Effects of climate change on biomass production and substitution in north-central Sweden

b i o m a s s a n d b i o e n e r g y 3 5 ( 2 0 1 1 ) 4 3 4 0 e4 3 5 5

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Effects of climate change on biomass production and substitution in north-central Sweden Bishnu Chandra Poudel a, Roger Sathre a,b, Leif Gustavsson b,*, Johan Bergh a,c, Anders Lundstro¨m d, Riitta Hyvo¨nen e a

Ecotechnology, Mid Sweden University, 83125 O¨stersund, Sweden Linnaeus University, Va¨xjo¨, Sweden c Southern Swedish Forest Research Centre, Swedish University of Agricultural Sciences, Alnarp, Sweden d Department of Forest Resource Management, Swedish University of Agricultural Sciences, Umea˚, Sweden e Department of Ecology, Swedish University of Agricultural Sciences, Uppsala, Sweden b

article info

abstract

Article history:

In this study we estimate the effects of climate change on forest production in

Received 3 June 2010

north-central Sweden, as well as the potential climate change mitigation feedback effects

Received in revised form

of the resulting increased carbon stock and forest product use. Our results show that an

1 August 2011

average regional temperature rise of 4
C over the next 100 years may increase annual

Accepted 4 August 2011

forest production by 33% and potential annual harvest by 32%, compared to a reference

Available online 10 September 2011

case without climate change. This increased biomass production, if used to substitute fossil fuels and energy-intensive materials, can result in a significant net carbon emission

Keywords:

reduction. We find that carbon stock in forest biomass, forest soils, and wood products also

Climate change

increase, but this effect is less significant than biomass substitution. A total net reduction

Forest production

in carbon emissions of up to 104 Tg of carbon can occur over 100 years, depending on

Biomass substitution

harvest level and reference fossil fuel.

Climate feedback

ª 2011 Elsevier Ltd. All rights reserved.

Sweden

1.

Introduction

The emissions of greenhouse gases, primarily carbon dioxide (CO2), into the atmosphere increased during the 20th century [1]. This has led to an increasing global surface mean temperature, which is projected to continue to rise further [2]. Continuing changes to the global climate patterns are likely during the 21st century [2]. The temperature increase is expected to be greater at higher latitudes [3], where northern Europe may face temperature increases up to 1e2
C in summer and 2e3
C in winter during the next 50 years [4]. Increased temperatures and elevated concentrations of CO2 may stimulate forest production in the boreal coniferous region, due to a longer growing season and more favourable

conditions for photosynthesis [5,6]. Increased soil temperature may also lead to increased soil biological activity and therefore increased mineralization and nitrogen (N) availability in the soil [7,8]. Several researchers have investigated the anticipated changes in boreal forest production due to climate change. Pussinen et al. [9] concluded that boreal forests will likely increase their productivity and reduce the length of rotation periods by 5e10 years, as an effect of anticipated climate change and increased N deposition. Briceno-Elizondo et al. [10] suggested that northern boreal forests will have higher production potential due to increased temperature. Kirilenko and Sedjo [11] reported that climate change is likely to increase the total standing forest biomass by 10e30% in the

* Corresponding author. Present address: Linnaeus University, 351 95 Va¨xjo¨, Sweden. Tel.: þ46 470 70 8997; fax: þ46 470 76 85 40. E-mail address: [email protected] (L. Gustavsson). 0961-9534/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.biombioe.2011.08.005

b i o m a s s a n d b i o e n e r g y 3 5 ( 2 0 1 1 ) 4 3 4 0 e4 3 5 5

next 100 years in northern Europe, if the current harvest level is maintained. Eggers et al. [12] claimed that climate change may increase forest productivity by 12e14% and carbon stock by 23e31% in the next 50 years in Europe. Bergh et al. [6,13] estimated that the temperature increase and elevated concentrations of CO2 could increase net primary production by 20e40% in northern Europe, which may lead to a 24e37% increase in forest growth over the next 100 years. Swedish Forest Agency [14] projected a 25% increase in annual stem wood production in Swedish forests due to the direct effects of climate change over the next 100 years. The increased productivity and standing biomass in boreal forests caused by climate change may in turn be used as a negative feedback on climate change due to greater potential for mitigation activities. The increased biomass production can be used to substitute fossil fuels and carbonintensive materials, leading to a net reduction of greenhouse gas emissions [15e18]. Biomass substitution affects energy and carbon balances through several mechanisms [19,20], including changes in the carbon stock in living forest biomass and soil; the use of biomass as biofuel to replace fossil fuels; the lower fossil energy used to manufacture wood products compared with non-wood materials; the avoidance of industrial process carbon emissions from e.g. cement manufacture; the physical storage of carbon in wood products; and the possible carbon sequestration in, and methane emissions from, wood residues deposited in landfills. In Sweden, the use of forest biomass-related products is increasing. For example, wood fuel sales have increased by 30% over the past 7 years [21]. There is also growing interest in Sweden for using wood material in place of concrete and other construction materials [22].

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The aim of this study is to estimate the effects of climate change on future forest production in north-central Sweden, and to explore the potential feedback effects of increased forest product use on climate change mitigation. We use the process-based model BIOMASS [23] and the empirical forecast model HUGIN [24] to estimate the effects of climate change on forest production for the next 100 years. Using various harvest scenarios, we calculate the potential to use the additional forest production to substitute for non-renewable materials and fuels. We use the Q model [25] to estimate the effects of climate change and biomass removals on the forest soil carbon stock. We then quantify the overall carbon balance based on the avoided fossil emissions due to biomass substitution and the changes in carbon stock in forest biomass, forest soils, and wood products.

2.

Methods and assumptions

2.1.

Study area

The studied forest area is in Ja¨mtland and Va¨sternorrland counties in north-central Sweden (Fig. 1). Ja¨mtland and Va¨sternorrland are characterised by a boreal climate, and range from 61
330 to 65
070 N latitude and from 12
090 to 19
180 E longitude. The elevation ranges from sea level to 1500 m altitude. Annual precipitation ranges from 600 to 700 mm in the eastern part to 1500 mm in the western part [26] and the range of growing season with mean temperature above þ5
C is approximately 120e160 days [27]. Average monthly temperature during the period 1961-1990 varied in winter between þ1
C and 12
C and in summer between 10
C and 14
C.

