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Strategies to manage forest carbon in British Columbia. Man, Cosmin D. 2015

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STRATEGIES TO MANAGE FOREST CARBON IN BRITISH COLUMBIA  by Cosmin D. Man  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  DOCTOR OF PHILOSOPHY in The Faculty of Graduate and Postdoctoral Studies (Forestry)      THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  April 2015 © Cosmin D. Man 2015 ii  Abstract This research uses the inventory of 3 actively managed forest estates located in the Coast, Southern Interior, and Northern Interior forest regions in British Columbia. The performance of two groups of forest management strategies (harvest reduction and increased growth rate strategies) is explored in order to determine the carbon storage potential. A sensitivity analysis is conducted for the strategy that reduces the harvest to a fixed target level to determine the cost to produce carbon credits. A new method is developed to reduce the cost to produce carbon credits through implementing fluctuating harvest schedules that allow the target harvest to fluctuate between a minimum accepted level and the baseline level. The results confirmed that at forest estate level, harvest reduction strategies outperform by a significant margin the increased growth rate strategies. There were no differences in terms of carbon storage and age class distribution between the various harvest reduction strategies analyzed in this study (fixed target harvest level, increased minimum harvest ages, and 2 strategies to increase area in reserves). Thus, the strategy that reduces the harvest to a fixed target level is preferred because it provides more flexibility in addressing natural disturbances and market fluctuations. The cost to produce carbon credits ranged from $3.9 to $40.8 tCO2e-1 (at 0% discount rate), out of which, the opportunity cost of reducing harvest represented 58%-97%. It was demonstrated that the inclusion of the opportunity cost of reducing harvest dominated the cost to produce carbon credits which, contrary to previous findings, increased with increasing forest productivity expressed both, as site index (i.e., top height in m at age 50) and average value of harvested timber (in $ ha-1). The new method that implements fluctuating harvest schedules reduced the cost to produce carbon credits by up to 17% (at 0% discount rate).  iii  Preface This research study was initiated by Dr. John D. Nelson and Dr. Gary Q. Bull in collaboration with the forest managers at the University of British Columbia’s (UBC) Alex Fraser Research Forest (Mr. Ken Day) and Malcolm Knapp Research Forest (Mr. Paul Lawson). The initial objective was to determine the potential of these forest estates to produce carbon credits and help the UBC offset their emissions as an obligation under the British Columbia’s Greenhouse Gas Reduction Target Act. Under the main supervision of Dr. Kevin C. Lyons, guided from the side by Dr. John D. Nelson and Dr. Gary Q. Bull, and supported with data and local experience knowledge by Mr. Ken Day, Mr. Paul Lawson and the staff at the two research forests, I was able to improve the models using the latest data available and develop a set of scenarios which allowed me to expand the research from the initial objective into a larger study with a more complex set of objectives. A version of Chapter 3 has been published. Cosmin D. Man, Lyons, K.C., Nelson, J.D., and Bull, G.Q., 2013. Potential of alternate forest management practices to sequester and store Carbon in two forest estates in British Columbia, Canada. For. Ecol. Manage. 305, 239-247. I conducted all the analysis and wrote most of the manuscript. A version of Chapter 4 has been published in January 2015. Cosmin D. Man, Lyons, K.C., Nelson, J.D., and Bull, G.Q., 2014. Cost to produce Carbon credits by reducing the harvest level in British Columbia, Canada. Forest Policy Econ 52:9-17. I conducted all the analysis and wrote most of the manuscript. A version of Chapter 5 will be submitted for publication during 2015. I will conduct all the analysis and write most of the manuscript.  iv  Table of Contents ABSTRACT ....................................................................................................................... ii PREFACE ......................................................................................................................... iii TABLE OF CONTENTS ................................................................................................ iv LIST OF TABLES ........................................................................................................... vi LIST OF FIGURES ........................................................................................................ vii LIST OF SYMBOLS AND ABBREVIATIONS ........................................................... ix ACKNOWLEDGEMENTS ............................................................................................ xi 1 INTRODUCTION..................................................................................................... 1 1.1 OBJECTIVES ............................................................................................................. 6 1.2 LITERATURE REVIEW ............................................................................................... 7 1.2.1 Increased carbon storage through fertilization .............................................. 7 1.2.2 Increased carbon storage through use of genetically improved stock ........... 9 1.2.3 Increased carbon storage through harvest reduction strategies .................. 10 1.2.4 Cost to produce forest-based carbon credits ................................................ 14 1.2.5 Assumptions in carbon financial analyses .................................................... 16 2 METHODS .............................................................................................................. 23 2.1 DESCRIPTION OF THE FOREST ESTATES ................................................................... 23 2.2 SIMULATION MODELS ............................................................................................. 25 2.3 BASELINE DETERMINATION .................................................................................... 29 2.4 MANAGEMENT STRATEGIES TO INCREASE CARBON STORAGE ................................ 31 2.5 CALCULATION OF CARBON CREDITS ....................................................................... 31 2.6 FINANCIAL ANALYSIS ............................................................................................. 33 3 ALTERNATE FOREST MANAGEMENT PRACTICES TO PRODUCE CARBON CREDITS .................................................................................................................. 36 3.1 OVERVIEW ............................................................................................................. 36 3.2 METHODS .............................................................................................................. 36 3.2.1 Scenarios to determine the magnitude of the difference between strategies that increase growth rates and strategies that reduce harvest level .................................... 37 3.2.1.1 Fertilization ............................................................................................... 37 3.2.1.2 Genetically improved stock ...................................................................... 37 v  3.2.1.3 Reduction of harvest to a fixed target level .............................................. 38 3.2.2 Scenarios to determine the performance of harvest reduction strategies ..... 39 3.3 RESULTS ................................................................................................................ 41 3.4 CONCLUSIONS ........................................................................................................ 56 4 COST TO PRODUCE CARBON CREDITS ....................................................... 59 4.1 OVERVIEW ............................................................................................................. 59 4.2 METHODS .............................................................................................................. 60 4.3 RESULTS ................................................................................................................ 64 4.4 CONCLUSIONS ........................................................................................................ 74 5 FLUCTUATING HARVEST SCHEDULES TO PRODUCE CARBON CREDITS…. ................................................................................................................................ 77 5.1 OVERVIEW ............................................................................................................. 77 5.2 METHODS .............................................................................................................. 78 5.2.1 Fluctuating harvest schedules ....................................................................... 78 5.3 RESULTS ................................................................................................................ 80 5.4 CONCLUSIONS ........................................................................................................ 90 6 FINAL CONCLUSIONS ........................................................................................ 93 REFERENCES .............................................................................................................. 102 APPENDIX I – LAND BASE DEFINITIONS ........................................................... 109 APPENDIX II – METADATA .................................................................................... 110         vi  List of Tables Table 3-1. Relevant constraints for the harvest reduction strategies over a range of harvest levels....................................................................................................................................................... 40 Table 3-2. Comparing the percentage increase of carbon storage from the baseline between strategies that reduce harvest and those that increase growth rates .............................................. 44 Table 3-3. Carbon storage differences at year 25 and year 100 for harvest reduction strategies: values indicate the percentage difference from the maximum stored carbon (identified as max) for each target harvest level. ......................................................................................................... 50 Table 4-1. Revenues, costs, and site productivity ........................................................................ 62 Table 4-2. Total break-even carbon credit price and its 2 components for a 25-year project life with all costs assumed to be expenses for a 5-year verification frequency .................................. 65 Table 4-3. Total break-even carbon credit price increase of moving from a 5-year to a 1-year verification frequency at 0% discount rate ................................................................................... 74 Table 5-1. List of scenarios with fluctuating harvest schedules defined by the starting target harvest level (STHL) set at 33%, 50%, and 70% of the baseline harvest level ............................ 80 Table 5-2. Number of carbon credits produced, total break-even carbon credit price (P), and opportunity cost (PH) for the 25-year carbon project life at 0% discount rate and 5-year verification frequency ................................................................................................................... 86          vii  List of Figures Figure 2-1. Location of the forest estates ..................................................................................... 24 Figure 3-1. Comparing the carbon storage between strategies that reduce harvest and strategies that increase growth rates .............................................................................................................. 43 Figure 3-2. Carbon storage (above) and harvested volume (below) throughout the entire planning horizon for increased MHA scenarios at MKRF when the LTSY is not recalculated .. 46 Figure 3-3. Carbon storage at AFRF for harvest reduction strategies over a range of harvest levels ............................................................................................................................................. 48 Figure 3-4. Carbon storage at MKRF for harvest reduction strategies over a range of harvest levels ............................................................................................................................................. 49 Figure 3-5. Standing volume by age classes at year 0 and year 100 for harvest reduction strategies over a range of harvest levels (MAL: minimum accepted level) ................................. 52 Figure 3-6. Percentage of total area by age classes at year 0 and year 100 for harvest reduction strategies over a range of harvest levels (MAL: minimum accepted level) ................................. 53 Figure 3-7. Carbon storage at AFRF over a range of mortality intensities in the reserve areas at year 10 of the simulation when using a rich sites first strategy to produce the MAL target harvest....................................................................................................................................................... 55 Figure 4-1. Carbon credits produced per ha over the 25-year life of the carbon project ............. 67 Figure 4-2. Annual total cost (i.e., sum of opportunity and carbon project costs) and carbon credits produced over the life of the project at the MKRF for a 30% target harvest level (i.e., minimum accepted level): costs are shown at 0% real discount rate. ........................................... 68 Figure 4-3. Comparing the total break-even carbon credit price at 0% real discount rate over a range of average values per ha harvested per year (12.2 at FE3, 22.9 at the AFRF, and 63.7 at the MKRF, measured in thousand $ ha-1 year-1) at 30% target harvest of the baseline level when the opportunity cost of reducing harvest is not included in the financial analysis (Panel A) and when it is included (Panel B): the values in the brackets represent the average site index for each forest estate. ............................................................................................................................................ 70 Figure 4-4. Comparing the total break-even carbon credit price at 0% real discount rate over a range of site indices (corresponding to 3 forest estates) at 30% target harvest of the baseline level when percentage reductions are applied to the timber net revenues (TNR) ................................. 71 viii  Figure 5-1. Comparing the carbon credits inertia (CCI) period at MKRF when the starting target harvest level (STHL) is held constant for a range of harvest reduction periods ........................... 81 Figure 5-2. Comparing the weighted average harvest age (by harvested area) and the net biome productivity (NBP) between the baseline scenario and the fluctuating harvest schedule scenarios: STHL is starting target harvest level. ........................................................................................... 83 Figure 5-3. The effect of the discount rate on the total break-even carbon credit price (P) reduction from maximum (with starting target harvest level (STHL) held constant over 25-year carbon project life) when STHL is at 50% of the baseline level for 10 years (highest P reduction from max)...................................................................................................................................... 88 Figure 5-4. Comparing the carbon credit market price range to the total break-even carbon credit price (P) (0% discount rate) for a range of timber net revenues (TNR) when the starting target harvest level (STHL) is set at 33% of the baseline level for 10 years (fluctuating harvest schedule) and 25 years (constant harvest schedule) ..................................................................... 90               ix  List of Symbols and Abbreviations AFOLU Agriculture, Forestry, and Other Land Use, used to define a specific set of protocols including the forestry sector. AFRF Alex Fraser Research Forest. AVHH Average Value per Hectare Harvested, the timber revenue (i.e. the timber selling price averaged from the last 5-10 years of financial records) multiplied by the 25-year average harvested volume per hectare per year (thousand $ ha-1). BEC Biogeoclimatic Ecosystem Classification in British Columbia. C The number of carbon credits produced (tCO2e). CBM-CFS3 Carbon Budget Model for Canadian Forest Sector. CC Total Carbon project cost, includes initial establishment and validation and ongoing verification cost ($). CCI Carbon Credits Inertia, used to define the CCI period for which the carbon credit production continues following the adjustment of the starting target harvest level to the baseline level (years). tCO2e Metric tonnes of carbon dioxide equivalent, one t of CO2e indicates the global warming potential of one t of carbon dioxide for various greenhouse gases as defined in ISO 14064-1(2006). In a forest ecosystem, the carbon storage is estimated in t of Carbon and then converted to tCO2e (1 t of carbon is 3.667 tCO2e). FCOP The Protocol for the Creation of Forest Carbon Offsets in British Columbia. FE3 Third Forest Estate. FPS-ATLAS Forest Planning Studio, a spatially explicit forest-level planning model. GIS Geographic Information Systems. H Harvested volume (B - baseline, C-Carbon project) (m3 year-1). IFM Improved Forest Management. LTSY Long Term Sustainable Yield, the maximum annual harvested volume that can be maintained for an entire planning horizon (Davis et al., 2005) (m3 year-1). MAL Minimum Accepted Level of harvesting for a forest estate (m3 year-1). MHA Minimum Harvest Age (years). MKRF Malcolm Knapp Research Forest. x  NVHH Net Value per Hectare Harvested, TNR multiplied by the 25-year average harvested volume per hectare per year (thousand $ ha-1 year-1). n Duration of the planning horizon and the break-even period for which a break-even Carbon credit price can be calculated (years). P Total break-even Carbon credit price (P= PH + PCC) ($ tCO2e-1). PCC The component of P due to the total Carbon project cost (initial project establishment and validation and ongoing verification) ($ tCO2e-1). PH The component of P due to the opportunity cost of reducing harvest ($ tCO2e-1). r Discount rate, real rate once inflation rate has been removed (%). STHL Starting Target Harvest Level (m3 year-1). t Simulation year. THLB Timber Harvest Land Base, the portion of land within a forest estates where harvest operations generating timber are allowed to occur (ha). TIPSY Table Interpolation Program for Stand Yields of managed stands (regenerated through planting following harvesting events). TNR Timber Net Revenue, calculated as the difference between the timber revenue (i.e., the timber selling price averaged from the last 5-10 years of financial records) and the average harvesting cost from the last 5-10 years of financial records ($ m-3). TPNR Total Present Net Revenue (B - baseline, C-Carbon project). VCS Verified Carbon Standard. VDYP Variable Density Yield Prediction for unmanaged stands (regenerated naturally following natural stand replacing disturbances).         xi  Acknowledgements There are many people to thank for in achieving this milestone in my academic career. My supervisor, Dr. Kevin C. Lyons deserves most of my gratitude for his leadership role, shared knowledge, support at all levels, inspiration, and advice in furthering my forestry and academic career. I am thankful to Dr. John D. Nelson for introducing me into the world of modeling, providing the solid starting steps into the philosophy of science, and giving me the opportunity to be his teaching assistant for two of the UBC forestry’s flagship courses. I thank Dr. Gary Q. Bull for his continuous insight into solving the financial analysis sections of my thesis and for providing guidance into the delicate financial issues related to carbon finance. Thanks also go to the UBC’s Alex Fraser and Malcolm Knapp Research Forest staff for their advice and support in completing this research. Mr. Ken Day and Mr. Paul Lawson were not shy in sharing their vast practical experience in managing small-scale forest estates and providing insight into many delicate problems in order to address them in my analysis. Mr. Mircea Rau and Mr. Ionut Aron were very helpful in providing the data to be used in the analysis I conducted as part of this research; their intimate knowledge of the area was very helpful in addressing most of the concerns. Mrs. Sheryl Power assisted me with silviculture data and information in order to reflect it as accurate as possible in the modeling scenarios. Mrs. Wendy Plain edited thoroughly the manuscript for English language, clarity of ideas, and consistency. Mrs. Judith Scholes also edited the manuscript for clarity and logic transitions between paragraphs. Mr. Patrick Bryant, a forest specialist, edited the manuscript for forest terminology, logic of ideas, and consistency of terms. Thanks to anonymous reviewers of the published research chapters.  1  1 Introduction Forest ecosystems can mitigate the anthropogenic global climate change by sequestering and storing carbon (i.e., carbon storage) in addition to the business as usual practice (i.e., the baseline) (Intergovernmental Panel on Climate Change, 2007). Proper forest management can increase the carbon storage in addition to the baseline (Cooper, 1983; Smith et al., 1993; Parker et al., 2000) while continuing to meet the need for timber, fiber, and energy (Kurz et al., 2002). Carbon storage in a forest ecosystem is expressed in units of carbon or carbon dioxide equivalents1 (CO2e) (1 unit of carbon equals 3.667 units of CO2e), which represent the mass in metric tonnes (t) of carbon stored in live and dead biomass above and below the forest floor. It is generally accepted that there are 3 major strategies to increase carbon storage in forest ecosystems: (1) increase the forest land base through afforestation, (2) conserve the existing forest land base by avoiding conversion to other land uses, and (3) use alternate forest management practices in the existing forests. Quantifying the carbon storage in addition to the baseline is a relatively well understood process in the case of the first 2 strategies (i.e., increasing the area occupied by forests or avoiding deforestation), but it is a complex problem when alternate forest management practices are used in existing forests. In existing forests, the complexity of quantifying the carbon storage in addition to the baseline is a result of the complex process of forecasting the current forest inventory and carbon storage based on forest management objectives and the predicted frequency and intensity of natural disturbances. The forest inventory includes a mosaic of stands with a range of species and                                                  1 One unit of CO2e indicates the global warming potential of one unit of carbon dioxide for various greenhouse gases as defined in Canadian Standards Association, 2009 (ISO 14064-1). 