Impacts of the Kyoto Protocol on boreal forest climate change mitigation

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change mitigation

Hanne K. Sjølie, Greg S Latta, Birger Solberg

To cite this version:

Hanne K. Sjølie, Greg S Latta, Birger Solberg. Impacts of the Kyoto Protocol on boreal forest climate

change mitigation. Annals of Forest Science, Springer Nature (since 2011)/EDP Science (until 2010),

2014, 71 (2), pp.267 - 277. �10.1007/s13595-013-0289-5�. �hal-01098405�

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ORIGINAL PAPER

Impacts of the Kyoto Protocol on boreal forest climate change mitigation

Hanne K. Sjølie_&Greg S. Latta_&Birger Solberg

Received: 12 June 2012 / Accepted: 8 April 2013 / Published online: 17 May 2013

#INRA and Springer-Verlag France 2013

Abstract

& Context The Kyoto Protocol allows the use of domestic

forest carbon sequestration to offset emissions to a limited degree, while bioenergy as an unlimited emission reduction option receives substantial financial support in many countries.

& Aim The primary objective of this study was to analyze

(1) whether these limits on forest carbon sequestration would be binding, thereby leading to inefficient mitigation, and (2) the total potential effect of the protocol on the greenhouse gas (GHG) fluxes in the forest sector.

& Methods A partial equilibrium model of the Norwegian forest

sector was used to quantify the GHG fluxes in a base scenario with no climate policy, a Kyoto Protocol policy (KP policy), and a policy with no cap on forest carbon sequestration (FC policy), assuming that the policies apply the rest of the century.

& Results Carbon offsets are higher under the KP policy than

in the base scenario and likewise higher than under the FC policy in the short run, but the KP policy fails to utilize the forest carbon sequestration potential in the long run as it

provides considerably less incentives to invest in forestry than the FC policy.

& Conclusion The KP increases the Norwegian forest sec-

tor’s climate change mitigation compared to no climate policy but less in the long run than a carbon policy with no cap on forest carbon credits.

Keywords Forest management . Bioenergy . Partial equilibrium . Forest sector modeling . Bioeconomic modeling . Climate policy

1 Introduction

In order to mitigate climate change, the Kyoto Protocol limits the greenhouse gas (GHG) emissions from industrialized countries participating in the treaty (Annex I countries) (UN1998).

Forests play an important role in the global carbon cycle (Watson et al.2000), with deforestation contributing 5–18 % of the global GHG emissions (Harris et al.2010; Nabuurs et al.

2007). On a global scale, however, forests are net carbon sinks with sequestration exceeding emissions (Watson et al. 2000) due to high net growth rates in temperate and boreal forests (Goodale et al.2002). Under the protocol, forest carbon sequestration in industrialized countries can be credited up to a given cap (den Elzen and de Moor2002), but if the cap is constraining, climate change mitigation efforts are inefficient.

Facing no limits under the treaty, forest-based energy is pro- moted in many Annex I countries; the total GHG emission impacts in the forest sector (i.e., forestry and forest industries) of its increased use remain however unclear (Chum et al.

2011). While this uncertainty in mitigation effectiveness has been addressed in studies with a bioenergy focus, the literature is silent on the potential inefficiencies of capped forest mitigation. This study addresses that void by evaluating the GHG implications of limiting forest mitigation possibilities and adds to the bioenergy literature by considering emission abatement efficiency within a forest market equilibrium context.

Handling Editor:Shuqing Zhao

Contribution of the co-authors Sjølie, Latta and Solberg designed the analysis, Sjølie carried out the model runs and the analysis, Sjølie, Latta and Solberg wrote the paper, and Solberg coordinated the research project.

The work was carried out at the Department of Ecology and Natural Resource Management, Norwegian University of Life Sciences, Ås, Norway.

Electronic supplementary material The online version of this article (doi:10.1007/s13595-013-0289-5) contains supplementary material, which is available to authorized users.

H. K. Sjølie (*)

:

_{B. Solberg}

Department of Ecology and Natural Resource Management, Norwegian University of Life Sciences,

Box 5003, 1432 Ås, Norway e-mail: hanne.sjolie@umb.no G. S. Latta

Department of Forest Engineering, Resources & Management, Oregon State University, Corvallis, OR, 97331, USA DOI 10.1007/s13595-013-0289-5

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The Kyoto Protocol sets binding targets for industrialized participating countries to limit the GHG emissions for the years 2008–2012 in order to reduce the overall emissions from these countries to minimum 5 % below the 1990 level (UN 1998). Forestry is included only to some extent as countries choose whether to include carbon credits from management of domestic, existing forests (Article 3.4) up to a predefined country-specific limit (UNFCCC2002). In aggregate, the forest carbon credit cap in Annex I countries (excluding the USA) is 69.9 Mt of carbon per year or 2.1 % of base-year emissions (den Elzen and de Moor2002). The 2011 UNFCCC negotiations extended the protocol through 2017 or 2020 (ClimateBrief 2011), with modifications to forestry including mandatory accounting of GHG fluxes from forest management and carbon stored in wood products (UNFCCC2012). However, resolution of forest carbon credits in the extended treaty was deferred to future negotiations (ClimateBrief2011).