Fig. 1 e The study area of Ja¨mtland and Va¨sternorrland counties, Sweden, (Data source: Department of Land Survey, Sweden).

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Climate scenarios BIOMASS HUGIN

Annual increment of forest Available harvest

Forest C-stock balance

Tree mortality and retained residues

Biomass substitution

Soil C-stock balance

C- Balance

Fig. 2 e Schematic structure of the analytical approach, showing the relation between scenario inputs, models, and outputs.

The forest land area of Ja¨mtland and Va¨sternorrland counties is about 3.5 and 1.9 million hectare, respectively. Of this area, productive forests, which have productive capacity of at least 1 m3 ha-1y1 and excludes protected areas, cover about 2.6 and 1.7 million hectare, respectively [21]. The dominant tree species are Norway spruce (Picea abies (L.) Karst.), Scots pine (Pinus sylvestris L.) and silver birch (Betula spp.). The mean annual increment (MAI) of forests in the counties has increased from 15.2 to 19.6 hm3 during the past 10 years, while the annual harvest of stem wood has maintained between 11.5 and 13.5 hm3. In comparison, the annual gross felling in Sweden as a whole has increased by 22% during the past 10 years [21].

2.2.

Modelling structure and scenarios

Our analytical approach is shown schematically in Fig. 2. Climate change scenarios are input into forest growth models.

These models give production forecasts for the next 100 years for the forest land, quantities of harvested biomass of different types, and input of dead biomass to the forest soil. A biomass substitution model determines the carbon balance effect of using the harvested biomass in place of fossil fuels and non-wood materials. Estimates of soil carbon stock changes are made based on litter input, biomass removals and climate scenarios. The resulting overall carbon balance incorporates carbon stock changes in tree biomass, forest soils and wood products, and avoided fossil carbon emissions due to biomass substitution. The time period of the study is from 2010 to 2109, broken down into 10-year time steps. We compare a Reference scenario that assumes an unchanged climate and a Climate change scenario that assumes a warmer climate and thus increased forest production. For each of these scenarios we consider 2 biomass use options: Stem wood and Whole tree. The stem wood option considers only the use of stem wood including bark, while the whole tree option considers the use of stem wood, bark, branches, foliage, tree tops, and recoverable stumps and coarse roots.

2.3.

Climate change

The Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenarios (SRES) describes climate scenarios of how greenhouse gas emissions, and resulting climate changes, might develop during the 21st century [2]. These scenarios are based on the main driving forces of greenhouse gas emissions, e.g. population growth, socioeconomic development and technological changes, considering their underlying uncertainties. The climate change scenario used in this analysis is based on SRES B2, corresponding to moderate emissions of greenhouse gases leading to an atmospheric CO2 concentration of 572 ppm by the year 2085. Other scenarios such as SRES A2 assume higher levels of greenhouse gas emissions (Fig. 3a). Raupach et al. [28] showed that actual emissions are greater than those assumed by SRES A2, though a concerted global effort to significantly reduce greenhouse gas emission may reduce emissions to the level of SRES B2. Given this uncertainty of future climate conditions, we discuss the implications of different climate scenarios in Section 4.

Fig. 3 e Global CO2 concentration history and scenario projections (left) [3,30], and past temperature fluctuation history and future temperature projections for north-central Sweden (right) [26].

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Regional climate scenarios based on IPCC global scenarios are made with the downscaled climate model RCA3 (Rossby Centre’s regional atmospheric model) that uses a grid of approximately 50 km  50 km [26]. In the calculations, RCA3 uses global driving variables from the general circulation model ECHAM4/OPYC3 [29]. The model covers the period of 1961e2100, of which 1961-1990 is taken as reference. Climate projections for north-central Sweden are for an increase in average regional annual temperature of more than 4
C by 2100 for SRES B2, and by over 5
C for SRES A2 (Fig. 3b). The variability and extreme changes in the minimum temperature are larger than those for the average temperature, and the expected temperature increase is higher in the eastern part than in the western part of the region [26]. For soil carbon modelling, we use a shift in latitude as a proxy for warmer mean temperatures expected in the Climate change scenario. The increased average temperature of 4
C corresponds to a shift of about 4 degrees of latitude towards the equator. Thus, in our soil carbon model we assume a latitude of 58e59
N, instead of the actual latitude of 62e63
N.

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induced production effect. An increase of SI by 4 m is assumed to occur linearly during the 100-year projection.

2.4.2.

HUGIN model

HUGIN is a model system for long-term forecasts of timber yield and potential harvest level. The system is used for planning on a regional level and for strategic planning for large companies. The model uses sample plots from the Swedish national forest inventory (NFI) to define initial conditions for the model, to describe the forest conditions. A basic assumption is that the natural site productivity and the climate conditions will remain unchanged during the studied period (normally 100 years), though we modified the model to incorporate the effects of a changed climate (see Section 2.4.1). The growth simulators are constructed to be valid for all forest land in Sweden, for all types of stands, and within a wide range of management alternatives. Different methods are used for describing the growth in young stands (stand establishment) and for the rest of the life-time of the stands (established stands).

2.4.2.1. Stand establishment. In order to obtain suitable data 2.4.

Forest production modelling

We use two models, BIOMASS and HUGIN, to estimate the effects of climate change on forest production. The BIOMASS model is used to incorporate the effects of climate change into the HUGIN model.

2.4.1.

BIOMASS model

BIOMASS is a process-based growth model describing the processes of radiation absorption, canopy photosynthesis, phenology, allocation of photosynthates among plant organs, litter-fall, and stand water balance. BIOMASS consists of a series of equations based on established theories of plant-physiological processes and soil-water dynamics. For a detailed description of the BIOMASS model, see McMurtrie et al. [23]. Climate data from the SRES B2 scenario, with a spatial resolution of 40  40 km, was used to run the BIOMASS model. Output data on net primary production (NPP) from the BIOMASS model was aggregated to mean values for the whole counties and used to determine the growth functions on a county level in the HUGIN model [24]. Transient simulations for 1961e2100 were performed with BIOMASS for silver birch, Norway spruce and Scots pine, and output data on NPP were summarized in four different periods: 1961-1990 (reference climate), 2011-2040, 2041-2070 and 2071-2100 [6]. The mean increase in NPP for Ja¨mtland county, for period 2071-2100 compared with 1961e1990, was 28.9, 21.4 and 21.6% for Norway spruce, Scots pine and silver birch, respectively [14]. The corresponding numbers for Va¨sternorrland county were 21.6, 11.0 and 13.9%. The effects of climate change on forest production are assumed to occur linearly, calculated for each tree species and county such that the full increase in NPP for each species and county is achieved after 100 years. The calculated effect is used for established forest stands in the HUGIN system, both in old and pre-mature forests. For young forests, a calibration of site index (SI), a relative measure of site quality based on the height of the dominant tree, is made to find the climate

