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Master's Theses and Capstones Student Scholarship

Spring 2016

Impacts of on Timber Markets in New England and New York

Iuliia Drach University of New Hampshire, Durham

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Recommended Citation Drach, Iuliia, "Impacts of Wood Energy on Timber Markets in New England and New York" (2016). Master's Theses and Capstones. 1081. https://scholars.unh.edu/thesis/1081

This Thesis is brought to you for free and open access by the Student Scholarship at University of New Hampshire Scholars' Repository. It has been accepted for inclusion in Master's Theses and Capstones by an authorized administrator of University of New Hampshire Scholars' Repository. For more information, please contact Scholarly.@unh.edu. IMPACTS OF WOOD ENERGY ON TIMBER MARKETS IN NEW ENGLAND AND NEW YORK

BY

IULIIA DRACH B.S. in Ecology Oles Honchar Dnipropetrovsk National University, Ukraine, 2009

THESIS

Submitted to the University of New Hampshire in Partial Fulfillment of the Requirements for the Degree of

Master of Science in Natural

May, 2016 This thesis has been examined and approved in partial fulfilment of the requirements for the degree of Master of Science in Natural Resources by:

Thesis Director, Theodore E. Howard, Associate Dean, Professor of Economics

Mark J. Ducey, Professor of Forest Biometrics and

John M. Halstead, Professor of Environmental and Economics

On April 8, 2016

Original approval signatures are on file with the University of New Hampshire Graduate

School.

This thesis is dedicated to my family for their endless love, support and encouragement.

ACKNOWLEDGEMENTS

I would like to express my sincerest gratitude to my research adviser and mentor

Professor Theodore Howard for his guidance and enormous patience throughout my graduate studies. Without his expertise and continuous support, the research presented in this thesis would not have been possible.

I wish to thank Professor Robert Abt, Jesse Henderson and Raymond Sheffield

(North Carolina State University) for providing their valuable insights on Sub‐Regional

Timber Supply model modifications and answering my repeated queries.

I am grateful to my dissertation committee (Professors Theodore Howard, Mark

Ducey and John Halstead) for guiding and supporting me through my thesis defense and graduation.

I wish to acknowledge and thank the collaborator from the United States Forest

Service Northern Research Station ‐ Elizabeth Burrill, for providing the forest inventory data that was used in this research.

Finally, I would like to thank my family and friends for all their support and words of encouragement during the difficult but very exciting and rewarding time at

UNH.

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TABLE OF CONTENTS

ACKNOWLEDGEMENTS ...... IV TABLE OF CONTENTS ...... V LIST OF TABLES ...... VII LIST OF FIGURES ...... VIII ABSTRACT ...... X I. INTRODUCTION ...... 1 II. LITERATURE REVIEW ...... 10 II.1. Review of trends ...... 10 II.1.1. National level ...... 10 II.1.2. Regional level ...... 14 II.2. Ecological impacts of wood energy ...... 15 II.3. Carbon neutrality ...... 16 II.4. Microeconomic analysis ...... 21 II.5. U.S. renewable instruments ...... 24 II.6. Modelling Approach ...... 28 III. METHODOLOGY ...... 30 III.1. Study area ...... 30 III.2. Sub‐Regional Timber Supply model ...... 32 III.3. Data source and modifications ...... 36 III.4. Modeling scenarios ...... 41 III.4.1. Traditional forest products demand ...... 43 III.4.2. Wood consumption scenarios ...... 44 III.4.3. Criteria for Evaluation ...... 48 IV. RESULTS ...... 50 IV.1. Section overview ...... 50 IV.2. Projection results for different scenarios ...... 50 IV.2.1. Reference case scenario ...... 50 IV.2.2. Scenario A1 ...... 51 IV.2.3. Scenario A2 ...... 53

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IV.2.4. Scenario A3 ...... 54 IV.2.5. Scenario B1 ...... 56 IV.2.6. Scenario B2 ...... 57 IV.2.7. Scenario B3 ...... 59 IV.3. Summary of projection results ...... 60 IV.4. Elasticity sensitivity analysis ...... 62 V. DISCUSSION AND CONCLUSIONS ...... 66 LIST OF REFERENCES ...... 69 APPENDICES ...... 78

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LIST OF TABLES

Table I.1. Existing policies by state (New England & New York, US), amount, and final phase‐in year ...... 4 Table II.1. U.S. renewable ...... 11 Table III.1. SRTS model management type aggregates for New England and New York ...... 36 Table III.2. FIA survey units and counties ‐New England and New York ...... 37 Table III.3. State private plot counts ...... 38 Table III.4. Area of timberland by SRTS management type and ownership class ...... 39 Table III.5. Detailed area of timberland by SRTS management type and ownership class ...... 40 Table III.6. Studied supply and demand price elasticities ...... 41 Table III.7. Six modeling scenarios for the SRTS ...... 45 Table IV.1 Summary of the projection results ...... 62 Table IV.2 Summary of the projection results for elasticity Case 1 (see Table III.6) ...... 63 Table IV.3 Summary of the projection results for elasticity Case 2 (see Table III.6) ...... 64

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LIST OF FIGURES

Figure I.1. State Renewable Portfolio Standards and goals ...... 5 Figure I.2. New England and New York wood facilities ...... 7 Figure II.1. Total forest carbon stock (C Mg/ha) from forest inventory plots, conterminous US, 2000‐2009 ...... 20 Figure II.2. Supply curves of generated from residues in the US...... 22 Figure II.3. Supply curves of carbon displacement resulting from substituting with logging residues in in the US...... 23 Figure II.4. REC generation model ...... 28 Figure III.1. Study region: New England and New York ...... 30 Figure III.2. Wood biomass energy facilities in New England and New York, existing and proposed ...... 31 Figure III.3. SRTS model structure and data flows* ...... 35 Figure III.4. New England and New York electric facilities ...... 47 Figure IV.1. Projections of inventory, removals and price change for (a) hardwood pulpwood, (b) hardwood sawtimber (c) softwood pulpwood, (d) softwood sawtimber market in New England and New York region under slow growth forest and constant biomass consumption scenario (reference scenario) ...... 51 Figure IV.2. Projections of inventory, removals and price change for (a) hardwood pulpwood, (b) hardwood sawtimber, (c) softwood pulpwood, (d) softwood sawtimber market in New England and New York region under steady growth bioenergy consumption (scenario A1) ...... 52 Figure IV.3. Projections of inventory, removals and price change for (a) hardwood pulpwood, (b) hardwood sawtimber, (c) softwood pulpwood, (d) softwood sawtimber market in New England and New York region under rapid development bioenergy consumption (scenario A2) ...... 54 Figure IV.4. Projections of inventory, removals and price change for (a) hardwood pulpwood, (b) hardwood sawtimber, (c) softwood pulpwood, (d) softwood sawtimber market in New England and New York region underwood bioenergy and hydropower consumption (scenario A3) ...... 55 Figure IV.5. Projections of inventory, removals and price change for (a) hardwood pulpwood, (b) hardwood sawtimber, (c) softwood pulpwood, (d) softwood sawtimber market in New England and New York region under steady growth bioenergy consumption and supply restrictions (scenario B1) ...... 57

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Figure IV.6. Projections of inventory, removals and price change for (a) hardwood pulpwood, (b) hardwood sawtimber, (c) softwood pulpwood, (d) softwood sawtimber market in New England and New York region under rapid development bioenergy consumption and supply restrictions (scenario B2) ...... 58 Figure IV.7. Projections of inventory, removals and price change for (a) hardwood pulpwood, (b) hardwood sawtimber, (c) softwood pulpwood, (d) softwood sawtimber market in New England and New York region under wood bioenergy and hydropower consumption and supply restrictions (scenario B3) ...... 60

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ABSTRACT

IMPACTS OF WOOD ENERGY ON TIMBER MARKETS IN NEW ENGLAND AND NEW YORK

by

Iuliia Drach University of New Hampshire May, 2016

Renewable energy has a number of environmental and socioeconomic advantages over the energy derived from finite fossil , which is why it has been increasingly promoted on the state and federal levels. Growing interest in renewable energy calls for detailed analysis of the effects the potential increase in production of such energy might have on the existing markets.

The obvious choice for the source of renewable energy in New England and New

York is wood biomass due to the region’s abundant forest resources and extensive wood harvesting and processing . To analyze the impacts of increased wood biomass consumption for energy production on timber market, a modified Sub‐Regional

Timber Supply model (NE‐SRTS) was used. The response of inventory, removals and price of four traditional wood product classes (hardwood pulpwood, hardwood sawtimber, softwood pulpwood and softwood sawtimber) to different scenarios of

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increased wood biomass consumption was modeled over the projection period of 50 years.

Six increased wood biomass consumption and one reference case scenario were analyzed using NE‐SRTS. In the reference case scenario, current (2015) levels of wood biomass consumption are assumed to remain constant over the entire projection period.

Increasing demand for wood biomass without and with policy driven restrictions on residuals removals is modeled in scenarios A1, A2, B1, B2, and increased competition from hydropower without and with residual removal restrictions is modeled in scenarios A3 and B3.

Modeling results show that increased wood biomass consumption would affect the pulpwood (hardwood and softwood) market resulting in price and removals increases compared to reference case scenario. On the other hand, sawtimber market would not be influenced by these scenarios, because sawtimber is too valuable for use in wood energy production.

To understand how sensitive the projection results are to changes in elasticity parameter values, all scenarios were re‐examined using sets of changes in elasticities favorable and unfavorable to wood biomass consumption. Results indicate that removals would be more sensitive to elasticity value changes than inventory.

The present study provides an initial analysis of the effects of increased wood biomass consumption on the timber markets in New England and New York region.

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This has some limitations which can be addressed in future studies. One of such limitations is requirement of constant elasticity values over the projection period. Future studies might also focus on state‐level modeling and additional modeling scenarios reflecting the developing state and federal renewable energy policies.

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I. INTRODUCTION

The relatively heavily forested northeastern United States is well‐positioned to tap its

renewable forest resources to produce energy. This research focuses on the impacts of wood

biomass consumption on traditional forest products markets in the context of national and

regional attention to the economic and ecological impacts of renewable energy markets and

policy.

The United States is currently heavily reliant on fossil fuels such as coal, and

oil. This dependence is costly to citizens, sending valuable dollars overseas and requiring large

expenditures. U.S. energy consumption in 2013 was estimated at 97.5 quadrillion

British thermal units (Btu) while production was only 84 percent of total U.S. energy demand

(81.7 quadrillion Btu). Eighty‐two percent of the total energy consumption came from fossil

fuels and only 10 percent from renewable energy sources (U.S. Energy Information, 2013).

Burning of fossil fuels generates many harmful byproducts including emissions,

acid deposition, and deposition of mercury into ecosystems (Earth System Research Laboratory

and Global Monitoring Division, 2006). Additionally, Americaʹs transportation system is

overwhelmingly dependent on conventional oil, which is not only responsible for 20

percent of emissions related to , but also threatens and

economic prosperity. However, the greatest threat to the U.S. standard of living may lie in its

dependence upon the continual and uninterrupted supply of this finite (Holdren, 1991).

Therefore, the U.S. needs to seriously rethink its and look for ways to find energy

sources that are renewable and produced near the point of consumption. Investing in renewable

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clean energy can produce economic savings. The United States has the ability today to produce this energy, and to help Americans use energy more efficiently in their homes, and .

A new national alternative energy system will need to be highly diversified to address energy needs and capitalize on the unique resources of different regions (Cormio et al., 2003;

Del Rio and Burguillo, 2008). Each location needs to play to its strengths. This means that areas will need to develop their systems independently from the greater whole because there is a wide diversity of circumstances that affect the efficient production of renewable energy. For example, is best where windy conditions are prevalent (e.g. U.S. Northern Plains), is best at southern latitudes with little cloud cover throughout the year (e.g. U.S.

Southwest), while energy from wood biomass does best where there are productive and abundant (e.g. U.S. East)

Policymakers have realized the need for replacing fossil fuels with renewable ones by passing the Energy Policy Act of 2005 (hereafter, EPAct 2005) (Energy Policy Act of 2005, 2005) and and Security Act of 2007 (hereafter, EISA 2007) (Energy independence and security act of 2007, 2007), and introducing a number of other mechanisms to expand renewable energy capacity such as state‐level Renewable Portfolio Standards (RPS).

EPAct 2005 requires federal agencies to reduce energy intensity every year in their , updates federal green standards with emphases on efficiency and sustainable , and sets federal government‐wide goals for renewable power purchases (at least 7.5% of all electricity consumption was to be derived from renewable resources by 2013 fiscal year). EISA

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2007 reinforces the energy reduction goals set by EPAct 2005 and introduces the Renewable

Fuel Standard and Corporate Average Fuel Economy Standard.

State‐level involvement in renewable energy policy predates federal efforts. The first renewable portfolio standard was established in 1983 by Iowa, although, the discussions of the detailed design of an RPS were not started until 1995 in California. Even though California chose not to implement an RPS at that time (California adopted RPS in 2002), the renewable energy advocacy quickly picked up the concept. As of 2015, thirty states (including all of New England and New York, Table I.1) and the District of Columbia have enforceable

RPS or other mandated renewable energy capacity policies (Figure I.1). The design of each state’s RPS may vary in structure, size or its application, but they have a common structure in the form of a periodic schedule that specifies the amount of electricity from eligible renewable sources. In addition, many such policies include trading of renewable energy certificates (RECs).

