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Global softwood lumber trade : a spatial equilibrium model Delcourt, Gregg Vincent 1995

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GLOBAL SOFTWOOD LUMBER TRADE: A SPATIAL EQUILIBRIUM MODEL by GREGG VINCENT DELCOURT B.Sc. (Agr.), University of British Columbia, 1993 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in v THE FACULTY OF GRADUATE STUDIES (Department of Agricultural Economics) We accept this thesis as confonriing to the required standard THE UNIVERSITY OF BRITISH COLUMBIA July 1995 © Gregg Vincent Delcourt, 1995 In presenting this thesis in partial fulfilment of the requirements for an advanced degree at the University of British Columbia, I agree that the Library shall make it freely available for reference and study. I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by the head of my department or by his or her representatives. It is understood that copying or publication of this thesis for financial gain shall not be allowed without my written permission. Department The University of British Columbia Vancouver, Canada DE-6 (2/88) 11 ABSTRACT This study uses a partial spatial equilibrium model to analyze changes in global softwood lumber trade. Based on work by Adams and Haynes (1980) and Cardellichio et al. (1990), trade flows from 15 different regions are analyzed using elasticity estimates and production, trade and price data from the year 1987. Unlike the earlier models, this one focuses on British Columbia (BC) and how proposed changes in the Annual Allowable Cut (AAC) will impact trade flows and prices. The model establishes a base case of expected future conditions from which a variety of different scenarios are tested. Prices, production, consumption and trade flows are calculated over a 38 year time period, 1987-2025. The results of the model indicate that changes in BC production cause an increase in the world average price until production responds to this shortage. As BC experiences a decline in production, exports to the United States are replaced by the increased production in the US South. However, BC continues to increase its exports to Japan and Interior Canada as demand for softwood lumber grows over time. Increases in transportation costs affect production and trade flows by forcing many importers to increase production and supply their domestic market. BC exports to Japan increase in response to this redistribution of trade. Welfare results indicate that, in the long-term, BC is better-off with a reduduction in AAC. A decline in production in BC causes world prices to rise, therby causing producer surplus to increase in the BC market. This comes at relatively small cost to the BC domestic consumer. These results suggest that provincial forest companies are not jointly maximizing returns from lumber production. iii TABLE OF CONTENTS ABSTRACT ii TABLE OF CONTENTS iii LIST OF TABLES v LIST OF FIGURES vi ACKNOWLEDGMENTS vii CHAPTER ONE 1 INTRODUCTION 1 1.1 INTRODUCTION 1 1.2 BACKGROUND INFORMATION 2 1.2.1 Demand Regions 6 1.2.2 Supply Regions 8 1.3 PURPOSE OF THE STUDY 9 1.4 OUTLINE OF STUDY 10 CHAPTER 2 11 LITERATURE REVIEW 11 2.1 LITERATURE REVIEW 11 2.2 TRADE MODELS 12 2.2.1 Two-Region, Nonspatial Models 12 2.2.2 Multi-region, Nonspatial Price Equilibrium Models 12 2.2.3 Spatial Equilibrium Models 13 2.2.4 Trade flow and market share models 14 2.2.5 Transportation models 15 2.3 FOREST SECTOR MODELS 15 CHAPTER 3 25 THEORETICAL MODEL 25 3.1 CONCEPTUAL MODEL 25 3.2 BACKGROUND OF THE SPATIAL EQUILIBRIUM MODEL 26 3.2.1 The Two Region Case 27 3.2.3 Multi-region Case.... 29 CHAPTER 4 32 EMPIRCAL MODEL 32 4.1 THE LUMBER TRADE MODEL 32 iv 4.2 OVERVIEW OF MODEL 32 4.3 THE MODEL 33 4.3.1 The Regions 34 4.3.2 Data ... -35 4.4 MODEL SPECIFICATION 36 4.4.1 Consumption Function 37 4.4.2 Production Function 39 4.4.3 Trade Functions 41 4.5.4 Transportation Costs 42 4.5 GENERAL ASSUMPTIONS OF THE SE MODEL 44 4.5.1 Homogeneity 44 4.5.2 Competitive Market Assumption .46 4.5.3 Preferences 46 4.5.4 Inertia 47 4.6 MODEL ASSUMPTIONS 48 4.7 MODEL VALIDATION 50 CHAPTER 5 53 BASE CASE RESULTS AND SIMULATIONS 53 5.1 BASE CASE SIMULATION 53 5.3 REGIONAL ASSUMPTIONS 56 5.3.1 British Columbia..... 57 5.3.3 Pacific Northwest 58 5.3.4 US South 59 5.3.5 Chile and New Zealand 60 5.4 WELFARE MEASURES 61 5.5 THE BASE CASE FORECAST 62 5.5.1 Export Regions 62 5.5.2 Import Regions... 