from young stands with known stand history, about 800 stands were inventoried. The inventory used systematic sampling with five circular plots with an area of 100 m2 in each stand. These data were used for the construction of growth functions for young stands [31]. The survey plots were remeasured after five growing seasons. The main objective was to improve the functions used for prediction of stand development. Furthermore, this data serves as a database for the projections of new stands. The stands in the database were then adjusted to correspond to unthinned state at 3 m height. After a final harvest on a NFI plot a stand quality index at 3 m height is estimated, as a function of site conditions and regeneration method. Depending on this index, to which a random component is added, one specific sample plot is selected from the database. Information about individual trees on the plot is then available for prediction of growth. In the stand establishment, the height is used as dependent variable in the growth functions. To obtain diameter distributions and volume growth, special functions are used to transform height to diameter, and diameter to volume. Fig. 4 shows a flow chart of how simulation in young stands is carried out. Functions are used to estimate damages and mortality in young forests [32]. These functions may be changed depending on what is known about different predators. When the average height of the trees on the plot has reached about 7 m, the stand is considered to be established and the method for growth forecast is changed. For established seedlings the early development of the young stands to the pole stage (i.e. mean height of approximately 7e8 m) is projected by height-age curves for the main crop trees. Diameter, age and volume are estimated by static relationships using height as the dependent variable. Mortality and damage are predicted by a partly stochastic model. Additional sub-models are also included in the growth simulator to simulate the effects of pre-commercial thinning, intensive fertilization and use of genetically improved material. Functions for mean height development in young stands were based on sample-tree data on height and total age from

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(i) Existing bare land at the initial state or simulated future regenerarion cuttings

Frequency

(ii) Existing sapling stands, mean height < 7.0m stand -level information Individual- tree information

Expected Regeneration success

Height , h

Tree list

Growth Prediction

Database with young stands (Nyskog)

Fig. 4 e Flow chart of the simulation of the stand establishment and early growth.

future crop trees (1600 stem ha1) in the HUGIN young stand survey. In total about 15 000 trees were measured. Height is expressed as a function of SI and total age. For spruce, SI is H100spruce. For pine, birch and aspen, SI is H100pine.

2.4.2.2. Established stands. A review of growth and yield forecasts in established stands is given by Ha¨gglund [33]. The simulators for growth in established stands are based on NFI data, and use the basal area growth for each single tree as dependent variable [34]. The growth period for the simulators is five years, and after each period a volume is obtained by applying static form height functions. To get the net growth for each period, functions for mortality and ingrowth (trees passing 5 cm in breast height) are used. The effect of thinning is estimated by means of thinning response functions based on experimental data [35]. 2.4.2.3. Treatments. The calculations of different treatments are based on single trees within sample plots, and the plots are selected according to priority rules. The priority rules are based upon a standardised silvicultural management. A varied amount (35e50%, depending on harvest methods) of harvested volume is randomly selected from the trees of NFI plots. The selected trees from the sample plots are treated for a specific growth period to simulate mismanagement and the stand-plot problem. These selected trees are considered as a unit of biomass measurement. Although many trees are measured, they are not considered as a stand since they represent different forest areas. The way to prescribe how treatments should be carried out in the system is very flexible. To make the use of the system easier, standard management programs are included, based upon what is considered as good management according to present standards. Division into different treatment classes is made according to relative age, where relative age is defined as the ratio of actual stand age and age for final felling. Depending on tree species, the age for final felling of each plot is determined from SI classes according to the Swedish Forest Act [36]. After a final felling, a young plot is chosen depending on site conditions and regeneration method (see Fig. 4). The user

gives the distribution for different regeneration methods in classes of SI and soil moisture. Whatever the regeneration method, i.e., planting, sowing or natural regeneration, the species to be used in planting and number of plants is specified in the same way. The system will then randomly choose the regeneration method and if needed tree species, by using the distribution provided by the user. When pre-commercial thinning is performed, the number of stems to be left depends on SI and species, based on the recommendations in the Swedish Forest Act [36]. Thinning is possible to perform on plots with a mean height over 11 m and with a relative age less than 0.9. To rank thinning plots a priority function (PT) is used: PT ¼ RBD þ a  H100 þ b  Hdom þ c  BL

(1)

where H100 is SI in metres; Hdom is dominant height in metres; BL is a ratio of the basal area of broadleaf trees to the total basal area and a, b and c are coefficients. This function is dependent on the relative basal area difference (RBD), SI and dominant height. The actual felling of trees, distributed among species and diameters, can be defined by parameterisation, dependent on SI and soil moisture. The RBD is calculated as: RBD ¼

basal area  gr gm  gr

(2)

where gr is recommended basal area after thinning according to thinning schedule and gm is maximum basal area before thinning according to thinning schedule.

2.4.2.4. Level of fellings. The level of fellings is based on the growth level in every period, and then calibrated depending on the state of the forest. The amount of the total fellings is calculated depending on age distribution and thinning rate, adjusted by the actual growing stock on thinning plots relative to expected growing stock on these plots.
Total fellings ¼ G$

G05 G610

a
b
g
d
3 V0 A1:0þ A0:910 A0:70:9 $ $ $ $ Vn R1:0þ R0:910 R0:70:9 (3)

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where G is total growth during latest ten year period, G0e5 is growth 0e5 years before actual year, G6e10 is growth 6e10 years before actual year, V0 is standing volume, Vn is calculated volume with actual age distribution and site conditions, A1:0þ ; A0:91:0 ; A0:70:9 are the areas over relative age 1.0, 0.9e1.0 and 0.7e0.9, respectively, R1:0þ ; R0:91:0 ; R0:70:9 are the desired areas over relative age 1.0, 0.9e1.0 and 0.7e0.9, respectively, and a, b, g, d, and 3 are coefficients. In the first two 10-year periods, the criteria for choosing plots for thinning and final felling is done by probability functions, based on how forest owners have made their priority for harvest according to permanent plots of the NFI in the beginning of 2000. After the first two periods, the thinning decision is made as described above, and decision for final felling is made by volume increment percentage. The felling volume for each decade is presented in cubic meter over bark, cubic meter under bark, and kilogram dry matter. Functions used to determine tree biomass were developed by Marklund [37,38] and Petersson and Sta˚hl [39]. The total carbon stock in standing forest biomass is also calculated by HUGIN in both standing stem wood and in whole tree biomass.