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Table I.1. Existing renewable energy policies by state (New England & New York, US), amount, and final phase‐in year

State / D.C. Amount Year Administering RPS

Connecticut 27% 2020 Department of Control

Class I 15% 2020 Massachusetts Massachusetts Division of Energy Resources Class II 7.1% 2009

Maine 40% 2017 Maine Public Utilities Commission

New Hampshire 24.8% 2025 New Hampshire Office of Energy and Planning

Rhode Island 16% 2019 Rhode Island Office of Energy Resources

Vermont* 20% 2017 Vermont Department of Public

New York 29% 2015 New York Public Service Commission

* The state has set voluntary goals for adopting renewable energy instead of a binding RPS

Source: ― State Renewable Portfolio Standards and Goals (National Conference of State Legislatures, 2015)

In New England and New York states, several factors such as technological constraints, cost considerations, and resource availability combine to limit the amount of renewable energy that will come from solar and wind sources. However, it is widely considered that the abundant wood biomass resources of the region will be available to meet much of the RPS‐ imposed demands for electricity derived from renewable sources. The forested are abundant and reasonably productive and have great potential as renewable energy feedstock

(Walsh et al., 2000). According to USDA Forest Service Forest Inventory and Analysis (FIA)

2013 data, the region’s forest cover is approximately 51.02 million acres (69% of the area) and more than 81% of this forestland in New England and New York is privately owned.

Private landowners are eager to participate in the new markets created by developing opportunities to meet renewable energy demand (Kingsley, 2012). Moreover, there are fewer

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regulatory or policy constraints for harvesting wood biomass from private land than on public land.

Figure I.1. State Renewable Portfolio Standards and goals

Source: National Conference of State Legislatures, 2015

New England and New York have already taken advantage of the abundant forest resources to generate energy. There are 31 wood biomass electricity producing units (Figure

I.2), as well as a significant number of industrial facilities (primarily and paper mills) that have large biomass units integrated into their production facilities. In addition, wood biomass provides about 4 percent of heating energy in New England and New York. Furthermore, New

England and New York incorporated wood‐based thermal power in their RPS program in relation to public buildings. According to data from the Wood2Energy project, 189 institutional facilities (schools, , government facilities, , correctional facilities, etc.) utilize

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wood chips or pellets as their heating source (Wood2Energy, 2014). US EIA also estimated that in 2009 nearly 2.9 million household in New England and New York consumed 123.9 trillion

Btu of wood for the heating purposes. Thus, New England and New York already make abundant use of wood biomass for energy and production.

The term “wood biomass” is not well defined and often leads to much confusion. The general definition of biomass includes organic matter of and animal origin. There are many biomass categories such as agricultural , crop and animal residues, trees and wood residues from management practices, wood residues, trees plantations and municipal solid waste. Although biomass includes a wide range of categories, this thesis will focus on wood biomass. USDA Forest Service defines wood biomass as “the trees and woody plants, including limbs, tops, needles, leaves, and other woody parts, grown in a forest, woodland, or environment, that are the by‐products of ” (USDA

Forest Service, 2013), but this thesis will use a broader definition, to include wood residues obtained indirectly from wood processing and manufacturing facilities or urban waste.

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Figure I.2. New England and New York wood power facilities

Source: (Energy Information Administration, 2015)

Development of new energy systems using wood for bioenergy certainly has many ecological and economic advantages. Wood biomass is a renewable source, which is considered by some researchers nearly “carbon neutral” when burned. In addition, growing demand for wood bioenergy creates new jobs and supports economic growth in the region (Buchholz et al.,

2011).

At the same time, utilizing wood biomass as an energy feedstock faces many challenges.

These include the high cost of new , insufficient biomass supply, limited existing infrastructure, and the potential to cause more harm than good when extracting renewable resources (Government Accountability Office, 2006). Additionally, there is a need to evaluate supply chains for wood energy markets and the effects of increased competition between wood

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bioenergy and traditional wood product markets. Moreover, the controversy around “carbon neutrality” highlighted by the Manomet study (Walker et al., 2010) raises questions about wood bioenergy’s time frame to achieve carbon neutrality. These concerns will need to be addressed as alternative wood‐based energy systems emerge.

Wood bioenergy production in New England and New York region is expected to increase significantly over the next several decades. A strong push in this direction may come from the state legislatures establishing RPSs for these states (Figure I.1). These rules would require each electric utility to supply a percentage of electricity sales from renewable energy resources located in New England and New York (Table I.1).

Voluntary consumer purchases of renewable energy certificates have grown rapidly, too.

Primarily this might have happened because more companies have been purchasing RECs for their electricity needs. Consumers are not required to have direct access to renewable energy sources to purchase RECs. They can buy RECs through their local utility company or a competitive electricity marketer without having to switch electricity suppliers. REC providers— including utilities, REC marketers, and other third‐party entities—may sell RECs alone or sell them bundled with electricity. RECs, also known as green certificates and renewable energy , “represent the attributes of electricity generated from renewable energy sources” (Holt and Bird, 2005). One REC indicates that one MWh of electricity was generated at a power facility using renewable sources. Each REC indicates the power source, location and year of power generation (Lau and Aga, 2008).

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Although wood biomass represents a renewable energy source for New England and

New York, concerns over the environmental impacts of policies promoting wood energy have also been raised. To avoid incentives that can lead to overharvesting, and subsequently to northeastern forests’ degradation, such policies need to account for a wide array of economic, ecological and social constraints on the sustainable supply of wood biomass in the region.

Moreover, there is an obvious knowledge gap that needs to be addressed regarding the impacts of increased policy‐driven bioenergy consumption on traditional wood and paper industries in

New England. Therefore, this thesis will focus on how the changing wood biomass energy consumption will affect New England timber markets, as well as how certain state‐level policies may affect these regional markets.

This thesis is organized as follows. Chapter II focuses on previous research on wood biomass on national, regional and local levels, concentrating on economic, ecological and policy aspects of wood biomass. Chapter III presents a modeling approach to regional analysis of wood biomass and timber markets by application of Sub‐Regional Timber Supply (SRTS) model and FIA data. Modeling results characterizing market reactions from changes in wood biomass consumption under selected market and policy scenarios are given in Chapter IV. Discussion and conclusions are presented in Chapter V.

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II. LITERATURE REVIEW

II.1. Review of consumption trends

II.1.1. National level

Currently, wood biomass energy comprises 2.2 percent of total US energy consumption

(Table II.1) and represents the second largest source of renewable energy after hydropower

(U.S. Energy Information, 2015). Wood biomass is widely used across residential, commercial,

industrial and electrical power sectors. Industry represents the dominant user of wood biomass,

consuming 58.3 percent of US wood (U.S. Energy Information, 2015). Within this

sector, pulp and paper product facilities are the largest energy consumers. Residential and

commercial users of wood energy are responsible for 26.7 percent and 3.3 percent, respectively,

while sector utilizes 11.6 percent of renewable energy in the form of wood

biomass. In addition to becoming an increasingly popular renewable choice,

wood biomass energy also offers great potential for purposes. However,

increasing wood biomass demand brings many questions about future consumption trends and

the factors that may affect them. These questions, along with long‐term projections of wood

energy use and review of the historic renewable energy consumption trends, have been studied

by a number of researchers over the past two decades (e.g. Aguilar et al., 2011 and Skog, 1993).

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Table II.1. U.S. renewable energy consumption

2013 2014 2015 2016

U.S. Renewables Consumption (quadrillion Btu)

Hydroelectric Power1 2.561 2.467 2.608 2.524

Geothermal 0.221 0.219 0.220 0.220

Solar 0.307 0.426 0.524 0.571

Wind 1.595 1.750 1.839 2.084

Wood Biomass 2.138 2.173 2.113 2.125

Ethanol 1.090 1.105 1.094 1.094

Biodiesel 0.205 0.194 0.196 0.196

Waste Biomass 0.476 0.472 0.489 0.495

Total 9.321 9.554 9.870 10.092

1Conventional hydroelectric power only. generated by pumped storage is not included in renewable energy. Source: U.S. Energy Information (2015)

Skog (1993) used a linear programming market equilibrium model to project industrial, commercial, residential and electrical power facilities’ wood biomass consumption in the US.

The results indicate that residential sector demand response to heating fuel price is slightly inelastic, meaning that the percentage increase in the quantity of wood energy demanded was smaller than the percentage increase in fuel price. However, the quantity of wood demanded increased with increasing household income. In contrast, commercial and electric utility sectors demand is very responsive to fuel prices as well as trends in wood energy demand, the main factor affecting industrial sector demand. However, Skogʹs (1993) projections do not account

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either for technological advances in conversion of wood to energy, or for government regulations or incentives. Modeling these factors would likely alter his results considerably.

Aguilar et al. (2011) analyzed historic trends and the impact of renewable energy prices and public policies on wood energy consumption. Their findings indicate that wood biomass consumption trends within industrial sector were largely associated with production levels in and, “to a lesser extent with oil prices”. In contrast to industrial demand, the residential sector’s wood energy demand was mostly affected by fuel prices and government policies. The authors also point out that consumption within electric power and commercial sectors greatly increased in 1990s, most likely due to policies promoting wood biomass energy. Nevertheless, they indicate that even though public policies play an important role in promoting wood energy, these policies should also be addressing the issue of wood energy price competitiveness.

Co‐firing with coal and wood has also become a popular choice among electric plant managers because many coal‐based electric facilities can incorporate wood biomass within already existing infrastructure with relatively minor investments. Goerndt et al. (2013) identified drivers of wood biomass co‐firing in US. These include adequate wood biomass supply and its competitive cost compared to fossil fuels alone, and short transportation distances. They also pointed out that environmental regulation might be affecting future willingness to co‐fire with wood biomass. Although the cost of electricity generated from co‐ firing with wood biomass is higher than that of electricity from fossil fuels, substituting wood

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biomass for some amount of coal represents an economically viable option for mitigating carbon emissions (Gan, 2007).

A wide range of wood biomass resources is available for transformation into energy. It comes in a form of fuel wood for residential and commercial consumption, urban wood waste, pulp and paper industry residues, forest residues and short rotation wood plantations. Among them, logging residues left after timber harvests represent a widely available option for energy generation. The residues availability and potential for electricity production were studied by

Gan and Smith (2006). They estimate that at a 70 percent residue recovery rate and a minimum plant size of 10 Megawatt (MW), recoverable logging residues from growing stock in the USA would reach 13.9 million dry tons available for energy production annually. Growing stock logging residues are defined as wood cut or downed during harvest (including branches and tops), but traditionally left at the harvest site. Other on‐site sources include downed dead and cull trees, and tree tops above a 10.16 cm growing‐stock top (smaller than

12.7 cm in diameter at breast height), not including stumps and limbs. If these other sources are included, the total residue volume expands to 36.2 million dry tons. The amount of recoverable logging residues is not evenly distributed across the United States and is concentrated in a few regions, particularly the Northeast and South. Gan and Smith (2006) estimated that utilization of the projected logging residue supply could generate 26 Terawatt‐hour (TWh) of electricity utilizing just growing stock residues and 67.5 TWh if other wood residue sources are included.

“This would correspondingly displace 6.8 and 17.6 million tons of carbon emitted from coal‐ fueled plants” (Gan and Smith, 2006).

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However, Gan and Smithʹs (2006) results reflected only logging residues supply and did not account for biomass from forest management treatments (e.g. fuel reduction or thinnings) and urban forests. Therefore, the total amount of available wood biomass supply for energy purposes was significantly underestimated. On the other hand, their projections did not account for any policy‐induced logging residues’ recovery restrictions or potential competition for these resources from other forest product industries.

II.1.2. Regional level

The use of wood for thermal energy production (e.g. residential wood‐burning stoves and commercial buildings heating) is one of the dominant uses of wood biomass in the New

England and New York. Wood biomass accounted for nearly 6% of energy supplied in the region in 2008 (U.S. Energy Information, 2015). A number of studies suggest that New England and New York have abundant forest biomass resources (e.g. Innovative

Solutions LLC, 2008; Milbrandt, 2005; Perlack et al., 2005). Benjamin et al. (2009) estimated over

1.3 billion cubic meters of growing stock in Northeastern forests and Smith et al. (2001) identified that recent harvest levels could be more than doubled without reducing total standing biomass over time. Vermont’s forests have the potential to provide significantly larger quantity of sustainably harvested wood biomass than current levels (Sherman, 2007). Volk et al.

(2010) estimated that New York’s inventory has an additional 4.8 to 6.4 million dry metric tons of wood biomass over current harvest levels that can be sustainably harvested. Similar studies have been done for Massachusetts (Kelty et al., 2008) and Maine (Dickerson et al., 2007).

