Estimating the net carbon balance of boreal open woodland afforestation: A case-study in Québec’s closed-crown boreal forest

Forest Ecology and Management 257 (2009) 483–494

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Estimating the net carbon balance of boreal open woodland afforestation: A case-study in Que´bec’s closed-crown boreal forest Simon Gaboury *, Jean-Franc¸ois Boucher, Claude Villeneuve, Daniel Lord, Re´jean Gagnon Universite´ du Que´bec a` Chicoutimi, De´partement des Sciences fondamentales 555, boul. de l’Universite´, Chicoutimi, Qc, G7H 2B1 Canada

A R T I C L E I N F O

A B S T R A C T

Article history: Received 3 April 2008 Received in revised form 10 September 2008 Accepted 11 September 2008

Black spruce (Picea mariana (Mill.) B.S.P.) is the dominant tree species in the Canadian province of Que´bec’s boreal ecosystem, particularly in the black spruce-feathermoss (BSFM) domain (between the 49th and the 52nd parallels). While black spruce is generally well adapted to regenerate after wildfires, regeneration failure can sometimes occur, resulting in the irreversible conversion of closed-crown BSFM to open black spruce-lichen woodlands (OW). With OWs representing approximately 7% (1.6 M ha) of Que´bec’s BSFM domain, the afforestation of OWs carries significant theoretical potential for carbon (C) sequestration, which has not yet been evaluated. The main objectives of the study were then: (i) to estimate the theoretical C balance of OW afforestation within the closed-crown BSFM domain in Que´bec’s boreal forest; (ii) to calculate, using the life cycle analysis (LCA) method, all the GHG emissions related to black spruce OW afforestation in the closed-crown BSFM domain of Que´bec. The CO2FIX v. 3.1 model was used to calculate the biological C balance between the baseline (natural OW of site index 9 at age 50) and afforestation (black spruce plantation of site index 6 at age 25) scenarios, using the best estimates available for all five recommended C compartments (aboveground biomass, belowground biomass, litter, deadwood, and soil). The simulation revealed a biological C balance of 77.0 t C ha1, 70 years following afforestation, for an average net sequestration rate of 1.1 t C ha1 year1. Biological C balance only turns positive after 27 years. When integrating the uncertainties related to both the plantation growth yield and the wildfire disturbance, the average sequestration rate varies between 0.2 and 1.9 t C ha1 year1. GHG emissions are 1.3 t CO2 equiv. ha1 for all afforestation-related operations, which is less than 0.5% of the biological C balance after 70 years. Thus, GHG emissions do not significantly affect the net C balance of the afforestation project simulated. Several recommendations are made, mostly centered on the factors influencing the growth rate of carbon stocks and the impact of natural disturbances, to minimize the range of uncertainties associated to the sequestration potential and maximize the mitigation benefits of an OW afforestation project. ß 2008 Elsevier B.V. All rights reserved.

Keywords: Afforestation Black spruce Boreal plantation Carbon balance Life cycle analysis Greenhouse gas mitigation

1. Introduction Scientific evidence of climate change is supported by a high number of international investigations (IPCC, 2007). The vast majority of these studies link this phenomenon to the humaninduced increase in greenhouse gas (GHG) concentrations in the troposphere (IPCC, 2001, 2007). GHG emissions are mainly caused by fossil fuel combustion, but also by land use changes, especially deforestation (IPCC, 2001, 2007). To extend the worldwide task of reducing GHG emissions, or to partly offset the deforestation, the

* Corresponding author. Tel.: +1 418 545 5011x2330; fax: +1 418 545 5012. E-mail address: [email protected] (S. Gaboury). 0378-1127/$ – see front matter ß 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.foreco.2008.09.037

Kyoto protocol (KP) considers reforestation and afforestation activities for carbon (C) sequestration accounting (IPCC, 2007). When deforestation occurs, the accumulated C is released more or less slowly, depending on vegetation decomposition time, litter and soil C accumulation, and/or human use of the harvested biomass. For the average hectare of forested land worldwide, between 50 and 120 t C are accumulated in aboveground vegetation (IPCC, 2000). The total aboveground forest C stock in the biosphere is estimated to be around 320–360 Gt (Dixon et al., 1994; FAO/FRA, 2006; IPCC, 2000). The boreal forest alone covers 14.5% of the world’s continents, contains 26% of terrestrial C stocks, and 31% of the C contained in all forest soils (Melillo et al., 1993; Dixon et al., 1994; Gower et al., 1997). Soil C is the largest C pool in the world’s forest ecosystems, with almost 700 Gt of C (Gower et al., 1997). Litter production and root decomposition, especially

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fine roots, are major processes causing soil C accumulation (Steele et al., 1997). Hence, density of aboveground vegetation can have a significant influence on the C accumulation rate of a forest ecosystem, both belowground and aboveground. In Canada’s Eastern boreal zone, the black spruce-feathermoss (BSFM) domain (between the 49th and the 52nd parallels) covers 28% of the province of Que´bec. Black spruce (Picea mariana (Mill.) B.S.P.) is the dominant tree species, representing more than 75% of the forest cover in the BSFM domain (Bergeron, 1996; Gagnon and Morin, 2001). While black spruce is generally well adapted to regenerate after wildfire (Heinselman, 1981; Viereck and Johnston, 1990), poor regeneration can sometimes occur, resulting in the irreversible conversion of closed-crown BSFM to open black spruce-lichen woodlands (hereafter shortened to open woodlands or OW) (Payette, 1992; Gagnon and Morin, 2001; Jasinski and Payette, 2005). To this day, there is no evidence of natural redensification of OWs, i.e. a shift to closed-crown BSFM stand (Payette, 1992; Jasinski and Payette, 2005). Moreover, a recent study showed a gradual increase in OW generation over the past 50 years (Girard et al., 2008). The most recent Que´bec forest inventory reveals that approximately 7% (1.6 M ha) of the BSFM domain is made up of OWs (Ministe`re des Ressources naturelles, de la Faune et des Parcs (MRNFP), 3rd decennial forest inventory), of which nearly 10% are less than 5 km from the existing road network in 2002 (Plante, 2003). Afforestation of open woodlands has been tested recently, with an experimental plantation network within Que´bec’s central boreal zone, where site-prepared OWs were compared to adjacent and managed BSFM stands (Girard, 2004; He´bert et al., 2006). The initial results show significant seedling survival and growth, within the 3-year post-plantation establishment window (He´bert et al., 2006). The large amount of available OWs within Que´bec’s closed-crown boreal forest – and probably across Canada (Rowe, 1972) – represents a significant theoretical potential for C sequestration that has not yet been evaluated. Since no long-term growth and yield results from boreal plantations are yet available, modelling is needed to project plantation C stock growth. The main objectives of the study were: (i) to estimate the theoretical C balance of OW afforestation within the closed-crown BSFM domain in Que´bec’s boreal forest; (ii) to calculate, using the life cycle analysis (LCA) method, all the GHG emissions related to black spruce OW afforestation in the closedcrown BSFM domain of Que´bec. A complete net C balance must include the biological C cycle as well as the industrial C cycle (Gower, 2003). 2. Methodology The general approach of the study was based on both the ISO 14 064 (ISO, 2006) and the GHG Protocol for Project Accounting (GHG Protocol Initiative, 2005) standards. The Good practices guidance for land-use, land-use changes and forestry (IPCC, 2003) approach was used for the biological C calculation of both the afforestation and baseline scenarios. Life cycle analysis (LCA) was used to quantify GHG emissions from forest operations (ISO, 1997). 2.1. Estimation of the C sequestration potential 2.1.1. Afforestation and baseline scenario postulates A common forest history was applied on both scenarios, i.e. a 75-year-old OW created after a wildfire in a dense BSFM stand (Payette, 1992; Jasinski and Payette, 2005) that completely released the leaf and branch biomass and 10% of the stem biomass (mostly bark), the remaining biomass being considered as woody debris.

