The private cost and timber market implications of increasing strict forest conservation in Finland

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1 Forest Policy and Economics 7 (2005) The private cost and timber market implications of increasing strict forest conservation in Finland Jussi Leppänen a, *, Mika Linden b,1, Jussi Uusivuori c,2, Heikki Pajuoja d,3 a The Finnish Forest Research Institute (Metla) Unioninkatu 40 A, FIN Helsinki, Finland b Department of Business and Economics, University of Joensuu, P.O. Box 111, FIN Joensuu, Finland c Department of Forest Economics, University of Helsinki, P.O. Box 27, FIN Helsinki, Finland d The Finnish Forest Research Institute (Metla), P.O. Box 18, FIN Vantaa, Finland Received 22 February 2002; received in revised form 3 February 2003; accepted 18 February 2003 Abstract In this paper the private costs of strict forest conservation are studied using Finnish data. First, the conservation-induced changes in the net present value of forest land are estimated together with the consequent compensation needs. Second, a dynamic reduced-form econometric model is estimated to derive the dynamic impacts of forest protection on the Finnish timber markets and forest sector. The estimations show that a conservation program that sets aside up to 3% (an additional ha) of the total productive forest land area in southern Finland could result in up to 1 milliard euros in compensation to private landowners. The results of the dynamic analysis of the forest sector indicate that conservation has a price-increasing and quantity-decreasing effect on the timber markets. However, the raw material supply effects on the forest industry s output and raw timber imports are less than expected. D 2003 Elsevier B.V. All rights reserved. Keywords: Economic impact assessment; Endangered species protection; Compensation; Substitution 1. Introduction Some 8% of all forest land worldwide is classified as protected. The actual meaning of a protected forest * Corresponding author. Tel.: ; fax: addresses: jussi.leppanen@metla.fi (J. Leppänen), mika.linden@joensuu.fi (M. Linden), jussi.uusivuori@helsinki.fi (J. Uusivuori), heikki.pajuoja@metla.fi (H. Pajuoja). 1 Tel.: Tel.: Tel.: varies from region to region and between countries. 4 There is also a worldwide realization that the current levels of conservation may not be sufficient to achieve the goal of preserving biodiversity and other conservation values. For example, it has been argued that a representative selection of 10 20% of the earth s land area should have legal protection as its priority land use form (WBGU, 2000). Both national and transnational initiatives have been taken to protect more 4 The forest conservation concept considered in this paper is defined as a strict protection of nature reserves, excluding private property rights and harvesting, but including the Finnish established practice of the open access to forests for public (everyman s right) /$ - see front matter D 2003 Elsevier B.V. All rights reserved. doi: /s (03)

2 72 J. Leppänen et al. / Forest Policy and Economics 7 (2005) forests. For example, the Habitat Directive in the European Union countries is leading to the NATURA 2000 network of protected areas, of which forests are an important element. In the European Union, the forests and their interaction with their environment have been monitored at quite different levels in member countries (Commission of the European Communities, 2002). Northern member countries, Finland and Sweden, have a long tradition of monitoring forest resources and endangered species in forests. In Finland, a governmental working group concluded that the current level of protected forests is too low from the ecological point of view (Ministry of the Environment, 2000). At the moment, approximately 2% of all forest land is under protection in the southern part of the country, but conservationists have called for up to 10% of productive forest land to be protected in that region. Considering the importance of forests and forest industries in Finland and the dominance of non-industrial private ownership, it is obvious that the private cost resulting from such an increase would be substantial. In this paper we aim to estimate the level of these costs using empirical data on Finnish forests, the economy and timber markets. In recent years, extensive forest conservation measures have also been implemented in the state-owned forests, mostly in northern Finland, administered by the Finnish Forest and Park Service (Ministry of the Environment, 2000). A new Forest Act came into effect in Finland in The law restricts timber harvesting on ecologically valuable sites. In addition, the law takes into consideration specific site restrictions of the Nature Conservation Act. The new regulations concerning forest management will have some effects on the timber markets, although the precise impact of these limitations on the actual timber harvest in privately owned forests is difficult to assess. Furthermore, under the EU-based NATURA 2000 program, some ha of commercial forests in Finland were identified as new protection areas. This represents a very small proportion of the over 20 million ha of forest land in the country. In contrast, the proposed protection of productive forests amounting to 2 10% of the total forest area in southern Finland (10.5 million ha) represents a significant conservation measure. From the viewpoint of investigating the private costs of conservation, a protection program that places a certain land area under protection gives a fairly straightforward basis for assessing the economic implications of forest conservation. Market effects, including the international trade implications resulting from national forest conservation acts, have been assessed by Sedjo et al. (1994) and by Sedjo (1996), as well as by Sohngen et al. (1999). The general conclusion of these studies is that forest conservation is likely to have some unexpected market and environmental effects, such as increased harvesting of previously unexploited forests. These effects may not respect national