Assessing the impacts of rising fuelwood demand on Swedish forest sector: An intertemporal optimization approach

Forest Policy and Economics 105 (2019) 91–98

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Assessing the impacts of rising fuelwood demand on Swedish forest sector: An intertemporal optimization approach

T

Jinggang Guo , Peichen Gong ⁎

Centre for Environmental and Resource Economics, Department of Forest Economics, Swedish University of Agricultural Sciences, SE-901 83 Umeå, Sweden

ARTICLE INFO

ABSTRACT

Keywords: Fuelwood Timber market Forest sector model Partial equilibrium model

An increase in the demand for fuelwood has the potential to affect traditional timber production. Based on a partial equilibrium model of the Swedish forest sector, this paper evaluates the supply and price of sawlogs, pulpwood and fuelwood under differing levels of future fuelwood demand. The results indicate that an increasing demand for fuelwood would intensify the competition between pulpwood and fuelwood, which would cause the prices of pulpwood and fuelwood to increase. The pulpwood supply would decrease and the fuelwood supply would increase in response to fuelwood demand expansion. An increasing demand for fuelwood would affect the harvest of sawlogs positively, though the effect would be small. It is concluded that an increase in the use of fuelwood would affect mainly the pulp and paper industry in terms of the provision of raw materials. Besides, the annual increment of forest standing volume would exceed the harvest in all the scenarios analyzed, implying that the Swedish forests are a carbon sink.

1. Introduction Sweden has a long history of promoting the use of bioenergy. The introduction of carbon tax, green electricity certificates and investments in combined heat and power have jointly paved the way for bioenergy development. Nowadays, bioenergy accounts for the largest share of the total energy consumed in Sweden. The total supply of bioenergy increased from about 84 TWh to 139 TWh between 2000 and 2016, and bioenergy accounted for around 25% of the final energy consumption in 2016 (Swedish Energy Agency, 2018). Biomass growth has been particularly impressive in the district heating sector and in 2015 biomass accounted for more than 63% of the total energy use (Swedish Energy Agency, 2017), of which fuelwood constituted an important share. With the supporting renewable energy policies progressing, the demand for fuelwood is likely to grow during a considerable period of time in the future. The increased demand can be met by using a larger share of harvested timber as fuelwood and/or increasing the total harvest volume. A question that naturally arises is how the rising fuelwood demand will impact timber supply for traditional forest industries. If increasing demand for fuelwood leads to increased timber harvest, it would also affect the dynamics of carbon stock in the forest over time, both directly through the harvest removal of carbon from the forest, and indirectly through the effect on forest growth and hence on carbon sequestration in the subsequent years after

⁎

each harvest. Another issue is therefore the impact of increasing fuelwood demand on the ability of forests to offset CO2 emission. Understanding the potential linkage among the production of sawlogs, pulpwood and fuelwood can help us to determine how traditional timber products and the total harvest respond to the rising fuelwood demand and identify the future role of Swedish forestry in mitigating CO2 emissions. The objectives of this paper are threefold: (i) to examine the changes in price and quantity for each timber product, (ii) to compare the changes in forest inventory and (iii) to analyze the changes in consumer and producer surplus in response to different growth rates of fuelwood demand. The results show that, when the future demand for fuelwood is projected in the low-growth-rate scenario, its effect on pulpwood production is limited. The additional demand is largely satisfied by increasing the timber harvest. Doubling the growth rate of the fuelwood demand requires more timber removal and at the same time leads to a larger reduction in the pulpwood supply, which in turn intensifies the competition between these two products. The results also provide some insights into the trade-off between fuelwood production and forest carbon. Despite the higher fuelwood demand being associated with an increased timber harvest, the annual harvest will still be below the annual growth, and the Swedish forest sector will continue to serve as a carbon sink. The remainder of the paper starts by presenting a literature review in Section 2. Section 3 contains details of the model and the three

Corresponding author. E-mail address: [email protected] (J. Guo).

https://doi.org/10.1016/j.forpol.2019.05.020 Received 30 October 2018; Received in revised form 10 May 2019; Accepted 15 May 2019 1389-9341/ © 2019 Elsevier B.V. All rights reserved.

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scenarios that we select (including the baseline scenario). The results of the comparative analysis are provided in Section 4. This is followed by a discussion and conclusion in Section 5.

industry and the heating industry in Sweden for the period 1970–2008. The pulp and paper industry would face rising competition as the use of wood by-products in heating industry increased. Both studies argued that traditional forest products were encountering growing competitive pressure from the increasing utilization of forest fuel in the energy sector. However, the degree of this competition was quite limited. In summary, these two papers are capable of revealing the dynamic relationships between three markets in the past using the historical data. They are less capable of capturing the impacts of future market change. Historically, fuelwood consumption keeps at a low level in Sweden. However, structure changes are taking place in forest products markets and the fuelwood harvest experiences a rapid increase since 2010, which sets the growing demand for the forward-looking analysis.