According to the IPCC Special Report on Renewable Energy, wood-based energy is the single largest renewable energy source supplying approximately 8 % of the global energy mix (Chum et al.2011). Bioenergy is considered an important mitigation option and subject to a range of promotion policies (ibid). However, the report states that the promotion of forest-based energy may have positive or negative impacts on forest carbon uptake but provides no quantification of the overall GHG emission impacts of policies targeted towards its increased use.

As clarified by Schlamadinger et al. (2007), characteris- tics unique to the land-use sector (including forestry) ham- per its easy integration to global climate policies. However, the magnitude of the forest carbon stocks and fluxes sug- gests that their exclusion may have large unintended conse- quences. Schulze et al. (2000) expressed concerns that the Kyoto Protocol may give incentives to replace old-growth forest with young stands, thereby causing higher emissions than in the absence of the climate policy. Marland and Schlamadinger (1999) and Schlamadinger and Marland (1998) concluded that as long as parts of the forest carbon system are excluded in the treaty, attempts to optimize parts of the system will likely create perversities and inequities elsewhere. As the IGBP Terrestrial Carbon Working Group (1998), they point in particular to the lack of integrating forest management in industrialized countries in the treaty.

In a stand-level analysis, Murray (2000) finds that the Kyoto Protocol creates unintended incentives to harvest excessive- ly in the presence of a carbon price as only reforested stands and not old stands are eligible for compensation. Zanchi et al. (2012) likewise point to the incomplete system of emissions accounting from the land-use sector under the treaty as only some countries count for some changes in the carbon stock, while the system was designed for all countries accounting for all carbon stock changes. Based on their

predictions, increasing harvest for supplying bioenergy may in some cases increase short-term net GHG emissions.

In spite of extensive criticism of the protocol’s treatment of forest carbon and several recent analyses pointing to negative potential GHG emission impacts of forest-based energy use (e.g., McKechnie et al.2010; Walker et al.2010), few analyses quantifying potential impacts of the policy have been carried out. The stand-level analyses of Marland and Schlamadinger (1999) and Murray (2000) are exceptions, but large-scale analyses at the regional or national level of potential impacts of the treaty on forest sector emissions in industrialized countries are lacking. Analyses are needed to gauge the extent to which application of the treaty provides aggregate GHG emission reductions compared to the case of no climate policy as well as determination of the effect of the restrictions placed on forest carbon credits on potential abatement. The elimination of these restrictions has been advocated by inter alia IGBP Terrestrial Carbon Working Group (1998) and Marland and Schlamadinger (1999).

This study uses Norway, which has ratified the protocol (UN1998), as a case study to quantify the potential impact of the Kyoto Protocol policy and its carbon offset restrictions on total forest sector GHG emissions. Our two primary objectives are to analyze the total potential GHG emission impacts in the forest sector under a continuation of the Kyoto Protocol compared to no climate policy and to assess the importance of the forest carbon credit constraint on the protocol’s efficiency. In the analysis, the protocol is assumed extended throughout the century. Only carbon offsets due to management of existing forest (Article 3.4) are considered, and offsets from afforestation/reforestation (Article 3.3) are excluded from the study. To accomplish the analyses, we employ a bio-economic model of the Norwegian forest sector projecting forest growth, forest management, production, and consumption of wood products as well as full GHG accounting in the sector.

In the analysis, society’s priority for mitigating climate change is reflected through a carbon price system where taxes are levied for emissions (from combustion of fossil fuels and bioenergy and decaying wood) and subsidies are paid for sequestration. Carbon prices ranging from 0 to 100

€/ton CO2eq in 12.5€/ton CO2eq increments are analyzed with and without forest carbon credit limits to determine potential efficiency sensitivity with respect to the carbon price and the cap.

Recent estimates of net annual CO2 sequestration in Norwegian forests range from 25 to 30 Mt, which corre- sponds to approximately half the national human-induced GHG emissions (Norwegian Climate and Pollution Agency 2010). Under the Kyoto Protocol, Norwegian forest carbon offsets are capped at 1.468 Mt CO2 per year (UNFCCC 2006), but the reference level of forest carbon sequestration has not yet been decided upon (UNFCCC2012). This work

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is underway, and Norway has proposed the use of the 1990 level of 11.4 Mt CO2eq per year (UNFCCC 2011), which we use in the analysis. The protocol sets an overall national reduction target relative to the 1990 emissions level (UN 1998), yet with the exception of the aforementioned forest carbon sequestration cap includes no specification of where the reductions should take place. Thus, there is no reference level for comparison of the emission reductions outside forestry; as a consequence, the tax/subsidy in the model arises for all these GHG fluxes, including those which would have occurred in the absence of the policy.

We evaluate the two following policies:

& KP policywith forest carbon sequestration credits being

paid for up to 1.468 million tons CO2eq above the 1990 reference level of 11.4 million tons CO2eq; no reference level and no caps on credits for emission offsets outside forestry including wood product carbon storage; carbon price ranging from 0 to 100€/ton CO2eq, scenarios denoted 12.5KP and so on.

& FC policy as the KP policy but with no caps on forest

carbon credits; carbon price ranging from 0 to 100€/ton CO2eq, scenarios denoted 12.5FC and so on.

