Forest sector impacts of the increased use of wood in energy production in Norway

Forest Policy and Economics 12 (2010) 39–47

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Forest Policy and Economics j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / f o r p o l

Forest sector impacts of the increased use of wood in energy production in Norway Erik Trømborg ⁎, Birger Solberg Department of Ecology and Natural Resource Management, Norwegian University of Life Sciences, PO Box 5044, NO-1432 Ås, Norway

a r t i c l e

i n f o

Keywords: Bioenergy Biomass Partial equilibrium model Wood supply

a b s t r a c t The main objective of this study was to analyse the impacts of increased energy prices on the traditional forest sector (forestry and forest industries) in Norway The study applied a regionalized partial equilibrium model covering forestry, forest industries and the bioenergy sector. In the model, an increase in the energy price from NOK 0.50/kWh (0.06 Euro/kWh) to NOK 0.70/kWh by the year 2015 reduces production by 12% for particleboard and by 4% for pulp (mainly sulphate), whereas the production of fibreboard was unaffected. The pulp and paper industries in Norway are mainly relying on spruce pulpwood, which is only partly affected by increased bioenergy prices. In the sawmill industries, the negative impact of higher energy prices (input of electricity) is compensated by higher prices received for chips, sawdust and bark. The production of pine sawnwood (accounting for about 31% of the sawnwood production in Norway) increased by 3% by 2015 when the energy price increased from NOK 0.50 to 0.70 NOK per kWh, whereas the production of spruce sawnwood (accounting for 69% of the sawnwood production) decreased by 0.4%. Future, improvements of the model should include even more detailed descriptions of bioenergy technologies, the supply of wood residues and the energy market, including consumer behaviour and investment decisions. © 2009 Elsevier B.V. All rights reserved.

1. Introduction Growing energy consumption, oil prices and greenhouse gas emissions have given increased attention to renewable energy, including bioenergy. The global primary energy supply of combustible renewables and waste (TPES) grew from 645 Mtoe in 1973 to 1185 Mtoe in 2006 (IEA, 2008). As global growth in total energy production and consumption was higher, the share of total final consumption of combustible renewables and waste dropped from 13.2% in 1973 to 12.9% in 2006. The use of biomass in energy production is expected to grow in importance. According to IEA (2008), the relative importance of renewables (TPES) will remain stable at the current 11% up to 2030 under current policies, whereas it is expected to grow to 14% based on policies under consideration. Global wood fuel production increased by 41% in the period 1961 to 2006, whereas industrial roundwood production increased by 63% (FAO, 2008a,b). The likely future increase in the use of biomass energy production is of high interest for forestry and forest industries. Increased demand for roundwood, by-products and harvesting residues, and the likely increased energy costs, may both directly and indirectly, affect the forest sector through higher alternative values for excess heat and wood residues. Total effects will vary based on wood availability, structure of the forest sector, technological development, political incentives and the development of energy prices.

⁎ Corresponding author. Tel.: +47 64 96 57 96; fax:+47 64 96 58 02. E-mail address: [email protected] (E. Trømborg). 1389-9341/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.forpol.2009.09.011

Issues related to the competition for wood fibre between energy use and forest industries, have only to a limited extent been analysed in quantitative studies. EEA (2006) applied the global forest sector model EFI-GTM to quantify wood utilisation for energy generation and its use for other purposes as part of an effort to calculate the potential for bioenergy in Europe. It was found that the energy potential in the EU-25 from competitive use of wood would increase from around 2 Mtoe in 2020 to more than 16 Mtoe by 2030 (EEA, 2006). Karjalainen et al. (2004) estimated the energy wood potential in Europe and found that 140 million m3 unutilized increment and felling residues for energy production was available in Europe, which corresponds to 280 TWh of energy or 37% of the current felling. This would be about 24% of the current use of renewable in EU25. Hoogwijk et al. (2005) analyzed the geographical and technical potential of energy crops for the years 2050–2100 based on future land-use patterns developed by the Intergovernmental Panel on Climate Change in its Special Report on Emission Scenarios. They found significant potentials equal to several times the present oil consumption and mainly at abandoned agricultural land. At a regional level, significant potentials are found in the Former USSR, East Asia and South America. Smeets and Faaij (2007) evaluated the global energy production potential of woody biomass from forestry for the year 2050 using a bottom–up analysis of key factors. The projection was performed by comparing the future demand with the future supply of wood, based on existing databases, scenarios, and outlook studies. Their results indicate that forests can, in theory, become a major source Broader analyses of fuel and fibre markets, but with limited quantification of effects, include those of Hillring (2006), Roberts (2008), the UN (2006 and 2008) and FAO (2008a,b).