2.5.

Soil carbon modelling

Soil carbon stock changes may be expected due to a warmer climate causing increased soil respiration leading to decreased soil C stock, increased tree growth and litter fall leading to increased soil C stock, and increased biomass removal leading to decreased soil C stock. We estimate soil carbon effects during the 100-year study period using a linear extrapolation between the reference climate in 2010 and the 4
C warmer climate in 2110. The soil carbon analysis uses forest production data calculated independently from that described in Section 2.4. The management plan for pine stands ranges from two thinnings during a rotation period of 145 years for the least productive stands to three thinnings during a rotation period of 90 years in the highest productive stands. Corresponding values for least productive spruce stands are two thinnings during a rotation period of 140 years and four thinnings during a rotation period of 70 years for the highest productive spruce stands. At thinnings, 14e35% of the basal area is removed depending on SI and stand age. At each thinning, the number of trees is decreased by the same fraction as the basal area. The thinnings are stem-only thinning.

2.5.1.

Growth functions

Development of basal area, BA (m2 ha-1) from age 0 up to an age t0 (year) of the stand is assumed to follow an exponential function: BAðtÞ ¼ N0 A0 ert where N0 is the initial number of trees ha1, which varies depending on SI, A0 is the basal area of a tree at age 0 and is assumed to be 104 square meter, and t0 is the age when the trees reach 10 m of height. The relative growth rate, r, is estimated from r¼

1 BAðt0 Þ ln t0 N0 A0

(5)

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For pines, the height of a tree after t0 is given according to Elfving and Kiviste [40] and basal area growth for trees older than t0 is taken from Elfving and Norgren [41]. For spruce, height growth for northern Sweden (regions 1e12) is taken from Ha¨gglund [42] and for southern Sweden (regions 13e30) from Ha¨gglund [43]. The height development is adjusted to give the right height at an age of 100 years. Basal area growth under bark for spruce has been simplified from Eriksson [44] and from which basal area over bark is calculated. Number of trees per hectare is empirically determined for SI ranging from 11 to 36 [44].

2.5.2.

Biomass allocation

We assume that all trees are of equal size, and from their average diameter over bark we calculate the biomass allocation to different tree components [38]. The tree components considered here are: needles, branches, tops, stems, stumps, coarse roots >5 cm, coarse roots <5 cm, and fine roots <2 mm. The ratio of mass of fine roots to mass of needles is set to 1.5. Pines are assumed to lose 25% of their needles and 5% of their branches per year [45]. The corresponding parameters for spruce are 10% and 2%, respectively [45]. These parameter values should vary with latitude but we do not have enough information to include this aspect. Fine root litter production is set to 1.5 times the needle litter fall [46]. Other types of litter are only formed when trees are felled (i.e., we neglect mortality of single trees between harvest events). At harvest, 25% of the needles, branches, and tops of the felled trees, 50% of the stumps and coarse roots, and 100% of fine roots are left on the site to decompose.

2.5.3.

Decomposition of tree litter and harvesting residues

The decomposition of the different litter fractions is calculated using the Q model [25]. The equation g(Lat,t) is the fraction of initial carbon remaining in a needle or fine root cohort after time t: gðLat; tÞ:¼ ð1 þ aðP; LatÞ$tÞz

(6)

where a is a function of latitude (Lat) such that:   a Lat :¼ fc $b$h11 $u0 ðLatÞ$qb0

(7)

The parameter fC stands for carbon concentration and it is assumed to be 50% of organic matter. The parameter b refers to abiotic factors and it is set to 7. The parameter h11 is a scale parameter and it is set to 0.36. The parameter q0 describes initial quality and is set to 1. The parameter u0 is coupled to decomposer growth rate and depends on climate, which is correlated with latitude such that: u0 ðLatÞ:¼ ½0:0855 þ 0:0157$ð50:6  0:768$LatÞ

(8)

The remaining mass of woody components is expressed by the functions G1(t,tmax) and G2(t,tmax) where invasion rates of litter types are taken into account. G1 describes decomposition until all wood is infected by decomposers and G2 describes decomposition after everything is infected, tmax is the time taken for decomposers to completely invade these litter components.

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Gðt; tmax Þ:




 
1 t ¼ $ $ ð1 þ a$tÞ1z  1  tmax a$ð1  zÞ tmax
h i 2 1 þ 2 $ 2 $ 1  ð1 þ a$tÞ2z tmax a $ð1  zÞ$ð2  zÞ 2  t þ 1 tmax

G2 ðt; tmax Þ :

2

(9)


1 2 1 ð1 þ a$tÞ1z þ 2 $ 2 tmax a$ð1  zÞ tmax a $ð1  zÞ$ð2  zÞ i h ð10Þ $ ½1 þ a$ðt  tmax Þ2z ð1  a$tÞ2z

¼

2

$

The parameter combination z is defined as follows: z¼

1  e0 b$h11 $e0

(11)

The parameter e0 is decomposer growth efficiency or production-to-assimilation ratio and it is set to 0.25 [47]. The calculations of remaining carbon are made by year for each litter component and each litter cohort separately such that the total soil carbon consists of the sum of remaining litter cohorts of different ages and from different tree components. There is no vertical partitioning of soil carbon in our model and all soil carbon should therefore be included. This assumes that we can neglect climatic variability with depth, as well as differences in the interaction between soil carbon and the mineral soil matrix.

2.6.