A growth yield corresponding to a site index of 9 m (dominant tree height at age 50) for a black spruce stand of low tree density, as found in growth and yield tables for black spruce natural stands within Que´bec’s spruce feathermoss domain (Pothier and Savard, 1998), was used for the baseline scenario. The time span of the OW used for the simulation was from ages 75 to 145, so that the growth yield matches the maximum theoretical volume of an unproductive OW (Direction des inventaires forestiers, 2002). These unproductive OWs also correspond to a tree crown cover of less than 25% (up to age 145), which is in accordance with the threshold adopted in Canada to identify unforested lands (Environnement Canada, 2006). The afforestation scenario included the plantation of 2000 black spruce seedlings per ha of a 75-year-old OW, which included the removal of all commercial stems (stem-only method). The rationale behind the simulated removal of the few trees in an OW before afforestation is that the growth and yield tables for a black spruce plantations in Que´bec include implicitly clear cutting (and soil scarification) prior to planting (MRNFP, 2003). The growth yield used for the plantation corresponded to the lowest site index for a black spruce plantation (site index 6 m, dominant tree height at age 25), as found in the growth and yield tables for black spruce plantations for the spruce feathermoss domain (Pre´gent et al., 1996). These tables have been used to estimate the growth yield beyond the age of the oldest plantation (approximately 35-yearold), so that site index 6 is estimated to reach a volume of 175 m3 at age 70, just over the expected maturity for a black spruce plantation (MRNFP, 2003). The beginning of the simulated afforestation scenario corresponds to an OW at age 75, so that the minimum theoretical plantation yield can be compared with the maximum theoretical OW yield (30 m3 ha1 at age 145). 2.1.2. Carbon model CO2FIX and C stock evaluated The CO2FIX v. 3.1 model was used to calculate the amount of C accumulated in both the baseline and afforestation scenarios (Masera et al., 2003; Schelhaas et al., 2004). Among others, the CO2FIX model has been used to predict the C dynamics of forestryrelated projects in Finland (Liski et al., 2001) and Canada (Lemprie`re et al., 2002). CO2FIX is an ecosystem-level model based on C accounting of forest stands, including forest biomass, soils and products (Masera et al., 2003). To calculate C stocks in soil, the ‘‘YASSO’’ module for soil C, adapted to the CO2FIX model, was used (Masera et al., 2003; Liski et al., 2005). When evaluating the net carbon balance of an afforestation project, it is good practice to include, or to justify the exclusion of the main forest carbon stocks (IPCC, 2003). Those stocks are: above ground biomass (merchant tree; non-merchant tree; understory vegetation); belowground biomass; litter; deadwood and soil. In this study, all but the nonmerchant tree (DBH < 9.1 cm) and understory vegetation stocks have been evaluated. While understory vegetation (including bryophytes) can be a quantitatively important compartment of young and poorly drained black spruce growing stands (BondLamberty et al., 2004; Bond-Lamberty and Gower, 2007; O’Connell et al., 2003), we have excluded this compartment in the simulation since no reliable numbers are available on an annual basis for either scenario, and particularly for the baseline scenario. Trees with a DBH below 9.1 cm were not considered because no information was available for this compartment in Que´bec’s growth yield table (Pothier and Savard, 1998). Because both stand types have an evenaged structure, this exclusion should have little impact on the net C balance at the end of the simulation, but could have a more significant effect at the beginning of the project, when commercial size has not yet been reached. Litter, deadwood and soil were treated under the soil C compartment in CO2FIX. For further details on the CO2FIX model, see Schelhaas et al. (2004) and Masera et al. (2003).

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Table 1 The model parameters used in the simulation. References and general remarks about their provenance and uses are also provided. Model parameters General parameters Stemwood density (t m3) Wood relative carbon content (ratio)

Specific aboveground biomass parameters Stand current annual volume increment (m3 ha1)

Branches and foliage growth (ratio form, relative to stem growth) Tree mortality (%) Annual turnover of each compartment Specific belowground biomass parameters Coarse and fine root growth (relative to total stem biomass growth) Annual coarse and fine root turnover (%)

References

Comments

Chen et al. (2002) Chen et al. (2002), IPCC (2003) and Liski et al. (2001)

Average value based on a literature review for black spruce Average value used for conifers by most studies

Pothier and Savard (1998), Pre´gent et al. (1996), MRNFP (2003), Kull et al. (2006) and Lambert et al. (2005) Lambert et al. (2005)

Includes the commercial stem part (from local yield table for black spruce), the tree top and the tree stump (average value for Que´bec’s conifers) and the bark (adapted from black spruce biomass compartment equation sets) Adapted from black spruce biomass compartment equation sets

Kurz and Apps (1999) Kurz and Apps (1999)

Average value estimated for Canadian conifer species Average values estimated for Canadian conifer species

Li et al. (2002)

Average values for Canadian conifer species

Kurz and Apps (1999)