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II.2. Ecological impacts of wood biomass energy

The literature suggests that increases in US wood biomass demand raises questions about ecological impacts and forest sustainability. As wood energy gains popularity and becomes an economically viable renewable energy option, it is likely that removal of traditionally non‐merchantable wood components (e.g. stumps, bark, tree tops, branches and leaves) will be increasing (Benjamin et al., 2010; White, 2010). High levels of wood biomass utilization could lead to reductions in the amount of aboveground carbon at rates that exceed reductions from conventional wood harvest levels (Berger et al., 2013). Berger et al. (2013) argue that it is reasonable to conclude that wood biomass removals and whole tree harvests have comparable ecological impacts on forest structure and functions. However, the long‐run consequences of the removal of non‐conventional wood components (e.g. dead trees, stumps and small‐diameter trees) remain unclear.

Studies such as Riffell et al. (2011), Lassauce et al. (2012) and Freedman et al. (1996) have shown that whole‐tree harvesting in the Southeast and Pacific Northwest regions has negative impacts on and leads to reduction in abundance and diversity of birds, invertebrates, as well as saproxylic species and bryophytes. Research exploring potential impacts to higher plant species composition and diversity is limited (see for example Haeussler and Kabzems, 2005). By contrast, Roxby et al. (2015) found no significant difference in tree diversity between whole tree and conventional harvesting in northern hardwood stands ten to twelve years after harvesting.

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Furthermore, Curzon et al. (2014) looked at the effects of logging residue recovery on compaction and its impacts on forest productivity in upper Great Lakes region. They found a significant influence on aboveground biomass production on sandy , but no negative effect on clayey or loamy soils. These findings highlight the importance of developing guidelines for energy wood harvesting for minimizing soil disturbance accounting for site diversity.

Some discussion of the importance of guidelines for harvesting wood biomass for energy can be found in Abbas et al. (2011). Their findings have shown that existing biomass harvesting guidelines are limited in number and mostly address wood energy plantations.

Curzon et al. (2014) suggest that it is essential to develop biomass‐specific and integrated harvesting guidelines, addressing soil impacts. Shepard (2006) expanded on previous suggestions by including hydrology and quality, habitat properties, and perspective into harvesting guidelines. Since biomass is already being harvested to supply growing energy demand (with or without specific guidelines), these authors point out that it is crucial to integrate relevant knowledge of the geographic areas where biomass is being extracted and update this regularly according to latest scientific discoveries.

II.3. Carbon neutrality

Initially, proponents of wood biomass, as a renewable energy source viewed its use as carbon neutral. However, as researchers have explored the time dimensions of neutrality, the assumption of carbon neutrality has recently been challenged. It has also been debated that

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using wood biomass as an energy feedstock will result in decreased forest stocks and, consequently, in net reduction of carbon sequestered by forests. Furthermore, most recent papers (Sedjo and Tian, 2012; Ter‐mikaelian et al., 2015) acknowledge the necessity of accounting for the future of forest carbon stocks in the absence of demand for wood bioenergy.

Wood biomass energy production releases greenhouse gas (GHG) emissions associated with growing and harvesting wood biomass, transporting it to the power plant, and burning or gasifying the material. Carbon neutrality may be achieved by balancing the amount of carbon released during energy production with an equivalent amount sequestered by future forest regeneration (Bright and Strømman, 2009; Sjølie et al., 2010; Zhang et al., 2010). Yet, forest regeneration does not occur instantly, so there can be a long delay before wood biomass resource achieves carbon neutrality (Vanhala et al., 2013).

In the 1990s, researchers debated that reduction of emissions by substituting wood energy for fossil fuels is time‐dependent and is not always carbon neutral (Schlamadinger and

Marland, 1996; Schlamadinger et al., 1997, 1995). Recent papers have supported those arguments via models that show that GHG reduction associated with wood energy production changes with respect to the considered timeframe (Cherubini et al., 2011; McKechnie et al., 2011;

Repo et al., 2011). Other studies have found that different wood biomass sources have various carbon emissions impacts. Utilization of easily decomposable wood residues can benefit GHG reduction (compared to fossil fuels) from the beginning of their use. Also, wood biomass from dedicated energy plantations does not cause evident carbon stock losses, and thus can be carbon neutral (Zanchi et al., 2012).

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In 2010, the Manomet Center for Conservation Sciences released its controversial study

(Walker et al., 2010) of the carbon cycle of wood biomass. The report concluded that the GHG emissions caused by burning wood biomass create an immediate “carbon debt” that can last for a period of time ‐ from five years to many decades ‐ before the carbon dioxide by new growing trees offsets the debt and creates “carbon dividends” (Walker et al., 2010). Based on

Manomet’s empirical methodology and modeling assumptions, replacement of fossil fuels with wood biomass from whole tree harvests would require 45 to 75 years to demonstrate carbon net benefits. Other researchers have expanded on Manomet’s findings (McKechnie et al., 2011;

Mitchell et al., 2012) and Schulze et al. (2012) concluding that increasing wood bioenergy production up to 20 percent of the global energy supply is not sustainable and could lead to increased GHG emissions. The Manomet report also led to the widespread public policy misrepresentations and publications in the press, such as in Associated Press by Steve LeBlanc :

“. Study: Wood power worse polluter than coal”(LeBlanc, 2010).

Other researchers and experts disagree with Manomet findings and argue that their study incorporates improper understandings about forest management. Strauss (2011) criticizes Manomet’s “debt‐then‐dividend” model by stating that it is incorrect to focus analysis only on stands that are harvested, omitting stands that are not being disturbed by harvests and continue to sequester carbon dioxide. Strauss’s paper demonstrates that carbon debt could be null if the forest system has been in a growth‐to‐harvest balance and the forest is managed in a sustainable manner without depletion of biomass stock. Furthermore, Sedjo and Tian (2012) criticized the static view of forest systems and noted that “forestry is a dynamic system in which

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markets generate changes in management on a broad scale involving multiple stands and multiple forests.” Any changes in wood biomass demand in one particular forest or stand can be transmitted throughout the multiforest system. Thus, anticipated increase in wood biomass demand will be addressed by expanded intensity of forest production and this in turn will offset the carbon released by wood energy production.

In the debate over reducing carbon emissions with displacing energy with wood biomass sources, scientists argue that emissions from these two sources should be differentiated. For example, Gunn et al. (2012), find that there is no difference between biogenic and fossil fuel carbon dioxide in terms of their GHG properties. The difference derives from where the carbon was sourced. Burning fossil fuels that are mined from centuries‐old deposits of carbon produces an influx of new carbon in the atmosphere, whereas burning wood biomass recycles renewable plant growth in a sustainable carbon balance. Fossil fuels also produce other greenhouse gases and pollutants with more negative environmental impacts than wood biomass. Gunn et al. (2012) also suggest that the prolonged use of fossil fuels in energy production is likely to lead to a significant increase in the levels of geologic carbon in the atmosphere.

On a regional level, Davis et al. (2012) analyzed the opportunities and impacts of growing bioenergy demand in the eastern US. Based on four modeling scenarios of biomass harvest in the eastern US (partial harvests of mixed hardwood forests, pine plantation management, short‐rotation woody cropping systems, and forest residue removal) they conclude that partial harvests and residue removals had greater carbon storage amount

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therefore, wood biomass could be sustainably harvested for energy production. The map of current levels of forest carbon stock is presented in Wilson et al. (2013) based on FIA data (see

Figure II.1).

Figure II.1. Total forest carbon stock (C Mg/ha) from forest inventory plots, conterminous US, 2000‐2009

Source: Wilson et al. (2013).

The arguments presented above suggest that carbon accounting for wood bioenergy is complicated and requires a thorough ‐cycle analysis. Thus, any policymaking decisions should incorporate long‐term research and planning to ensure wood biomass actually reduces carbon dioxide emissions.

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II.4. Microeconomic analysis

As an energy source, wood biomass directly contributes to the national economy.

However, determining its true value can be difficult since a large portion of wood energy is not sold in the market and wood energy production and consumption (e.g. environmental and social impacts) are very difficult to evaluate. One of the most widely used approaches to measure the contribution of an activity to the economy is the calculation of the value added produced by that sector. Value added reflects the difference in economic value between the total in the sector and the inputs into production purchased from other sectors (Sathre and Gustavsson, 2009). Since the most cost‐effective production of biomass for energy occurs when it is produced simultaneously with other higher valued products or in coordination with stand improvement, the total value of wood biomass production is a good approximation of the value added in the sector. Thus, market price of wood biomass is used as a rough estimate of the value of wood biomass energy (Adams and Haynes, 1996; Newman and

Wear, 1993).

The amount of wood energy produced and consumed in an economy has been heavily dependent on the local forest resource availability. Yet, technological advances and renewable energy policy incentives have brought wood energy markets to a regional scale. A recent study by Jones et al. (2013) indicates that the growing wood is more influenced by delivered biomass prices than by transportation costs and the location of nearby wood resources. Therefore, development of well‐distributed small local markets could better support

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economical biomass removal versus larger markets requiring wood biomass supplies drawn from long distances.

The current cost of electricity production from logging residues ranges from $47/MWh

(marginal cost) to $50/MWh (full cost) (Figure II.2), which is slightly higher than the cost of producing electricity from fossil fuels (e.g. $35/MWh from coal) (Gan and Smith, 2006).

However, an earlier study (Ahmed, 1994) estimates that in favorable conditions the current cost of electricity production from logging residues can be significantly reduced.

Figure II.2. Supply curves of electricity generated from logging residues in the US.

Source: Gan and Smith (2006)

Gan and Smith (2002) also noted that imposing an emissions of $25/ton of carbon dioxide is needed to make wood biomass energy competitive with coal energy at current prices

(Figure II.3). Short and Keegan (2002) predict that the cost of biomass energy production could fall by 15 to 20 percent over the next 20 years, which could make biomass energy an economically viable alternative to fossil fuels use.

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Wood biomass energy production creates a market for low‐grade forest residues. In

New England and New York, foresters report that, depending on the forest type and stand conditions, around 20 to 80 percent of the harvested volume can be sent to a low‐grade market

(North East State Foresters Association, 2013; Sendak et al., 2003). The removal of forest residues has a number of environmental benefits (e.g. improved forest health and productivity, habitat development) and could promote best forest management practices by avoiding high‐grading. By providing a market for low‐grade forest products landowners more willingly engage in practicing sustainable forestry with the benefit of ancillary revenue from their forestland (Shivan and Mehmood, 2010).

Figure II.3. Supply curves of carbon displacement resulting from substituting coal with logging residues in electricity generation in the US.

Source: Gan and Smith (2006)

Previous studies indicate that utilization of wood biomass and other renewable sources creates more total employment than fossil fuel energy production (Kammen et al., 2004). Wood bioenergy production requires the highest amount of labor inputs per unit of energy produced:

100 to 170 person‐days per terrajoule for fuelwood and 200 to 350 person‐ days per terrajoule

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for charcoal (Remedio and Domac, 2003). However, the benefit of this employment generation depends on the value of the labor used for production (Luoga et al., 2000).

Potential competition for raw wood material between bioenergy plants and traditional forest product sectors has been indicated as one of the main concerns of the wood biomass energy markets in the US (Aguilar et al., 2011; Scott and Tiarks, 2008). However, a European empirical analysis found that high‐value solid wood product markets would less likely be affected by growing wood bioenergy demand compared with pulpwood markets, since they would not compete with wood biomass destined for the energy market (Aulisi et al., 2008).

Galik et al. (2009) also found that US southern pulpwood markets would likely be negatively influenced by emerging wood bioenergy industry. However, these studies have not considered forest landowner behavior. Susaeta et al. (2012) found that an increased price of wood biomass energy induced by emerging bioenergy markets would lead to an increase in pulpwood production rate and demand for forest labor, which could bring financial benefits to landowners. Further econometric simulations (Susaeta et al., 2013) indicated that an increase in wood bioenergy demand leads to an increase in sawtimber and pulpwood prices, but a decrease in their respective quantities.

II.5. U.S. renewable energy policy instruments

Over the past several decades, the U.S. has adopted a number of public policy instruments promoting wood bioenergy production and use. Such instruments can be differentiated by level of government (federal or state) and viewed from several perspectives:

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incentives versus mandates and research versus industry development. Public policies may be divided between those that (1) create markets for wood biomass and wood energy generation and (2) incentives relying on existing markets to improve cost competitiveness of wood energy generation, , and consumption (Guo et al., 2007). Financial incentives may be classified by those (1) promoting the demand/supply of renewable energy feedstocks, (2) reducing start‐up costs through cost‐share programs, (3) lowering the cost of capital necessary to generate renewable energy, and (4) providing financial incentives based on energy output

(Aguilar and Saunders, 2010).

In the early 1990s, as part of the Energy Policy Act of 1992, two federal programs were introduced providing incentives for electrical generation from green energy sources: Renewable

Energy Production Incentive (REPI) and Renewable Energy Production Tax (REPTC)

(Peksa‐Blanchard et al., 2007). REPI provides monetary incentive payments for electricity generated and sold by new qualifying renewable energy generation facilities. Annual incentive payments of 1.5 cents per kWh (1993 dollars and indexed for inflation) were available to qualifying facilities for the first ten year period of their operation. REPTC provides the same 1.5 cents per kWh (1993 dollars) in the form of a tax credit to facilities generating electricity from wind, closed‐loop biomass, or poultry waste. Initially REPTC was a ten‐year program, however in 2004 it was renewed and list of eligible energy sources expanded to include open‐looped biomass, solar, municipal solid waste, geothermal, and small power (Peksa‐Blanchard et al., 2007).