64 5.5.3 Prices 65 5.6 ALTERNATIVE SCENARIOS 66 5.6.1 Decrease in BC Production 67 Welfare Changes 69 5.6.2 Increased Production in the Former USSR 70 Welfare Changes 71 CHAPTER 6 72 SUMMARY AND CONCLUSIONS 72 6.1 SUMMARY AND CONCLUSIONS 72 6.2 MODEL LIMITATIONS 74 6.3 SUGGESTIONS FOR FURTHER STUDY 75 REFERENCES 110 V LIST OF TABLES Table 4.1: Exporting and Importing Regions 77 Table 4.2: Total Quantity Produced 78 Table 4.3: Lumber Supply and Demand Elasticities 79 Table 4.4: Transportation Costs 80 Table 4.5: Value Differential 80 Table 4.6: Regional Assumptions of Future Supply Conditions - Base Case 81 Table 5.1: Simulated Production, Consumption, Exports and Prices 82 Table 5.2: Simulated Production, Consumption, Imports and Prices 87 Table 5.3: Direction of Trade - Base Case 91 Table 5.4: Projected Real Softwood Lumber Prices 93 Table 5.5: Projected Softwood Lumber Price Index 93 Table 5.6: Direction of Trade - Reduced AAC in BC (Scenario a) 94 Table 5.7: Direction of Trade- Reduced AAC in BC (Scenario b) 96 Table 5.8: Direction of Trade - Increased Production in the Former USSR 98 Table 5.9: Change in Producer and Consumer Surplus - Scenario: Change in AA Ca.... 100 Table 5.10: Change in Producer and Consumer Surplus - Scenario: Change in AACb.... 101 Table 5.11: Change in Producer and Consumer Surplus - Scenario: Increased Production in USSR 102 vi LIST OF FIGURES Figure 1.1: Comparative Forest Productivity 103 Figure 1.2: BC Lumber Shipments 104 Figure 2.1: Two Region Trade Model 105 Figure 5.1: Projected Exports - 1987-2025 106 Figure 5.2: Projected Imports - 1987-2025 ...107 Figure 5.3: World Softwood Lumber Exports - Market Share 108 Figure 5.4: World Softwood Lumber Imports - Market Share 109 Vll ACKNOWLEDGMENTS I would like to thank my advisor, Dr. Casey van Kooten, for his assistance, guidance and encouragement throughout my academic career. He has been a great help and a great 11:30am work-out partner during my stay at UBC. Dr. Mary Bohman, a member of my thesis committee, has always been a great help and excellent teacher since the first class I took as an undergraduate. Her direction was highly useful in the writing of this thesis. As well, I would like to thank Dr. W.R. Wilson who gave me the opportunity to develop this project. His input was greatly appreciated. I would also like to thank my colleagues at Agricultural Economics and FEPA for their help and input. A special thanks to Casey for the financial assistance he has provided me during my years at UBC. I would also like to thank my family for their encouragement and support. Finally, I offer my deepest thanks to Michelle Johansen for her love, reassurance and understanding throughout my work; I will always be grateful. 1 C H A P T E R O N E I N T R O D U C T I O N 1.1 I N T R O D U C T I O N BC is experiencing a reduction in the supply of merchantable timber. Pressure from the public and environmental groups has forced the provincial government to legislate new harvesting regulations including smaller clear-cuts and more intensive reforestation activities. Projections of harvest levels indicate there will likely be a fall-down period in coming years. This has facilitated the proposal of a reduction in the current annual allowable cut (AAC) by 20 to 25 percent from its current levels (Ministry of Forest 1994; Smyth 1994). Such a proposition could cause serious problems for the BC forest industry with its current capital-intensive structure. Unemployment, mill closures and increased production costs are only a few of the foreseeable problems that such an event could cause. The commodity-driven BC forest industry should consider investing more money in areas where it can compete more effectively. High quality old-growth timber is BC's competitive advantage and should be marketed in a manner that yields the highest net revenue per cubic metre. World softwood lumber supply is increasing, however there are wide variations in regional production. Emerging regions and other alternative sources are threatening BC's market share. The US South, New Zealand and Chile are producing softwood lumber on rotation periods many times shorter than in BC. The former USSR is another producer that has enormous potential to supply softwood lumber. 2 This study examines the effects of forest policies and increased global supply on international trade flows and, in particular, the BC softwood lumber industry. A partial equilibrium trade model is used to predict how different regions react to different supply and demand conditions. The model predicts changes in softwood lumber trade, production, consumption and prices for 7 demand regions and 8 supply regions over a 38 year period spanning from 1987 to 2025. The base case scenario, which is derived from explicit assumptions about future supply and demand conditions, yields projections regarding future market conditions. From the base case, alternative circumstances are tested and the results are discussed. The paper examines the following scenarios: 1. a reduction in provincial AAC with an increase in AAC over time, 2. a reduction in provincial AAC with no increase in AAC over time, and 3. an increase in supplies from alternative sources. 