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The main objective of this study was to analyse the impacts on the Norwegian forest sector (i.e. forestry and forest industries) of increased demand for wood-based bioenergy caused by increased energy prices. Particular focus has been on taking simultaneously into account the competition for raw materials from the forest industries, regional differences regarding heat demand and wood fibre supply, as well as important spatial aspects connected with interregional transport and trade in wood. A second and more derived objective was to explore the weak and strong points of applying partial equilibrium modelling in this kind of analysis, and of identifying promising improvement possibilities. The remaining part of the article is structured as follows: First, an overview of the Norwegian energy market and forest sector is given in Section 2. Section 3 is a description of the material and methods applied in the study, and the results are presented in Section 4. In Section 5 method and results are discussed and main conclusions drawn. 2. Current state of the biomass and energy market in Norway 2.1. Characteristics of the Norwegian energy market Large resources of oil and gas imply that net domestic energy consumption in Norway is only about 8% of the production of primary energy bearers. The production of hydro-electric power is also high corresponding in 2005 to 40% of production in the EU 27 (EUROSTAT, 2007). Bioenergy constituted 6% of the Norwegian consumption, electricity 49% and fossil fuels 45%. Fifty-five percent of the domestic energy consumption is renewable, and, of this, hydro-electric power made up 88% (2006 figures from Statistics Norway). Electricity occupies a high share of energy consumption in Norway (Fig. 1). Power consumption per capita is roughly 10 times the world average — the main reasons being extensive power-intensive manufacturing and that electricity is a more common source of heating than in other countries. Liberalisation and internationalisation of the electricity market since the early 1990s have resulted in more equal prices between different energy commodities, but also in increased electricity prices, although still relatively low compared to prices in other European countries. The high dependency on electricity for heating and industrial purposes renders Norwegian society vulnerable to high electricity prices. The Norwegian climate-change targets are to become carbonneutral by the year 2030 and to reduce annual greenhouse gas emissions by 15–17 million tonnes of CO2 equivalents by 2020 relative to the 1990 emissions, including increased carbon uptake in forests. The emissions totalled 53.8 million tonnes CO2 equivalents in 2008 (Statistics Norway, 2009). Measures in the field of renewable energy and energy efficiency will be important in fulfilling the greenhouse gas reduction targets. The government has proposed a

Fig. 1. Energy consumption by sector 2006 (TWh). Source: Statistics Norway (2009)

national target of 14 TWh/50 PJ increased use of bioenergy by 2020, which is a doubling of current production. 2.2. The forest sector Productive forests occupy 24% of the land area in Norway, with forestry and the forest industry making up 0.84% of GDP, a share that has been steadily declining. The forest industries in Norway comprise 290 sawmills, 10 pulp and paper mills, 3 mills producing particleboard and 3 fibreboard mills. The turnover in the forest industries was NOK 44.2 billion (about 5 billion Euros) in 2007, whereas the value of roundwood sold to the manufacturing industry was NOK 3.2 billion. Forestry provides employment for 4000 people (man-years), the sawnwood industries 16,000 and the pulp and paper industries 6500 people. The forest sector employs 1.0% of total employment (Statistics Norway, 2009). The production of forestry products in Norway is less than 2% of total European production. The most important product in terms of export is newsprint, with Norwegian production constituting 5.7% of total European production (2005 figures from FAO, 2008a,b). Annual commercial roundwood removal in Norway was 8.2 million m3 in 2007 and wood imports 3.4 million m3 (roundwood equivalents). Only about one-third of the annual increment of roundwood is harvested annually. Forest resources therefore represent the major potential for increased bioenergy production in Norway. The sustainable potential use of biomass for energy production is uncertain, but is estimated to be around 39 TWh (140 PJ), which is close to three times the current production, and of this 65% is from forest resources, 31% from waste and 4% from energy crops. The theoretical potential if all biomass resources were used for energy production would be around 180–210 PJ (50–55 TWh) (Bernard and Bugge, 2006; Langerud et al., 2007). 3. Materials and methods 3.1. Modelling approach In order to analyse the impact of increased energy prices on the Norwegian forestry sector, a regionalised partial equilibrium model (NTM II) covering forestry, forestry industries and the bioenergy sector was applied (Bolkesjø, 2004; Bolkesjø et al., 2006). The model simulates the behaviour of profit-maximizing forestry industry firms that buy timber in competitive national and international markets and sell their output of paper, sawnwood and bioenergy at prices obtained in these markets. NTM II belongs among the same class of models as the Global Trade Model (GTM) developed at IIASA in the 1980s (Kallio et al., 1987), the Global Forest Products Model (GFPM) (Buongiorno et al., 2003), the EFI-GTM (Kallio et al., 2004) and SF-GTM for the Finnish forest sector (Ronnila, 1995). NTM II is a spatial model covering 19 domestic regions, Sweden and two other regions for export and import to/from the rest of the world. Twenty-three products are included, six of which are roundwood assortments, three pulp grades, two board grades, three sawn wood products, five paper and paperboard products, recycled paper, energy wood, wood chips, sawdust, bark and harvesting residuals. The objective function of the model maximises net social pay-off for the products and regions included, and the model thus simulates market equilibria of competitive markets as shown by Samuelson (1952). The net social pay-off is calculated as the sum of the area under each demand curve minus production costs in the forest industries and the sum of transportation costs resulting from trade between regions. New capacity in the forest and bioenergy sectors can be determined endogenously by the model: Whenever the endogenously determined market prices covers the per unit capital and variable production costs of new capacity, new investments will take place with new specified technologies for given product and region. Alternatively, the capacity

