery2 projects in 2008 totaling $89.8 million over four year s. In addition, Citrus Energy, LLC, Florida Crystals Corporation, Gulf Coast Energy, and Southeast Biofuels, LLC, in Florida are currently planning commercial or smallscale facilities and have received state grants and other financial investments. Besides biorefineries, bio power (biomass power) uses biomass to generate heat or electric power. Direct fired systems burn bioenergy feedstocks directly to produce steam, and this steam drives a turbine which turns a generator, creating electricity. For instance, Gainesville Regional Utilities is planning to build a 100 MW power plant in Gainesville, Florida that will utilize cellulosic biomass for electricity production. Co firing is the combustion of two different types of material at the same time. In the case of coal fired power plants, a proportion of fossil fuel is often substituted with solid biomass to produce electricity. An existing power plant facility can blend biomass up to 5% with coal or inject biomass separately up to 20% into the boiler. Cofi ring with biomass may reduce GHG emissions. However, biomass cofiring still has to overcome some issues, su ch as ash deposition, corrosion, and feedstock selection among others (Hughes, 2000; Tillman, 2000). The Southeastern region currently has 24 electr ic power plants with the ability to co fire biomass with fossil fuels. The region generated about 25 trillion watt hours of electricity from biomass resources in 2007. Of that, Alabama and Florida generated about 4 trillion and 4.4 trillion watt 2 The US DOE defines a commercial scale bio refinery as one that uses at least 700 tons of feedstock per day to produce 1020 million gallons per year and a small scale bio refinery as one that uses about 70 tons of feedstock per day to yield at least 1 million gallons per year.

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23 hours of e lectricity from biomass resources, respectively; Georgia and Louisiana each generated about 3 trillion watthours (Alavalapati et al ., 2009). Because most current bio power plants are based on direct combustion in small, biomass only plants with relatively low electric efficiency, total system efficiencies for combined heat and power (CHP) can approach 90%3. However, there are no CHP plants set up in the Southeastern region so far. On the other hand, concerning bioproducts, technology for producing wood pe llets is being continuously improved using softwood and hardwood. Rather than only using sawdust from mills for producing pellets, the plants have developed a method to use whole tree and wood chips to produce pellets. Some new, large scale plants producin g pellets have been constructed in the region, such as the Green Circle Plant in Florida. Cellulosic Bioenergy Feedstocks Many bio refineries utilize multiple cellulosic materials, such as feedstocks. The forestry sector provides varied forest biomass that can be utilized to produce cellulosic ethanol or to generate heat and electricity. This study classifies feedstocks into three categories: forest biomass resources, bioenergy plantations, and waste products. Forest biomass resources Forest biomass resources are comprised of logging residues, residues from mills, smalldiameter trees from thinnings, and stands damaged by natural disturbances, such as wildfire, pest outbreaks, hurricanes, and other events. Although logging residues are one of the largest sources of forest biomass, they have not been utilized in the U.S. In the past, the unused portions of trees, such as branches, were left on sites after the traditional timber harvest. I t is currently estimated that the Southeastern region has a total of 25.8 million dry tons of logging residues (Jackson, 3 http://www1.eere.energy.gov/biomass/abcs_biopower.html (Accessed on Oct 10, 2009)

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24 2007). T he 2002 Forest Inventory Analysis (FIA) (Smith et al. 2004) showed about 60 million dry tons of logging residues per year available at harvest sites in the U.S Perlack et al (2005) indicated tha t around 40 million dry tons of these residues could be regained annually in the U.S. over 90% from privately owned lands. Meanwhile, mill residues are one of the most readily available sources of forest biomass. The Southeastern region produces about 15.98 million dry tons of mill residues per year, such as shavings, sawdust, and bark (Jackson, 2007). Perlack et al (2005) showed that about 97% of primary wood processing mill residues are already being utilized in the U.S. It indicated that only a small amount of mill residues can be further used for bioenergy under current market conditions. Smalldiameter trees from fuel treatment thinnings can be considered good sources for forest bioenergy; thinning operations can also help prevent wildfire hazards and attacks by pests such as the southern pine beetle. Natural disturbances in the region most often include hurricanes, tornadoes, wildfires, and pest and disease outbreaks. For example, Hurricanes Katrina and Rita damaged over 2.5 billion dry tons of timber along the Gulf Coast in 2005 (USDA Forest Service, 2005). Coulson et al. (2005) estimated that an average of 1.36 million tons of available biomass are killed annually by pests in the Southeastern states, and about half of this amount could be used for bioenergy. Bioenergy p lantations Research and developme nt of short rotation woody crops (SRWC s ) has been undertaken in the Southeastern U.S. since the 1960s (Andersson et al. 2002) SRWCs can produce woody biomass for bioenergy, as well as mulch, pulpwood, and other products Perlack et al (2005) indicated that about 5 million dry tons of biomass from SRWC plantations could be used annually for bio energy production in the U.S. Fast growing species such as Populus Salix and

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25 Eucalyptus and their respective hybrids, can be planted as SRWCs that enable high productivity and have a variety of inherent logistical benefits and economic advantages relati ve to other lignocellulosic energy crops, such as swtichgrass and miscanthus. The Southeastern region has about 12,000 acres of intensive SRWCs for hardwood plantations ( Hinchee et al ., 2009). Two major silvicultural systems in the region include: a) moderately dense stands of cottonwood; and b) dense stands wi th 1 to 4 year rotations usually applying willows ( Salix species) or sycamore ( Plantanus occidentalis ). L oblolly pine ( Pinus taeda) and sweetgum ( Liquidambar styraciflua) can be also considered f or bioenergy applications (Davis and Trettin, 2006; Dickmann, 2006; Rockwood et al ., 2008; Hinchee et al ., 2009). Among the conifers, the loblolly pine shows particular promise since genetic variation is readily exploitable and as a native North American s pecies (Rockwood et al ., 2008). Additionally, of all hardwood species, Eucalyptus (introduced species) has the greatest potential to produce the greatest amount of biomass per acre compared to other potential biomass crops such as E. benthamii E. macarthurii and E. grandis (ArborGen, 2009). However, the estimates of biomass supply in the region vary with different estimation methods and land availability. More research is necessary to provide a consensus est imate In addition, herbaceous energy cr ops are perennials that are harvested annually after taking two to three years to reach full productivity. These include switchgrass, miscanthus e lephant grass ( Pennisetum ) sweet sorghum, tall fescue, kochia, wheatgrass, and others. These can be used as bioenergy feedstock as well. Waste products Waste products can be one of the cellulosic bioenergy feedstocks and include urban wood residues, yard waste, construction waste and municipal solid waste In addition, construction scraps have high quality biomass while demolition mater ials are typically contaminated unless

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26 highly processed. Jackson (2007) indicated that urban wood residues have a potential to contribute 10.1 million dry tons of biomass in the Southeastern region each year. Key Factors Affecting Forest Bioenergy Development Forest bioenergy development may be affected by many factors and this section addresses a succinct discussion on some key factors. These are divided into economic, environmental, and social factors. Economic Factors Thi s study investigated three economic factors: increasing demand for forest bioenergy, bioenergy technology, and food price effect. Increasing demand for bioenergy Under the situations of soaring oil prices and continually increasing consumption of energy, the demand for bioenergy is expected to increase as well. Total energy consumption in the U.S. increased from 98.2 quadrillion Btu (QBtu) in 2003 to 101.5 QBtu in 2007. Biomass based energy use in the U.S. also rose, from 2.8 QBtu in 2003 to 3.6 QBtu in 2007. EIA (2008b) forecast that U.S. liquid biofuel production will expand by an average of 3.3% per year until 2030. The total consumption of electricity in the Southeastern region was about 254.4 million megawatt hours in 2008, and the trend of electricit y sales for all the Southeastern states also showed that the consumption of electricity was continuously increasing4. The demand for forest bioenergy is also expected to grow, since it could potentially make a higher contribution to environmental and energy security benefits (World Bank, 2007). 4 ht tp://www.eia.doe.gov/cneaf/electricity/epm/table5_4_b.html (accessed on May 30, 2009)

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27 Bioenergy technology There are some opportunities and challenges relating to conversion of cellulosic feedstocks to bioenergy. Although research indicates that the process of breaking down cellulose, the woody part s of plants and trees, into simple sugar is technologically hard and expensive (Orts et al ., 2008), many technology companies declare that forest bioenergy technology can be improved by enhancing conversion and enzyme efficiency in the next few years. Howe ver, the timeline of the deployment of these technologies to achieve economic scale is still uncertain. One of the main weaknesses is that conversion technologies are still under trial and no information is available about their performance at the commerci al scale (Dwivedi and Alavalapati, 2009). Bioenergy growth depends on its competitive strength compared to other markets, such as traditional wood products and conventional energy markets. Competition can be in price, quality and /or services Thus, effect ive development of forest bioenergy would need sufficient information and technology about the growth, resilience, and adaptability of forests; silvicultural techniques and management guidelines; and energy efficiency, handling, and processing technologies for woody biomass (PattonMallory, 2008). Food prices effect There is much debate about the link between grain based biofuels and food prices, and some argue that developing bioenergy is the reason why food prices are increasing. H unt (2008) argued that biofuel production is not the major reason for rising food prices ; droughts and a surging demand for meat and milk products in Asia have probably played a significantly larger role in increasing food prices. However, with rapid growth in grainbased biofuels, bioenergy production may become a crucial factor contributing to the increase of food prices in the long run (Childs and Bradley, 2007; World Bank, 2007). Hence, it is prudent to focus on incentives that would bring forest bioenergy to markets th at do not compete with food production.

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28 Environmental Factors This research considered sustainable development and land use change as key environmental factors that affect forest bioenergy development. Sustainable development The use of forest bioenergy ca n decrease GHG emissions over fossil fuels and be an important tool to influence forest processes that contribute to economic, environmental, and social sustainability (Patton Mallory, 2008). C ellulosic bioenergy production, when compared to grain based et hanol production, offers greater energy and GHG benefits (reducing up to 86% of GHG emissions) (Wang et al ., 2007). Searchinger et al (2008) argued that life cycle studies incorporated carbon benefits without considering the costs of carbon storage and se questration and showed that corn ethanol would increase GHG by a worldwide agricultural model. Contrarily, Hunt (2008) pointed out that cellulosic ethanol reduces net GHG emissions by 70 to 90% and has more benign environmental impacts. Fargione et al (2008) also demonstrated that biofuels made from waste biomass or bioenergy plantations grown on degraded and abandoned agricultural lands catch little or no carbon debt, so they can provide sustained GHG advantages. Since it is unclear whether biomass removal from forests may result in environmental degradation, bioenergy systems have to develop some sustainable criteria. Buchholz et al (2009) analyzed 35 sustainability criteria for bioenergy systems through an expert survey, and each criterion was rated as critical at least once. The authors found that energy balance and GHG balance were perceived as crucial criteria by more than half of the experts in terms of importance, relevance, practicality, and reliability attributes. Participation, soil protectio n, ecosystem protection, water management, natural resource efficiency, and microeconomic sustainability were rated as critical or highly important criteria by over 75% of the respondents.

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29 Land u se change One of the major concerns of bioenergy is the competition for l and between bioenergy production and food/ animal feed production. As previously discussed, research has shown that producing grainbased biofuels would lead to the conversion of rainforests, savannas, or grasslands into croplands, and this sit uation would release more GHGs, rather than reducing them (Fargione et al ., 2008; Searchinger et al ., 2008). However, when developing forest bioenergy, many studies have indicated that woody biomass does not compete with agricultural land and can reduce GH G emissions (Wang et al ., 2007; Fargione et al ., 2008; Hunt, 2008). In addition, Reilly and Paltsev (2007) found that a biofuel industry with cellulosic biofuels supplying a substantial share of liquid fuel demand would have significant impact s on land use and conventional agricultural markets in the U.S. Social Factors This study considered public and landowners preference and awareness and policies and programs as key social factors that impact forest bioenergy development. Public/Landowners preference and awareness Non industrial private forest (NIPF) landowners own about 60% of the forestland in the Southeastern region. Educating the landowners and public about the benefits derived from using woody biomass will improve and increase interest in forest biomass utilization. Accurate and sufficient information on the bioenergy issue is important. Lack of information would result in potential consumers and forest landowners having a vague or even incorrect understanding of bioenergy benefit s Mayfield et al (2008) indicated that education and community engagement play important roles in the development of a bioenergy industry in rural communities throughout the region.

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30 Polic ies and programs Government policies and programs are one of the key factors in bioenergy development. B ioenergy incentive policies provide substantial support to the bioenergy industry, thereby making it competitive relative to conventional energy industries. Dwivedi and Alavalapati (2009) demonstrated that g overnment support is essential when forest bioenergy begins to develop a new market in a region. The state governments in the Southeast have implemented many policies and program s to promote bioenergy development, including funding bioenergy initiatives, e nsuring state agency coordination of bioenergy efforts, requiring assessments of existing bioenergy programs, encouraging the production and use of bioenergy, and promoting the development of infrastructure to distribute bioenergy. The state governments ar e attempting to assist their citizens through these bioenergy policies, while at the same time attempting to ensure that these policies contribute to their states positive economic growth. Bioenergy Policies in the Southeastern States Besides the comprehensive state energy plans in 7 of the 13 Southeastern states, each of the states has implemented a number of bioenergy policies. Alavalapati et al (2009) organized the relevant bioenergy policies and program s in the region into three major categories: 1) r egulatory mechanisms, 2) incentive based policies, and 3) support based programs. The r egulatory mechanisms include those polices setting goals for renewable energy production or consumption. For instance, Renewable Fuel Standards (RFS) set blending requi rements for transportation fuel to contain a certain ratio of biofuels like E10 (10% e thanol and 90% gasoline) or B2 (a 2% blend of biodiesel and 98% fossil diesel ); Renewable Energy Portfolio standards (RPS) create a set of policies aimed at ensuring that a certain percentage of energy is derived from renewable energy sources such as wind, solar, biomass, and geothermal

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31 energy; and Alternative Fuel Vehicle (AFV) acquisition regulations are state mandates that set priorities for purchasing alternative fuels vehicles for state or local agencies. With regard to incentive based policies, financial incentives for bioenergy producers and consumers to develop bioenergy initiatives are provided. These policies can be identified as: issuing tax incentives to individ uals and businesses for energy efficient products or biomass based productions; offering subsidies or grants to decrease financial risk to individuals or companies; and establishing a low or nointerest loan system for people, local government, and institutions for developing bioenergy needs. Support based programs are geared toward developing and implementing education and outreach information for local government, communities, industries, landowners, and consumers. The programs include biofuel infrastructure development, technical assistance, public educational outreach, and advancement of bi oenergy technologies. As previously discussed, this research concluded that three factors (economic, environmental, and social) may influence bioenergy development. However, the condition of those factors in a state also impact policy formulation. For exam ple, a wealthier state would have more financial resources to develop an incentive policy. To connect the relationship among economic, environmental, and social factors and the three categories of bioenergy policies, the study chose gross state product per capita, growth rate of population, and number of biorefineries to represent the economic factor/condition. While the percentage of forestland in total area by state and share of coal in total net energy generation were chosen to represent the environment al factor/condition, the percentage of biomass in total net energy generation and education level were chosen to reflect the social factor/condition. Variable choice will be addressed in detail in the next section. The study ma de some hypothese s, and the r elations show n

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32 in Figure 2 1 were given an arbitrary interval scale (where 1 = low, 2 = medium, and 3 = high) to represent the level of economic, environmental, and social factors. First of all, the study assumed that regulatory mechanisms would be developed by a state with high environmental, medium social, and low economic conditions. A state with abundant natural resources might have sufficient motives to conserve the resources. A state government might promote new polices like enacting regulations and blending requirements, while the public or landowners still need to be educated. Bioenergy development is expected to generate new job opportunities and improve income distribution. In the meantime, t he processes to set up the regulatory mechanisms needed t o negotiate with different institutions, interest groups, and the public have a relatively high transaction cost (Feiock, 2007). Next, the study assumed that the incentive based policy would be chosen in a state with high economic, medium environmental, and low social conditions. It was expected that a n incentive based policy needed a high level of financial support, so that a state with good economic conditions might prefer to develop more policies and programs with economic incentives. Focacci (2003) obse rved a greater use of natural resources and a higher output of pollutants/GHGs during the initial stages of economic growth, following the concept of the Environmental Kuznet Curve. Given this situation, both public and landowners may lack sufficient knowl edge of forest bioenergy and its benefits, so knowledge of the benefits of utilizing bioenergy still needs to be extended. The final hypothesis was that a state would prefer to develop support based policies in a situation with high social, medium economi c, and low environmental conditions. In a state with a lower environmental condition, which may indicate a greater use of natural resources and/or higher GHG emissions, the public would look for cleaner and more environmentally friendly

