Potential for climate change mitigation through afforestation: an economicanalysis of fossil fuel substitution and carbon sequestration benefits

Peter J. GrahamCanadian Forest Service, Natural Resources Canada, 580 Booth Street, 7thFloor, Ottawa, Ontario, Canada,K1A 0E4; (tel.: 613-947-9070; fax: 613-947-9020; e-mail: pegraham@nrcan.gc.ca)

Key words: Ethanol, Greenhouse gas, Renewable energy, Wood waste

Abstract

The impetus for this paper is Canada’s commitment under the Kyoto Protocol to reduce national greenhouse gasemissions as well as reducing dependency on fossil fuels. This research assesses the economic viability of usingbiomass from afforested lands and industrial wood waste as a feedstock for ethanol production to substitute forfossil fuels in the transportation sector. Afforestation can increase the size of the carbon sink and also provide asource of renewable energy. Ethanol offers an excellent opportunity for greenhouse gas mitigation due to marketpotential, an ability to offset significant emissions from the transportation sector, and reduce emissions fromCO2-intensive waste-management systems. A case study of the economics of a hypothetical ethanol productionfacility found that a facility capable of producing 122 million litres of ethanol annually could have a net presentvalue of CDN$245 million over a planning horizon of 36 years. This facility would require a supply of up to 960oven-dry tonnes of wood-biomass per day and would result in net annual reductions of greenhouse gas emissionsof approximately 349,000 tonnes of CO2. This includes the carbon sequestered through the afforestation as wellas emissions avoided through fossil fuel substitution. Using biomass from afforested lands and industrial woodwaste as a fuel for energy production can be an economically viable tool for reducing greenhouse gas levels inthe atmosphere, reducing reliance on fossil fuels and reducing the sensitivity of transportation fuel prices tochanges in gasoline prices.

Introduction

The impetus for this paper comes from two relatedissues: climate change and the international commit-ment to its mitigation; and reducing dependency onfossil fuels for energy. The question posed here is: canusing biomass from afforested lands and industrialwood waste as a fuel for energy production be aneconomically viable tool to reduce greenhouse gaslevels in the atmosphere?To answer this, the twostages of afforestation’s role in reducing greenhousegas levels are examined: its initial use as a carbonsink; and then its use as a renewable energy sourcethat substitutes for fossil fuels. Next the benefits ofusing biomass to produce ethanol for use in the trans-portation sector are examined. Integral to such ananalysis is an assessment of the potential supply of

biomass from afforested lands as well as from indus-trial wood waste. A case study provides analysis ofthe economics of a hypothetical wood-ethanol pro-duction facility.

Case Study

To further our understanding of the potential of suchtechnologies, a case study of a hypothetical ethanolproduction facility located in Grande Prairie, Alberta,was conducted examining the economics of affores-tation for ethanol. Mathematical programming wasused to determine the optimal allocation of resources�afforested land and wood residues� from the point ofview of a profit-maximizing ethanol producer. Theallocation of land and resulting production of ethanol

© 2003 Kluwer Academic Publishers. Printed in the Netherlands.Agroforestry Systems 59: 85–95, 2003. 85

was then examined in terms of the impacts on netCO2 emissions. The facility uses wood biomass fromsawmill residues and hybrid poplar harvested fromafforested land as feedstock. In each planning period,the decisions to be made are: how much wood wasteto use; how much land to afforest; and how much toharvest. The cost components of the mathematicalmodel include: land rental; transportation; plantationestablishment and maintenance; harvesting; and capi-tal and operating costs. Financing costs are notincluded and wood waste is assumed to have no valueto the supplier. Revenue is a function of the totalamount of feedstock delivered to the facility in eachperiod. The model is constrained to ensure an even-flow of wood biomass to the facility over the plan-ning horizon, while allowing for construction timeand for plantations to accumulate biomass. Conver-sion rates, model variables and coefficients are listedin Appendix 1, and all equations are shown inAppendix 2. Note that monetary Figures are in Cana-dian dollars �US$1 � CDN$1.61, September 2002�.