Average values estimated for Canadian conifer species

2.1.3. Data and model parameters The model parameters were selected from the most recent studies relevant to black spruce ecosystems and are shown in Table 1. For the soil compartment, the Yasso model uses the turnover of each biomass compartment as inputs. Deadwood is considered immediately returned as litter to the soil surface, so that it is included in the litter pool. Regional climatic data are integrated to estimate the litter decomposition rate (Schelhaas et al., 2004; Masera et al., 2003; Liski et al., 2005). The climatic data used were: the amount of precipitation during the growing season (May to September), the sum of degree-days above zero, and the potential evapotranspiration. They were acquired from the regional climate annual data, using 1960–2000 Bonnard weather station yearly averages (508 430 N 718 30 O, altitude 506 m) (Environnement Canada, 2000). This weather station is one of a few stations in Central boreal Que´bec representative of the area covered in the present study. 2.1.4. Treatment of uncertainties Uncertainties associated with C accumulation in biomass and soil in this study are mainly of three sources: (i) wood yield, (ii) reversibility, (iii) calculation parameters. First, all OWs do not have the same wood yield, before and after afforestation. To address this uncertainty, a sensitivity analysis was performed using a simulation with low and high plantation yields, in addition to the original scenario (site index 6 m at 25 years or 175 m3 ha1 at age 70). The objective was to identify the influence of plantation wood yield on the net C sequestration of the project. For the low yield scenario, it was assumed that the plantation would perform as a natural stand of site index 12 (m at age 50) from Pothier and Savard’s (1998) growth and yield tables for natural stands of black spruce, as there is no site index lower than 6 (m at age 25) in Pre´gent et al. (1996) growth and yield tables for black spruce plantations. Site index 8 (m at age 25), the highest plantation site index found for boreal black spruce plantations (Pre´gent and Ve´giard, 2000), was used as the high yield scenario. Reversibility must be considered when evaluating the net C balance of a project (GHG Protocol Initiative, 2005), that is the risk that a natural disaster (wildfire, insect infestation, windthrow) occurs and reduces the carbon accumulation of the mitigation project. The stochastic character of these natural events impedes a precise determination of their impacts on C stocks. As an indication, Bergeron et al. (2004) estimated that 0.11% of central

Que´bec forests burned annually, compared to 0.79% in the past and less than 0.00001% in the future. The approach used to address this uncertainty and illustrate its impact was to subtract from the C sequestration of both the afforestation and the baseline scenarios a percentage equivalent to the annual factors (with both low 0.11% and high 0.79% fire severity rates) determined by Bergeron et al. (2004), multiplied by the time horizon studied. This approach allows for an assessment of the magnitude of the C losses that could be attributed to wildfires in large afforestation projects (Lemprie`re et al., 2002). The third uncertainty type is the precision of the parameters used. Validation of each of the C stocks calculated from regression equations or ratios and used as inputs in the model is to some degree lacking for both scenarios studied. The parameter variability reported in the literature has not been subjected to further analysis in the present study. Moreover, the available data does not allow for estimating, with minimum reliability, the impact of the compartments ignored in the simulation. The uncertainty associated with these exclusions was not evaluated, but is addressed in the discussion. 2.2. Calculation of GHG emissions from afforestation operations 2.2.1. Operations examined All necessary operations for the OW afforestation were taken into account during the LCA. These operations were first classified into five major processes commonly reported in the literature (Berg and Karjalainen, 2003; Berg and Lindholm, 2005; Johnson et al., 2005): 1. seed production, 2. seedling production, 3. harvesting operations, 4. site preparation, 5. plantation. Four other operations needed to be accounted for, to yield a complete picture, for a total of nine processes: 6. land access, 7. housing and accommodation, 8. monitoring, 9. transportation. Similarly, the transport needed between each process was integrated (Fig. 1). For each process, the GHG output was calculated by multiplying the material or energy input by its associated general emission factor (see Section 2.2.2). The data for each operation and process came from two general sources. The primary source of data was taken from both the 2004 and 2005 annual reports of companies involved in local forest management, and from direct inquiries during the same years. When primary sources were not available, a complementary source of data obtained from the relevant literature, i.e. where the technological and socio-economical contexts were comparable to the present study context, was used.

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Fig. 1. Main processes for the LCA of the operations related to OW afforestation. Dotted arrows represent processes that are not always required.

As in several other studies (Berg and Karjalainen, 2003; Berg and Lindholm, 2005; Johnson et al., 2005, White et al., 2005; Sonne, 2006), the production of infrastructures (buildings and equipments) was not included in the LCA. GHG included in the LCA are carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O), which were expressed in CO2 equivalent (CO2 equiv.) for a 100year horizon (IPCC, 2001). 2.2.2. General emission factors used For GHGs emitted by all sources of fossil fuel combustion (gasoline, diesel, propane, heating oil), the emission factors used were those suggested in Environnement Canada (2006), depending on the different transportation modes or energy production. Data from GHGenius (2006) were used to calculate fossil fuel precombustion factors (PCF), corresponding to the emissions associated with the production of various types of fossil fuel. This model was designed to assess GHGs associated with the life cycle of different transportation modes in a Canadian context (GHGenius, 2006). Hydro-Que´bec (the supplier for all electrical installations in this study) power generation is divided between hydro, natural gas, nuclear energy, wind power and biomass, and the emission factors (per kWh) found in the literature were used for those electricity production modes (Gagnon, 2003). The average GHG emissions for the production of 1 kWh were then calculated and used as the average emission factor in the calculations. 2.2.3. Data presentation The accuracy of data was provided for each process and operation presented, i.e. a qualitative assessment based on data relevance with regard to geographical, technological and temporal considerations, in accordance with the ISO 14040 standard (ISO, 1997). Assumptions were made regarding the allocation of GHG emissions between different types of seedlings, the productivity of

OW logging operations (compared to normal operations on productive sites) and the emission factors associated with the production of the following materials (for which no primary data was available): perlite and vermiculite (Kellenberger et al., 2004), HDPE plastic (Boustead, 2005), herbicide and fertilizer (GHGenius, 2006), and peat moss (Cleary et al., 2005). 2.2.4. Treatment of uncertainties In order to test the impact of uncertainties on the outcome of GHG emissions, sensitivity analyses were performed. Activities or emission factors having a marked influence on the results, i.e. which made up more than 5% of the total emissions and did not come from primary sources, were replaced by other assumptions representing the maximum extent of variation found in the specific context of the local environment. Sensitivity analyses were then performed on the three following activities. 2.2.4.1. Road construction. The assumption used in the simulation was that GHG emissions to access 1 ha of OW, related to road construction, were equivalent to those emitted to access 1 ha of land on average, based on data collected in northern Que´bec for this study. The maximum GHG emission assumption postulated that the new road construction to access 1 ha of OW was equivalent to the amount of road needed to access 1 ha of productive BSFM. The minimum assumption stated that the OW was already accessible by the road network in place. 2.2.4.2. Roundwood transportation. The assumption used in the simulation was that commercial timber harvested to allow OW afforestation was transported by truck to the mill, with an average two-way distance of 450 km, corresponding to the average distance between the processing mill and the forestry operations in the study area in 2005. The minimum assumption stated that the