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33 technologies (Focacci, 2003) and be aware of the benefit s of bioenergy. However, a low economic condition may not be conducive for policymakers to adopt a policy approach to encourage bioenergy development, such as technology support, infrastructure development, education outreach, and extension. Cluster Analysis: Data Sources and Techniques Cluster analysis is a statistical method for classifying observations into similar sets or groups. The groups are identified by minimizing the statistical variance within the groups and maximizing the variance among the groups (Ketchen and Shook, 1996). The techniques of cluster analysis are applicable in a wide range of areas. For instance, cluster analysis has been applied to describe and compare vegetation or communities of organisms i n ecosystem classification (Dolan and Parker, 2005); classif y the choice of work search methods for 2006); classify industries into different categories and evaluate the performance and s ustainability of industries in market research (Zeng et al ., 2008); and identify groups of similar state programs that help to customize Medicaid interventions and strategies in health policy analysis ( Roy et al ., 2009). Hierarchical and nonhierarchical ar e two basic methods of clustering algorithms The hierarchical method builds a treelike structure by agglomerative or divisive clustering algorithms. The agglomerative method starts with each observation being regarded as different cluster s and then proce eds to combine them until all observations belong to a specific cluster. Contrarily, the divisive method begins with all of the observations in one cluster and then proceeds to partition them into smaller cluster s Average linkage clustering, complete linkage clustering, single linkage clustering, and Wards linkage clustering are four well known

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34 and Stokus 2006; Zeng et a l ., 2008). The differences among them are in the mathematical procedures used to calculate the distance between clusters. The other clustering algorithm is nonhierarchical, also known as K means or the iterative method. It is usually applied to a sample s ize of over 10,000 observations (SAS Institute, 1990). Since this study had 13 states as observations and chose seven variables, it was more appropriate to apply the agglomerative hierarchical clustering method for the small sample size When organizing d ata into different groups, cluster analysis can present the characteristics of association and structure in data that may allow deduction of sensible and useful conclusions. Therefore, this study applied cluster analysis techniques to group the 13 states i nto homogeneous categories based on selected factors that may help policymakers to customize bioenergy policy instruments and strategies for a region rather than an individual state. All analyses were done using the software Stata version 10.0 for Windows. Data Sources for Variables The seven variables chosen were gross state product per capita (GSP), growth rate of population (GRP), number of biorefineries (Bio refineries) percentage of forestland in total area (FOR), share of coal in total net energy generation (COAL), percentage of biomass in total net energy generation (Bioenergy) and education level (Education) A brief description of the variables is presented in Table 2 1. The data sources for each variable follow. GSP is the gross market value of the goods and services attributable to labor and property located in a state. It is the state counterpart of the nations gross domestic product (GDP). Following the concept of the environmental Kuznets inverted U hypothesis, studies have shown a positi ve relationship between societal affluence and public preference for environmental quality (Padilla and Serrano, 2006; Verbeke and Clercq, 2006). Hence, GSP may have a significant impact on bioenergy development. This study used 2007 data from the U.S. Cen sus

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35 Bureau. The range of GSP was between $24,460.43 in Mississippi and $41,623.44 in Virginia per capita, with an average value of $32,665.92 (Table 21). Population growth rates (GRP) were calculated by using the following formula: ( ) = ln [ ( + 1 ) ( ) ] (2 1) where R(t) is the growth rate from year t to year t+1 and G(t) is population at year t To simplify the calculation, the study measured the GRP from 2000 to 2007 (data from the U.S. Census Bureau). The growth rate varied from negative (Louisi anas growth rate was 0.04% ) to positive values (Georgia had the highest with 0.15% followed by Texas and Florida), while the average growth rate in the region was about 0.07% It was hypothesized that states with higher growth rates of population would pursue more diverse energy sources, since the demand for energy would be higher. Alternatively, one could expect that higher population growth might pose challenges to the pursuit of environmentally benign energy sources. The number of cellulosic biorefineries in a state (Bio refineries) reflected the investment opportunity for forest bioenergy. Florida had six cellulosic biorefineries completed, under construction, or in the planning stage. On the contrary, there were no cellulosic bio refineries in Mississippi, Oklahoma, Texas, or Virginia. This variable represented the bioenergy technology development of using cellulosic bioenergy, and a larger number of biorefineries indicated higher technology in the state. Percenta ge of forestland in total area of a state (FOR) can reflect land use and the potential for forest bioenergy development; more forestland may represent a higher potential for forest bioenergy development. The study used 2003 data from the U.S. Census Bureau and 2009 Statistical Abstract data. The average forestland area in the region was 42.77%, with the highest in Alabama (64.40%) and the lowest in Texas (6.20%).

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36 The variable of share of coal in net energy generation (COAL) was measured as the ratio of net energy generation from coal in a state. Values of this variable ranged from 0.30 in Louisiana to 0.93 in Kentucky, while the average share of coal for the regions was 0.51. A state with a higher share of coal energy might be less likely to pursue bioenergy development, because locals whose livelihoods depend on coal production may not support alternative energy sources. At the same time, this variable represented the levels of GHG emissions in the states. The variable of bioenergy was calculated as the percentage of net energy generation from biomass, including landfill gas, municipal solid waste, agriculture byproducts/crops, and wood and derived fuels. This variable represented the current situation of bioenergy use in each state and reflect ed current impl ementation of bioenergy policies. The mean value of this variable was 2.46%, with the lowest being 0.48% in Kentucky and the highest 3.65% in Oklahoma. The variable of education was calculated as the percent of people 25 years old and over with at least a bachelors degree. Higher education was expected to be positively correlated with a persons knowledge of the negative consequences of fossil fuel use and political problems associated with a higher dependency on foreign oil. The average percentage with a bachelors degree or higher was 23.49%, with the highest in Virginia (33.6%) and the lowest in Mississippi (18.9%). Techniques of Cluster Analysis This study applied two hierarchical cluster ing algorithms, namely average linkage method and Wards linkage method. The dissimilarity measure used was squared Euclidean distance between standardized values of the variables, which was computed as the square root of the sum of the squared differences in value for every variable. The Euclidean distance for objects can be expressed as the following equation:

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37 ( ) = = ( 1 1)2+ ( 2 2)2+ + ( )2 (2 2) where X and Y are searching objects, ( 1, 2, ) and ( 1, 2, ) are objects X or Y chosen m features vectors. Average linkage treated the distance between clusters as the average distance between pairs of observations, while the distance in Wards method attempted to minimize the sum of squares (SS) of any two clusters that could be formed at each step. Wards met hod used a variance approach to evaluate the distance between clusters. For the average linkage method, assume that the distance ( ) between and of the cluster A and B (where i= 1,,k and j= 1,,l ) is measured with fixed criteria The distance d (A, B) was calculated by the equation: ( ) =1 1 1 (2 3) With regard to Wards method, the equation is shown as: ( ) = 2+ 2 (2 4) = + + (2 5) whe re and are numbers of cluster A and B. Since variables with large values may give more weight in defining a cluster than those with smaller values, Ketchen and Shook (1996) suggested that analyses should be undertaken by using standardization and nonstandardization to look for clusters that are consistent across the two solutions. This researc h standardized all variables by transform ing the distribution of each variable to a mean of zero and a standard deviation of one, except for the variable of biorefinery. It found that clusters were inconsistent between the average linkage and Wards methods before standardizing the variables in this study. After standardizing the variables, the study achieved almost identical clusters with the two methods.

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38 Results and Discussion The dendrogram used for cluster analysis, shown in Figure 22, is a graphical representation of the cluster tree. It is structured by applying Wards agglomerative method to the 13 states and 7 variables selected. The vertical axis s hows the dissimilarity measure on the basis of rescaled squared Euclidean distance. The horizontal axis arrays the states identified by their abbreviations, and three distinct clusters have been grouped. The average linkage method has consistent results in membership of the three clusters and a similar dendrogram. The only difference between the two dendrograms is the distance among groups, since the method to measure distance is different. Thus, the study only reports the results from Ward s linkage method. Table 2 2 reports cluster means for each variable measure within each final cluster and F and P values in variables. As the table shows, the variables percentage of forestland area and bioenergy are statistically significant at 5%, and variables gross st ate product per capita ( GSP ) growth rate of population ( GRP ) share of coal, and education are significant at 10%. Additionally, the study arbitrarily assigned a number scale of 3 to represent the highest cluster mean, 2 to the medium cluster mean, and 1 to the lowest cluster mean value of each variable5, and it re calculated the mean value of variables in each cluster to represent economic, environmental, and social factors. Figure 23 was drawn by using transformed means and showed the characteristics o f the clusters. Compared to Figure 2 1, the trends between the two distribution graphs are generally similar as the hypothese s ma de previously. 5 There is an exception for share of coal variable. The study assigned 3 to represent low level of share, 2 to medium, and 1 to high. The reason is that higher level of e nvironmental factor shows higher environment quality, and the higher share of coal may represent higher GHG emission, which is harmful to environment.

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39 Cluster 1 is form ed by Alabama (AL), Arkansas (AR ), Louisiana (LA), Mississippi (MS), and South Carolina (SC). T he characteristics of this cluster are that mean values of variables GSP, GRP, share of coal, and education have the lowest mean values compared to those in the other two clusters. However, the mean value of the percentage of forestland area (52.38%) is the highest among the three clusters. Generally speaking, this cluster is relative ly low in economic and social conditions and higher in natural resources. Given these results, it is expected that that these states are more suitable to develop regulatory mechanisms to promote forest bioenergy. In Cluster 2, there are five states, including Florida (FL), Georgia (GA), Kentucky (KY), Tennessee (TN), and North Carolina (NC). This cluster has higher values of GRP, number of biorefineries, and share of coal, whe reas it also has the lowest value of net energy generation from bioenergy (1.48%). Other variables, GSP, forestland area (44.54%), and education, are in the medium range. Therefore, these five states are high in economic development and relatively medium to low in both environmental and social conditions. Given these details, the results indicated that incentive based policies would be most suitable for these five states. Cluster 3 consists of the remaining states, Oklahoma (OK), Texas (TX), and Virginia (V A). This cluster has the highest average of GSP and highest average of bioenergy generation (3.60%) and education, but the lowest average forestland (23.80%) and no biorefineries. It can be concluded that these three states have higher social development, medium economic growth, and relatively medium to low environmental resources. This suggests that support based polic ies to aid bioenergy suppliers, producers, or consumers would be more suitable for these states. Conclusions Study results from cluster ana lyses showed that it is possible to classify the 13 Southeastern states into three separate clusters that are distinctly different based on the characteristics of their member states. Selected variables, including gross state per capita ( GSP )

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40 growth rate of population ( GRP ) number of biorefiner ies in a state (Bio refinery), percentage of forestland area in total area (FOR) share of coal in total net energy generation (COAL) percentage of biomass in total net energy generation (Bi oenergy ) and education level (Education) were applied as explanatory factors. Based on the hypothese s, the study found that three categories of policies, regulation mechanisms, incentive based policies, and support based policies, can be identified from economic, environmental and social criteria within clusters. Results suggest that Alabama, Arkansas, Louisiana, Mississippi, and South Carolina can adopt regulatory mechanisms for bioenergy policy; incentive based bioenergy policies are more applicable for Florida, Georgia, Ke ntucky, North Carolina, and Tennessee; and, Oklahoma, Texas, and Virginia are more suitable for support based bioenergy policies. The findings of the clusters and their characteristics can provide valuable information for policy makers to develop target spe cific forest bioenergy policies at a regional scale to increase the chances of their success

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41 Table 21. Descriptive statistics for variables used in the analysis Variable Mean Std. dev Min. Max. Unit GSP 32665.92 4748.29 24460.43 41623.44 $ per capita GRP 0.07 0.05 0.04 0.15 % Bio refinery 1.38 1.61 0.00 6.00 unit FOR 42.77 16.28 6.20 64.40 % COAL 0.51 0.17 0.30 0.93 ratio Bioenergy 2.46 1.05 0.480 3.65 % Education 23.49 4.04 18.90 33.60 % Table 22. Cluster means for classification variables from Wards clustering Variable Cluster F P 1 2 3 GSP 29186.76 33966.99 36296.07 3.35 0.077* GRP 0.03 0.10 0.09 3.16 0.086* Bio refinery 1.2 2.4 0 2.77 0.110 FOR 52.38 44.54 23.80 4.79 0.035** COAL 0.41 0.63 0.46 3.10 0.090* Bioenergy 2.77 1.48 3.60 11.36 0.003** Education 20.70 24.06 27.20 3.58 0.067* Significant at ** p < 0.05, *p < 0.10 with degree of freedom = (2, 10) Numbers in boldface means highest value among 3 clusters.

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42 Figure 2 1. Impacts of different levels of economic, environmental, and social factors on the three types of bioenergy policies ( regulatory mechanism, incentivebased, and support based) Figure 2 2. Dendrogram for cluster analysis by Wards method in t he Southeastern states 0 1 2 3 economic environmental social Regulatory mechanisms Incentive based policies Support based policies High Medium Low 0 10 20 30 40 L2squared dissimilarity measureAL SC AR MS LA FL GA NC TN KY OK TX VADendrogram for cluster analysis in the Southeastern states

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43 Figure 2 3. Distribution graph showing levels of economic, environmental, and social conditions 0.5 1 1.5 2 2.5 3 economic environmental social Regulatory mechanisms (C1) Incentive based policies(C2) Support based policies (C3) High Medium Low

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44 CHAPTER 3 A STATIC COMPUTABLE GENERAL EQUILIBRIUM ANALYSIS OF FOREST BIOENERGY POLICY IN TH E SOUTHEASTERN UNITE D STATES Introduction As the price of crude oil increases, the U.S. government is looking for alternative energy sources. Previous studies have shown that bioenergy is renewable, environmentally benign, and socially desirable, and could serve as an alternative energy source (Hill, et al ., 2006; Rabe, 2006) Thus, the U.S. government set a goal to produce 36 billion gallons of biofuels per year by 2022 through the Energy Independence and Security Act of 2007. Of that, c orn ethanol product ion is capped at 15 billion gallons per year starting in 2015 and t he remainder is anticipated to be met by cellulosic based biofuels and other advanced biofuels. Meanwhile, the Farm Bill of 2008 provides a $1.01 per gallon subsidy for cellulosic ethanol production. T hese polic ies are expected to stimulate new market opportunities for forest bioenergy At the state level many bioenergy programs and policies have been initiated to promote bioenergy development as well. Some states, such as Arkansas, Florida, Louisiana, and South Carolina, have set a blending mandate ( i.e., Renewable Fuel Standards or RFS) requiring that all marketed fuels contain a specific percentage of biofuels like E10 (10% ethanol and 90% gasoline) or B2 (a 2% blend of biodiesel and 98% fossil diesel ) Some states have instituted consumption tax incentives for individuals and businesses for the use and/or production of energy efficient products as well as subsidies for bioenergy. For instance, Florida provides a credit against the state sales and use tax for 75% of all capital operation and maintenance, and research and development costs for the product ion, storage, and distribution of biodiesel and ethanol up to a maximum of $6.5 million per fiscal year. Georgia has adopted a grant based initiative for E85 (85% ethanol and 15% gasoline) fueling infrastructure projects where up to $20,000 or one third of the total planned project costs are subsidized.

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45 The regional focus of this study is the 13 Southeastern U.S. states of Alabama, Arkansas, Florida, Georgia, Kentucky, Louisiana, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, and V irginia. This region consists of around 214 million acres of forest lands that have a high potential to produce forest bioenergy. When forest bioenergy policies are implemented, it is important to understand the macroeconomic effects of forest bioenergy dev elopment and how markets adjust to these government programs. Although many studies can be found that explore bioenergy issues, economy wide analysis in the Southeastern U.S. is still rare. Thus, this study applies a static computable general equilibrium ( CGE) model ( Lofgren et al ., 2002; Holland et al ., 2007) to shed light on important inter sectoral linkages and capture the economy wide impacts of policy implementation The following section provides an overview of CGE applications in bioenergy. Section 3 presents the modeling framework, the data, and the scenarios to be implemented in the analysis. Results and discussion are provided in section 4. The chapter concludes with a summary of the key findings and policy implications. CGE Applications in Bioenergy CGE model s have been widely used to assess the effects of environmental policies and bioenergy issues ( J orgenson and Wilcoxen, 1990; Das et al. 2005; Zhang et al ., 2005; Abdula, 2006; Reilly and Paltsev, 2007; Banse et al ., 2008; Kancs and Wohlgemuth, 2008; Taheripour et al ., 2008; Arndt et al ., 2009; Winston, 2009). This class of models is particularly effective in accounting for inter sectoral linkages and capturing the economy wide impacts of a policy instrument. Altho ugh Input O utput (IO) and Social Accounting M atrix (SAM) models can be useful for applied policy analysis they are not without their limitations IO models assum e that prices of input s and output s are constant and technological coefficients are fixed The re are also no

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46 constraints on the supply of primary factors and no tradeoffs between sectors since the f inal demand for the output of each sector is exogenous and thus may lead to biased estimates. Comparatively, a CGE modeling framework is thought to provide more flexibility and less biased estimates by incorporating a set of equations that represent the behavior of economic actors and a theoretical structure of the economy in question (Alavalapati et al ., 1998) Consumers maximize utility subject to their budget constraints, leading to the demandside specification of the model. Producers maximize profits, which describes the productionside specification of the model. Market and factor prices are determined by the equilibrium conditions requiring that demand must be equal to supply for all commodities and factors. Zero profit conditions are also satisfied for each industry with the assumption of constant returns to scale (Dixon et al ., 1992; Shoven and Whalley 1992) Abdula (2006) emplo yed a CGE model with a land use change model to examine bio energy policy implications on land use. Gan and Smith (2006) investigated the cost competitiveness of woody biomass for electricity production in the U.S. Their research showed that logging residues would be competitive with coal if emission s were taxed. McDonald et al. (2006) applied a multi region CGE model to evaluate the effects of substituting a biomass product (switchgrass) for crude oil in the production of petroleum in the U.S. Results indi cated that the new production process is less efficient and GDP declines slightly. The world price of crude oil falls, while the U.S. import demand declines. Scaramucci et al (2006) also applied a CGE model to estimate the economic impacts of constraining the supply of electric energy in Brazil and showed that the sugarcane agroindustrial system could ease the economic impacts of an electric energy shortage crisis on GDP.