borders. In the United States, where large tracts of federal land have been protected in the North West of the country, empirical evidence demonstrates the impact of the conservation programs. From 1989 to 1994 in the states of California, Oregon and Washington, timber removals declined by some 46% in the regions where the federal forests had been protected (Raettig and Christensen, 1999; Toppinen et al., 2001). At the county level, however, there was considerable variation in the impacts. In the most affected counties, timber harvesting fell by 75%, while in others timber harvesting increased between 1989 and In another study investigating the effects of no-harvest forest reservations in western Washington, Lippke et al. (1998) reports a 23% reduction in the net present value of forests in the area concerned. Furthermore, Guan and Munn (2000) identified a transfer of capital investments from the Pacific Northwest to the southern states of the United States as a consequence of the forest protection. In the present study, the cost effects of increased strict forest conservation in Finland are explored at two levels. First the direct cost implications of taking timber assets out of commercial use are estimated. This is based on a net stumpage earnings approach that evaluates the reduction in the timber selling income flows as a result of forest protection focusing particularly on non-industrial private landowners. The derived estimates serve as approximations of the size of the state budget outlay required to compensate landowners for the lost future income from the protected timber. Second, a dynamic timber market analysis is carried out. In this part, an econometric vector-autoregressive model is estimated. The model incorporates timber quantities, prices, and growth on the domestic markets, as well as timber imports and the output of the forest industry.

3 J. Leppänen et al. / Forest Policy and Economics 7 (2005) In the next section of the paper, the methodology employed to assess the protection effects is presented. The data section describes the series used to estimate the direct, and dynamic impacts of strict forest conservation in Finland. Results are then presented, followed by discussion and conclusions. 2. Methods 2.1. Estimating the decrease in net stumpage earnings at regional and ownership level A working group established by the Ministry of the Environment of Finland listed those nature characteristics in the country that it considered worth protecting or re-establishing by increasing forest conservation efforts (Ministry of the Environment, 2000). Because of the lack of any detailed information of the areas to be protected an accurate conservation cost analysis cannot be made. Nevertheless, the approach presented in this section allows the direct conservation effects to be identified regionally (at the level of Forestry Centres) and between the main ownership categories (non-industrial, industrial, public). In this study, the estimation of the direct costs of strict forest conservation employs the approach of Hildén et al. (1998). They estimated the net present values of (1) average land with standing timber and (2) land with standing timber mature for final fellings, before and after forest income taxes; when evaluating the cost of establishing the Finnish NATURA 2000 network, in which an additional ha of nonindustrial private forest (NIPF) and ha of stateowned forests were protected. Applied at regional and ownership levels, this evaluation allows minimum and maximum estimates for strict conservation costs, to be calculated from the general forest statistics without detailed inventory data on the additional forest protection areas. The theory of the evaluation is based on small-project cost benefit analysis (Johansson, 1993). The evaluation can be extended to the calculation of private costs, i.e. compensation need analysis, by adding public financial assistance and deducting taxes. In this section, the possible market effects are ignored, but they will be analyzed in the dynamic impulse response analysis. The minimum estimate of yearly private conservation costs, C L, is derived by employing the aggregate average even timber-flow assumption and an age class distribution form close to a normal forest. According to the results of the National Forest Inventory for southern Finland (Finnish Statistical Yearbook of Forestry, 2001), this assumption is justified. In a normal forest, the age class areas are evenly distributed over the optimal rotation period and the timber-flow is therefore even over time. The market equilibrium timber-flow and consequent net stumpage earnings are shown to continue under discounting as a cyclical stationary flow from present to future (Salo and Tahvonen, 2002). Therefore, it is appropriate to estimate the cost of additional protection as a reduction in the long-term equilibrium of timber-flow. 5 The minimum cost estimate is calculated as follows: C L ¼ Xm i¼1 X n j¼1 ðr þ s sþ ij x ij ð1þ where the hectare-based private surplus term (R+s H ) ij consists of the yearly average net stumpage earnings R (see data Section 3.1 for the calculation of earnings) and public financial assistance s less taxes H of the forest owner category i region j. x ij is the additional protected forest area. The cost C L corresponds to the conservation-induced decrease in net stumpage earnings after net taxes. This decrease is constant from year to year ceteris paribus. The constant decrease in the annual fellings is obtained, if the (R+s H ) ij is replaced by the regional and ownershiprelated and hectare-based annual average cutting volume Q ij. The infinite annual private cost series can be capitalized by discounting. This result equals the capital value of private conservation costs, and can be defined as follows: NPV L ¼ CL r ; ð2þ where NPV L is the minimum estimate of the capital value of forest conservation cost and r is the long-term real market interest rate. 5 Harvesting for stumpage sales, which is the principal timber sales method in Finland, is carried out only according to the evenage forest management regime.