2. Previous studies The dynamic interlinks between bioenergy use and traditional timber uses (e.g., sawlogs and pulpwood) have been subject to extensive analysis. A number of studies have examined the interactions between the prices and quantities of timber products and bioenergy using econometric models (e.g., Brännlund et al., 1985; Du and Runge, 2014; Geijer et al., 2011; Kristöfel et al., 2016; Lundmark and Olsson, 2015; Susaeta et al., 2013). Some studies have applied computable general equilibrium (CGE) models to capture the interactions between the forest sector and the rest of the economy resulting from the increased demand for bioenergy (e.g., Kretschmer and Peterson, 2010; Suttles et al., 2014). Since each sector is represented at a highly aggregate level in the CGE model, this type of model is less suitable for disaggregated analysis of a specific sector. Partial equilibrium (PE) models have dominated the analysis of impacts of increased bioenergy demand, allowing the evaluation of forest sector responses to bioenergy demand with varying levels of detail (Greber and Wisdom, 1985; Galik et al., 2009; Schwarzbauer and Stern, 2010; Sjølie et al., 2010; Ince et al., 2011; Buongiorno et al., 2011; Kallio et al., 2011; Kong et al., 2012; Sedjo and Tian, 2012; Lecocq et al., 2011; Latta et al., 2013, Abt et al., 2014, Johnston and van Kooten, 2016; Nepal et al., 2018). Among them, some focus solely on fuelwood expansion. For example, Lecocq et al. (2011) examined the impact of fuelwood subsidy on French forest sector and concluded that forest industries using pulpwood could be worse off due to the increased use of energy wood. Kong et al. (2012) modeled an integrated market for sawlogs, pulpwood and forest bioenergy in southern Sweden and examined the wood market's response to changes in the prices of fossil fuels. With continuous promotion of bioenergy and relatively high fossil fuel prices, the use of pulpwood for power generation would become attractive, which would generate competition between wood energy producers and pulp and paper mills. Another strand of PE analysis includes a broader coverage of biomass feedstock for bioenergy. Kallio et al. (2011) developed a partial equilibrium model focusing on the forest chip market in Finland and investigated the trade-offs among various energy policy targets; Ince et al. (2011) argued that increasing the use of logging residue for the energy production would help to reduce the negative impact of wood energy expansion on the production of wood pulp and pulpwood-based products; Moiseyev et al. (2013) used the European Forest Institute Global Trade Model (EFI-GTM) to analyze the potential role of the forest sector in providing wood-based energy in different scenarios of coal, gas and carbon prices up to the year 2030. Their results suggested that, with low coal and gas prices, wood-based electricity would be limited to the use of low-cost logging residues. Nepal et al. (2018) confined their focus to the U.S. and found that about 37 million m3 of pulpwood would be diverted to energy sector due to the expansion in wood energy consumption while the logging residues made limited contributions to the biomass feedstock. The above-mentioned studies contain detailed representations of the demand for forest products; however, the supply-side is highly aggregated with regard to forest dynamics. In addition, the cross-price effects and the influence of the growing stock on the timber supply are not explicitly represented. For Sweden Geijer et al. (2011) and Lundmark and Olsson (2015) examined the relation between the forest industry and the energy sector by econometrically estimating the cross-price elasticity of the demand and supply. Three main wood categories were taken into account by Geijer et al. (2011) to examine the trade-offs between forest conservation and fuelwood production. Lundmark and Olsson (2015) provided a more comprehensive analysis of factor substitution and production structure among the sawmill industry, the pulp and paper

3. The model The Swedish Timber Market Model (STIMM) is an intertemporal partial equilibrium model that integrates the dynamics of forest resources and timber supply and the demand in Sweden (Gong et al., 2013). The model was constructed for analyzing the impacts of revenant national policies on timber harvest and roundwood market in Sweden. For this purpose, the model focuses on the market equilibrium amount and price of domestically produced roundwood, whereas timber imported to Sweden is excluded. Timber harvested in Sweden is either consumed by the domestic forest industry and energy sector, or is exported to other countries. Export of roundwood from Sweden has historically been low. In 2011–2018, roundwood export varied between 0.6 and 0.9 million m3 per year, corresponding to about 1% of the total harvest in Sweden (SCB, 2018a). In the STIMM, we assumed that Because of the low volume, roundwood export is zero.1 The model determines the timber supply function that represents the rational behaviors of forest owners in a competitive market with exogenously given demand functions and uses the obtained supply function to simulate the market equilibrium price–quantity combination. A distinctive feature of the STIMM is that the timber supply function coefficients are dependent on the “policy context,” implying that a policy change could lead to changes in the coefficients of the supply function. In other words, the elasticities of the timber supply are endogenous, and changes in policy, technology or other external factors could affect the timber supply indirectly through their impacts on the supply function coefficients.2 Using the supply function to determine the market equilibrium prices and quantities, instead of determining the optimal quantities directly, dramatically reduces the number of decision variables and greatly simplifies the optimization model. Briefly, the application of the STIMM involves two steps. First, the model is solved to find the optimal values of the timber supply function coefficients. After the coefficients of the supply function have been optimized, the model is run to simulate the annual harvests and prices of timber, the updates of the forests over time and other related variables. For the purpose of policy assessment, these two steps are repeated for a benchmark scenario and for each policy option. The impacts of different policy options, in terms of changes in the price, production and welfare, are estimated by comparing the results associated with different scenarios. In the previous studies, the STIMM has been applied to assess the economic effects of biotechnological progress on the timber sector (Gong et al., 2013) and to estimate the net present value of non-timber forest products (Gong and Guo, 2017). To accommodate our research needs, in addition to using the updated data set, two major updates to the STIMM are made in this paper: a set of subdivided timber markets is now included and the cross-price effects 1 Import and export of roundwood were treated in the same way in the Swedish forest sector model presented in Geijer et al. (2011). 2 A brief explanation of the principle of the method is presented in the Appendix A of Guo and Gong (2017).

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between industrial roundwood and fuelwood are taken into account. Below we will present the revised specifications of the STIMM in detail and explain some key parameters used in the current paper.