E. Trømborg, B. Solberg / Forest Policy and Economics 12 (2010) 39–47

Fig. 2. Availability of harvest residues. The figure shows total availability at different roadside prices.

can be changed exogenously based on announced investments or mill closure. NTM II comprises four sub-models: • A sub-model for timber supply, including the connection between harvesting level and roundwood price, and between today's levels and future levels. • A sub-model for the forest industry and bioenergy production describing how timber and other wood resources are transformed into intermediate and end products, and how capacity, locations and production costs change over time. • A sub-model relating product demand for forest products to price, volume, economic growth and exchange rates. • A sub-model for trade between regions that relates a location of timber resources and forest industry to the demand and supply of forestry products. The specific model structure of NTM II is outlined in Bolkesjø (2004), while mathematical specification of the objective function and model constraints are presented in Kallio et al. (2004). NTM II has previously been used for different types of market studies and policy analysis (Bolkesjø et al., 2005; Bolkesjø, 2005). Roundwood supply is specified according to exogenously determined roundwood price elasticities and observed prices and quantities of the base year (2003) defining the level of supply functions. Supply shifts annually in each region according to changes in the standing timber stocks calculated as annual growth minus removals. Six roundwood assortments are currently included in NTM II; sawlogs and pulpwood of spruce, pine and non-coniferous species. The model includes logging residues as a possible raw material for bioenergy, but prices and quantities for the base year are not available for this assortment because only to a limited extent has it been traded in the market. Instead, the supply of logging residues is defined as a stepwise supply function reflecting increasing transport costs. The available logging residue volumes within various transport distances are based on Aalde and Gotaas (1999), and the costs for logging residues applied in the model are shown in Fig. 2.

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Sawdust and bark are residues from the forest industry and are the remaining sources of biofuel included in the study. NTM II defines chips supplied in the pulpwood market as residue from sawmilling. Surpluses of sawdust and bark not used for heating and drying at the mill are included in the model. These assortments may in the model be supplied in the energy market or used for other purposes (sawdust as input in particle board production, and bark in gardening and for sanitary purposes, etc.). Prices of forest industry residues are endogenous in the model. Trade between regions occurs when the price difference between regions exceeds transport costs. Commodities can be transported by truck, train or ship and the cheapest possible alternative is chosen. The innovation of NTM II compared to previous forestry sector models is the integration of the bioenergy sector on a regional basis where the market potential of different heating technologies is analysed at county level heat markets. Based on a survey of available technologies and related costs, eight bioenergy technologies are defined, varying from wood stoves to large central heating systems as shown in Table 1. The bioenergy production is modelled in the same way as the forest industries with existing and potential new capacities for each technology and region. Individual production facilities ranged from wood stoves to larger district heating plants are aggregated in production groups per region, similar to the method for sawmills where the number of mills is too large to be modelled on plan level. It is assumed that bioenergy production level, which is low compared to that of electricity and oil, does not affect general energy price level. This partial approach implies that bioenergy demand in the model is assumed to be fully elastic, or having horizontal demand curves. The price of energy is a scenario parameter in the model that affects external energy costs produced in forestry industries, as well as the revenue of bioenergy producers. Bioenergy production increases until marginal production costs equal the exogenously given energy (heat) prices. As such, quantities of bioenergy are determined endogenously in the model, but production level depends on specific assumptions regarding the general energy price in the given scenario, in addition to the specific bioenergy production costs and competition for wood fibre in each market segment and region. The energy price also affects the production cost in the forest industries due to their input of procured energy. Changes in energy prices have no direct effect on transport costs in the model. All bioenergy technologies have a certain technical potential restricted by total energy consumption and the construction of existing buildings and of new buildings within each region. Therefore, upper bounds restrict the total possible bioenergy consumption of each technology. In addition, restrictions on the growth of consumption from one year to the next are taken into account to reflect observed inertia in investment and consumption behaviour. These restrictions are defined for each technology and region according to regional population densities (population densities are considered since some technologies require densely populated areas to be efficient). The national numbers are allocated to counties using official statistics on population densities and density of industrial buildings and on the total area of buildings under construction in 2002 (Statistics