In addition, we include abase scenario with no climate policy (thus, zero carbon price) in order to assess the GHG fluxes in the absence of climate policies. The next section provides a description of the bio-economic model used for the analyses. Levels of harvest, forest management, industrial production, and GHG fluxes in the scenarios are contrasted in the result section. The results and methods are discussed and conclusions drawn in“Discussion and conclusions.”

2 Methods and material

For the analysis, we use the partial and spatial equilibrium model of the Norwegian forest sector, NorFor, which pro- jects forest management, harvest, forest growth, production and consumption of wood products, and trade and maintains full accounting of their associated GHG fluxes. An abbreviated description along with simplified equations are provided here, while a comprehensive mathematical representation of the model is provided in theElectronic supplementary material and further documentation of the model structure and data can be found in Sjølie et al. (2011) and Trømborg and Sjølie (2011), respectively.

The model simulates the behavior of utility-maximizing forest owners, consumers of wood products, and a profit- maximizing forest industry. A simplified form of the optimization problem can be written as:

Maximize welfare¼Dx;ðQx;tÞ Ex;tþCt

ð1þiÞ ^t;

where Dx is the area under the demand curve for product x as a function of volume Q E are expenditures associated with producing the market clearing quantity Q of productx, andCis the carbon tax/payment determined by carbon price and carbon fluxes additional to base. Forest owners are assumed to maximize profit from timber sales and the utility from having old-growth forest; all forest older than 90 years is assumed yielding a utility of 0.63€/m³. (Throughout the paper, an exchange rate of 8 NOK/€is used.) All agents are assumed having perfect foresight and access to perfect cap- ital markets. The 19 Norwegian counties form the domestic regions, and two foreign regions (Sweden and “rest of world”) ensure balance in the markets. The model is solved for 20 5-year periods starting in 2010 using a discount rate of 4 %. The results focus on the first 19 periods to avoid bias in the last period due to terminal condition constraints.

Timber supply is based on a harvest scheduling approach with the basic unit being the National Forest Inventory (NFI) plots. Harvest quantities are calculated as:

H_x¼F_n;mx Y_x;n;m;

where Fn,m is the forest base represented by NFI plot n enrolled in silvicultural management regime m and Yx,n,m

is the associated yield of productx. Forest growth is simulated using the stand level model Gaya (Hoen and Eid1990) for each of the approximately 9,000 NFI plots covering all productive forest land in Norway for the mset of regeneration and management options. Regeneration, forest management and timing of final harvest for each hectare of land are determined in the optimization process with no harvest being an option for all stands. Growth is simulated for a set of regeneration and management options for clear-cut stands, and upon final harvest, the optimal alternative is chosen for each hectare of land.

Forest owners supply spruce, pine, and birch sawlogs and pulpwood to the forest products manufacturing facilities or to the export market. Each product is ensured balanced through the following equation:

Q_x¼H_x M_xx⁰þB_x⁰_xþI_x E_x;

whereHxis the harvest of productx,Mxx′is the consumption of productxin the manufacturing of productx′,Bx′xis the amount ofxproduced usingx′as input in the manufacturing process,Ixis the imports, andExis the exports. Sawlogs are processed into sawn wood or can be used as a substitute for pulpwood. Pulpwood is used as an input for production of pulp, paper, boards, and bioenergy feedstocks. Forest and manufacturing residues along with roundwood pulpwood can be processed into the bioenergy feedstocks chips, pellets, and firewood to produce heat. Norway’s approximately 20 pulp, paper, and board mills are represented individually in the model, while the production of sawn wood, bioenergy

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feedstocks, and bio-heat is modeled on the county level.

County-specific demand varies with price, projected Norwegian national gross domestic product and population growth in each county. Bio-heat is defined for the market segments waterborne heating consisting of district and local heating centrals, space heating (firewood and pellets stoves), and industry other than forest industry, each having specific market prices, alternative fuels, production costs, and potentials. The price for each part of the heat market is defined by electricity prices (as most heat in Norway is electricity- based), grid rents, and the variation of electricity consumption and spot prices over the year. County-level potentials for each segment are found by using population and building census data, population forecasts, employment, and income data, while production costs are depending on cap- ital costs for heating facilities and grids, non-fuel production costs, and energy efficiency, in addition to the endogenous fuel costs. Energy efficiency varies from 54 % in wood- stoves to 80–90 % in waterborne heating installations and pellets stoves (Trømborg and Sjølie 2011). As bioenergy constitutes a minor share of the overall heating market, its production levels is assumed to have limited influence on the heat price and a price elasticity of demand of −5 is assumed for water-borne heating and bioenergy in industry.

For space heating, elasticity is assumed equaling −0.7 for capturing demand effects related to the extra work for consumers of using firewood compared to electricity.