2  age classes as a result of historic disturbances (natural and anthropogenic). In order to overcome many technological limitations (e.g., database limitations, processing time) (Nelson, 2003a), the complexity of the forest inventory is reduced by grouping together stands with similar characteristics (e.g., historic disturbance, tree species, ecological unit, site productivity etc.). The forest management objectives include timber and non-timber objectives, which establish silvicultural systems, species composition, spatial adjacency constraints, size of reserves, and percentage of retention. Typically, the current forest inventory and predicted frequency and intensity of natural disturbances are forecast in a timber supply model, which produces a harvest schedule that meets the forest management objectives. The forecasted inventory and harvest schedule are then transferred into a carbon budget model in order to estimate the carbon storage over time. The complexity of quantifying the carbon storage was overcome in the past by using theoretical forests with normal age class distributions, a reduced number of species, and simplified management objectives (Harmon and Marks, 2002; Seely et al., 2002) or by excessively grouping similar stands using non-spatial models (Taylor et al., 2008). In the case of small-scale actively managed forest estates (e.g., less than 50,000 ha in size), the complexity of quantifying the carbon storage is better handled because (1) the grouping of similar stands does not have to be excessive to reduce the inventory size, (2) all management objectives can be included in forecasting the future conditions without significantly affecting the complexity of the analysis, (3) the inventory is typically more accurate for small-scale forest estates, and (4) in most cases, increased efforts are made to reduce the loss of timber due to natural disturbances. In existing forests, the alternate forest management practices that are focussed on carbon storage in addition to the baseline include strategies that either (1) reduce the harvest below the baseline level, (2) maintain the baseline harvest level while promoting increased growth rates, or 3  (3) combine category (1) and (2). Strategies that reduce the harvest below the baseline level include reducing the harvest to a fixed target level, increasing minimum harvest ages, increasing the area in reserves, and switching from even-aged to uneven-aged silvicultural systems. Strategies that increase growth rates include the use of fertilizers and genetically improved planting stocks. There are examples in the literature suggesting that, in temperate forests, strategies that increase growth rates result in less carbon storage in addition to the baseline than harvest reduction strategies. In a 300-year simulation, Seely et al. (2002) found that carbon storage in addition to the baseline was increased by 13% by using fertilizers in trembling aspen (Populus tremuloides) stands. Empirical data from Aspinwall et al. (2012) showed that carbon storage in addition to the baseline increased by 13% over 40 years for genetically improved loblolly pine (Pinus taeda) stands. Simulations over 500-year periods conducted by Harmon and Marks (2002) showed that a 20% harvest reduction increased the carbon storage in addition to the baseline by 140%. Tripled minimum harvest ages for trembling aspen, lodgepole pine (Pinus contorta), and white spruce (Picea glauca) stands (estimated harvest reductions of 62% to 88% from the baseline level) increased the carbon storage in addition to the baseline by 43%-75% in 300-year simulations (Seely et al., 2002). The results presented here depended largely on study location, intensity of the strategy considered, natural disturbance dynamics (e.g., wildfire), and length of the planning horizon. In addition, when comparing the performance of the harvest reduction strategies, the emphasis was more on carbon storage and less on the ability of the forest manager to meet other management objectives. For example, in the case of increased minimum harvest ages, the studies did not consider whether there would be a reduction in the annual harvest volume if the forest manager tried to maintain baseline harvest levels. 4  Implementation of alternate forest management practices into forest-based carbon projects generates another stream of revenue for the forest manager in addition to the timber revenue. In a carbon project implementing alternate forest management practices, carbon storage in addition to the baseline is quantified into carbon credits, which are sold via carbon markets (i.e., 1 tCO2e quantified in addition to the baseline is equivalent to 1 carbon credit). The costs to produce carbon credits and the financial viability of the carbon project are determined through a financial analysis which selects and calculates the financial indicators specific to the carbon project. The selection of the proper financial indicators to be calculated is often complicated by the large number of factors involved (e.g., accuracy of timber and carbon credit estimates, accuracy of costs and revenues, market prices, discount rate) (Golden et al., 2011; Greig and Bull, 2011; Galik and Cooley, 2012; Buongiorno, 2014; van Kooten et al., 2014). Boyland (2006) proposes the use of the carbon supply curve (i.e., plotting the carbon credits produced against the marginal cost to produce them), yet the efforts to determine the marginal costs produced a wide range of results. For the forest management practices, van Kooten et al. (2009) found marginal costs between $59 to $88 tCO2e-1, McKinsey and Company (2009) determined abatement costs of $6 to $12 tCO2e-1, while the average carbon credit price on the voluntary markets was $9.8 tCO2e-1 in 2012 (Peters-Stanley et al., 2013). In addition, the marginal costs derived from the average costs and revenues (which are usually available in a forest estate) can be misleading because they can overestimate the number of carbon credits that can be produced at a given carbon credit price (Boyland, 2006). Comparing the break-even carbon credit price (i.e., the total cost of the carbon project divided by the number of carbon credits produced) with the market carbon credit price is an alternate strategy to determine the financial viability of carbon projects. 5  The total cost of the carbon project includes the typical costs for harvesting and silviculture plus the carbon project specific costs (opportunity due to harvest reduction, initial establishment and validation, and ongoing verification). Depending on the overall objectives, some of these costs are not explicitly included in the financial analysis. In the case of harvest reduction strategies, the opportunity cost of the timber left standing as opposed to generating revenue from harvesting (i.e., the opportunity cost of reducing harvest) is not always included in the financial analysis. For example, Huang and Kronrad (2001) did not explicitly include the opportunity cost of reducing harvest, and this resulted in lower average costs to store one additional tCO2e for stands with a higher site index (i.e., top height in meters at age 50). Lower break-even carbon credit prices for higher site indices were also found for strategies that increase growth rates, which do not have opportunity costs of reducing harvest (Bull, 2010). Financial analyses of carbon projects that include the opportunity cost of reducing harvest are needed in order to provide better estimates for the break-even carbon credit price when considering actively managed forest estates. However, using site index as the universal measure of site productivity can be problematic when comparing different forest estates composed of different species and site conditions. Thus, it is necessary to develop a metric that represents the opportunity cost of reducing harvest in favour of storing carbon. This new metric will have to be sensitive to site productivity, tree species, and log quality. The opportunity cost of reducing harvest is determined by the timber price and the different harvest schedules of the baseline and project scenarios. In the case of forest estates actively managed for profit, there is a need to design harvest schedules that minimize the opportunity cost of reducing harvest while continuing to produce carbon credits. Past studies showed that reducing the harvest to a constant level below the baseline produced carbon credits 6  from year 1 (e.g., Harmon and Marks, 2002). The effects on carbon credit production and the potential financial benefits were not investigated when the target harvest is allowed to fluctuate between a minimum accepted level and the baseline level. To determine these effects, fluctuating harvest schedules will need to be developed and analyzed.  1.1 Objectives This present study proposes to investigate 3 objectives: (1) The first objective is to analyze the performance of 4 harvest reduction strategies (harvest reduction to a fixed harvest level, harvest reduction through increasing minimum harvest ages, and 2 harvest reduction strategies that increase the area in reserves) in terms of carbon storage and based on forest age class distribution over a range of fixed harvest levels. (2) The second objective is to develop a new metric that represents the opportunity cost of reducing harvest in favour of storing carbon and to analyze how the break-even carbon credit price varies with the new metric over a range of forest types and opportunity costs of reducing harvest. (3) The third objective is to develop fluctuating harvest schedules and analyze their potential to produce carbon credits at lower costs.   7  1.2 Literature review In this section, I review both the alternate forest management strategies for increasing carbon storage in addition to the baseline and the studies that analyzed the costs of producing forest-based carbon credits.   1.2.1 Increased carbon storage through fertilization Increased growth through fertilization was examined by Farnum (1983) for 2 theoretical single-species stands. In the case of planted Douglas-fir (Pseudotsuga menziesii) stands (1,000 stems per ha at age 17) located in Washington State with a site index of 32 m, multiple fertilizations of 200 kg of nitrogen per ha were applied every 5 years starting at age 30. In the case of planted loblolly pine stands (1,500 stems per ha at age 5) located in North Carolina with a site index of 21 m, a single phosphorus fertilization was applied before planting and multiple fertilizations of 224 kg of nitrogen per ha were applied every 5 years starting at age 10. The study concluded that multiple fertilization applications can at least maintain the increased growth resulting from the initial fertilization application and that they added 20% (in the case of Douglas-fir) and 90% (in the case of loblolly pine) more volume at peak age compared to natural unfertilized stands. Seely et al. (2002) evaluated the potential to increase growth and carbon storage through fertilization on a theoretical trembling aspen stand on a mesic site (estimated site index of 20 m) in northern British Columbia. In a 300-year simulation, fertilizations of 250 kg of nitrogen per ha were applied in years 10 and 25 of a 30-year harvest cycle. They concluded that stand growth production increased by 37%, which corresponded to a 13% increase in carbon storage. 8  Jassal et al. (2010) summarized the 2-year results of 3 fertilization trials in Douglas-fir stands in the Coast forest region of British Columbia. A fertilization of 200 kg of nitrogen per ha was applied aerially in a 58-year-old stand (130 ha in size, 1,400 stems per ha, with a site index of 35 m) and in a 19-year-old stand (110 ha in size, 1,200 stems per ha, with a site index of 29 m), while 60 kg of nitrogen per ha fertilization was applied manually in a 7-year-old stand (32 ha in size, 1,100 stems per ha, with a site index of 32 m). They concluded that the highest growth increase (68%) was in the case of the 7-year-old stand followed by the 19-year-old stand (32% growth increase), and that it was significantly lower in the 59-year-old stand (8% increase). They also showed that the losses to the atmosphere through ecosystem respiration at younger ages of stand development were higher than the growth of trees in the 7-year-old stand, which is amplified by the fertilization application and losses of N2O from the soil. This resulted in less carbon storage for the 7-year-old stand. The N2O losses to the atmosphere may be significant if large areas of young stands are being fertilized because the N2O has a higher global warming potential than CO2 (approximately 300 times). Thus, fertilization application in young stands (e.g., less than 10 years old) has a negative short-term effect on carbon storage. The level of response to fertilization application in relation to the site index was examined by Schroeder (1991). He analyzed the 10-year cumulative growth increase from previous fertilization trials (224 kg of nitrogen per ha) on Douglas-fir stands in the Pacific Northwest. He concluded that there is an inverse relationship between site index and nitrogen fertilization response in Douglas-fir stands (a higher fertilization response on stands with a lower site index). However, the quality of timber products resulting from low site index stands (e.g., less than 12 m) is questionable. Thus, in an actively managed forest estate, the growth increase 9  strategy that will likely increase carbon storage and deliver acceptable timber products most efficiently is to fertilize stands with site indices within the average range.  1.2.2 Increased carbon storage through use of genetically improved stock Increased growth through the use of genetically improved planting stock in newly established stands was examined by Farnum (1983) on the same theoretical single-species stands used in the fertilization example above. The use of 12% genetically improved planting stock for Douglas-fir stands added 20% more volume at peak age compared to natural unfertilized stands. The use of 24% genetically improved planting stock for loblolly pine stands added 70% more volume at peak age compared to natural unfertilized stands. Farnum (1983) concluded that the gain in mean annual yield for Douglas-fir and loblolly pine stands could be increased by 10% to15% per generation while also improving stem quality characteristics, disease resistance, wood density, and product uniformity. Stoehr at al. (2011) examined if 12-year-old Douglas-fir stands established in the Coast forest region of British Columbia using genetically improved planting stock were growing as predicted by the current models. Low elevation (100 to 550 m) stands with a range of site indices (32 to 42 m), spacing densities (625 to 3,906 stems per ha), and 2 genetic classes (10% and 18% volume gain at age 60) were used in the analysis. They concluded that the current models underestimated the volume increase by 8% to 9% for 12 years after stand establishment. Aspinwall et al. (2012) examined the growth and carbon storage increase on a genetically improved planting stock for loblolly pine plantations established between 1968 and 2007 in the southeast United States. The average site index at age 25 was 18.3 m; the average spacing density was 1,482 stems per ha; and the range of genetic gains was 10% to 33% (gain in mean 10  annual yield). They concluded that the volume increased by 17% and carbon storage by 13% compared to loblolly pine plantations without improved planting stock.  1.2.3 Increased carbon storage through harvest reduction strategies Harvest reduction strategies that switch from even-aged to uneven-aged systems were examined by Taylor et al. (2008) in red spruce (Picea rubens) dominated stands in Nova Scotia, Canada. The analysis was conducted on managed stands (regenerated after clear-cuts) in a non-spatial model, and only the timber objective was implemented. The uneven-aged system was designed with the first cut at age 80 (50% removal) followed by 50% removal cuts every 40 years over a 240-year planning horizon. The even-aged system had 80-year clear-cut removals. They concluded that the carbon storage increased by 7% over 240 years when uneven-aged systems were implemented compared to even-aged systems. Harmon et al. (2009) examined the effect of the uneven-aged systems on carbon storage in a theoretical forest of Douglas-fir and western hemlock (Tsuga heterophylla) typical to the Pacific Northwest forest region in the United States. The analysis was conducted in a non-spatial model, and only the timber objective was implemented. A range of removal percentages (20% to 100%) from mature stands was analyzed over a range of rotation intervals (20 to 250 years). They concluded that the uneven-aged system with frequent but small removals (20%) can store as much carbon as clear-cut removals (100%) on less frequent intervals over a 250-year planning horizon. Thus, the use of uneven-aged systems provides the highest carbon storage benefits in short to intermediate terms (20 to 100 years). Nunery and Keeton (2010) examined the potential of uneven-aged systems to increase carbon storage in hardwood-conifer forests in the northeastern United States. The area was 11  aggregated into 32 stand types based on ecoregion, forest type, stand origin, slope, and site productivity. The analysis was conducted over a 160-year planning horizon, in a non-spatial model, and only the timber objective was implemented. The uneven-aged systems were designed to remove 15% of the basal area every 15 or every 30 years and 35% of the basal area every 15 or every 30 years. The even-aged system had 80- to 120-year clear-cut removals. They found that the uneven-aged systems increased carbon storage by 23% to 57% compared to even-aged systems over 160 years. The effect of harvest reduction to a fixed target on carbon storage was examined by Harmon and Marks (2002) in a theoretical forest of Douglas-fir and western hemlock typical to the Pacific Northwest forest region in the United States. The analysis was conducted in a non-spatial model, and only the timber objective was implemented. Two harvest utilizations (100% and 80% of tree mass removed) were directly compared in terms of carbon storage over a 500-year planning horizon. They concluded that the 20% harvest reduction increased the carbon storage by 140% after 500 years. Peng et al. (2002) analyzed the carbon storage increase due to harvest reduction on a boreal forest transect in central Canada. The analysis was conducted in a non-spatial model, and only the timber objective was implemented. They compared the clear-cut system (with branches and needles left on site) to the no harvest scenario at 2 locations on the boreal forest transect (Prince Albert and Thompson). They concluded that a harvest reduction of 100% (from clear-cut to no harvest) increased the carbon storage by 1.8 to 3.4 times over a 500-year planning horizon. Colombo et al. (2012) examined the effect of harvest reduction on carbon storage in the boreal forest of eastern Canada. The study area encompassed 3.4 million ha aggregated into 4 management units (detail aggregation of forest types is not reported). The analysis was 12  conducted in non-spatial models, while a range of timber and non-timber management objectives were implemented. The scenarios simulated no harvest and 38% to 73% (average of 52%) future harvest reduction from the baseline. Wildfires were also simulated at 63 to 85 cycles. They concluded that the no harvest scenario stored 10% more carbon than the baseline, while the 38% to 73% harvest reduction scenario stored 6% more carbon than the baseline over a 100-year planning horizon. The increased rotation age effects on carbon storage was examined by Peng et al. (2002) in a boreal forest transect in central Canada. The analysis was conducted in a non-spatial model, and only the timber objective was implemented. They compared the effect of 30-, 60-, 90-, and 120-year rotation ages on carbon storage for a clear-cut system (with branches and needles left on site) at 2 locations within the transect (Prince Albert and Thompson). They found that the carbon storage increased by 71% to 92% when the rotation age was increased from 30 to 120 years for a 300-year planning horizon. Seely et al. (2002) evaluated the potential to increase growth and carbon storage through increased rotation ages in theoretical trembling aspen, white spruce, and lodgepole pine stands on a mesic site (estimated site index of 20 m) in northern British Columbia. The analysis was conducted over a 300-year planning horizon in a non-spatial model, and only the timber objective was implemented. The rotation ages analyzed were 30 to 90 years for trembling aspen, 60 to 200 years for white spruce, and 30 to 150 years for lodgepole pine stands. They found that carbon storage increased by 43% for trembling aspen stands (rotation age increased from 30 to 90 years), 43% for spruce stands (rotation age increased from 60 to 200 years), and 75% for pine stands (rotation age increased from 30 to 150 years). 13  Harmon and Marks (2002) examined the effect of increased rotation age on carbon storage in a theoretical forest of Douglas-fir and western hemlock typical to the Pacific Northwest forest region in the United States. The analysis was conducted in a non-spatial model, and only the timber objective was implemented. The rotation age was increased from 40 to 120 years for a clear-cut system with high utilization, and the simulations were run for a 500-year planning horizon. They found that carbon storage increased by 2.5 times when the rotation age increased from 40 to 120 years. Nunery and Keeton (2010) examined the increased rotation age effects on carbon storage in hardwood-conifer forests in the northeastern United States. The area was aggregated into 32 stand types based on ecoregion, forest type, stand origin, slope, and site productivity. The analysis was conducted over a 160-year planning horizon in a non-spatial model, and only the timber objective was implemented. They found that increasing rotation age from 80 to 120 years in an even-aged management system increased carbon storage by 3% over a 160-year planning horizon. The Darkwoods Forest Carbon Project (The Nature Conservancy of Canada, 2011) was established on a 55,000 ha area in the Southern Interior forest region of British Columbia. The Darkwoods project has forecast a100% increase in carbon storage over a project length of 100 years when the harvest level was reduced below the baseline. However, the reduction level has varied greatly (0% to 97%) as the baseline level was not constant throughout the planning horizon. While the additional carbon storage from the baseline will be expected to be relatively high after 100 years, comparison with other findings is difficult due to the baseline calculation. The TimberWest Strathcona Ecosystem Conservation Project (Pacific Carbon Trust, 2011) was established on a 25,000 ha area in the Coast forest region of British Columbia. The 14  TimberWest project forecasts an increase of 8.75 106 tCO2e over the baseline for a project length of 25 years; given the project area of 25,000 ha, the carbon storage per ha is 350 tCO2e after 25 years. Based on its project plan summary document (Pacific Carbon Trust, 2011), 350 t per ha of additional carbon storage indicates that the baseline for the TimberWest project assumes most of the old growth area is operable, and that the harvest level for this old growth is reduced to zero. However, since the remaining old growth tends to be at higher elevations and in more difficult terrain, it is likely that a portion of the old growth is not accessible, and the baseline harvest level could be overestimated. Thus, the magnitude of the additional carbon storage could also be overestimated.  