Table 1 Bioenergy technologies included in NTM II. Technology

Description

Fuel

Wood stove Pellets stove Wood-based central heating — single houses Wood-based central heating

Traditional wood stoves in private households. Stoves/kamins in private households using wood pellets. Bio boilers in private households with water-borne heat distribution. Bio boilers in buildings in service sectors and multi-dwelling buildings with water-borne heat distribution. Water-borne distribution to several buildings from a central bio boiler. Bio boilers in industrial buildings. Existing energy production for heating and drying in forest industries.

Firewood Pellets Wood, pellets or briquettes Wood, pellets or briquettes

Wood-based district heating Bioenergy in industries Bioenergy in forest industries

Wood chips, or forest fuel (waste) Wood, pellets or briquettes Bark and residues

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Table 2 Timber supply and forest products demand parameters in Norwegian Trade Model II (NTMII). Product category

Price elasticity GDP elasticity Base-year productiona Average base year priceb Capacity No of mills

Product

Roundwood assortments

Spruce pulpwood Pine sawlogs Non-coniferous sawlog Spruce pulpwood Pine pulpwood Non-coniferous pulpwood Mechanical forest industry products Spruce sawnwood Pine sawnwood Non-coniferous sawnwood Particleboard Fibreboard c Pulp Mechanical pulps Chemical pulp Special pulp Paper and paperboard Newsprint Uncoated printing paper Coated printing paper Linerboard Other paper and paperboard

0.4–0.6 0.3–0.5 0.3–0.5 0.4–0.6 0.4–0.6 0.4–0.6 − 0.5 − 0.5 − 0.5 − 0.4 − 0.3

0.7 0.7 0.7 1.0 1.2

− 0.5 − 0.5 − 0.8 − 0.8 − 0.3 − 0.3

0.8 0.5 0.5 0.5 0.8 0.8

3034 957 8 2770 592 54 1730 479 13 476 74 188 227 153 858 834 101 337 245

427 403 426 278 244 259 1585 1440 1730 1345 7054

2595 719 22 548 96 210 240 153

4550 5211 5892 4824 6976 8200

– – – 5 3 2 2 1 3(2) 5 2 2 7

Notes: – sawnwood production is not modeled at mill level. a For roundwood, sawnwood and particle boards, volumes are measured in 1000 m3, for pulp and paper volumes are measured in 1000 tonnes. b Prices are measured in NOK. In 2003, 1 Euro ≈ 8.00 NOK, 1 US$ = 7.08 NOK. c Mechanical and chemical pulp are modeled as intermediate products where demand is generated endogenously by paper production.

Norway, 2002). Quantification of these constraints is to some extent based on ad hoc assessment, since these market segments are relatively novel and historical estimates are not available. 3.2. Data The model uses comprehensive data for the forest sector and heat market. The year 2003 is used as the base year (starting point) and the impacts of different energy prices are evaluated at year 2015 — giving a medium-term horizon that caters for investments to be implemented. Since the main focus here is the bioenergy sector, most attention is directed at bioenergy technologies and assumptions regarding the heat market. Data for the forest sector, such as base-year prices, production levels, capacities and forest industry technologies, are based on Bolkesjø (2004). The cost structure and capacity in the pulp and paper industry were collected directly from each mill, while data for the sawmilling industry were specified for group of mills defined by annual production. The GDP elasticities for forest products demand were based on econometric demand studies like Simangunsong and Buongiorno (2001). Due to expected impacts from information technology on paper demand, in line with Bolkesjø et al. (2003), the assumed income elasticities for newsprint and printing paper are assumed to be some lower than most estimates based econometric studies of historical data. The elasticities and base year prices and volumes applied in the study are summarised in Table 2. All exogenous input prices were assumed to be constant in real terms over the forecasting period. Base year prices, production, export and import of forest products is based on official statistics from Statistics Norway. GDP growth, driving the demand for forest products, was