Cost accounting

The approach to estimating the cost of establishingcarbon-sequestering tree plantations in this study issimilar to that described by Jepma et al. �1996�. Theapproach first involves the estimation of cost func-tions. In this study, the cost of leasing land is repre-sented by a function derived from a survey oflandowners. The next step is to refine the point esti-mates for plantation establishment and maintenancecosts by taking into account local or regional siteconsiderations �e.g., climate, soil zone, transportationdistance�. For example, Marland and Marland �1992�showed that the best afforestation scenario, basedonly on carbon flows, will depend on site-specificcharacteristics such as the expected growth-rate andthe accessibility for and efficiency of harvest �Ma-cLaren 1999�. Finally, a discounting procedure isbuilt into the methodology to account for the timevalue of money and reduced GHG emissions.

The significance of time in carbon mitigationstudies

Due to the growth characteristics of trees, carbon gainis low in the initial years of establishment. Thismeans that afforestation and reforestation programswill not contribute a great deal to carbon-dioxide re-ductions during the first commitment period, 2008 �

2012. However, over time and with successively moreland being afforested each year, the benefits will con-tinue to increase for decades �Nilsson and Schopf-hauser 1995�. The results of a study by van Kooten�2000� highlight the influence of discount rates on thetime value of carbon �C� and the marginal value ofafforestation. The time value of carbon depends onthe path of marginal damages �Richards, 1997�; inother words, the value of reducing GHG emissionstoday depends on when the damages being avoidedare expected to occur and how much they will cost.Of course, this inherently includes the discounting offinancial costs.Whereas the financial discount rate re-flects the opportunity cost of money, the carbon dis-count rate is a function of the marginal rate ofsubstitution between damages from emissions nowversus damages from emissions later �Marland et al.1997�. The carbon discount rate used in the followingcase study is relatively arbitrary due to the level ofscientific and economic uncertainty related to climatechange. The rates used in the literature range from 0to 6%.

Methodology

The study area is assumed to be a circle, with a ra-dius of 200 km from the ethanol facility at its centre,divided into four zones �d� of 50 km. Planning peri-ods �t� are four years in length, with decisions madeat the beginning of each period. The planning hori-zon is 36 years �t � 1,2,3,...,9�, allowing for a rea-sonable life span for the facility and the potential formultiple tree-crop rotations. The age �a� of an area ofafforested plantation is measured from the year of itsestablishment and measured in planning periods �a �1,2,3,...,9�.The decision variables are:Pd,t � the area �in hectares� of marginal farmland in

zone d afforested �i.e. planted� in period t;Ha,d,t � the area �in hectares� of afforested plantation

of age a, in zone d, harvested in period t;Wd,t � oven-dry weight �in metric tonnes� of wood

waste acquired in period t from mills in zone d.

Objective function

The objective is to maximize net present value �NPV�to the owner of the ethanol production facility by de-ciding what area to afforest, how much to harvest, andhow much wood waste to use in each period. The ob-jective function, NPV, is equal to the difference be-tween the discounted value of revenues �PVR� and the

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discounted value of costs �PVC� resulting from theland-use decisions over the planning horizon.

RevenueThe total present value of revenue �PVR� from salesof ethanol over the planning horizon is a function ofthe yield from harvesting and wood waste acquisi-tions. In each period, the yield from harvest is aproduct of the area �ha� of plantation in each age classharvested in each zone �Ha,d,t� and the volume of bio-mass �m3� per hectare produced in each age class �Ya�.The merchantable yield �m3/ha� for hybrid poplar iscalculated using the Chapman-Richards function:yield � 329 x �1 – e–0.156�a x p��3 �van Kooten et al.,1999a�. The resulting total volume �of biomass� after12 years of growth is 290 m3/ha. The estimates of to-tal aboveground biomass �i.e. including tree-tops andbranches� are derived from the merchantable yieldfunction and an expansion factor of 1.454 �NawitkaRenewable Resource Consultants Ltd., pers. comm.,1999; van Kooten et al., 1999b�. It is assumed that100% of the aboveground biomass is removed duringharvesting. This assumption is made due to the sig-nificant variation in harvesting efficiency dependingon harvesting systems. The quantity per hectare ofaboveground biomass, Ba, is presented in Table 1 withthe associated value of ethanol, Ea. The total revenueat the end of each period is discounted at a rate �r�equal to 6% per year from the middle of each periodas described in Equation 1.