1.2.4 Cost to produce forest-based carbon credits Huang and Kronrad (2001) examined the cost to produce one additional tonne of carbon in theoretical loblolly pine plantations in the southern United States. They calculated the net present revenue for a scenario that maximized timber production and a scenario that maximized carbon sequestration (through increased rotation ages). The analysis was conducted for a range of site indices (24 to 37 m) and discount rates (2.5% to 15%). The carbon project costs and the opportunity cost of reducing harvest (through increased rotation ages), as opposed to generating revenue from harvesting, were not included in the analysis. They found that the cost to sequester one additional tonne of carbon in intensively managed loblolly pine plantations decreased with an increasing site index (at a 5% discount rate, from $113.5 per t carbon ($30.9 tCO2e-1) at a site index of 24 m to $19.3 per t carbon ($5.3 tCO2e-1) at a site index of 37 m), and increased with an increasing discount rate (for a site index of 37 m, from $4.18 per t carbon ($1.1 tCO2e-1) at a 2.5% discount rate to $93.0 per t carbon ($25.4 tCO2e-1) at a 15% discount rate). 15  The cost to produce carbon credits through the use of fertilizers was examined by Bull (2010) in theoretical forests for the Coast forest region in British Columbia. The analysis was conducted for a range of site indices (15 to 30 m); carbon project costs (planning, maintenance, monitoring, verifying, reporting, and transactions), planting costs, and management investment costs (fertilization). The discount rate used to calculate the cost to produce carbon credits was not specified. He found the cost to produce carbon credits decreased with an increasing site index (from $29.3 tCO2e-1 for a site index of 15 m to $6.8 tCO2e-1 for a site index of 30 m). Elgie et al. (2011) examined the effect of a range of carbon credit prices ($3 to $40 tCO2e-1) on the harvest level in a theoretical boreal forest typical to north-central Alberta. The analysis was conducted in a non-spatial linear programming model, which included timber and non-timber objectives and maximized the combined net present value of timber harvest and carbon credits. The opportunity cost of reducing harvest and the costs specific to carbon projects (establishment, validation, and verification) were not included in the calculation of the net present value. Various other costs were included, which resulted in a timber net revenue of $7 per m3 (silviculture costs were provided in $ ha-1 and were estimated to $2 m-3). The analysis was conducted for discount rates between 3% and 7%. They found that harvest reductions to 70%, 50%, and 30% of the baseline level at a 3% discount rate were possible for carbon credit prices of $8, $9.6, and $14.4 tCO2e-1, respectively. They also found that the carbon credit prices increased with increasing discount rates (for a 30% harvest reduction, from $14.4 tCO2e-1 at a 3% discount rate to $35.2 tCO2e-1 at a 7% discount rate). Boyland (2006) reviewed in detail the literature on the economics of forest management for carbon storage. He concluded that (1) timber and non-timber objectives should be part of the analysis; (2) leakage  should be accounted for (i.e., reduced harvest in one forest might result in 16  an increase harvest in other forest in order to fill in the wood demand); (3) the discounting method (which discounts the costs, revenues, and carbon credits) should be used to calculate the carbon credit price; (4) carbon supply curves (i.e., the amount of carbon credits produced over a range of carbon credit prices) should be produced; and (5) both average and marginal costs should be published. However, it is important to recognize that not all of these indicators can be estimated in a forest estate, mostly because of data limitations. The cost to produce carbon credits was examined by van Kooten et al. (2009) in a meta-regression analysis with 1,047 observations from 68 studies. While there was a great deal of inconsistency across the studies included in the analysis, the authors concluded that the marginal cost to produce carbon credits through forest management practices was US$59 to US$88 tCO2e-1. An additional US$30 tCO2e-1 could be added when the opportunity cost of land (not equivalent to opportunity cost of reducing harvest) was included. In comparison, the average market carbon credit price for carbon projects involving forest management practices during 2012 was US$9.8 tCO2e-1 ($6 to $45 tCO2e-1) (Peters-Stanley et al., 2013).  1.2.5 Assumptions in carbon financial analyses A typical financial analysis determines the net revenue of an activity as the sum of all revenues minus the sum of all costs. More meaningful financial results are obtained when the time effect on money is included through means of discounting based on the general perception that money, goods, services, and products have a time value (i.e., early availability is more valuable than late availability). With respect to forestry activities, basic financial analyses include assumptions to quantify the forest products and assumptions to estimate the money value of the forest products. The timber, as a forest product, is typically quantified via a timber supply 17  analysis which forecasts the existing forest inventory and determines the maximum sustainable rate of harvest that meets a specific set of management objectives. This process is well established in temperate forests (e.g., Davis et al., 2005) and is not reviewed here. In this section, I review the key assumptions to estimate forest-based carbon credits (as another forest product) and to estimate the money value of the carbon credits because these two sets of assumptions have the most significant outcomes in carbon financial analyses. The forest-based carbon credits are generated when the project scenario carbon storage in the forest ecosystem and in the timber products life cycle is higher than the baseline’s. Given the complexity to track the carbon in the forest ecosystem and in the timber products life cycle, many assumptions are made along the way, which ultimately affect the accuracy of the carbon storage estimates and the carbon credits that a project proponent can claim. Some key assumptions include: (1) Estimate the carbon storage in a forest ecosystem. The carbon storage in a forest ecosystem is affected by the carbon uptake from the atmosphere (total photosynthesis less autotrophic respiration) and by the carbon release through heterotrophic respiration. Kurz et al. (2013) reviews the 4 approaches to estimate carbon storage in forest ecosystems (repeated field measurements of forest characteristics, summation of carbon flux estimates, modelling of ecosystem processes, and atmospheric inversion models) and argue that all these approaches should be used to estimate carbon storage in forest ecosystems and reduce uncertainties. Information about all 4 approaches is included into the carbon budget model for the Canadian forest sector (CBM-CFS3) (Kurz et al., 2009) 18  which is widely used to model the carbon dynamics within Canadian forest ecosystems. (2) Estimate the carbon in timber products life cycle. Sophisticated schemes were developed to track over time, the carbon fraction that is harvested from the forest ecosystem, the energy inputs to manufacture the timber-based products, the greenhouse gas emissions from decaying of the timber products in use or in landfills, and the capture of the greenhouse gasses from landfills. A simpler approach includes the use of decay curves (e.g., Smith et al, 2006) which indicate the fraction of carbon that is stored over time in various timber products and in landfills. (3) Estimate the substitution benefits. Some of the harvested carbon is used to replace materials that have a higher carbon footprint and thus, reduce the greenhouse gas emissions. However, the accounting methodology is difficult because of the complex tracking mechanism of the various sources for raw materials and energy used in manufacturing.  Carbon credits protocols were developed to guide the project proponents in addressing the carbon accounting assumptions summarized previously. However, selecting the proper protocol is a difficult task. Galik et al. (2009) analyzed two of the mainstream protocols - the California Climate Action Reserve (Climate Action Reserve, 2012) and Verified Carbon Standard (VCS) (Verified Carbon Standard, 2012a) - and assessed the carbon credits that could be claimed for the same forest and the same management strategy. The results showed that carbon credits estimates between the two mainstream protocols varied by approximately 50% after 100 years. In addition, some protocols are trusted more than others on the marketplace, the 19  trust being affected by the credibility of the organisation monitoring the carbon accounting and by the other environmental services attached to the claimable carbon credits. For example, the carbon credits claimed through California Climate Action Reserve which has a higher level of standardization (Climate Action Reserve, 2012) or through Climate, Community, and Biodiversity protocol which adds environmental co-benefits to the project (Climate, Community, and Biodiversity Alliance, 2013), have a higher price tag than the carbon credits clamed through VCS (Peters-Stanley, 2013; World Bank, 2014). Therefore, selecting the guiding protocol can have a significant effect on the number and market value of the claimable carbon credits. Leakage, increased risk of non-permanence, and the temporary positive effects are the three main concerns that reduce the performance of forest-based carbon credits on the marketplace. Leakage occurs when harvesting is reduced in one forest estate for the purpose to produce carbon credits and it increases in other forest estates in order to fill the gap in timber demand. Some guiding protocols have higher carbon credits deductions than others. For example, the Protocol for the Creation of Forest Carbon Offsets in British Columbia (FCOP) (British Columbia Ministry of Environment, 2011) deducts 55-65% of the claimable carbon credits, while VCS deducts up to 40%. The risk of non-permanence refers to the unpredictable characteristic of natural or illegal anthropogenic disturbances which can reverse the benefits of increased carbon storage. A widely used and accepted methodology to assess the risk of non-permanence was developed by the VCS (Verified Carbon Standard, 2012b) which deducts a fraction of the claimable carbon credits and stores it into a buffer pool for latter release. Regardless of the scrutiny level in accounting the carbon credits, the positive benefits on the atmosphere are temporary because the carbon is in a state of flux between the land and aquatic 20  ecosystems and the atmosphere. Thus, the carbon sequestered today by a tree will eventually be released in the atmosphere at a later time. The money value of the carbon credits depends largely on the costs to establish and monitor the carbon project, the value of the standing timber (i.e., timber price), and the time effect of discounting. The costs to establish and monitor the forest-based carbon projects were investigated in the United States by Galik et al. (2012). Afforestation, extended rotations, and improved productivity activities were considered for a range of project sizes (100-10,000 ha). The results indicated that the total costs (establishing and monitoring) can vary between $500 ha-1 and $1,800 ha-1 for a 100-ha forest estate, and between $360 ha-1 and $1,400 ha-1 for a 10,000-ha forest estate. In British Columbia, there are no published such costs, yet the range found by Galik et al. (2012) is not unheard of (Clive Welham, PhD, personal communication, 2013). The approaches to estimate timber prices were reviewed by Leefers and Ghani (2014). Administered charges (or values) are commonly used in developing countries, whereas the residual value and the transaction evidence methods, which are closely linked to log markets, are widely used in North America.  The residual value method uses the end-wood-product price as a base price, from which it subtracts the logging, transporting, and manufacturing costs along with a risk factor. The transaction evidence method (also used in British Columbia) uses the statistical analysis of recent timber sales and their characteristics. Leefers and Ghani (2014) argue that none of the three approaches to estimate timber prices is superior because there is an absence of competitive markets in many regions of the world. The time effect of discounting in financial analyses is discussed in detail by Price (2014). Because of the long time period between expenditure and ensuing benefit and the rapidly changing circumstances (e.g., political, economic), discounting can affect significantly forest 21  investments and forest activities. The optimal rotation, thinning regimes, optimal intensity of the silvicultural intervention, and harvesting investment are unprofitable when discounting is considered. Maintenance of the current silvicultural system is favoured by discounting, yet discounting delays adopting new regimes that might result in higher timber volumes or improved environmental services. Similarly, activities (forestry-related included) that have the potential to mitigate the anthropogenic global climate change are delayed when relatively high discount rates are used in the cost-benefit analysis (Howarth, 2009). Arguments for low discount rates usage (<1%) include intergenerational fairness (Howarth, 2009; Price, 2014), decision-makers’ behaviour in capital markets (Howarth, 2009), supporting aggressive steps to stabilize the anthropogenic global climate change (Cline, 1992), and utilitarian moral reasoning (i.e., equal weight to the welfare of each present and future person) (Stern, 2007). Arguments for discounting include the opportunity cost of investment funds, the time preference, diminishing marginal utility, and risk. These are discussed in detail by Price (2014). The opportunity cost of investment funds is measured by the real rate of return on alternate investments. It is argued that “the real rate of return as a discount rate indicates whether the net benefits of forestry investments are greater than the net benefits foregone elsewhere in the economy”. However, this argument depends on the reality of the alternative investments. Like environmental values, some benefits are inheritably un-marketed, consumption-based, and incapable of direct reinvestment. The time preference for earlier over later consumption misinterprets what people want and neglects intergenerational justice. The diminishing marginal utility applies only to a few products and services because it discounts only those products and services that are known to be more abundant for future generation. The risks to profitability should not be included in discounting because it gives less significance to future costs and thus, 22  risky policies are more likely to be accepted. Finally, the concept of declining discount rate with time developed to address some of the discounting issues creates major difficulties for forestry activities.                      23  2 Methods 2.1 Description of the forest estates The Alex Fraser Research Forest (AFRF) is a 9,812 ha forest subdivided into 2 blocks near Williams Lake, British Columbia (Figure 2-1). Detailed land base description is included in APPENDIX I. The Knife Creek block (3,487 ha) is adjacent to the San Jose Valley in the Interior Douglas-fir biogeoclimatic ecosystem classification (BEC) zone, dry-cool variant. Small areas of this block are in the Interior Douglas-fir BEC zone, very-dry variant and Sub-boreal Pine-Spruce BEC zone, moist-cool variant. Interior Douglas-fir (Pseudotsuga menziesii var. glauca) and lodgepole pine dominate these forests, which have been shaped since the mid-1940s by frequent wildfires and logging activity into an uneven-aged stand condition. The Gavin Lake block (6,315 ha) is located adjacent to the Beaver Valley in the Sub-boreal Spruce BEC zone, dry-warm variant and Interior Cedar-Hemlock BEC zone, moist-cool variant. Interior Douglas-fir, hybrid spruce (Picea glauca x Picea engelmanii) and western redcedar (Thuja plicata) dominate these mid-seral and mature stands. The western half of the Gavin Lake block has been shaped since the early 1960s into an uneven-aged stand condition by wildfires and logging activity, while the eastern half has been converted to a timber production area of even-aged stands with age classes ranging from 20 to 250+ years. The average site index (i.e., top height in meters at age 50) estimated for the forested portion of the land base is 22.1 m (range 15 to 26 m).    24   Figure 2-1. Location of the forest estates  The Malcolm Knapp Research Forest (MKRF) is a 5,157 ha forest estate located in the foothills of the Coast Mountains, approximately 60 km east of Vancouver, British Columbia (Figure 2-1). It falls entirely into the Coastal Western Hemlock BEC zone, with the southern half in the dry maritime subzone, and the northern half in the very wet maritime subzone. The average site index for the forested portion of the land base was estimated to be 25.6 m (range 20 to 40 m). Coniferous trees dominate these stands, the most common being coastal Douglas-fir (Pseudotsuga menziesii var. menziesii), western redcedar (Thuja plicata), and western hemlock (Tsuga heterophylla). Wildfires and logging have created a mosaic of even aged stands; the 25  western half is covered by 120-year-old stands, the eastern half by 70-year-old stands, and some small patches of 400+-year-old stands are spread throughout the northern portion of the forest estate. In addition, forest harvesting since 1949 has led to a range of second- and third-growth age classes from 1 to 60 years. The third forest estate (FE3) is 14,920 ha in size and is located in the boreal plains, approximately 40 km southeast of Dawson Creek, British Columbia (Figure 2-1). It falls entirely in the Boreal White and Black Spruce BEC zone, with the western third in the dry cool subzone and the rest in the moist warm subzone. The average site index for the forested portion of the land base was estimated to be 14.7 m (range 6 to 22 m). Lodgepole pine covers approximately half of the land base while the other half is covered by mixed stands of white spruce, black spruce (Picea mariana), and trembling aspen. Mountain pine beetle (Dendroctonus ponderosae) has disturbed most of the lodgepole pine stands since 2003 at an average attack intensity of 30%. Wildfires and forest harvesting since 1978 have created a mosaic of even-aged stands; 76% of the land base is covered by 80- to 160-year-old stands.  2.2 Simulation models The Forest Planning Studio (FPS-ATLAS) (Nelson, 2003b), a spatially explicit forest-level planning model, was used to report for each planning period and for every spatial unit the volume harvested and the standing volume. Two major sources of information were required to build the FPS-ATLAS database: (1) GIS (Geographical Information Systems) databases for the forest estates and (2) growth and yield information of the forest inventory. The GIS databases provided the spatial (e.g., coordinates, area), ecological (e.g., BEC zone), forest inventory (e.g., species, site index, age), and planning (e.g., land-use change) information. These were used to 26  develop the spatial planning units (i.e., polygons) in FPS-ATLAS. The combination of silvics of the preferred species, site and climate factors, and management objectives determined the silvicultural systems to be implemented at the 3 forest estates. At the AFRF, there are 8,670 polygons grouped into 213 stand types based on forest inventory variables, ecological information, and planning information. There are 3 silvicultural systems at the AFRF:  (1) The clear-cut system, which is implemented on 15% of the timber harvest land base (THLB), consists of one 40% commercial thinning at age 60 and a final cut at age 100.  (2) The shelterwood system, which is implemented on 2% of the THLB, consists of one 40% commercial thinning at age 60, a seeding cut (50% volume removal) at age 100, and a final cut at age 110.  (3) The uneven-aged system, which is implemented on 83% of the THLB, consists of one 40% commercial thinning at age 60 and 20% to 35% volume removal every 20 to 40 years at age 160 to 200 years (depending on wildlife habitat and visual objectives).  The FPS-ATLAS model for the MKRF was developed by Moreira-Munoz (2009) which includes 2,160 polygons grouped into 104 stand types, and only the clear-cut silvicultural system is used. The clear-cut system consists of one 40% commercial thinning at age 80 for stands that were regenerated naturally; one 40% commercial thinning at age 40 to 50 (depending on site index) for stands that were regenerated artificially; and a final cut at age 60 to 120 (depending on site index, regeneration type, and species composition). For the AFRF and the MKRF, the commercial thinning was capped at 20% of the annual harvested volume with a treatment window of up to 20 years. The polygons that missed the commercial thinning window were 27  subsequently scheduled for final cuts. At FE3, there are 2,737 polygons grouped into 32 stand types, and only the clear-cut system is used; this consists of one cut at age 60 to 170, depending on site index, regeneration type, and species composition. Detailed information of the types of data used to build the FPS-ATLAS databases is included in APPENDIX II. Growth and yield information was extracted either from the Timber Supply Area Analysis Report (British Columbia Ministry of Forests, 2001; British Columbia Ministry of Forests, 2002; British Columbia Ministry of Forests, 2003) where the forest estates reside or developed from the existing inventory using stand-level yield prediction systems that account for losses due to a range of factors (e.g., open areas in the productive space, disease and pests, decay, waste, and breakage) (British Columbia Ministry of Forests, Lands and Natural Resource Operations, 2012a). Variable Density Yield Prediction (VDYP), an empirical yield prediction system intended for use in unmanaged, natural stands of pure or mixed species composition, was used to generate the growth and yield curves for the stands regenerated naturally following a stand-replacing disturbance (e.g., wildfire). The Table Interpolation Program for Stand Yields (TIPSY), a growth and yield program that retrieves and interpolates yield tables from the managed stand yield databases for various even-aged species, was used to generate the growth and yield curves for the stands regenerated artificially following harvesting-planting events. The carbon storage in the forest ecosystem was modeled with the Carbon Budget Model for the Canadian Forest Sector (CBM-CFS3), a non-spatial stand- and landscape-level model that simulates carbon dynamics in a forest ecosystem (aboveground biomass, belowground biomass, and dead organic matter pools) (Kurz et al., 2009). The FPS-ATLAS databases and harvesting schedules for all scenarios were translated into CBM-CFS3-specific import tables through a series of queries built into a Microsoft Access database (F2C (FPS-ATLAS to CBM-CFS3) tool). 