4 74 J. Leppänen et al. / Forest Policy and Economics 7 (2005) Forest protection efforts are likely to be aimed at forests representing high biodiversity value. This means that forests with old trees, rotting wood and old hardwood mixtures are preferred as strict conservation sites. These characteristics are most likely to be found in forests that are either close to or have passed the age of financial maturity. Therefore, in order to derive the maximum estimate (upper-limit) of the additional private conservation costs, the net stumpage earnings are calculated for stands classified as mature for regeneration harvest. The maximum value for the private forest conservation costs can be now expressed as follows: C U ¼ Xm X n x ij i¼1 j¼1 X q a¼1 p aj v aij s ij þ LV j!; ð3þ where C U is the upper-limit private cost of additional forest protection, p aj is the average competitive market real unit stumpage price of roundwood assortment a in region j and v aij is the corresponding per-hectare growing stock volume of an average mature stand. The LV j is defined as the private optimum of the Faustmannian bare land value, i.e. the net present value of optimal rotations after net taxes. Consequently, Eq. (3) gives the decrease in private income from the final harvest and the land value. The direct economic cost attributable to decreasing final fellings is obtained, if taxes H are removed from the formula and the bare land value LV is defined as the economic optimum before net taxes. Harvesting financially over-mature forest stands is generally not carried out during a single year, but within a certain period normal to optimal roundwood supply behavior (Salo and Tahvonen, 2002). For example, under Finnish conditions, the timber volumes implied by a 10% strict conservation proportion aimed at mature growing stocks correspond to final felling volumes of timber that enter the timber markets during a 10-year period. This is accounted for by deriving an estimate, based on Eq. (3), for the maximum value of the timber lost to the market: NPV U ¼ XT t¼0 1 T C Uð1 þ r gþ t ; ð4þ where r is the real interest rate (average long-term interest rate 4%), g is the annual growth rate of the timber (c2%), and T=10 years. 6 NPV U corresponds to the upper-limit of the lost capital value from the additional strict forest conservation areas Estimation of the dynamic forest sector effects Estimation of the direct compensatory costs is static in nature. In reality, markets adjust themselves to the conservation measures, and any effects on industrial timber consumption may differ from the supply effects implied by conservation. Forest conservation planning is therefore strengthened by the knowledge of likely dynamic market impacts of the conservation measures. Next, a method is introduced to analyze the dynamic forest sector effects of forest conservation. Under the assumption of competitive timber markets and with declining demand and increasing supply curves with respect to timber market prices, stateauthorized strict forest conservation acts as an exogenous reduction in timber supply. Part of the forest land is taken out of commercial timber production. Thus, conservation shifts the supply curve to the left. In the short-run, this leads to higher market prices with lower market quantities compared to the state before protection. The precise magnitude of price and quantity movements depends on the demand and supply elasticities of the timber markets. However, the long-run effects of conservation on timber markets are not so easily derived. Determining the dynamic long-run timber market effects of forest conservation is econometrically a demanding task. Various approaches can be used depending on the data. In a model based on time series or cross-section observations, where land conservation has already taken place, an econometric timber market model incorporating conservation dates or locations as dummy variables can be used. However, a priori, where no extensive conservation has yet been implemented, a different approach must be employed. An approach 6 The difference between adopted market interest (4%) and growth rate of timber-value in mature forests (2%) may be partly caused by the reforestation requirement and harvesting restrictions of the Forest Act and official silvicultural and harvesting recommendations in Finland (Hyytiäinen and Tahvonen, 2001). Capitalized values other than that from timber production, such as capitalized value of amenities related to standing timber (Hartman, 1976) are not considered, because most of them, even after strict conservation, remain available to the forest owner in question.