Costs

The total present value of costs �PVC� associated withthe afforestation and ethanol production over the

planning horizon is calculated as the sum of the fol-lowing present-value costs: capital cost �CC�; operat-ing cost �OC�; transportation cost �TC�; plantationcost �PC�; land rental cost �LC�; and harvesting cost�HC�.The cost of acquiring wood waste is assumed tobe zero. Linear representations of each of the capital,operating, and land rental cost functions require twoseparate equations to describe their behaviour ade-quately. Although non-linear equations may betterdescribe the behaviour of the systems, the functionshave been estimated by piece-wise linear functions inorder to permit the use of linear or quadraticprogramming. Capital cost �CC� is assumed to be in-curred in the middle of the first period �t � 1�, allow-ing two years to build the facility and have it capableof operating at full capacity. CC is a function of theaverage annual feedstock supply �in oven-dry tonnes,ODt� to the facility.Given the early stage of develop-ment of this technology, the cost coefficients used inEquations 2a and 2b are very rough estimates; theyare not based on a detailed study of the system re-quirements of the wood-ethanol conversion facility.For comparison, a study by Kadam et al. �2000�models a similar system in greater detail. The twolinear functions described by Equations 2a and 2b arederived from the combination of a non-linear functiondeveloped by Kaylen et al. �2000� and linear estima-tions from B. McCloy and D. O’Connor �pers. comm.1999�. The source of the operating cost �OC� func-tions is the same as that of the capital cost functions.A non-linear form for OC was estimated by two lin-ear functions �Equations 3a and 3b�.The cost of rent-ing the land required for afforestation �LC� is afunction of Aa,d,t, the area �ha� available for harvest�planted and not yet harvested� as described in Equa-tion 4; where Pa,d,t is the total area of age a in zone dplanted until period t. This measure keeps track of thetotal amount of land afforested in any given periodand is therefore useful in ensuring that land availabil-ity constraints are not exceeded.Although previousstudies on the potential of afforestation to contributeto Canada’s Kyoto commitment have assessed thephysical availability of suitable land, few haveassessed its economic availability. It is unrealistic toassume simply that substitution will occur if the valueof tree production is greater than the value of the cur-rent agricultural regime, as there are many factorsother than crop value that affect land rent or the land-owners’ willingness to accept afforestation �Jepma etal. 1996�. Factors range from relatively quantifiablevalues such as efficiencies of scale and transaction

Table 1. Values of merchandable yield �Ya�, above-ground biomass�Ba�, and value of ethanol per afforested hectare �Ea� used in mod-eled scenarios.

a �period� Ya �m3/ha� Ba �ODt/ha� Ea �$/ha�1

1 47.85 17.27 2,418.352 173.33 62.57 8,760.343 289.84 104.63 14,648.414 369.57 133.42 18,678.245 417.75 150.81 21,113.256 445.21 160.72 22,500.897 460.40 166.21 23,268.798 468.68 169.20 23,687.339 473.16 170.81 23,913.61

1Based on $0.40/litre of ethanol�350 l of ethanol/ODt per hectare.

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costs, to non-market values such as visual quality�aesthetics� and resistance to change. The land rentalcost �$/ha� used in this study is based on results froma survey of western Canadian landowners �Suchaneket al., 2001� and is a measure of the landowners’ will-ingness to accept tree planting on their land.Suchaneket al. �2001� estimate that the minimum an individuallandowner would accept is $40 per acre �$98.84/ha�.At this annual rate the average landowner was will-ing to offer 143 acres �57.87 ha�. An assumption madein this study is that any compensation less than$98.84/ha �$342.49/ha per 4-year period� would re-turn zero hectares. When restated for the purposes ofthis model, the cost of establishing plantations on anyarea less than 57.87 hectares per landowner will be atleast $342.49/ha per period. If the area required forafforestation is greater than 57.87 hectares per land-owner, the cost per hectare in each period follows thelandowners’ willingness-to-accept function describedin Equation 5b.In order to avoid non-linearity in theprogram, the land rental rate has been divided intotwo segments, one constant and the other linearly in-creasing, thereby necessitating at least two scenariosof the model. The split occurs at 57.87 hectares perlandowner; below this point the rate is constant at$342.49 per hectare �Equation 5a, Appendix 1�, andabove this point the rate follows the function LC2

�Equation 5b� �Figure 1�. The coefficient 9.76 inEquation 5b is expressed in units of $ per farmer perhectare. These forms of the LC1 and LC2 functionsimply the combination of a linear and quadratic formof the land rental function.