28  The CBM-CFS3-specific import tables were needed to create CBM-CFS3-specific project files and thus forecast the carbon storage. CBM-CFS3 was instructed to use the default built-in parameters for 3 terrestrial ecoregions, depending on the locations of the 3 forest estates; British Columbia Montane Cordillera at the AFRF, British Columbia Boreal Plains at the FE3, and British Columbia Pacific Maritime at the MKRF. Details on how CBM-CFS3 initializes dead organic matter carbon pools and calibration can be found in Kurz et al. (2009). The reports generated by CBM-CFS3 can be used to track a wide variety of biomass and dead organic matter pools on a yearly basis throughout the planning horizon. In this analysis, the total ecosystem carbon storage was used to quantify the carbon credits. Forest response to climate change is expected to differ depending on the BEC zones where the forest estates analyzed in this study reside. The changes predicted to occur to the BEC zones are (1) changes to BEC size and shifting of BEC boundaries (Burton and Cumming, 1995; Hamann and Wang 2006), (2) changes to species habitat and frequency (i.e., ground cover percentage for each species) (Burton and Cumming, 1995; Hamann and Wang, 2006), and (3) increases in the intensity and frequency of natural disturbances (Swift and Ran, 2012). In the case of the AFRF, the Interior Douglas-fir and Interior Cedar-Hemlock BEC zones are expected to increase in size, while the Sub-boreal Pine-Spruce BEC zone is expected to shrink in size as it shifts northward and towards higher altitudes. In the case of FE3, the range of white spruce and trembling aspen is predicted to shrink, while the range of lodgepole pine will increase. In the case of the MKRF, the Coastal Western Hemlock BEC zone is predicted to expand to higher altitudes. In order to accommodate the predicted changes in this analysis, the growth and yield curves for the future managed stands have been adjusted to promote the species that may have a competitive advantage under a warmer climate. At the AFRF, a mix of interior Douglas-fir, 29  hybrid spruce, western redcedar, and lodgepole pine was promoted; at FE3, lodgepole pine-dominated stands with small pockets of trembling aspen and white spruce were promoted; and at the MKRF, a mix of western redcedar, coastal Douglas-fir, western hemlock, and red alder (Alnus rubra) were promoted.  2.3 Baseline determination Two approaches could be used to determine the baseline harvest level for the forest estates in this analysis: (1) historic harvest level and (2) long term sustainable yield (LTSY). The LTSY is defined as the maximum annual harvested volume that can be maintained for an entire planning horizon (Davis et al., 2005). The historic harvest levels for the AFRF and MKRF forest estates have been strongly controlled by log prices, and this has resulted in the historic harvest at both forest estates being below the LTSY. It is difficult to predict whether log prices will continue to constrain the harvest to levels below the LTSY, and so it is difficult to use historic harvest levels in simulations that will extend far into the future. Current forest management planning in British Columbia employs a 250-year planning horizon to determine the annual allowable cut volume (British Columbia Ministry of Forests, Lands and Natural Resource Operations, 2012b). For consistency, however, it was determined that a 250-year LTSY is not suitable for this analysis: the MKRF is located on private land, and the current management plan employs a 100-year LTSY; the AFRF is located on Crown land, and the allowable cut is guided by a hybrid approach (area and LTSY control); and FE3 is located on Crown land, and the allowable cut is guided by a 250-year LTSY. In addition, British Columbia’s Greenhouse Gas Reduction Targets Act stipulates that the atmospheric effect of a greenhouse gas reduction achieved by a carbon project has to endure for at least 100 years, while the validation period of 30  forestry offset projects expires after 25 years (British Columbia Ministry of Environment, 2013). Thus, a 100-year planning horizon was selected for determining the LTSY, and this will be used as the baseline harvest level in this analysis. Using the above approach, the baseline LTSYs were determined to be 14,800 m3 year-1 for the AFRF, 33,000 m3 year-1 for the MKRF, and 27,000 m3 year-1 for FE3, while satisfying a series of constraints imposed by the forest management objectives of the forest estates. The constraints consist of minimum harvest ages (MHA); spatial adjacency constraints; buffer size around protected areas (water bodies, wetlands, wildlife corridors, and visual quality); in-block retention levels; and harvesting priorities. MHAs for the AFRF and the MKRF were determined by considering a combination of culmination of mean annual increment, desired wood products, and existing research objectives. For FE3, MHAs were referenced from the timber supply analysis report (British Columbia Ministry of Forests, 2002). MHAs ranged between 100 and 200 years for the AFRF, between 60 and 120 years for the MKRF, and between 60 and 170 for the FE3. Spatial adjacency constraints (10 to 20 years), buffer sizes (10 to 50 meters), and in-block retention levels (8% to 20%) were assigned based on the forest management plans for the 3 forest estates, which are compliant with British Columbia’s current forest legislation. The harvesting priority algorithm was programmed to treat oldest stands (and mountain pine beetle-infested stands at FE3) first. Stands scheduled for commercial thinning were given priority over stands scheduled for final cuts (clearcuts, shelterwood, and those with uneven-aged management systems), and stands scheduled for clearcuts were given priority over stands scheduled for other final cuts. The simulations were run for 100 years.  31  2.4 Management strategies to increase carbon storage Throughout this analysis, 2 types of management strategies are used to explore the research objectives: (1) strategies that reduce the harvest below the baseline level and (2) strategies that maintain the baseline harvest level while promoting higher growth rates. The first group includes strategies that reduce the harvest level to a fixed predetermined target (i.e., reduced allowable harvest), increase the MHAs, or increase the area in reserves. The second group includes strategies that promote higher growth rates through the use of fertilizers or genetically improved planting stock. Each research objective is explored using a particular combination of strategies, which is described in more detail in the respective chapters.  2.5 Calculation of carbon credits Given the location of the 3 forest estates analyzed here, the Protocol for the Creation of Forest Carbon Offsets in British Columbia (FCOP) (British Columbia Ministry of Environment, 2011) was selected to estimate the claimable carbon credits. It should be noted the FCOP is seeking formal recognition under the international Verified Carbon Standard (VCS) (Pacific Carbon Trust, 2012b). The controlled and affected carbon pools and related carbon sources considered by the FCOP include (1) live and dead forest carbon pools (which are significantly larger than all other carbon pools and sources); (2) carbon stored in harvested wood products in use and in landfill; (3) emissions due to fossil fuel production and combustion for vehicles, for equipment, and for transportation of material, equipment, inputs, and personnel to site; (4) emissions due to processing the harvested wood products; (5) emissions due to the anaerobic decay of the harvested wood products in landfill; and (6) external harvest shifting leakage due to harvest reduction (estimated at 61.27% for the AFRF, 53.47% for the MKRF, and 65% for FE3). 32  In addition to the controlled and affected carbon pools and related carbon sources, every forest carbon project carries a risk of reversal as forests are subjected to natural disturbances that reduce forest growth and carbon storage. In order to mitigate the risk of reversal, the offset programs (i.e., the regulatory bodies that register projects and enforce systems and rules for carbon accounting, monitoring, reporting, verification, and certification) create a buffer pool of carbon credits corresponding to the risk of reversal. The carbon credits in the buffer pool cannot be sold immediately by project proponents; instead the project proponents must follow release schedules. This analysis uses the VCS (Verified Carbon Standard, 2012a) buffer pool release schedule, which releases 15% of the buffer pool every 5 years. The number of carbon credits held back due to the risk of reversal was assessed at 10% of the carbon credits produced for all 3 forest estates analyzed here, using the VCS Tool for AFOLU Non-Permanence Risk Analysis and Buffer Determination (Verified Carbon Standard, 2012b). Ongoing verification must be conducted periodically in order to allow project proponents to sell carbon credits. Most of the offset programs require that at least one verification event should occur every 5 years for the improved forest management strategies (e.g., Verified Carbon Standard, 2012c). The verification frequency, payment strategy (ex-ante or ex-post), and payment schedule are established through negotiations between buyers and producers and are specific to each project. Since verification frequency has a significant effect on the number of carbon credits that can be claimed, the financial analysis was conducted for 2 levels of verification: 1 year and 5 years. The 2 levels permit project proponents either to sell the carbon credits as soon as they are generated (1-year verification) or to sell accumulated carbon credits at 5-year intervals (5-year verification).  33  2.6 Financial analysis The goal of the financial analysis is to determine the financial viability of the management actions that result in selling a product or a service of the forest. As detailed in the introductory chapter, there is a debate in the literature regarding what financial indicators to use in assessing the financial viability of implementing a forest carbon project. In order to explore the research objectives of this analysis, I estimated the net revenues of each forest estate for the baseline scenario and for the carbon project with the corresponding strategy to produce carbon credits. Then, I estimated the minimum value per carbon credit that was needed to make the 2 net revenues (baseline and project) equivalent. The total present net revenue for the baseline scenario (TPNRB) and the total net present revenue for the carbon project (TPNRC) for an entire planning horizon of n years are   nttBtB rTNRHTPNR0 1 (2-1)   ntttttCtC rCCCPTNRHTPNR0 1 (2-2) Here, H is the harvested volume (B - baseline, C - carbon project); TNR is the average timber net revenue per cubic meter (i.e., the difference between the average timber revenue and the harvesting cost); P is the price per carbon credit; C is the number of carbon credits; CC is the carbon project cost, which includes the initial establishment, validation, and ongoing verification cost; r is the discount rate; and t is the year. Set TPNRB equal to TPNRC and isolate the sum containing P:       ntttCtBtnttttrCCTNRHHrCP00 11  (2-3) 34  The right-hand side of Equation (2-3) represents the total cost needed to generate Ct in year zero dollars for a given r. In order to determine a minimum value per carbon credit, solve for P in Equation (2-3), assuming that P is a constant over the planning horizon of n years:    ntttntttCtBtrCrCCTNRHHP0011 (2-4) P calculated as in Equation (2-4) is defined as the total break-even carbon credit price (year zero $ per carbon credit), and it is assumed to be a constant value for the entire planning horizon of n years. Equation (2-4) is identical to the levelization equation from Richards and Stokes (2004) when using the same discount rate for the cash flows (numerator) and carbon credits (denominator); it is also identical to the discounted carbon equation from Boyland (2006).  The right-hand side of Equation (2-4) can be expanded in order to calculate 2 components of the total break-even carbon credit price:     ntttnttCtBtHrCrTNRHHP0011 (2-5)   ntttntttCCrCrCCP0011 (2-6) Here, PH is the component of the total break-even carbon credit price due to the opportunity cost of reducing harvest and PCC is the component of the total break-even carbon credit price due to 35  the carbon project cost. Note that the total break-even carbon credit price (P) calculated in Equation (2-4) is the sum of PH and PCC. In previous financial analyses on carbon cost (e.g., van Kooten et al., 2009), n was assumed to be the entire life of the project. In the case of British Columbia, the contract term for forest carbon projects is 25 years with the option of renewal, yet permanence of the emissions offsets should be ensured for 100 years following the end of the contract (British Columbia Ministry of Environment, 2013). This is expected to be achieved by continuing to harvest the baseline level (set prior to the implementation of the carbon project) for the next 100 years following the end of the 25-year contract. In this analysis, n is defined as the break-even period and is set to 25 years. It is also assumed that all costs are expenses at the time of occurrence. The costs and revenues used in the financial analysis are detailed in section 4.2.             36  3 Alternate forest management practices to produce carbon credits 3.1 Overview Alternate forest management practices can either reduce harvest levels from the baseline level or maintain the baseline harvest level while promoting higher growth rates. It is also possible to combine these 2 types of strategies (e.g., reduce the harvest while promoting higher growth rates). In this chapter, I will compare the 2 types of strategies on 2 different actively managed forest estates in British Columbia (AFRF and MKRF) to determine if the finding that harvest reduction will outperform increased growth rate holds true. Furthermore, for the forest management practices that employ harvest reduction strategies, I will consider the performance of 4 strategies: fixed harvest level, increased minimum harvest ages, and 2 strategies that increase the area in reserves. These 4 strategies will be compared based on carbon storage and forest age class distribution over a range of harvest levels. Finally, I will discuss from the forest manager perspective, the reasons to favour one of these strategies over the other 3, and whether this choice is the same for both forests considered (AFRF and MKRF).  3.2 Methods The forest estates, simulation models, and baseline determination have been described in detail in Chapter 2. What follows is a description of the methods relevant only to this chapter, which are in addition to the methods described in Chapter 2. This chapter uses for analysis the AFRF and MKRF forest estates. 37  3.2.1 Scenarios to determine the magnitude of the difference between strategies that increase growth rates and strategies that reduce harvest level 3.2.1.1 Fertilization This scenario was designed to maximize growth responses using fertilizers. While applying fertilization is financially attractive towards the end of the rotation (Bevege, 1984), and the response to a single fertilizer application lasts 5 to 8 years (Stegemoeller and Chappell, 1991), multiple applications can sustain the increased growth rates for longer periods of time (Farnum et al., 1983; Huettl and Zoettle, 1992; Seely et al., 2002). Starting in year 1 of the simulation, for all existing stands over 10 years old on medium productivity areas (i.e., those with a site index of 15 to 26 m for the AFRF and 18 to 34 m for the MKRF), 5 consecutive applications at 10-year intervals were applied, 5 being the maximum number of applications that could be simulated in TIPSY. All growth and yield curves were adjusted using standard growth responses embedded in TIPSY for 80% fertilization efficiency. These newly developed growth and yield curves were updated in the FPS-ATLAS database for all the stands undergoing fertilization. This strategy for modeling fertilization was also applied to the newly established managed stands following harvest throughout the simulation (i.e., 5 consecutive applications at 10-year intervals starting at age 10). The target harvest level was set at the baseline level, and the simulation attempted to achieve this harvest level while meeting the same constraints as the baseline scenario.  3.2.1.2 Genetically improved stock This scenario was designed to use genetically improved planting stock in order to maximize growth responses. Previous studies have reported volume gains of up to 48% (Stoehr 38  et al., 2011) using TIPSY’s procedure for predicting breeding values in British Columbia (Xie and Yanchuk, 2003). Therefore, TIPSY was instructed to generate growth and yield curves that simulated a 30% expected gain at age 60, 30% being the maximum response that could be simulated in TIPSY. These newly developed growth and yield curves replaced the existing curves in the FPS-ATLAS database and were used for all future stands regenerated from planting under the clear-cut system. The target harvest level was set at the baseline level, and the simulation attempted to achieve this harvest level while meeting the same constraints as for the baseline scenario.  3.2.1.3 Reduction of harvest to a fixed target level Using the same FPS-ATLAS database and the same constraints as for the baseline scenario, 2 scenarios were simulated: (1) with the target harvest level set from year 1 of the simulation to a minimum accepted level (MAL) and (2) with the target harvest level set from year 1 of the simulation to zero (no harvest). MAL was determined by the forest managers of the forest estates. In the case of the AFRF, the need to meet wildlife habitat and wildfire protection objectives, to continue current research, and to establish future research projects constrained MAL to 7,500 m3 year-1 (5,000 m3 year-1 for the Gavin Lake block and 2,500 m3 year-1 for the Knife Creek block). In the case of the MKRF, in addition to meeting the research and wildfire protection objectives, the forest manager had to provide a minimum volume for the onsite sawmill, and this constrained MAL to 11,000 m3 year-1.  39  3.2.2 Scenarios to determine the performance of harvest reduction strategies Four harvest reduction strategies were considered in this analysis: fixed harvest level (i.e., reduced allowable harvest), increased MHA, and 2 strategies that increase the area in reserves. For each of these strategies, stored carbon and forest age classes were compared over 3 harvest levels: MAL and 2 equally distanced levels between the baseline and MAL. Harvest levels are permitted to fluctuate from year to year in the forest estates considered in this analysis; however, overcutting in a period is constrained so that the LTSY can be maintained over the long term. If constraints such as the MHAs and the area in reserves were increased, it would not be possible to maintain the LTSY that was determined for the baseline. Thus, to compare these 4 harvest reduction strategies, it was necessary to determine the percentage increase in the MHA or the percentage of area in reserves that would produce an LTSY equivalent to the fixed harvest level being considered (Table 3-1). This provided 4 strategies to achieve a particular reduced harvest level.  40  Table 3-1. Relevant constraints for the harvest reduction strategies over a range of harvest levels  AFRF (baseline at 14,800 m3 year-1) Harvest level for comparison (m3 year-1) (% reduction from the baseline) 12,367 (16%)  9,933 (33%)  7,500 (49%)  Fixed harvest level (m3 year-1)*  12,367  9,933  7,500 Increased MHA (%) 17 44 54 Area in reserves (poor sites first) (%) 25 38 49 Area in reserves (rich sites first) (%) 21 36 48  MKRF (baseline at 33,000 m3 year-1) Harvest level for comparison (m3 year-1) (% reduction from the baseline) 25,667 (22%) 18,333 (44%) 11,000 (67%)  Fixed harvest level (m3 year-1)   25,667  18,333  11,000 Increased MHA (%) 65 84 125 Area in reserves (poor sites first) (%) 26 48 69 Area in reserves (rich sites first) (%) 19 35 53 * For the fixed harvest level strategy, the values represent annual harvested volumes equal to the harvest level considered for comparison. For the increased MHA and area in reserves strategies, the values represent the percentage increase (of MHA or area in reserves) that would produce an LTSY equivalent to the harvest level considered for comparison.  The fixed harvest level strategy used the same FPS-ATLAS database and the same series of constraints as the baseline. The harvest levels for comparison that the simulations attempted to achieve are presented in Table 3-1. The increased MHA strategy altered MHA constraints from the baseline constraints by applying the percentage increase to the current MHAs (Table 3-1) that produced an LTSY equivalent to the fixed harvest level being considered for comparison. Two strategies were used in this study to increase the area in reserves: (1) allocating poor sites first (i.e., lowest site index first), and (2) allocating rich sites first (i.e., highest site index first). While there are other strategies to increase the area in reserves (e.g., increasing buffer size for water streams, lakes, and wetlands; increasing size and frequency of wildlife tree patches; adding areas around existing reserves; adding stands above a certain age and site index; and adding low operability areas), the poor sites and rich sites strategies represent the extreme cases. Modeling 41  the poor and rich sites reserve strategies was achieved by increasing the area in reserves until the LTSY was equivalent to the fixed harvest level being considered for comparison (Table 3-1). The area in reserves was increased by adding polygons sorted by site index (e.g., poor first or rich first) and then ascending by size. Table 3-1 presents the increased area in reserves as a percentage increase from the baseline constraint.  