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timber was left on site, with no GHG emissions allocated to roundwood transportation. The maximum assumption doubled the average distance between the harvesting site and the mill. 2.2.4.3. Logging, hauling and lopping operations. The GHG emissions reported for these operations are typically influenced by the chosen emission factor, which is an average of fuel consumption per m3 of harvested timber on productive land (95 m3 ha1 for the study area). For less productive land, or lands close to the commercial limit of 30 m3 ha1, fuel consumption is normally increased by 30% for one harvested m3, as reported by forest operators, since the harvesting machinery has to cover nearly the same area. Consequently, the assumption used for the simulation increased average emissions per harvested m3 by 30%. The minimum assumption stated that no harvesting was necessary before plantation. The maximum assumption was based on an OW already near the commercial limit (30 m3 ha1) when logging, hauling and lopping operations were made. 3. Results 3.1. Biological carbon balance 3.1.1. Modelling The outputs from the CO2FIX model indicate that the hypothesized afforestation project would sequester 87.0 t C ha1 after 70 years. C accumulated in the afforestation project is predominantly found in the total stems, with 46% of the total C stock (Fig. 2). The commercial part of the stems alone accounts for 39% of total C. Soil C is the second largest C stock, with 26% of total C, followed by roots, with 14%, and then foliage and branches, with 7% each, all after 70 years (Fig. 2). The total amount of C per ha drops from 17.0 to 14.0 t C ha1 during the first 20 years following planting, which corresponds to the period when decomposition of organic matter surpasses primary productivity from trees. Tree biomass itself becomes significant thereafter. Net carbon balance turns positive after 23 years, and then increases steeply, in conjunction with the exponential growth of tree stems, with an average of 1.9 t C ha1 year1. The amount of C in the soil compartment regains its initial value after 38 years and increases progressively thereafter. After 70 years, C contained in all compartments of the plantation has increased by 572%.

Fig. 2. Carbon accumulated in the different biomass compartments of the afforestation project over 70 years. Results are from the CO2FIX model (Masera et al., 2003; Schelhaas et al., 2004).

Fig. 3. Carbon accumulated in the different biomass compartments of the OW, ascribed as the baseline scenario. Results are from the CO2FIX model (Masera et al., 2003; Schelhaas et al., 2004).

CO2FIX outputs for the baseline scenario results in a C sequestration of 10.0 t C ha1 after 70 years of growth, for a total of 30.0 t C ha1 estimated in the 145-year-old OW (Fig. 3). Most of the C in the baseline scenario is in the soil compartment (52%), which remains stable during the entire simulated period, with a maximum variation of 2% (Fig. 3). Decomposition occurring in the OW is, therefore, more or less balanced by biomass turnover. Total stem represents 29% of total C stock, roots 9%, foliage 6% and branches 4%. After 70 years, C in all compartments of the OW increased by 156%. Biological C balance from the OW afforestation results in a net C emission during the first 20 years of the afforestation project, compared to the baseline scenario (Fig. 4). The biological C balance turns positive only 7 years later, at 27 years. After 70 years, the biological C balance is 77.0 t C ha1, with a maximum annual net sequestration rate (2.3 t C ha1 year1) occurring between 30 and 35 years. The average C accumulation is 1.1 t C ha1 year1 during the simulated period. The compartment with the highest contribution to the C balance is the stem, with 53% of the total

Fig. 4. Biological carbon balance of the afforestation project over 70 years. Results are from the CO2FIX model (Masera et al., 2003; Schelhaas et al., 2004).

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Fig. 5. Impacts of uncertainties related to fire and plantation wood yield on global C balance of an OW afforestation project. The fire severity impact is from Bergeron et al. (2004).

net C sequestration (Fig. 4). The roots and soil share second place with 16% of the total net C stock, while the foliage (8%) and branches (7%) complete the picture. 3.1.2. Uncertainties Depending on the lower and higher plantation yields simulated, the biological C balance of the afforestation scenario is 62% less or 70% higher after 70 years, compared to the original yield used in this study (Fig. 5). The C balance turns positive between 22 and 34 years, and the average annual sequestration rate over the duration of the project goes from 0.4 to 1.9 t C ha1 year1 (Fig. 5). Regarding reversibility, data extrapolated from Bergeron et al. (2004) was used to estimate that about 8% of the territory would be burned over the 70-year simulation, based on the current fire Table 2 GHG emissions (CO2 equiv.) from the seed production process related to OW afforestation. Sources are qualified as primary (P) if taken on-site, complementary (C) if from other sources. Accuracy is a qualitative analysis based on the geographical, technological and time representativeness of the data (high: H; moderate: M; low: L). Process

Seed production Black spruce cone harvesting Cone transportation Building and installation heating Cone processing Seed storage Seed extraction and drying Others Total a

Inputs

Sources

Accuracy

Total emissions (kg CO2 equiv. ha1)

Gasoline

Pa

H H

23

Diesel

Pb

M M

3 4

Heating oil

Pc

Electricity

Pc

M–H

1

Electricity 31

Monique Gilbert, pers. comm., Centre de semences forestie`res de Berthier, Ministe`re des Ressources naturelles et de la Faune du Que´bec (MRNF), 2006. b Pierre Girard, pers. comm., MRNF, 2006. c Normand Brault, pers. comm., MRNF, 2006.