25–35% of the total harvest volume in Sweden (Swedish Forest Agency, 2015). In practice, the timing and intensity of thinning is determined based mainly on the development of dominant height and basal areal of each stand and, therefore, the thinning schedule (number of thinnings per rotation, stand age when each thinning is conducted and timber volume harvested at each thinning) varies greatly among different site indexes (Agestam, 2015). In this study the productive forest land is grouped into two categories with respect to site productivity. We assume that the total thinning removal during each rotation is, on average, 40 m3/ha for forests on low-productivity sites and 80 m3/ha for high-productivity land. Because each of the two site productivity classes consists of land with varying site indexes, we do not specify the exact ages at which thinning should be performed. Instead, we assume that thinning will be conducted each year in 5% of the forests in the age classes 40–60, which is the most probable age interval when thinning is made. These assumptions lead to fairly stable harvest volume from thinning which is comparable with observed volume in the past decade. The final harvest is carried out by cutting the oldest age class on the high-productivity site first. The age class distribution is updated by moving all post-harvest age class distributions to the next higher age class. The areas harvested in each period are required to be regenerated immediately and constitute the 1-year-old age class in the next period. The total costs of harvest-related activities are comprised of two components: harvest cost, a linear function of the volume being harvested and regeneration cost, a linear function of the areas being regenerated. Non-timber benefits are represented by a logistic function of forest stand age multiplied by forest area in each age class. These parameters remain the same as those in the previous work by Guo and Gong (2017).

3.1. Timber supply The extended model distinguishes between three primary timber products: sawlogs, pulpwood and fuelwood. For each timber product, the market is assumed to be perfectly competitive, and all forest owners are expected to act optimally. The optimal harvest level in a given year depends on the state of the forest, timber prices, management costs, interest rate and so on. In this paper, we model the total harvest of each timber assortment as a function of the roundwood prices and the amount of timber stock in mature forests, assuming that all other factors that affect the optimal harvest level remain constant over time. The timber supply function (Wear and Parks, 1994) is formulated as:

Ssl, t = Ssl, t ( ) = e 1 It 2 Psl,3t Ppw4 , t P fw5, t

(1)

4 P 5 Spw, t = Spw, t ( ) = e 1 It 2 Psl,3t Pp w, t fw, t

(2)

4 P 5 Sfw, t = Sfw, t ( ) = e 1 It 2 Psl3, t Ppw , t fw, t

(3)

where Si, t (for i = sl, pw, fw) stands for the supply of sawlogs, pulpwood and fuelwood in year t; the price of product i at time t is denoted by Pi, t; and It is the inventory of mature standing timber at time t. This is a commonly used functional form for modeling market supply of timber (e.g., Gong and Löfgren, 2003; Bolkesjø et al., 2010; Lecocq et al., 2011). The coefficients of the supply functions (α, β, γ) are determined endogenously in the STIMM. In Eqs. (1)–(3), it is reasonable to expect the own-price elasticity of supply for each timber product (α3, β4, γ5) to be positive. In the numerical solution, the model does not impose any constraint on the cross-price elasticities (α4, α5, β3, β5, γ3, γ4) to account for all the possible relationships between different timber products. Intuitively, an increase in the standing stock will lead to a higher annual harvest level in the short term while holding all other variables constant; we thus set the signs of (α2, β2, γ2) to be positive. The position parameters (eα1, eβ1, eγ1) always ensure a positive supply curve.

3.4. Determination of supply function coefficients Under the assumption of perfect competition in all markets, the optimal harvest behavior of forest owners leads to the maximum present value of the total surplus (Lyon and Sedjo, 1983). Since we model the timber harvest using a set of supply functions, the coefficients of the supply functions can be determined by maximizing the total surplus, which is equivalent to the present value of the sum of consumer and producer surplus plus the non-timber benefits, subject to the market clearing conditions (6) and the constraints controlling the forest growth:

3.2. Timber demand The demand function for each assortment of domestically produced timber takes the same form as it did in the previous version of the model (Gong et al., 2013), which can be specified as:

Di = Ci Pidi

T

Max , ,

Si, t

rt

t=1

(4)

+

where Ci are calculated using the average prices and quantities during the 2010–2015 and the price elasticity of demand di. Lundmark and Olsson (2015) reviewed some previous studies on the price elasticity of the demand for sawtimber, pulpwood and wood fuel in Sweden. They found that the results varied greatly among different studies. In this paper, the price elasticities of sawlog pulpwood and fuelwood demand we use in the paper are from Geijer et al. (2011), Lundgren and Sjöström (1999) and Brännlund and Lundgren (2004), which is −0.71, −0.26 and −0.63, respectively.

e e 1

i

0

r (T + 1)

e

Si, T

r

i

0

120

Di, t1 (p;

i ) dq

c Xt ,

Si, t + i

(f (k ) Ak, t ) k=1 120

Di, T1 (p;

i ) dq

c XT ,

Si, T + i

(f (k ) Ak, T ) k=1

(5) s.t.

Si, t ( ; ; ) = Di, t (p; i ) for i = s l, pw, fw

(6)

where (α, β, γ) is the array of decision variables, representing the coefficients of the timber supply functions; Di, t−1(p; ϕi) is the inverse demand function for each timber product; ϕi is a 1 × 2 array of demand function parameters. Xt is the age-class distribution of the forests at the beginning of year t and Si, t is the production of i at time t . c (Xt , i Si, t ) is the sum of regeneration and harvest costs. Ak, t is the area of the forest stand at age class k and f(k) is the non-timber benefit function modeled as a function of k3. The initial age class distribution of the forest is given according to the Swedish National Forest Inventory (NFI). The last term in the objective function represents the remaining value of the standing

3.3. Forest dynamics Forest dynamics originates from two sources: the natural growth governed by an age-based growth function and the annual timber harvest determined by the market equilibrium. The analysis is conducted at the national level and includes 85% of the productive forestland in Sweden. Forests in national parks, nature reserves, and other nature protection area, as well as forests older than 120 years are excluded. Here we assume that the total supply comes from thinning and final harvesting. During 2007/08–2016/17, thinning contributed with

3

f (k ) =

1000 60 k 15

1 + exp

(Guo and Gong, 2017). 93

is the per ha value of non-timber services of age-class k

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stock at the end of the simulation, that is, the value from period T + 1 to infinity. In this paper, we simply assume that the market activities and forest condition in the last period T will continue until infinity. The market clearing condition (6) ensures the market equilibrium that the supply will always be equal to the demand in each period in each submarket.