In order to determine the number of landownerswithin each zone, an estimate of the average farm sizeis used in conjunction with the total area of farmlandin each zone. Within each zone, the area of farmlandphysically available for afforestation �Ld� is based onplanimetric measurements of improved land �Alberta

Provincial Base Map 1984�. Statistics Canada �pers.comm.� estimates that there are 5,650 farms withinthe study area. The number of landowners per zone�qd� is estimated based on the average size of farmsin the study area �� total area / total number of farms�and the available area in each zone. The area of landafforested in each zone is assumed to be equally dis-tributed amongst the total number of eligible land-owners in the respective zone. As the survey�Suchanek et al. 2001� indicated that only 75% oflandowners would consider planting trees, the mea-sured area was reduced by 25% per zone �given afixed, average farm size�. The total land rental costsper period are discounted from the beginning of eachperiod.

Transportation cost �TC� is a function of the weightof harvested biomass �ODt� delivered from each zoneto the facility in each period �Equation 6�. The costper tonne of biomass �chipped trees and industrialwood waste� delivered to the facility is a function ofan average distance to each zone. The cost includesthe round-trip distance, assuming that the outward-bound trucks are empty. The Figures used in thismodel are based on 1999 Saskatchewan Wheat Poolrates, and are similar to transportation costs used inrelated studies �Intergroup Consulting EconomistsLtd., pers. comm. 1982; Lindenbach 2000�. Transpor-tation costs are discounted from the middle of eachperiod.

Plantation cost �PC� is made up of establishmentand maintenance costs including site preparation,planting, brush and weed control, and operating over-head as described in Equation 7. The per hectare cost�m� is estimated at $1575.50/ha in this scenario.1

Harvesting cost �HC� is a product of the areaharvested �Ha,t,d�, the yield �m3/ha� in each age class�Ya�, and the harvesting cost per cubic metre �v�, asdescribed in Equation 8. The values of Ya used in thisscenario are presented in Table 1. The value of v isset at $12.00/m3 in this scenario �van Kooten et al.1999b�.

Constraints

The physical constraints include the area of land andwood waste available. Additional constraints are re-quired to ensure that harvested areas do no exceed

1The components of this cost include site preparation estimated at$180/ha, planting at $950/ha, herbicide application at $240/ha �in-cludes multiple applications�, and a 15% operating overhead.

Figure 1. Graphical representation of the Land Rental Ratefunction.

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planted area and that very young plantations are notharvested. An even-flow requirement is also includedto provide a stable supply of ethanol and a measureof employment stability for the local community. Asthe size of the facility is determined by the averagefeedstock supply over its lifetime, a relatively stableflow of biomass must be maintained in order to avoidsignificant feedstock excess or deficit. The periodicsupply �in ODt/period� is constrained within a limitof 25% above and below the average periodic supplyover the planning horizon.As mentioned previously inreference to land rental cost, the rental rate for affor-ested land is a function of the area required by theethanol producer and the number of eligible landown-ers �qd�. A different constraint on the rental rate func-tion is required for the two linear models. Theconstraints corresponding to land rental cost func-tions, Equations 5a and 5b, are presented in Equations9a and 9b respectively. Constraints on capital and op-erating cost functions were required in order to dif-ferentiate between the small and large-scale facilities.The point of differentiation was set at 350,000 ODt/year, the point where the two functions intersect.Harvesting constraints are set up to ensure that theharvested area in each period is not greater than thearea of plantations physically available. Harvesting isalso restricted to plantations older than 4 years.Theamount of waste produced by a mill is a function ofthe amount and size distribution of roundwood enter-ing the mill and the process efficiency of the mill. Inthis model, the maximum available levels areassumed to be fixed over the planning horizon and arebased on current sawmill operations in the study area�Table 2�. It is also assumed that there are no com-peting uses for the wood waste and it has zero valueto the mill. Of course, if the wood waste constraint iseffective, wood waste becomes valuable to the

forestry operation or mill owner, and it is then pos-sible to determine its shadow prices.