return rates for Que´bec’s central zone, compared to 55% using the past rates and 0% using the predicted future rate for the same region. The net C balance of the original afforestation project would then be 70.8, 34.7 or 77.0 t C ha1, assuming that both scenarios have equivalent susceptibility to fire and that the burnt areas in the plantation and the OW contain the same post-fire biomass (Fig. 5). 3.2. GHG emissions from OW afforestation-related operations 3.2.1. Processes Seed production is the lowest GHG emitter of all the processes related to OW afforestation (Table 6), with most (75%) of the emissions caused by cone collection operations (Table 2). Thirtysix percent of emissions from the seedling production process are from perlite/vermiculite mining and processing operations and the production of seedling containers, and 55% by system maintenance and nursery heating (Table 3). Roundwood transportation to the mill, over an average distance of 225 km, accounts for 48% of total GHG emissions caused by harvesting operations, which is slightly above those of logging, hauling and lopping operations (38%) (Table 4). These are the third and fourth highest GHG emissions, respectively, for the operations in all afforestation-related processes (Table 7). Scarification alone makes up 92% of total emissions for the site preparation process (Table 4). The remaining operations therefore only contribute slightly to total GHG emissions. Seedling transportation from nursery to plantation causes 43% of GHG emissions associated with the tree-planting process (Table 4). Road construction alone accounts for 33% of total GHG emissions linked the afforestation scenario (Table 7). Road maintenance is the second largest GHG-emitting operation of the entire afforestation project, with 13% of total emissions. Worker accommodations account for 95% of all GHG emissions in the housing and accommodation process (Table 5). Plantation monitoring accounts for 3% of the total GHG emissions for the project (Table 6). Overall, the afforestation of 1 ha of OW releases 1.25 t CO2 equiv. or 0.34 t C ha1. Twenty-nine percent of those emissions are caused by fossil fuel PCFs (Table 6).

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Table 3 GHG emissions (CO2 equiv.) caused by seedling production processes related to OW afforestation. See Table 2 for abbreviations.

Table 4 GHG emissions (CO2 equiv.) caused by harvesting, site preparation and plantation processes related to OW afforestation. See Table 2 for abbreviations.

Process

Processes

Inputs

Sources

Accuracy

Harvesting operations Logging, hauling and lopping

Diesel

Pa Pa

M–H M–H

94

Oil

Pa

M–H

4  101

a

Seedling production Seed production Seedling handling Seedling box production Seedling box transportation Peat moss uses Peat moss extraction Peat moss transportation Herbicide uses Herbicide production Herbicide transportation Fertilizer uses Fertilizer production

Fertilizer transportation Perlite and vermiculite uses Perlite extraction Perlite processing Vermiculite extraction Vermiculite processing Perlite and vermiculite transportation Building and nursery heating, electricity uses and maintenance

Inputs

Sources

Accuracy

Diesel HDPE Diesel

Pa Pa Cb Pc

H H H M H

Peat Diesel

Pa Cd Pa,c

H M H

3  101 1

a

8  101 0

2  101 10 1

Pesticides Diesel

P Ce Pa

H M M

N P K Ca Diesel

Pa Ce Ce Ce Ce Cf

H M M M M M

1 0 0 0 2  101

Diesel

P Cg Cg Cg Cg P–Ca,h

H M–L M–L M–L M–L M–H

0 10 0 3 1

Diesel

Pa

H

22

a

Perlite Vermiculite

Gasoline Heating oil Electricity Propane Total

Total (kg CO2 equiv. ha1)

Pa Pa Pa Pa

H H H H

12 7  101 6  101 3  101 63

a Guy Marinaud, pers. comm., La Pe´pinie`re de Normandin, Ministe`re des Ressources naturelles et de la Faune (MRNF), 2006. b Boustead (2005). c Michel Prebinski, pers. comm., Laidlaw carrier Inc., 2006. d Cleary et al. (2005). e GHGenius (2006). f Environnement Canada (2006). g Kellenberger et al. (2004). h Re´jean Boivin, pers. comm., Transport Alfred Boivin, 2006.

3.2.2. Uncertainties The operations with the largest GHG emissions have been subjected to a sensitivity analysis: road construction, roundwood transportation, and logging, hauling and lopping operations (Table 7). Since the data collected for both road maintenance and soil scarification are from primary sources, no sensitivity analysis has been performed on these operations. If all minimum assumptions were applied, total GHG emissions for the afforestation project would decrease by 50%. By applying all maximum assumptions, total GHG balance would increase by 44%. The activities whose emissions were estimated from complementary sources outside of Canada, namely the production of perlite, vermiculite, plastic and herbicide, account for only 1.8% of total GHG emissions. 3.3. Net C balance Total GHG emissions caused by the operations related to the afforestation of 1 ha of OW make up only 0.4% of project net C balance (Table 6; Fig. 4). Hence, the net C balance is virtually

Loading Roundwood transportation Machinery transportation

Diesel Diesel

P Pa

M–H M–H

5 117

Diesel

Pa

H

30

Total Site preparation Machinery transportation Soil scarification

Operator transportation

246

Diesel

Pb Pb

H H

5  101

Diesel Oil

Pb Pb

H H

92 2  101

Diesel

Pb

H

7

Total Plantation Seedling transportation to camp Tree planter transportation

Seedling transportation to site

Total (kg CO2 equiv. ha1)

100

Diesel

Pb Pb

H H

23

Diesel

Pb

H

20

Gasoline

P

b

H

3

Diesel

Pb

H

4

Gasoline

Pb

H

Total

4 54

a Jean-Franc¸ois Coˆte´ and Benoıˆt Bouchard, pers. comm., Bowater Inc. division Mistassini, 2006. b Je´roˆme Simard, pers. comm., Coope´rative forestie`re de Girardville, 2006.

equivalent to the biological carbon balance in this study, when numbers are rounded up, for a total of 77.0 t C ha1 after 70 years. The net C balance only turns positive after 27 years. When considering the uncertainties assessed, the net C balance of the afforestation project could change markedly. Indeed, after 70 year of plantation growth, results range from 13.0 t C ha1 for a weak plantation growth yield scenario where fire frequency is high, to 131.0 t C ha1 for a scenario without wildfire and high plantation growth yield (Fig. 5). The annual net C sequestration rate for the project would then vary from 0.2 to 1.9 t C ha1, with an average of 1.0 t C ha1 year1. Assuming the worst case scenario, GHG emissions caused by afforestation operations could make up 1.2–3.5% of net C balance, depending on the sensitivity analysis. 4. Discussion 4.1. Estimated net C sequestration from afforestation The net sequestration rate of the simulation, of around 1 t C ha1 year1, is among the lowest of reported afforestation projects in the boreal zone, which range between 0.8 and 2.4 t C ha1 year1, according to Brown et al. (1996). In southern Que´bec, a net sequestration rate of around 1.5 t C ha1 year1 has been found for 50-year-old white spruce (Picea glauca (Moench)) plantations on fallow lands, without considering belowground

S. Gaboury et al. / Forest Ecology and Management 257 (2009) 483–494

490

Table 5 GHG emissions (CO2 equiv.) caused by land access, housing and accommodation, and monitoring processes related to OW afforestation. See Table 2 for abbreviations. Processes