Table 1 Reference harvest levels and prices used in STIMM.

3.5. Scenario settings

a No official figures are as yet available for the fuelwood price. We use the price for sawmill by-products as a proxy for the fuelwood price (Olsson & Lundmark, 2014), close to the estimate of around 277 SEK/m3 by Carlsson (2012).

Currently there are no specific targets regarding the share of fuelwood in the district heating system for 2030, to tease out the possible change in parameters of the supply function, we simply employ three alternative growth rates in our simulation. In the base scenario (S0), no changes are assumed in the demand for fuelwood throughout the simulation. To simulate an increase in the demand for fuelwood, the shift parameter in the fuelwood demand function is simply moved upwards (e.g., Abt et al., 2012; Johnston and van Kooten, 2016). In the lowgrowth scenario (S1), the annual growth rate of the demand for fuelwood is 1.5% during the period 2015–2030 and remains constant at the 2030 level thereafter; in the high-growth scenario (S2), we double the annual growth rate of the fuelwood demand to 3.0% during the period 2015–2030. All three scenarios hold the demand for sawlogs and pulpwood constant. For each of these scenarios, the market equilibrium prices and quantities of wood products, changes in standing stock and total welfare are computed. Table 1 summarizes the reference harvest level and price used in the model obtained from the Swedish Forest Agency (2014). Regeneration cost in the low- and high-productivity land is 7000 and 9000 SEK/ha, respectively. The harvest cost is 160 SEK/m3 for thinning and 90 SEK/m3 for final felling (Swedish Forest Agency, 2014). The updated version of the STIMM is programmed in MATLAB and solved using TOMLAB/LGO4. The model is solved over a span of 36 years (2015–2050) and we focus on the results for the first 15 years to reduce the impact of the terminal condition of the forests on study results over the period of interest. The results are presented for two periods, 2015–2020 and 2015–2030, to examine the short- to medium-term effects of increasing the demand for fuelwood. (See Table 2.)

Product

Base-year production (million m3)

Base-year pricea (SEK/m3)

Sawlog Pulpwood Fuelwood

42.8 37.1 8.4

504 277 277

among the three scenarios, indicating that an increase in the fuelwood demand does not have an obvious impact on the sawlog supply. The cross-price elasticity of the pulpwood supply with respect to the fuelwood price decreases from 0.08 to −0.48 in Scenario S1 and to −0.38 in Scenario S2, suggesting that an increased fuelwood demand will lead to an increased degree of substitution between them. The negative change in the cross-price elasticity of the fuelwood supply with respect to the pulpwood price exhibits a high degree of consistency across each scenario considered. The parameters (eα1, eβ1, eγ1) determine the position of the supply function. A rise can be found in eα1 and eγ1, which shifts the curve to the right. An opposite shift occurs in the pulpwood market, where eβ1 experiences a decrease. The coefficient estimates provide some insights into the interaction among different types of timber production. To determine further the extent to which the increasing fuelwood demand will exert an impact on the Swedish forest sector, the market equilibrium in three different scenarios is simulated in the following section. 4.2. Market equilibrium harvests and prices To explain how the prices and production of different timber products respond to the increased fuelwood demand, we first outline the equilibrium prices and production for the baseline scenario in Table 3 followed by detailed comparisons of the market equilibrium in 2015, 2020 and 2030 across scenarios in Tables 5–7. The baseline scenario for the STIMM model is solved assuming that there is no change in the demand for any timber product. Table 3 shows that the timber production for sawlogs, pulpwood and fuelwood remains relatively constant at the 2015 levels throughout the entire simulation period. The minor change in each type of timber production in the short term (2020) and the medium term (2030) is mainly caused by the change in forest inventory. Correspondingly, the price of each product and the total harvest level show little variation for the period from 2015 to 2030. Table 4 presents the changes in the market equilibrium harvests and prices for the S1 and S2 scenarios relative to the baseline scenario in 2015. Since the fuelwood demand is almost at the same level in the initial year, the equilibrium price and quantity do not show many differences across the scenarios. Most of the differences are less than 1% in comparison with the baseline. For scenarios S1 and S2, in the short term, the total timber harvest increases by 0.63 Mm3 and 1.41 Mm3 relative to the baseline level in 2020 (Table 5). The difference in pulpwood production among these three scenarios is marginal, a reduction of only 0.08 Mm3 and 0.1 Mm3 relative to 37.01 Mm3 in S0. In contrast, the estimated fuelwood production shows an increase of 0.29 Mm3 and 0.73 Mm3 above the baseline in S1 and S2. In the short term, the demand for fuelwood in S1 and S2 is only moderately higher than the baseline; in this case, the increase in fuelwood production can be met by additional harvesting with limited use of pulpwood as a substitute. The continued growing demand for fuelwood reaches a high level in the long term, giving rise to a significant increase in the total harvest and fuelwood production. The total harvest experiences an increase of 1.25 Mm3 and 2.73 Mm3. To satisfy the growing demand, the fuelwood

4. Results In this section, we firstly list the optimized coefficients of the supply functions and then conduct a detailed comparison of the prices, production and standing volume across scenarios and markets. Finally, we assess the welfare changes of the four major participants in the Swedish forest sector in the face of the rising fuelwood demand. 4.1. Coefficients of the supply functions The values of the optimized supply function coefficients are presented in Table 1. Most of the coefficients have the expected signs. All the own-price supply elasticities are positive, and all except one of the elasticities are less than or equal to 1, which means that timber products are not very sensitive to price changes. Compared with the ownprice elasticity, the cross-price elasticities of the sawlog supply with respect to the prices of pulpwood and fuelwood are even lower (both are 0.2), indicating that the quantity of sawlogs supplied is less dependent on price changes in the other timber products. Furthermore, the coefficients of the sawlog supply function show little variation 4 Calibration of the STIMM involves two steps. First, the model is solved to maximize total surplus with respect to the coefficients of timber supply function. The process continues until harvest level generated by the model closely matches the observed in the base year using the solved coefficients. We then adjust the searching range around the values of these coefficients to narrow down the difference between the optimized harvest level and reference level.