Tree carbon content is based on Marklund (1988) functions for above and below-ground biomass, and net emission decay rates for dead wood and soil are taken from the Yasso model (Liski et al.2005). The Marklund functions include single tree biomass functions for stem wood, bark, living branches, dead branches, stump, fine roots, and coarse roots for pine, spruce, and birch with the exception of below-ground functions for birch. Coarse birch roots biomass is assumed equaling 0.1×the sum of biomass in stem, bark, living, and dead branches, and fine birch roots biomass 0.0978×the same sum. Yasso consists of three types of litter compartments (non-woody, fine woody, and coarse woody litter) that enter five different decomposition compartments, each with its proper fractionation rate that determines the proportion of content being emitted in each time period. Emission rates are assumed to be independent of the carbon stock, and no initial carbon stock is therefore accounted for. Carbon in trees that die is treated as an instant emission and assumed to decay immediately upon tree death and harvest residues over time. GHG emissions accounting from silvicul- ture, harvesting, transport, and industrial processing are based on life cycle assessment data. Carbon stored in sawn wood, boards, and paper are included along with estimates on the product’s lifetime and the proportion retired from use at each point in time following production. Upon going out of use, wood products are assumed to be combusted and replace domestic heating oil. Waterborne bio-heat and bio-heat in

industry are treated as substitutes for domestic heating oil, reducing the GHG emissions by 301 kg CO2/MWh heat (Sjølie et al. 2010), while firewood and pellets in stoves displaces half hydro- and half coal-based electricity as current Norwegian electricity consumption is a mix of those. The corresponding substitution effect is assumed to be 379 kg CO2/MWh heat (ibid). Sawn wood is assumed to replace half concrete and half steel, yielding an overall substitution effect of 431 kg CO2/m³(Petersen and Solberg2005).

GHG fluxes are calculated as the difference in stocks from one period to the next and divided by 5 to obtain the annual fluxes. The carbon accounting equation is:

C¼Pc ½ðFc McþScþKcÞ

Fcbase McbaseþScbaseþKcbase

ð Þ;

where Fc is the net forest carbon sequestration, Mc is the GHG emissions from manufacturing, Sc are the avoided GHG emissions when substituting other building materials and fossil fuels, and Kc is the carbon stored in long-lived wood products.

The carbon price Pc is paid only for the carbon fluxes additional to base scenario levels. A terrestrial view is taken of the carbon cycle with positive fluxes being additions to terrestrial carbon and negative fluxes being transfers from terrestrial to atmo- spheric carbon accounts. The yearly GHG emission reductions are found as the difference between the flux under the policy and in the base scenario (with no climate policy) for the given period. Thus, the base scenario GHG fluxes do also fluctuate over time and should not be confounded with the 1990 reference level. The 1990 reference level determines the forest carbon sequestration payments, with forest owners being paid for offsets above this level (only up to the cap under the KP policy) and being imposed taxes if sequestration for any period is lower than this level. Following Stavins and Richards (2005), the GHG emission reductions are defined as the discounted annualized net emission deviation from base scenario levels, i.e., yearly deviations are discounted with a 4 % discount rate to obtain the net present value of future offsets. This value is annualized over the given period to yield the annual offsets.

In the analysis,“forest carbon”refers to carbon sequestration in forests, while “non-forest carbon”includes all other GHG fluxes in the sector as emissions from harvesting, timber transport, and processing in industry, as well as carbon storage in wood products and substitution of non-wood products.

Forest carbon is thus subject to the 1990 reference level and the KP policy cap, while there is no reference level and no cap for the non-forest carbon.

3 Results

Results are concentrated on the carbon prices 12.50, 50, and 100€/ton CO2eq. We first present results from the base run,

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followed by those for the two climate policies (KP and FC) focusing on forestry, then wood products markets, and fi- nally GHG accounts.

3.1 Base scenario

In the base scenario, harvests start at 9.5 million m³ and increase gradually for eventually reaching 12 million m³in 2100. With the exception of the first period where more than 80,000 hectares are regenerated due to harvested stands lacking regeneration in the data, 27,000–48,000 hectares are an- nually regenerated. Over the century, on average 53 % of the land is regenerated naturally with spruce, 23 % with birch, 13 % with pine, and 11 % are planted with spruce.

In industry, the pulp and paper production increases from 2.6 million tons the first period to 3.2 million tons in the last periods. Production of solid wood products (sawn wood and boards) starts at 2.3 million m³and levels off at 3.1 million m³after 2050. With expansions in waterborne heating and pellet-based space heating, bioenergy production increases stepwise from first period’s 5 to 10 TWh in 2025 for eventually reaching 11 TWh. The production of chips and pellets increases with the expansions in waterborne heating, starting at 0.5 TWh and stabilizing at 10 TWh the last periods. In parallel, the production of firewood is reduced, from its initial level at 8 to 5 TWh in 2100.

Net annual imports of timber fluctuate between 1.5 and 2 million m³, while net exports of pulp and paper gradually decrease from 950,000 tons in the second period to 400,000 tons in 2100. Net imports of solid wood increase from 250,000 m³/year the second period to 1.5 million m³/year the last period, and domestic annual consumption of solid wood products increases in parallel from 2.9 million m³to 4.7 million m³. Paper consumption increases also gradually over the model time horizon, from 1.5 million tons to 2.5 million tons/year.

The annual forest sector GHG fluxes fluctuate between 6 and 13 million tons CO2eq, reaching the minimum in 2035 and increasing from there. Between 2 and 10 million tons CO2eq is yearly sequestered in forests, while the non-forest carbon offset beyond 2020 is stable around 6 million tons CO2eq.