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US Federal EPAct 2005 (Energy Policy Act of 2005) created the Federal Green Power

Purchasing Goal that required “to the extent it is economically feasible and technically practicable,” the total amount of renewable electric energy consumed by the federal government should be at least 5% from 2010 to 2012 and 7.5% after 2013. Also under the EISA

2007 a university‐based program was enacted to widen the use of renewable energy (Energy independence and security act of 2007). State‐level regulations complement federal policies. While federal support promotes nationwide efforts, state‐level regulations reflect on state‐specific conditions and adopt approaches deemed most appropriate for the circumstances prevalent in that state or region. By January 2015, 30 states in the United States and the District of Columbia have adopted enforceable RPS or other mandated renewable capacity policies (DSIRE, 2015).

The US Biomass Crop Assistance Program provided an incentive in the form of a government payment per unit of biomass supplied for the production of bioenergy (Stubbs,

2011). In addition, (Aguilar et al., 2011) noted that the use of bonds created by the Energy Improvement and Extension Act of 2008 has been an effective policy instrument for financing energy projects powered by renewable energy, including wood biomass.

Aguilar et al. (2011) also mentioned that most public incentives have targeted electricity production from woody feedstock whereas other sectors, such as the residential, have not received as much government support. Research by Song et al. (2012) also stresses the importance of the residential sector and the potential of greater wood energy use in rural areas.

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Public policy implementation has been influential in the recent growth of wood biomass production and generation of wood energy. However, there are also direct and indirect impacts that are important to point out. Policies promoting use of wood biomass that previously had little or no commercial value will certainly have effects on current timber markets. Additional removals of materials from forestlands can also result in undesired impacts on the forest resource (Aguilar and Garrett, 2009). In addition, on a regional level, following the Manomet study (Walker et al., 2010) the Massachusetts Department of Energy Resources (DOER) finalized the Massachusetts Renewable Portfolio Standard Class I regulations for biomass eligibility

(Massachusetts DOER, 2012). The final standards require all wood biomass plants to generate power at a minimum 50 percent efficiency to receive one‐half of a renewable energy credit

(REC), and 60 percent efficiency to receive one full REC, a sharp increase from the previous requirement of 25 percent. The new goals are likely unattainable for most plants. All plants must also achieve a 50 percent reduction in carbon lifecycle emissions over 20 years.

These new standards are expected to influence the regional forest product industry, and some argue that they could affect regulation throughout the country. Twenty plants in New

England and New York that can sell power in Massachusetts could be affected by the new rules, which will require them to boost their efficiency if they want to claim state RECs.

Massachusetts, like other states, uses RECs to create incentives for companies to produce renewable power and reduce use of fossil fuels. As a tradable , RECs represent the right to the environmental, social and other non‐power qualities of renewable energy generation (Holt and Bird, 2005). These certificates are also being used for establishing

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compliance with state requirements (RPS) for power producers to achieve a certain level of electricity generation from renewable resources (Frenkil and Yaffe, 2012). One REC can be created for every 1 megawatt‐hour of renewable energy generated (Figure II.4)(USEPA, 2008).

Figure II.4. REC generation model

Source: USEPA (2008)

II.6. Modelling Approach

There are numerous attempts to model and analyze the response of US timber market to various wood biomass demand scenarios. Typical approaches include (a) assessment of potential wood biomass availability that do not include models of biomass demand (Biomass

Research and Development Board, 2008; Gan and Smith, 2006; Perlack et al., 2005); (b) market projections of supply and demand based on policy‐driven biomass consumption (Abt et al.,

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2010a, 2010b; Galik et al., 2009; Ince et al., 2011; Rossi et al., 2010); and (c) facility location and plant size evaluations (Wu et al., 2011). In recent years, GHG impacts influencing wood bioenergy markets have been assessed on national (Gan and Smith, 2006) and regional (Abt et al., 2010b; Walker et al., 2010) levels. To evaluate wood biomass supply impacts of various levels of biomass demand in the US South, a Sub‐Regional Timber Supply (SRTS) model was developed. The model produces supply estimates from a supply function based on USDA FIA data sets and proposed demand scenarios (Abt et al., 2009; Prestemon and Abt, 2002). More detailed information about SRTS model and its applications in US Northeast can be found in the methodology chapter.

There are published studies in which the importance of the social, economic and environmental impacts of increased wood biomass demand in the southeastern United States were evaluated and assessments of meeting specific biomass utilization targets at the national level were made. However, the potential implications of wood biomass use across multiple levels of bioenergy demand and under multiple environmental and economic constraints in the

New England and New York region remain to be assessed. Given the variation observed in the literature surrounding prognoses of forest sector market and trade impacts that could result from increased wood energy consumption under the current and potential policy scenarios, the goal of this thesis is to assess the tradeoffs between the traditional timber markets and emerging wood bioenergy industry. The research question is addressed by evaluating wood biomass supply variation through time and the associated price, inventory, and removal responses.

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III. METHODOLOGY

III.1. Study area

This analysis is concentrated on the New England and New York region of the United

States and includes Connecticut, Maine, Massachusetts, New Hampshire, New York, Rhode

Island, and Vermont (Figure III.1).

Figure III.1. Study region: New England and New York

Source: ESRI 2011. ArcGIS Desktop: Release 10

This region was selected for various reasons. First, the region has large areas of

forestland that potentially could benefit from additional forest management practices associated

with sustainable wood biomass harvests (Smith et al., 2009). Second, it hosts a large number of

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wood‐consuming energy facilities (Figure III.2 and Appendix A), and wood biomass combustion was identified as a major potential source of renewable energy in the region

(Aguilar and Garrett, 2009). Third, this research adds to the literature assessing possible impacts of wood bioenergy demand on timber markets in the southeastern US (Abt et al., 2012, 2010a;

Susaeta et al., 2013) by extending the work to New England and New York region.

Figure III.2. Wood biomass energy facilities in New England and New York, existing and proposed

Source: (2010)

As mentioned above, the seven‐state region has sizeable forest resources. However, distribution of species and wood biomass is uneven within the region. Southern New England consists of large hardwood stands which are parcelized by rapidly developing urban . In contrast, a large portion of northern Maine is represented by an undeveloped

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softwood landscape. Overall, the New England and New York region features an average accessible forestland area of 69 percent of total land, ranging from a low 47 percent in Rhode

Island to a high 83.2 percent in Maine (USDA Forest Service, 2015). Based on 2013 FIA inventories, total accessible forestland area totals 51.09 million acres (excluding water areas) (see

Appendix C).

III.2. Sub‐Regional Timber Supply model

The Sub‐Regional Timber Supply model (SRTS) is a partial equilibrium market simulation model that can be used for analysis of various forest resource and timber supply scenarios under different demand projections (Abt et al., 2000). It uses biological growth, economic resource allocation and land change frameworks to forecast future timber prices, removals and inventories given exogenous assumptions about forest resource dynamics.

SRTS models both sides of the market (demand/supply) as functions of stumpage price and their respective shifters. The supply function is determined by the following equation

(Sendak et al., 2003):

Q V ∙P ∙I (1)

where quantity supplied Q for a harvest unit i in year t is a function of price P, beginning of period inventory I and other supply shifters V. β represents the supply‐price elasticity; γ represents the supply‐inventory elasticity.

The demand function is defined by:

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Q Z ∙P (2)

where quantity demanded Q in year t is a function of price P and other demand shifters Z.

The demand‐price elasticity is represented by α. SRTS model assumes that all elasticities are constant (Abt et al., 2009) and that market is initially in equilibrium:

Q ∑ Q (3)

The approach for reaching equilibrium is based on the work of Samuelson (1952), who suggested simultaneously solving a system of demand and supply equations. The estimated aggregate regional price levels for products are used to estimate harvest levels that need to be provided by aggregating responses across sub‐regions. The aggregate price level in year t for

∗ each product is thus found by using an algorithm that determines the market‐clearing price P , by summing the supply response across sub‐regions and owners:

∗ P f∑∑ Q) (4) where i represents industrial and non‐industrial landowners, j represents 24 FIA survey units in the New England and New York region, and f represents four products used in our study. The demand, supply and inventory equations defined through their respective elasticity values and past harvest stumpage price levels, are used to forecast the market‐clearing price and quantity levels for the New England and New York region. The estimated product prices and quantity levels are used to allocate the harvests in different sub‐regions and to project the next period‘s inventory values.

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The inventory accounting in the model uses ten‐year age classes and species/survey unit/owner/ forest type cells to account for inventory change and is presented in Equation 5:

∗ I ∑,,,, I∑,,,, Q∑,,,, G) (5)

∗ where I is inventory in time t and l represents species (hardwood (HW) and softwood (SW)), m

– forest types, n –age class of stands, Ik – represents initial inventory, Qk – harvests in the current year, and Gk – growth of forest inventory since preceding period. The definitions of i and j are the same as in Equation 4. The inventory growth in each of the two species groups (HW, SW) and management type combinations is estimated using growth per acre (GPA) regression

Equation 6 (McNulty et al., 2000). The data used in Equation 6 are obtained from FIA databases.

GPA fstate, owner, age class, age class owner interaction (6)

SRTS’s interface allows application of various modelling assumptions and comprehensive display of the results. Inputs include elasticities and aggregate harvest projection for the region, while output consists of price projections by product, harvest allocation among owner/region/type, and inventory trends by product/region/owner. The model runs on sub‐regions (24 survey units in the study region) and produces outputs aggregated to a state level. Figure III.3 illustrates the structure and data flows in the model.

The model accesses FIA data of inventory, growth and removals to project inventory changes. It also uses input from the user in the form of modeling scenarios (wood biomass consumption trends) and market conditions (demand, supply and inventory elasticity values).

Product supply is modeled as a function of product stumpage price and inventory. Given the

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demand by year and product, and supply from changing inventory, SRTS model estimates sawtimber and pulpwood demand allocations for softwoods and hardwoods. Their proportions, defined from market equilibrium conditions, are used to forecast the market‐ clearing price and quantity levels, which in turn are used to allocate sub regional harvest. The

Goal Program then categorizes the total wood requirement by management type and age class and makes allocations to sub regions, owners, and products (Abt et al., 2009).

*DBH – diameter at breast height

Figure III.3. SRTS model structure and data flows*

Source: Adapted from Abt et al. (2009)

The original SRTS model was adapted for a project for the North East State Foresters

Association (NEFA) (Sendak et al., 2003). The NEFA version of SRTS was modified to accommodate northeastern market module specifics such as partial harvests and included a

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limited number (six) of management types corresponding to FIA forest types or aggregations of

FIA forest types. A Northeastern variant of the SRTS (NE‐SRTS) was developed specifically for the current project. It maintained the partial harvest option of the NEFA model, but the management types were expanded to nine from six to correspond to the broader management types present in New England and New York region (Table III.1). In addition, the geographic scope was expanded to include Massachusetts, Connecticut and Rhode Island.

Table III.1. SRTS model management type aggregates for New England and New York

SRTS Management Forest‐type Groups USFS Type Code Type Number 1 White/Red/Jack Pine 100 2 Spruce‐fir 120 3* Other Softwoods 160, 170, 200 380 4 Oak‐Pine 400 5 Oak Hickory 500 6 Elm/Ash/Cottonwood 700 7 Maple/Beech/Birch 800 8 Aspen/Birch 900 9* Other Hardwoods 960, 600, 999 *These management types were eliminated from the model

III.3. Data source and modifications

SRTS uses the USDA Forest Service FIA datasets of inventory, growth, removals, and acreage by forest type, private ownership category, species group, and age class to model inventory change by product for multi‐county areas (survey units). These data are collected periodically and updated annually in all states and are available from the USDA Forest Service

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upon request or from the FIA (USDA Forest Service, 2015). For the purposes of this research, the 24 FIA survey units in the New England and New York region were selected as a geographic unit (Table III.2).

Forests in the Northeast are largely under private ownership, although the last decade shows significant changes in ownership class, and parcel fragmentation. In remote northern areas of New England and New York, large industrial ownerships have been sold to private groups such as Timber Investment and Management (TIMOs)

(Hagan et al., 2005). Therefore, it is crucial to differentiate private non‐corporate and TIMOs and similar ownership categories. Although TIMOs and similar organizations do not always use the corporate business organization form, these ownerships will be categorized as private corporations for this analysis.

Table III.2. FIA survey units and counties ‐New England and New York

FIA Number of Survey Number of State Code Units Counties Connecticut 9 1 8 Maine 23 9 16 Massachusetts 25 1 14 New Hampshire 33 2 10 New York 36 8 62 Rhode Island 44 1 5 Vermont 50 2 14

Due to USDA Forest Service disclosure protocols, the data details by ownership class that can be used in the NE‐SRTS model were constrained by the representation in the FIA plot data of an ownership sub‐class for a chosen geographic unit (survey unit). Therefore, within a survey unit and forest type, if there were fewer than three individual owners within an

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ownership class, the plots were excluded from the analysis. The 1.38 percent of plots with corporate ownership code excluded from the analysis (Table III.3) were in forest types that were of minor importance in the relevant survey units and were either uncommon types (e.g. oak‐hickory in eastern Maine), or were “other hardwoods” or “other softwoods”. These few exclusions allow us to maintain corporate and non‐corporate categories of private ownership for the entire study region in the SRTS model without breaking USDA Forest Service disclosure protocols.