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Table 2 Calculated coefficients of the supply function for each submarket. Ssl

Constant Inventory Sawlog price Pulpwood price Fuelwood price

α1 α2 α3 α4 α5

Spw

S0

S1

S2

−6.85 0.40 0.83 0.20 0.20

−6.83 0.40 0.83 0.20 0.20

−6.24 0.43 0.70 0.20 0.20

β1 β2 β3 β4 β5

S0

S1

S2

−4.11 0.36 −0.20 1.00 0.08

−4.37 0.39 0.20 1.13 −0.48

−4.50 0.43 0.20 1.00 −0.38

Table 3 Equilibrium prices (SEK/m3) and quantities (million m3) of each timber product in the baseline scenario.

Psl Ppw Pfw Ssl Spw Sfw Stotal

Pulpwood Fuelwood Total

Price Quantity Price Quantity Price Quantity Quantity

Pulpwood Fuelwood Total

−4.45 0.40 0.00 −0.25 0.87

−4.42 0.43 0.15 −0.47 0.88

S1

514.84 279.97 275.01 42.40 37.02 8.48 87.90

515.29 280.41 275.33 42.37 37.01 8.47 87.85

513.81 278.95 274.28 42.46 37.04 8.51 88.01

Absolute changes

Percentage changes

Absolute changes

Percentage changes

−12.13 0.77 12.19 −0.24 28.09 0.72 1.25

−2.36 1.81 4.37 −0.65 10.24 8.46 1.42

−23.86 1.54 20.49 −0.39 56.38 1.58 2.73

−4.64 3.63 7.35 −1.05 20.56 18.57 3.10

Sawlogs Pulpwood Fuelwood Total

Absolute changes

Percentage changes

Absolute changes

Percentage changes

−3.67 0.23 0 0 −1.38 0.05 0.28

−0.71 0.54 0 0 −0.50 0.59 0.32

−5.49 0.34 −3.01 0.06 −7.07 0.29 0.69

−1.07 0.80 −1.08 0.16 −2.57 3.42 0.78

Percentage changes

Absolute changes

Percentage changes

−6.84 0.43 4.28 −0.08 8.73 0.29 0.63

−1.33 1.01 1.53 −0.22 3.17 3.42 0.72

−12.32 0.78 5.16 −0.10 14.02 0.73 1.41

−2.39 1.84 1.84 −0.27 5.09 8.62 1.61

Price Quantity Price Quantity Price Quantity Quantity

Sawlog consumers: Pulpwood consumers: Fuelwood consumers: Forest owners: Non-timber benefits: Total surplus:

S0

S1

S2

411.09 131.23 16.57 258.79 86.80 904.49

413.86 130.55 19.03 260.58 86.67 910.70

416.24 130.31 22.16 261.64 86.51 916.86

has lower prices; thus, we can conclude that a portion of the further increases in fuelwood production will be obtained through the conversion of pulpwood. In all the scenarios, the absolute values of the percentage changes in the sawlog supply are all below 5%. The sawlog supply is not severely affected by the expansion of the fuelwood production, which is in line with our findings in Table 3 showing that not much difference exists in the sawlog supply function across scenarios. When comparing the dynamics of the forest inventory, we find that S0 has a higher total standing volume than S1 and S2, as shown in Fig. 1. In addition, each curve exhibits a growing trend throughout the period, except for the decrease in the initial years, which suggests that, in all the cases considered, Swedish forests can be viewed as net carbon sinks. Considering fuelwood's small share in the total harvest, a modest change in the fuelwood harvest will not cause an obvious impact on the total standing volume and in turn on the forest carbon.

S2

Absolute changes

Δ Δ Δ Δ Δ Δ Δ

S2

Table 7 Changes in welfare in different markets compared with the baseline scenario during the period 2015–2030, billion SEK.

S2

S1

Price Quantity Price Quantity Price Quantity Quantity

−4.74 0.44 0.00 −0.25 0.87

2030

Table 5 Changes of quantity and price in each timber market compared with the baseline level (S0) in 2020.

Δ Δ Δ Δ Δ Δ Δ

S2

2020

Note: Absolute changes in the quantities and prices are measured by million m3 and SEK/m3, as in the following Tables 4 and 5.

Sawlogs

S1

2015

S1

Δ Δ Δ Δ Δ Δ Δ

γ1 γ2 γ3 γ4 γ5

S0

Table 6 Changes in the quantity and price in each timber market compared with the baseline level (S0) in 2030.

Table 4 Changes of quantity and price in each timber market compared with the baseline level (S0) in 2015.