assumed to be 1.5% per year in all regions. The forest industry was assumed to have an annual capacity growth of 0.5% in addition to capacity changes based on new investments or exogenously defined closure of specific mill. Data from Statistics Norway on heating systems in private households, and energy consumption in different sectors at county and municipality levels, are used to estimate the base-year production and potential for the given technologies. They represent segments that make up the complete market for heat based on forestry biomass in each region. Average input data were collected for each technology and then validated by energy consultants in Norway. The estimated total economic potential for bioenergy based on wood is 21.8 TWh (Table 3) or about double of the current production, and if electricity used for heating in urban areas is substituted by water-born heating based on bioenergy, the potential would increase with about 12 TWh. Hence, the theoretical potential for bioenergy for heating in Norway is about 36 TWh including waste, or about 25% of the net total stationary energy consumption. Potential electricity production based on wood comes in addition. Replacement of electrical heating systems as well as electricity production based on wood is not profitable within the analysed price ranges and are not included in this study. The market price for central heating installations in the industries is set to 70% of the energy price for the other technologies to reflect lower alternative costs for oil and electricity in the industries compared to households and service sectors. Table 3 summarize base year production and potential for each technology, whereas Fig. 3 shows the regional picture. The high bioenergy production in Buskerud and Østfold counties are caused by high pulp and paper production in these counties. Specific input data for bioenergy technologies are described in Table A1 (Appendix A) and in Trømborg et al. (2007).

Table 3 Net production of bioenergy from forest fuels in Norway 2003 and estimated potential for increased bioenergy production by increased use of wood stoves and replacement of fossil fuels. Technology

Wood stove

Pellets stove

Central heating — single houses

Central heating

District heating

Bioenergy in industries

Bioenergy in forest industries

Total

Net production 2003 Potential 2003

4 717 7 243

69 1 896

0 889

0 4 358

497 3 584

0 1 996

5 371 5 371

10 654 22 752

Note that the potential for wood based district heating and wood based central heating cannot be added as replacement of fossil fuels in service sectors and multi-dwelling buildings in urban areas are included in both. The potential grows according to the rate of building construction in 2002.

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Fig. 5. Projected prices of pulpwood and chips in 2015 for different energy price scenarios relative to a heat price of NOK 400/MWh. Fig. 3. Net bioenergy production and market potential by region. Finmark county is not included in the model and Hedmark county is split in two regions.

4. Results 4.1. Bioenergy production The model results show how the bioenergy competitiveness, and thus production levels, depends heavily on the development of the general energy price. Fig. 4 shows the net projected bioenergy production by the year 2015 under different energy price scenarios, not including internal heat production in the industrial sector (approx. 4.4 TWh in 2006, of which pulp and paper was 3.1 TWh and sawmills 1.3 TWh). The stepwise volume is caused by the regional capacity constraints for the different technologies being reached, such as availability of water-borne heat distribution in existing buildings. The actual average energy price for district heating was NOK 0.505 per kWh in 2007, a drop from NOK 0.532 in 2006 (Statistics Norway, 2009). The model results show that the production increases most in regions with high population density compared to forest resources, like in Oslo, Akershus, Rogaland and Hordaland counties and for both wood stoves and district heating systems. Hence increased bioenergy consumption for heating purposes implies more transport of biomass from forest regions to consumption areas. 4.2. Wood prices and harvest Raw material prices are strongly affected by bioenergy production, as shown in Fig. 5 for pulpwood and wood chips (priced equally in the

Fig. 4. Projected bioenergy production in 2015 in the “Base” scenario for different energy prices (exclusive of VAT). Energy production in the forest industries and installation based on waste are not included in the figures.

model). An increase in the price of energy from NOK 500/MWh to NOK 700/MWh implies an 8 TWh increase in bioenergy production, which results in turn in a 20% increase in the price of pine pulpwood prices by 2015, whereas the prices of non-coniferous and spruce pulpwood increase by 31% and 5%, respectively. The timber harvest is estimated to be 18% higher at an energy price of NOK 700/MWh compared to NOK 500/MWh. The raw material distribution in the model for forestry-based bioenergy at a price level of NOK 700/MWh were about 1/3 roundwood, 1/3 residues from forest industries and 1/ 3 harvest residues. The use of harvesting residues as well as wood import dampens the effect of roundwood prices. The annual wood increment in Norway of more than double the annual harvest ensures a potentially sustainable bioenergy production within the scenario. The industrial roundwood harvest in Norway was 8.2 million m3 in 2007, with a gross value of NOK 3.1 billion. Fig. 6 shows the model results for how wood consumption for energy production can reach 9 million m3 and the value of NOK 2 billion at a heat price of NOK 0.7 per kWh. 4.3. Forest industry impacts Increased bioenergy production in Norway will mainly affect the production of particleboard and chemical pulp — the use of low-grade wood input and low profitability in baseline scenarios being the main reasons for the negative impact. The reduction in chemical pulp production concerns only the sulphate pulp, because its production is

Fig. 6. Wood fuel consumption and gross value of feedstock in 2015 at different energy prices. The value of the feedstock is based on modelled prices for roundwood and chips (bark, sawdust and harvesting residues).