3.2 KP and FC policy results

In this section, impacts of the two policies are contrasted.

3.2.1 Forestry

Both policies lead to reduced harvest levels compared to base, with the exception of the highest carbon price in the KP policy after 2060 (Fig.1). Initial harvest levels are lower in the KP than under FC. But while harvests decline over time in the FC policy, the more with higher carbon price, harvests increase over time under the KP policy, and more with higher carbon

price. However, harvest levels tend to eventually converge in the KP and FC scenarios as they stabilize under KP and increase under FC towards the end of the century.

The average rotation age does not differ much between the policies with the exception of high carbon prices at low site indexes (SI), where the KP leads to longer rotations than base while FC does not. (The site indexes refer to the average height of the 100 largest trees per hectare at 40 years breast height (Fitje1989)). The difference is mainly caused by an average increase in rotation age of about 50 years at SI 8 the first decades under the FC. Both policies lead to shorter rotations than base at high SI and carbon price (Table1).

Investment in regeneration increases with both climate policies; compared to base, it more than doubles in the 100KP scenario and more than triples in the 100FC scenario. The carbon price leads to less natural regeneration with birch. At low carbon price, larger areas are regenerated naturally with spruce but as the price increases, planting of spruce becomes the dominant method, with the shifts being more pronounced in the FC than in KP. High carbon price leads likewise to less thinning and higher standing volumes under both policies compared to base, with impacts being larger under FC than KP.

3.2.2 Wood markets impacts

The two policies have different impacts on industrial production, with the exception of pulp and paper manufacturing which is reduced to about the same degree with the two policies (Fig.2). Compared to base, solid wood production reduces over the century under the FC policy but increases under KP. Bio-heat in high-effective waterborne heating systems and pellet stoves (“modern bioe”) increases considerably in both policies with a high price on carbon, up to 60 % in the 100FC and 75 % in the 100KP compared to the base. Firewood-based bio-heat which has relatively low efficiency (“Trad. Bioe”) is reduced with a high price on carbon, up to 50 % in the 100KP and 65 % in the 100FC relative to base scenario levels. Total bioenergy consumption is up to 48 % higher under the KP policy compared to base contrasted to a maximum of 32 % increase under FC.

Exports of pulp and paper products are similarly impacted by the two policies, while net imports of solid wood products increase considerably under the FC policy and only moderately in the KP (Table 2). Trade of timber and bioenergy feedstocks sees similar trends with higher net imports under the FC policy than under KP.

Net imports of timber is stable over time under the 100KP scenario on about 3 million m³; under 100FC, imports start at 1.9 million m³ but increase to 4.7 million m³ in 2085. Over the model time horizon, net exports of pulp and paper are in both 100FC and 100KP scenarios reduced from 380,000 to −340,000

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tons. Net imports of bioenergy feedstocks fluctuate in both policy scenarios, but while they gradually increase in the 100FC, they reduce over time in the 100KP.

Timber prices rise as harvests are reduced and consequently more under the FC policy than under KP (Table2). Processed product prices are less affected by the policies as they follow international prices more closely.

While saw log prices in the KP policy are higher than in base throughout the horizon, the 100FC saw log prices do not exceed the base scenario levels the first two periods.

While the consumption of paper is barely influenced by the policies, solid wood consumption is higher under both policies relative to the base scenario. Solid wood consumption leaps in the second period in both 100KP and 100FC to 3.5 million m³. The long-term consumption level is 300,000– 400,000 m³ higher in the 100KP than in base, while the difference to the base diminishes over time in the 100FC.

3.2.3 GHG emission reductions

The GHG emissions offsets above the base scenario levels in the sector in the form of discounted, annualized fluxes vary between the two policies. Until 2020, potentials are higher under the KP policy than under FC, with potentials at a price of 100€/ton CO2eq equaling 5.4 million tons CO2eq and 4.7 million tons CO2eq/year for the KP and FC policies, respectively (Fig. 3). Until 2050, about the same emission offsets can potentially be materialized under the two policies, while the potential for climate change mitigation until year 2100 is in the KP about the same as the 2050 potential, 7.7 million tons CO2eq/year for the highest carbon price.

The mitigation potential over the century is in the 100FC scenario 9.2 million tons CO2eq/year.

The path of GHG emission reduction varies between the policies. Forest carbon sequestration is in the KP policy

6 000 000 7 000 000 8 000 000 9 000 000 10 000 000 11 000 000 12 000 000 13 000 000

National harvest level (m3)

Year

Base 12.5KP 12.5FC 50KP 50FC 100KP 100FC

Fig. 1 National harvest level in the base scenario with no carbon policy and in carbon policy scenarios. KP indicates carbon policy with cap on forest carbon credit and FC carbon policy without such cap, 12.5, 50, and 100 indicate carbon price in€/ton CO₂eq.y-axis starts at 6 million m³(12.5KP is partly hidden under 12.5FC)

Table 1 Base scenario silvicultural activity levels and impacts of policies (relative deviations in percent from base scenario). Numbers refer to average over the 2010-2100 period