Sawlogs

Sfw

production increases by 0.72 Mm3 in S1 and 1.58 Mm3 in S2, 8.5% and 18.6% higher than the 2030 S0 level. Conversely, the pulpwood production decreases by 0.24 Mm3 and 0.39 Mm3 compared with the S0 level of 37 Mm3. This strong fuelwood demand also has a positive effect on the sawlog production, which rises by 0.77 Mm3 and 1.54 Mm3, respectively. The results presented in Table 5 suggest that a greater increase in the fuelwood demand not only results in an increase in the timber harvest but also decreases the pulpwood production, which is less affected in a low-demand scenario. Despite the model allowing fuelwood feedstock to substitute either sawlogs or pulpwood, pulpwood

4.3. Welfare implications The examination of changes in the market equilibrium leads us to investigate the welfare implications of a rising fuelwood demand for each sector. The key results are presented in Table 7. In brief, except for pulpwood consumers, the increasing fuelwood demand will benefit all other market participants. The gains of the forest owners and the sawlog and fuelwood consumers will exceed the loss of the pulpwood consumers and the non-timber benefits, and the total surplus will increase. 95

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2330

Total standing stock (million m3)

2320 2310 S0

S1

S2

2300 2290 2280 2270 2260 2250 2015

2020

2025

2030

Fig. 1. The development of forest standing stock for each demand scenario during the years 2015–2030.

Since, in the intertemporal framework, the market participants are assumed to have perfect foresight (Sjølie et al., 2015), forest owners can anticipate the growing demand for fuelwood, adjust the harvest schedule to take advantage of the rising fuelwood prices induced by this growing demand and improve their welfare. The increased price and decreased supply will deteriorate the welfare of pulpwood buyers, which implies that the pulpwood-based industry will suffer from the growing demands for fuelwood. As expected, the fuelwood consumers' surplus will increase due to the increased demand, which seems obvious. Additionally, the fuelwood demand expansion is associated with a minor reduction in the non-timber benefits due to the rise in the total timber harvest.

Overall, since the fuelwood harvest accounts for a small share of the total harvest, the rising fuelwood demand only has a moderate effect on the Swedish forest sector. The results of this study also provide some insights to the modeling of timber supply. Currently most forest sector models only use own-price variables in the supply functions and crossprice variables are not explicitly represented. The results of this study illustrate the necessity of modeling the cross-price effect on the timber supply. Our simulation results, to some extent, are consistent with the work of Schwarzbauer and Stern (2010), who evaluated the impacts of a “wood-for-energy” policy on the Austrian forest sector. The authors predicted that the pulp and panel sector would be worse off due to increased input costs while forest companies and sawmills would benefit from such a policy. One difference between our results and those of Schwarzbauer and Stern (2010) is that in their study the increase in welfare for sawmills comes largely from the rising profit of using sawmill residues as wood fuel, whereas in our study sawmills benefit only due to the decrease in the sawlog price. Our results reinforce the prediction made by Lindner et al. (2007). By taking industrial and harvest residues into account, Lindner et al. (2007) assessed the changes in the mix of potential biomass sources for energy use and predicted that, when higher bioenergy prices appear, more wood will be switched from competing uses, particularly chemical pulp, to energy production. Though an examination of the specific policies influencing the forest sector is beyond the scope of this paper, some general policy implications may be drawn from our results. Policies designed to stimulate the fuelwood demand (thus increasing the fuelwood prices) will yield more fuelwood and sawlog production but less pulpwood. Pulpwood-based industry, such as the pulp and paper sector, will be adversely affected by the rising input costs. Due to the non-linearity of the objective function, it is difficult to distinguish between a local optimal solution and the global optimal solution. The intertemporal characteristic of the model makes it even more complex to escape local solutions. Though most of the coefficients presented in Table 1 have a similar magnitude to those estimated by econometric methods for Sweden, some of them should be interpreted more carefully. In addition, the results are contingent on a number of parameters, such as the price elasticity of demand. The inelastic pulpwood demand results in a large price increase with a relatively small production change, in line with the results of Abt et al. (2010) and Geijer et al. (2011). Sensitivity analysis is still needed to evaluate the robustness of results to changes in these parameters. Like any other forest sector model, the model applied in this paper was constructed based on many simplifications. For the purpose of this paper we included only fuelwood, but not the by-products of forest industry and logging residues in the analysis. In Sweden, the major source of biomass feedstock for bioenergy production is the by-product

5. Discussion and conclusion In this paper, we develop a partial equilibrium model of the Swedish forest sector for assessing the interactions between the supply of fuelwood and that of other primary timber products. The optimized coefficients of the supply function indicate that: a) the sawlog supply is less sensitive to changes in its own price compared with the pulpwood and fuelwood supply functions; b) a negative cross-price effect exists between pulpwood and fuelwood and c) competition emerges between pulpwood production and fuelwood production with a growing demand for fuelwood. Most of the coefficients of the supply function optimized by the STIMM model have a similar magnitude to those estimated by Geijer et al. (2011). One exception is that the own-price elasticity of the pulpwood supply and the fuelwood supply is larger than the estimates of Geijer et al. (2011), being respectively 0.14 and 0.55. The differences may originate from the fact that we do not set any limit on the interconversion between different timber products that enables forest owners to have greater potential to increase their supply when wood prices rise. Our model estimates that the inventory elasticities of the supply are centered around 0.4 and that there are no significant differences among the three primary timber products. The result is consistent with the finding of Buongiorno et al. (2003) that the timber supply generally shows an inelastic response to inventory changes. We then use the optimized supply functions to simulate the market equilibrium in three scenarios with different growth rates of demand for fuelwood and find that a modest fuelwood demand increase by 2020 has a minor impact on the pulpwood production and the additional fuelwood demand can be met by increasing the timber harvest. As the fuelwood demand continues to increase, the pulpwood production will be negatively affected. The production competition between pulpwood and fuelwood will become intense. We also compare the changes in forest inventory in different fuelwood demand scenarios, which show that the forests in Sweden will continue to act as a carbon sink. Besides, the rising demand for fuelwood is synergic to the sawlog production. 96