Table III.3. State private ownership plot counts

Number of plots Percent of plots with in survey unit in a Total number of less than 3 corporate State forest type with plots in private owners of total less than 3 private owner class private plot count corporate owners Connecticut 0 264 0 Maine 43 3170 1.36 Massachusetts 4 385 1.04 New Hampshire 7 642 1.09 New York 48 2770 1.73 Rhode Island 0 92 0 Vermont 0 46 0 Total 102 7369 1.38

The SRTS’s accounting module tracks inventory by eleven ten‐year age classes (with aggregated upper end to include 100+ year old forests) for the seven forest management types

(Table III.1) and for three ownership classes (public, corporate and other private). After careful consideration “Other hardwoods” and “Other softwoods” management types were also excluded from the NE‐SRTS model. In many cases, these two management types were of marginal commercial value and generally represented minor fractions of the total forestland.

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Moreover, in the case of “Other softwoods”, this management type had inadequate area to stand alone in the projection model (Table III.4).

In the case of the “Other hardwood” in the “Oher hardwoods” management type, we had some concern about eliminating 620,100 acres (Table III.5). Allocating them to different hardwood types would be driven largely by subjective judgments and would greatly vary by survey unit. Lacking any consistent decision criteria, and for such a small (<1.5%) area, it was logical to exclude the “Other hardwoods” management type from the model. Overall, excluding

1.5 million acres of mostly non‐commercial wood types, scattered across 46.6 million acres, from the modeling projections is not expected to significantly influence modeling results.

Table III.4. Area of timberland by SRTS management type and ownership class

All Ownership class National Forest Other Private‐ Corporate Management type public individual Thousand Thousand acres Thousand Thousand Thousand acres acres acres acres White/Red/Jack pine 3471.67 31.63 456.68 2405.43 577.92 Spruce‐fir 6740.64 148.72 403.74 1838.49 4349.68 Other softwoods 464.08 – 131.71 286.14 46.23 Oak‐pine 1804.03 5.45 312.83 1269.56 216.19 Oak Hickory 6477.64 26.95 1077.21 4446.66 926.82 Elm/ash/cottonwood 2105.82 1.64 302.64 1535.49 266.05 Maple/beech/birch 21515.23 693.08 1815.58 12004.58 7001.99 Aspen/birch 3039.68 87.89 165.58 1435.12 1351.09 Other hardwoods 1000.63 3.52 153.16 665.37 178.58 All 46619.42 998.9 4819.13 25886.84 14914.56

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Table III.5. Detailed area of timberland by SRTS management type and ownership class

All Ownership class National Other Private‐ Corporate Management Detailed Forest type Forest public individual type Thousand Thousand Thousand Thousand Thousand acres acres acres acres acres Other Pitch pine 121.23 . 68.55 41.99 10.69 softwoods Eastern redcedar 63.12 . 3.13 45.94 14.05 Douglas‐fir 2.73 . 1.72 1.01 . Scotch pine 120.33 . 9.03 97.5 13.8 Norway spruce 132.25 . 42.72 86.78 2.75 Introduced larch 24.42 . 6.56 12.91 4.95 All 464.08 . 131.71 286.14 46.23 Other Sweetbay / swamp 102.34 . 38.22 55.09 9.03 hardwoods tupelo / red maple Other hardwood 620.1 3.52 66.13 421.45 129.01 Other exotic 25.96 . 5.92 13.77 6.26 hardwoods Nonstocked 252.23 . 42.89 175.07 34.27 All 1000.63 3.52 153.16 665.37 178.58

Finally, in our analysis, we evaluate four forest product types: softwood pulpwood (SW

P), softwood sawtimber (SW S), hardwood pulpwood (HW P) and hardwood sawtimber (HW

S). SRTS models growth and harvest regimes in response to demand for pulpwood and sawtimber, where the term “pulpwood” is used to describe the growing stock volume of softwood trees of less than 9 inches DBH or hardwood trees of less than 11 inches DBH.

Hardwood and softwood volumes of larger diameters were considered sawtimber. In other words, the product type is a function of species group and diameter; wood quality is not a classification factor. Summary of input parameters used in NE‐SRTS model can be found in

Appendix D.

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III.4. Modeling scenarios

The supply and demand price elasticities can vary by product, as can the inventory supply elasticity. The consensus regarding these elasticities is that they are inelastic (Liao and

Zhang, 2008; Pattanayak et al., 2002), implying that the percentage response in harvest or consumption is smaller than the percentage change in price. Adams and Haynes (1996) defined elasticities at a broad regional level and estimated the inventory supply elasticity to be unitary

(1.0) for the northeastern region. Sendak et al. (2003) also assumed inventory supply elasticity to be unitary (1.0) and demand price elasticity to be 0.5. Supply price elasticities for softwood sawtimber and non‐sawtimber were estimated to be 0.5 and hardwood sawtimber 0.45, non‐ sawtimber 0.7 (Abt, 2015).

Throughout the analysis, the values of demand and supply price elasticities remain constant over the projection period. Our initial results employ values currently available to us in the literature. To understand the sensitivity of our results to potential different elasticity values, we compare results obtained using the original and modified elasticities. Table III.6 summarizes the studied elasticity values.

Table III.6. Studied supply and demand price elasticities

Case/ Demand Supply Product HW P HW S SW P SW S HW P HW S SW P SW S Initial Results 0.50 0.50 0.50 0.50 0.70 0.45 0.50 0.50 Case 1 0.45 0.45 0.45 0.45 0.75 0.50 0.55 0.55 Case 2 0.55 0.55 0.55 0.55 0.65 0.4 0.45 0.45

In our sensitivity analysis we investigate two cases: Case 1 and Case 2. Compared to the

Initial Results (see Table III.6), Case 1 corresponds to uniform decrease in demand elasticity

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values for all products by 0.05 and simultaneous uniform increase of all supply elasticity values by 0.05. For the wood market this means conditions favorable to more wood use – higher supply elasticity means that more wood will come more readily to the market with price increase, while lower demand elasticity means that with price increase quantity demanded will not decrease as much compared to the Initial Results. Lower demand elasticity may be explained by unavailability of substitute products. Case 2 is the opposite of Case 1 – it represents less favorable conditions for the wood market. Compared to the Initial Results, in

Case 2 demand is more responsive while supply is less responsive. It means that with price increase quantity supplied will not increase as much, while quantity demanded will decrease more compared to Initial Results.

The sub‐regional supply is equilibrated with a regional demand curve. In each year, supply is shifted by biological accounting module, and product demand is shifted based on the user‐defined scenario and must be allocated among product groups. Species group preference has been established for regional wood bioenergy production. We assumed that up to 100 percent of hardwood and softwood pulpwood is available for use as wood biomass. Based on the average total merchantable height of sawtimber trees removed (4 logs), average number of merchantable sawlogs in such trees (2 logs), and linear interpolation of the data in Table 1 from

Mesavage and Girard, 1946 we determined that average sawlog volume in each harvested sawtimber tree accounted for 63 percent of the total tree volume. The remaining 37 percent was pulpwood. Therefore, based loosely on the above‐mentioned calculations, we assume that roughly 40 percent of HW and SW volume of sawtimber‐sized trees can be used for bioenergy

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consumption. The maximum percentage of logging residues that can be utilized to offset wood biomass demand are set to 70 percent.

III.4.1. Traditional forest products demand

To determine the effects of increased wood biomass energy demand on timber markets in New England and New York, a reference case in terms of constant demand for wood bioenergy has been assumed. In the reference case, the initial demand for standing timber is based on the recent FIA annual survey data on growing stock removals in New England and

New York region. The future demand is expected to slowly grow to reflect changing prices and is determined based on the market equilibrium calculations by product, subregion and owner.

The growth is estimated to be average of 0.47 percent yearly for HW pulpwood, 0.25 percent for

SW pulpwood, 0.63 percent for HW sawtimber and 0.48 percent for SW sawtimber. The demand generated by traditional wood industries (e.g. paper and ) is the same in all considered scenarios. Bioenergy demand is estimated to be 8.25 million green ton of wood and is assumed to stay constant over the projection period of 50 years.

Comparing the NE‐SRTS projections with the base harvest for timber industry defines the additional harvesting associated with the increased wood bioenergy consumption (new removals). Comparing new removals with projecting scenarios’ requirements provides an estimate of the wood that could be diverted from timber industry for wood biomass energy production.

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For example, a lower level of timber demand from pulp and paper mills and sawmills would lead to reduction in harvest levels and logging residues availability. If only residues are allowed to qualify as a renewable source, then wood bioenergy is tied to the future of the traditional timber markets. If roundwood was considered as a viable source for bioenergy, then timber markets would face some impacts. A lower level of traditional harvest could lead to fewer available residues, higher inventory levels and lower roundwood prices, which would increase roundwood utilization for bioenergy purposes.

III.4.2. Wood bioenergy consumption scenarios

Demand for wood biomass for bioenergy depends on (1) policies such as state RPS and renewable fuel standards, (2) economic and physical availability of alternative sources of renewable energy such as wind, solar and hydropower, (3) economic and physical availability of alternative sources of biomass, such as energy crops, crop residues and municipal waste, and

(4) the alternative value of wood biomass for carbon storage or use in traditional industries. In the scenarios below, we do not address the impacts of fuel standards, we take as given existing assumptions by EIA (U.S. Energy Information, 2015) and Creech et al. (2009) on the physical and economic availability of all other sources of renewable energy, and we do not address the value of timber for carbon storage. Our attention is on policies affecting wood biomass energy use and the potential impacts of wood bioenergy consumption on traditional timber markets in

New England and New York.

44

We assume wood bioenergy scenarios based on wood consumption data from existing and proposed biomass facilities within the New England and New York region (Wilderness

Society, 2010, see Appendix A, Appendix B), RPS legislatures, EIA regional estimates (U.S.

Energy Information, 2015), and projected future wood biomass consumption under favorable conditions given by the Forisk resource assessment (Mendell et al., 2010). This study uses six scenarios to project changes in inventory, growing stock removals, estimates of stumpage price change, and to test sensitivity to the parameters changes over 50 years. These six scenarios will be compared to the reference case as well as with each other.

The consumption scenarios are categorized as follows (Table III.7): A1 – steady growth wood bioenergy consumption; A2 – rapid development wood bioenergy consumption; and A3

– wood bioenergy consumption with an increased share of hydropower energy. All three categories have no wood biomass supply restrictions. B1, B2 and B3 scenarios fall under the same consumption categories only with supply restrictions simulating Massachusetts‐type RPS regulations (Massachusetts DOER, 2012) applied to the entire study region

Table III.7. Six modeling scenarios for the SRTS

Case No supply restriction Massachusetts‐like supply restrictions

Steady growth wood bioenergy A1 B1 consumption

Rapid development wood A2 B2 bioenergy consumption

Wood bioenergy plus Hydropower A3 B3

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Although the study describes consumption scenarios, it is important to understand that they are not demand projections, as the model does not specify price‐responsive demand relationships for wood biomass and traditional forest products. The steady growth‐ consumption scenarios (A1, B1) assume that in the first year 8.25 million green tons of biomass will be consumed and that consumption will grow 1.9 percent yearly. These estimates are based on existing biomass facilities’ wood consumption (Appendix A) and Energy Information

Administration (U.S. Energy Information, 2015) reference case projections. The rapid development‐consumption scenarios (A2, B2) assume that within next 5 years with significant growth of wood bioenergy based on policy instruments (e.g. RPS mandates) all of the proposed biomass facilities (Appendix B) will be built the future wood biomass consumption will reach

15.85 million green tons (18 percent yearly growth rate) and stay constant for the next 45 years.

Hydropower is an eligible technology in most of the states’ RPS, but there are generally restrictions on which hydro projects can be included, because of the technology’s maturity, established financial constraints, and environmental concerns. Several states, including

Connecticut and Maine are currently considering revisions to their RPS that would change the way hydropower is treated in meeting renewable energy targets. In addition, Connecticut,

Maine, Massachusetts, New Hampshire and New York are considering strengthening their environmental qualifications for hydropower and allowing purchases of hydropower from large utilities in Canada to qualify for RECs. Such legislative measures combined with a large number of hydropower energy facilities present within the region (Figure III.4) provide the basis for scenarios A3 and B3, in which hydropower is assumed to displace half of the biomass

46

used to generate electric power under the steady growth‐consumption scenarios (4.125 million green tons). We do not model hydropower displacement of the high wood energy demand because wood energy would be too expensive relative to hydropower, making it unlikely that a high wood energy demand would ever be achieved.

Figure III.4. New England and New York hydropower electric facilities Source: Energy Information Administration (2015)

Scenarios A1, A2 and A3 have no policy restrictions on wood biomass supply, while B1,

B2 and B3 impose certain restrictions on the amount of logging residues that can be recovered.