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based on pine pulpwood, which is more used for energy production than spruce pulpwood. In the model, an increase in the energy price from NOK 0.50/kWh to 0.70 kWh by the year 2015 reduces the production by 12% for particleboard and by 4% for chemical pulp. The production of fibreboard is unaffected by higher energy prices in the model. The pulp and paper industries in Norway are mainly relying on spruce pulpwood, which is only partly affected by increased bioenergy prices. In the sawmill industries, the negative impact of higher energy prices (caused by production use of electricity) is compensated by higher prices received for chips, sawdust and bark. The production of pine sawnwood (about 30% of the sawnwood production in Norway) increases by 3% when the energy price increases from NOK 0.50/kWh to 0.70 kWh by 2015, whereas the production of spruce sawnwood (69% of the production) decreases only by 0.4%. In an economic review of the 20 main sawmills in Norway in 2005, (external) energy costs constituted 2.6% of running costs, whereas income from bi-products was 11.2% of revenues. This relation shows that the increased wood residues prices are more important than the increased energy costs for the sawmill industry. 4.4. Employment Fig. 7 shows that, according to the model, the gross value of bioenergy production can reach NOK 10 billion and the direct employment 6000 man-years at an energy price of NOK 0.70 per kWh and a production level of 14 TWh (in the heat market outside the forest industries). The employment impact is calculated based on figures from district heating. In 2007, the total employment in district heating was 380 persons producing 2.8 TWh to consumers (all fuels). In addition comes the employment in fuel production, which represents approximately 1/3 of the total costs for district heating With a capital share of 50% in fuel production and a cost per man-year of NOK 500,000, the employment per TWh in district heating will be around 300 man-years. Central heating as well as pellet and wood stoves are more labour intensive than district heating, and are assumed here to provide 400 and 500 man-years per TWh, respectively. Gross economic turnover increases steadily with increasing energy prices, since it consists of prices multiplied by production, whereas employment is directly linked to the production level. We emphasize that these figures are rough estimates, but they give an indication of the employment impacts of bioenergy. The actual employment in forestry was 4000 persons in 2007, whereas total turnover in the forest industries was NOK 44.2 billion (SSB 2008). In terms of employment and income, these results indicate that bioenergy can be equally important as forestry.

Fig. 7. Employment and gross value of energy production in 2015 at different energy prices.

5. Discussion and conclusions We have applied a rather detailed forest sector model for Norway in analysing impacts on forest harvests and the forest industries of increased bioenergy prices, assuming profit maximizing behaviour. The model results show that in Norway the overall impacts on forestry of increased bioenergy production are positive. Forestry (harvest of roundwood and other woody biomass) benefits because demand for wood fuel increased. Sawmills increase their production because they can sell sawmill residues as biomass for energy production at higher prices. The particleboard industries are negatively affected owing to their low-grade wood use which also are demanded for bioenergy production, and low profit margins. Fibreboard production, which in Norway is characterised by high product prices and a relatively low cost share for wood input (see Table 2), does not decline in the model despite lower profit margins as wood costs increase. This result illustrates that changes in production costs or prices not necessarily imply changes in the short-run production level in a given market, when the production is constrained by mill capacities and a limited number of mills and the marginal revenue (the market price) exceeds marginal costs. The sulphate pulp production is reduced due to higher pine pulpwood prices and relatively low profitability in the Norwegian markets. In our study, impacts on the sulphite pulp production (special pulp) and the paper industry production are negligible because of their high ability to pay on the margin for maintaining high capacity utilisation. In other markets than the Norwegian, where wood chips attract initially low prices but are commercially viable for energy production, the impacts will be higher. Market opportunities for excess heat, wood availability and share of procured energy in production will vary between mills and regions according to regional production capacities and forest structure, and general conclusions about effect on the pulp and paper industries have to be drawn with care. The total welfare effect on the forest sector (forestry and forest industries combined) must be positive because the demand for biomass increases. However evaluation of welfare impacts on specific industries should ideally be based on effects on producers and consumers surplus, the net social pay-off. The objective function of the NTMII maximizes net social pay-off for the products and regions, but the current model version do not explicitly identify changes in producers or consumers surplus for specific products. We have therefore not been able to quantify these welfare impacts. There is also a difference between short-run (in our study the impacts after 20 years) and long-run effects, as higher wood prices will affect not just profitability in the short run, but also expected profitability of new investments and thus log-run production and wood input. In our study, however, in a 20-years perspective, these effects are negligible. In most cases, forest owners will receive higher prices for wood when energy prices increase. Wood fuel assortments and pulpwood will increase in price, and harvesting residues can be delivered together with roundwood. The model results show that in most regions included in this study, harvesting residues can be a significant resource for bioenergy production, and the use of this mainly unused biomass can dampen the impact on roundwood prices. The “stumpage” price for harvesting residues will however be low, (currently close to zero in Norway), as collection and transport costs are higher per energy unit compared to roundwood. In the short run, the main gain for the forest owner will be easier replanting and employment opportunities. The model results show that a larger proportion of woody biomass is likely to be used in energy production in the future compared to the current situation. The proportion will be different in different regions, depending on factors like the existing forest structure deciding the regional wood supply potential, regional forest industry capacities, development of energy prices, political incentives, international trade and the demand for forest industrial products. The spatial economic