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47 Evaluating bioenergy issues in a CGE framework presents some challenges, however. B i oenergy production, in particular second generation bioenergy (forest bioenergy) is often not recorded in the national accounts or is produced at a very low level. Kretschmer and Peterson (2008) identify three approaches to overcome this data limitation. The first is an implicit approach in which the amount of biomass required to achieve a bioenergy production target is specified without explicitly modeling a bioenergy sector Banse et al (2008) adopted this approach using an extended version of the GTAP E model. The authors modeled biofuels as intermediate inputs to the petroleum industry and adjusted the database to derive initial biofuel shares in the petroleum industry. A policy scenario introduced a mandatory blending requirement while the subsidy re quired to achieve the ratio was determined endogenously. The study showed significant shif ts in land use resulting from the implementation of bioenergy policies in the European Union (EU). The second approach models latent technologies that exist, but are inactive and unprofitable in the base year of the Social Accounting Matrix (SAM) In counterfactual scenario s the latent technologies can become profit able endogenously through changes in relative input or output prices or exogenously through a new polic y. Reilly and Paltsev (2007) used this approach to incorporate biomass energy production and competition for land. The authors found that with second generation biofuels suppl ying a substantial share of liquid fuel demand significant effects on land use a nd conventional agricultural markets in the U.S. would result The third approach is to disaggregate bioenergy production sectors from existing sectors directly from a SAM Based on relevant production information, the deficiencies can be removed with more research and improved data ( Taheripour et al ., 2008). Kretschmer and Peterson (2008)

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48 indicated that bioenergy data limitations are likely to be overcome in the near future, since bioenergy production is expected to become economically viable. Meanwhile, t hey compared the results of this approach to the latent technology approach and found similar outcomes. Therefore, Kretschemer and Peter (2008) suggest that disaggregating a bioenergy sector in the SAM approach is the most promising approach to modeling bi oenergy and, as such, is adopted in this research. Modeling Framework This study applies a CGE model developed by Lofgren et al (2002) and customized by Holland et al (2007) for compatibility with the IMPLAN (IMpact analysis for PLANning) data set to ass ess the impacts of bioenergy policies in the Southeastern U.S. The model was customized to include a more robust representation of transfers between institutions and the inclusion of indirect business taxes. In addition, the government, investment account, and households receive income from the primary factors of production. In the modeling framework, producers are modeled to maximize profits with a twolevel production technology. At the first level, intermediate and primary inputs (labor, capital, and lan d) are demanded in fixed proportions to produce each unit of output. At the second level, the aggregate intermediate input is specified by a Leontief function of disaggregated intermediate inputs, while value added is captured by a constant elasticity of s ubstitution (CES) function of the primary inputs. The institutions in the model are: three household income classes; the state and federal government (including their disaggregated investment and expenditure); general investment; and the rest of the world. The households receive income from the primary factors of production and transfers from other institutions; they make payments to the direct tax account, save, consume, and make transfers to other institutions. Household consumption is assumed to maximize a

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49 Stone Geary utility function, which leads to a linear expenditure system (LES) demand function. The government collects taxes, which are fixed at ad valorem rates, and receives transfers from other institutions. Government consumption is fixed in quanti ty, and government transfers to households and the investment account are indexed by the consumer price index (CPI). The general investment institution receives payments from the primary factors and transfers from other institutions. Investment demand is f ixed and defined as the baseyear quantity multiplied by an adjustment factor. Transfer payments from the rest of the world, domestic institutions, and factors are all fixed in foreign currency units. Regarding trade, domestic and imported goods are considered imperfect substitutes by the Armington assumption, which applies a CES function to aggregate domestic and imported goods into a composite good. The demand of each sectors output is obtained by minimizing the cost of the composite good subject to the CES function. Composite commodity supply is a function of the price of imports and the price of regionally produced commodities. The export supply function is derived from a constant elasticity of transformation (CET) function and specifies the value of ex ports based on a ratio of domestic and export prices. The CET function assumes imperfect substitutability between products produced for the domestic and export market by a given industry. Equilibrium prices are endogenously determined (commodity prices, fa ctor prices, and the exchange rate) to clear the product, factor, and foreign exchange markets All prices of commodities and primary factors are normalized to unity in the initial equilibrium. Given prices normalized to one, the values in the SAM accoun ts can be interpreted as physical quantity indices in the commodity and factor markets. The parameters of these functional forms are calibrated with the Southeastern regions SAM.

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50 There are three major alternative factor closures to equilibrate supply and demand in factor markets (labor, capital, and land). The first closure fixes the quantity of each factor at the observed level and enables the economy wide wage/rent to adjust. In this closure, the factor is fully employed and mobile across sectors. The s econd closure fixes the economy wide wage/rent and the factor may go unemployed. Finally, the third closure fixes factor demand and the economy wide wage; however, the industry specific wage/rent and supply are variable. Each industry uses the base year qu antities of each factor (Lofgren et al ., 2002). In this research, labor supply is modeled as flexible in supply and mobile across sectors within the state, capital is activity specific and fixed in supply, and land is fixed in supply and mobile across sect ors. For the savings and investment closures, savings are generated from private households, governments, and foreign trade in the national economy. Private savings, government savings, and foreign savings consist of macro saving balances. The sum of all three macro balances is equal to gross private domestic investment. There are three types of savings and investment closures, namely Johansen, neoclassical, and Keynesian closures. In the Johansen closure, foreign savings are exogenous and the savings inve stment balance is reached through adjustment in household consumption. The neoclassical closure results in a savings driven model where foreign borrowing is fixed and aggregate gross private domestic investment is determined by aggregate savings. In the Ke ynesian closure, investment is fixed and all macro saving balances are allowed to adjust. This study applies the Keynesian closure, since it can aggregate employment as a link to macro variables and has been used in other studies (Julia Wise et al ., 2002; Lofgren et al ., 2002). Additionally, the macro saving balances should be calculated carefully at the regional level since major capital projects (both public and private) are likely to be financed with little or no consideration of regional saving (Robinson et al ., 1994). Financial

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51 capital is totally mobile across regional boundaries. This implies that total regional investment should be treated as exogenous, with outside capital flows adjusting to equate total savings with investment. Concerning government expenditures, state government revenues are endogenous and drive state expenditures in the current account; as a result, the state government savings or deficit remains fixed (exogenous) at the baseline level. Federal government expenditures are treated as fixed in nominal terms, while federal revenues are endogenous with federal government savings or deficit reflecting the difference. Furthermore, the foreign exchange rate is assumed flexible and the import price is a function of the world price import tariffs, and the exchange rate. The CPI is set to be the numeraire. The General Algebraic Modeling System (GAMS) software is used to solve the model as a mixed complementary problem using the PATH solver. Database The datab ase is derived from 2006 Southeastern regional IMPLAN data and includes 509 sectors1. To focus on sectors of interest for this study, the 509 sectors were aggregated into 14 sectors (Figure 3 1) : agriculture, logging, sawmill products, pulpmill products, other wood products, conventional electric power electric power from grain based bioenergy (i.e., electric power from first generation bioenergy or 1GB), electric power from forest bioenergy (i.e., electric power from second generation bioenergy or 2GB), conventional energy excl uding electric power, grain based bioenergy excluding electric power (i.e., non electric energy from 1GB), forest bioenergy excluding electric power (i.e., nonelectric energy from 2GB), 1 Between 1990 and 2000, IMPLAN data included 528 sectors based on the Standard Industrial Classification (SIC) system. From 2001 onward, datasets were modified to include 509 sectors based on the North American Industry Classification System (NAICS) codes.

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52 manufacturing, transportation, and all other sectors. Figure 3 1 repr esents the linkage among the sectors most important to this research (agriculture, forestry, and energy sectors). Sector code 30 in the IMPLAN data, power generation and supply, represents the conventional electric power sector. Based on EIA (2007), renewa ble energy consumption in the nations energy supply was about 7%, of which biomass accounted for 53%. Based on EIA (2007) data for the Southeastern region, biomass based electricity generation was estimated at 3.7%, of which 7% was produced from agricultural byproducts/crops and 93% was produced from woody biomass. In this study, electric power from 1GB and electric power from 2GB were disaggregated from the conventional electric power sector by 0.26% (share of biomass based electricity generation multiplies the ratio from agricultural byproducts and crops, 3.7%*7%) and 3.44% (share of biomass based electricity generation multiplies the ratio of electricity generation from woody biomass, 3.7%*93%), respectively, of total conventional electric energy product ion. With regard to the conventional energy sector excluding electric power, nonelectric energy from 1GB, and nonelectric energy from 2GB, this sector includes related coal, oil, and natural gas activities. S ector 151 in the IMPLAN data, other basic orga nic chemical manufacturing, includes four major activities: gum and wood chemical manufacturing, cyclic crude and intermediate manufacturing, ethyl alcohol manufacturing, and all other basic organic chemical manufacturing2. Nonelectric energy from 1GB mai nly refers to corn based ethanol (i.e., ethyl alcohol). Lacking data on the relative importance of ethyl alcohol manufacturing in sector 151, the 1GB sector was disaggregated based on the assumption that the other basic 2 The sector code 151 in the IMPLAN is referred to the sector code 32519 in NAICs. The following docum ent has more detail about related activities in the sector ( http://www.census.gov/eos/www/naics/2007NAICS/2007_Definition_File.pdf ), accessed May 25, 2009

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53 organic chemical manufacturing sect or produces gum and wood chemicals, cyclic crude and intermediate products, ethyl alcohol, and all other basic organic chemicals in equal proportions. Additionally, IMPLAN does not provide explicit information on forest bioenergy since forest bioenergy wa s not produced in significant quantities in 2006. Thus, the intermediate and primary factor consumption of the nonelectric energy from 2GB sector is disaggregated from the logging, sawmill products, and pulpmill products sectors according to a ratio based on the literature (Taheripour, et al ., 2008; Winston, 2009). IMPLAN describes nine householdincome classes. To simplify analysis, households were aggregated into three income categories: low ( annual income less than $ 15,000), medium ($1 5,000 to $40,000) and high (greater than $40,000) income categories In constructing the S outheastern U.S. SAM from IMPLAN, some imbalances between symmetrical row and column sums were unavoidably created. This study applied the cross entropy (CE) balancing approach devel oped by Robinson et al (2001) to eliminate the se imbalances. The procedures of the CE balancing approach and the GAMS program code is available in Robinson and El Said (2000). Appendix A presents the SAM developed for the S outheastern U.S. reference year 2006. The Policy Scenarios This research investigates four specific scenarios to analyze the economy wide and welfare impacts of biofuel production in the Southeastern U.S. The following scenarios are considered: Bioenergy substitution : The U.S. governmen t has proposed a number of policies to promote forest bioenergy development. Renewable Portfolio Standards (RPS) and Renewable Fuels Standards (RFS) are two major programs that regulate, mandate, or restrict fuel consumption and production to promote fores t bioenergy.

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54 The RPS, also referred to as Renewable Electricity Standards (RES), are a set of policies aimed at ensuring that a certain percentage of energy is derived from renewable energy sources such as wind, solar, biomass, and geothermal energy. The essence of RPS is to generate Renewable Energy Credits (RECs), for example by demonstrating that a certain number of kilowatthours (kWh) of electricity is generated from renewable resources and then sold to an end user in the state. An energy generating company or a retail supplier has to gain credits equivalent to the state specified RPS percentage of their entire annual energy sales. Retail suppliers or generators can acquire RECs by c onstruct ing and operat ing renewable energy facilities ; alternatively, they can purchase RECs from other energy generators or retail suppliers. Most RPS laws require states to increase the percentage of renewable power sources used from the current amount to between 10% and 20% over about 20 years. Currently, only three of th e 13 Southeastern states (North Carolina, Texas, and Virginia) are adopting an RPS policy. Several other states, Florida for example, are considering an RPS policy ( Energy Efficiency and Renewable Energy State Activity & Partnerships 2009) Therefore, in this research, an RPS policy is simulated by substituting 1 % of conventional electric power production with electric power produced from forest biomass bioenergy. The RFS are policies which require a minimum amount of renewable fuels in transportation fuel ; the states of Arkansas, Florida, Louisiana, and South Carolina have set such a blending mandate. For instance, in Arkansas, all diesel powered motor vehicles owned or leased by a state agency have to be oper ated with at least 2% biodiesel. In Florida, al l gasoline sold must contain 10% ethanol by volume by the end of 2010. It is anticipated that the vast majority of the RFS will be met using ethanol produced from corn starch or grainbased biomass. Although there are plans underway to build plants capable of producing cellulosic biofuel,

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55 cellulosic biofuel is still at the very early stages of production and commercialization, and high production costs could have significant impacts on these near term plans. Therefore, in this research, the socioeconomic i mpacts of RFS are considered by developi ng a policy scenario in which 1% of conventional liq uid fuel is substituted with cellulosic ethanol. Equation 31 is a mathematical representation of the domestic supply function. = + (3 1) w here variable is quantity of domestic output C, variable is level of activity A, variable is the quantity of institutional make matrix3, and parameter is the yield of output C per unit of activity A. To simulate both bioenergy substitution scenarios, the model shocks the parameter to simulate the substitution of 1 % of conventional electric power with forest biomass electric bioenergy in the RPS scenario and to simulate the substitution of 1 % of conventional liquid fuel with cellulosi c ethanol in the RFS scenario. Bioenergy incentive: Since concern for r ising GHG emissions is leading a shift from fossil fuel s to renewable energy sources, a price support for bioenergy or a tax on conventional energy could be used to simulate shifting preferences for clean and efficient energy sources. As previously discussed, most ethanol subsidies are applied to grain based ethanol or first generation bioe nergy production. To encourage forest bioenergy development, the 2008 Farm Bill provides a $1.01 subsidy on cellulosic ethanol per gallon produced. To simulate this policy, a number of assumptions are required. First of all, it is assumed that the nonelectric energy 2GB sector produces cellulosic ethanol only. In 2006 in the Southeastern SAM, this sectors total output was about $1698.83 million. Based on Winston 3 The make mat rix shows the value of output of each commodity C produced by different institution I.