5 J. Leppänen et al. / Forest Policy and Economics 7 (2005) proposed by Linden and Uusivuori (2002) relying on vector-autoregressive modeling (VAR) is used here. The VAR approach is a useful and flexible method that provides all the dynamic interactions and feed-back effects between the model variables. The VAR model can be cast in the moving average (MA) form, which gives the impulse response interpretation of the model without losing relevant information. The impulse response method detects all the dynamic short, interim and long-run effects of shocks in different endogenous variables on all other endogenous variables. Thus, irrespective of what the basic dynamic timber market model is a highly structured or VAR model without exogenous variables the MA form is almost always derived and impulse responses are derived. In this context a semi-structural VARX model is employed, where some of the variables of the system are exogenous, but zero parameter restrictions are not used, as in the highly structured market model by Linden and Uusivuori (2002). A VARX(p,0) model has the following form y t ¼ a 0 þ X p A i¼1 iy t i þ B 1 X t þ u t ; ð5þ where t=1,2,..., T. Vector y t =( y 1t, y 2t,..., y Nt ) stands for the endogenous variables, while matrix X t gives the exogenous variables {x 1t, x 2t,..., x Mt }. The vector of constant terms is a 0 and u t gives the disturbances of the system that are temporally non-correlated but may correlate across the equations. Matrices A i and B 1 provide all the relationships between the N endogenous variables up to lag order p and the impact of exogenous variables upon them. Note that variables X t may also include lagged terms. If the number of variables (N+M) and the lag orders are large, the system contains too many coefficients for reliable estimation with typical economic data sets. However, if the dimension of the system is low (N, M<5, p<2), VARX system estimation provides a complete picture of all the dynamic effects between the variables. Impulse response analysis is obtained after an appropriate VARX model has been estimated. The estimated system is inverted to MA form, leading to the following expression (Lütkepohl, 1993): Matrices C i are the impulse response matrices that show the impact of shocks in the innovations u t in different time periods. If an external shock, such as land conservation, takes place in the system, its impact on all future values of endogenous variables y t can be traced, since matrices C i provide all the estimated dynamic feed-back connections between the endogenous variables in MA form at the various time points. The sum of matrices C i provides the accumulated impulse effects over the chosen time period. In practice, this means that a low dimensional VARX system that describes the Finnish timber markets is initially estimated. Secondly, MA form is calculated and a 10% shock is introduced to the innovations of the equation describing wood production. Thus, the conservation shock first hits the quantity and the supply side of the timber market. Lastly, the impulse responses and accumulated effects on the endogenous variables used in the system are calculated for a suitable time period ahead (say 30 years). The advantage of the impulse response method in the analysis of conservation effects stems from the fact that it can answer the questions, (a) what are the magnitude and the duration of conservation shock and (b) what is the total effect of conservation shock over the chosen time span? The analysis is conducted with a five-equation system describing the timber markets in Finland. The endogenous variables of the system are annual domestic pulp wood timber harvests, stumpage prices for pulp wood, increment of commercial timber stock in NIPFs, wood pulp production and imports of roundwood timber { Q, P, g, Qp, RawImp}. The exogenous variables used are the exports of pulp and paper industry, bank loan interest rate and import prices of roundwood {Expv, r, ImpP}. The model assumes that these variables are determined independently of the domestic timber markets, although they affect the economic decisions made by forest owners and the wood processing industry. In contrast to the compensation analysis in previous chapter, the econometric analysis is confined to the pulpwood sector, because of the wood imports data which are not separated between logs and pulpwood more accurately describe the wood importing behavior of the pulp industry. 7 y t ¼ c 0 þ X l i¼0 D ix t i þ X l i¼0 C iu t i : ð6þ 7 Over the studied time period, more than 90% of all wood imports into Finland consisted of pulpwood.