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from the wood-processing industries, with black liquor having the largest share (Swedish Energy Agency, 2018). An extended version of the model where the by-products of forest industry and logging residues are explicitly modeled has been presented in Guo et al. (2019). We assumed that the demand functions for sawtimber and pulpwood are constant over time. Sawmills as well as pulp & paper industry may choose to increase its production capacity, which will cause the demand function for sawtimber or pulpwood to shift upwards. In principle, investment in production capacity should be optimized directly in a forest sector model. On the other hand, it seems that there has not been any significant change in the production capacity of the Swedish forest industry in the past two decades. The value of fixed assets (buildings, land, machinery, and equipment) of the pulp and paper industry in Sweden in 2017 is merely 1.3% higher than the value in 2000, whereas the value of fixed assets of the wood processing industry decreased 1.5% in this period (SCB, 2018b). However, large investments have been made in recent years in the Swedish forest industry and it is estimated that these investments will increase the production capacity by over 10% (Hallsten et al., 2019). Given this background, it seems reasonable to assume that the production capacity will remain constant in the coming decades. Another notable simplification is that the whole country is modeled as one market and therefore the transportation cost between different regions are ignored. A common practice in forest sector modeling is to include a number of regional markets and allow for trade between different regions. However, the volume of inter-regional trade of roundwood in Sweden is relatively small (SDC, 2018). Including transportation cost by dividing the country into different regions probably would not result in any significant change in the results. Moreover, the demand function for sawtimber used in this study was estimated using a mix of roadside price (for 1966–1994) and mill gate price (for 1995–2006), with means that transportation cost for sawtimber is partly embedded in the demand function. In this case, it is not necessarily proper to include transport cost in the same way as it is commonly done in some other forest sector models. On the other hand, the importance of non-timber benefits for non-industrial private owners, who own 50% of the forest land in Sweden, vary significantly among different regions. The large forest companies own about 40% of the forest land in Sweden, and they typically impose an even-flow constraint in forest harvest decision-making. There is a need to divide the country in different regions and to distinguish between different owner categories in order to achieve a better description of forest management objectives.

(2), 69–77. Geijer, E., Bostedt, G., Brännlund, R., 2011. Damned if you do, damned if you do not—reduced climate impact vs. sustainable forests in Sweden. Resour. Energy Econ. 33 (1), 94–106. Gong, P., Guo, J., 2017. Forest owners' valuation of non-timber benefits. Unpublished manuscript. Department of Forest Economics, Swedish University of Agricultural Sciences, SE-901 83, Umeå, Sweden. Gong, P., Löfgren, K.G., 2003. Risk-aversion and the short-run supply of timber. For. Sci. 49 (5), 647–656. Gong, P., Löfgren, K.G., Rosvall, O., 2013. Economic evaluation of biotechnological progress: the effect of changing management behavior. Nat. Resour. Model. 26 (1), 26–52. Greber, B.J., Wisdom, H.W., 1985. A timber market model for analyzing roundwood product interdependencies. For. Sci. 31 (1), 164–179. Guo, J., Gong, P., 2017. The potential and cost of increasing forest carbon sequestration in Sweden. J. For. Econ. 29, 78–86. Guo, J., Gong, P., Brännlund, R., 2019. Impacts of increasing bioenergy production on timber harvest and carbon emissions. J. For. Econ (Accepted). Hallsten, K., Heinsoo, K., Niklasson, M., Persson, T., 2019. Marknadsrapporten “Så går det för skogsindustrin” (Economic and Market Developments of the Swedish Forest Industry), Febuary 2019. Skogsindustrierna. https://www.skogsindustrierna.se/ skogsindustrin/rapporter-och-analyser/. Ince, P.J., Kramp, A.D., Skog, K.E., Yoo, D.I., Sample, V.A., 2011. Modeling future US forest sector market and trade impacts of expansion in wood energy consumption. J. For. Econ. 17 (2), 142–156. Johnston, C.M., van Kooten, G.C., 2016. Global trade impacts of increasing Europe's bioenergy demand. J. For. Econ. 23 (2016), 27–44. Kallio, A.M.I., Anttila, P., McCormick, M., Asikainen, A., 2011. Are the Finnish targets for the energy use of forest chips realistic—assessment with a spatial market model. J. For. Econ. 17 (2), 110–126. Kong, J., Rönnqvist, M., Frisk, M., 2012. Modeling an integrated market for sawlogs, pulpwood, and forest bioenergy. Can. J. For. Res. 42 (2), 315–332. Kretschmer, B., Peterson, S., 2010. Integrating bioenergy into computable general equilibrium models—a survey. Energ Policy 32 (3), 673–686. Kristöfel, C., Strasser, C., Schmid, E., Morawetz, U.B., 2016. The wood pellet market in Austria: a structural market model analysis. Energ Policy 88, 402–412. Latta, G.S., Sjølie, H.K., Solberg, B., 2016. A review of recent developments and applications of partial equilibrium models of the forest sector. J. For. Econ. 19 (4), 350–360. Lecocq, F., Caurla, S., Delacote, P., Barkaoui, A., Sauquet, A., 2011. Paying for forest carbon or stimulating fuelwood demand? Insights from the French Forest Sector Model. J. For. Econ. 17 (2), 157–168. Lindner, M., Eggers, J., Zanchi, G., Moiseyev, A., Tröltzsch, K., Eggers, T., 2007. Environmentally Compatible Bio-Energy Potential From European Forests. European Environment Agency, Copenhagen, Denmark. Lundgren, T., Sjöström, M., 1999. A dynamic factor demand model for the Swedish pulp industry—an Euler equation approach. J. For. Econ. 5 (1), 1–23. Lundmark, R., Olsson, A., 2015. Factor substitution and procurement competition for forest resources in Sweden. Int. J. Prod. Econ. 169, 99–109. Lyon, K.S., Sedjo, R.A., 1983. An optimal control theory model to estimate the regional long-term supply of timber. For. Sci. 29 (4), 798–812. Moiseyev, A., Solberg, B., Kallio, A.M.I., 2013. Wood biomass use for energy in Europe under different assumptions of coal, gas and CO2 emission prices and market conditions. J. For. Econ. 19 (4), 432–449. Nepal, P., Abt, K.L., Skog, K.E., Prestemon, J.P., Abt, R.C., 2018. Projected market competition for wood biomass between traditional products and energy: a simulated interaction of US regional, national, and global Forest product markets. For. Sci. 65 (1), 14–26. SCB, 2018a. Utrikeshandel med varor och tjänster (Foreign trade in goods and services). In: Statistics Sweden's Statistical Database, . https://www.scb.se/en/findingstatistics/. SCB, 2018b. Balansräkningsposter enligt Företagens ekonomi efter näringsgren SNI 2007 (Balance sheet items according to Corporate finances by industry SNI 2007). In: Statistics Sweden's Statistical Database, . https://www.scb.se/en/finding-statistics/. Schwarzbauer, P., Stern, T., 2010. Energy vs. material: economic impacts of a “wood-forenergy scenario” on the forest-based sector in Austria—a simulation approach. For. Pol. Econ. 12 (1), 31–38. SDC, 2018. Virkesförbrukning 2017 (Forest Industry's Consumption of Roundwood and Production of Forest Products). https://www.sdc.se/default.asp?id=2055&ptid=. Sedjo, R., Tian, X., 2012. Does wood bioenergy increase carbon stocks in forests? J. For. 110 (6), 304–311. Sjølie, H.K., Trømborg, E., Solberg, B., Bolkesjø, T.F., 2010. Effects and costs of policies to increase bioenergy use and reduce GHG emissions from heating in Norway. For. Pol. Econ. 12 (1), 57–66. Sjølie, H.K., Latta, G.S., Trømborg, E., Bolkesjø, T.F., Solberg, B., 2015. An assessment of forest sector modeling approaches: conceptual differences and quantitative comparison. Scand. J. For. Res. 30 (1), 60–72. Susaeta, A., Lal, P., Alavalapati, J., Carter, D.R., 2013. Modelling the impacts of bioenergy markets on the forest industry in the southern United States. Int. J. Sustain. Energ. 32 (6), 544–561. Suttles, S.A., Tyner, W.E., Shively, G., Sands, R.D., Sohngen, B., 2014. Economic effects of bioenergy policy in the United States and Europe: a general equilibrium approach focusing on forest biomass. Renew. Energy 69, 428–436. Swedish Energy Agency, 2017. Energy in Sweden: facts and figures 2017. In: Spreadsheet With Statistics, . http://www.energimyndigheten.se/en/facts-and-figures/ publications/ (Accessed on 10 June 2018).