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56 (2009), the input cost of cellulosic ethanol is approximately $5.02 per gallon. Therefore, one can estimate that the Southeastern region produced 338.41 million gallons of cellulosic ethanol (1698.83/5.02). Meanwhile, the total indirect business tax of nonelectric energy from 2GB sources was about $16.72 million, so that each gallon of cellulosic ethanol was taxed by 4.9 cents per gallon in 2006. To simulate the impacts of this policy in the 2008 Farm bill, a fuel tax reduction (20.6 $0.049 = $1.01) was applied to the nonelectric energy sourced from 2GB sector. This tax reduction can be conside red an incentive for the production of cellulosic bioenergy. Equation 32 represents the function of indirect business tax in the model. = (3 2) w here variable is indirect business taxes receipts for each government unit, the variable is the price of activity A, variable is the level of activity A, parameter is the share of indirect business taxes for each government unit, and parameter is the indirect business tax rate. To account for the tax reduction, the parameter was updated by ( ) 20.6. Technological progress : Due to the high cost of energy production from woody biomass with current technology, energy companies are still less likely to use forest biomass to produce energy. It is expected, however, that technological advancements will eventually render the production of biomass based bioenergy economically feasible. There are a number of policy alternatives that may be implemen ted to increase forest bioenergy production. Policy incentives to reduce the cost of biomass transportation or a production subsidy would stimulate bioenergy production. Improvements in production, harvesting, collection, densification, transportation, and storage and conversion of woody biomass can reduce the cost of biomass based bioenergy production. Meanwhile, c ost sharing capital investments in constructing woody fuel bioenergy

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57 plants would lead to a reduction in the uni t cost of bioenergy production. To simulate technological gains, a final scenario reduces the 2GB electric power and 2GB nonelectric power sectors intermediate consumption of logging, sawmill, and pulpmill products by an arbitrary amount of 10%. Equation 33 describes the intermediate input demand function. = (3 3) w here variable is the quantity of intermediate use of commodity C by activity A, variable is the level of activity A, and parameter is the quantity of C a s intermediate input per unit of activity A. Simulation Results In this section, simulation results are presented. The results report policy impacts on supply price and quantity, government revenue and expenditure, factor demand, and welfare. Supply Price and Quantity Since the prices of commodities are normalized to unity in the initial equilibrium, all impacts of scenarios on supply prices of commodities are reported as the percent change from the normalized price of $1. The supply price of conventional electric power co mmodities increase by 1.71%, while the price of 2GB electric power commodities decrease significantly by 27.56% in the bioenergy substitution RPS sc enario (Table 3 1). Meanwhile, the RPS scenario has small negative impacts on the supply pr ices of logging and 1GB electric power by 0.01% respectively. For other sectors, the RPS scenario does not have significant impacts on the supply prices. Moreover, t he supply of 2GB electric power increases by $1850.20 million (28%) and that of logging ris es negligibly in the RPS scenario (Tables 3 2 and 33) while the supply of

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58 conventional electric power decreases by $1850.20 million ( 1%). S upply is affected negatively for all other sectors. With regard t o the RFS scenario, results (Table 3 1) show negat ive impacts on the supply price of 2GB nonelectric energy ( 10.11%) and that of other wood products ( 0.02%). Contrarily, the RFS scenario has positive impacts on t he supply price s of conventional energy e xcluding electric power ( 0.66% ), follow ed by logging (0.39%), agriculture (0.22%), transportation (0.07%), 1GB nonelectric energy (0.04%) and sawmill products (0.03%). Furthermore, t he suppl y of conventional energy excluding electric power decreases by $7613.42 million (1%) while the supply of 2GB nonelectric energy increase by $7647.34 million (446.41%) in response to this policy scenario (Tables 32 and 33). The supplies of logging, sawmill products, pulpmill products, and other wood products rise by $679.09 million (3.57%), $157.56 million (0.43%), $79.83 million (0.11%), and $17.56 million (0.08%), respectively. The supply of all commodities drops, with the exception of 2GB non electric energy, which increases by $346.63 million (20.23%)(Tables 32 and 33). In the bioenergy incentive scenari o, the supply price of 2GB nonelectric energy decreases by 5.08% (Table 3 1) while there is a slight negative change in the supply price for other wood products. Nevertheless, the bioenergy incentive policy results in an increase in the supply prices of logging (0.14%), agriculture (0.08%), 1 GB electric power (0.03%), 2GB electric power (0.03%), conventional electric power (0.03%), and sawmill products (0.01%). The supplies of most commodities increase in the bioenergy incentive scenario (Tables 3 2 and 33), especially 2GB nonelectric energy, which increases by $1221.16 million (71.28%). While agricultural supply decreases by about $30.33 million ( 0.04%), quantities of manufacturing and 1GB non electric energy drop only slightly.

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59 For the technological progress scenario, the supply price of the 2GB electric power and 2GB nonelectric energy commodities drops by 0.01% and 0.98% (Table 3 1) respectively. However, the supply prices of agriculture and logging commodities increase by 0.02% and 0.04%, respe ctively. Generally speaking, the technological progress results in most supply prices increasing, with the exception of other wood products, which tend to decrease slightly. The quantity of 2GB electric power and 2GB non electric energy increases by $0.4 m illion (0.01%) and $151.71 million (8.85%), respectively (Tables 32 and 32). The quantity of agriculture, 1GB nonelectric energy, and manufacturing commodities drops slightly in the technological scenario. Primary Factor Demand and the Government With a fixed labor wage and flexible labor supply, the results show that total l abor demand decreases by 6,360 ( 0.01 %) jobs in the bioenergy substitution RPS scenario In the case of the substitution RFS, bioenergy incentive and technological progress scenarios total labor demand increases by 20,787 (0.04%), 8,377 (0.014%) and 581 (0.001%) jobs, respectively. In addition, with a fixed land supply, there is a contraction in agricultural demand for land and an increase in the logging sectors demand for land in a ll scenarios (Table 3 4). In order to clear the capital market, the price of capital decreases for all sectors with the exception of the conventional electric power s ector where it increases by 0.54 % in the RPS scenario. The RFS scenario has positive impacts on the price of capital for 2GB nonelectric energy (847.09%), logging (5.59%), sawmill products (0.77%), agriculture (0.23%), pulpmill products (0.19%), and other wood products (0.06%) sectors, while the price of capital decrease for other sectors. Mor eover the price of capital increases for all sectors, with the exception of the 2GB electric power ( 0.02%), 2GB nonelectric energy ( 69.92%), and manufacturing sectors ( 0.02%) in the bioenergy incentives scenario. In the technological progress scenario, sim ilarly, the price of capital increases for all sectors with the exception of the 2GB electric power ( 0.02%),

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60 1GB nonelectric energy ( 0.001%), 2GB nonelectric energy ( 7.67%), and manufacturing ( 0.003%) sectors. The impacts of the policy simulations on the government are presented in Table 3 5. In the RPS scenario, the federal government revenue and expenditures decrease around $42.30 million and $0.71 million, respectively, while the regional state government revenue and expenditures both increase by $ 12.65 million. By contrast, the federal revenue and expenditures rise by $30.80 million and $71.86 million, respectively, while the regional state government revenue and expenditures both decrease by $29.45 million in the RFS scenario. Furthermore for the bioenergy incentive and technological progress scenarios, the federal government revenue and expenditures both increase; the regional state government revenue and expenditures also rise slightly. Additionally, the federal and regional state governments collect more indirect business taxes ($ 22.65 million, $601.51 million, and $4.60 million in the RPS, bioenergy incentive, and technological progress scenarios, respectively). Only the federal and regional state governments in the RFS scenario collect less indire ct business taxes ($46.45 million). Household and Welfare Impacts Household utility decreases slightly for all three househol dincome classes in both RPS and RFS scenarios L ow income households were worse off than medium and high income households. However, household utility increases slightly for medium and high household income classes in both the bioenergy incentive and technology progress scenarios while declining for the low household income class. Results show that most commodity supply prices increase; thus, the negative impacts on low income households may be explained by a negative substitution effect which is greater than the positive income effect.

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61 This study applies the Hicksian equivalent variation (EV) as a measure of both price and inco me effects rather than simply a measure of change in household income. Equivalent variation is measured at the level of prices and income present prior to the implementation of a policy. It is the minimum payment the consumer would accept to forgo the poli cy change. In other words, it is the amount the consumer would need to receive to be as well off as if the policy had been implemented. In the RPS scenario, the EV decreases for low, medium, and high income classes by $73.45 million, $ 190.26 million, and $117.82 million (Table 3 6) respectively, and by $131.06 million, $ 298.05 million, and $177.97 million, respectively, in the RFS scenario. For the bioenergy incentive scenario, the EV decreases for low income households by $8.35 million and increases for medium and high income households by $40.37 million and $40.58 million, respectively. In the technological progress scenario, the EV decreases for low income households by $0.56 million and increases for medium and high income households by $2.10 million a nd $2.25 million, respectively. Finally, the Southeastern gross regional product decreases by $188.16 million and $ 799.24 million in the bioenergy substitution RPS and RFS scenarios, respectively. In contrast, the Southeastern gross regional product incre ases by $384.57 million and $21.90 million in the bioenergy incentive and technological progress scenarios, respectively. Conclusions Private forests in the Southeastern region have a high potential to produce forest biomass that can be utilized to produce cellulosic ethanol or to generate electricity through co firing. It is believed that promoting forest bioenergy can create job opportunities and stimulate economic growth. This research assessed the socioeconomic impacts of a set of potential forest bioenergy scenarios on the regional economy. The scenarios evaluated included a substitution of

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62 conventional electric power by forest bioenergy, a substitution of liquid fossil fuel by cellulosic biofuel, an incentive for second generation bioenergy production, and technological gains in second generation bioenergy production. Overall, both bioenergy substitution scenarios resulted in decreases in social welfare, household utility, and the gross regional product. Because the fossil fuel industry is a fundamenta l industry for many other sectors, a number of industries depend on energy from fossil fuels. Household utility and social welfare drop when forest bioenergy replaces a portion of conventional energy in the production of fossil fuel or electric power, sinc e this involves a productivity loss in the conventional energy sector. The results also showed that land demand shifts from agricultural production to forest based activities. Most importantly, while the supply price of logging and 2GB electric power comm odities decrease in the bioenergy substitution RPS scenario, the supply quantities of logging and 2GB electric power commodities rise. This implies that the supply curves of logging and 2GB electric power increase in the RPS scenario. Since the logging sector is a major source of forest biomass, an increasing supply of 2GB electric power resulted in increased logging sector production. Meanwhile, this leads to compress the supply of sawmill and pulpmill products, since the logging sector is also a major sou rce for those products. In the RFS scenario, displacing 1% of conventional energy excluding electric power by increasing supply of 2GB nonelectric energy leads to an increase in production of forest ry sectors, including logging, sawmill products, pulpmil l products and other wood products sectors. This may be an indication of the fact that largely increased 2GB nonelectric energy market would result in an additional demand for forest biomass and therefore an increase in the price biomass material. Meanwh ile, total labor demand increases in the RFS scenario. It may be

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63 explained by the fact that forestry sectors and 2GB nonelectric energy sector are relatively labor intense sectors t hough the forest bioenergy production may pull labor from conventional ene rgy sectors. The results also indicated that technological progress and providing incentives for the forest bioenergy sector s (2GB electric power and 2GB nonelectric energy sectors) would lead to increased welfare and gross regional product, and land shifting from agricultural production to forest based activities. T o maximize positive policy outcomes, compl e mentary policies may be required to offset the small reduction in the income of low income households. Both the bioenergy incentive and technological progress scenarios resulted in reduced production costs of 2GB nonelectric energy and a decline in the supply prices of 2GB nonelectric energy. Although both federal and state gove rnment expenditures increased as governments planned to develop forest bioenergy by implementing incentives or improving technologies, both federal and state government revenues increased as well. Therefore, t he implementation of incentives for the produc tion of forest bioenergy may generate new market opportunities for forest biomass and increase the demand for forest bioenergy resulting in overall positive outcomes for the economy. Investment in technology may reduce the cost of bioenergy production and further stimulate the production of forest bioenergy. Moreover, given increases of supply prices and quantities in the forest markets (such as logging, sawmill products, and pulpmill products) incentives for bioenergy and technological progress would inc rease demand for woody biomass. One implication for landowners is that increasing the level and frequency of forest thinning could result in increased income and also improve forest health, reduce wildfire risk, and enhance biodiversity. In addition, loggi ng residues or unused portions of trees, which were left on sites after traditional timber harvesting in the past,

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64 may prove to be an important source of forest bioenergy. Land currently left idle or considered marginal for the production of crops may be used to produce forest biomass, depending on how bioenergy, fore stry, and land markets evolve.

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65 Table 3 1. Percent change in producer commodity prices Bioenergy Substitution RPS Bioenergy Substitution RFS Bioenergy Incentive Technological P rogress Agriculture 0.0 0 0.22 0.08 0.02 L ogging 0. 01 0.39 0.14 0.04 Sawmill products 0.0 0 0.03 0.01 0.01 Pulp mill products 0.00 0.00 0.00 0.00 Other wood products 0.0 0 0.02 0.00 0.00 Conventional electric power 1.71 0.01 0.03 0.00 Electric power from 1GB 0. 01 0.01 0.03 0.00 Electric power from 2GB 27.56 0.01 0.03 0.01 Conventional energy excluding electric power 0.00 0.66 0.01 0.00 Non electric energy from 1GB 0.0 0 0.04 0.00 0.00 Non electric energy from 2GB 0.0 0 10.11 5.08 0.98 Manufacturing 0.0 0 0.01 0.00 0.00 Transportation 0.0 0 0.07 0.01 0.00 Others 0.0 0 0.00 0.01 0.00 Table 3 2. Percent change in quantity of commodity supply by scenario Bioenergy Substitution RPS Bioenergy Substitution RFS Bioenergy Incentive Technological P rogress Agriculture 0. 01 0.16 0.04 0.01 L ogging 0. 00 3.57 1.22 0.30 Sawmill products 0. 02 0.43 0.14 0.02 Pulp mill products 0.04 0.11 0.07 0.01 Other wood products 0. 01 0.08 0.04 0.00 Conventional electric power 1 .00 0. 01 0.01 0.00 Electric power from 1GB 0. 00 0. 01 0.01 0.00 Electric power from 2GB 28.00 0. 01 0.01 0.01 Conventional energy excluding electric power 0.00 1.00 0.00 0.00 Non electric energy from 1GB 0. 01 0.39 0.00 0.00 Non electric energy from 2GB 0. 02 446.41 71.28 8.86 Manufacturing 0. 02 0.14 0.01 0.00 Transportation 0. 01 0.13 0.02 0.00 Others 0. 01 0.02 0.01 0.00

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66 Table 3 3. Change in the level of com modity supply ($ million s ) Bioenergy Substitution RPS Bioenergy Substitution RFS Bioenergy Incentive Technological P rogress Agriculture 9 75 138.70 30.33 11.33 L ogging 0.75 679.09 231.07 57.53 Sawmill products 5.85 157.56 53.10 8.02 Pulp mill products 30.55 79.38 48.56 5.54 Other wood products 2.23 17.56 7.85 0.70 Conventional electric power 10 85.91 8.07 9.42 0.54 Electric power from 1GB 0. 01 0.01 0.03 0.01 Electric power from 2GB 1085.20 0.30 0.34 0.40 Conventional energy excluding electric power 19.92 7613.42 19.23 2.04 Non electric energy from 1GB 1.47 46.58 0.30 0.05 Non electric energy from 2GB 0.28 7647.34 1221.16 151.71 Manufacturing 349.71 2177.06 139.92 23.24 Transportation 17.67 343.52 42.95 3.83 Others 294.88 777.48 544.09 24.51 Table 3 4. Percent change in demand for land Bioenergy Substitution RPS Bioenergy Substitution RFS Bioenergy Incentive Technological P rogress Agriculture 0. 00 0.81 0.27 0.07 L ogging 0. 02 4.43 1.48 0.38 Table 3 5. Change in the level of government revenue and expenditure ($ millions ) Bioenergy Substitution RPS Bioenergy Substitution RFS Bioenergy Incentive Technological P rogress Federal government revenue 42.30 30.80 130.73 2.84 Federal government expenditure 0.71 71.86 15.38 0.76 S tate government revenue 12.65 29.45 539.87 5.02 S tate government expenditure 12.65 29.45 539.87 5.02

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67 Table 3 6. Change in the level of social welfare by equivalent variation ($ millions) Numbers of HH (% of total HH) Bioenergy Substitution RPS Bioenergy Substitution RFS Bioenergy Incentive Technological P rogress Low HH 5,963,404 (15.88%) 73.45 131.06 8.35 0.56 Medium HH 12,165,184 (32.39%) 190.26 298.05 40.37 2.10 High HH 19,432,041 (51.73%) 117.82 177.97 40.58 2.25

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68 Figure 3 1. Structure of sector mapping. Shaded cells represent final sectors disaggregated in the model and the numbers in parentheses indicate the number of sectors within the primary sector. The dashed and dotted lines represent the main source for different types of bioenergy. Agriculture sector(1) Forestry sectors(4) Non electric energy from 2GB Electric Power from 2GB Forest products and logging Conventional energy excluding electric power Sawmill products Energy sectors(6) All other sectors (3) Non electric energy from 1GB Electric power from 1GB Conventional electric power Pulp -mill products Other wood products

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69 CHAPTER 4 A RECURSIVE DYNAMIC COMPUTABLE GENERAL EQUILIBRIUM ANALYSIS O F FOREST BIOENERGY POL ICY IN THE SOUTHEASTERN UNITED STATES Introduction Chapter 3 evaluated the policy impacts of forest bioenergy development with a static CGE model for the economy of the Southeastern U.S. When the static model was shocked with a series of bioenergy policy scenarios, the results indicated changes in the levels of economic indicators. Although the static CGE model can provide information on tradeoffs between sectors and capture the economy wide impacts of policy implementation, policymakers are also interested in how policies may impact future socioeconomic conditions and economic transition paths. Therefore, the major goal of the work described in this chapte r wa s to investigate the impacts of forest bioenergy policies from 2006 to 2025 in the Southeastern region. To achieve this goal, a recursive dynamic CGE modeling framework wa s developed and applied. A dynamic CGE analysis can account for issues related t o timing and lagged effects of policy implementation and adaptation by producers and consumers. This type of model can also explicitly trace each variable and predict the economys trajectory through time. One of the main advantages of this class of models is their ability to shed light on the economic transition path resulting from a policy shock and the short term costs and longer term gains resulting from policy implementation (Cattaneo, 1999). This chapter is organized as follows: section 2 provides an overview of dynamics in CGE models; section 3 describes the dynamic extension to the standard static CGE model in GAMS; section 4 briefly presents the database and the scenarios to be implemented; section 5 provides results and discussion; and the final s ection presents key findings and policy implications.