6 76 J. Leppänen et al. / Forest Policy and Economics 7 (2005) Data 3.1. Net stumpage earnings, fellings and net taxes The available regional and ownership level data from the forest statistics 8 are employed in order to operationalize the net stumpage earnings calculations. The applicable forest owner categories in the statistics are non-industrial private, forest industrial and state forests. The decrease in annual net stumpage earnings and harvest volume estimates are calculated for all categories. The private costs (the decrease in net stumpage earnings after net forestry taxes) are calculated only for the NIPF owners. 9 For private costs, the net tax implications are estimated by utilizing regional information concerning public financial assistance for forestry investments, and the forest land proportions of two different forest income taxes employed in Finland. The private costs correspond to the reimbursements made by the government to purchase the forest land from the owners to compensate for the lost income resulting from the strict conservation program. In the even timber-flow assumption based on some form of normal forest, the lower limit analysis employed here is defined as an average from the available regional data period The annual net stumpage earnings and the private surplus (net stumpage earnings after net taxes) for the minimum cost estimate are calculated based on the following accounting scheme (sources in parentheses): Gross stumpage earnings (STAT, calculated according to FSIS felling quantities and stumpage prices) Silvicultural works (FSIS): Preparation of regeneration areas, Seeding and planting Tending of seedling stands, Improvement of young stands, Pruning and forest fertilization 8 The regions are the 10 southern Forestry Centres and the Province of Åland. The area included here is somewhat less than that defined by the Ministry of the Environment (2000) as the conservation target area. 9 Regarding the state forests and industrial private forests, only the net stumpage earnings are meaningful. This is because the income from state-owned forests is related more or less directly to state finance, and also because industrial forests are usually managed as part of global forest industry. Forest improvements (FSIS): Forest drainage, Construction and maintenance of forest roads = Net stumpage earnings (calculated for all ownership categories) + Public financial assistance for NIPF (FSIS) Direct regular forestry income taxes on NIPF (estimation by employing NFI and FSIS information) = Private surplus where STAT, Statistics Finland; FSIS, Metla Forest Statistics Information Service; NFI, Metla National Forest Inventory. The accounting is converted to hectare-based units: first net stumpage earnings or private surplus per hectare are estimated, and then these unit values are employed in estimating forest income lost due to conservation of the area in analysis. In the final harvest assumption used in the maximum cost estimate, use is made of the available National Forest Inventory (Metla NFI) regional statistics for forest owners measured regionally over the Finland during The stumpage price information has been collected by the Metla Forest Statistics Information Service (Metla FSIS) and the available regional average price data from 1995 to 1999 are used here. The maximum cost estimate is based on the following accounting scheme: For all forest ownership categories Gross stumpage earnings = final harvest assortment volumes (NFI) stumpage prices (FSIS) F Faustmannian bare land value (estimation) = Direct economic surplus For NIPF Gross stumpage earnings = Final harvest assortment volumes (NFI) Stumpage prices (FSIS) Direct regular forestry income taxes on NIPF (estimation by employing NFI and FSIS information) = Private surplus before discounted surplus of new rotations FFaustmannian bare land value (after net taxes value, estimated as zero) = Private surplus

7 J. Leppänen et al. / Forest Policy and Economics 7 (2005) The minimum and maximum hectare-based estimates are calculated for every Forestry Centre, by assuming that all regions should aim at similar strict conservation percentage proportions. At the ownership level, the assumption is that all ownership groups reserve an equal proportion of their unprotected productive forest land for the additional conservation. However, in the maximum estimate more weight is given to NIP forests, because regionally there are not enough mature forests for final harvest in the industrial and state forests. Finally, the essential phase in calculating net present value for forestry income is to choose the real long-term interest rate. Normally, the government bond real interest rates are considered risk-free. In Finland, 10-year government bonds after tax have yielded between 1986 and 1998 on average: approximately 4% in real terms (Hyytiäinen and Tahvonen, 2001), with variation from minimum 0% to maximum 7.5%. This period is chosen because the liberalisation of the capital markets was completed by Data used in the dynamic analysis The dynamic analysis data employed here consist of yearly observations from the period 1952 to 1998 (47 observations). The sources of data are the Finnish Statistical Yearbook of Forestry (various years) and the data base of Research Institute of Finnish Economy (ETLA). The VARX model was estimated only for the pulpwood market series. Endogenous Q t, market pulpwood fellings in privately owned forests in year t (m 3 ); P t, stumpage prices of pulpwood in year t deflated by the cost-of-living index (FIM/m 3 ); g t, the growth increment in private forests in a year (m 3, observations for the years between the national forest inventories were obtained by interpolation); Qp t, the quantity of wood pulp production in Finland in year t (metric ton); RawImp t, the quantity of forest industry timber imports in year t (m 3 ). Exogenous Exp v t, volume index for pulp and paper plus printing industry exports from Finland in year t; r t, the average bank loan interest rate deflated by the inflation rate based on the cost-of-living index; Imp P t, the real unit import price of timber faced by forest industry. 