Biomass use modelling

In the stem wood biomass use option, we assume that we harvest and remove 95% of the felled stem wood from the forest. Large diameter pine and spruce stem wood is used for production of wood construction material, with the processing residues used for bioenergy. Small diameter pine and spruce stem wood, and all deciduous stem wood, is used for bioenergy. We assume a minimum diameter of 20 cm for large diameter stem wood. Stem wood is sorted entirely by diameter without consideration of quality aspects, thus we are likely to overestimate the quantity of stem wood used for construction material. Small-diameter stem wood is generally used in Sweden for pulp and paper production. In this study we analyse the use of additional quantities of biomass produced due to climate change, but we have not analysed whether there is demand for additional pulpwood in the pulp industry. We therefore show the climate effects of using this small diameter stem wood for bioenergy, acknowledging that this may not be the most economically beneficial use of this wood [48]. In the whole tree biomass use option, in addition to harvesting 95% of the felled stem wood, we also assume that 75% of needles, branches and tops, and 50% of stumps and coarse roots are harvested and used for bioenergy. According to the Swedish forest agency [14], current Swedish harvest guideline recommends 20% of logging residues should be left at the site and 15e25% of the stumps at clearfelling, and there is no

restrictions to harvest both above-ground biomass and stumps from the same harvest site. We assume that all recovered biomass for energy purposes (biofuels) are used to replace either coal or fossil gas in stationary plants with conversion efficiencies of 100% and 96% relative to the conversion efficiency of the respective fossil fuel-fired plants [20]. Values of specific carbon emission from fossil fuels as atomic carbon are assumed to be 30 kg GJ-1 for coal, 22 kg GJ-1 for oil, and 18 kg GJ-1 for natural gas, and include emissions during the entire fuel-cycle from the natural resource to the delivered energy service [49]. Energy used for recovery and transport of biofuels is assumed to be diesel fuel, calculated as a percentage of the heat energy content of the biofuel. We assume this percentage is 10% for stumps, 5% for slash and small diameter stem wood, and 1% for processing residues [50]. Emissions from forest management activities are based on Berg and Lindholm [51]. Large diameter stem wood is assumed to be used for producing wood construction material that substitutes in place of conventional reinforced concrete. The material substitution effects are based on a case study of a multi-story apartment building constructed in Sweden using wood structural framing, compared to a functionally equivalent building constructed with a reinforced concrete frame [20]. Calculations take into account the differences between the buildings due to the emission from fossil fuels used to manufacture and transport the materials, the cement calcination and carbonation process emissions, and substitution of fossil fuel by biomass residues from wood processing and construction. The reference concrete building uses some wood materials, and the substitution benefit is calculated based on the reduction in net carbon emission per unit of additional wood needed to make the wood-frame building. Large roundwood is converted into a mix of sawn lumber, plywood, and particleboard, with a product/roundwood dry weight ratio of 0.53. Some wood processing residue is used for particleboard manufacture, some is used internally as bioenergy for e.g. kiln-drying lumber, while the remainder is available externally for use as biofuel. We assume a building life span of 100 years, and calculate the carbon stock stored in the wood building materials during that time.

3.

Results

3.1.

Forest biomass production and harvest

The annual forest biomass production and harvest under the scenarios are presented in Table 1. The whole tree biomass production was significantly larger under the Climate change scenario compared to the Reference scenario. The annual biomass production increased by 1.84 Tg y-1 dry mass in the Reference scenario, and by 5.60 Tg y-1 dry mass in the Climate change scenario. The annual production increased during the 100-year study period by 33% more in the Climate change scenario than in the Reference scenario. In the Reference scenario, the calculated average harvest of whole tree biomass over 100 years was 8.95 Tg y-1, while in the Climate change scenario it was 10.18 Tg y-1. The harvest of stem wood biomass followed a similar trend. The annual harvest of

Table 1 e Average annual forest biomass production and harvest during each 10-year period for each scenario (Tg yL1 dry mass), cumulative totals for 100 years, and differences between the scenarios.

Climate change scenario Biomass production Whole tree biomass Stem wood biomass Biomass harvest Whole tree biomass Stem wood biomass

2020-2029

2030-2039

2040-2049

2050-2059

2060-2069

2070-2079

2080-2089

2090-2099

2100-2109

Cumulative total (Tg)

10.99 6.22

11.64 6.15

11.46 6.17

12.18 6.48

12.41 6.60

12.16 6.48

11.97 6.38

12.05 6.40

12.67 6.66

12.83 6.74

1204 643

8.17 5.00

8.07 4.91

8.80 5.36

8.85 5.40

9.40 5.75

9.45 5.85

9.06 5.63

9.04 5.63

9.09 5.63

9.60 5.97

895 551

11.34 6.40

12.15 6.42

12.40 6.67

13.53 7.18

13.98 7.44

14.17 7.58

14.37 7.68

14.77 7.86

16.10 8.51

16.94 8.97

1397 747

8.27 5.06

8.33 5.09

9.35 5.70

9.53 5.82

10.19 6.27

10.52 6.51

10.83 6.77

10.91 6.84

11.42 7.19

12.41 7.77

1018 630

0.94 0.50

1.35 0.70

1.57 0.84

2.01 1.10

2.40 1.30

2.72 1.46

3.43 1.85

4.11 2.23

194 104

0.55 0.34

0.68 0.42

0.79 0.52

1.07 0.66

1.77 1.14

1.87 1.21

2.33 1.56

2.81 1.80

122 79

Difference between Climate change and Reference scenarios Biomass production Whole tree biomass 0.35 0.51 Stem wood biomass 0.18 0.27 Biomass harvest Whole tree biomass 0.10 0.26 Stem wood biomass 0.06 0.18

b i o m a s s a n d b i o e n e r g y 3 5 ( 2 0 1 1 ) 4 3 4 0 e4 3 5 5

Reference scenario Biomass production Whole tree biomass Stem wood biomass Biomass harvest Whole tree biomass Stem wood biomass

2010-2019

4347

4348

b i o m a s s a n d b i o e n e r g y 3 5 ( 2 0 1 1 ) 4 3 4 0 e4 3 5 5

2100-2109

Bark

2090-2100

Slash

2080-2089

Stumps

2070-2079

0

Stemwood

2010-2019

2

0 2100-2109

2 2090-2100

4

2080-2089

4

2070-2079

6

2060-2069

6

2050-2059

8

2040-2049

8

2030-2039

10

2020-2029

10

2010-2019

Climate

12

Bark

2060-2069

Slash

2050-2059

Stumps

2040-2049

Stemwood

2030-2039

12 Dry mass Tg y-1

14

Reference

2020-2029

14

Year Fig. 5 e Average annual biomass harvest by tree component over time for Reference (left) and Climate change (right) scenarios (Tg yL1 dry mass).