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70 Dynamic Computable General Equilibrium Models for Policy Analysis Dynamic CGE models are applied to simulate the impacts of a policy on the economy for a definite time period since a static impact analysis does not reveal the effects of policy shocks through time. The short and long term impacts of a policy may be very different and it is therefore interesting to investigate an economys transition path and whether or not the transition is a smooth one. It is also important to investigate the differences in timing and amplitude of the oscillation of certain variables between the short and long terms (Dixon et al ., 1992). Moreover, policy evaluations based on a single period static equilibrium can be misleading since dynamic elements such as population movements or growth and capital accumulation abound in the real world. Hence, in many cases, applying a dynamic model provides a more realistic interpretation of the socioeconomic impacts of policy implementation. Typically, dynamic CGE models are developed by incorporating functions for capital accumulation and investment and growth into key parameters such as population, labor force, factor productivity, and government consumption. D ynamic CGE models have been used to study bioenergy development Arndt et al. (2009) assessed the implications of large scale investments in biofuels on growth and income distribution using a recursive dynamic CGE model. They found that in Mozambique, inve stment in biofuels improved growth and reduced poverty despite some displacement of food crops by biofuels; biofuel investment increased Mozambiques annual economic growth by 0.6% and decreased the incidence of poverty by around 6% over a 12year period. Kretschmer et al. (2009) applied a recursive dynamic CGE model to assess the economic impacts and optimality of different aspects of the EU climate package. The authors showed that the EU emission targets would lead to only minor increases in biofuel production. Subsidies are necessary to reach the 10% biofuel target while European agricultural prices could increase by

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71 up to 7%. Compared to a cost effective scenario in which a 20% emission reduction target is reached, welfare losses could occur because of separated carbon markets and the renewable quotas. Winston (2009) modified the United States Agricultural General Equilibrium model (USAGE), a large scale dynamic CGE model of the U.S. economy, to enhance its utility in agricultural and bioenergy policy an alysis. The modifications included the disaggregation of several new industries and commodities from the USAGE database, the addition of land matrices for acreages and land rentals in agriculture covering 72 types of land, and technical modifications to th e USAGE theory for land use and by product production (e.g., crop residues ) In principle, the dynamic mechanisms of CGE models are represented by different solution approaches for the model and the expectation s of economic actors. Dixon and Parmenter (1996) identified four approaches to deal with investment and capital accumulation in the dynamic modeling framework. In the first approach, investment is exogenous and the availability of investment funds is diminishing. This approach assumes that if one industry has attracted substantial investment funds with a high rate of capital growth, the industry has a high expected rate of return to attract the marginal investor (Dixon and Rimmer, 2002). This type of model implies the static expectation that decision p arameters are fixed over time. In contrast the second approach assumes investment is endogenous and that installation costs continually increase. Investment depends on rates of return, while savings and total consumption are fixed shares of income. This c lass of models implies adaptive expectations and has been adopted by many modelers (Dixon et al. 1992; Jorgenson and Wilcoxen, 1993; Thurlow, 2004; Reilly and Paltsev, 2007; Arndt et al., 2009). This approach assumes that investment funds are available in an infinitely elastic supply at the going rate of interest. The

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72 idea of rising installation costs is the main mechanism through which a realistic allocation of investment spending is reached (Dixon and Rimmer, 2002). The two approaches described above are both solved recursively. A recursive solution method is based on the recursive dynamic principle ; this type of dynamic model is in essence a series of static or one period CGE models which are solved sequentially. The variables in subsequent periods in the reference path or baseline are estimated with assumptions about exogenous and endogenous endowments (Yang, 1999). The results from subsequent periods in the absence of a policy shock can be considered as quantity (endowment) shocks to the calibrated initial period. Therefore, the baseline scenario in a recursive dynamic CGE model is a series of counter factual equilibria with respect to the initial period. The third approach is a nonrecursive multiperiod model w ith endogenous investment. This approach applies forwardlooking expectations where decisions on production, consumption, and investment are based on perfect expectations and assumes that economic actors know exactly what will happen in all periods of time covered by a modeling exercise (Yang 1999; Babiker et al ., 2009). Furthermore, an agent expects that rates of return to capital for a given year should be equal to the actual rates of return for that year. This class of models is designed to solve all time periods simultaneously. This strategy is computed by maximizing a function of the consumption path subject to all of the intra and inter temporal trade and production constraints under which the economy operates (Dixon and Rimmer, 2002). In the fourth approach, investment behavior is also modeled endogenously and a n iterative method is used for handling nonrecursive computations. Dixon and Rimmer (2002) applied this approach in the dynamic MONASH models for Australia. In solving the model, a path for the expected rates of return is first guessed and then the model is solved recursively. This estimated

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73 expectation is used to compute an implied path for the expected rates of return, which is used to modify the initial guessed path. This iterative process continues until the guessed path and the implied path are the same. Although forward looking expectations allow one to better address economic and policy issues, this approach still presents some challenges. For example, the full model should contain eco nomically sensible relationships between the path of capital stock accumulation and rates of return. In the real world, however, perfect foresight is not broadly representative of how investment behaves. In addition, Babiker et al. (2009) compared forwardlooking versus recursive dynamic modeling in climate policy analysis. They found that their recursive model produced similar price behavior in the energy sector and provided greater flexibility in the modeling framework. Finally, forward looking solutions involve a considerable computational burden. To avoid these drawbacks, this study applies the second approach, in which investment is endogenous with adaptive expectations ; the model is solved recursively. The Dynamic CGE Model for the Southeastern United States This study applies a modified version of the recursive dynamic CGE model developed by Thurlow (2004), which is an extension of the IFPRI standard CGE model (Lofgren et al., 2002; Holland et al ., 2007) The within period specification of the model is identical to the standard static model described in Chapter 3. A series of dynamic equations update various parameters and variables from one year to the next while a simple set of adaptive expectation rules is chosen so that investment is allocated based on current relative prices. Thus, the between period specification in the recursive dynamic model includes a set of accumulation and updating rules, such as endogenous capital accumulation, exogenous population and labor force growth, and total factor pr oductivity changes (Thurlow, 2004).

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74 For capital accumulation, changes in total capital supply are all endogenous. The total available capital in a given time period is based on the capital stock and investment spending in the previous period. New investme nts are directed to each sector in response to differential rates of return and are adjusted by the ratio of each sectors profit rate to the average economy wide profit rate. Therefore, the sectoral allocation of capital is a function of the rate of capit al depreciation and differential rates of return. A sector with a higher than average rate of profit will capture a greater share of investment than its average share in aggregate capital income (Thurlow, 2004). The procedure for capital accumulation invo lves four steps. First the average economy wide rental rate of capital for year t is calculated The average rate equals the sum of the rental rates of every sector weighted by the sector s share of total factor capital demand. Second, the share of new capital investment for each sector is calculated by comparing its rental rate to the economy wide average initially and then it is multiplied by the existing share of capital stock. Next, the quantity of new capital for each sect or is estimated by multiplying the share of new capital investment for each sector by the total quantity of new capital. The total quantity of new capital is calculated as the value of gross fixed capital formation divided by the price of capital. Finally, the new aggregate quantity of capital and the sectoral quantities of capital are adjusted from their previous levels to include new additions to the capital stock. These changes involve a loss of capital which is considered to be depreciation (Thurlow, 2004). With regard to population growth, it is assumed that population growth has a positive impact on private household consumption. This assumption implies that household consumption of a particular commodity is adjusted upwards to explain higher consumption demand as the population grows. Thus, income independent demand is adjusted upwards according to the

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75 growth rate of population (GRP) (Thurlow, 2004). The GRP is calculated by the same formula as applied in Chapter 2: ( ) = ln [ ( + 1 ) ( ) ] (4 1) where R(t) is the growth rate from year t to year t+1, and G(t) is population at year t. The study applies the average GRP per year (from 2000 to 2007), which is about 1.04% for the Southeastern region (data from the U.S. Census Bureau). As discussed in Chapter 3, there are three alternative closures for each factor market. Growth in total labor supply is specified exogenously. The dynamic model updates relevant parameters to show changes in labor supply for each closure. In th e modeling exercises that follow, it is assumed that labor may go unemployed and nominal wage s are fixed in order to observe how bioenergy policies may impact demand for labor in each sector. Meanwhile, labor supply grow th is updated exogenously by the rate of labor force growth (Thurlow, 2004). Since this growth rate is close to th at of population, the labor force growth rate is assumed to also be 1.04% In addition to changes in factor supply, total factor productivity is also updated. A technological parameter is used to calculate the quantity of aggregate primary factors (Thurlow, 2004). The shift parameter for the Leontief CES production function is adjusted upwards by the total facto r productivity growth rate. The growth rate of total factor productivity in the Southeastern region is about 1.13% according to a USDA report1. T his dynamic extension to the standard CGE model is programmed and solved as a mixed complementary problem in G AMS with the PATH Solver. All equations unique to the dynamic model are listed in Appendix B. 1 http://www.ers.usda.gov/data/agproductivity/ (Accessed October 1, 2009)

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76 Data and Policy Scenarios Database The database is essentially the same as in Chapter 3 and is based on the 2006 Southeastern regional IMPLAN data. To focus on se ctors of interest in this study, the 509 sectors were aggregated into 14 sectors : agriculture, logging, sawmill products, pulpmill products, other wood products, conventional electric power, electric power from grainbased bioenergy (i.e. electric power f rom first generation bioenergy or 1GB), electric power from forest bioenergy (i.e., electric power from second generation bioenergy or 2GB), conventional energy excluding electric power, grain based bioenergy excluding electric power (i.e., non electric en ergy from 1GB), forest bioenergy excluding electric power (i.e., nonelectric energy from 2GB), manufacturing, transportation, and all other sectors. Households were aggregated into three income categories: low ( annual income less than $ 15,000), medium ($15,000 to $40,000) and high (greater than $40,000) income categories. The model is calibrated so that the initial equilibrium reproduces the base year values from the Social Account Matrix (SAM). Policy Scenarios Five scenarios are simulated in this chapte r. In addition to the four scenarios implemented in Chapter 3, a baseline scenario is simulated which projects the economy from 2006 to 2025 in the absence of any policy intervention. Since all scenario results significantly depend on the baseline forecast s, the baseline scenario forecast is important for evaluating the long term welfare implications of public policy. In the baseline scenario, labor supply and population are assumed to grow at a rate of 1.04% per year. Unbiased technological change is simul ated by increasing the shift parameter on the Leontief CES production function by 1.13% per year and capital accumulation is simulated as described in the previous section

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77 The f our policy scenarios are designed to simulate bioenergy development in the So utheaster n U.S. from 20062025: s cenario 1 is a bioenergy substitution Renewable Portfolio Standards ( RPS ) scenario where an RPS policy is simulated by substituting 1 % of conventional electric power production with electric power produced from forest biomass bioenergy; s cenario 2 is a bioenergy substitution Renewable Fuels Standards ( RFS ) scenario where 1 % of conventional liquid fuel is substituted with cellulosic etha nol; s cenario 3 is a bioenergy incentive scenario in which a fuel tax reduction is applied to the nonelectric energy from 2GB sector to simulate the impacts of the 2008 Farm bill; and the final scenario is a technological progress scenario that reduces th e 2GB electric power and 2GB nonelectric energy sectors intermediate consumption of logging, sawmill, and pulpmill products by an arbitrary amount of 10%. The difference between the baseline and policy scenarios is the policy effects on the Southeastern regions economy. Simulation Results In this section, r esults are reported for the baseline scenario and the bioenergy policy impacts on supply price and quantity, government expenditure and investment, factor demand, and welfare. Supply Price and Quantit y Changes in producer commodity prices and quantities in the baseline scenario are presented in Table s 41 and 42. The policy impacts on the average annual growth rate (AAGR) for the four specific bioenergy scenarios are compared with the AAGR results of the baseline scenario.

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78 In the bioenergy substitution RPS scenario, t he conventional electric power sectors AAGR of output increases at a slower rate2 (1.46%) and the supply price increases at a faster rate (0.49%); the supply price of 2GB electric po wer decreases at a rate of 1.48 % (rather than increasing as in the baseline scenario), and the output of 2GB electric power increases at a faster rate (2 .76%). Meanwhile, the RPS policy only has slight impacts on the forestry sectors. T he AAGR for the supply price of logging commodities increases by 0.19% and that for output rises by a rate of 1.8 0%. The AAGR for the supply prices of sawmill and other wood products commodities decrease by 0.07% and 0.31%, respectively and the output s of sawmill and other wo od products increase at slower rate s of 2.51% and 2.44%, respectively. With output increas ing at the slower rate of 2.94 % compared to the baseline, t he RPS policy has a small impact on the supply price of pulpmill products Figure s 413 and 42 show annual policy impacts on supply prices and output. Results indicate that the logging sector would benefit by this policy; however, sawmill and other wood products sectors are negatively affected. Furthermore, the RPS policy reduces the produc tion efficiency of conventional electricity and increases that of the 2GB electric power sector In the bioenergy substitution RFS scenario, the r esults show that the supply price of conventional energy excluding electric power increases at a faster rate ( 0.15%), while output increases at a slower rate (1.37 %); the price of 2GB nonelectric energy d ecreases at a faster rate ( 0.55%), while output of 2GB nonelectric energy increases at a faster rate (11.07 %) (Tables 41 and 42). Moreover, results for the forestry sector are quite similar to those of the RPS scenario. 2 The study defines a slower growth rate as a smaller AAGR in the policy scenario when compared to that of the baseline and a faster growth rate as a larger AAGR in a policy scenario when compared to that of the baseline scenario. 3 In all figures, there is a spike in the year 2007, since this is the year in which the four bioenergy policies were implemented.

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79 T he AAGR of the supply price of loggi ng commodities increases by 0.21 % and that of the output increases by 1.98 %. The outputs of sawmill and other wood products increase at the slower rates of 2.54% and 2.45%, respectively while t he supply prices of sawmill and other wood product commodities decrease at the rates of 0.07% and 0.31%, respectively The supply of pulpmill products i ncreases at a lower rate of 2.94 % compared to the baseline whil e the supply price of pul pmill products is unaffected. In addition, Figure s 43 and 44 show annual policy impacts on supply prices and output. Results show that the logging sector would benefit, but the sectors of sawmill and other wood produc ts would not In the case of the bioenergy incentive scenario, the main policy impacts are that the supply price of 2GB nonelectric energy decreases at a faster rate ( 0.30%) and the output of 2GB nonelectric energy increases at a faster rate of 5.46% compared to t he baseline case (Tables 41 and 42). Additionally, the AAGR of the supply price and output of logging commodities increase faster (0.20% and 1.87%, respectively). The supply prices and outputs of sawmill products, pulpmill products, and other wood products decrease by similar rates to those in the baseline case. Figures 45 and 46 display annual policy impacts on supply price s and output and show that the output of 2GB nonelectric energy increases, while the supply price decreases indicating a positive policy impact on this sector. In the technological progress scenario, the supply price of 2GB nonelectric energy decreases at a faster rate ( 0.05%) while the output of 2GB nonelectric energy increases at a faster rate (2.81% ) (Tables 41 and 42). Improved technology has little impact on the logging, sawmill products, pulpmill products, and other wood products sectors. The A AGR of the supply prices of logging commodities increases and that of the outputs decreases while the AAGR of

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80 supply prices decrease and output s increase for the other three forestry sectors. Figures 4 7 and 48 show the annual policy impacts on supply pri ces and output s Factor Markets In the labor market, given the fixed labor wage and flexible labor supply closure, all sectors increase their demand s for labor in the baseline scenario (Table 4 3). Compared to the baseline scenario, results show that tota l labor demand for all sectors decreases by 2,383 and 7,515 jobs per year from 2006 to 2025 in the RPS and RFS scenarios, respectively. The AAGR of labor demand for most other sectors declines, with the exception s of the 2GB nonelectric energy logging, a nd sawmill products sector s in the RFS scenario where labor demand (13.99 % 2.61%, and 2.50%, respectively ) increases faster when compared with the baseline case (2.48% 2.35%, and 2.47%, respectively ). Total average demand of labor increases slightly by 407 and 29 jobs per year in the bioenergy incentive and technological progress scenarios, respectively compared to the baseline scenario. In the logging sector, t he AAGR of demand for labor increases by 2.46% in the bioenergy incentive scenario and by 2.38% in the technological progress scenario. All other sectors show slight or no response in the se two substitution scenarios. W ith a fixed land supply, there is a contraction in agricultural demand for land and an increase in the logging sectors demand for land in the early stages of policy implementation, from 2007 to 2008 (Figure s 49 and 410). The RFS scenario is the most efficient policy scenario to reduce the pressure on agricultural demand for land, followed by the bioenergy incentive, technologic al progress, and RPS scenarios. However, as time progresses, agricultural demand for land increases with population growth and increased demand for food. Therefore, the AAGR of agricultural demand for land increases while the logging sectors demand for la nd decreases in all scenarios (Table 4 4).