Land access Road construction

Road maintenance

Inputs

Sources

Accuracy

Diesel Oil

Pa P–Ca NA

H H

411

Diesel Oil

Pa NA

H

159

Total Housing and accommodation Tree planters

Land preparation operators

Other employees

571 b

H

Diesel Propane Gasoline

Pb Pb Pb

H H H

4 0 0

Diesel

Pb

H

2  101

Propane Gasoline

Pb Pb

H H

3  101 3

Heating oil Propane Gasoline

Pa Pa Pa

H H M

102 7 31

P

Total Monitoring Transportation

Total (kg CO2 equiv. ha1)

148 Cb P–Cb

Gasoline

M M

37

a

Jean-Franc¸ois Coˆte´ and Benoıˆt Bouchard, pers. comm., Bowater Inc. division Mistassini, 2006. b Je´roˆme Simard, pers. comm., Coope´rative forestie`re de Girardville, 2006. Table 6 Total GHG emissions (CO2 equiv.) of each process related to OW afforestation and their relative significance. Significance of the pre-combustion factor (PCF) in terms of GHG emissions is shown for each process. Processes

% emissions from PCF

Emissions (kg CO2 equiv. ha1)

Seed production Seedling production Harvesting operations Site preparation Plantation Land access Housing and accommodations Monitoring

22 16 24 24 24 24 16 24

37

3

Total

22

1249

100

31 63 246 100 54 571 148

% of global GHG emissions 2 5 20 8 4 46 12

biomass (Tremblay et al., 2006). Lemprie`re et al. (2002) evaluated the potential net sequestration rate of a white spruce reforestation project, over 3300 ha of previously degraded forested lands. Using 50-year-old simulated white spruce plantations with a previous Table 7 Operations which generate at least 5% of overall GHG emissions (CO2 equiv.), relative to OW afforestation. Operations

Emissions (kg CO2 equiv. ha1)

% of global GHG emission

Road construction Road maintenance Roundwood transportation Logging, hauling and lopping Soil scarification

411 159 117 94 92

33 13 9 8 7

Total

874

70

version of the CO2FIX model, Lemprie`re et al. (2002) found a mean net sequestration rate of just over 0.3 t C ha1 year1, without considering the belowground biomass, the soil and the litter stocks, and integrating a 30% loss caused by fire and insect damage. Those numbers are comparable to the bottom rate of expected sequestration ranges in this study. In a study comparing subarctic OWs and BSFMs, Moore and Verspoor (1973) found that OW total aboveground biomass reached 10.0 to 29.0 t ha1 (i.e. approximately 5.0–14.5 t C ha1), 1), while total BSFM aboveground biomass reached 78.0– 163.0 t ha1 (39.0–81.5 t C ha1). Those estimates are in the lower to middle range of those found in this study, which is likely for natural stands located in a northernmost ecozone. Auclair (1985) found that a 110-year-old OW had a total stand biomass of 84.5 t ha1 (42.3 t C ha1), which is slightly higher than the stand biomass estimated herein, and could indicate the quantitative importance of C stocks in the understory vegetation, which has not been considered in the simulation. Many uncertainties are associated with the soil and litter C dynamics, in particular in the boreal forest (Gower et al., 2001). The estimates herein result in a mean annual net sequestration rate of between 0.1 and 0.2 t C ha1 year1 in this compartment, which is in the same order of magnitude as those of known afforestation projects around the world (Paul et al., 2003). However, C accumulation in the litter of boreal forests was previously estimated to be higher than in other ecosystems, due to slow decomposition rates (IPCC, 2000). The significance of this compartment may well be underestimated in this study, especially because fine roots are not specifically treated in the CO2FIX model. Fine roots have been shown to be of great significance in the C dynamics of both the soil and the litter compartments (Steele et al., 1997). Tree growth yield is the most significant input in the simulated net C balance of OW afforestation. However, many uncertainties remain regarding the long-term growth of afforested OW, because known OW plantations were only established in 1999 (He´bert et al., 2006). Pre´gent and Ve´giard (2000) evaluated the black spruce plantation growth yield in Que´bec’s boreal zone and found that 34% of plantations had lower growth than what was chosen as the minimum site index herein. However, a similar proportion was found to have a higher site index. He´bert et al. (2006) found that 3year-old afforested OWs had slightly but significantly lower growth rates than those of comparable 3-year-old reforested BSFMs. Thiffault et al. (2004) found that afforested open ericaceous woodlands, similar to the OW described herein, had a higher growth yield after 10 years than that of the 6-m plantation site index. Since testing the growth and yield of afforested OWs in the Canadian boreal forest is recent (He´bert et al., 2006), further research is needed to assess the longer-term productivity of afforested OWs. Even if Brown et al. (1996) did not recommend including the understory vegetation in an afforestation project, this compartment should still be given some consideration. Understory vegetation is mainly composed of lichen in OW stands, and of mosses in BSFM stands. Ericaceous shrubs can also be a major component in both types of stand, but especially in OWs (Bloom and Mallik, 2004; Thiffault et al., 2004; He´bert et al., 2006). NPP for both understory vegetation and bryophytes in black spruce regenerating stands after fire have been shown to be relatively high, especially under age 40 and in poorly drained stands (BondLamberty et al., 2004; Bond-Lamberty and Gower, 2007; O’Connell et al., 2003). Longton (1992) found an average NPP for ground lichens of less than 0.1 t C ha1 year1. For mosses, the NPP found in mature black spruce forest studies ranges from 0.1 to 0.6 t C ha1 (Chen et al., 2002). While mosses have been reported