In all B‐scenarios, we assume that New England and New York states will follow Massachusetts

DOER’s lead (Massachusetts DOER, 2012) and implement similar logging residues eligibility restrictions where the amount of recoverable residues is specified as percentages of total timber harvests by weight: (a) zero percent in forests with poor soils; (b) twenty five to thirty percent in

47

forests with good soils. Furthermore, these restrictions imply leaving downed, cavity and den trees behind, and prohibit harvesting on steep slopes. While such residue recovery restrictions have an ecological basis, given high variability of soils in the study region it would be operationally very difficult and arguably hard to enforce them on the ground. In the extreme case, the net effect of such regulations may prevent the use of logging residues for generating electricity and the associated RECs. Thus, in B1, B2 and B3 scenarios we set the amount of recoverable logging residues to zero. These restrictions will increase demand of the biomass energy market for pulpwood and thus force wood‐energy products to compete with other wood consuming industries.

III.4.3. Criteria for Evaluation

To compare different scenarios we propose to look at overall trends and rates of change of price, inventory and removals averaged over the planning horizon of 50 years and all regions for the following products: (1) hardwood pulpwood, (2) hardwood sawtimber, (3) softwood

pulpwood, (4) softwood sawtimber. We introduce %δ as the mean rate of change of price in percent per year for a given product as:

%δ ∙ 100% (7)

where 50 is the number of years in our projection timeline, P is the price in the final

projected year, and P is the initial price. Mean rate of change of inventory %δ is introduced in a similar way as:

48

%δ ∙ 100% (8)

where I is inventory in the final projected year, and I is initial inventory. Finally, mean

rate of change of removals %δ is

%δ ∙ 100% (9)

where Q is removals in the final projected year, and Q is initial removals.

Note, that the quantities %δ, %δ and %δ are calculated based on the initial and final values of price, inventory and removal values respectively as opposed to fitting a linear regression model to all available data points. We believe it to be a reasonable approach due to monotonic nature of all but two points showing quantity (price, inventory and removals) change over time, see Section IV.

From the perspective of the traditional forest products sector, one desirable outcome for the New England and New York region would be minimal change in timber prices with increases of wood biomass energy use. In other words, based on the metrics above, small or no

changes in %δ when comparing the reference case with different biomass consumption scenarios would imply minimal disruption to traditional wood products markets.

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IV. RESULTS

IV.1. Section overview

This section is organized as follows. Subsection IV.2 presents the projections obtained

using NE‐SRTS software for price, inventory and removals quantities for different scenarios

(Table III.7). The results of these projections are summarized in Subsection IV.3. Finally, the

analysis of projections’ sensitivity to small changes in demand price and supply price elasticity

values is presented in Subsection IV.4.

IV.2. Projection results for different scenarios

The results are presented as plots showing percentages of price, inventory and removals

over 50 years relative to the initial year (2015) values set at 100 percent. These projections

illustrate NE‐SRTS output based on our defined consumption scenarios.

IV.2.1. Reference case scenario

In the reference case, demand for forest products is assumed to slowly grow to reflect

changing prices and the current levels of wood biomass energy utilization are assumed to stay

constant over the 50 years. The results of the NE‐ SRTS model run for four products in this

scenario are shown in Figure IV.1. The results are consistent with the southern forest product

demand analysis presented in Wear et al. (2011), i.e. removal levels increase slowly at a constant

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rate, which leads to increase in harvest levels by a small amount, inventory levels also increase, and timber prices decline over time.

200 200 Inventory Removals Price Inventory Removals Price 180 180

160 160

140 140

120 120

Percent 100 Percent 100

80 80

60 60 HW Pulpwood HW Sawtimber 40 40 2015 2025 2035 2045 2055 2065 2015 2025 2035 2045 2055 2065 Year Year (a) (b)

200 200 Inventory Removals Price Inventory Removals Price 180 180

160 160

140 140

120 120

Percent 100 Percent 100

80 80

60 60 SW Pulpwood SW Sawtimber 40 40 2015 2025 2035 2045 2055 2065 2015 2025 2035 2045 2055 2065 Year Year (c) (d)

Figure IV.1. Projections of inventory, removals and price change for (a) hardwood pulpwood, (b) hardwood sawtimber (c) softwood pulpwood, (d) softwood sawtimber market in New England and New York region under slow growth forest industry and constant biomass consumption scenario (reference scenario)

IV.2.2. Scenario A1

In scenario A1 we assume constant growth of wood bioenergy consumption of 1.9% per year. The projection results for scenario A1 are shown in Figure IV.2. Hardwood (HW) pulpwood inventory, removals and price grow steadily over the projection period (Figure IV.2

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(a)). Softwood (SW) pulpwood removals grow uniformly at a high rate over the projection period, while inventory seems to grow at the same rate as removals at first and slow down considerably after year 2030 (Figure IV.2 (c)). This causes a gradual increase in price after 2030.

The trends of price, inventory and removals of HW and SW sawtimber are virtually the same as in reference scenario, see Figure IV.2(b),(d).

200 200 Inventory Removals Price Inventory Removals Price 180 180

160 160

140 140

120 120

Percent 100 Percent 100

80 80

60 60 HW Pulpwood HW Sawtimber 40 40 2015 2025 2035 2045 2055 2065 2015 2025 2035 2045 2055 2065 Year Year (a) (b)

200 200 Inventory Removals Price Inventory Removals Price 180 180

160 160

140 140

120 120

Percent 100 Percent 100

80 80

60 60 SW Pulpwood SW Sawtimber 40 40 2015 2025 2035 2045 2055 2065 2015 2025 2035 2045 2055 2065 Year Year (c) (d)

Figure IV.2. Projections of inventory, removals and price change for (a) hardwood pulpwood, (b) hardwood sawtimber, (c) softwood pulpwood, (d) softwood sawtimber market in New England and New York region under steady growth bioenergy consumption (scenario A1)

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IV.2.3. Scenario A2

In scenario A2 we assume rapid growth of wood bioenergy consumption of 18% over 5 years and hold that level constant over the next 45 years. The projection results for scenario A2 are shown in Figure IV.3. We observe sharp short‐term increase in removals as well as price of

HW and SW pulpwood over the first 5 years corresponding to rapid increase in wood bioenergy consumption (Figure IV.3 (a), (c)). After these 5 years, removals of both products grow at a much slower rate while the prices start decreasing because increases in inventory eventually overwhelm the effects of high consumption levels of biomass.

The trends of price, inventory and removals of HW and SW sawtimber are virtually identical to the reference scenario (Figure IV.3 (b), (d)).

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200 200 Inventory Removals Price Inventory Removals Price 180 180

160 160

140 140

120 120

Percent 100 Percent 100

80 80

60 60 HW Pulpwood HW Sawtimber 40 40 2015 2025 2035 2045 2055 2065 2015 2025 2035 2045 2055 2065 Year Year (a) (b)

200 200 Inventory Removals Price Inventory Removals Price 180 180

160 160

140 140

120 120

Percent 100 Percent 100

80 80

60 60 SW Pulpwood SW Sawtimber 40 40 2015 2025 2035 2045 2055 2065 2015 2025 2035 2045 2055 2065 Year Year (c) (d)

Figure IV.3. Projections of inventory, removals and price change for (a) hardwood pulpwood, (b) hardwood sawtimber, (c) softwood pulpwood, (d) softwood sawtimber market in New England and New York region under rapid development bioenergy consumption (scenario A2)

IV.2.4. Scenario A3

In this scenario, we assumed that hydropower displaces half of the woody biomass electric power initially generated under A1 scenario, so that we start at half of A1 wood bioenergy consumption level. Steady growth of 1.9% per year is assumed over the next 50 years.

The projection results for scenario A3 are shown in Figure IV.4. HW pulpwood inventory and removals increase smoothly over the projection period although inventory gains outpace

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removals; price remains at a relatively constant level (Figure IV.4(a)). SW pulpwood inventory and removals grow at an approximately the same rate until year 2045. After the year, the inventory rate change levels off, while removals continue to grow at the original rate. This results in a fairly constant price over the period 2015‐2045 and price increases after 2045. The trends of price, inventory and removals of HW and SW sawtimber are virtually the same as in reference scenario (Figure IV.4(b),(d)).

200 200 Inventory Removals Price Inventory Removals Price 180 180

160 160

140 140

120 120 Percent Percent 100 100

80 80

60 60 HW Pulpwood HW Sawtimber 40 40 2015 2025 2035 2045 2055 2065 2015 2025 2035 2045 2055 2065 Year Year (a) (b)

200 200 Inventory Removals Price Inventory Removals Price 180 180

160 160

140 140

120 120 Percent 100 Percent 100

80 80

60 60 SW Pulpwood SW Sawtimber 40 40 2015 2025 2035 2045 2055 2065 2015 2025 2035 2045 2055 2065 Year Year (c) (d)

Figure IV.4. Projections of inventory, removals and price change for (a) hardwood pulpwood, (b) hardwood sawtimber, (c) softwood pulpwood, (d) softwood sawtimber market in New England and New York region underwood bioenergy and hydropower consumption (scenario A3)

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IV.2.5. Scenario B1

In addition to conditions described Subsection IV.2.2 for A1 (1.9% annual growth in energy related wood biomass consumption), in scenario B1 we also assume zero amount of recoverable logging residues for biomass energy production. The projection results for scenario

B1 are shown in Figure IV.5. We observe steady growth of inventory, removals and price of

HW pulpwood over the entire projection period (Figure IV.5(a)). Similar to the results of scenario A1, SW pulpwood removals grow uniformly at a high rate over the projection period, while inventory seems to grow at the same rate as removals at first and slow down after year

2045 (Figure IV.5(c)). This causes an increase in price change rates after 2045. While the trends of HW and SW pulpwood in B1 resemble those in A1, the mean rates of change of price, inventory and removals are somewhat different. When comparing B1 to A1 in the case of HW

pulpwood, %δ is higher (0.52 vs 0.39), %δ is the same (1.24) and %δ is higher (1.18 vs 1.10,

which explains the greater increase in price). In the case of SW pulpwood, %δ is lower (0.28 vs

0.72), %δ is a little higher (0.67 vs 0.60) and %δ is lower (0.86 vs 1.13). The trends of price, inventory and removals of HW and SW sawtimber are almost the same as in reference scenario

(Figure IV.5(b),(d)).

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200 200 Inventory Removals Price Inventory Removals Price 180 180

160 160

140 140

120 120

Percent 100 Percent 100

80 80

60 60 HW Pulpwood HW Sawtimber 40 40 2015 2025 2035 2045 2055 2065 2015 2025 2035 2045 2055 2065 Year Year (a) (b)

200 200 Inventory Removals Price Inventory Removals Price 180 180

160 160

140 140

120 120

Percent 100 Percent 100

80 80

60 60 SW Pulpwood SW Sawtimber 40 40 2015 2025 2035 2045 2055 2065 2015 2025 2035 2045 2055 2065 Year Year (c) (d)

Figure IV.5. Projections of inventory, removals and price change for (a) hardwood pulpwood, (b) hardwood sawtimber, (c) softwood pulpwood, (d) softwood sawtimber market in New England and New York region under steady growth bioenergy consumption and supply restrictions (scenario B1)

IV.2.6. Scenario B2

In scenario B2 we use the conditions of scenario A2 and the additional restriction on zero amount of recoverable logging residues. The projection results for scenario B2 are shown in

Figure IV.6. We observe sharp increases in removals as well as price of HW and SW pulpwood over the first 5 years corresponding to rapid increase in wood bioenergy consumption (Figure

IV.6(a),(c)). After these 5 years, removals of both products grow at a much slower rate while the

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prices start decreasing. Overall the plots for HW and SW pulpwood in this scenario are similar to the corresponding plots obtained for scenario A2 (Figure IV.3(a),(c)). When comparing the

mean rates of change of B2 to A2 in the case of HW pulpwood, %δ is a little higher (0.16 vs

0.07), %δ is the same (1.22) and %δ is slightly higher (0.92 vs 0.87, which explains the greater

increase in price). In the case of SW pulpwood, %δ is lower (0.02 vs 0.30), %δ is a little higher

(0.64 vs 0.56) and %δ is lower (0.60 vs 0.75). The trends of price, inventory and removals of

HW and SW sawtimber are almost the same as in reference scenario, see Figure IV.6(b),(d).

200 200 Inventory Removals Price Inventory Removals Price 180 180

160 160

140 140

120 120

Percent 100 Percent 100

80 80

60 60 HW Pulpwood HW Sawtimber 40 40 2015 2025 2035 2045 2055 2065 2015 2025 2035 2045 2055 2065 Year Year (a) (b)

200 200 Inventory Removals Price Inventory Removals Price 180 180

160 160

140 140

120 120

Percent 100 Percent 100

80 80

60 60 SW Pulpwood SW Sawtimber 40 40 2015 2025 2035 2045 2055 2065 2015 2025 2035 2045 2055 2065 Year Year (c) (d)

Figure IV.6. Projections of inventory, removals and price change for (a) hardwood pulpwood, (b) hardwood sawtimber, (c) softwood pulpwood, (d) softwood sawtimber market in New England and New York region under rapid development bioenergy consumption and supply restrictions (scenario B2)

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IV.2.7. Scenario B3

The only difference of scenario B3 compared to A3 is the additional condition of zero amount of recoverable logging residues. The projection results for scenario B3 are shown in

Figure IV.7. HW pulpwood inventory and removals grow at a constant rate over the projection period, while the price appears to remain at the initial level until the year 2045 and then grow slightly over the remaining 20 years (Figure IV.7(a)). Rates of change of SW pulpwood inventory and removals appear to be similar over the projected 50 years, while the price seems to drop to a level of about 95% over the first 25 years and then increase very slowly until 2065 up to the level of about 98%. When comparing the mean rates of change of B3 to A3 in the case

of HW pulpwood, %δ is a little higher (0.13 vs 0.02), %δ is the same (1.26) and %δ is slightly higher (0.95 vs 0.87, which explains the greater increase in price). In the case of SW pulpwood,

%δ is lower (‐0.07 vs 0.30), %δ is a little higher (0.72 vs 0.67) and %δ is lower (0.61 vs 0.88).