Results

To solve the model, four scenarios are run using dif-ferent combinations of the two land cost functions�Equations 5a and 5b� and the two sets of capital andoperating cost functions �Equation 2a & 3a and 2b &3b� in the objective function. The behaviour of eachscenario, and the sensitivities of the solutions, are ex-amined before drawing conclusions from the results.Only two of the four scenarios need to be examinedfurther as the other two resulted in negative netpresent values.A linear programming model underScenario-A considers a small-scale ethanol produc-tion facility capable of processing a maximum of350,000 ODt of biomass per year. Based on currentgrain-fed ethanol plants, this size of facility is aboutaverage and would be near the upper limit for the in-dustrial application of a new technology such as en-zymatic hydrolysis. The supply of biomass fromafforested land is limited to less than 57.87 hectaresper farmer �see Equation 9a�. The land rental rate isconstant at $342.49/ha �see Equation 5a� and the cor-responding land rental function is linear.The optimumallocation of resources for the small-scale facility re-sults in a discounted net present value �NPV of rev-enues minus NPV of costs over the planning horizon�of $244.8 million. The scale of the production facil-ity in this case requires a capital investment of $136.3million for the capacity to produce 122 million litresof ethanol annually. This scale of operation requiresall of the available wood waste from all zones in ev-ery period. It also requires that a total of 65,785 hect-ares be planted over 24 years; 19,289 ha in the firstperiod and 9,299 ha in periods 2 to 6. While harvest-ing begins in the second period, the first plantation isnot completely harvested until the fourth period,when the marginal yield is highest. Harvesting inyounger age classes is required to offset the initial in-vestment and maintain an even-flow of biomass laterin the planning horizon.The demand for wood wasteis higher than for afforested biomass simply becausethe only cost directly associated with wood waste inthis model is transportation; therefore it is obviouslya less costly feedstock. The shadow price of woodwaste shows that the ethanol producer would be will-ing to pay between $7 and $50 per additional oven-dry tonne �ODt� of wood waste, depending on zone

Table 2. Total mill residues by distance zone �Rd�. Source: AlbertaForest Products Association, 2001.

DistanceZone �d�

Number ofmills

Total Quantity of Residue perYear �tonnes�

Produced Available �Rd�

1 4 598536 1976442 0 0 03 7 243592 2246784 2 397508a 870231

1Includes 65,000 ODt of wood waste from mills in British Colum-bia within a 2-hour drive �McCloy & O’Connor, 1999�.

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and period. For example, the ethanol producer couldpay up to $33.03 for one additional ODt from zone 1in period 1 without reducing the net present value ofthe business.Scenario-D involves a large-scale etha-nol production facility supplied with biomass fromwood waste and from a limited area of afforestedfarmland. The scale of the ethanol production facilityis constrained to have a capacity of at least 350,000ODt per year. The capital and operating cost functionsfor a facility of this size follow a shallower slope thanfor the smaller facility represented by Scenario-A.The land rental cost function and associated con-straints used in the model under Scenario-A are em-ployed under Scenario-D.The optimum allocation ofresources in Scenario-D results in a discounted netpresent value of $2,150 million, the optimal solutionof the model. The scale of the production facility inthis case requires a capital investment of $401 mil-lion for the capacity to produce 726 million litres ofethanol annually. This scale of operation requires thata total of 722,782 hectares be planted over 32 years,and requires all of the available wood waste from allzones in every period. The shadow price for woodwaste ranges from $19 to $102 depending on zoneand period.All available land is planted in the firstperiod and the plantations in zone 1 occupy themaximum area over the whole planning horizon.Plantations in zones 2, 3 and 4 are not maintained tothe end of the planning horizon. The majority of theharvesting occurs in 12- to 16-year-old plantationswhen the marginal yield is highest. All plantations arecompletely harvested by the end of the planning ho-rizon. In comparison to the small-scale facility, thegreater demand for feedstock for the large-scale fa-cility of Scenario-D is reflected in the increase in af-forestation as well as the shadow price of woodwaste.

Sensitivity analysis

The land rental cost, a result of planting and harvest-ing decisions, is a key factor in the net present value�NPV� of the model. In the case of the small-scale fa-cility, the objective function value �NPV� is sensitiveto the costs related to planting and harvesting in zone1. A decrease in planting cost of $27.15/ha in the firstplanning period, or an increase of $21.29 in the sec-ond period, can result in a change in the amount ofland afforested. The model is most sensitive to costsand revenues associated with harvesting 12- to16-year-old plantations due to the growth function.