References Abt, R.C., Abt, K.L., Cubbage, F.W., Henderson, J.D., 2010. Effect of policy-based bioenergy demand on southern timber markets: a case study of North Carolina. Biomass Bioenergy 34 (12), 1679–1686. Abt, K.L., Abt, R.C., Galik, C., 2012. Effect of bioenergy demands and supply response on markets, carbon, and land use. For. Sci. 58 (5), 523–539. Abt, K.L., Abt, R.C., Skog, K.E., 2014. Wood energy and competing wood product markets. In: Wood Energy in Developed Economies. Routledge. Agestam, E., 2015. Gallring. Skogsskötselserien Skogstyrelsen, Jönköping. Bolkesjø, T.F., Buongiorno, J., Solberg, B., 2010. Joint production and substitution in timber supply: a panel data analysis. Appl. Econ. 42 (6), 671–680. Brännlund, R., Lundgren, T., 2004. A dynamic analysis of interfuel substitution for Swedish heating plants. Energy Econ. 26 (6), 961–976. Brännlund, R., Johansson, P.O., Löfgren, K.G., 1985. An econometric analysis of aggregate sawtimber and pulpwood supply in Sweden. For. Sci. 31, 595–606. Buongiorno, J., Zhu, S., Zhang, D., Turner, J., Tomberlin, D., 2003. The Global Forest Products Model. Elsevier. Buongiorno, J., Raunikar, R., Zhu, S., 2011. Consequences of increasing bioenergy demand on wood and forests: an application of the Global Forest Products Model. J. For. Econ. 17 (2), 214–229. Carlsson, M., 2012. Bioenergy From the Swedish Forest Sector (Licentiate Thesis). Swedish University of Agricultural Sciences. Du, X., Runge, T., 2014. Price dynamics in Wisconsin woody biomass markets. Biomass Bioenergy 63, 250–256. Galik, C.S., Abt, R., Wu, Y., 2009. Forest biomass supply in the southeastern United States—implications for industrial roundwood and bioenergy production. J. For. 107

97

Forest Policy and Economics 105 (2019) 91–98

J. Guo and P. Gong Swedish Energy Agency, 2018. Energy in Sweden: facts and figures 2018. In: Spreadsheet With Statistics, . http://www.energimyndigheten.se/en/facts-and-figures/ publications/ (Accessed on 13 June 2018). Swedish Forest Agency, 2014. Swedish Statistical Yearbook of Forestry 2014. Swedish Forest Agency, Jönköping.

Swedish Forest Agency, 2015. Forests and Forestry in Sweden. https://www. skogsstyrelsen.se/globalassets/in-english/forests-and-forestry-in-sweden_2015.pdf, Accessed date: 10 June 2018. Wear, D.N., Parks, P.J., 1994. The economics of timber supply: an analytical synthesis of modeling approaches. Nat. Resour. Model. 8 (3), 199–223.

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