The trends of price, inventory and removals of HW and SW sawtimber are virtually the same as reference scenario, see Figure IV.7(b),(d).

59

200 200 Inventory Removals Price Inventory Removals Price 180 180

160 160

140 140

120 120

Percent 100 Percent 100

80 80

60 60 HW Pulpwood HW Sawtimber 40 40 2015 2025 2035 2045 2055 2065 2015 2025 2035 2045 2055 2065 Year Year (a) (b)

200 200 Inventory Removals Price Inventory Removals Price 180 180

160 160

140 140

120 120 Percent 100 Percent 100

80 80

60 60 SW Pulpwood SW Sawtimber 40 40 2015 2025 2035 2045 2055 2065 2015 2025 2035 2045 2055 2065 Year Year (c) (d)

Figure IV.7. Projections of inventory, removals and price change for (a) hardwood pulpwood, (b) hardwood sawtimber, (c) softwood pulpwood, (d) softwood sawtimber market in New England and New York region under wood bioenergy and hydropower consumption and supply restrictions (scenario B3)

IV.3. Summary of projection results

The results of the projections given in Subsection IV.2 are summarized in Table IV.1 in

the form of quantities %δ, %δ and %δ (mean rate of change in price, inventory and removals respectively; introduced in Section III.4.3) for each product and scenario. From the analysis of

Table IV.1 it is evident that introduction of increased wood biomass energy consumption (i.e. scenarios A1‐3 and B1‐3) does not significantly affect projections for two products, HW and SW

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sawtimber as expected (see discussion in Section II.4). The primary use of these two products is not related to wood biomass energy due to much higher value of sawtimber compared to pulpwood. However, the small variations in mean rates of change in price and inventory between different scenarios can be explained by use of a small portion of low‐grade sawtimber in cases when removals of pulpwood are not sufficient to satisfy consumption for bioenergy.

At the same time, in all increased wood bioenergy consumption scenarios (A1‐3 and B1‐

3), mean rates of change of price and removals for HW and SW pulpwood are considerably greater than in the reference scenario. In reference scenario, the price change for HW and SW pulpwood is projected to decrease monotonically over the entire projection period (Figure

IV.1(a),(c)), while in all other scenarios the final prices are greater than initial.

When comparing the results for HW pulpwood corresponding to scenarios A1‐A3 in

Table IV.1, it is evident that the highest rates of price increase and removals are observed in scenario A1. This can be explained by the largest increase in wood biomass consumption over the projection period (increase of 150% of the initial level over 50 years) compared to A2

(increase of 18% of the initial A1 level over the first 5 years) and A3 (increase of approximately

28% of the initial A1 level over 50 years). B1 is the “worst case” scenario among B1‐B3 and rates of price increase and removals are greater than in A1. This is explained by a restriction on the use of logging residues in B1, which means that the removals in B1 will have to be increased at higher rate than inventory level leading to additional price increase compared to A1. Scenarios

A2 and A3 result in almost the same predictions for %δ, %δ and %δ. Similarly, projections for scenarios B2 and B3 are very close to each other.

61

Similar to HW, in the case of SW pulpwood, the highest rate of change of price and removals is observed in scenario A1 among A1‐A3 and B1 among B1‐B3.

Note, however, that the mean rate of change metrics (%δ, %δ and %δ), calculated from end point values, may not be the best choice for description of plots corresponding to HW and SW pulpwood scenarios A2 and B2 due to their non‐monotonic nature, see Figure

IV.3(a),(c) and Figure IV.6(a),(c). Price and removals curves in all four plots are characterized by rapid increases over the first 5 years followed by gradual price decrease and leveling off of removals over the remaining 45 years.

Table IV.1 Summary of the projection results

HW pulpwood HW sawtimber SW pulpwood SW sawtimber Scenario/Mean %δ %δ %δ %δ %δ %δ %δ %δ %δ %δ %δ %δ rate of change, % Reference ‐0.50 1.30 0.53 ‐0.68 1.80 0.74 ‐0.52 0.81 0.26 ‐0.91 1.21 0.55

A1 0.39 1.24 1.10 ‐0.67 1.78 0.74 0.72 0.60 1.13 ‐0.83 1.11 0.53

A2 0.07 1.22 0.87 ‐0.65 1.76 0.74 0.30 0.56 0.75 ‐0.80 1.09 0.54

A3 0.02 1.26 0.87 ‐0.67 1.79 0.74 0.30 0.67 0.88 ‐0.85 1.15 0.55

B1 0.52 1.24 1.18 ‐0.66 1.77 0.74 0.28 0.67 0.86 ‐0.86 1.15 0.55

B2 0.16 1.22 0.92 ‐0.65 1.76 0.73 0.02 0.64 0.60 ‐0.84 1.14 0.55

B3 0.13 1.26 0.95 ‐0.67 1.78 0.74 ‐0.07 0.72 0.61 ‐0.88 1.18 0.55

IV.4. Elasticity sensitivity analysis

The results of the elasticity sensitivity analysis are shown in Table IV.2 (Case 1) and

Table IV.3 (Case 2). Comparing the projections for Case 1 to the Initial Results (Table IV.1) we see that quantity demanded for all four products is less responsive and quantity supplied is

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more responsive to price changes, which correlates with the changes in demand price and supply price elasticity changes for Case 1 (see Table III.6). For example, in A1 scenario for HW pulpwood, the mean rate of price change value of 0.31 corresponds to mean rate of removals

(quantity demanded) change of 1.09 in Case 1 compared to 0.39 and 1.10 in Initial Results respectively. We can conclude that quantity demanded is less responsive because when recalculated, same price increase results in higher removals (quantity demanded) in Case 1 compared to Initial Results. At the same time, the aforementioned mean rates of price change result in 1.25 and 1.24 rates of inventory (quantity supplied) change in Case 1 and Initial Results respectively. When recalculated, the same price change results in higher inventory in Case 1 compared to Initial Results.

Table IV.2 Summary of the projection results for elasticity Case 1 (see Table III.6) HW pulpwood HW sawtimber SW pulpwood SW sawtimber Scenario/Mean %δ %δ %δ %δ %δ %δ %δ %δ %δ %δ %δ %δ rate of change, % Reference ‐0.52 1.32 0.48 ‐0.72 1.82 0.67 ‐0.52 0.82 0.24 ‐0.91 1.22 0.49

A1 0.31 1.25 1.09 ‐0.70 1.79 0.66 0.73 0.59 1.17 ‐0.82 1.11 0.47

A2 0.02 1.24 0.85 ‐0.69 1.78 0.66 0.29 0.56 0.76 ‐0.79 1.09 0.48

A3 ‐0.05 1.28 0.84 ‐0.71 1.80 0.67 0.31 0.67 0.90 ‐0.86 1.16 0.49

B1 0.41 1.25 1.16 ‐0.70 1.79 0.66 0.28 0.67 0.88 ‐0.86 1.16 0.48

B2 0.10 1.23 0.91 ‐0.69 1.77 0.66 0.01 0.63 0.59 ‐0.83 1.14 0.50

B3 0.06 1.27 0.92 ‐0.71 1.80 0.66 ‐0.08 0.73 0.61 ‐0.89 1.20 0.48

Comparing the projections for Case 2 to the Initial Results (Table IV.1) we see that the situation is reversed compared to Case 1. Quantity demanded for all four products is more responsive and quantity supplied is less responsive to price changes. For example, in A1

63

scenario for HW pulpwood, the mean rate of price change value of 0.49 corresponds to mean rate of removals (quantity demanded) change of 1.11 in Case 2 compared to 0.39 and 1.10 in

Initial Results, respectively. We can conclude that quantity demanded is more responsive because when recalculated, same price increase results in lower removals (quantity demanded) in Case 2 compared to Initial Results. At the same time, the aforementioned mean rates of price change result in 1.23 and 1.24 rates of inventory (quantity supplied) change in Case 2 and Initial

Results respectively. When recalculated, the same price change results in lower inventory in

Case 2 compared to Initial Results.

Table IV.3 Summary of the projection results for elasticity Case 2 (see Table III.6) HW pulpwood HW sawtimber SW pulpwood SW sawtimber Scenario/Mean %δ %δ %δ %δ %δ %δ %δ %δ %δ %δ %δ %δ rate of change, % Reference ‐0.40 1.28 0.62 ‐0.63 1.78 0.83 ‐0.52 0.80 0.29 ‐0.90 1.19 0.61

A1 0.49 1.23 1.11 ‐0.61 1.76 0.83 0.74 0.60 1.10 ‐0.83 1.11 0.59

A2 0.17 1.21 0.90 ‐0.60 1.75 0.82 0.29 0.57 0.74 ‐0.81 1.09 0.59

A3 0.10 1.25 0.90 ‐0.62 1.77 0.83 0.31 0.66 0.86 ‐0.85 1.14 0.62

B1 0.63 1.23 1.18 ‐0.60 1.75 0.82 0.29 0.66 0.84 ‐0.85 1.14 0.61

B2 0.28 1.21 0.96 ‐0.59 1.74 0.82 0.001 0.64 0.59 ‐0.83 1.12 0.61

B3 0.24 1.24 0.98 ‐0.61 1.76 0.83 ‐0.06 0.71 0.62 ‐0.86 1.16 0.62

Two conclusions can be drawn from these results:

1) As was hypothesized before, Case 1 results in more favorable conditions for the wood

market (higher supply elasticity means that more wood will come more readily to the

market with price increase, while lower demand elasticity means that with price increase

quantity demanded will not decrease as much compared to the Initial Results); Case 2

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results in less favorable conditions (quantity demanded is more responsive while

quantity supplied is less responsive, which means that with price increase quantity

supplied will not increase as much, while quantity demanded will decrease more

compared to Initial Results);

2) Overall, removals (quantity demanded) seems to be more sensitive to elasticity value

changes than inventory (quantity supplied). When averaged over all scenarios, Case 1

resulted in mean changes (compared to Initial Results) in inventory of 0.014, 0.016, 0.000

and 0.007 corresponding to HW pulpwood, HW sawtimber, SW pulpwood and SW

sawtimber respectively. Case 2 resulted in mean changes in inventory of ‐0.027, ‐0.034, ‐

0.004 and ‐0.019. At the same time, the average changes in removals in Case 1 were ‐

0.024, ‐0.076, 0.009, ‐0.061 and in Case 2: 0.057, 0.163, ‐0.016 and 0.123.

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V. DISCUSSION AND CONCLUSIONS

This study explores the potential effects of wood biomass use for energy on the wood

markets of New England and New York. In particular, we used a modified version of the Sub‐

Regional Timber Supply model (SRTS), NE‐SRTS, to predict changes in timber inventory,

removals and prices over 50‐year period in response to timber use for wood biomass energy. In

our modeling, we focused on four timber products (SW and HW sawtimber, and SW and HW

pulpwood) and seven scenarios of wood biomass use (see Table III.7 for details).

Our modeling shows that in all scenarios involving increased consumption of wood

biomass for energy (A1‐A3, B1‐B3), the prices as well as removals increase dramatically for SW

and HW pulpwood compared to the reference case (wood biomass use for energy remains at

the current level). Scenario A1 (corresponds to first year wood biomass consumption of 8.25

million green tons and steady growth of 1.9% over the remaining 49 years) results in the

greatest increase in price and removals of HW pulpwood compared to other scenarios.

Meanwhile, scenario B1 (same as A1 with policy‐driven limitations on the use of logging

residues) leads to the greatest increase in price and removals of SW pulpwood. Our results

show that for scenarios A1‐A3 and B1‐B3 wood biomass consumption for energy had no

significant effect on SW and HW sawtimber inventory, removals or prices. These results are

likely due to the high cost of sawtimber stumpage which may make sawtimber prohibitively

expensive to use for wood biomass energy production.

Wood bioenergy has a great potential as a renewable energy source, however more

policy support is still needed. As seen from the results of scenario B1, restrictions on use of

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logging residues for wood bioenergy production clearly put additional pressure on the pulpwood market. If the majority of the states adopted logging residues restrictions, the competition of traditional and bioenergy industries for pulpwood would drive the prices up, which in the long term could lead to wood being an infeasible source for energy production.

The restrictions could also put greater constraints on (and potentially destroy) the low‐grade market. Presence of an outlet for low‐quality wood market within a region is very important since it encourages thinning and other responsible forestry practices that support long‐term value generation through the growth of high quality timber. Moreover, these restrictions may also discourage landowners from managing their lands. Harvesting logging residues not only creates additional revenues but also brings aesthetic benefits – some landowners are displeased at the slash and other residues left after conventional logging operations.