For example, the harvesting schedule could change ifharvesting costs in the 4th planning period were todecrease by only $2.16/ha, from $1,369.13/ha ��$12/ha x 289.84 m3/ha x �1�r�–16� for 12-year-oldplantations. A relatively small change in plantationyield or transport cost could cause a similar changein the solution. The sensitivity of Scenario-A to thesevariables declines with increasing distance from thefacility and over time.The large-scale facility is lesssensitive to planting costs due to the high demand forbiomass. This sensitivity �about $2/ha� increases sig-nificantly after period 2; however the effect would beseen mostly in the timing of planting rather than achange in the area planted. Transportation cost has asignificant effect on the area chosen for afforestation.In Scenario-A the transportation costs restrict plant-ing to zone 1 �maximum 50 km from facility�. InScenario-D, transportation costs affect the harvestingcosts in the same manner as in Scenario-A, exceptthat the lower marginal capital and operating costsoffset higher transportation costs associated with har-vesting farther from the facility.The requirement foran even-flow of biomass to the facility has a stronginfluence on the optimal solution. While the relax-ation of the even-flow constraint increases NPV overthe planning horizon, the periodic fluctuations be-come quite significant, resulting in periodic lossesduring planting years. Despite a lower NPV, the etha-nol producer may prefer to have an even-flow con-straint to maintain cash flows, or may consideracquiring feedstock from somewhere else.

Analysis of results in terms of GHG emissionsreduction

The results obtained from the maximization of theobjective function, for both Scenario-A and Scenar-io-D, are now examined in terms of greenhouse gas�GHG� emissions. All GHG emissions are presentedin terms of CO2 or CO2-equivalents, and a physicalcarbon discount rate of 2.5% is used �a mean valueof estimates by Stennes 2000; van Kooten et al.1999b; Price 1997; Marland et al. 1997�.2 The GHGpools examined in this case study include soil organic

2The discounting of GHG emissions remains a contentious issue.The view taken in this paper is that given regulatory requirements�i.e. a ratified Kyoto Protocol� and the emergence of carbon mar-kets, a discount rate �although arguably artificial� is appropriate.However, a precise rate applicable to this situation is unknown andtherefore the rate of 2.5% is chosen arbitrarily, but a mean valuebased on previous studies.

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carbon �SOC�, belowground carbon, abovegroundcarbon, emissions from plantation operations, andemissions avoided through the use of ethanol ingasoline.Scenario-A results in a net discounted emis-sions reduction of 8.5 Mt-CO2-equivalents over 36years. The costs �total PVC divided by total tonnageof CO2 sequestration and avoided emissions over theplanning horizon� incurred amount to $98/t-CO2. Thelarger scale of operation represented in Scenario-Dresults in a net discounted emissions reduction of 51.2Mt-CO2 equivalents, at a rate of $84/t-CO2. These re-sults and their sensitivity to the even-flow constraintsare presented in Table 3.

Case study conclusions

The results of the mathematical models presentedhere indicate that the land rental rate significantly re-stricts the economic success of afforestation as asource of biomass for ethanol production. The resultsalso show that a large-scale facility has a higher netpresent value and contributes substantially more toGHG emissions reductions than a smaller-scale facil-ity. However, there are considerations not accountedfor in the model that would make the smaller-scalefacility more attractive at this time.

Although the large-scale facility is more economi-cally efficient than the small-scale facility due toeconomies of scale, it is highly unlikely that thelarge-scale ethanol production system would be builtat this time. The enzymatic-hydrolysis technologyapplied in these scenarios is presently untested at anindustrial scale and this represents the greatest risk forthe ethanol producer. In addition, the land rental ratefunction used is based on a sample of landownersfrom all western provinces and may not accurately

reflect the willingness-to-accept compensation ofthose in the study area. Another consideration not in-cluded in the scenarios is the limitation of the currenttransportation infrastructure. A supply of 5,685 tonnesof feedstock per day to the larger-scale facility maynot be possible given current road and rail networks.A smaller facility could more easily meet itsfeedstock requirements using existing transportationnetworks.