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81 I n order to clear the capital market the AAGR of the price of capital increases more slowly for each sector with the exception of the conventional electric power sector where it increases at a faster rate (1.89 % ) in the bioenergy substitution RPS scenario (Table 45). Similarl y in the bioenergy substitution RFS scenario, the price of capital rises at a slower rate for each sector with the exception s of the 2GB non electric energy logging, and sawmill products sector s in which AAGR for these sectors increases at a faster rate of 14.14%, 2.64%, and 2.53%, respectively In contrast, the price of capital increases at a faster or similar rate for every sector in the bioenergy incentive scenario; the price of capital for the logging, sawmill, and pulpmill sectors grows at 2.48%, 2.51%, and 3.29%, respectively. The one exception is the AAGR of the price of capital for the 2GB nonelectric energy sector in which it declines at a rate of 3.30%. In the technological pr ogress scenario, there is a positive impact on every sector ; the AAGR increases at a similar rate as in the baseline case, with the exception of the price of capital for the 2GB nonelectric energy sector which increases at a slower rate of 2.09%. Governm ent, Household, and Welfare Impacts Average annual changes in macroeconomic indicators in the baseline and in the four scenarios are presented in Table 4 6. Since government policies influence the level of federal and state government revenue and expenditu re, the AAGR of federal government revenue increases by 0.97% while federal government expenditure declines at a rate of 0.10% in the baseline scenario Both the AAGR for state government revenue and expenditure rise at the same rate of 0.52% in the basel ine scenario In the RFS scenario, the AAGR of federal government revenue, state government revenue, and state government expenditure incr ease at the slower rates of 0.96%, 0.51%, and 0.51 %, respectively, while federal government expenditure declines at the same rate ( 0.10%) as in the baseline case. The RPS, bioenergy incentive, and technological

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82 progress scenarios have nearly no impact on the AAGR of both federal or state government revenue and expenditure. In the long term, federal and regional state governments collect more indirect business taxes in all scenarios (Table 4 6). The AAGR of indirect business taxes increases at a rate of 1.78% ($5,593 million) in the baseline scenario and increase at the same rate in the RPS and the technological progress scenarios. In the bioenergy incentive scenario, more indirect business tax is collected with an AAGR of about 1.79% ($5,630 million). In the RFS scenario, however, less indirect business tax is collected comp ared with the baseline scenario where the AAGR is around 1.77% ($5, 565 million). With regard to household utility, it grows in the baseline scenario with AAGR s of 0.09%, 0.13%, and 0.15% for low, medium, and high income classes, respectively. All scenari os reveal positive impacts and AAGR s are similar (nearly no change) to those in the baseline scenario (Table 4 6). To measure improvements in social welfare, the AAGR in Hicksian Equivalent Variation (EV) is reported since EV can consider as a measure of both price and income effects rather than simply a measure of change in household income. Equivalent variation is measured at the level of prices and income present prior to the implementation of a policy. It is the minimum payment the consumer would accept to forgo the policy cha nge. Thus it is the amount the consumer would need to receive to be as well off as if the policy had been implemented. The AAGR for EV s in the baseline scenario grow by 1.23%, 1.31%, and 1.32% ($69 million $303 million and $245 million) for low, medium, and high income class es respectively. In the bioenergy incentive and technological progress scenarios, EV increases at the same rate as in the baseline scenario. However, the AAGR in EV in the RPS scenario rise at the slower rate s

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83 of 1.22% and 1.30% for low and medium income classes, respectively; the EV in the bioenergy substitution RFS scenario also increase at the slower rates of 1.22%, 1.30 %, and 1.31% for low, medium, and high income classes, respectively. The AAGR for the real Southeastern gross regional product at market prices increases by 2.20% ($90,319 million) in the baseline scenario. The AAGR of the bioenergy substitution RPS and RFS scenarios increase more slowly, at 2.19% ($89,909 million) and 2.18% ($8 9,498 million), respectively while the AAGR of gross regional product in the bioenergy incentive increase at a faster rate of 2.21% ($ 90,730 million) and technological progress scenarios rise at a similar rate to that of the baseline scenario. Conclusions This study applied a recursive dynamic CGE model to examine the economic impacts and optimality of different bioenergy development scenarios in the Southeastern U.S. Besides the baseline scenario, the study considered four cou nter factual scenarios, including a s ubstitution of conventional electric power by forest bioenergy, a substitution of liquid fossil fuel by cellulosic biofuel, an incentive for forest bioenergy production, and technological progress in forest bioenergy production. These policy scenarios we re compared to the baseline scenario during 2006 and 2025 in the Southeastern U.S. The results showed that the logging sector would benefit since both supply price and output increase from the producers perspective in the long run. The logging sector supplies most of the forest biomass used to generate forest biomass based electricity and cellulosic bio liquids. Meanwhile, the model showed that the bioenergy policies simulated would result in lower rates of growth in supply prices and greater growth in output from the 2GB electric power and 2GB nonelectric energy sectors. This resulted in a direct increase in demand for logging commodities. However, since the logging sector also supplies raw materials for other forestry

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84 sectors such as the sawmill, pulpmill, and other wood products sector s, these sectors expand at a slower rate in the long run as a result of bioenergy development. In addition, both bioenergy substitution scenarios reduce the supply of conventional energy, which works toward the goal of red ucing dependency on fossil fuels. The bioenergy incentive and technological progress scenarios have little effect on the supply of conventional energy. Results show that the demand for labor increases in the baseline scenario in the long run. However, the policy impacts in the substitution scenarios reveal that labor demand increases at a slightly slower rate for most of sectors This may be explained by the fact that increased forest bioenergy production pulls labor from the conventional energy sectors. Fu rthermore, most sectors still depend on conventional energy as an intermediate input. Under these circumstances, one may expect that the bioenergy substitution scenarios would decrease the demand for labor in sectors depending on conventional energy as wel l. In contrast, labor demand increases at faster rates in the bioenergy incentive and technological progress scenarios in the forestry and forest bioenergy sectors. T he logging sector s demand s for land increase in the first couple of years, leading to a contraction in agricultural demand for land in all policy scenarios. The RFS scenario is the most efficient policy scenario to reduce the pressure on agricultural demand for land, followed by the bioenergy incentive, technological progress, and RPS scenari os. Nevertheless, as population increases, demand for food and feedstock also rises and the demand for agricultural land tends to adjust to this increase. In the absence of bioenergy development, household utility, social welfare, and gross regional product increase in the long run. The four counterfactual scenarios have no effect on household utility, though social welfare and the gross regional product in both bioenergy

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85 substitution scenarios rise at a slower rate. In the bioenergy incentive and technolo gical progress scenarios there is a positive but negligible impact on social welfare and gross regional product. This is likely a function of the importance of the fossil fuel industry to other sectors, such as the manufacturing and transportation sectors; replacing a portion of conventional energy by forest bioenergy without reductions in the production costs of forest bioenergy may result in increased costs thereby negatively impacting consumers Overall, the bioenergy substitution RPS and RFS policies c an reduce dependency on fossil fuels and reduce the price of conventional energy. These polices directly promote forest bioenergy development and further help reduce greenhouse gas emissions. However, the RPS and RFS may reduce social welfare, with slower growth in the gross regional product and employment. Meanwhile, the implementation of incentives for the production of forest bioenergy would spur demand for forest bioenergy and could create new market opportunities resulting in overall positive outcomes for the economy. Investment in technology to improve forest bioenergy not only reduces bioenergy production costs but also stimulates production. Since all results demonstrated that the demand for woody biomass would increase, one can expect that landowners may increase the level and frequency of forest thinning and thoroughly use logging residues. This may result in increas ed landowners incomes and also improve forest health, reduce wildfire risk, and enhance biodiversity.

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86 Table 4 1. Average annual g rowth rate in quantity of commodity supply during 2006 to 2025 (%) Initial value ($ millions) Base Case Bioenergy Substitu tion RPS Bioenergy Substitu tion RFS Bio energy Incentive Techno logical Progress Agriculture 85025.73 2.03 2.0 2 2.01 2.02 2.03 L ogging 18997.20 1.80 1.8 0 1. 98 1.87 1.82 Sawmill products 36781.59 2.52 2. 5 1 2.5 4 2.53 2.52 Pulpmill products 74628.52 2.95 2. 94 2. 94 2.95 2.95 Other wood products 21636.70 2.45 2.4 4 2.4 5 2.45 2.45 Conventional electric power 108506.59 1.59 1.46 1.5 9 1.59 1.59 Electric power from 1GB 292.96 1.57 1.56 1.5 6 1.57 1.57 Electric power from 2GB 3876.05 1.59 2. 76 1.5 8 1.59 1.59 Conventional energy excluding electric power 761593.78 1.51 1.50 1.37 1.51 1.51 Non electric energy from 1GB 11974.98 2.22 2. 21 2.17 2.22 2.22 Non electric energy from 2GB 1713.08 2.36 2.3 5 11.07 5.46 2.81 Manufacturing 1604968.01 2.99 2.9 8 2.9 9 2.99 2.99 Transportation 273724.30 2.67 2.6 7 2. 65 2.67 2.67 Others 5170769.88 2.06 2.0 5 2.0 5 2.06 2.06 Table 4 2. Average annual growth rate in producer commodity prices during 2006 to 2025 (%) Base Case Bioenergy Substitution RPS Bioenergy Substitution RFS Bioenergy Incentive Technolo gical Progress Agriculture 0.17 0.1 7 0.1 8 0.17 0.17 L ogging 0.19 0.1 9 0. 21 0.20 0.19 Sawmill products 0.07 0.0 7 0.07 0.07 0.07 Pulpmill products 0.00 0.00 0.00 0.00 0.00 Other wood products 0.31 0.31 0.3 1 0.31 0.31 Conventional electric power 0.25 0.49 0. 24 0.25 0.25 Electric power from 1GB 0.14 0.1 3 0. 13 0.14 0.14 Electric power from 2GB 0.24 1.48 0. 23 0.24 0.24 Conventional energy excluding electric power 0.06 0.06 0. 15 0.07 0.06 Non electric energy from 1GB 0.04 0.0 4 0.0 4 0.04 0.04 Non electric energy from 2GB 0.00 0.00 0.55 0.30 0.05 Manufacturing 0.14 0.14 0.14 0.14 0.14 Transportation 0.37 0.38 0.3 7 0.37 0.37 Others 0.45 0.45 0.4 5 0.45 0.45

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87 Table 4 3. Average annual growth rate in quantity demand of labor by activity during 2006 to 2025 (%) Initial value (jobs) Base Case Bioenergy Substitu tion RPS Bioenergy Substitu tion RFS Bio energy Incentive Techno logical Progress Agriculture 1,290,780 2.63 2. 62 2.6 3 2.64 2.63 L ogging 64,402 2.35 2.3 5 2. 61 2.46 2.38 Sawmill products 168,395 2.47 2.4 6 2. 50 2.48 2.47 Pulp mill products 165,842 3.25 3. 23 3. 25 3.26 3.25 Other wood products 158,946 2.03 2.02 2.03 2.03 2.03 Conventional electric power 145,083 1.78 1.87 1. 76 1.78 1.78 Electric power from 1GB 392 1.62 1. 62 1. 61 1.63 1.62 Electric power from 2GB 5,183 1.76 1. 42 1. 75 1.77 1.76 Conventional energy excluding electric power 535,309 1.77 1.7 6 1.56 1.77 1.77 Non electric energy from 1GB 4,456 1.97 1.9 6 1.54 1.97 1.97 Non electric energy from 2GB 5,368 2.48 2.4 7 13.99 3.27 2.07 Manufacturing 4,056,890 3.09 3.0 8 3.06 3.09 3.09 Transportation 2,052,550 2.19 2.1 9 2. 16 2.19 2.19 Others 48,598,300 1.61 1. 60 1. 60 1.61 1.61 Total changes in jobs 2383 7515 407 29 C ompared to the baseline scenario rather than initial value of jobs. Table 4 4. Average annual growth rate in quantity demand of land by activity during 2006 to 2025 (%) Initial value ($ millions) Base Case Bioenergy Substitu tion RPS Bioenergy Substitu tion RFS Bio energy Incentive Techno logical Progress Agriculture 9328.61 0.04 0.04 0.0 02 0.03 0.04 L ogging 1715.46 0.24 0.2 3 0. 016 0.15 0.22

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88 Table 4 5. Average annual growth rate in rental of capital by activity during 2006 to 2025 (%) Base Case Bioenergy Substitution RPS Bioenergy Substitution RFS Bioenergy Incentive Technolo gical Progress Agriculture 2.66 2. 64 2. 65 2.66 2.66 L ogging 2.37 2.3 7 2. 64 2.48 2.40 Sawmill products 2.49 2.4 8 2. 53 2.51 2.49 Pulpmill products 3.28 3. 26 3. 28 3.29 3.28 Other wood products 2.05 2.0 4 2.0 5 2.05 2.05 Conventional electric power 1.80 1.89 1. 78 1.80 1.80 Electric power from 1GB 1.64 1.6 4 1. 63 1.64 1.64 Electric power from 2GB 1.78 1.4 3 1. 76 1.78 1.78 Conventional energy excluding electric power 1.79 1.78 1.57 1.79 1.79 Non electric energy from 1GB 1.99 1.9 8 1.55 1.99 1.99 Non electric energy from 2GB 2.50 2.4 9 14.14 3.30 2.09 Manufacturing 3.12 3. 11 3.0 9 3.12 3.12 Transportation 2.21 2. 21 2. 18 2.22 2.21 Others 1.62 1.6 2 1. 61 1.63 1.62

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89 Table 4 6. Average annual growth rate in macroeconomic indicators during 2006 to 2025 (%) Initial value ($ millions) Base Case Bioenergy Substitu tion RPS Bioenergy Substitu tion RFS Bio energy Incentive Techno logical Progress Federal government revenue 1,170,894 0.97 0.96 0.96 0.97 0.97 Federal government expenditure 1,154,992 0.10 0.10 0.10 0.10 0.10 State government revenue 1,235,916 0.52 0.52 0.51 0.52 0.52 State government expenditure 1,235,780 0.52 0.52 0.51 0.52 0.52 Indirect business taxes 314,439 1.78 1.78 1.77 1.79 1.78 Household(HH) utility for Low income class 12.37* 0.09 0.09 0.09 0.09 0.09 HH utility for medium income class 13.33* 0.13 0.13 0.13 0.13 0.13 HH utility for high income class 13.08* 0.15 0.15 0.15 0.15 0.15 Equivalent variation (EV) for low income class 5,640 1.23 1.22 1.22 1.23 1.23 EV for medium income class 23,132 1.31 1.30 1.30 1.31 1.31 EV for high income class 18,519 1.32 1.32 1.31 1.32 1.32 Real GDP at market prices 4,105,426 2.20 2.19 2.18 2.21 2.20 L evel of household utility

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90 Figure 4 1. Economy wide output impacts of displacing 1 % of conventional electric power with forest biomass based electric power. Figure 4 2. Economy wide supply price impacts of displacing 1 % of conventional electric power with forest biomass based electric power. 20 0 20 40 60 80 2006 2008 2010 2012 2014 2016 2018 2020 2022 2024 (%) Agriculture Logging Sawmill products Pulp mill products Other wood products Conventional electric power Electric power from 1GB Electric power from 2GB Conventional energy excluding electric power Non electric energy from 1GB Non electric energy from 2GB 0.6 0.7 0.8 0.9 1 1.1 1.2 2006 2008 2010 2012 2014 2016 2018 2020 2022 2024 Supply price ($) Agriculture Logging Sawmill products Pulp mill products Other wood products Conventional electric power Electric power from 1GB Electric power from 2GB Conventional energy excluding electric power Non electric energy from 1GB Non electric energy from 2GB

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91 Figure 4 3. Economy wide output impacts of displacing 1 % of conventional liquid fuels with second generation biofuels Figure 4 4. Economy wide supply price impacts of displacing 1 % of conventional liquid fuels with second generation biofuels 200 0 200 400 600 800 2006 2008 2010 2012 2014 2016 2018 2020 2022 2024 (%) Agriculture Logging Sawmill products Pulp mill products Other wood products Conventional electric power Electric power from 1GB Electric power from 2GB Conventional energy excluding electric power Non electric energy from 1GB Non electric energy from 2GB 0.85 0.9 0.95 1 1.05 2006 2008 2010 2012 2014 2016 2018 2020 2022 2024 Supply price ($) Agriculture Logging Sawmill products Pulp mill products Other wood products Conventional electric power Electric power from 1GB Electric power from 2GB Conventional energy excluding electric power Non electric energy from 1GB Non electric energy from 2GB

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92 Figure 45. Economy wide output impacts of a $1.01 per gallon subsidy for cellulosic ethanol production Figure 4 6. Economy wide supply price impacts of a $1.01 per gallon subsidy for cellulosic ethanol production 0 50 100 150 200 2006 2008 2010 2012 2014 2016 2018 2020 2022 2024 (%) Agriculture Logging Sawmill products Pulp mill products Other wood products Conventional electric power Electric power from 1GB Electric power from 2GB Conventional energy excluding electric power Non electric energy from 1GB Non electric energy from 2GB 0.9 0.95 1 1.05 2006 2008 2010 2012 2014 2016 2018 2020 2022 2024 Supply price ($) Agriculture Logging Sawmill products Pulp mill products Other wood products Conventional electric power Electric power from 1GB Electric power from 2GB Conventional energy excluding electric power Non electric energy from 1GB Non electric energy from 2GB