1.2 BACKGROUND INFORMATION BC continues to dominate the international softwood lumber market. In 1991 BC accounted for almost one-third of all softwood lumber exports and one-tenth of global production (COFI 1993). However declining domestic supply, rising wood costs and deflated international prices have created uncertainty about the future direction of the BC forest industry. Provincial legislation is requiring a reduction in AAC at the same time that stumpage costs are increasing. BC firms are objecting to higher stumpage costs saying that they are unable to compete in times of depressed prices (Hamilton 1995). The forest industry 3 is being forced to alter its focus as these issues, as well as government, special interest groups and international pressure create an uncertain environment for the marketing of BC wood products. As timber demand rises, traditional timber production and consumption patterns are modified. Production changes include the utilization of marginal species and smaller trees, investments in plantation forests (including on agricultural land), and biotechnological investments in enhancing tree growth (intensive margin); and the use of marginal forest areas and removal of remaining trees from agricultural land (extensive margin). Plantation forests have become common-place in non-traditional wood producing regions such as parts of South America. Other plantation intensive regions include Australia, New Zealand and the US South. In times of inadequate supply, New Zealand radiata pine has become a substitute for ponderosa pine and other alternative US species (Apthorp 1994). As these plantations mature and become areas of reliable timber supply, consumers of BC lumber may look to alternative supplies. The US has been investing in plantation forest for over half a century. Much of the US South is second and third growth timber. The US reforested over one million hectares (ha) in 1987 and continues to plant at a rate of approximately a million ha every year. Other countries such as New Zealand, Chile, Australia and Brazil are investing in plantations with high yield/short rotation species and are expected to make an impact on world supply in the near future. Another area with abundant resources is the former USSR. Since BC lumber has a quality advantage over these regions, direct comparisons must be made carefully. 4 Due to its large inventory and low rates of regeneration, BC forests have had little investment in replanting and intensive reforestation Until the last couple of decades. In fact, silviculture investment still makes little economical sense in most regions (Benson 1988; Thompson et al. 1990). In the immediate future, BC will continue to compete effectively as an international supplier although future supply conditions are in question. As indicated in Figure 1.1, BC growth rates are one-fifth of those in the southern hemisphere, making forest management plans even more important over the long-term. BC needs to pursue a forest management plan whereby BC's resources are used in a manner that maximizes provincial welfare, not harvested timber volume. As BC supplies decrease, the cost and access of timber becomes an additional constraint for BC producers. Continual harvest of old-growth alters specie composition and end-use products. The 60-100 year rotation length of BC forests creates a great deal of uncertainty regarding silvicultural investment and harvesting patterns. Since future demand for lumber is uncertain, investment in alternative species are a consideration for the BC forest industry. Currently timber is harvested for lumber, and the residual is used for pulp and paper. An option exists, for some regions, to grow timber used exclusively for pulp and paper. Hardwoods, such as alder and poplars, with much shorter rotation lengths are favourable for this allocation of fibre. Although future timber demands are uncertain, BC does recognize that its comparative advantage over the emerging regions lies in its supply of higher quality appearance grade timber. Flora, Anderson and McGinnis (1991) conclude that commodity grades will experience flat or declining real prices due to increased supply in plantations and higher rates 5 of utilization, but that 'performance' grade lumber will experience a steady and strong rise