The economic viability of the facilities modelledare also sensitive to planting and harvesting costs. In-creases or decreases in these costs by as little as $2/hacan change the amount of area afforested or the tim-ing of planting and harvesting. Transportation costsalso affect these activities, but less so; in this modelthey are constant within each zone and the differencebetween zones is significant enough to be unaffectedby small changes in cost. The shadow prices of woodwaste in Scenario-A indicate that the mills couldcharge at least $11 per ODt, depending on distancefrom the ethanol facility, and the ethanol producerwould continue to purchase the wood waste. Of sig-nificant consideration not included in the model arethe characteristics of the available wood waste.Whitewood sawmill residue is much preferred overbark due to their relative ethanol conversion rates.

With some government incentives designed to re-duce, or compensate for, the risk to the producer, asmall-scale wood-ethanol production facility locatedin Grande Prairie, Alberta, would be an economicallyviable project in the short term. In the long term, alarge-scale facility may have a greater chance of suc-cess given some maturing of the technology. A greaterwillingness of landowners to plant trees will likely benecessary in the future to make up for reductions in

Table 3. Summary of GHG emissions statistics of Scenario-A and of Scenario-D. A-25% and D-25% represent a 25% even-flow tolerance,A-50% and D-50% have a 50% tolerance, and A-100% and D-100% have no even � flow constraints.

Scenario A-25% A-50% A-100% D-25% D-50% D-100%

NPV �$�106� 244.8 254.7 263.9 21249.9 2200.3 2350.0Annual Ethanol Production �L�106� 122.5 122.5 122.5 726.3 739.6 792.8Total Disc. Cost/Disc. tCOd 77 75 74 83 81 82Total Disc. Cost/Non-Disc. tCOd 52 49 50 56 55 54Initial Aboveground CO2 max. �kt-CO2�

1 905 2,013 4,990 9,887 13,546 20,362Net disc. balance �kt-CO2�

2 8,467 8,258 8,567 51,137 52,080 54,409Net non-disc. balance �kt-CO2�

3 12,551 12,552 12,552 76,164 77,581 83,048

1The maximum amount of aboveground CO2sequestered prior to harvesting. If the planning horizon was infinite, and the plantations weremanaged on a sustainable basis, this would be the equilibrium level of sequestered aboveground CO2; 2Net quantity of CO2sequestered andavoided over the planning horizon, and discounted at a rate of 2.5% per annum; 3Net quantity of CO2sequestered and avoided over theplanning horizon, but not including discounting of carbon.

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wood waste supplies due to increased efficiency ofmills and increased competition from other users.

Future reductions in the availability of low-costwood waste will increase the demand for wood-bio-mass from fast-growing plantations. The potential forafforestation to meet this demand is limited mainly bythe cost and productivity of marginal farmland and itssuitability for growing trees. Increasing demandcombined with reductions in the costs associated withplantation establishment and harvesting will result inincreased productivity �due to better land and moreintensive management� and therefore greater potentialto compete with wood waste.

Discussion

There are technological obstacles to overcome beforewood-ethanol production can begin to realise its mar-ket potential. An increase in federal incentives in theform of tax concessions on ethanol-blended fuel,combined with provincial incentives designed to helpovercome the large capital costs and associated risks,will provide the industry with the necessary boost toget it up and running. A successful ethanol industrycan help Canada not only reduce greenhouse gasemissions, but also increase economic activity,particularly in rural areas.

The case study presented in this paper finds that,given the assumptions made in the model formula-tion, positive net present value could be achievedfrom the point of view of the ethanol producer. Dueto the significantly lower acquisition costs comparedto afforestation and harvesting, wood waste was thepreferred feedstock in the model. However, to takeadvantage of some economies of scale while satisfy-ing even-flow requirements, some afforestation is alsoeconomically viable. Land rental and transportationcosts are the most significant costs limiting afforesta-tion potential.

In terms of health benefits, the use of ethanol in thetransportation sector can reduce the emissions of pol-lutants including a number of greenhouse gasses. Forexample, the improved combustion efficiencies ofethanol-blended gasoline reduce the formation oflow-level ozone, a principle component of smog �En-vironment Canada 2002�. Ethanol can also replaceother gasoline blending agents, such as the two most

common octane-boosting additives, MMT3 andMTBE4, that have been linked to negative health andenvironmental impacts. Also, instead of incineratingwood waste, using it for ethanol production can di-rectly improve air quality.