3.3 Results Duration of the carbon project is an important aspect when analyzing alternate forest management practices that have the potential to result in additional carbon storage. In British Columbia, the validation period of carbon projects expires after 25 years, but the atmospheric effect of a greenhouse gas reduction achieved by a sequestration project should endure for at least 100 years (British Columbia Ministry of Environment, 2013). In addition, differences between year 25 and year 100 results are expected to occur, and these influence the decision of the manager in terms of the contract length and the forest management practice to implement in order to achieve additional carbon storage. Therefore, the results in this chapter are presented for both 25- and 100-year terms. Strategies that increase growth rates show only slight increases in carbon storage from the baseline (Figure 3-1): less than 0.4% over 25 years and less than 5.3% over 100 years (Table 3-2). The results for the fertilization strategy are comparable to the results found by Seely et al. (2002), who reported a less than 13% increase in carbon storage over 300 years for trembling aspen. The results for the genetically improved stock strategy are comparable to the results found by Aspinwall et al. (2012), who reported a less than 13% increase in carbon storage over 40 years for loblolly pine plantations. The time lag required for these strategies to accumulate more 42  live tree biomass than the baseline scenario is causing a delay in carbon accumulation, which explains the small increase at year 25. At year 100, fertilization had the highest increase in carbon storage at the AFRF, while the genetically improved stock strategy had the highest increase in carbon storage at the MKRF. This difference between the 2 forests is explained by the design of the strategies that increase growth rates and the choice of silvicultural system. Recall that the genetically improved stock strategy was designed to regenerate areas under the clear-cut system using genetically improved stock; at the AFRF the clear-cut system is applied to 15% of the THLB, while at the MKRF it is applied to the entire THLB. The fertilization strategy was designed to maximize growth on polygons with medium site index regardless of the silvicultural system. Polygons with medium site index account for 92% of the THLB at the AFRF and 49% at the MKRF. Thus, at year 25 the fertilization strategy results in more carbon storage than the genetically improved stock strategy at both forests, while at year 100 the fertilization strategy still outperforms the genetically improved stock strategy at the AFRF but not at the MKRF.  43   Figure 3-1. Comparing the carbon storage between strategies that reduce harvest and strategies that increase growth rates   44  Table 3-2. Comparing the percentage increase of carbon storage from the baseline between strategies that reduce harvest and those that increase growth rates Strategy Year 25  Year 100 AFRF MKRF  AFRF MKRF  No harvest  4.3%  11.0%   15.7%  48.9% Reduced harvest to MAL 1.9% 7.0%  7.6% 30.4% Fertilization 0.3% 0.4%  2.0% 2.8% Genetically improved stock 0.0% 0.0%  0.7% 5.3%  Strategies that reduce harvest levels resulted in the largest increase in carbon storage from the baseline (Figure 3-1). When harvest levels are reduced to MAL (49% harvest reduction from the baseline at the AFRF and 67% at the MKRF), the carbon storage increase from the baseline was up to 7.0% at year 25 and up to 30.4% at year 100 (Table 3-2), with the MKRF performing approximately 3.5 times better than the AFRF. When harvest levels are reduced to zero (no harvest), the carbon storage increase from the baseline was up to 11.0% at year 25 and up to 48.9% at year 100 (Table 3-2). These results are comparable to the results found by Colombo et al. (2012), who reported a 5% carbon storage increase over 100 years for 52% harvest reduction, and a 10% increase for 100% harvest reduction. These results are also within the range of earlier theoretical findings (Harmon and Marks, 2002; Peng et al., 2002; Taylor et al., 2008; Harmon et al., 2009; Nunery and Keeton, 2010) and the 2 existing forest-based carbon projects in British Columbia (The Nature Conservancy of Canada, 2011; Pacific Carbon Trust, 2011), which indicated that strategies that reduce harvest levels resulted in a 6% to 140% carbon storage increase from the baseline. However, due to the differences in location, baseline calculation, accounting for natural disturbance dynamics (e.g., wildfire), and the length of the planning horizon, the effect of harvest reduction level on carbon storage is difficult to compare. 45  The differences between the 2 forest estates observed in Figure 3-1 and Table 3-2 are, for the most part the result of differences in location, size, and silvicultural systems. Location determines the range in site indices and thus the growth rates. Site index is generally lower at the AFRF compared to the MKRF, yet at year 0 and year 100 for the no harvest scenario, both forest estates have similar carbon storage values. This result is explained by the fact that the area of the AFRF is 91% larger than that of the MKRF. The choice of silvicultural system explains why carbon storage for the baseline scenario is increasing slightly at the AFRF and decreasing at the MKRF and why strategies that reduce harvest levels perform better at the MKRF than at the AFRF. Recall that the AFRF applies uneven-aged systems to 85% of the land base, while the MKRF does not use uneven-aged systems. Previous studies (Harmon and Marks, 2002; Taylor et al., 2008; Harmon et al., 2009) have indicated that switching to uneven-aged systems could increase carbon storage. Thus, in the baseline scenario, the AFRF is moving from a highly disturbed young forest to a forest with more mature trees and more carbon storage, which causes the upward trend observed for the baseline scenario. The upward trend of the baseline scenario reduces the performance of the strategies that reduce harvest levels at the AFRF. Problems can arise when performing a sensitivity analysis that alters the constraint level from that used in the baseline scenario. Recall that the target harvest level (i.e., LTSY) is strongly dependent of the constraint levels, and if the constraints are increased without recalculating the LTSY, then there will be a reduction in harvest volumes in later years of the planning horizon. FPS-ATLAS is instructed to harvest the target volume without violating the constraints; if a particular constraint is arbitrarily increased, this may become limiting, preventing FPS-ATLAS from scheduling the full harvest for a particular year. Figure 3-2 shows the reduction in harvest volume that can be expected when the MHA is increased while the target 46  harvest volume remains at the baseline level. Administratively, this reduction in harvest volume is not permitted. Thus, when comparing the performance of harvest reduction strategies, it was necessary to select target volumes for comparing the different scenarios, and then to find the constraint levels that produced these target volumes.   Figure 3-2. Carbon storage (above) and harvested volume (below) throughout the entire planning horizon for increased MHA scenarios at MKRF when the LTSY is not recalculated  Harvest reduction strategies resulted in small carbon storage differences for both forest estates (Figure 3-3, Figure 3-4, and Table 3-3). The largest difference at the AFRF was at year 100, when the harvest level was set to 9,933 m3 year-1; the fixed harvest level strategy stored the least carbon, 0.6% less than the increased MHA strategy, which stored the maximum amount (identified as max in Table 3-3). The largest difference at the MKRF was at year 100, when the 47  harvest level was set to 25,667 m3 year-1; the increased area in reserves using the poor sites first strategy stored the least carbon, 4.1% less than the rich sites first strategy, which stored the maximum amount (identified as max in Table 3-3). In addition, these differences become smaller as the harvest level considered for comparison approached MAL at the MKRF. However, this trend was not apparent at the AFRF. This result is explained by the general differences between the forest estates mentioned earlier (e.g., site index distribution and silvicultural systems) coupled with small overcuts and an overlap of the area in reserves. Small overcuts occur when FPS-ATLAS attempts to harvest a volume as close as possible to the target level by fully treating the polygons scheduled for harvesting. Since undercuts are not permitted, small overcuts are possible, depending on the combination of polygons that provide the harvest. In the case of the AFRF, when the target level is set to 9,933 m3 year-1, these overcuts were larger for the fixed harvest level strategy than for the other strategies; thus, the difference in carbon storage was larger. There may be an overlap between the set of polygons assigned to reserves based on the rich sites first strategy and the set from the poor sites first strategy, and this overlap will increase as the area in reserves increases. Figure 3-4 shows that, for the MKRF, the difference between the rich sites first and poor sites first strategies diminishes as the harvest level approaches MAL. The MKRF has a large range in site indices, and when the overlap between the sets of polygons is smaller, the carbon storage capacity of the polygons in reserve based on the rich sites first strategy is noticeably greater. This result is not seen at the AFRF because there is a smaller variation in site indices.   48   Figure 3-3. Carbon storage at AFRF for harvest reduction strategies over a range of harvest levels  49   Figure 3-4. Carbon storage at MKRF for harvest reduction strategies over a range of harvest levels 50  Table 3-3. Carbon storage differences at year 25 and year 100 for harvest reduction strategies: values indicate the percentage difference from the maximum stored carbon (identified as max) for each target harvest level. Target harvest level (m3 year-1) Fixed harvest level Increased MHA Increased area in reserves,  poor sites first Increased area in reserves, rich sites first   AFRF year 25  12,367  0.2%  max  0.1%  0.1% 9,933 0.1% max 0.1% 0.1% 7,500 0.1% max 0.0% 0.0%   AFRF year 100  12,367  0.1%  max  0.2%  0.4% 9,933 0.6% max 0.2% 0.3% 7,500 0.2% 0.1% max 0.1%   MKRF year 25  25,667  0.5%  0.4%  0.5%  max 18,333 0.0% max 0.0% 0.3% 11,000 0.1% 0.0% max 0.1%   MKRF year 100  25,667  3.9%  3.1%  4.1%  max 18,333 2.6% 1.9% 2.6% max 11,000 2.0% 1.5% 0.9% max  The harvest reduction strategies over the 3 target harvest levels selected for comparison showed little difference in the distribution of standing volume and area by age classes (Figure 3-5 and Figure 3-6). There is a shift towards older age classes except in the case of the MKRF, where the harvest level is set at 25,667 m3 year-1. In this particular case, there is a more uniform volume distribution by age class and more area in younger age classes compared to the other 2 51  harvest levels. This result can be explained by the choice of silvicultural system. In the case of the AFRF, the implementation of uneven-aged systems on 85% of the area keeps most of the forest in older age classes (100+ years), while in the case of the MKRF, the implementation of the clear-cut system on 100% of the area converts the current distribution to a normal forest. Thus, for the MKRF, as more volume is harvested, less area is locked in older age classes. The 2 strategies to increase area in reserves resulted in slightly larger differences at the MKRF than at the AFRF. While at the AFRF the distribution of standing volume and area is almost identical for all harvest levels, at the MKRF the rich sites first strategy has less area and standing volume in older age classes (160+ years) than the poor sites first strategy. This result is explained by the difference in the areas allocated to the reserve by each of the 2 strategies. While at the AFRF the difference in the reserve area between the poor and rich sites first strategies is less than 8%, at the MKRF this difference is less than 16% (Table 3-1). Thus, in the case of the MKRF, at the same harvest level, the poor sites first strategy allocates more area to reserves than the rich sites first strategy, which results in a slightly higher standing volume for older age classes.  52   Figure 3-5. Standing volume by age classes at year 0 and year 100 for harvest reduction strategies over a range of harvest levels (MAL: minimum accepted level)   53   Figure 3-6. Percentage of total area by age classes at year 0 and year 100 for harvest reduction strategies over a range of harvest levels (MAL: minimum accepted level)   54  While the results of the analysis conducted in this chapter indicated virtually no difference in the performance of harvest reduction strategies when carbon storage and age class distribution were analyzed, the increased MHA and increased area in reserves strategies can create problems for a forest manager. Older forests do not necessarily produce more valuable timber; some products from younger stands can be very valuable. At the MKRF, cedar poles, for example, are available in the 60- to 120-year old stands, and are worth significantly more than standard sawlogs (British Columbia Ministry of Forests, Lands, and Natural Resource Operations, 2014a). If the MHA strategy is used to increase the stored carbon in the forest, and the increase in MHA makes the stands with cedar poles unavailable for harvesting, then the forest manager loses an important revenue source. If a fixed harvest level strategy had been used instead to reduce the harvest level and increase the carbon storage in the forest, it would be possible to alter harvesting priorities in order to take advantage of changing markets. When an increased area in reserve strategy is being used to increase the carbon storage in a forest, the LTSY of the forest estate is based on the part of the forest that is not locked into reserves. Thus, the manager can continually harvest at the LTSY level even if the forest locked in reserves is severely damaged by insects or wind. However, if trees in the reserve area are killed, it is not possible to salvage them, and they will eventually rot, returning much of the stored carbon to the atmosphere. The combination of these 2 factors can result in an overall reduction of the carbon storage in the forest even though the forest manager is meeting the reserve and harvesting constraints. Figure 3-7 shows a simulation of the carbon storage at the AFRF after significant mortality in the reserves in year 10 of the simulation. The sensitivity analysis considered 5%, 25%, and 50% mortality in the reserves when using a rich sites first strategy. The results indicate that 25% mortality is sufficient to reduce carbon storage below the 55  baseline at year 25, while over 50% mortality reduces the carbon storage to levels below the baseline for the rest of the planning horizon. If a fixed harvest level strategy had been used instead to reduce the harvest level and increase the carbon storage in the forest, it would have been possible to shift the harvest to the damaged stands, and the salvaged wood would have been part of the planned annual harvest.   Figure 3-7. Carbon storage at AFRF over a range of mortality intensities in the reserve areas at year 10 of the simulation when using a rich sites first strategy to produce the MAL target harvest  The response of the forest to climate change can have an important effect on carbon storage, yet the published literature for the BEC zones where the 2 forest estates are located indicates contradictory findings. First, an ecosystem can shift north and in elevation. For the general location of the AFRF, Hamann and Wang (2006) predict that the Interior Cedar-Hemlock and Interior Douglas-fir BEC zones are expected to increase in size, to the detriment of the Sub-56  boreal Spruce and Sub-boreal Pine-Spruce BEC zones. Burton and Cumming (1995), on the other hand, predict relatively no change for the Interior Douglas-fir BEC zone, but expect the Interior Cedar–Hemlock BEC zone to increase timber productivity substantially (by 55% to 65%) due to the prevalence of western hemlock. For the general location of the MKRF, Hamann and Wang (2006) predict an increase in size for the Coastal Western Hemlock BEC zone, while Burton and Cumming (1995) predict a total collapse for this BEC zone due to frequent frost events induced by colder winters. Second, species habitat and frequency also change following shifts in the pattern of the ecosystem. For the general location of the 2 forest estates analyzed in this chapter, Hamann and Wang (2006) predict an increase in habitat and frequency for the Douglas-fir, western redcedar, and western hemlock, to the detriment of lodgepole pine and hybrid spruce, while Burton and Cumming (1995) predict an increased frequency for lodgepole pine. Third, natural disturbances (e.g., wildfire, insect disturbance, and windthrows) are predicted to grow in intensity and frequency (Swift and Ran, 2012), thus increasing the risk of losing carbon storage. As the responses of the 2 forest estates analyzed in this chapter can vary under various climate change scenarios, so will the estimates of carbon storage. However, the differences between the alternate forest management strategies analyzed here are not expected to change significantly.  3.4 Conclusions Strategies that reduce harvest levels outperformed strategies that increase growth rates in terms of carbon storage for both forest estates analyzed in this chapter. At the AFRF, carbon storage increased by 7.0% above the baseline level when the harvest volume was reduced to MAL (49% reduction from the baseline), while the fertilization strategy increased the carbon 57  storage by only 2.0%. At the MKRF, carbon storage increased by 30.4% above the baseline level when the harvest volume was reduced to MAL (67% reduction from the baseline), while the genetically improved stock strategy increased carbon storage by only 5.3%. The fertilization and genetically improved stock strategies produced different results for the 2 forest estates; these differences are explained for the most part by differences in site indices and associated growth rates, area, and silvicultural systems (clear-cut versus uneven-aged systems). The 4 harvest reduction strategies analyzed in this chapter resulted in similar results for both forest estates in terms of carbon storage and the distribution of age classes. All 4 strategies shifted the forest estates to older age classes, and this trend became more evident as the harvest reduction level was decreased from the baseline. For the same harvest level, the rich sites first strategy allocates less area in reserves while storing more carbon than the poor sites first strategy. The fixed harvest level strategy provides more flexibility to adapt to changing markets and to respond to natural disturbances while maintaining the forecasted harvest level and carbon storage increase. It was demonstrated when employing an increased reserve area strategy that 25% mortality in the reserves would reduce carbon storage down to the baseline levels at year 25. This reduction could have been at least partially avoided if a fixed harvest reduction strategy had been used, because then it would be possible to shift the harvest to the dead timber. This chapter considered 2 general types of strategies to increase carbon storage in a forest ecosystem: increased growth and reduced harvest. Within each of these types, there was only a small difference in the performance of the different strategies considered. In this chapter, carbon storage was the metric used to compare the performance between these 2 types of strategies, and this metric indicates that reduced harvest strategies outperform increased growth strategies. However, using carbon storage as the metric for comparison ignores the financial performance of 58  the strategies in that the cost of implementing the strategies, and the revenue from sources such as timber sales and carbon credits, are not considered. A financial analysis will require a detailed parameter analysis due to uncertainty in discount rates and product values. Thus, the results from this chapter will simplify the parameter analysis by eliminating the need to consider multiple strategies within each type and multiple values for the parameters with uncertain values.    59  4 Cost to produce carbon credits 4.1 Overview Comparing the break-even carbon credit price (i.e., the total cost of the project divided by the number of carbon credits produced) with the market price of a carbon credit is a useful strategy to determine the financial viability of carbon projects. The various costs included in the financial analysis to quantify the total costs depends on the overall objectives of the carbon project. For example, in the case of harvest reduction strategies, the opportunity cost of reducing harvest, is not always included in the financial analysis (e.g., Huang and Kronrad, 2001). In the case of forest estates actively managed for profit, the opportunity cost of reducing harvest needs to be included in the financial analysis in order to provide better estimates of the break-even carbon credit price. However, using site index as the universal measure of site productivity can be problematic when comparing different forest estates composed of different species and site conditions. Thus, it is necessary to develop a metric that is sensitive to site productivity, tree species, and log quality and that represents the opportunity cost of reducing harvest in favour of storing carbon. This chapter considers 3 small-scale actively managed forest estates located in the Coast, Southern Interior, and Northern Interior forest regions in British Columbia that cover a wide range of forest types and timber net revenues. The objectives of this chapter are (1) to propose a new metric that represents the opportunity cost of reducing harvest in favour of storing carbon, and (2) to examine how the break-even carbon credit price varies with the new metric developed in (1) for the 3 forest estates.  