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model NTMII incorporates many of these factors, but the model represents per definition a simplification of real life's rather complex relations within the forest and bioenergy sectors and there are many uncertainties regarding the parameters applied in the model. Future development of demand for forest products, roundwood supply and investment behaviour in the forest industries and bioenergy sectors vary over time and between regions, and situations outside the historical references regarding demand for forest products, prices and technologies create uncertainties about the validity of parameters estimated based on econometric studies of historical data. However, the model utilises best available estimates for the parameters, and the effect of the uncertainties is also reduced as the parameters except energy prices are kept constant between the scenarios. The main advantage of the forest sector model approach applied in this study is the relatively detailed modelling of biomass supply and demand at regional level and that trade between regions are explicitly included. It is also a considerable advantage compared to pure econometric models that NTM II makes it possible to define technologies rather accurately at regional level, thus allowing for improved specification of new technologies and increased capacities. In general energy market models, biomass prices are mainly exogenously specified and consistency regarding prices and available volumes is lacking because the main alternative wood demanders, the forest industries, are not included in a realistic way. The spatial disaggregation of the market is especially important in bioenergy analyses as transport cost is a large share of the biomass costs and the geographical variation of building structure and density affects the feasibility of different heating technologies. The availability of empirical production cost data for the existing bioenergy production in Norway is however limited, due to limited volumes of district heating. Whereas transport cost and capacities of different bioenergy technologies area explicit modelled per region, the input/output parameters for each bioenergy technology have common parameters that represent state of the art in Norway for the given technology. In reality there might be significant differences between individual projects, and more detailed data on production costs of specific technologies in different regions will improve the accuracy of the analyses. Consumer behaviour concerning change of energy sources and investment in new energy technologies is also uncertain, especially as energy prices reach levels outside historical references. Investments for increased bioenergy production take place in the model when the market price in a given period covers marginal cost given by marginal production plus the capital cost defined by the costs of construction, depreciation time and interest rate. Uncertainties about future development of energy prices and political incentives make it hard to forecast long-run investments in bioenergy. An advantage of the approach presented in this study is that it shows the impact of different energy price levels that represent different expectations of revenues given by market prices and subsidies for bioenergy. Forecast of energy prices must be done in holistic modelling approaches both in terms of sectors and markets. Recent developments in international markets regarding economic growth, oil prices and interest rates illustrate the challenges of long-run economic analyses. The partial equilibrium approach applied in this study is useful for if–then or ceteris paribus analyses in a medium-term time horizon, where factors like existing production capacities in the forest industries, wood harvest levels and prices strongly influence the market situation. In long run analyses, technological development and potential higher energy prices will influence the feasibility of new technologies competing with bioenergy like e.g. heat pumps and solar heating and possibly reduce the market potentials for bioenergy. Generally, the heat market is important in the future development of bioenergy and will affect the economic viability of both electricity based on biomass and second generation biofuel production. However, energy policies as well as technological development will influence prices for heat,