S. Gaboury et al. / Forest Ecology and Management 257 (2009) 483–494

to be 2–60 times more productive than lichens, no study considered respiration in their analysis and very few attempted to quantify lichen NPP (Chen et al., 2002). Auclair (1985) evaluated that in northern Canada Taiga OWs, ericaceous shrubs and lichens can account for up to 18% of total mature stand biomass. More research is therefore needed to evaluate the relative impact of this compartment on both scenarios. This study illustrates that the impact of natural disturbances can lead to a significant decrease in the net C balance of a boreal afforestation project, especially if fire frequency tends to be higher (Bergeron et al., 2004). However, more comprehensive studies are needed to fully integrate, and eventually manage, the impact of natural disturbances on the C balance of an afforestation project. More precisely, the impact of other types of disturbance (including insects and windthrow), and the use of a spatially explicit modelling approach should help to better assess this important type of sequestration calculation uncertainty. 4.2. GHG emissions from OW afforestation related operations As in this study, several studies also showed that the CO2 equivalent of GHG emissions from forest operations is far below the CO2 sequestered in the afforested stand (Berg and Karjalainen, 2003; Liski et al., 2001; Schwaiger and Zimmer, 2001; White et al., 2005). Several studies have shown that logging and hauling operations and roundwood transportation are the highest GHG emitters of all forestry-related operations, before the processing of wood products (Berg and Karjalainen, 2003; White et al., 2005; Sonne, 2006). Because an OW supports only a few commercial stems prior to afforestation, those operations are not as significant in this study; road network construction is, instead, the main driver of GHG emissions from forest operations. GHG emissions related to seed and seedling production or site preparation have been shown to be of minor relative significance in this and other studies, accounting for less than 1% of overall GHG emissions from forest operations in the Pacific Northwest (Sonne, 2006), or less than 10% in harvested and replanted sites in Sweden (Berg and Lindholm, 2005). Precombustion factors used herein are of significant importance, being directly responsible for 29% of all GHG emissions caused by the OW afforestation project. When using another model adapted to the North American context in general (GREET, 2004), an overall GHG emission reduction of 4% is observed. Recent regulations, requiring a more efficient refinery process, may explain this difference (GHGenius, 2006). If natural tree regeneration causes tree density to increase too much, thinning operations may be required for the desired plantation growth yield to be achieved. Karjalainen and Asikainen (1996) evaluated the GHG emissions from thinning operations to be around 16 kg CO2 equiv. ha1. Thus, emissions from thinning would probably not be significant in terms of GHG emissions, but could be significant in changing the biological C dynamic of the plantation, as outlined by Eriksson (2006) for Norway spruce. In fact, while thinning operations could lead to higher commercial wood volumes, total stand biomass can remain higher in nonthinned stands (Eriksson, 2006). Further research would be needed to assess the effects of thinning in an OW afforestation project. As previously highlighted, infrastructure-related GHG emissions were not included in this analysis, as no data were available. Only one consulted LCA study presented data for energy consumption associated with harvesting machinery production and maintenance (Klvac et al., 2003). In this study, 11% of all energy requirements for harvesting operations were related to machinery production and maintenance. However, translating those data into GHG emissions was impracticable. Thus, considering GHG emissions from machinery production and maintenance could con-

491

siderably affect the GHG profile of forest operations. Further research is needed to specifically address this topic in a Canadian context. The specific context in this study allows for excluding from the simulations other GHG emissions than CO2 related to vegetation management, mainly in CH4 and N2O forms. These emissions are normally attributed to slash burning, fertilizer application, organic matter burial and decomposition under anaerobic conditions, plantation of nitrogen fixing plants, and land drainage or flooding (IPCC, 2003). Because these activities are unnecessary in normally prescribed forest operations within the boreal forest of Que´bec (MRNFP, 2003), these emissions were considered to be zero in the simulated project herein. 4.3. Mitigation potential and recommendations The small relative significance of GHG emissions from afforestation operations compared to the estimated biological sequestration of an OW plantation opens the door to interesting mitigation potential. Black spruce plantation growth yield in OWs and natural disturbances are the two main topics that should be addressed first to minimize uncertainties on the calculation of the mitigation potential related to OW afforestation. Studies on natural disturbances should focus on their impact both spatially and temporally, and on how to reduce their occurrence. Preventive fire management, which could include land vulnerability characterization, plantation web distribution that ensure good spacing between each afforested site, soil fuel removing, etc., should be explored. In all cases, a preventive factor should be subtracted to the C sequestration of an afforestation project in the boreal zone, to account for possible losses caused by natural disturbances (Lemprie`re et al., 2002). The evolution of C stocks in the baseline scenario (natural OW) is also poorly documented. Finally, C stocks not considered in this study could change the biological C balance. To maximize the potential sequestration rate of an OW afforestation project, many silvicultural strategies should be analysed. Afforestation with more productive species than black spruce, like jack pine, white spruce and tamarack (Larix laricina (Du Roi) K. Koch), or a mix of these, should be tested, since these species are also naturally present in the Canadian boreal forest. Afforestation projects should also target the more recently created OWs, for which stand characteristics are already known, and those with very few trees, so that the time before net positive sequestration is attained would be shortened. Also, already accessible OWs should be targeted first. By removing the emissions related to land access and harvesting operations, the GHG emissions would drop by 66%, based on the findings in this study. However, the selection of OWs for afforestation should not focus on sites were growth conditions are limited by physical factors other than regeneration failure (Kurz and Apps, 1995). The afforestation of OWs could impact climate in other ways than just removing carbon from the atmosphere. Some studies suggest that the mitigation potential of a boreal afforestation project could be diminished or even offset by the resulting diminution of the surface albedo, especially in northern zones where snow and lichens are abundant (Betts, 2000; Gibbard et al., 2005; Bala et al., 2007). However, atmospheric CO2 removal has a global effect, while albedo reduction has a local to continental effect, so comparing both on an equivalent basis is not straightforward (IPCC, 2007; Betts, 2008). Also, albedo is not the only surface effect created by land-use change: Juang et al. (2007) evaluated, for the temperate zone in USA, that while the afforestation of fallow lands decreases surface albedo, other surface effects, especially changes in the eco-physiological and aerodynamic attributes of the land surface, have a cooling effect