4. Results 4.1. Decrease in net stumpage earnings and fellings, and private NIPF costs The minimum estimates of the strict conservation effects on annual net stumpage earnings and fellings are illustrated in Fig. 1. The estimate of decreasing fellings at 10% conservation level is also employed in the dynamic analysis in the following section. If the protection efforts are aimed at average productive forest lands, then the net stumpage earnings decrease yearly by 12.6 million euros and fellings by 0.4 million m 3 per additional strict conservation percentage. These correspond yearly to o113 and 3.8 m 3 per additionally protected hectare. Under the assumption that protection efforts are carried out in proportion to the current ownership distribution of forest land, the major impact will be felt in NIPF because of their dominance in forest land ownership in Finland. In NIP forests, the sawlog supply is decreasing slightly more than the pulpwood supply. Because of the fact that standing and harvested timber volumes per hectare differ across the ownership categories, the annual harvest and stumpage earnings effects of strict conservation are not proportional to the number of hectares protected in each category. When the reductions in net stumpage earnings and fellings are calculated, the NIPF share of the total conservation losses is larger than indicated by the areas protected. The maximum estimates of the strict conservation effects are illustrated as follows. First, the net present values of the minimum (Eq. (2)) and maximum (Eq. (4)) conservation costs before net taxes are calculated from the average and final fellings by employing a 4% real discount rate. These are then compared to the net present values calculated from the total net stumpage earnings before the assumed strict conservation. The results suggest that at the 4% real interest rate level, due to substantial increase of

8 78 J. Leppänen et al. / Forest Policy and Economics 7 (2005) Fig. 1. Conservation-induced reductions in annual net stumpage earnings and timber harvesting in southern Finland based on average fellings. Numbers in parentheses following the ownership categories indicate percentages of the total protected area, conservation-induced reduction in net stumpage earnings and timber harvesting, respectively. The existing official forest conservation areas are taken into account at the 2% level. sawlog volume protected the maximum conservation costs will, on average, be doubled compared with the minimum cost estimates. In industrial and state forests the maximum conservation cost could even be tripled, because in these cases almost all stands mature for final felling would be placed under strict conservation. However, if a higher real interest rate was applied, the relative effects would become much higher for the maximum estimates because of the final fellings to be realized in the near future (i.e. during the next 10 years, during which a 2% yearly growth rate in value has been assumed here). As in the case of the minimum conservation effects, the relative maximum effects on NIPF are also greater because of the higher level of hectare-based net stumpage earnings compared with other ownership groups. To illustrate the compensatory effects of forest conservation, Fig. 2 depicts the net present values of the private annual costs, i.e. financial assistance is added and taxes deducted from the estimated decreases in the net stumpage earnings of the nonindustrial forest owners. The results indicate that every percentage point of protected NIPF land corresponds a cost of million euros to NIP forest owners and the corresponding need for compensation because of lost property rights. This corresponds to 2600 o5200/ ha protected, which is in line with forest land real estate market prices. 10 However, the results here are to some extent sensitive to the assumptions in tax calculations Dynamic market effects A five-equation system describing the timber markets in Finland was specified in Section 2.2. The endogenous variables of the system were {Q t, P t, g t, Qp t, RawImp t }. The exogenous variables were {Exp v t, r t, Imp P t }. The impulse response analysis conducted after the VARX estimation is based on the idea that conservation reduces the commercial timber 10 Forest land real estate (>10 ha) market prices in southern Forestry Centre regions in 2001 have been from first quartile o716/ ha (South Ostrobothnia) up to third quartile o3418/ha (Häme- Uusimaa). The median prices have been in regions between 1089 and o2620/ha, respectively, and the mean prices a little higher (Finnish Statistical Yearbook of Forestry, 2001). The mean prices (o1869/ha in all southern Forestry Centres in 2001) according to Hannelius (2000) correspond to woodlands that on average have 30 40% less standing volume than on average land with standing timber (assumption of our minimum estimate). 11 One reason for this is that two simultaneous forest tax systems are being employed in Finland during the period from 1993 to The old site-productivity taxation is in a transitional stage and a new system based on timber sold less expenses is being adopted by holdings in two phases: approximately 2/3 of the forest holdings land has been in the new system since the beginning of 1993 and 1/3 will adopt it from the beginning of 2006 (Pesonen and Räsänen, 1994).