In conclusion, the use of biomass from afforestedlands and wood waste as fuel for energy productioncan be an economically viable tool to reduce green-house gas levels in the atmosphere. Afforestation canincrease the size of the terrestrial carbon sink, whichin turn contributes to net reductions in GHGemissions under the Kyoto Protocol. While the sinkbenefit is essentially finite due to a finite amount ofland available, the opportunity for long-term green-house gas reductions lies in the use of the afforestedbiomass as feedstock for energy production. Wood-ethanol reduces our reliance on fossil fuels, thus re-ducing emissions that are harmful to our health andthe environment.

Whereas the potential damage resulting from cli-mate change may be perceived by many as not criti-cal enough to require immediate action, the effects ofdecreased air quality in rapidly-growing cities arepushing policy and law makers to demand cleanersources of transportation fuel. As a “no-regrets” op-tion, the production of ethanol from wood waste andafforested wood-biomass can be economically viableand contribute to the mitigation of greenhouse gasemissions.

Recommendations for further study

To make afforestation more affordable there are op-portunities to derive multiple economic benefits fromeach rotation of trees. For example, suitable logs canbe sold to pulp and paper mills, while the remainingwood-biomass in the form of treetops and branches�and possibly roots� could be used as a feedstock forethanol or other forms of energy production. If mar-kets for the lignin co-product of the bio-ethanol con-version process can be found �within the chemicalindustry for example�, the value of the lignin is likelyto be higher than when it is burned for process steamand electricity in the bioconversion process. The use

3MMT �Methylcyclopentadienyl Manganese Tricarbonyl� is an ad-ditive used to boost octane in unleaded gasoline.4MTBE �Methy Tertiary Butyl Ether� is a by-product of the naturalgas industry used as an additive to increase oxygen content ofgasoline.

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of lignin-based chemicals could replace currentpetroleum-based chemicals, thereby further reducingthe use of fossil fuels. In addition to NPV, furthereconomic analyses should also include internal ratesof return in order to better compare the economicperformance of alternative scales of production.

Acknowledgements

This paper is based on research for the author’sgraduate thesis and was funded by the SustainableForestry Management Network through a project leadby Dr. J. Saddler, UBC. The author would also liketo thank Dr. G.C. van Kooten, Dr. E. Krcmar-Nozic,Dr. G. Bull, Dr. A. Esteghlalian, P. Suchanek, Dr. B.Bogdanski, and D. Park for their valuable contribu-tions at various stages of the research. The views ex-pressed in this paper are not necessarily those of theGovernment of Canada or the Canadian Forest Ser-vice.

Appendix 1

Table A1. Conversion rates and model variables and coefficients.

Conversion ratesInside bole �merchantable� volume to aboveground biomass expansion factor: 1.454Wood volume to dry-matter biomass �ODt / m3�: 0.361Ethanol per unit biomass �litres / ODt�: 350.0Avoided emission from ethanol use �tCO2-equivalent / litre�: 0.00269Soil organic carbon gain relative to baseline agricultural soil �tC ha–1yr–1�: 0.2CO2-equivalent emitted in planting operation �tCO2/ha�: 0.2667Electricity/Steam from lignin co-product �kWh / litre�: 1.1Physical carbon to CO2-equivalent conversion factor �tCO2 / tC�: 3.6667Market value of ethanol used in case study �CDN$/litre� 0.40

Model variables and coeffıcientsEquation 2a coefficient: b1 �$/ODt� 4437.5Equation 2b coefficient: b2 �$/ODt� 175.0Equation 2b coefficient: c �$� 87,500,000Equation 3a coefficient: f1 �$/ODt� 94.5Equation 3b coefficient: f2 �$/ODt� 37.8Equation 3b coefficient: g �$� 18,900,000Area in zone L1 �hectares� 424,500Area in zone L2 �hectares� 459,750Area in zone L3 �hectares� 561,000Area in zone L4 �hectares� 135,000Number of landowners in zone q1 1138Number of landowners in zone q2 1233Number of landowners in zone q3 1504Number of landowners in zone q4 362Transportation cost from zone TF1 �$/ODt� 4.2Transportation cost from zone TF2 �$/ODt� 12.6Transportation cost from zone TF3 �$/ODt� 21.0Transportation cost from zone TF4 �$/ODt� 29.4

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Appendix 2

Equations

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