Silvicultural activity (unit) Details Base value Kyoto Policy Full Carbon Policy

12.5 50 100 12.5 50 100

Relative deviations (%) from base scenario Average harvest age existing stands

(year)

SI 8 142 15 18 18 16 12 1

SI 14 96 −0 2 2 −1 1 −1

SI 20 83 0 −2 −12 −1 −9 −8

Silvicultural investments (€/year)

Regeneration 6,108,411 17 81 125 16 183 227

Precommercial thinning 247,809 −8 −19 4 −6 −22 12

Fertilizing 664,367 161 144 129 168 28 −23

Regeneration methods

(share of total regenerated area)

Planting spruce 11 % 0 91 157 −6 269 335

Natural regeneration spruce 53 % 22 17 5 24 −11 −18

Natural regeneration birch 23 % −48 −60 −58 −50 −73 −77

Thinning share of total harvest (%) 15 11 −10 −29 13 −40 −69

Average standing volume (m³/hectare) 123 7 12 12 7 19 21

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constrained by the given cap after 2040 for a price of 100

€/ton CO2eq (Fig. 4). At a price of 12.5€/ton CO2eq, the constraint is binding only after 2070. In the FC policy with no constraints, forest carbon sequestration increases considerably over the horizon, reaching 28.4 million tons CO2eq/year in 2085 at a price of 100€/ton CO2eq contrasted to the base scenario maximum of 9.7 million tons/year in 2070.

GHG fluxes outside forests (i.e., from the use of ma- chines for harvest and transport, processing, use of wood products with carbon storage and substitution effects) are slightly higher throughout the horizon in KP than in FC with a maximum difference of 1 million ton CO2eq/year in 2085.

More substitution and carbon storage in wood products make up almost the entire deviation in carbon offsets outside forests between the two policies. This is also the most important factor in the difference in non-forest carbon offsets between base and the climate policy scenarios; however, reduced emissions from industrial processing count in this case for about 400,000–500,000 tons CO2eq/year.

4 Discussion and conclusions

Compared to the FC policy with no constraints on forest carbon credit, the KP policy with limitations on forest carbon credits yields larger carbon offset potentials for the same carbon price in the short run, approximately the same in the medium run, and considerably less in the long run.

The larger short-run mitigation potential in the KP is in large part determined by lower harvest levels through this period.

However, once the forest carbon sequestration cap becomes binding, further avoidance is not incentivized in the KP which leads to a long-term mitigation potential (through year 2100) close to the 2050 levels. Long-term mitigation potential under the FC is much higher beyond 2050 as further reductions in harvest coupled with the fruition of earlier investments in planting yield large sequestration rates over the remainder of the century. Albeit there is lack of comparable studies in other countries, our primary findings can be generalized to many other industrialized countries with forest carbon sinks well above the 1990 reference level

-80 -60 -40 -20 0 20 40 60 80 100

Relative deviation of industrial production from Base level (%)

Year

P&P-100KP P&P-100FC Solid wood-100KP Solid wood-100FC Modern bioe-100KP Modern bioe-100FC Trad. bioe-100KP Trad. bioe-100FC Fig. 2 Relative deviations of

industrial production levels in 100KP (carbon policy with cap on forest carbon credit and carbon price of 100€/ton CO₂eq) and 100FC (carbon policy without such cap and carbon price of 100€/ton CO₂eq) scenarios from the base level with no carbon policy (0=base scenario level).P&Pinclude pulp and paper,solid woodsawn wood and plates,modern bioe bioenergy in waterborne heating systems, andtrad.bioefirewood in stoves

Table 2 Base scenario trade (net export quantities), average domestic prices, and impacts of policies (relative deviations in percent from base scenario). Numbers refer to average over the 2010-2100 period

Product (units) Base value Kyoto Policy Full Carbon Policy

12.5 50 100 12.5 50 100

Relative deviations (%) from base scenario

Net export Timber (m³) −1,869,000 0 35 60 19 63 100

Pulp and paper (tons) 695,000 −18 −40 −103 −19 −42 −100

Solid wood (m³) −761,000 2 −1 6 2 24 54

Bioenergy feedstocks (MWh) −115,000 34 77 153 37 139 281

Prices Spruce sawlog (€/m³) 42 2 10 42 1 43 125

Spruce pulpwood (€/m³) 33 4 14 28 5 29 46

Spruce sawn wood (€/m³) 186 −2 −5 −7 −2 −2 3

Newsprint (€/ton) 524 0 −1 0 0 −1 2

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(den Elzen and de Moor2002). However, fluctuating reference levels will be used for post-Kyoto carbon sink credits which may change the incentives for such mitigation efforts in the future (Frieden et al.2012).

The price of carbon reflects the necessary price level for agents to adjust their production and consumption levels to sequester more carbon or avoid GHG emissions. For forest owners, this price includes the opportunity costs of with- holding stands beyond the economic maturation age and harvesting stands other than those that would be economi- cally optimal with no price on carbon as well as the direct costs of intensified planting. The price of carbon causes changes in the relative prices of manufacturing inputs and outputs, thereby leading to an adaptation of the optimal production mix; the optimal bundle of consumption goods changes likewise for the consumers. However, this approach excludes the potential total welfare impacts faced by society when introducing a carbon policy.