60  4.2 Methods The forest estates, simulation models, and baseline determination have been described in detail in Chapter 2. This chapter uses for analysis 3 forest estates: the Alex Fraser Research Forest (AFRF), the Malcolm Knapp Research Forest (MKRF), and Forest Estate 3 (FE3). The various strategies to reduce the harvest below the baseline level have been investigated in the past (Harmon and Marks, 2002; Seely et al., 2002; Peng et al., 2002; Harmon et al., 2009; Nunery and Keeton, 2010), and I demonstrated in Chapter 3 that there is little difference in carbon storage among these strategies. Chapter 3 suggested that reducing the harvest to a fixed target level offers more flexibility to the forest manager, since this poses fewer constraints than increasing rotation ages or increasing the area in reserves. Thus, the strategy of reducing the harvest to a fixed target level is used to conduct financial analyses. In order to continue to meet the objectives of the 3 actively managed forest estates considered here, a minimum accepted harvest level (MAL) had to be determined. For these forest estates, MAL varied from 50% to 30% of the baseline harvest level. To permit comparisons among the 3 forest estates, MAL was set to 30% of the baseline harvest level for all forest estates (i.e., MAL equals 0.3 × baseline harvest level). Seven scenarios were simulated by gradually reducing the target harvest level in steps of 10% down to 30% of the baseline level. The minimum harvest ages (MHA), spatial adjacency, buffer size around protected areas, retention levels, and harvesting priorities constraints were kept identical to those in the baseline scenario. Throughout the planning horizon, the target harvest level was constant, and the simulations were run for 100 years. The present-day costs and revenues used in the financial analysis are detailed in Table 4-1; these were considered to increase with the inflation rate over the 25-year life of the carbon 61  project. In the case of the AFRF and the MKRF, the timber revenue (i.e., average market timber selling price) and harvesting cost were averaged from the last 10 years of financial data, while in the case of FE3, these were averaged from 8 cutting permits that are typical for the area where FE3 resides, taken from the last 5 years. Table 4-1 also shows the average site index (i.e., top height in meters at age 50) of the 3 forest estates and metrics representing the opportunity cost of reducing harvest, expressed as the average value per ha harvested (AVHH) and as the net value per ha harvested (NVHH).  62  Table 4-1. Revenues, costs, and site productivity Revenues, costs, and site productivity Forest estates AFRF FE3 MKRF  Timber revenue ($ m-3)  67  48  85  Harvesting cost ($ m-3)a  51  44  50  TNR (timber revenue less harvesting cost) ($ m-3) 16 4 35  Carbon project establishment and validation ($ ha-1)b  5.61 (all forest estates)  Verification ($ ha-1 event-1)b  1.52 (all forest estates)  Average site index (top height in meters at age 50)  22.1  14.7  25.6  25-year average harvested volume (m3 ha-1 year-1) (range shown in brackets) 342 (276-441) 255 (236-275) 750 (679-785)  AVHH (25-year average harvested volume × average timber revenue) (thousand $ ha-1) 22.9 12.2 63.7  NVHH (average annual harvested volume × TNR) (thousand $ ha-1 year-1) 5.5 1.0 26.3 a Includes tree to truck, hauling, road construction, road deactivation, road maintenance, silviculture, scaling, administrative overhead, stumpage (at FE3 and the AFRF), and fire protection (at the AFRF and the MKRF). b Costs estimated from Galik et al. (2012).  The AVHH and the NVHH are calculated for each forest estate only for the baseline scenario because this is the only scenario that represents the potential of the forest to produce a non-declining yield. The AVHH is calculated as the timber revenue multiplied by the 25-year average harvested volume (Table 4-1). The 25-year average harvested volume (in m3 ha-1) is calculated as the average of                                                               . The annual harvested volume is a direct output from FPS-ATLAS, while the annual effective treated area is estimated from FPS-ATLAS outputs to account for the in-block retentions (10% to 20%) in the case of 63  even-aged systems (only applicable at the MKRF), the effective treated area of each spatial polygon (30%) in the case of even-aged systems, and the effective treated area of each spatial polygon (40%) in the case of commercial thinnings. The NVHH is calculated as the timber net revenue (TNR) multiplied by the 25-year average harvested volume (Table 4-1). Given the variety of species, forest types, and timber qualities of the 3 forest estates, the use of the AVHH to represent the opportunity cost of reducing harvest is more appropriate for the purpose of this analysis because it takes into account the timber value, which is a function of species, forest type, and wood quality. Site index does not take into account the timber value and wood quality. The NVHH requires information about other costs (harvesting, fire protection, etc.), which is not always available; these costs can vary greatly within the forest estate in question. For many years, it has been debated what discount rate (r) should be used in the financial analysis of forestry projects (e.g., Row et al., 1981; Cline, 1992; Howarth, 2009; Price, 2014). In section 1.2.5 I reviewed the current thinking on discounting and presented the arguments of both sides. The scope of this study is not to determine the correct discount rate to be used in financial analyses, but to determine the effect of the discount rate on the total break-even carbon credit price. Recent financial analyses on carbon projects have used discount rates (i.e., real rates once inflation has been removed) of 2.5% to 15% (Richards et al., 1993; Newell and Stavins, 2000; Huang and Kronrad, 2001; Galik and Cooley, 2012). However, the discount rate used in the financial analysis can be lower than 2% (Stern, 2007) or much higher than 15% (Covell, 2011). Thus, discount rates of between 0% and 16% were used to evaluate the effect on the total break-even carbon credit price.  64  4.3 Results The number of carbon credits produced and the total break-even carbon credit price (P), with its 2 components (PH and PCC), are presented in Table 4-2, at 0% and 16% discount rates. It can be seen that PH is relatively independent of the target harvest level (i.e., percentage reduction of the baseline harvest level) because in Equation (2-5), both the opportunity cost of reducing harvest (numerator) and the number of carbon credits produced (denominator) increase at similar rates as the target harvest reduces from 90% to 30% of the baseline harvest level. The implication of this result is that PH is a function of TNR; a higher TNR (e.g., MKRF) results in a higher PH. It can be seen that PCC drops slightly as the target harvest level is reduced; this is because in Equation (2-6), the carbon project cost (i.e., initial establishment, validation, and verification) is constant while the number of carbon credits produced increases. The overall effect is that P is relatively independent of the target harvest level because PH is much larger than PCC, and so it dominates this relationship. When the discount rate is set to 0%, it can be seen that PH represents more than 58% of P at FE3, more than 79% of P at the AFRF, and more than 97% of P at the MKRF. The exception observed at the AFRF, where P is not independent of the target harvest level for target harvest levels that are 60% to 90% of the baseline level, is explained by the reduced number of carbon credits produced. As the target harvest level is reduced from 90% to 60% of the baseline harvest level, carbon credit production at the AFRF increases at a slower rate than the opportunity cost of reducing harvest (the numerator in Equation (2-5)), and thus, PH and ultimately P, decrease instead of being relatively constant.  65  Table 4-2. Total break-even carbon credit price and its 2 components for a 25-year project life with all costs assumed to be expenses for a 5-year verification frequency THL* Carbon credits (tCO2e 103) 0% discount rate  16% discount rate PH ($ tCO2e-1) PCC ($ tCO2e-1) P=PH + PCC ($ tCO2e-1)  PH ($ tCO2e-1) PCC ($ tCO2e-1) P=PH + PCC ($ tCO2e-1)  AFRF          90% 1 326.5 87.9 414.4  132.6 95.5 228.1 80% 9 125.0 14.1 139.1  69.8 17.4 87.2 70% 30 59.8 4.4 64.1  48.4 8.1 56.5 60% 46 48.8 2.8 51.6  47.9 6.8 54.7 50% 89 32.7 1.5 34.2  38.4 3.9 42.3 40% 114 31.0 1.1 32.1  39.2 3.1 42.3 30% 129 31.1 1.0 32.1  38.4 2.7 41.2  FE3          90% 79 3.5 2.5 6.0  4.7 7.4 12.2 80% 177 3.2 1.1 4.3  4.6 3.5 8.1 70% 257 3.2 0.8 4.0  4.6 2.4 7.0 60% 333 3.2 0.6 3.8  4.7 1.9 6.6 50% 407 3.3 0.5 3.8  4.8 1.5 6.3 40% 477 3.4 0.4 3.8  4.9 1.3 6.2 30% 538 3.5 0.4 3.9  5.0 1.1 6.1  MKRF          90% 67 40.6 1.0 41.6  56.4 2.9 59.3 80% 128 40.7 0.5 41.3  58.9 1.6 60.4 70% 175 42.1 0.4 42.4  59.7 1.1 60.8 60% 255 45.1 0.3 45.4  61.4 0.8 62.2 50% 321 44.4 0.2 44.6  60.9 0.6 61.5 40% 393 44.2 0.2 44.4  60.7 0.5 61.2 30% 501 40.7 0.1 40.8  59.5 0.4 60.0 *THL: target harvest level shown as % of the baseline harvest level  Figure 4-1 presents the total number of carbon credits produced over the life of the project divided by the forest area for each of the forests considered in this study. The numbers 66  presented for the MKRF align well with the estimates found by Harmon and Marks (2002) for a similar forest type in the Pacific Northwest. It can be seen that the AFRF produces fewer carbon credits per ha over the life of the project than FE3 or the MKRF for all target harvest levels. This difference can in part be explained by the productivity of the forest estates. The current average volume per ha is 497 m3 ha-1 for the MKRF, 194 m3 ha-1 for the AFRF, and 172 m3 ha-1 for FE3 (at the MKRF, the current average standing volume per ha is 2.9 times larger as compared to FE3 and 2.6 times larger as compared to the AFRF). When the target harvest is reduced to 30% of the baseline level, the number of carbon credits produced per standing volume at the end of the 25-year carbon project life is 0.18 tCO2e m-3 for FE3, 0.16 tCO2e m-3 for the MKRF, and 0.05 tCO2e m-3 for the AFRF (at FE3 the number of carbon credits produced per standing volume is 1.1 times larger as compared to the MKRF and 3.4 times larger as compared to the AFRF). The reason for the much lower performance of the AFRF is that 83% of the timber harvesting land base is managed under uneven-aged systems, which results in higher carbon storage for the baseline than under an even-aged system (Taylor et al., 2008; Harmon et al., 2009). Thus, the target harvest has to be at a lower level in order for the AFRF to produce a larger number of carbon credits.   67   Figure 4-1. Carbon credits produced per ha over the 25-year life of the carbon project  An unexpected result in Table 4-2 is that PH and PCC increase with increasing discount rates, except at the AFRF for the 60% to 90% target harvest levels. Figure 4-2 shows that the annual production of carbon credits at the MKRF is greater in the later years of the carbon project, while the annual total cost (i.e., opportunity cost of reducing harvest and the carbon project cost) is constant over the life of the carbon project. Note on the left-hand side of Equation (2-3) that a larger number of carbon credits are produced later in the carbon project, while on the right-hand side, the costs are uniformly distributed over the life of the carbon project; this makes the left-hand side more sensitive to an increase in the discount rate. In order to preserve equality in Equation (2-3) as the discount rate is increased, it is necessary to increase P when P is considered a constant value. The exception observed at the AFRF for 60% to 90% target harvest levels is explained by the higher percentage of carbon credits being produced in the earlier years of the project. Recall that the AFRF uses uneven-aged systems on 83% of the THLB, and a 68  lower target harvest level is needed in order to produce an increasing number of carbon credits throughout the project life.   Figure 4-2. Annual total cost (i.e., sum of opportunity and carbon project costs) and carbon credits produced over the life of the project at the MKRF for a 30% target harvest level (i.e., minimum accepted level): costs are shown at 0% real discount rate.  The average site indices of the MKRF, the AFRF, and FE3 are respectively 25.6 m, 22.1 m, and 14.7 m, while the AVHHs of the MKRF, the AFRF, and FE3 are respectively 63.7, 22.9, and 12.2 (measured in thousand $ ha-1) (Table 4-1). For the 3 forest estates considered in this analysis, a higher site index corresponds to a higher AVHH. However, this is not always the case. For example, high timber value species such as yellow-cedar (Chamaecyparis nootkatensis) growing on low site indices can have a high AVHH. The average value per cubic meter harvested follows a trend similar to the site index and AVHH, and for the MKRF, the AFRF, and FE3, it is respectively $35 m-3, $16 m-3, and $4 m-3. Figure 4-3 presents the total 69  break-even carbon credit price as a function of AVHH and site index and compares the trends in two scenarios: (1) when the opportunity cost of reducing harvest is not included in the calculation of the total break-even carbon credit price (Panel A) and (2) when it is included (Panel B). Recall that a significant portion of the AFRF uses uneven-aged systems, which result in low carbon credit production and high total break-even carbon credit prices when the target harvest is 60% to 90% of the baseline harvest level. The effect of the uneven-aged system is lessened when the target harvest is reduced to 50% of the baseline harvest level. Thus, to use the MKRF, the AFRF, and FE3 in an analysis of the sensitivity of the total break-even carbon credit price to AVHH and site index, the target harvests were set to 30% of the baseline level. When the opportunity cost of reducing harvest is not included, the trend shown in Figure 4-3 (Panel A), where the total break-even carbon credit price is lower for the forest estates with higher site index and higher AVHH, is similar to that found by Huang and Kronrad (2001). In contrast, when the opportunity cost of reducing harvest is included in the analysis (Figure 4-3, Panel B), the trend is reversed, and the total break-even carbon credit price increases as the AVHH and site index of the forest estate increase. This is explained by the higher average timber net revenue for higher site index forest estates (Table 4-1), which results in a higher opportunity cost of reducing harvest and higher AVHH. In addition, even a 90% reduction of the TNR (Figure 4-4), which drives the opportunity cost of reducing harvest, does not show the trends found by Huang and Kronrad (2001). It should be noted that in the case of the AFRF, the total break-even carbon credit price has the potential to be lower if an even-aged system is used instead of the uneven-aged system. In a separate analysis conducted at the AFRF, the uneven-aged system was changed to an even-aged system, and the target harvest was set at 30% of the baseline level. This resulted in a total break-even carbon credit price at a 0% discount rate of 70  $14.1 tCO2e-1 (PH = $13.9 and PCC = $0.2). The trends in Figure 4-3 become clearer when these values are used for the AFRF (shown in gray in Figure 4-3).  Figure 4-3. Comparing the total break-even carbon credit price at 0% real discount rate over a range of average values per ha harvested per year (12.2 at FE3, 22.9 at the AFRF, and 63.7 at the MKRF, measured in thousand $ ha-1 year-1) at 30% target harvest of the baseline level when the opportunity cost of reducing harvest is not included in the financial analysis (Panel A) and when it is included (Panel B): the values in the brackets represent the average site index for each forest estate. 71   Figure 4-4. Comparing the total break-even carbon credit price at 0% real discount rate over a range of site indices (corresponding to 3 forest estates) at 30% target harvest of the baseline level when percentage reductions are applied to the timber net revenues (TNR)  The opportunity cost of reducing harvest is not always important in the financial analysis of a carbon project. For example, the TimberWest Strathcona Ecosystem Conservation Project (Pacific Carbon Trust, 2011) and the Darkwoods Forest Carbon Project (The Nature Conservancy of Canada, 2011) were established on the premises that preservation of the current forest structure is more important than financial return. Where financial return is the main objective, the opportunity cost of reducing harvest has to be taken into account when conducting a financial analysis; in such cases, the opportunity cost of reducing harvest has a significant effect on the total break-even carbon credit price, which increases for forest estates with higher AVHH. 72  The current market prices for improved forest management (IFM) projects are between $5 and $16 per carbon credit, with a slight increase since 2006 (Peters-Stanley et al., 2013). For the P values in Table 4-2, only FE3 could profitably undertake a carbon project. Forecasting carbon credit market prices into the future is a difficult task, and contradictory arguments are found in the literature. While Sohngen and Mendelsohn (2003) predict an increase of the carbon credit market prices towards the end of the century due to higher accumulated carbon concentrations in the atmosphere, most predict a decrease of carbon credit market prices, either because carbon sequestration is viewed as a short-term strategy to allow for new technologies to emerge (Feng et al., 2002) or because of the decreased attractiveness of carbon sequestration (Stavins, 1999). Despite the uncertainty, carbon projects can still be profitably undertaken (Haim et al., 2014); an optimal time path is to immediately implement the projects and maintain them until the atmospheric carbon concentration is stabilized (Feng et al., 2002). In order to implement IFM projects that are immediately financially feasible at the AFRF and the MKRF at the current carbon credit market prices, the TNR should be 60% to 80% less (at a 0% discount rate) (Figure 4-4) than the values shown in Table 4-1. The lowest value for the TNR in the last 10 years of financial data from the AFRF and the MKRF was 18% less than the values shown in Table 4-1. The British Columbia timber market reports for the last 10 years (British Columbia Ministry of Forests, Lands and Natural Resource Operations, 2014a) indicate that the lowest timber prices were 32% to 34% less than the average timber prices for the same period. Thus, reaching a balance between timber and carbon credit market prices that would permit the implementation of financially feasible IFM projects at the AFRF and the MKRF seems difficult in the near future. In the case of forest estates with low productivity and relatively low TNR (similar to FE3), forest 73  managers should consider immediately implementing IFM projects in order to be as close as possible to the optimal time path suggested by Feng et al. (2002). The carbon project proponent might prefer a 1-year verification frequency in order to sell carbon credits annually to offset the opportunity cost of reducing harvest. Using the verification cost from Table 4-1, it was determined that, compared to a 5-year verification frequency, a 1-year verification frequency increases the carbon project cost (for initial establishment and ongoing verification) by 3.30 times at a 0% discount rate and by 2.14 times at a 16% discount rate. Using the increased carbon project cost due to the 1-year verification frequency in Equation (2-4), the total break-even carbon credit price in Table 4-2 increased by 22% at FE3 ($0.8 per carbon credit), by 7% at the AFRF ($2.3 per carbon credit), and by 1% at the MKRF ($0.1 per carbon credit) when the target harvest was set to 30% of the baseline level (Table 4-3). The highest percentage increase for the total break-even carbon credit price was observed in the case of FE3 because the carbon project cost represents a large percentage of the total cost (at FE3, PH represents more than 58% of P while PCC represents up to 42% of P). At the other extreme is the MKRF, where the carbon project cost is less than 3% of the total cost, and the added cost of adopting a 1-year verification frequency increases the total break-even carbon credit price by the lowest percentage. When the discount rate is set to 16%, the percentage increase for the total break-even carbon credit price is similar to that for a 0% discount rate for the AFRF and the MKRF and lower for FE3 because PCC represents a higher proportion of P and it is discounted more. Thus, a 1-year verification frequency can be more advantageous where the carbon project cost represents a relatively small percentage of the total cost; the added verification cost has little effect on the total break-even carbon credit price.  74   Table 4-3. Total break-even carbon credit price increase of moving from a 5-year to a 1-year verification frequency at 0% discount rate   THL* AFRF  FE3  MKRF +$ +%  +$ +%  +$ +%  90% 202.2 49%  5.7 96%  2.3 6% 80% 32.3 23%  2.6 60%  1.2 3% 70% 10.0 16%  1.8 45%  0.9 2% 60% 6.4 12%  1.4 36%  0.6 1% 50% 3.4 10%  1.1 29%  0.5 1% 40% 2.6 8%  1.0 25%  0.4 1% 30% 2.3 7%  0.8 22%  0.3 1% *THL: target harvest level shown as % of the baseline harvest level  4.4 Conclusions Three actively managed forest estates were analyzed in this chapter, each representing one of the main forest regions in British Columbia: the Coast (MKRF), Southern Interior (AFRF), and Northern Interior (FE3). For each of these forest estates, the total break-even carbon credit price was estimated. When the opportunity cost of reducing harvest was included in the analysis, it represented 58% to 97% (at a 0% discount rate) of the total break-even carbon credit price. The total break-even carbon credit price ($ tCO2e-1) was $3.9 at FE3, $32.1 at the AFRF, and $40.8 at the MKRF when the target harvest was reduced to 30% of the baseline level and for a 0% discount rate. Under the current voluntary carbon credit market prices, only FE3 could profitably undertake a carbon project that reduces the harvest below the baseline level. The total break-even carbon credit price was relatively independent of the target harvest level (i.e., percentage reduction of the baseline harvest level) because when the target harvest decreased from 90% to 30% of the baseline harvest level, the portion of the opportunity cost of reducing 75  harvest that represents the largest portion of the total cost increased at a similar rate to the number of carbon credits produced. In addition, a higher discount rate increased the total break-even carbon credit price because the number of carbon credits produced was larger in the later years of the 25-year carbon project life, while the annual total cost was constant over the carbon project life. However, when the number of carbon credits produced was larger in the beginning of the project (e.g., AFRF, with a harvest target at 60% to 90% of the baseline level), a higher discount rate decreased the total break-even carbon credit price. The forests considered in this analysis provide 3 points from a larger spectrum of forest estates and associated costs to produce carbon credits; the range in site index (i.e., top height in meters at age 50) was 14.7 to 25.6 m, the range in average value per ha harvested (i.e., the metric representing the opportunity cost of reducing harvest) was 12.2 to 63.7 thousand $ ha-1 year-1, and the range in average timber net revenue was $4 to $35 m-3. The results of this study indicate that including the opportunity cost of reducing harvest results in higher total break-even carbon credit prices for forests with higher average values per ha harvested (which corresponded to higher site indices for the 3 forest estates analyzed here). However, when the opportunity cost of reducing harvest was not included in the analysis, the total break-even carbon credit price drops as the site index increases, and this is similar to previous findings.  