9 J. Leppänen et al. / Forest Policy and Economics 7 (2005) Fig. 2. Capital values of private costs in southern Finland per additional forest conservation percentage unit of NIPFs and additional correspondingly protected NIPF land. Average harvest corresponds to the minimum estimate, final harvest corresponds to the maximum estimate and the best estimate is their average. The maximum estimate consists of a larger proportion of NIPF forests than the minimum estimate because of the shortage of mature forests in industrial and state forests. stock and thus represents a negative shock to it. As a result, the annual growth of commercial timber stock g t also declines after the conservation influencing the wood producing and wood consuming sectors of the economy. If the harvesting per timber stock remains constant, this naturally has an adverse effect on the commercial timber production Q t in the medium and long-run. Because of this, the wood consuming industry may face a smaller domestic timber supply, which may reduce the forest industry s production Qp t. However, the supply reduction can partly be replaced by timber imports RawImp t. If the shock is severe and wood importing opportunities are limited, the wood consuming industry may be forced to reduce its scale. In a small open economy, this outcome is not simply a theoretical curiosity. In the competitive global forest product markets with fixed export prices Finnish industry faces an exogenously given export demand Exp v t. Likewise, the import prices Imp P t are given. If the domestic raw material price level increases because of conservation, the low import price may induce extensive wood imports only if good substitutes are found for the domestic timber supply. The exogenous real interest rate has been found to have negative effects on supply prices (Linden and Uusivuori, 2002). The logarithmic transformation was used for all series except for real interest rates. Model selection criteria (adjusted AIC, residual autocorrelation and normality tests) supported the VARX(2,1) model for the pulpwood series (Appendix A). The impulse responses were calculated assuming a 10% negative shock in the equation for ln g t. Impulses were calculated for a period of 30 years. The graphs plotted in Fig. 3 present the accumulated responses of a 10% negative conservation shock on ln g t on all endogenous variables of the system. The accumulated effects are the cumulative sums of the yearly effects, giving the long-run impact of the shocks. The pulpwood market quantity effects are negative. 1. Upper left panel shows that in the longrun (some 20 years) harvest levels fall by approximately 12.2%, but in the medium term the decrease is greater. Some upward correction thus takes place between years 8 and 16. The price effects of conservation 2. upper right panel are positive and stabilize in 8 years at a level of 12.3%. The initial negative conservation shock in the forest stock increment 3. Middle left panel induces a 34.5% reduction in the stock increment in 20 years. This indicates that conservation might have a profound effect on the growth of Finnish commercial forests, the overall renewal capacity being hampered by conservation. The conservation effect on the pulp production 4. Middle left panel and pulpwood imports 5. lower left panel is less than expected. Production decreases in the long-run by

10 80 J. Leppänen et al. / Forest Policy and Economics 7 (2005) Fig. 3. Accumulated impulse responses of a 10% reduction in the yearly growth of NIPF in Finland with 95% confidence intervals. Vertical axis: responses in percentages (%). Horizontal axis: response periods in years.