Some have expressed concerns that the biomass market would divert high quality wood from and veneer markets. As can be seen from our analysis, high quality HW and SW sawtimber are not considerably affected by any of the considered wood biomass consumption scenarios. It is possible that some higher quality wood could eventually find its way to biomass facilities because the volume of such wood at some timber sales may be too small to be marketed separately. However, we do not expect this to happen on significant scale.

While the present study provides useful insight, it is important to recognize its limitations as this is the first attempt to use NE‐SRTS for New England and New York and its first application to wood biomass issues. One of such limitations is related to the supply and demand price elasticities used in the model. Even though we performed a sensitivity analysis

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by looking at the effect of small changes in elasticity values on the predictions for all scenarios, in all analyses, we assumed the elasticities to be constant over the entire projection period.

Elasticity values may change over time, which can be accounted for in future modeling of the region’s timber market behavior. Additionally, we echo Sendak et al. (2003) call for empirical studies to develop supply, demand, and inventory elasticities specific to the study region.

Moreover, we treated the entire region as a whole and not state by state. State economies may have a significant impact on the local markets, and detailed state by state analysis should be included in future modeling efforts. Finally, other modeling scenarios should be investigated.

For example, global competition within the pulp and paper sector may result in region‐wide closures of pulp and paper facilities. This in turn would decrease the demand on pulpwood, which would drive the prices down and affect the wood biomass market considerably. There might also be other additional sources of renewable energy besides hydropower investigated in this thesis, which would affect wood biomass consumption. Moreover, new policies regulating renewable energy markets may be introduced in the near future, which would merit detailed analysis in the form of new modeling scenarios. Future studies may also incorporate potential land use changes over the projection period as well as unbiased investigations of when the use of wood bioenergy becomes ecologically, economically and socially undesirable.

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APPENDICES

Appendix A: Existing wood biomass energy facilities in New England and New York

Wood Use, Energy MapID Facility Location State Type Unit green ton Output Wheelabrator CT‐1 Lisbon CT Electric 200,000 15 MW Lisbon Pinetree Power MA‐1 Westminster MA Electric 180,000 17 MW Fitchburg MA‐2 Ware CoGen Ware MA Electric 50,000 9 MW

ME‐1 Boralex Stratton Stratton ME Electric 325,000 45 MW Boralex Livermore ME‐2 ME Electric 350,000 36 MW Livermore Falls Falls Boralex ME‐3 Sherman ME Electric 300,000 20 MW Sherman Energy Boralex Fort ME‐4 Fort Fairfield ME Electric 400,000 42 MW Fairfield ME‐5 Boralex Ashland Ashland ME Electric 350,000 40 MW Covanta ME‐6 (Indeck) Jonesboro ME Electric 300,000 24 MW Jonesboro Covanta ME‐7 Enfield ME Electric 300,000 24 MW (Indeck) Enfield Greenville ME‐8 Greenville ME Electric 200,000 16 MW Steam Co. Worcester ME‐9 Deblois ME Electric 250,000 25 MW Energy Partners Catamount Co‐ Electric Co‐ ME‐10 generation Rumford ME n/a 102 MW Fire, Mill (NewPage) Electric, ME‐11 Verso Paper Jay ME 245,000 n/a MW Mill East Milliinocket East Electric, ME‐12 ME 300,000 n/a MW Mill Millinocket Mill S.D. Warren ‐ Electric Co‐ ME‐13 Westbrook ME n/a n/a MW SAPPI Fire, Mill Aroostook and ME‐14 Bangor Reload Mattawamkeag ME CHP n/a 1 MW Co. ME‐16 Robbins Lumber Searsmont ME CHP n/a 1 MW

ME‐18 Lavallee Lumber Sanford ME CHP n/a 2 MW Hancock Bethel ME‐19 Bethel ME CHP n/a 2 MW Geneva Wood ME‐20 Strong ME Pellet 46,000 23,000 tons/yr Fuels

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Maine ME‐21 Athens ME Pellet 330,000 165,000 tons/yr Pellet Company Northeast ME‐22 Ashland ME Pellet 50,000 23,000 tons/yr Pellets, LLC Corinth Wood ME‐23 Corinth ME Pellet 540,000 270,000 tons/yr Pellets Pinetree NH‐1 Tamworth NH Electric 300,000 20 MW Tamworth Pinetree NH‐2 Bethlehem NH Electric 230,000 15 MW Bethlehem Bridgewater NH‐3 Bridgewater NH Electric 225,000 15 MW Power and Light Springfield NH‐4 Power LLC Springfield NH Electric 200,000 16 MW (Hemphill) DG Whitefield NH‐5 Whitefield NH Electric 180,000 16 MW Power and Light Northern Wood NH‐6 Power Station Portsmouth NH Electric 500,000 50 MW (Schiller) Indeck NH‐7 Alexandria Alexandria NH Electric 200,000 15 MW Energy Center West NH‐8 Bioenergy Corp. NH CHP n/a 13 MW Hopkinton New England NH‐9 Jaffrey NH Pellet 150,000 63,000 tons/yr Wood Pellet Lakes Region NH‐10 Barnstead NH Pellet 18,000 9,000 tons/yr Pellets Boralex NY‐1 Chateaugay Chateaugay NY Electric 268,000 20 MW Power American Ref‐ Electric Co‐ NY‐2 Niagara NY n/a 9 MW Fuel of Niagara fire Lyonsdale Biomass NY‐3 (Catalyst Lyonsdale NY Electric 255,000 19 MW Renewable, CH Energy Group) AES Greenidge, Electric Co‐ NY‐4 Dresden NY 100,000 113 MW LLC fire Black River Electric Co‐ NY‐5 Fort Drum NY n/a 25 MW Generation fire Covanta Electric Co‐ NY‐6 Westbury NY n/a 79 MW Hempstead fire Trigen Syracuse Electric Co‐ NY‐7 Syracuse NY n/a 101 MW Energy fire WPS Power ‐ Niagara Electric Co‐ NY‐8 Niagara NY n/a 56 MW Generating fire Facility

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Associated NY‐9 Lafargeville NY Pellet 10,000 5,000 tons/yr Harvest Co. Dry Creek NY‐10 Arcade NY Pellet 50,000 25,000 tons/yr Products Schuyler New NY‐11 England Wood Schuyler NY Pellet 100,000 50,000 tons/yr Pellet Curran NY‐12 Renewable Massena NY Pellet 10,000 5,000 tons/yr Energy LLC InstantHeat NY‐13 Wood Pellets, Addison NY Pellet 50,000 25,000 tons/yr Inc. Hearthside NY‐14 Stamford NY Pellet 10,000 5,000 tons/yr Wood Pellets Enviro Energy NY‐15 Unadilla NY Pellet n/a n/a tons/yr LLC NY‐16 Mascoma Rome NY Biofuel 2,400 200,000 gallons/yr International Electric Co‐ NY‐21 Ticonderoga NY n/a n/a MW Paper Fire, Mill Electric Co‐ NY‐22 Finch Paper Glens Falls NY n/a n/a MW Fire, Mill Inferno Wood RI‐1 Rumford RI Pellet n/a n/a tons/yr Pellet, Inc. VT‐1 McNeil Burlington VT Electric 400,000 50 MW

VT‐2 Pinetree Ryegate Ryegate VT Electric 250,000 20 MW Gilman Paper VT‐3 Gilman VT CHP n/a 4 MW Mill Vermont Wood North VT‐4 VT Pellet 20,000 10,000 tons/yr Pellet Company Clarendon

Source: Wilderness Society (2010)

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Appendix B: Proposed wood biomass energy facilities in New England and New York

Wood Use, Energy MapID Facility Location State Type Unit green ton Output Plainfield Renewable CT‐2 Plainfield CT Electric 400,000 30 MW Energy NRG Energy CT‐3 Uncasville CT Electric 350,000 30 MW Montville Watertown CT‐4 Watertown CT Electric 300,000 30 MW Renewable Power MA‐3 Russell Biomass Russell MA Electric 510,000 50 MW Pioneer Renewable MA‐4 Greenfield MA Electric 500,000 47 MW Energy Palmer Renewable MA‐5 Springfield MA Electric 73,000 38 MW Energy (Caletta) MA‐6 Berkshire Generations Pittsfield MA Electric 400,000 40 MW Bates Energy ME‐15 Lewiston ME CHP 8,000 1 MW Associates International Wood ME‐24 Burnham ME Pellet 200,000 100,000 tons/yr Fuels, LLC ME‐25 Aroostook Pellets Houlton ME Pellet n/a n/a tons/yr Old Town Fuel and ME‐26 Old Town ME Biofuel n/a 1,500,000 gallons/yr Fiber Electric, ME‐27 Verso Paper Mill Bucksport ME n/a n/a MW Mill Clean Power NH‐11 Berlin NH Electric 250,000 25 MW Development Barnstead Power and NH‐12 Barnstead NH Electric 60,000 5 MW Light Laidlaw Berlin NH‐13 Berlin NH Electric 700,000 70 MW Biopower NH‐14 Greenova LLC Berlin NH Pellet 200,000 100,000 tons/yr

NH‐15 Presby Environmental Whitefield NH Pellet 100,000 50,000 tons/yr Clean Power NH‐16 Development ‐ Winchester NH Electric 200,00 20 MW Winchester NH‐17 Concord Steam Concord NH CHP n/a 17 MW

NY‐12 Woodstone USA Moreau NY Pellet 200,000 100,000 tons/yr Griffis Utility Services NY‐17 Rome NY Electric 140,000 10 MW Biomass Electric Co‐ NY‐18 Jamestown Jamestown NY n/a 43 MW fire Catalyst Renewables NY‐19 (Onondaga Geddes NY Electric 540,000 40 MW Renewables)

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Watkins NY‐20 U.S. NY CHP 150,000 n/a MW Glen Alliance Energy Electric, NY‐21 Ogdensburg NY 250,000 n/a MW Renewables Conversion CHP, NY‐22 Lockheed Martin Owego NY 25,000 n/a MW Conversion New England Wood NY‐23 Deposit NY Pellet 100,000 50,000 tons/yr Pellets Woodland Bio ‐ Newton NY‐24 NY Biofuel n/a 20,000,000 gallons/yr Newton Falls Falls North Springfield VT‐5 Springfield VT Electric 400,000 25 MW Project Vermont Renewable VT‐6 Island Pond VT Pellet 22,000 11,000 tons/yr Energy Company Vermont Pellet Works VT‐7 Lyndonville VT Pellet 150,000 75,000 tons/yr Corporation VT‐8 Burke Sawmill site Sutton VT Pellet 200,000 100,000 tons/yr Southern Vermont Electric‐ VT‐9 Energy Park (Beaver Pownal VT 520,000 29 MW Pellet Wood Energy) Electric‐ VT‐10 Beaver Wood Energy Fair Haven VT 520,000 29 MW Pellet VT‐11 Access Energy Ludlow VT Electric 300,000 25 MW

Source: Wilderness Society (2010)

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Appendix C. New England and New York Region

Area of forest land by Forest type group and Ownership group (in acres)

Ownership group Forest‐type groups Total Forest Other State and local Private Service (10) federal (20) govʹt (30) (40) White / red / jack pine group (100) 31,634 41,797 641,121 2,993,863 3,708,415

Spruce / fir group (120) 235,954 76,658 819,498 6,317,541 7,449,651

Loblolly / shortleaf pine group (160) ‐‐ 17,014 109,984 52,679 179,677

Other eastern softwoods group (170) ‐‐ ‐‐ 3,130 59,985 63,115

Douglas‐fir group (200) ‐‐ ‐‐ 1,717 1,011 2,728

Exotic softwoods group (380) ‐‐ ‐‐ 75,697 218,690 294,387

Oak / pine group (400) 5,451 37,848 331,099 1,485,761 1,860,159

Oak / hickory group (500) 29,819 89,316 1,251,947 5,376,862 6,747,943

Oak / gum / cypress group (600) ‐‐ ‐‐ 43,134 64,119 107,253

Elm / ash / cottonwood group (700) 1,643 18,806 345,278 1,850,044 2,215,771

Maple / beech / birch group (800) 864,173 126,567 4,164,412 19,006,605 24,161,757

Aspen / birch group (900) 139,913 30,302 376,382 2,795,011 3,341,608

Other hardwoods group (960) 3,524 19,408 59,508 556,681 639,121

Exotic hardwoods group (990) ‐‐ ‐‐ 5,924 20,036 25,960

Nonstocked (999) ‐‐ 9,106 53,447 226,448 289,002

Totals: 1,312,111 466,823 8,282,279 41,025,336 51,086,549

Source: USDA Forest Service (2015)

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Appendix D. Summary of NE‐SRTS user input parameters

New England and New York: 24 FIA survey Region units

Ownership types Public, corporate and other private

Softwood pulpwood, softwood sawtimber, Product classes hardwood pulpwood and hardwood sawtimber

Management types 7 management types

Age classes 11 ten‐year age classes

Driver type Demand

Projection period 2015 – 2065 (50 years)

Constant over the entire projection period

• Inventory supply elasticity: unitary (1.0)

• Demand price elasticity: 0.5 Elasticity values • Supply price elasticities for softwood sawtimber and pulpwood: 0.5

• Supply price elasticities for hardwood sawtimber: 0.45 and pulpwood: 0.7

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