A 1-year verification frequency could be considered where the verification cost represents a low percentage of the total cost. For projects following an ex-post payment schedule, it is necessary to periodically validate and verify the carbon credits produced before payment is made. Thus, moving to a 1-year verification frequency can supply a more uniform revenue stream. However, care must be taken to ensure that the benefits of a more uniform revenue stream outweigh the increased cost incurred due to the more frequent verification events. 76  This is another area that requires more research as the result is likely to be strongly dependent on the financial model of the forest.                      77  5 Fluctuating harvest schedules to produce carbon credits 5.1 Overview In the case of forest estates actively managed for profit, the reduction of the opportunity cost of reducing harvest is an important consideration. In Chapter 3, I showed that the inclusion of the opportunity cost of reducing harvest increased the break-even carbon credit price by 58% to 97%. However, the harvest reduction strategies analyzed so far reduced the harvest below the baseline level to a fixed target level, which was held constant over the entire carbon project life. In Chapter 4, I also confirmed the previous finding (e.g., Harmon and Marks, 2002) that reducing the harvest to a constant level below the baseline produced carbon credits from year 1. Some potential financial benefit could be achieved if the harvest schedule fluctuates during the carbon project life (so the opportunity cost of reducing harvest is lowered) while carbon credits continue to be produced. While fluctuating harvest schedules can be designed in various ways, as shown in Chapter 4, the lower the fixed target level is below the baseline harvest, the more carbon credits are produced during the 25-year life of the carbon project. Thus, a fluctuating harvest schedule needs to have a starting target harvest level (STHL) that is below the baseline level in order to trigger carbon credit productions from year 1 of the carbon project life. In order to reduce the opportunity cost of reducing harvest, the harvest is adjusted from STHL to the baseline level at a later stage in the carbon project’s life. What happens to the carbon credit production following the harvest adjustment from STHL to the baseline level is still unknown. This question is important because maintaining the baseline level longer during the carbon project life can lower the opportunity cost of reducing harvest. I showed in Chapter 3 that a reduced harvest below the baseline level shifts the forest into older age classes. An older forest has higher carbon storage, 78  and resuming the baseline harvest level after a number of years with a reduced harvest will deplete the older stands given the oldest first harvesting algorithm. The depletion of the older stands is expected to be gradual because the older stands produce higher volumes and fulfill the baseline harvest request faster. Thus, carbon credit production is expected to continue for a number of years following the harvest adjustment from STHL to the baseline level, yet there is little information about the key factors affecting the continued production of carbon credits and the potential financial benefits.  In this chapter, the MKRF is considered for analysis because it has higher growth rates and less complex silvicultural systems, which facilitate a clearer analysis of the key factors affecting carbon credit production. The objectives of this chapter are to determine (1) the effect of fluctuating harvest schedules on carbon credit production and (2) if there is a financial advantage in using fluctuating harvest schedules.  5.2 Methods The MKRF, simulation models, and methodology used to determine the baseline harvest level are described in detail in Chapter 2, while the financial analysis methodology is detailed in section 4.2.  5.2.1 Fluctuating harvest schedules Fluctuating harvest schedules allow the target harvest level to fluctuate between a minimum accepted level (MAL) and the baseline level. In the case of the MKRF, the baseline level was determined to be 33,000 m3 year-1 (section 2.3), and MAL was set by the forest manager to 11,000 m3 year-1 (33% of the baseline level) in order to achieve a minimum set of 79  objectives (research, wildfire protection, and a minimum volume for the onsite sawmill). Starting from the baseline FPS-ATLAS database and keeping the same set of constraints (e.g., minimum harvest ages) as for the baseline scenario, 3 sets of 5 scenarios were simulated. Each of the 3 sets was defined by the STHL, which was set at 33% (MAL), 50%, and 70% of the baseline level (Table 5-1). For each of these 3 sets, 5 scenarios were simulated by holding the STHL constant for 5, 10, 15, 20, and 25 years (the harvest reduction period) and then suddenly adjusting the harvest to the baseline level. A sudden harvest adjustment from the STHL to the baseline level is more advantageous financially compared to a gradual harvest adjustment because, as shown in Chapter 4, the break-even carbon credit price is independent of the target harvest level (i.e., percentage reduction of the baseline harvest level). The last harvest adjustment from the STHL to the baseline level was scheduled at year 25 because the contract length of a carbon project in British Columbia is a minimum of 25 years (British Columbia Ministry of Environment, 2013). The simulations were run for 100 years in order to allow enough time to observe how carbon credit production is affected once the harvest has been adjusted from the STHL to the baseline level. For each scenario, the change in carbon storage was tracked for the entire 100-year planning horizon and 4 metrics were quantified: (1) the length of time (in years) that the carbon credits continue to be produced following the harvest adjustment from the STHL to the baseline level, or the carbon credits inertia (CCI) period, (2) the number of carbon credits produced during the CCI period, (3) the number of carbon credits produced during the 25-year period corresponding to the contract length of the carbon project, and (4) the total break-even carbon credit price for the 25-year carbon project.   80  Table 5-1. List of scenarios with fluctuating harvest schedules defined by the starting target harvest level (STHL) set at 33%, 50%, and 70% of the baseline harvest level Harvest level in years: 1-5 6-10 11-15 16-20 21-25  STHL  Baseline  Baseline  Baseline  Baseline STHL STHL Baseline Baseline Baseline STHL STHL STHL Baseline Baseline STHL STHL STHL STHL Baseline STHL STHL STHL STHL STHL  5.3 Results Following the harvest adjustment from the STHL to the baseline level, carbon credit production continues for 4 to15 years, resulting in 8 to 103 thousand tCO2e (Figure 5-1). The CCI period and carbon credit production during the CCI period are at the highest levels when the harvest reduction period is between 10 and 15 years, regardless of the STHL. After reaching the highest levels, the CCI period and carbon credit production during the CCI period decrease with increasing harvest reduction periods. The percentage reduction of the STHL (33%, 50%, or 70% of the baseline level) does not affect the patterns of the CCI period or carbon credit production during the CCI period, but it has a visible effect on the scale of carbon credit production during the CCI period (which is highest when the STHL is at 33% of the baseline level).Thus, the most efficient fluctuating harvest schedule holds the STHL constant for 10 to 15 years, followed by a sudden adjustment to the baseline level for the remaining period of the 25-year carbon project life. 81   Figure 5-1. Comparing the carbon credits inertia (CCI) period at MKRF when the starting target harvest level (STHL) is held constant for a range of harvest reduction periods  The variations in the CCI period and carbon credit production during the CCI period are explained by the interaction between the net biome productivity (NBP) of the baseline scenario and the fluctuating harvest schedule scenarios. The NBP is equivalent to the annual change of carbon storage, which is calculated as the annual change of the sum of all biomass carbon 82  production minus losses due to decomposition and disturbances (natural and anthropogenic). Carbon credit production occurs when the NBP of the fluctuating harvest schedule scenario is higher than the baseline scenario. The CCI period starts when the harvest is adjusted from the STHL to the baseline level, and it ends when the NBP of the fluctuating harvest schedule scenario becomes lower than the NBP of the baseline scenario. The NBP is sensitive to the starting age class structure of the forest estate and the age of the stands being harvested because older stands have typically higher carbon storage. In the case of the MKRF, the average harvest age (weighted by harvest area) (Figure 5-2) is relatively high in the beginning of the planning horizon, indicating that the MKRF has a surplus of older stands on the THLB. Recall that the FPS-ATLAS harvest algorithm was instructed to harvest the oldest stands first. Thus, the weighted average harvest age is a good indicator of the age classes older than the minimum harvest ages and the rate at which older stands are depleted from the THLB as the forest estate transitions to a more regular age class structure.      83   Figure 5-2. Comparing the weighted average harvest age (by harvested area) and the net biome productivity (NBP) between the baseline scenario and the fluctuating harvest schedule scenarios: STHL is starting target harvest level.84  In the case of the baseline scenario, where the depletion of the older stands is faster compared to the fluctuating harvest schedule scenarios, the NBP decreases slightly for the first 25 years followed by a gradual increase as the weighted average harvest age declines at lower levels (Figure 5-2). The cyclic pattern of the NBP in the case of the baseline scenario is important because it affects the length of the CCI period and the number of carbon credits produced during the CCI period. The maximum levels for CCI period and carbon credit production during the CCI period are achieved when the NBP of the baseline scenario is declining and the NBP of the fluctuating harvest schedule scenario is decreasing at a slower rate. A slower rate of decrease for the NBP of the fluctuating harvest schedule scenario occurs when the removal of the older stands is delayed for a shorter period of time (e.g. when the harvest reduction period is 5 years). A longer delay of the older stands removal causes the weighted average harvest age to be higher for longer periods of time following the harvest adjustment from the STHL to the baseline level. This indicates that higher carbon storage in the older stands is removed faster when the STHL is adjusted to the baseline level, causing the NBP to decrease at a faster rate. For example, when the harvest reduction period is 5 years, the NBP of the fluctuating harvest schedule scenario is decreasing at a slower rate compared to when the harvest reduction period is 25 years. This pattern occurs for all STHLs, but at different scales. Thus, the NBP of the fluctuating harvest schedule scenario with harvest reduction periods closer to 25 years falls sooner under the NBP of the baseline scenario and reduces the CCI period and the carbon credits produced during the CCI period. Furthermore, the highest levels of the CCI period and carbon credit production during the CCI period are achieved when the harvest reduction period is set to 10 to 15 years because the NBP of the fluctuating harvest schedule scenario falls under the NBP of the baseline scenario around year 25. 85  The maximum number of carbon credits is produced when the STHL is 33% of the baseline level (MAL) for the entire 25-year carbon project life (identified as max in Table 5-2). The number of carbon credits produced (for the 25-year carbon project life) by the range of fluctuating harvest schedules analyzed here is 8% to 88% of the maximum. The large variation from the maximum is explained by the STHL, the harvest reduction period, and to a lesser extent by the CCI period and the number of carbon credits produced during the CCI period. However, in order to maximize the properties of the CCI period and the number of carbon credits produced during the CCI period, the STHL should be held constant for 10 to 15 years. Fluctuating harvest schedules with the STHL for 10 to 15 years achieves 21% to 70% of the maximum. For example, approximately 50% of the maximum number of carbon credits produced for the entire 25-year carbon project life can be achieved if the STHL is at 33% of the baseline level for 10 years, at 50% for 15 years, or at 70% for 25 years (Table 5-2). In the case of the fluctuating harvest schedule with the STHL at 70% for 25 years, the CCI period properties are not used and the potential to lower the opportunity cost of reducing harvest and the total break-even carbon credit price is lost. Thus, the range of fluctuating harvest schedules in Table 5-2 should be assessed in conjunction with the properties of the CCI period and the number of carbon credits produced during the CCI period shown in Figure 5-1.       86  Table 5-2. Number of carbon credits produced, total break-even carbon credit price (P), and opportunity cost of reducing harvest (PH) for the 25-year carbon project life at 0% discount rate and 5-year verification frequency Harvest reduction period (years)  Number of carbon credits produced  P  Opportunity cost of reducing harvest  tCO2e % of max  $ tCO2e-1 % reduction from max  PH ($ tCO2e-1) Value ($ 106) Value % of max  STHL at 33% of the baseline level (MAL)  5  105,523 21%  35.6 8%  34.9 3.7 19% 10  228,421 46%  32.6 16%  32.3 7.4 39% 15  344,061 70%  33.0 15%  32.8 11.3 59% 20  433,963 88%  35.1 9%  34.9 15.2 80% 25  492,283 100%  38.7 0%  38.6 19.0 100%  STHL at 50% of the baseline level  5  80,798 16%  35.6 8%  34.7 2.8 15% 10  180,419 37%  32.2 17%  31.8 5.7 30% 15  261,154 53%  32.5 16%  32.3 8.4 44% 20  331,465 67%  34.9 10%  34.7 11.5 61% 25  376,255 76%  38.0 2%  37.8 14.2 75%  STHL at 70% of the baseline level  5  38,152 8%  41.1 –6%  39.3 1.5 8% 10  105,061 21%  33.4 14%  32.8 3.4 18% 15  153,072 31%  34.3 11%  33.9 5.2 27% 20  194,768 40%  35.3 9%  34.9 6.8 36% 25  227,039 46%  38.5 1%  38.2 8.7 46%  The total break-even carbon credit price (P) at a 0% discount rate for the range of fluctuating harvest schedules analyzed here varies between $32.2 and $41.1 tCO2e-1 (Table 5-2). The highest P reduction from maximum (17%) occurs for the fluctuating harvest schedule where STHL is at 50% of the baseline level for 10 years. Similar P reductions from maximum (14% to 87  16%) occur when the STHL is at 33% of the baseline level for 10 to 15 years, 50% for 15 years, or 70% for 10 years. These results correlate well with the properties of the CCI period and the number of carbon credits produced during the CCI period found in Figure 5-1. A somewhat surprising result is that P could only be reduced from the maximum by 17% despite implementing the baseline harvest level for the last 15 years of the carbon project life. Recall that in Chapter 4, I found that P is relatively independent of the target harvest level because of the high proportion of PH (over 98% in the case of the MKRF). In the case of the fluctuating harvest schedule with the highest P reduction from maximum (17%), the value of the opportunity cost of reducing harvest is lowered from $19.0 106 to $5.7 106 (30% of the maximum), but PH still represents a high proportion of P ($31.8 out of $32.2 tCO2e-1). Thus, P reduction through implementing fluctuating harvest schedules is limited by the difference between the opportunity cost of reducing harvest and the carbon project cost. In the case of the MKRF, the difference is between $106 for the opportunity cost of reducing harvest and $104 for the carbon project costs. When the discount rate is increased to 4% to 6%, the P reductions from maximum shown in Table 5-2 are reduced by half (Figure 5-3). The P reductions from maximum become less than 2% when the discount rate is increased to more than 14%. The effect the discount rate has on P reductions from maximum is explained by Equation (2-3), used to calculate P. Recall that when the target harvest level is held constant over the 25-year carbon project life, P (as a constant value) needs to increase with an increasing discount rate in order to preserve the equality in Equation (2-3). This is because the left-hand side of Equation (2-3) (which includes an increasing annual carbon credit production) is more sensitive to the discount rate increase than the right-hand side (which includes a relatively constant annual total cost). During the post-harvest reduction period of fluctuating harvest schedules, when the STHL is adjusted to the 88  baseline level, annual carbon credit production is decreasing while the total costs are reduced to the carbon project costs (i.e., the opportunity cost of reducing harvest is zero). Because the post-harvest reduction period occurs towards the end of the 25-year carbon project life, the discounting of both carbon credit production and total costs for fluctuating harvest schedules is less and causes P (as a constant value) to increase at a slower rate compared to a constant harvest schedule. The implication of these results is that a higher discount rate reduces the financial advantages gained through implementations of fluctuating harvest schedules.   Figure 5-3. The effect of the discount rate on the total break-even carbon credit price (P) reduction from maximum (with starting target harvest level (STHL) held constant over 25-year carbon project life) when STHL is at 50% of the baseline level for 10 years (highest P reduction from max)   0%4%8%12%16%0% 2% 4% 6% 8% 10% 12% 14% 16%Preduction from maxDiscount rateSTHL at 50% of the baseline level for 10 years89  The P reductions from maximum shown in Table 5-2 are not sufficient to produce carbon credits at prices competitive with the current voluntary market prices of $5 to $16 tCO2e-1 (Peters-Stanley et al., 2013). This is because the opportunity cost of reducing harvest still represents a high proportion of the total cost. The opportunity cost of reducing harvest is driven by the TNR and by the difference in harvested volume between the baseline scenario and the carbon project scenario. Various options were explored to reduce the difference in harvested volume, yet the lowered opportunity cost of reducing harvest did not produce significant financial advantages for the carbon project scenario. The TNR is the only variable left to be changed in order to reduce P to values within the range of the carbon market prices. In the case of the MKRF, the TNR should be less than $15 m-3 to produce carbon credits within the market price range (Figure 5-4). A fluctuating harvest schedule may be useful when the TNR is at the lower limit, which makes carbon projects financially viable. For example, a TNR of $15 m-3 cannot produce carbon credits at competitive prices using the approach with a constant STHL at 33% of the baseline for the entire 25-year carbon project life (Figure 5-4). When a fluctuating harvest schedule is used with an STHL at 33% of the baseline for 10 years, carbon credit production becomes efficient enough (i.e., a lower P) to make the carbon project financially viable for the same TNR of $15 m-3.  90   Figure 5-4. Comparing the carbon credit market price range to the total break-even carbon credit price (P) (0% discount rate) for a range of timber net revenues (TNR) when the starting target harvest level (STHL) is set at 33% of the baseline level for 10 years (fluctuating harvest schedule) and 25 years (constant harvest schedule)  5.4 Conclusions Following the harvest adjustment from the STHL to the baseline level, carbon credit production continues for 4 to 15 years (the CCI period) resulting in 8 to 103 thousand tCO2e. The highest levels for CCI period and carbon credit production during the CCI period resulted when the STHL was 10 to 15 years followed by a sudden harvest adjustment to the baseline level for the remainder of the 25-year carbon project life. The interaction between the net biome productivity (NBP) of the baseline scenario and the fluctuating harvest schedule scenarios explained the existence of the CCI period and the carbon credit production during the CCI period. The NBP was sensitive to the older starting age class structure of the forest estate and to 0102030400 5 10 15 20 25 30 35P($ tCO2e-1)Timber net revenue ($ m-3)Market range ($5-$16)STHL at 33% of the baseline level for 25 yearsSTHL at 33% of the baseline level for 10 years91  the depleting rate of older stands as the forest estate transitioned to a more regular age class structure. Thus, the NBP of the baseline scenario had a cyclic pattern, decreasing for the first 25 years because the existing older stands were harvested first. The NBPs of the fluctuating harvest schedule scenarios were higher than the baseline scenario during the harvest reduction period followed by a gradual decrease caused by the harvest adjustment from the STHL to the baseline level. The NBP of the fluctuating harvest schedule scenarios with STHL for 10 to 15 years decreased to below the NBP of the baseline scenario around year 25, which maximized the CCI period and carbon credit production during the CCI period. When the STHL was held for longer than 15 years, the NBP of the fluctuating harvest schedule scenarios decreased to below NBP of the baseline scenario past year 25. Because the NBP of the baseline scenario rose after year 25, the CCI period and carbon credit production during the CCI period were at lower levels for fluctuating harvest schedules with an STHL held for longer than 15 years. The fluctuating harvest schedules with the STHL for 10 to 15 years were able to achieve 21% to 70% of the maximum number of carbon credits produced for the 25-year carbon project life. The maximum number of carbon credits was realized when the STHL was at 33% (MAL) of the baseline level for 25 years. The total break-even carbon credit prices (P) at a 0% discount rate were between $32.2 and $41.1 tCO2e-1 for the fluctuating harvest schedules. The highest P reductions from the maximum at a 0% discount rate (14% to 17%) were achieved when the STHL was held for 10 to 15 years, which correlated well with the CCI period findings. A discount rate increase to 4% to 6% reduced the P reductions from the maximum by half. The P reduction from the maximum through implementing fluctuating harvest schedules was limited by the difference between the opportunity costs of reducing harvest and the carbon project costs. While the opportunity costs of reducing harvest were lowered significantly when the STHL was 92  held for 10 to 15 years, they still accounted for the largest portion of the total costs (over 96%). Another option to lower the opportunity costs of reducing harvest is to lower the timber net revenue. This study showed that, in the case of the MKRF, the timber net revenue should be under $15 m-3 in order to enable implementing financially feasible carbon projects with harvest reduction strategies.                   