whole tree biomass in the Reference scenario increased about 18% over the 100-year period, while in the Climate change scenario it increased by 50% during the period. Average annual harvest of forest biomass broken down by tree component is shown in Fig. 5. Stem wood biomass production averaged 6.30 Tg y-1 in the Climate change scenario, compared to an average of 5.51 Tg y-1 for Reference scenario. Stump biomass production increased by 47% for Climate change scenario, but only increased 14% for Reference scenario over the 100-year period. The quantities of stump biomass may be overestimated, because Marklund’s revised function [40] used by the HUGIN model estimates deciduous tree stump values based on spruce stumps. Pine harvest increases significantly particularly in Climate change scenario, while spruce harvest decreases in early years and then increases after 30 years of management (Fig. 6). For deciduous species, the harvest is higher during the initial 50 years but thereafter it decreases in Reference scenario. However, in Climate change scenario, deciduous species increase significantly. The higher increase for deciduous species might be

2100-2109

2090-2100

2080-2089

Deciduous

2070-2079

Spruce

2060-2069

Pine

2050-2059

2010-2019

2100-2109

0

2090-2100

0

2080-2089

2

2070-2079

4

2

2060-2069

6

4

2050-2059

6

2040-2049

8

2030-2039

8

2020-2029

10

2040-2049

Deciduous

10

Dry mass Tg y-1

Climate

12

2030-2039

Spruce

The average annual net carbon balance for each 10-year period, composed of carbon-stock increases in trees, soil and wood products, and avoided fossil and cement process emissions due to material and fuel substitution, is shown in Table 2. The net substitution values in the table are net values after deducting forest operations emissions from the substitution benefits. Biomass substitution is the largest single contributor to the

2020-2029

Pine

Carbon balance

14

Reference

12

3.2.

2010-2019

14

because of the low presence of birch in older forests today due to earlier silviculture practices when birch was unwanted. Moreover, HUGIN considers the past data and removes less birch in thinning considering few trees in an area, thus birches remains uncut in each thinning which might increase the volume in the long run. Another explanation might be that the HUGIN model assumes deciduous trees live longer than they actually do, which will overestimate the volumes in a longterm perspective, especially in reserves and environmental care areas where the trees are not felled.

Year Fig. 6 e Average annual biomass harvest by species over time for Reference (left) and Climate change (right) scenarios (Tg yL1 dry mass).

b i o m a s s a n d b i o e n e r g y 3 5 ( 2 0 1 1 ) 4 3 4 0 e4 3 5 5

carbon balance. The increased biomass production in the Climate change scenario can be used to replace more fossil fuels and non-wood products, resulting in substantially more carbon emission reduction than in the Reference scenario. Whole tree biomass usage results in greater substitution benefits because the slash and stump biomass is used to replace fossil fuels. When coal instead of fossil gas is replaced, the substitution benefits increase by about 38% for stem wood usage and about 48% for whole tree usage. The carbon stock in forest biomass continues to increase in both Reference and Climate change scenarios, but increases at a faster rate in the Climate change scenario. Soil carbon stock increases in both scenarios, but increases more in the Climate change scenario, presumably due to the greater amounts of litter fall produced by the faster growing trees. Removing slash and stumps causes the soil carbon to increase at a slower rate than when only stem wood is removed. However, the carbon emission benefits of using the slash and stumps to replace fossil fuel are significantly greater than the net decrease in soil carbon accumulation. When coal is the reference fossil fuel, the increased substitution benefits are about 5.2 times greater than the soil carbon net decrease, and for fossil gas are about 2.6 times greater. Carbon stock in wood products is greater in the Climate change scenario than the Reference scenario because part of the additional biomass production is stored in additional wood products. Fig. 7 shows cumulative avoided carbon emission over 100 years for the Reference and Climate change scenarios, assuming whole tree biomass use and coal reference fuel. Biomass substitution is the largest single factor contributing to avoided C emission. Carbon stock increases in soil, tree biomass and wood products each have roughly the same magnitude. Total avoided emissions during the 100-year period are 104 Tg C greater in the Climate change scenario than in the Reference scenario for whole tree biomass use with coal reference fuel, and 91 Tg C greater for stem wood biomass use with coal reference fuel. Table 3 shows the six factors contributing to the avoided carbon emission due to biomass substitution. Stem wood use includes the reduced fossil energy and cement process emissions when wood construction materials are produced instead of reinforced concrete materials, and the biomass from wood processing residues and small-diameter stem wood used to replace fossil fuel. Whole tree use includes these factors plus slash and stumps used as biofuel to replace fossil fuel. Each of these substitution factors is greater in the Climate change scenario than the Reference scenario, because more biomass is produced and used for substitution. Fig. 8 shows the differences in cumulative avoided carbon emissions between the Climate change scenario and the Reference scenario. The difference is greatest with whole tree harvesting and when coal is the reference fossil fuel. The difference in avoided carbon emission between whole tree use and stem wood use is roughly the same as the difference between coal and fossil gas reference fuel.

4.

Discussion and conclusions

We developed an integrated analytical approach to calculate forest biomass production, harvest, carbon stock changes, and