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93 Figure 4 7. Economy wide output impacts of technology progress by reducing 10% of intermediate inputs for second generation bioenergy Figure 4 8. Economy wide supply price impacts of technology progress by reducing 10% of intermediate inputs for second generation bioenergy 0 20 40 60 80 2006 2008 2010 2012 2014 2016 2018 2020 2022 2024 (%) Agriculture Logging Sawmill products Pulp mill products Other wood products Conventional electric power Electric power from 1GB Electric power from 2GB Conventional energy excluding electric power Non electric energy from 1GB Non electric energy from 2GB 0.9 0.95 1 1.05 2006 2008 2010 2012 2014 2016 2018 2020 2022 2024 Suppy Price ($) Agriculture Logging Sawmill products Pulp mill products Other wood products Conventional electric power Electric power from 1GB Electric power from 2GB Conventional energy excluding electric power Non electric energy from 1GB Non electric energy from 2GB

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94 Figure 4 9. Policy impacts on agricultural land in the Southeastern U.S. Figure 4 10. Policy impacts on forestland demand in the Southeastern U.S. 9150 9200 9250 9300 9350 9400 9450 2006 2008 2010 2012 2014 2016 2018 2020 2022 2024 ($million dollars) Base case Bioenergy Substitution RPS Bioenergy Substitution RFS Bioenergy Incentive Technological progress 1550 1600 1650 1700 1750 1800 2006 2008 2010 2012 2014 2016 2018 2020 2022 2024 ($million dollars) Base case Bioenergy Substitution RPS Bioenergy Substitution RFS Bioenergy Incentive Technological progress

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95 CHAPTER 5 SUMMARY AND CONCLUSIONS Private forests in the Southe astern region have a high potential to produce forest biomass that can be utilized to produce cellulosic ethanol or to generate electricity through co firing. It is believed that promoting forest bioenergy can create job opportunities and stimulate economi c growth. This research assessed the socioeconomic impacts of a set of potential forest bioenergy policies on the regional economy. The study includes three major component s : first, the 13 Southeastern states are classified into homogenous groups based on potential determinants using cluster analysis; second, a static computable general equilibrium (CGE) analysis is conducted to assess the socio economic impacts of forest bioenergy development; and, third, a recursive dynamic CGE analysis is conducted to ev aluate the long term impacts of potential forest bioenergy policies implemented in the Southeastern region. The cluster analyses showed that it is possible to classify the 13 Southeastern states into three separate groups that are distinctly different based on the characteristics of their member stat es. Selected variables including gross state product per capita (GSP), growth rate of population (GRP), number of biorefineries, percentage of forestland in total area, share of coal in total net energy generation, bioenergy generation, and education level were applied as explanatory factors. T he study predicted three categories of policies ( regulat ory mechanisms, incentive based policies, and support based policies ) based on s tates economic, environmental, and social criteria within clusters. Alabama, Arkansas, Louisiana, Mississippi, and South Carolina are likely to adopt regulatory mechanisms for bioenergy policy. These states possess abundant natural resources and have suf ficient motives to conserve the ir resources. These states are expected to promote new bioenergy policies through enacting regulations and blending requirements These policies may

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96 create a demand for natural resources including forest biomass for bioenergy Along with these policies, appropriate bioenergy markets outreach efforts targeting landowners and the general public would produce desired economic benefits and job opportunities. Florida, Georgia, Kentucky, North Carolina, and Tennessee are more amenable to incentive based bioenergy policies. These states with their better economic conditions may be willing to provide the required financial support to promote forest bioenergy development. However, with a greater use of natural resources a higher output of pollutants/GHGs during the initial sta g es of economic growth is expected. Undertaking outreach activities about the production and use of forest biomass based bioenergy would help the public and landowners to actively engage in this emerging market. O klahoma, Texas, and Virginia are more suitable for support based bioenergy policies. With lower environmental quality conditions which suggest a greater use of natural resources and/or higher GHG emissions, the public in the se state s might prefer to have cleaner and more environment ally friendly technologies However, poor economic condition s may not be conducive for policymakers to adopt polic ies such as technolog ical support, infrastructure development, education outreach and extension to encourage bioenergy development. Results of this analysis provide valuable insights in to strategies for promot ing forest bioenergy. First, states grouped through cluster analysis could get together to explore the question of what poli cies would be more appropriate t o pursue Second, states could explore possibilities to coordinate their policies and programs to leverage resources and to harness economies of scale. Third, states could explore the possibilities of developing regional initiatives to reduce the cost of f orest bioenergy, attract more investments, and pursue outreach activities.

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97 T he second major component of this study develops a static CGE model to assess the impacts of a suite of potential pol i cies relating to bioenergy F our bioenergy policy scenarios we re evaluated : a) a Renewable Portfolio Standards ( RPS ) scenario where 1 % of conventional electric power production is re placed w ith forest biomass based electric power ; b) a Renewable Fuels Standards ( RFS ) scenario where 1% of conventional liquid fuel is substituted with cellulosic ethanol; c) a bioenergy incentive scenario where a fuel tax reduction ($1.01 per gallon) is applied to cellulosic ethanol production; and d) a technological progress scenario that would reduce the second generation bioenergy sector s demand for intermediate inputs from logging, sawmill, and pulpmill products by 10%. T he forest based bioenergy sectors expand in response to all policy scenarios. This is expected since all policy scenarios are designed to promote forest bioenergy development. One should notice that the logging sectors also expand in the four counterfactual scenarios This is likely because bioenergy development would create an additional demand for forest biomass. On e implication for landowners is that increasing the level and frequency of forest thinning could result in increased income and also improve forest health, reduce wildfire risk, and enhance biodiversity. In addition, logging residues or unused portions of trees which were left on sites after a traditional timber harvest in the past may prove to be an important source of forest bioenergy. The results, however indicate that the supply of other three conventional forestry sectors (i.e., sawmill, pulp mill and other wood products sectors) decreases in the RP S scenario With increased intermediate demand for forest biomass from the bioenergy sectors, the production of logging, sawmill, pulpmill and other wood products tends to decline as a result. L and demand shi f ts from agricultural production to forest based activities in respons e to all scenarios Land currently left idle or considered marginal for the production of crops may be

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98 used to produce forest biomass depending on how bioenergy, forestry and land markets evolve. Howev er, it is possible that some erodible cropland or other environmentally sensitive acreage might be convert ed to bioenergy plantations such as switchgrass which would be a negative consequence of bioenergy promotion policies. Policymak er s should consider these complex linkages and exercise caution in bioenergy development policies The RPS and RFS scenarios showed that social welfare and gross regional product decrease overall This result is expected since the bioenergy promotion policies force the replacement of a portion of conventional energy while the technology to produce second generation bioenergy is not yet cost effective. Low income households were generally worse off t han medium and high income households. This is likely a function of the fact that the ratio of conventional energy consumption for high income households is lower than that for medium and low income households T o maximize positive policy outcomes, comple m entary policies may be required to offset the reduction in the income of low income households. Furthermore the bioenergy incentive and technological progress scenarios for forest bioenergy would lead to an increase in welfare, gross regional product, and job opportunities. The implementation of incentives for the production of forest bioenergy and investment in technology may generate new market opportunities and reduce the cost of bioenergy production resulting in overall positive outcomes for economy T hese results are consistent with m uch previous research on bioenergy development The results of this static CGE analysis offer useful in sight in to the forest bioenergy development. First, the model results show how the outputs and prices of forestry sectors (and other related sectors) are affected by the policies Second, labor demand and land use change can be assessed enabling a more pragmatic approach to prevent environmental damage and spur

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99 bioenergy development and to increase job opportunities. Third, information on the welfare and distributional impacts of bioenergy policies is generated including effects on gross regional product. C omplementary policies may be developed to maximize benefits and minimize n egative impacts resulting from the implementation of forest bioenergy policies In the third component of this research, a recursive dynamic CGE model was applied to trace each variable and predict the economys trajectory through time. The model assesses the same four bioenergy policy scenarios as the static model. R esults suggest that the logging sector expand s in response to all policy scenarios. This is expected because bio e nergy markets would create an additional demand for forest biomass and thus an i ncrease in the price of biomass. An increase in the price of biomass would trigger the supply of forest biomass. This might benefit existing forestland owners by rais ing the profitability of their business. However, in the long run, when new landowners ent er into the market and more biomass is supplied, the price might decline. Results show that conventional forest products sectors would contract in response to all policy scenarios. Again, this result is expected because emerging bioenergy markets would be in direct competition with these sectors for raw material. Furthermore, policy incentives and increased investments toward the bioenergy sector might increase its competitiveness relative to the pulpwood and sawtimber sectors. This may explain why there i s considerable push back from conventional forest product industries at state, regional, and national level s A more pragmatic approach that can limit the negative impacts on conventional forest products sectors but allow the benefits of bioenergy market s to be harnessed should be explored. B oth RPS and RFS scenarios are shown to cause the conventional energy sector to contract This result is expected since the substitution policies directly force reduce d dependency

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100 on the conventional energy sector. T he bioenergy incentive and technological progress scenarios have very little effect on the conventional energy sector The RPS and RFS scenarios result in reduced labor demand in the long term This may be explained by the fact that increased forest bioenergy production pulls labor from the conventional energy sectors. Furthermore, most sectors still depend on conventional energy as an intermediate input. Under these circumstances, one may expect that the RPS and RFS scenarios would decrease the demand for labor in sectors depending on conventional energy as well. By contrast, labor demand increases in the bioenergy incentive and technological progress scenarios in the forestry and forest bioenergy sectors. The logging sectors demand for land increases in the f irst couple years, leading to a contraction in agricultural demand for land in respond to all policy scenarios. All policy scenarios could efficiently reduce the agricultural demand for land initially; however as population increases, demand for food and feedstock also rises and the demand for agricultural land would tend to adjust to this increase in the long term Idle land or land that was considered marginal for the production of crops may be used to produce forest biomass. However, some erodible cropland or other environmentally sensitive acreage might be convert ed to bioenergy plantation s which would be a negative environmental consequence of bioenergy development These types of negative externalities require p olicymakers to exerci se caution in designing policies for bioenergy development. Results also indicated that social welfare and the gross regional product decrease in the RPS and RFS scenarios. In the bioenergy incentive and technological progress scenarios there is a positive but negligible impact on social welfare and gross regional product. This is a likely a function of the importance of the fossil fuel industry to other sectors, such as the manufacturing

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101 and transportation sectors; replacing a portion of conventional energ y by forest bioenergy without reductions in the production costs of forest bioenergy may result in increased costs thereby negatively impacting consumers Meanwhile, the implementation of incentives for the production of forest bioenergy would spur demand for forest bioenergy and could create new market opportunities resulting in overall positive outcomes for the economy. Investment in technology to improve forest bioenergy not only reduces bioenergy production costs but also stimulates production. Althoug h the study showed a reduction in welfare in response to the RPS and RFS scenarios, the non monetary benefits of a decrease in dependency on foreign oil, potential reduction in GHG emissions, and an increase in rural community stability associated with the se scenarios may compensate this loss Results of this study can contribute important insights for policy makers, industry, and civil society in the long term. Meanwhile, to maximize benefit s and minimize negative impacts complementary policies may be req uired Several limitations associated with this study must be noted. First, as f orest bioenergy is not recorded in the national accounts and is currently produced at a very low level data on the bioenergy sectors was drawn from existing sectors of the eco nomy. Second, i nter sectoral flows of intermediate inputs and primary factor allocations to the newly created sectors were made based on the literature and reasonable assumptions For example, the intermediate and primary factor consumption of the nonelectric energy from forest bioenergy sector data were disaggregat ed from the logg ing, sawmill products, and pulpmill products sectors according to a ratio specified in the literature. Creation of new sectors based on the literature has implications for the validity and reliability of the results derived from this study. As b ioenergy production and consumption expands, it is likely that bioenergy sectors would find an explicit pla ce in the

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102 national accounts and social account ing matrices Third, lack of data on elasticity parameters is another problem with the regional data Because CGE models are dense with parameters, these parameters must be calibrated if econometric estimates are unavailable. There are numerous topics for further res earch. First and most important ly land availability and land restrictions could be incorporated in a more sophisticat ed way. Further modeling is needed to investigate the potential environmental benefits and carbon release from land conversions as well. A more sophisticated model would be needed to support a substantial bioenergy industry. T he implications of converting unused land to bioenergy production should be considered in the context of GHG emissions calculations It is likely that the mode of conve rsion and carbon sequestration by bioenergy plantations could substantially influence the GHG emission balance. Second, world prices and export prices w ere assumed constant in the dynamic modeling experiment. However, world price s are often updated accordi ng to projections make by industry experts, depending on the time horizon of analysis. Since the Southeastern Social Account Matrix ( SAM ) was highly aggregated in this study, it is not conducive to exogenously updating world prices. Meanwhile, while export prices remain constant, domestic prices tend to grow due to imperfect substitution between imported goods and domestically produced goods. Given fixed world prices, a lower growth rate in exports would occur than if world prices were updated according to industry projections. A f inal issue is with regard to trade in bioenergy. Empirical evidence shows that the trade regime may strongly affect a sectors growth and productivity and different trade strategies would affect factor prices through the composition of output. As b ioenergy production is

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103 expected to become economically viable, trade patterns would begin to emerge more clearly. Models should be improved to reflect evolving trad e patterns as well.

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104 APPENDIX A SOCIAL ACCOUNTING MATRIX FOR THE SOUTHEASTERN U.S. 2006 The Southeastern Social Accounting Matrix A ccounts Account Description AGRI A Agricultural activities LOG A Forest products and logging activities SAW A Sawmill products activities PULP A Pulpmill products activities WOPRODA Other wood products activities Elec A Conventional electric power activities ElecB1 A Electric power from first generation bioenergy activities ElecB2 A Electric power from second generation bioenergy activities Non_Elec A Activities of conventional energy excluding electric power Non_ElecB1A Activities of first generation bioenergy excluding electric power Non_ElecB2 A Activities of second generation bioenergy excluding electric power MANU A Manufacturing activities TRANP A Transportation activities OTH A All other activities AGRI C Agricultural commodities LOG C Forest products and logging commodities SAW C Sawmill products commodities PULP C Pulpmill products commodities WOPROD C Other wood products commodities ElecC Conventional electric power commodities ElecB1 C Electric power from first generation bioenergy commodities ElecB2 C Electric power from second generation bioenergy commodit ies Non_Elec C Commodities of conventional energy excluding electric power Non_ElecB1 C Commodities of first generation bioenergy excluding electric power Non_ElecB2 C Commodities of second generation bioenergy excluding electric power MANU C Manufacturing commodities TRANP C Transportation commodities

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105 Account Description OTH C All other commodities LAB Labor CAP Capital LAND Land INDT Indirect b usiness t axes HHD1 Low income household (<$15K) HHD2 Medium income household ($15K $40K) HHD3 High income household ($40K+) FGOVND Federal g overnment nondefense FGOVD Federal g overnment defense FGOVI Federal g overnment i nvestment SGOVNE State local non e ducation SGOVE State local government e ducation SGOVI State Local government i nvestment INV Investment and inventory FT Foreign t rade DT Domestic t rade

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106 Social Accounting Matrix for the S outheastern U.S. 2006 ($ million ) AGRI A LOG A SAW A PULP A WOPRODA ElecA ElecB1 A ElecB2 A Non_Elec A AGRI C 14289.40 4193.65 0.84 0.00 0.03 287.62 LOG C 8.67 6626.12 8103.58 2933.28 122.68 15.31 SAW C 11.34 20.28 6437.75 1699.17 4137.02 69.72 0.19 2.49 120.35 PULP C 156.04 2.96 65.62 15553.23 300.74 41.48 0.11 1.48 619.73 WOPROD C 88.35 570.27 59.07 716.37 20.91 0.06 0.75 73.14 ElecC 715.38 11.79 342.58 1079.23 136.79 3.03 0.01 0.11 3611.08 ElecB1 C 2.63 0.17 1.24 3.55 0.56 0.75 0.00 0.03 14.08 ElecB2 C 26.31 0.58 12.58 39.22 5.09 0.84 0.00 0.03 134.45 Non_Elec C 4269.51 719.97 461.66 3434.51 112.13 11159.53 30.13 398.64 429199.98 Non_ElecB1C 9.10 302.78 1.74 4.54 0.01 0.16 1853.23 Non_ElecB2 C 2.65 205.23 316.32 265.05 48.79 1.94 0.01 0.07 13.84 MANU C 18840.63 505.94 2012.88 9659.57 1694.78 979.26 2.64 34.98 29995.70 TRANP C 1613.02 44.33 1445.82 3216.18 754.45 2600.12 7.02 92.88 15481.11 OTHC 11638.75 1446.23 4998.56 13836.47 4019.54 3729.26 10.07 133.21 84856.93 LAB 10864.29 1511.37 6722.85 12686.06 6266.91 13687.52 36.95 488.94 45326.50 CAP 10762.51 1047.10 5136.56 9854.20 3384.46 55508.68 149.87 1982.86 165721.00 LAND 9328.61 1715.46 INDT 1156.96 247.58 178.22 719.04 116.56 11616.74 31.36 414.97 19902.26 TOTAL 83775.07 18298.73 36815.58 75340.63 21818.61 99425.15 268.44 3551.63 797226.29