93  6 Final Conclusions The use of forest property to sequester and store carbon is seen by most authors as a real option for mitigating the possible negative effects of recent anthropogenic global climate change (Intergovernmental Panel on Climate Change, 2007). It is also argued that proper forest management can store additional carbon within forest ecosystems without impacting society’s need for timber (Smith et al., 1993). As part of the global effort to mitigate the potential negative effects of anthropogenic global climate change, the signatory countries of the internationally binding agreements had to develop financial mechanisms (e.g., carbon trading markets) to encourage forest managers to consider carbon as a management objective. In recent years, many studies looked at various forest management strategies that can successfully combine timber and carbon objectives in the short and long term alike (Harmon and Marks, 2002; Harmon et al., 2009; Stoehr et al., 2011; Aspinwall et al., 2012). Three types of forest management strategies emerged: (1) afforestation, (2) avoidance of deforestation (conservation), and (3) alternate strategies for managing existing forests. Two of these indicated the greatest potential, mostly due to a simpler process to account for the additional carbon storage (i.e., afforestation) or carbon loss (i.e., avoiding deforestation). Two approaches exist to develop alternate forest management strategies: (1) reduce the harvest below the baseline level and (2) increase the growth rate of existing and future stands while maintaining the baseline harvest level. Studies of the alternate forest management strategies are challenged by many factors that are interlinked in time and space in a forest estate that is actively managed for profit. Such factors determine the accuracy of forecasting the existing forest inventory, the estimates of timber production, and the additional carbon storage that can be achieved under a set of management objectives. Most studies analyzing alternate forest management strategies in temperate forests indicate that the strategies 94  that increase growth rates are less efficient at storing carbon than strategies that reduce the harvest below the baseline (e.g., Seely et al., 2002). The sensitivity analysis I conducted in chapter 3 confirmed the finding that harvest reduction strategies outperform increased growth rate strategies in terms of carbon storage. I found that carbon storage increased by 7.0% above the baseline level at the AFRF and by 30.4% above the baseline level at the MKRF when the harvest was reduced to the minimum accepted harvest level set by the forest managers of the 2 estates. The increased growth strategies (i.e., intensive fertilization and planting genetically improved stock to regenerate the clear-cut stands) increased carbon storage by only 2.0% at the AFRF and 5.3% at the MKRF. The results for the increased growth strategies are likely overestimated because of the assumptions used in the modeling. Here, I used an intensive fertilization schedule, yet the response of various species to the fertilizer is not fully understood. I used TIPSY fertilization response information where available (British Columbia Ministry of Forests, Lands and Natural Resource Operations, 2012a) and estimated the response of other species growing at the 2 forest estates I analyzed. I used 30% genetically improved planting stock to regenerate the clear-cut stands, yet such high gains are unlikely for the species growing at the 2 forest estates. However, the results are within the range of previous findings, for example, Seely et al. (2002) for fertilization, and Aspinwall et al. (2012) for the use of genetically improved stock. The difference between the 2 types of strategies is caused mainly by the time required for the stands to respond to the actions that increase growth rates. When the harvest is reduced to below the baseline level, less carbon storage is removed, resulting in an immediate gain over the baseline scenario. This gain is significantly higher compared to the increased growth strategies. Thus, the increased growth strategies are less appealing for forest managers because of the time lag required to accrue the gains, the 95  uncertainties around forest response to increasing the growth rates, and the smaller gains compared to the harvest reduction strategies. Subsidizing the costs for fertilization and for the use of genetically improved stock is one option to encourage forest managers to more aggressively pursue strategies that increase growth rates. Forest managers continue to accrue timber revenues while using the subsidies to increase forest growth. The increased forest growth can be used to generate revenue from additional carbon storage or from higher volumes available for harvesting in the future. In chapter 3, 4 harvest reduction strategies were analyzed for their carbon storage performance: fixed harvest level, increased rotation age, and 2 strategies that increase the area in reserves. I found that the carbon storage and distribution of age classes were similar for all 4 harvest reduction strategies over a range of harvest reduction levels. The carbon storage gains were in line with previous findings (Harmon and Marks, 2002; Colombo et al., 2012). Furthermore, my results support the finding that switching to an uneven-aged system does increase carbon storage (Taylor et al., 2008; Nunery and Keeton, 2010). The uneven-aged system is implemented on 83% of the timber harvest land base at the AFRF, and this resulted in significantly lower carbon storage gains compared to the MKRF, where only the clear-cut system is implemented. Because the area of the AFRF is 1.9 times larger than that of the MKRF and the site index range at the AFRF is almost half of that at the MKRF (15 to 26 m compared to 20 to 40 m), it was expected that carbon storage gains at the 2 forest estates would be similar for similar strategies. All 4 strategies shifted the forest estates to older age classes, and this trend became more evident as the harvest was decreased from the baseline level. While no significant differences between the 4 harvest reduction strategies were found, preference might be directed towards the fixed harvest level strategy because of the flexibility to 96  adjust to natural disturbances and market changes. I showed in chapter 3 that increasing the reserve area posed a higher risk because a 25% mortality occurring in the reserves at year 10 would reduce the carbon storage down to the baseline levels by year 25 of the 100-year planning horizon. This reduction could have been at least partially avoided if a fixed harvest reduction strategy had been used, because then it would be possible to shift the harvest to the dead timber. Similar risks are posed by using a harvest reduction strategy that increases the minimum harvest ages. Older minimum harvest ages would constrain the forest manager’s option to shift harvesting in areas susceptible to higher risks (e.g., natural disturbances). Thus, a fixed harvest level strategy would provide the highest flexibility for forest managers to shift harvesting within the land base in order to respond to natural disturbances, implement various research projects (in the case of research forests), or adapt to changing markets without impacting the forecasted harvest level and carbon storage. Previous findings suggested that carbon credit production costs are lower for forests with higher site indices (e.g., Huang and Kronrad, 2001). In the sensitivity analysis conducted in chapter 4, I calculated the total break-even carbon credit price (i.e., the total cost of the carbon project divided by the number of carbon credits produced) for 3 forests estates actively managed for profit with a site index range of 14.7 to 25.6 m and a corresponding timber net revenue range of $4 to $35 m-3. I found that the total break-even carbon credit price increased with an increasing site index: $3.9 tCO2e-1 for a site index of 14.7 m, $32.1 tCO2e-1 for a site index of 22.1 m, and $40.8 tCO2e-1 for a site index of 25.6 m when the target harvest was reduced to 30% of the baseline level and for a 0% discount rate. The inclusion of the opportunity cost of reducing harvest explained this trend. The opportunity cost of reducing harvest represented 58% to 97% (at a 0% discount rate) of the total break-even carbon credit price, and it was independent of the 97  target harvest level (i.e., percentage reduction of the baseline level) because the number of carbon credits produced increased at similar rates as the target harvest was reduced from 90% to 30% of the baseline harvest level. This trend continued to hold true even when the timber net revenue (and the opportunity cost of reducing harvest) was reduced by 90%. When the opportunity cost of reducing harvest was not included, I found results similar to previous findings, where the total break-even carbon credit price reduces as the site index increases (Huang and Kronrad, 2001). In order to avoid issues with using site index as the universal measure of site productivity over a range of forest estates with different species and site conditions, I developed in chapter 4 a new metric that represents the opportunity cost of reducing harvest in favour of storing carbon. The new metric was called the average value per ha harvested (AVHH) and it was calculated as the timber revenue (including harvesting and silviculture costs) multiplied by the 25-year average harvested volume per ha per year for the baseline scenario. The AVHH for the range of site indices (14.7 to 25.6 m) was estimated to be 12.2 to 63.7 thousand $ ha-1. Like the site index, the total break-even carbon credit price increased with increasing AVHH when the opportunity cost of reducing harvest was included and decreased with increasing AVHH when the opportunity cost of reducing harvest was excluded. Thus, under the current voluntary carbon credit market prices, only the forest estate with an AVHH of 12.2 thousand $ ha-1 (site index of 14.7 m, timber net revenue of $4 m-3) could profitably undertake a carbon project that reduces the harvest below the baseline level while taking into consideration the opportunity cost of reducing harvest. Furthermore, I showed that increasing the carbon project verification frequency from 5 years to 1 year (in order to sooner accrue the revenue generated by selling the carbon credits) could be considered where the verification cost represents a low percentage of the total 98  cost, so the increased cost of the carbon project has little impact on the total break-even carbon credit price. In chapter 5 I developed fluctuating harvest schedules that lowered significantly the opportunity cost of reducing harvest while continuing to produce carbon credits. The fluctuating harvest schedules adjusted the starting target harvest level (STHL), which is set below the baseline level, to the baseline level at year 5, 10, 15, and 20 during the 25-year life of the carbon project. I found that carbon credit production continued for 4 to 15 years following the STHL adjustment to the baseline. The fluctuating harvest schedules with the STHL held for 10 to 15 years and then adjusted to the baseline level for the remaining life of the carbon project were the most efficient at producing carbon credits. These fluctuating harvest schedules can achieve 21% to 70% of the maximum number of carbon credits that can be produced during a 25-year carbon project and can reduce the total break-even carbon credit price by up to 17% at a 0% discount rate. Similar number of carbon credits can be produced by a range of fluctuating harvest schedules, yet the schedule that fits best to a particular forest estate depends on the local conditions of the forest and the timber and carbon markets. The maximum number of carbon credits that can be achieved occurs when the STHL is set at 33% of the baseline level (the minimum accepted level) for the entire 25-year carbon project. Discount rates of 4% to 6% reduced the financial benefits by half. The financial benefits of reducing the total break-even carbon credit price were limited by the difference between the opportunity costs of reducing harvest and the carbon project costs. While the opportunity costs of reducing harvest were lowered significantly when the STHL was held for 10 to 15 years, they still accounted for the largest portion of the total costs (over 96%). I also showed that the timber net revenue should be 99  under $15 m-3 (2.3 times less than the actual timber net revenue of $35 m-3 in the case of the MKRF) in order to enable implementing financially feasible carbon projects with harvest reduction strategies. In this study, 3 actively managed forest estates located in the 3 major forest regions of British Columbia (Coast, Southern Interior, and Northern Interior) were analyzed in terms of their potential to produce carbon credits and the cost to produce the credits. The 3 forest estates covered a wide range of temperate forest species (from coastal Douglas-fir to black spruce), site productivities (average site indices of 14.7 to 25.6 m), silvicultural systems (clear-cut, clear-cut with thinnings, shelterwood, and uneven-aged), timber net revenues (from $4 to $35 m-3), and costs to produce carbon credits (from under $4 to $45 tCO2e-1 at a 0% discount rate). I showed that when opportunity cost of harvest is included, the cost to produce carbon credits increased with increasing forest productivity. This trend might not hold true in the case of forest estates where high value species grow on low productivity sites (e.g., yellow-cedar). Moreover, a larger spectrum of forest estates and associated costs to produce carbon credits can be used to determine the type of forests where viable carbon projects could be implemented. The density of the points in the spectrum can be increased by using data that is currently public and easily accessible. Such datasets include the British Columbia Vegetation Resources Inventory (British Columbia Ministry of Forests, Lands and Natural Resource Operations, 2014b), the harvesting costs for each timber supply area accessed via TIPSY, the timber market prices (British Columbia Ministry of Forests, Lands and Natural Resource Operations, 2014a), and the carbon credit market prices (Peters-Stanley et al., 2013). Other specific factors can be included in the analysis from the timber supply analysis reports (British Columbia Ministry of Forests, Lands and Natural Resource Operations, 2014c). Using similar spatially explicit timber supply models 100  and carbon budget models, combined with a financial analysis similar to that presented in this study, the spectrum can be populated with more points in a relatively short period of time. A rapid extension of this spectrum is critical because the implementation of viable carbon projects should occur as soon as possible in order to be on the optimal path suggested by Feng et al. (2002): immediate implementation and maintenance of the forest-based carbon projects until the atmospheric carbon concentration is stabilized. A quick analysis of the latest vegetation resources inventory and previous timber supply analysis reports for the Northern Interior forest region of British Columbia indicates that approximately 4.33 106 ha (a conservative estimate) from the timber harvest land base has a site index of between 10 and 15 m (a similar range to FE3) that could potentially be used for carbon credit production. The production of FE3 is approximately 36 carbon credits ha-1 when the harvest is reduced to 30% of the baseline, which indicates the Northern Interior forest region could potentially produce 100 106 carbon credits (100 106 tCO2e) over a 25-year carbon project. Thus, the need for British Columbia to meet the public sector carbon neutrality goal based on the Greenhouse Gas Reduction Targets Act, which is estimated at 800,000 tCO2e year-1 (Pacific Carbon Trust, 2012a) could be fulfilled for 100 years. However, allocating large tracts of forested land for carbon credit production could have significant socio-economic impacts, especially in regions where forestry is the only industry. This is an area that requires further research. The impact of stand succession on carbon storage when harvest reduction strategies are implemented is another area that requires future research. Stand succession refers to the natural processes where stands with a certain age start to break-up as some older trees decay and die and new ones emerge. In the case of the boreal forest, the stands reach succession age much sooner 101  compared to the stands in the Coast forest regions. When harvest reduction strategies are implemented, stands are delayed from harvesting and depending on the harvest reduction period, some stands could be delayed for long enough so the break-up age is reached. During the succession period, carbon losses to the atmosphere from heterotrophic respiration are potentially higher. Thus, carbon storage could be negatively impacted by the succession processes. The present study contributed to the research through the following: (1) demonstrated that there is no difference in carbon storage between the various strategies that reduce the harvest below the baseline level, yet the fixed harvest reduction strategy might be preferred because of its flexibility to adapt to unforeseen natural disturbances and market changes, (2) demonstrated that the inclusion of the opportunity cost of reducing harvest drove the cost to produce carbon credits which, contrary to previous findings, it increased with increasing forest productivity expressed both, as site index and average value per ha of harvested timber, and (3) found a new method to reduce the cost to produce carbon credits by up to 17% (at 0% discount rate) through implementing fluctuating harvest schedules.          102  References Aspinwall, M.J., McKeand, S.E., and King, J.S., 2012. 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For. 18, 88-100.                109  APPENDIX I – Land base definitions AFRF Land Base element Total Area (ha) % of Total Area Gross Area 9,812 100.0% Less     Non-Forest 535 5.5% Productive Land Base 9,277 94.5% Less:     Reserves, Low Productivity 887 9.0% Timber Harvest Land Base 8,390 85.5% FE3 Land Base element Total Area (ha) % of Total Area Gross Area 14,920 100.0% Less     Non-Forest 40 0.3% Productive Land Base 14,880 99.7% Less:     Reserves, Low Productivity 0 0.0% Timber Harvest Land Base 14,880 99.7% MKRF Land Base element Total Area (ha) % of Total Area Gross Area 5,157 100.0% Less     Non-Forest 537 10.4% Productive Land Base 4,620 89.6% Less:     Reserves, Low Productivity 839 16.3% Timber Harvest Land Base 3,781 73.3%        110  APPENDIX II – Metadata The resultant GIS file used to build the FPS-ATLAS model was developed from a series of data sources (e.g., inventory, administrative, and management) using geo-processing tools (e.g., clip, union, topology). The table below indicates the fields, data types, and a description of each field. Field Data Type Description GIS Resultant   Site_Index Double Top height in m at age 50 Volume _12_5 Double Standing volume for 12.5 cm DBH merchantable Non_Forest Text Non Forest indicator Species_1 Text Species 1 label PCT1 Short Integer Pct Species 1 Species_2 Text Species 2 label PCT2 Short Integer Pct Species 2 Species_3 Text Species 3 label PCT3 Short Integer Pct Species 3 Species_4 Text Species 4 label PCT4 Short Integer Pct Species 4 Species_5 Text Species 5 label PCT5 Short Integer Pct Species 5 Species_6 Text Species 6 label PCT6 Short Integer Pct Species 6 DATE_EST Double Establish date (date of last stand-replacing disturbance) Operability Text Operability indicator BEC Text Biogeoclimatic Ecosystem Classification AGE_2013 Short Integer Stand age in years ATLAS_SG Text FPS-ATLAS Stand Group ID ATLAS_id Short Integer FPS-ATLAS Polygon ID (links to FPS-ATLAS Polygon_Id in model database) Shape_Area Double Area of polygon in square metres    FPS-ATLAS Inventory   Polygon_Id Integer Primary key of table Zone_Id Integer Foreign key of zone StandGroup_Id Integer Foreign key of stand group HarvestSystem_Id Integer Foreign key of harvest system State_Id Integer Foreign key of state Age Integer Polygon age in years 111  Field Data Type Description Distance Integer Distance to the mill Area Double Polygon area in ha Description Text Description for the polygon X Integer X coordinate of Description in UTM's Y Integer Y coordinate of Description in UTM's    FPS-ATLAS Stand Group  StandGroup_Id Integer Foreign key of stand group StandGroup_Category_Id Integer Category of stand group for reporting purposes LogValue Integer Log value in $/m3 Priority Integer Priority in relation to other stand groups Cycle_Constraints Boolean Are constraints cyclic Period_Constraints Integer True = by Period; False = by Year Description Text Description for the stand group    FPS-ATLAS Stand Group Treatment  StandGroup_Id Integer Foreign key of stand group Treatment_Id Integer Foreign key of treatment (1-5) Enabled Boolean Apply  treatment? Goto_StandGroup_Id Integer Where to go if treatment is applied Minimum_Age Integer Minimum treatment age Maximum_Age Integer Maximum treatment age Treatment_Value Integer Treatment specific value (e.g. percent removal) Ignore_Adjacency Boolean Spatial Adjacency    FPS-ATLAS Stand Group Curve  StandGroup_Id Integer Foreign key of stand group CurveType_Id Integer Yield curve type (Volume, ECA) Curve_Id Integer Foreign key of yield curve    FPS-ATLAS Curve   Curve_Id Integer Foreign key of yield curve X_Initial Integer Initial value along the x axis Delta_X Integer Interval value along x axis Description Text Description of curve    FPS-ATLAS Curve Data  Curve_Id Integer Foreign key of yield curve Sequence Integer X axis location Y_Value Integer Y value for this X axis location  

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