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biomass substitution effects under Reference and Climate change scenarios. The results suggest that there will be a significant increase in forest production in the next 100 years in northcentral Sweden due to climate change. Our forest modelling shows a 49% increase in total annual forest biomass production for the Climate change scenario during the next 100 years, slightly higher than the estimate by Pussinen et al. [52] for the whole of Europe. Compared to the increased biomass production in our Reference scenario, the annual forest biomass production for the Climate change scenario is 33% greater at the end of the 100-year period. The reasons why the production increases in the Reference scenario are because large areas with low productive forests are replaced with new planted forests with improved genetic material. This has been going on for several decades and will continue into the future. Some of these planted forests have now reached their most productive phase and the amount will increase further in the future. The total effect caused by genetic improvement in the Reference scenario is about 7% at the end of the scenario. Other explanations for growth increase are soil scarification during plantation, increased pre-commercial thinning, and fertilization. Soil scarification is done in 36 000 ha y-1 and pre-commercial thinning is carried out in 31 000 ha y-1. Nitrogen fertilizer is applied in the young stands of Norway spruce at an average rate of 20.9 kg ha-1 y-1 throughout the study period. The area of fertilization at the beginning is 9200 ha y-1 and reaches 10 000 ha y-1 at the end of the study period. The total fertilized area at the end of study period is 25% of the total production forest. Per hectare of productive forest, our results suggest that annual whole tree biomass production should be 3.9 Mg ha-1y1 dry mass for Climate change scenario in 2110. This is smaller than the forecast of 5e6 Mg ha-1y1 by Bergh et al. [13] for northern Swedish forests, but is higher than the projection of 2.4 Mg ha-1y1 by Kellomaki et al. [53] for Finnish forests. The increased harvest level in Climate change scenario gives increased possibility of forest product use, resulting in significant additional substitution benefits during the 100-year period. The modelled climate change also results in increased carbon stock in standing tree biomass, forest soil, and wood products. Whole tree harvesting can increase usable biomass by about 58% compared to stem wood-only harvesting. However, stump biomass calculation for broadleaf trees in the HUGIN model is based on spruce stumps, which overestimates stump biomass for broadleaf trees. Nevertheless, the recovery of slash and stumps can significantly increase the amount of biofuel per hectare and rotation period. Carbon stock in living tree biomass continuously increases in both Reference and Climate change scenarios, though it increases more in the Climate change scenario. Our average forest carbon stock increase is smaller than the estimate by Pussinen et al. [52], who assumed the SRES A2 climate scenario and intensive forest harvesting in the whole of Europe. In our analysis, increased harvest levels does not result in reduced carbon stock, as the forest production increase due to climate change is greater than the harvest increase. Although early studies reported that increasing temperature does not necessarily increase the soil respiration and thus soil carbon decomposition [54], later studies have

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Table 2 e Average annual carbon stock increases and avoided emissions due to biomass substitution during each 10-year period (Tg yL1 carbon). 2010-2019

Climate Scenario Forest C stock change Soil C stock change Stem wood biomass use Whole tree biomass use Wood product C stock change Net biomass substitution: Coal Stem wood biomass use Whole tree biomass use Net biomass substitution: Fossil gas Stem wood biomass use Whole tree biomass use Total (Coal ref) Stem wood biomass use Whole tree biomass use Total (Fossil gas ref) Stem wood biomass use Whole tree biomass use

2030-2039

2040-2049

2050-2059

2060-2069

2070-2079

2080-2089

2090-2099

2100-2109

0.38

0.76

0.22

0.55

0.34

0.20

0.35

0.40

0.67

0.45

0.78 0.58 0.77

0.78 0.58 0.72

0.78 0.58 0.62

0.78 0.58 0.63

0.78 0.58 0.65

0.78 0.58 0.67

0.78 0.58 0.72

0.78 0.58 0.74

0.78 0.58 0.74

0.78 0.58 0.76

3.78 4.75

3.63 4.61

3.59 4.63

3.63 4.67

3.82 4.91

3.89 4.96

3.88 4.90

3.93 4.94

3.93 4.96

4.12 5.20

2.81 3.30

2.68 3.17

2.56 3.08

2.59 3.11

2.72 3.27

2.77 3.32

2.81 3.33

2.85 3.37

2.86 3.38

2.98 3.53

5.70 6.48

5.90 6.67

5.22 6.05

5.59 6.42

5.59 6.48

5.54 6.42

5.73 6.55

5.85 6.67

6.12 6.95

6.11 6.99

4.73 5.03

4.94 5.24

4.18 4.51

4.55 4.87

4.49 4.84

4.43 4.77

4.66 4.97

4.78 5.09

5.05 5.37

4.97 5.32

0.49

0.85

0.35

0.80

0.63

0.53

0.45

0.61

0.96

0.75

0.78 0.58 0.79

0.79 0.58 0.75

0.79 0.59 0.64

0.80 0.60 0.66

0.81 0.61 0.74

0.82 0.62 0.77

0.82 0.63 0.86

0.83 0.64 0.86

0.84 0.64 0.96

0.84 0.65 1.06

3.83 4.82

3.78 4.77

3.79 4.89

3.88 4.99

4.21 5.39

4.37 5.57

4.67 5.88

4.69 5.89

5.05 6.30

5.51 6.89

2.85 3.35

2.79 3.29

2.69 3.25

2.76 3.32

3.01 3.61

3.13 3.73

3.38 4.00

3.38 4.00

3.68 4.32

4.03 4.73

5.89 6.68

6.16 6.96

5.58 6.47

6.14 7.05

6.39 7.37

6.49 7.49

6.80 7.82

6.99 8.00

7.80 8.87

8.16 9.36

4.90 5.21

5.17 5.48

4.48 4.83

5.02 5.38

5.19 5.59

5.24 5.65

5.52 5.93

5.68 6.10

6.44 6.88

6.68 7.19

b i o m a s s a n d b i o e n e r g y 3 5 ( 2 0 1 1 ) 4 3 4 0 e4 3 5 5

Reference scenario Forest C stock change Soil C stock change Stem wood biomass use Whole tree biomass use Wood product C stock change Net biomass substitution: Coal Stem wood biomass use Whole tree biomass use Net biomass substitution: Fossil gas Stem wood biomass use Whole tree biomass use Total (Coal ref) Stem wood biomass use Whole tree biomass use Total (Fossil gas ref) Stem wood biomass use Whole tree biomass use

2020-2029

4351

b i o m a s s a n d b i o e n e r g y 3 5 ( 2 0 1 1 ) 4 3 4 0 e4 3 5 5

Fig. 7 e Cumulative avoided carbon emission (Tg carbon) for Reference (left) and Climate change (right) scenarios, assuming whole tree biomass use and coal reference fuel.

suggested that a warmer climate may decrease soil carbon as CO2 is released to the atmosphere [55]. Our results show soil carbon stock increasing during the study period in both the Reference and Climate change scenarios. The carbon increase is slightly greater (60 kg ha-1 y-1 by 2010) in the Climate change scenario than in the Reference scenario, suggesting that increasing temperature has a net positive effect on soil carbon stock in boreal forests. Stro¨mgren and Linder [7] also found that soil respiration was not increased by temperature as an effect of a shift in the respiration response to temperature. The role of warmer climate and elevated CO2 concentration on soil C stock is still under debate [56e62]. Nevertheless, we expect that the uncertainty in soil carbon stock change will have very little effect on our conclusions. In the Climate change scenario, our estimate of soil carbon stock change is about 12% and 8% of the total carbon balance with coal reference fuel, with stem wood and whole tree biomass use, respectively (Table 2). Thus, if our estimate of soil carbon stock change is over- or underestimated by 20%, the total carbon balance will vary by only 2e3%. Hence, we expect that the uncertainty in soil carbon stock change will have a minor impact on our results and will not change our conclusions. We have assumed that 75% of needles from felled trees are harvested and used as bioenergy. Tree needles are rich in