11 J. Leppänen et al. / Forest Policy and Economics 7 (2005) % and imports increase only by 1%. These mild effects may be explained in part by the fact that domestic industry has its own wood reserves, technological progress in wood processing may take place and the dependency on foreign wood reserves is not structural. Wood imports into Finland have increased during the latter part of the 1990s. The econometric model was estimated with data starting from the 1950s. The results concerning the wood imports elasticity with respect to domestic conservation measures may therefore be biased downwards. Furthermore, more rigorous modeling of pulp production (e.g. production function modeling) would perhaps give larger magnitude for the conservation effects on production. Nevertheless, the mild negative impacts of forest conservation on the pulp production do reveal a tendency for industrial production to partially recover from the initial negative conservation-induced shock on timber supply. Note that the 95% confidence intervals for accumulated responses are large, indicating that the exact numeric values given above are rather imprecise. 5. Discussion and conclusions The paper has examined the private costs of strict forest conservation. Finnish data have been used to determine the magnitude of static direct and dynamic forest sector effects of forest set-asides. Because the focus has been on the cost side, the analysis cannot be considered to be a cost-benefit analysis of forest conservation. The direct costs calculated in this study reflect the value of the standing timber stock, as well as the value of forest land in timber production. The derived figures serve as a basis for assessing what the compensatory costs of strict forest conservation would be for the taxpayers. The results indicate that approximately 1 million ha of protected forest land in southern Finland would generate annual direct economic losses in forestry of o120 million and, in addition, o2 5 millard in compensation for NIPF owners due to decreased income and lost property rights. These estimates reflect the current growing stock and timber price levels in Finland. Timber prices in Finland are among the highest in the world, and this should be kept in mind when interpreting the results. The dynamic analysis of the conservation effects on the forest sector applied only for the pulpwood market implies that, in line with findings in other studies, some transfer effects will occur on nonprotected forest land as a result of putting some land under protection. In general, the net income of forest landowners from timber sales after the protection program remains at the same level as before conservation. The supply will be reduced but the timber prices will increase at almost the same relative rate. Consequently, it is the forest industries that would be the biggest loser from the conservation measures. The results further suggest that the affect of conservation on imported timber could be fairly small. It would seem that a conservation measure carried out at the national level in Finland would not lead to a significant direct international transfer effect. However, the analysis did not consider the possible effect of forest industries moving out of the country as a result of higher timber prices and reduced domestic timber supply. Such an event would lead to investment transfers from Finland to other countries, thus transferring the conservation effects across the national borders in the long-run. In fact, it is possible that such behavior by the Finnish forest industries might already have begun, as suggested by the recent tendency of Finnish forest companies to invest mostly outside Finland in the form of mergers, buy-outs and alliances with foreign companies. In discussing the practical relevance of a cost analysis of strict forest conservation it can be argued that both in the United States and in Europe, forest protection programs, and the political processes underlying them, have initially given too limited attention to the related economic consequences (see e.g. Raettig and Christensen, 1999). Political decisions might not have been based on a careful consideration of the benefits and costs of land protection schemes. This study argues that political decisionmaking would benefit from careful studies of the economic costs of land protection programs. This is all the more important since there is evidence among the general public that the cost to the national economy is a popular argument against nature protection programs (Pouta et al., 2000). It is true that an economic cost analysis such as the one offered here may over-estimate the economic

12 82 J. Leppänen et al. / Forest Policy and Economics 7 (2005) cost of a forest conservation measure by ignoring the supply side effects of increasing resources (labor, capital, forest land) freed from the forest sector to other economic activities. For example, in retrospect, it can be argued that in the northwestern United States old-growth forest conservation measures may have speeded up economic development by forcibly freeing capital and labor resources from an oldeconomy sector for use in the new economy (see also Marsinko et al., 1997). This could also happen in Finland should large forest conservation programs be implemented. However, while shock therapy may have positive effects through accelerating the restructuring of the economy, the received wisdom in economics is that under perfectly competitive markets such restructuring would happen even without forest conservation programs. Thus, given perfect markets, any enforced deviation from the natural speed of economic restructuring must represent welfare losses to society. Acknowledgements We thank Jari Kuuluvainen and Markku Ollikainen, and two referees of this Journal for their valuable comments on the earlier versions of this paper. Appendix A. Statistical results for VARX(2,1) model for Finnish forest sector (OLS-estimation) DATA: 1954 to 1998, 45 observations Variable Coefficient ln Q t ln P t ln g t ln Qp t ln RawImp t Constant * 0.237* Trend * ln Q t * ln Q t ln P t * ln P t * 0.488* ln g t * ln g t * ln Qp t * * ln Qp t * ln RawImp t * * ln RawImp t * 0.008* 0.071* ln Imp P t * ln Imp P t ln Exp v t 1.162* 1.216* 0.011* 0.466* ln Exp v t * 0.970* * r t 0.017* 0.015* 0.003* r t * Standard deviations of endogenous variables and model residuals Correlation of actual and fitted AIC: lags 1: : : : Vector AR 1 2 F(50, 62)=1.087 ( P-value: 0.311). Vector normality m 2 (10)= ( P-value: 0.143). *Statistically significant at 10% critical level.

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