Higher carbon prices lead to more harvest over time in the KP policy but to less harvest in the FC policy, while initial harvest levels are higher in the FC policy than in the

KP. This difference may be caused by the harvesting of stands that underutilize their production potential in order to ensure higher carbon sequestration rates later in the FC policy; this can also be seen in the lower average rotation age on low-productive sites the first decades under FC.

Lower benefits of future carbon sequestration due to the limit on payments under the KP policy leads to lower initial harvest as agents maximize the net present value of forest and carbon values. For a price of 100€/ton CO2eq, the KP cap is reached in 2040 even with the low initial harvest level as forest owners modify forest management and plant and fertilize more and reduce thinnings. Thus, over time, harvests can be increased as the cap is fully utilized with the changes in the forest management taking place.

Capping the forest carbon payments leads to higher emission reduction in the wood product markets where there is no limit on emission offsets. Beyond the forest carbon credit cap, there is no trade-off between such offsets and forest carbon sequestration, enhancing GHG emission avoidance in the wood product markets. Along with forest products manufacturing, alterations in domestic harvest

0 10 20 30 40 50 60 70 80 90 100

0 2 000 000 4 000 000 6 000 000 8 000 000 10 000 000 Marginal costs of GHG emission reduction abve Base level ( /ton CO2eq)

National GHG emission reduction above Base level (tons CO₂eq/yr) KP 2020 FC 2020 KP 2050 FC 2050 KP 2100 FC 2100 Fig. 3 Marginal costs of

discounted, annualized GHG emission reductions above the base scenario up to year 2020, 2050, and 2100 in the KP policy with cap on forest carbon credit and FC policy without such cap

0 5 000 000 10 000 000 15 000 000 20 000 000 25 000 000 30 000 000

GHG fluxes (tons CO2eq/year)

Year

Base Forest carbon 100KP Forest carbon 100FC Forest carbon Base Non-forest carbon 100KP Non-forest carbon 100FC Non-forest carbon Fig. 4 GHG fluxes over time

in forest (forest carbon) and outside forest (non-forest carbon) in the base scenario with no carbon policy, 100KP (carbon policy with cap on forest carbon credit and carbon price of 100€/ton CO₂eq), and 100FC (carbon policy without such cap and carbon price of 100€/ton CO₂eq) scenarios

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levels lead to changes in international wood products trade.

Pulp and paper with its high energy needs and zero substitution values see a reduction in domestic production and a deterioration of exports. The impacts on the solid wood product markets vary between the policies as domestic production yields bioenergy byproducts, while imports do not. The consequence is an increase in production and stable trade values under KP, but harvest reductions under FC leave Norwegian sawn wood demand to be filled with increased imports. Sawn wood demand increases slightly in the climate policy scenarios when the values of carbon storage and substitution effects are considered compared to the base. Bioenergy imports increase in both policy scenarios with FC leading to a twofold increase compared to KP. This change in the relative value of production to trade in both policies lead to carbon leakage, i.e., increase in GHG emissions in other countries; the impact is however biggest with the FC policy where the value of keeping old stands is greatest.

Total bioenergy consumption increases under both policy schemes but more under KP than under FC. The carbon opportunity costs of using bioenergy under KP is zero when the forest carbon credit cap is reached, while under FC, the climate benefits of bioenergy use has to be weighed against the forest carbon values at all production levels. Total bioenergy use averaged over the century grows from the base’s level of 9.8 to 12.7 TWh/year in 100FC and to 13.8 TWh/year in 100KP. Imports of bioenergy feedstocks increase alongside, by 0.08–0.33 TWh/year compared to base levels in 100KP. Differences in imports from base fluctuate m o r e i n t h e 1 0 0 F C , r e a c h i n g a l o w e r l e v e l o f 0.067 TWh/year in 2015 and a top of 0.68 TWh/year in 2080, averaging 0.32 TWh/year over the century. However, due to the possible use of pulpwood for bioenergy, the total leakage effects may exceed those values.

While there are no similar studies that have been carried out, it is of interest to compare our results with the recent debate of bioenergy promotion as a climate change mitigation tool (e.g., McKechnie et al.2010; Walker et al.2010).

In line with these studies, harvest decreases under climate policies. However, our findings of bioenergy production diverge from these studies which conclude that its increased use leads to higher short-run emissions. Accounting for carbon in the entire value chain as done here while considering the joint production of various timber qualities and wood products, their relative market values, trade, and pre- and post-harvest forest growth lead to the conclusion that increased use of bioenergy does not preclude short-term GHG emission reductions. Instead, according to our predictions, traditional bioenergy use is replaced by modern bioenergy systems which offer substantially higher efficiency, thus emitting less per megawatt-hour. In that regard, the aforementioned studies are correct in problematizing that the omission of bioenergy GHG emissions leaves no

incentive to use bioenergy more efficiently. In addition to the changes within the bioenergy market, we find shifts in the use of pulpwood from paper products to bioenergy. To exclude the leakage factor in policy assessments, future studies could evaluate the policy impacts keeping trade levels fixed to base levels, thus not allowing for increased imports of timber, feedstocks, or manufactured products.