492

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which offsets the albedo warming effect, making the net surface effect a cooling one. The production of aerosol by the vegetation is another issue concomitant to increased sequestration (IPCC, 2001). Kurten et al. (2003) studied the aerosol effect on the climate of a conifer plantation in Finland, and concluded that it has a cooling effect in the same order of magnitude as the warming effect of decreasing albedo. In the near future, carbon balance estimations should also include the aspects linked to wood products and bioenergy use, especially in the case of relatively low biomass growth after afforestation of boreal OWs. Olsson and Kja¨llstrand (2006) have shown that energy production from wood could be environmentally attractive when combustion is optimized. It could then have further GHG mitigation benefits if this energy was used to replace fossil fuels (Baral and Guha, 2004; Fleming et al., 2006). Substitution by wood products of energy intensive construction materials, like concrete and steel, can also achieve further GHG mitigation benefits (Perez-Garcia et al., 2005). Some authors suggested that, in order to obtain the greatest GHG mitigation potential in afforestation projects and to better address the issue of sequestration permanence, the anthropogenic uses of biomass must be considered in the long run (Kirschbaum, 2006). Elsewhere, climate change is not necessarily the only issue to consider when analyzing OW afforestation. Boreal ecosystem biodiversity is a fundamental issue that needs to be assessed before changing the status of a natural ecosystem. OW afforestation can be seen as a means to partly counterbalance the natural deforestation of the black spruce feathermoss ecosystem (Gagnon and Morin, 2001; Jasinski and Payette, 2005), a much less abundant ecosystem than OWs across Canada (Rowe, 1972). On the other hand, extraordinary efforts to afforest OWs within the close-crown boreal forest could lead to the rarefaction of open habitats, which can be needed by some species (e.g. woodland caribou, Courtois et al., 2007). However, the current occurrence and recent progression of OWs within the close-crown boreal forest (Girard et al., 2008) suggest that the afforestation of a part of available OWs would not put pressure on species that depend on open habitats, if interventions are properly distributed over the area. Finally, the cost of the afforestation of boreal OWs needs to be determined. Forestry-based GHG mitigation projects are generally reported to be relatively inexpensive, but costs can vary greatly depending on growth yield, discounting method, time horizon, carbon credit distribution over time, and the inclusion or exclusion of harvest activities (Boyland, 2006). In fact, commercial exploitation of afforested OWs lessens the C sequestration potential on the long-term, unless a specific strategy is used to ensure that the harvested biomass is used in such a way as to yield greater GHG mitigation benefits (Kirschbaum, 2006). This could be done by using the harvested biomass to manufacture wood products to replace more energy-intensive materials, or using residual biomass as biofuels to substitute for fossil fuels. Regeneration of harvested plantations must also be guaranteed. Consequently, it is recommended that OWs afforested for climate purposes should not be included in the traditional provincial timber yield calculations on Crown land, at least until a specific land tenure is created for sequestration objectives. 5. Conclusion In light of the findings in this study, the afforestation of OWs within the close-crown boreal forest could theoretically lead to a net sequestration rate of about 1 t C ha1 year1, over 70 years. GHG emissions from all the afforestation-related operations are insignificant with regards to the biological C balance. However, almost 30 years of plantation growth are needed to obtain a

positive carbon balance following afforestation. After including uncertainties, related mainly to plantation growth yield and natural disturbances, the OW afforestation project could have net C balances that vary between 0.2 and 1.9 t C ha1 year1. Several recommendations were made, mostly centered on the factors influencing the growth rate of carbon stocks and the impact of natural disturbances, to minimize the range of uncertainties associated with the sequestration potential, and maximize the mitigation benefits of an OW afforestation project. The extensive area presently covered by natural OWs within the close-crown boreal forest of Canada, and their recent progression, highlights the need to continue the investigation on an integrated approach that could help mitigate climate change and address other relevant issues, like biodiversity and socio-economic needs. Acknowledgments We would like to thank Pierre Bernier and three anonymous reviewers for their helpful comments on earlier versions of the paper, the Consortium de recherche sur la foreˆt bore´ale commerciale and the Chaire en E´co-Conseil for funding, and Bowater Inc. for technical assistance. Also, the Cooperative forestie`re de Girardville, Ministe`re des Ressources Naturelles et de la Faune, Centre interuniversitaire de recherche sur le cycle de vie des produits, proce´de´s et services (CIRAIG), and the Carbon Modelling Team of the Canadian Forest Service provided helpful assistance and advice. References Auclair, A.N.D., 1985. Postfire regeneration of plant and soil organic pools in a Picea mariana–Cladonia stellaris ecosystem. Can. J. For. Res. 15, 279–291. Bala, G., Caldeira, K., Wickett, M., Phillips, T.J., Lobell, D.B., Delire, C., Mirin, A., 2007. Combined climate and carbon-cycle effects of large-scale deforestation. Proc. Natl. Acad. Sci. USA 104, 6550–6555. Baral, A., Guha, G.S., 2004. Trees for carbon sequestration or fossil fuel substitution: the issues of cost vs. Carbon benefit. Biomass Bioenergy 27, 41–55. Berg, S., Karjalainen, T., 2003. Comparison of greenhouse gas emissions from forest operations in Finland and Sweden. Forestry 76, 271–284. Berg, S., Lindholm, E.L., 2005. Energy use and environmental impacts of forest operations in Sweden. J. Cleaner Prod. 13, 33–42. Bergeron, J.-F., 1996. Domaine de la pessie`re noire a` mousses. In: Manuel de foresterie, Les Presses de l’Universite´ Laval, Que´bec, Canada. Bergeron, Y., Flannigan, M., Gauthier, S., Leduc, A., Lefort, P., 2004. Past, current and future fire frequency in the Canadian boreal forest: implications for sustainable forest management. Ambio 33, 356–360. Betts, R.A., 2000. Offset of the potential carbon sink from boreal forestation by decreases in surface albedo. Nature 408, 187–190. Betts, R., 2008. Comparing apples with oranges. Nat. Rep. Climate Change 2 (January), 7–8. Bloom, G.B., Mallik, A.U., 2004. Indirect effects of black spruce (Picea mariana) cover on community structure and function in sheep laurel (Kalmia angustifolia) dominated heath of eastern Canada. Plants Soil 265, 279–293. Bond-Lamberty, B., Gower, S., 2007. Estimation of stand-level leaf area for boreal bryophytes. Oecologia 151, 584–592. Bond-Lamberty, B., Wang, C., Gower, S.T., 2004. Net primary production and net ecosystem production of a boreal black spruce wildfire chronosequence. Global Change Biol. 10, 473–487. Boustead, I., 2005. Eco-profiles of the European Plastics Industry. A report prepared for PlasticsEurope, Bruxelle, Belgium. Boyland, M., 2006. The economics of using forests to increase carbon storage. Can. J. For. Res. 36, 2223–2234. Brown, S., Sathaye, J., Cannel, M., Kauppi, P., 1996. Management of forests for mitigation of greenhouse gas emissions. In: Watson, R.T., Zinyowera, M.C., Moss, R.H. (Eds.), Climate Change 1995, Impacts, Adaptations and Mitigation of Climate Change: Scientific-Technical Analyses. Report of Working Group II, Assessment Report, IPCC, Cambridge University Press, Cambridge, UK, pp. 773– 797. Chen, W., Chen, J.M., Price, D.T., Cihlar, J., 2002. Effects of stand age on net primary productivity of boreal black spruce forests in Ontario, Canada. Can J. For. Res. 32, 833–842. Cleary, J., Roulet, N.T., Moore, T.R., 2005. Greenhouse gas emissions from Canadian Peat Extraction, 1990–2000: a life cycle analysis. Ambio 34, 456–461. Courtois, R., Ouellet, J.-P., Breton, L., Gingras, A., Dussault, C., 2007. Foraging across a variable landscape: behavioral decisions made by woodland caribou at multiple spatial scales. Ecoscience 14, 491–498.

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