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electricity and transport fuel differently and may justify more detailed treatment of energy prices than done in this study. International trade with biomass is increasing due to higher bioenergy production in many countries and currently lower shipping prices. Import of biomass represents an opportunity for bioenergy producers in Norway, but international competition for biomass can also increase biomass prices in Norway. Modelling of impacts of international trade must be done in an international context where developments in energy policies in different countries are an important part. The international forest sector experiences significant changes, and increased competition for biomass for energy production is one of the many factors that will influence the forest sector both in Norway and internationally. A major strength of the forest sector modelling approach applied in this study is that competition for wood and synergies between the forest industry and the bioenergy sector are included in a consistent and relatively detailed manner. In addition, the model includes the interregional and international trade of raw materials and end products. A further strength of this methodology is that it allows for assessment of the economic potential of bioenergy under different policy alternatives, and takes into account the competition for raw materials from the forest industries, regional differences regarding heat demand and wood fibre supply, as well as important spatial aspects connected with interregional transport and trade in wood. One should, of course, be careful in drawing strict conclusions from one single study like this, but we regard the following as plausible: i. Expectations about even minor, permanent, increases in the price level of electricity and oil could release substantially increased levels of bioenergy production in Norway. ii. Investors and policy makers must account for, ceteris paribus, increasing raw material costs as the bioenergy sector grows in volume. iii. Increased biomass demand opens up opportunities and at the same time threatens the forestry sector. Most affected would be the production of wood-based panels because of the use of lowgrade biomass input well suited for energy production and with often low profit margins. The impact on the pulp and paper industries depends strongly on biomass supply and the market situation for wood residues. The impact on the sawnwood industry is likely to be positive. In the Norwegian case, the overall impact for the forest sector in the short and medium term is positive. iv. The forest sector should take advantage of bioenergy opportunities and their capacity in relation to wood logistics, processing knowledge and heat demand. v. Use of a relatively detailed spatial forestry sector model covering regional differences, competition for biomass as well as dependencies between forest industries and bioenergy production has been advantageous. Future analyses in Norway should include more detailed bioenergy technology and market data, development of more detailed supply functions for harvest residues, and more advanced modelling of the energy market, including consumer behaviour and investment decisions. It would also be of high interest to include the technological (and commercial) development of second-generation biofuels, as forest sector models are well suited for analysing the up-stream and down-stream market effects of different development scenarios. Acknowledgements This study was funded by the Research Council of Norway through the programme “Forest based bioenergy in Norway: Economic potential, market interactions and policy means" (158896) and with support from Agder Energi as. We thank Torjus Folsland Bolkesjø and Hanne Kathrine Sjølie for their contributions.

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Appendix A

Table A1 Input–output coefficients for different bioenergy technologies included in the study. Technologya Biofuel

Raw materialb Capital costsc Maintenance costs Efficiency % Raw material processing costs Raw material input (solid m3) Predicted priced

Wood stove Firewood SPWD Firewood PPWD Firewood NPWD

0 0 0

0 0 0

60

442 382 343

0.88 0.76 0.69

668 516 518

Wood stove — new capacity Firewood SPWD Firewood PPWD Firewood NPWD

91 91 91

0 0 0

75

354 306 275

0.71 0.61 0.55

626 504 506

305 305 305 305 305 305

56 56 56 56 56 56

90

280 242 217 266 222 356

0.59 0.51 0.46 0.56 0.56 0.75

791 692 695 761 669 806

286 258 66 57 52 63 315 272 245 249 400

0.57 0.52 0.66 0.57 0.52 0.63 0.66 0.57 0.52 0.63 0.84

953 955 801 724 749 780 950 838 842 813 967

57

0.57

998

66 57 52 315 272 245 299 249 400

0.66 0.57 0.52 0.66 0.57 0.52 0.63 0.63 0.84

355 278 303 544 432 436 511 407 561

Wood based central heating — conversion from oil (including bioenergy in industrial buildings) Chips SPWD 256 120 80 66 Chips PPWD 256 120 57 Chips NPWD 256 120 52 Pellets SPWD 256 60 315 Pellets PPWD 256 60 272 Pellets NPWD 256 60 245 Pellets Logging res. 256 60 299

0.66 0.57 0.52 0.66 0.57 0.52 0.63

612 534 559 800 689 692 767

Pellet stove Pellets Pellets Pellets Pellets Pellets Pellets

SPWD PPWD NPWD Logging res. Sawdust Bark

Wood based central heating — single houses (conversion) Firewood PPWD 366 200 Firewood NPWD 366 200 Chips SPWD 366 200 Chips PPWD 366 200 Chips NPWD 366 200 Chips Logging res. 366 200 Pellets SPWD 366 100 Pellets PPWD 366 100 Pellets NPWD 366 100 Pellets Sawdust 366 100 Pellets Bark 366 100 Wood based central heating — new single houses Chips PPWD 640 200 Wood based central heating — existing bio burners Chips SPWD 0 Chips PPWD 0 Chips NPWD 0 Pellets SPWD 0 Pellets PPWD 0 Pellets NPWD 0 Pellets Logging res. 0 Pellets Sawdust 0 Pellets Bark 0

120 120 120 60 60 60 60 60 60

80

Prices and costs are NOK/MWh exclusive of VAT.

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