Until 2050, about the same emission offsets occur under the KP and FC policies, while the potential for climate change mitigation to year 2100 is about 20 % higher in 100FC compared to 100KP. Referring to the European Low Carbon 2050 roadmap and its aim of reducing GHG emissions by 80 % by 2050, our results therefore indicate that, under the assumptions made in this study for Norway, the FC policy would be preferable.

As with most modeling exercises, this study is burdened with considerable uncertainty. The most significant in terms of their influence on the results are most likely mortality rates in older forests, substitution effects of wood products, and carbon leakage. The mortality functions utilized are based on few stands older than 120–130 years and could underestimate the mortality in old-growth forest leading to an overestimation of the potential carbon offset in forest management. This would be particularly true with the lower harvest levels of the FC policy. Potential carbon offsets may however be underestimated as old forest is assumed to decay immediately upon tree death instead of gradually over time. This approach was chosen to avoid asymmetry in the growth modeling: The stand simulator provides estimates of net stand growth, while the gross growth and tree death are unknown. If accounting for decay over time when the net growth is negative (when we know the tree death rates), asymmetry arises. Optimally grad- ual decay would have been counted for both when the stand has positive and negative net growth; use of an individual tree simulator would make that possible.

The utility value of owning old-growth forest was determined by evaluation of the difference between the simulated behavior of profit-maximizing forest owners and observed harvest levels but is yet uncertain and subject to adjustment as the forest and society change. Forest carbon sequestration rates in the base scenario are well below recent historic levels. While the models currently used in Norway are thought to under-predict growth, future forest growth is expected to be considerably lower than recent levels as investments in forestry are much lower than 30–50 years ago. However, our predictions of national forest growth and thus carbon sequestration are well below the predictions of other models in Norway (Norwegian Climate and Pollution Agency2010). This is probably to a large extent caused by differences in assumptions of forest owner behavior; our optimization mechanism maximizes forest owners’ profit, thereby raising harvest above recent levels as well as keeping investments low as long as there is no compensation for

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carbon sequestration. Changes in harvest levels and forest management under KP may be overestimated in our pro- jections, as carbon offsets can be substantially increased beyond the base level before reaching the cap. Future carbon sequestration in Norwegian forests may possibly exceed the KP cap even in the absence of climate policies thus leaving less incentives for changing the forest management under a KP policy. Consequently, differences between FC and KP long-term potentials are likely to be conservative. Since most reference levels are determined independently of policy analyses, inconsistencies between modeled base line activities and those of the reference level may influence estimates of policy effectiveness. Future studies are needed to evaluate the impacts of different reference level method- ologies as well as cap on mitigation potentials and costs.

Bioenergy is assumed to displace domestic oil and electricity based on a mixture of domestically produced hydropower and imported coal power, while sawn wood displaces 50 % steel and 50 % concrete. Albeit these estimates are based on the best available knowledge, few assessments of actual substitution effects exist. Substitution effectiveness could also be bolstered by substituting the worst emission sources first.

Carbon leakage in any forest carbon offset program may be considerable (Murray et al.2004) but is not included in our GHG estimates. The changes in net trade indicate substantial carbon leakage possibilities. The magnitude of that potential leakage is largely a function of the elasticities of foreign supply and demand which are uncertain. Multi-country partial equilibrium models of the forest sector such as the EFI-GTM (Kallio et al.2004) could be useful for providing estimates on these elasticities. It should however be noted that a small country will probably not introduce such policies alone with their trading partners not participating. The relevance of this study does not lay in the probability of the policies being carried out but in the assessment of mitigation potentials in the forest sector in the face of different policies.

Another source of uncertainty is the policy application period. In the model, forest owners make management decisions assuming the policy to be permanent. The current status of the Kyoto Protocol is that it will be extended to 2017 or 2020 (ClimateBrief2011), and it is not yet known whether any climate policy will apply to the forest sector at a later date. Rather, different results could emerge if the policy only applies for a shorter period than assumed in this study, as forest owners then would optimize their management considering the post-policy situation. The study results should be interpreted with the aforementioned uncertainties in mind.

However, despite uncertainty, we demonstrate that the choice of policy and limitations on forest carbon sequestration payments may have a substantial impact on the GHG emissions reduction potential and that spatial models like NorFor with rather detailed forest management and industry

are useful in estimating potentials of the GHG impacts of climate change mitigation policies.

Our results suggest that under long-term policies, the KP policy offers higher GHG emissions reduction potential in the short term than the FC policy with no constraints on forest carbon sequestration. However, mitigation potential over the entire century is substantially higher under the FC policy than under a continued KP policy. At a price of 100

€/ton CO2eq, the potentials are 9.2 million tons CO2eq and 7.7 million tons CO2eq above base, respectively. The higher short-term emissions reduction under the KP policy is caused by a sizeable decrease of the initial harvest level, which on the other hand leaves less room for increased carbon sequestration in the long term. The sector’s long- term potential to mitigate climate change is not fully utilized if forest carbon sequestration credits are capped as in the KP.

Funding Funding for this work was provided in part by the Research Council of Norway, the Norwegian Forest Owner Association, and the Norwegian forest industries under the project “Klimatre”(project number 415764), the project “ClimPol”(project number 415735), and the project“CenBio”(project number 415758).

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