nutrients, and continued removal of needles may lead to longterm nutrient deficiency [63]. In practice, however, needles often do not detach from the tree branches, even when felling residue is left in the forest to dry before harvesting. Thus, the quantity of needles that is harvested is uncertain and difficult to control. Nevertheless, this factor will have minimal impact on our conclusions. If 25% of needles are harvested instead of 75%, the reduced amount of bioenergy used to substitute fossil fuels will change the total carbon balance by about 1%. At the same time, however, soil carbon stock will increase faster if fewer needles are harvested, making the net effect on total carbon balance even less significant. The residual removal after thinning and clear felling may reduce forest growth by 10e15% and the reduction tends to be higher for Norway spruce compared with Scots pine [64e66]. The growth reduction is due to temporary reduction because of loss in nitrogen primarily in needles [65]. This reduction is likely to occur after extended periods of residue removal when there is no nutrient compensation provided. The nutrient can be compensated by N-fertilization [66]. There appears to be no reduced growth of trees due to stump harvest [66]. Carbon stock in wood products increases more in the Climate change scenario compared to Reference scenario, because of the greater quantities of stem wood converted into

Table 3 e Average carbon emission avoided (Tg yL1 carbon) due to the six factors comprising biomass substitution, with reference fossil fuel of coal or fossil gas. Coal fuel

Material substitution: Production energy Material substitution: Cement emissions Biofuel: Wood processing residues Biofuel: Small stem wood Biofuel: Slash Biofuel: Stumps

Fossil gas fuel

Reference

Climate change

Reference

Climate change

0.96 0.99 0.92 1.00 0.59 0.54

1.10 1.14 1.06 1.13 0.66 0.61

0.73 0.99 0.53 0.56 0.33 0.29

0.84 1.14 0.61 0.63 0.37 0.33

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b i o m a s s a n d b i o e n e r g y 3 5 ( 2 0 1 1 ) 4 3 4 0 e4 3 5 5

Avoided carbon emissions Tg

120

Cumulative differences: Climate and Reference scenario

100 Whole tree, coal fuel 80

Whole tree, fossil gas Stemwood, coal fuel

60

Stemwood, fossil gas

40 20

2100-2109

2090-2099

2080-2089

2070-2079

2060-2069

2050-2059

2040-2049

2030-2039

2020-2029

2010-2019

2000-2009

0

Year

Fig. 8 e Difference in cumulative avoided carbon emission (Tg carbon) between the Climate change scenario and the Reference scenario.

long-lived wood building materials. We assume a 100-year life span of the buildings produced with the forest biomass, thus the carbon stock in building materials increases continuously during the 100-year study period because no wood products are yet removed from service. At the end of this period, however, buildings will begin to be demolished and the annual carbon stock change in wood products will be the difference between new wood products entering service and old wood products leaving service. Post-use wood products can be a significant source of bioenergy [17], which would appear in our carbon balance calculations if the time period were longer. Using forest biomass as raw material for construction materials and bioenergy provides permanent and cumulative emissions reductions by avoiding fossil emissions. Storing increasing amounts of carbon in living biomass is not a viable climate change mitigation strategy in the long-term, as forests will eventually reach a dynamic equilibrium where carbon uptake is balanced by carbon release [50]. Moreover, biomass fractions such as slash and stumps that are left in the forest after fellings will also decompose over time and release most of their stored carbon into the atmosphere. Instead of forest biomass decaying naturally in the forest, while simultaneously using fossil fuels and materials to provide services to society, the forest biomass could provide those services resulting in less net CO2 emission to the atmosphere [20]. The time dynamics of forest residue oxidation vary. Forest residues left to decompose naturally in the forest slowly release CO2 into the atmosphere over a time scale of decades, while residues removed from the forest and used as biofuel release CO2 when burned. This can result in varied radiative forcing, the significance of which depends on the time horizon under consideration [67]. This effect is more pronounced for slower-decaying biomass such as stumps. The avoided fossil CO2 emissions are greater when coal is substituted, due to its greater carbon intensity. International trade in bioenergy is increasing rapidly, made feasible by the development of efficient long-distance transport methods allowing biofuel produced in one region to replace

fossil fuel in another region [68]. Gustavsson et al. [69] showed that woody biofuels can be economically transported internationally, and the GHG reduction per unit of biofuel depends more on the fossil fuel replaced than on the transport distance. By exporting biomaterials and biofuels to be used in applications that result in high greenhouse gas emission reductions per unit of biomass, the total emission reduction from the available supply of biomass could by increased. There are significant uncertainties in our analysis. The degree of temperature change in the future is not known with certainty, and will depend on several factors including economic growth, population, and implemented climate mitigation activities. We have used the SRES B2 scenario of temperature increase requiring significant mitigation efforts. Regional temperatures may rise by more than we modelled, and the forest biomass production would further increase. Furthermore, the HUGIN model overestimates biomass in deciduous stumps and in mature deciduous trees that are not felled. Climate change may result in natural disturbances, for example, wind damages, fire risks, pathogens, and insect outbreaks, which may affect forest growth and mortality [70e75]. Some of these potential disturbances do not have direct effect on growth, such as forest fires and storm-felling, however it can damage timber or make more difficult to harvest. Other disturbances like pathogens and insects can have major effect on growth following the disturbances in tree stems and in the leaf area. A Swedish governmental survey [76] pointed out these uncertainties and risks as possible threats to future forestry in Sweden. In Swedish forestry, however, because of intensive forest management practices and easy access to the forest, it is possible to take precautions to reduce potential disturbances. Although these uncertainties may alter the exact values that we have calculated, our general conclusions appear to be robust. We expect future climate change to increase both biomass production and substitution potential in forests in north-central Sweden.

Acknowledgement We gratefully acknowledge the support of European Commission: Objective II, European Structural Funds, County Administrative Board of Ja¨mtland, Sveaskog AB, SCA Forest ˚ TAB, and Ja¨mtkraft AB. This analysis Products, Norrskog/SA was done partly in collaboration with the project Future Forests at the Faculty of Forestry at SLU, Umea˚. We also thank the anonymous reviewers of this paper.

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