PAGE 107

107 Non_ElecB1 A Non_ElecB2 A MANU A TRANP A OTH A AGRI C LOG C SAW C AGRI A 83495.48 LOG A 18298.73 SAW A 36425.50 PULP A 32.90 WOPRODA 287.88 Elec A ElecB1 A ElecB2 A Non_Elec A Non_ElecB1 A Non_ElecB2 A MANU A 33.78 TRANP A OTH A AGRI C 11.12 129.71 46446.12 4.98 5407.55 LOG C 3.76 316.45 999.85 78.23 SAW C 5.66 82.82 2175.00 149.31 21090.46 PULP C 12.52 157.86 18934.69 322.77 16839.88 WOPROD C 1.85 6.36 1537.40 40.55 16411.42 Elec C 35.94 14.73 10763.71 301.11 25091.57 ElecB1 C 0.14 0.05 31.49 2.76 36.29 ElecB2 C 1.33 0.54 387.84 13.06 859.33 Non_Elec C 2447.67 61.62 58970.74 34494.03 57789.15 Non_ElecB1 C 113.32 3.16 4771.33 16.22 1010.03 Non_ElecB2C 0.35 12.23 245.28 6.99 362.55 MANU C 764.96 133.54 596623.88 13506.79 291085.38

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108 Non_ElecB1A Non_ElecB2A MANUA TRANP A OTHA AGRI C LOG C SAW C OTH C 832.18 234.97 370728.45 52940.75 1339187.41 LAB 503.92 242.78 242553.29 91940.95 1805027.12 CAP 430.27 236.84 148537.73 37200.92 1102130.47 LAND INDT 37.73 16.72 19846.72 8351.55 251802.61 HHD1 HHD2 HHD3 FGOVND 273.53 47.24 FGOVD FGOVI SGOVNE 1037.56 651.23 SGOVE SGOVI INV 219.16 1.53 FT 9270.51 1643.88 6147.65 DT 26591.65 7219.54 12490.77 TOTAL 5301.33 1698.83 1566583.97 268437.44 4999922.19 120887.89 27860.63 55420.01

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109 PULP C WOPROD C Elec C ElecB1 C ElecB2 C Non_Elec C Non_ElecB1 C Non_ElecB2 C MANU C AGRI A LOG A SAW A 341.45 48.63 PULP A 74022.55 5.64 1105.29 WOPROD A 21006.55 2.93 521.25 Elec A 89944.08 8544.59 ElecB1 A 242.86 25.58 ElecB2 A 3213.06 293.96 Non_Elec A 748229.51 6174.59 42822.19 Non_ElecB1 A 128.73 1104.19 4068.41 Non_ElecB2 A 3.75 0.19 1682.01 12.89 MANUA 576.43 278.60 1567.99 4690.38 6.17 1552663.07 TRANP A OTH A 18562.52 50.10 662.99 2494.56 869.31 FGOVND 272.71 1.48 81.21 FGOVD FGOVI SGOVNE 20.17 378.45 SGOVE SGOVI INV 29.54 6.36 36.15 0.32 2397.31 FT 12662.04 5156.03 5.80 0.02 0.21 107378.36 2211.19 240.79 511223.26 DT 52268.68 7283.76 1203.25 3.25 42.98 82943.75 2646.51 877.29 737558.80 TOTAL 139559.24 34076.50 109715.65 296.22 3919.23 951915.89 16832.68 2831.16 2853750.06

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110 TRANP C OTHC LAB CAP LAND INDT HHD1 HHD2 HHD3 AGRI A 279.59 LOG A SAW A PULP A 174.25 WOPROD A ElecA 936.48 ElecB1 A ElecB2 A 44.62 Non_Elec A Non_ElecB1A Non_ElecB2 A MANU A 6767.56 TRANP A 268437.44 OTHA 5283.74 4971998.97 AGRI C 3549.73 8254.63 5411.74 LOG C SAW C 20.91 83.71 63.64 PULP C 1623.32 3700.92 2505.59 WOPROD C 878.70 2784.82 3033.07 Elec C 12076.60 22265.92 11349.25 ElecB1 C 34.23 55.43 32.91 ElecB2 C 432.27 786.72 407.86 Non_Elec C 16067.85 36787.82 21081.55 Non_ElecB1 C 23.99 47.07 28.86 Non_ElecB2 C 21.26 49.40 35.73 MANUC 101832.99 249164.26 159243.23 TRANP C 9889.10 24892.07 20835.24 OTH C 408859.67 1049079.89 796755.35

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111 TRANP C OTHC LAB CAP LAND INDT HHD1 HHD2 HHD3 CAP LAND INDT HHD1 164951.31 42143.04 922.08 443.11 1285.16 4321.53 HHD2 941588.20 241035.58 5267.09 2504.10 7264.96 24622.31 HHD3 869470.03 221995.19 4854.90 2306.60 6691.85 22676.70 FGOVND 1871.30 251956.33 13544.26 40854.54 12492.30 131233.44 160626.07 FGOVD FGOVI 657.11 SGOVNE 157164.91 5934.57 2519.67 273584.46 3015.10 31974.61 36858.92 SGOVE SGOVI 4208.39 INV 3.11 26666.70 2274.91 1010609.05 50334.58 75896.63 FT 4024.97 5098.72 828.58 1239.18 6701.81 7397.30 DT 44794.61 913977.52 1684.11 14447.43 0.01 12.48 10.33 TOTAL 322543.88 6089846.13 2237859.45 1542083.46 11044.07 314439.01 577310.99 1633451.57 1353193.79

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112 FGOVND FGOVD FGOVI SGOVNE SGOVE SGOVI INV AGRI C 91.65 1.59 1131.57 460.16 1879.51 LOG C 8.12 126.89 SAW C 13.88 77.47 0.58 345.32 233.20 87.28 810.46 PULP C 81.75 29.22 2523.11 1417.72 483.98 WOPROD C 24.19 27.23 144.83 150.69 101.35 559.53 2157.41 ElecC 247.30 312.07 11017.37 4617.22 ElecB1 C 6.09 2.39 31.98 14.21 ElecB2 C 15.95 12.97 395.29 166.64 Non_Elec C 744.19 7785.02 20250.49 7531.34 76489.92 Non_ElecB1C 7.08 2.49 398.50 78.17 125.96 Non_ElecB2 C 7.64 2.70 0.29 35.17 18.94 2.20 29.38 MANU C 5775.57 28245.89 28348.33 43902.38 13863.47 22897.02 239413.07 TRANP C 941.35 7541.53 185.41 8770.42 5687.29 330.11 4057.38 OTHC 71089.57 164011.05 8338.41 229113.97 230349.94 142382.04 441567.87 HHD1 154751.78 9674.12 186510.19 HHD2 240141.95 46586.67 105393.84 HHD3 70849.32 4051.17 142442.84 FGOVND 312590.13 FGOVD 208017.61 14.19 FGOVI 36340.99 SGOVNE 109019.29 187996.11 SGOVE 264538.43 SGOVI 162053.62 INV 15901.99 133.09 2.05 1.29 156213.32 FT 11787.11 5.29 5.28 6.17 5.29 1.27 14.18 DT 0.00 14.55 14.46 6.17 2.02 1.27 14.21 TOTAL 925864.37 208031.80 36998.11 805115.71 264538.43 166262.01 1858330.81

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113 FT DT TOTAL AGRI A 83775.07 LOG A 18298.73 SAW A 36815.58 PULP A 75340.63 WOPROD A 21818.61 ElecA 99425.15 ElecB1 A 268.44 ElecB2 A 3551.63 Non_Elec A 797226.29 Non_ElecB1A 5301.33 Non_ElecB2 A 1698.83 MANU A 1566583.97 TRANP A 268437.44 OTHA 4999922.19 AGRI C 9130.92 20205.38 120887.89 LOG C 774.72 7742.98 27860.63 SAW C 2075.39 15606.59 55420.01 PULP C 9064.83 65119.69 139559.24 WOPROD C 783.58 3904.59 34076.50 Elec C 402.29 5320.58 109715.65 ElecB1 C 5.45 19.78 296.22 ElecB2 C 19.97 200.37 3919.23 Non_Elec C 21717.52 139910.93 951915.89 Non_ElecB1 C 3067.82 4967.12 16832.68 Non_ElecB2 C 139.64 1007.50 2831.16 MANUC 238140.02 757082.89 2853750.06 TRANP C 24530.12 51593.95 322543.88 OTH C 80913.44 578792.17 6089846.13

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114 FT DT TOTAL CAP 1542083.46 LAND 11044.07 INDT 314439.01 HHD1 10654.20 1654.48 577310.99 HHD2 12692.95 6353.91 1633451.57 HHD3 3920.62 3934.58 1353193.79 FGOVND 5.29 14.55 925864.37 FGOVD 208031.80 FGOVI 36998.11 SGOVNE 0.00 0.00 805115.71 SGOVE 264538.43 SGOVI 166262.01 INV 274984.41 242619.32 1858330.81 FT 612.40 693635.57 DT 1906051.36 TOTAL 693635.57 1906051.36 33242129.56

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115 APPENDIX B COMPLETE MODEL SETS, PARAMETERS, VARIABLE S, AND EQUATION LIST ING Index sets used in the model: A A ctivities C C ommodities CM C C ommodities which have at least one source of imports (from rest of world or from rest of the U.S. or from both) CE C C ommodities which have at least one destination for exports (from rest of world or from rest of the U.S. or from both) CNM C C ommodities which are not imported CNE C C ommodities which are not exported CM1 C C ommodities which have exactl y one import source CE1 C C ommodities which have exactly one export destination CM2 C C ommodities which are imported from both sources CE2 C C ommodities which are exported to both destinations F F actors of production and indirect business taxes FF F F actor s of production I I nstitutions H I H ouseholds G I G overnment units HG I H ouseholds and government units FG G F ederal government units SG G S tate government units T T rading regions (FT: rest of world, DT: rest of U.S. ) Parameters in the model: Shift parameter for production function

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116 Share parameter for A rmington demand function Shift parameter for export transformation function Shift parameter for A rmington import function Shift parameter for A rmington demand function Exponent for A rmington demand function Shift parameter for supply transformation function Marginal budget share parameter for StoneGeary utility function Weight of commodity C in the consumer price index Share parameter for CES production function Share parameter for export transformation function Demand elasticity for factors of production Engel aggregation weight Exponent for export transformation function Elasticity of substitution between regional output and imports Elasticity of transformation between foreign and regional exports Elasticity of substitution between foreign and regional imports El asticity of substitution for production function Elasticity of transformation between regional output and exports Frisch parameter for Stone Geary utility function Budget share parameter for investment utility function Quantity of C as intermediate input per unit of activity A Engel aggregation weight for commodity investment Investment demand flexibility ( = 1 implies no minimum investment level) Subsistence level parameter for investment expenditures

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117 Investment on commodities elasticity Income elasticity Subsistence level parameter for StoneGeary utility function Sha re parameter for A rmington import function Marginal propensity to save Exponent parameter for A rmington import func tion Government consumption Exponent for production function Share parameter for supply trans formation function Initial state government budget balance Institutional share of factor income Exponent for supply transformation function Indirect business tax rate Government unit share of indirect business taxes Consumption tax rate (paid only by households) Export tax rate Yield of output C per unit of activity A Import tax rate Sales tax rate Sales tax rate on intermediate inputs Inter household transfers Household income tax rate Price for factor FF in activity A Elasticity of demand for world export demand function

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118 Shift parameter for world export demand function Sector share of new capital (dynamic model) Capital depreciation rate (dynamic model) Variab les in the Model Price variables (endogenous): Average capital rental rate in time period t (dynamic model) Activity price Regional price of regional output Composite export price in regional currency Regional export price in regional currency Composite import price in regional currency Regional import price in regional currency Composite commodity price Value added price World export price in foreign currency Producer price Average price (e.g. wage or rental rate) for factor FF Exchange rate Quantity variables (endogenous): Institutional make matrix (quantity) Total indirect taxes Activity level Quantity of regional output supplied to regional demanders Composite export quantity

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119 Regional exports Quantity of factor FF demanded by activity A Household consumption Quantity of intermediate use of commodity C by activity A Investment demand Investment demand by institutions Composite import quantit y Regional imports Composite quantity supplied to regional demanders Quantity of regional output Other accounting variables (endogenous): Rest of the U.S. savings (import row) Rest of the U.S. savings (export column) Federal government expenditure State government expenditure Foreign savings (import row) Foreign savings (export column) Total investment expenditures on capital goods (commoditie s) Net household income WALRAS dummy variable (should be 0) Transfer of income to institution I from factor FF Federal government income Gross household income State government revenue Quantity of new capital by activity a for time period t (dynamic model)

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120 Exogenous variables: Consumer price index Factor supply Factor price distortion factor Investment adjustment factor Institutional investment adjustment variable 1 Investment equation adjustment variable 2 Stone Geary investment adjustment variable Savings adjustment factor State government spending adjustment factor Factor supply equation shift variable Model E quations Regional foreign import price equation: = 1 + ( B 1) Regional domestic import price equation: = 1 + ( B 2 ) Regional foreign export price equation: = 1 ( B 3) Regional domestic export price equation: = 1 ( B 4) World (including rest of world and rest of the U.S. ) export demand function: = ( B 5) Armington import composite equation:

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121 2= 2 2 2 2+ ( 1 2) 2 2 1 2 ( B 6) Rest of world and rest of the U.S. import ratio: 2 2 = 2 2 21 2 1 1 + 2 ( B 7) Quantity for an import ed commodity: 1= 1+ 1 ( B 8) Price for an imported commodity: 1= 1+ 1 ( B 9) Value of imports: 2 2= 2 2 ( B 10) Armington export composite equation: 2= 2 2 2 2+ ( 1 2) 2 21 2 ( B 11) Rest of world and rest of the U.S. export ratio: 2 2 = 2 2 21 2 1 2 1 ( B 12) Value of exports: 2 2= 2 2 ( B 13) Quantity for an exported commodity: 1= 1 + 1 ( B 14) Price for an exported commodity: 1= 1 + 1 ( B 15)

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122 Absorption equation: = ( 1 + ) ( + ) ( B 16) Domestic output value: = + ( B 17) Activity price equation: = ( B 18) Value added price equation: = ( 1 ) ( 1 + ) ( B 19) Leontief CES production function: =1 1 ( B 20) Factor demand equation: =1 1 1 1 ( B 21) Intermediate input demand equation: = ( B 22) Output function: = + ( B 23) Armington commodity composite equation: = + ( 1 ) 1 ( B 24) Import (domestic) demand ratio: = 1 1 + 1 ( B 25)

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123 Composite supply for nonimported commodities: = ( B 26) Output transfor mation (CET) equation: = + ( 1 ) 1 ( B 27) Export (domestic) supply ratio: = 1 1 1 ( B 28) Output transformation for nonexported commodities: = ( B 29) Factor income equation: = , ( B 30) Household income equation: = + + + + + 1 ( B 31) Net household income equation: = 1 1 ( B 32) Household consumption demand: = + ( 1 + ) / [( 1 + ) ] ( B 33) Investment demand equation: = 1 ( B 34) Institutional investment demand:

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124 = ( B 35) Investment on capital commodities: = + ( 1 ) + ( ) + + + + ( B 36) Investment demand for commodities: = 2 [ + ( ) /] ( B 37) Federal government revenue: = + + + + + + + + ( B 38) Federal government expenditures: = + + ( B 39) State government revenue: = + + + + + + + + ( + )+ + ( B 40) State government expenditure: = + + ( B 41)

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125 State government budget balanced: = + ( B 42) Factor market equation: = ( B 43) Composite commodity market equation: = + + + + ( B 44) R est of world current accounting balance: + + + = + + + ( B 45) Rest of the U.S. curr ent account balance: + + + = + + + ( B 46) Savings investment balance: + 1 + + + + ( ) + = + + + + ( B 47) Price normalization equation: = ( B 48) Indirect taxes calculation: = ( B 49) Factor supply equation: = ( B 50)

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126 Capital accumulation for dynamic model: = (B 51) = 1 + 1 (B 52 ) = (B 53) = (B 54) + 1= 1 + (B 55) + 1= 1 + A (B 56)

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BIOGRAPHICAL SKETCH Born and r aised in Taichuang, Taiwan Ming Yuan Huang received her B.S. in Forestry from the National Taiwan University (NTU). Soon after her graduation, she took a job as a research assistant at the Taiwan Forestry Research Institution and undertook a project on biodiversity conservation and valuation in Taiwan. She then returned to pur sue a higher degree and received her M.S. in Forestry at NTU. Her thesis investigated an international comparative study on resource management policies for the convention of biological diversity. Upon completion of her master s degree she also worked as a research assistant at the Resource Inves tigate and Analysis Lab in the D epartment of Forestry at NTU and undertook various projects in forest management and geographic information systems (GIS) development support ed by t he Taiwan government. She commenced her Ph.D in 2005 in the School of Forest Resources and Cons ervation, University of Florida focusing on assessing economy wide impacts of forest bioenergy polic ies in the southeastern U.S. She received her Ph.D from the University of Florida in the Spr ing of 2010.