in prices. There has already been a steady increase in 'performance' grade lumber prices over the past 15 years, and industry has responded with new technology and higher recovery rates, while consumers have responded by accepting lower grades and veneer overlays, and by substituting non-wood products. An example of technology and substitutability of appearance grade lumber is found in wooden doors. Until the 1970s these products were made of solid material using an estimated 81 foot board measure (fbm) of wood per door. When the same door is made of a construction grade core with a veneer overlay there is a reduction of over 90% in clear material used (MacMillan Bloedel 1994). Evidently rising prices and declining supplies are affecting technology, production, tastes and the consumption of lumber. Technological change in lumber products may work to BC's disadvantage. As technology develops, fiber could substitute for sawtimber and cause a decline in demand for BC's construction material. Historic trade patterns will prevail over time, but it is the eroding of market share that is of concern in BC lumber trade. Estimates of future timber supplies can be made through existing and future planting and growing stock (however there is still a high degree of uncertainty), whereas demand fluctuates due to a variety of market, social and political conditions. The following is a summary of demand and supply regions that are of particular interest in global lumber trade. 6 1.2.1 Demand Regions The BC forest industry is one that invests little in value-added goods and specializes in large volumes of commodity forest products. Exports of lumber outside Canada account for well over 75 percent of total volume produced. The US imports about 58 percent of total production (COFI 1993). Other consumers of BC lumber are Canada (20 percent), Japan (14 percent), and the UK (3 percent). Figure 1.2 illustrates how exports have fluctuated over time. The figure also shows how BC production is closely tied to US imports. Domestic production in BC's two primary export markets, the US and Japan, differ considerably. The US is the world's largest producer and consumer of softwood lumber, whereas Japan imports most of its timber leaving its domestic forest lands relatively untouched. Both of these markets have strong currencies compared to the Canadian dollar, thereby making BC wood products more attractive. The two demand regions are briefly discussed below. The US is the world's number one producer of softwood lumber accounting for approximately 25 percent of world production, of which less than 10 percent is shipped outside of the country. Until the late 1980s the US West dominated US lumber production, however the areas preserved for the spotted owl has caused a dramatic decrease in timber harvest in this area and has allowed the US South to increase its market share of US lumber production by over 10 percent. US South production is expected to continue to grow to account for over 50 percent of US lumber market share by the year 2000 (Widman 1994). Although the US is the largest producer, it is also the largest importer and consumer. In 1992, over 58 percent (19.6 million cubic metres) of BC lumber was sent to the US (COFI 7 1993) . This dependency on the US market could cause BC exporters to scramble to find alternative markets as plantations from the US South become more competitive (Smyth 1994) . Japan is BC's other primary international export destination. There is a demand for large dimension appearance grade timber in Japan. BC old-growth possesses favourable qualities which are well suited for use in the Japanese construction industry for decorative purposes. Structural lumber is being considered more closely as the Kobe earthquake of 1995 proved the viability of the 2-by-4 frame construction style. Houses of Vancouver Village in Kobe were completely undamaged compared to houses built using the traditional post and beam structure (Column One 1995). Exports to Japan have more than doubled over the past 10 years and trade is favorable between the two regions due to location. BC's market share in Japan's sawtimber trade has increased considerably, but cost advantages in Chile, Brazil and New Zealand could cause a change in the demand for 'performance' grade lumber. Japan's ability to provide for its domestic demand should also be considered. As Japan's domestic stock of timber matures, it will become more self-sufficient in timber resources. Over 40 percent of Japan's forest base has been planted over the past 100 years. After W.W.II there was a large depletion of forest resources in order to rebuild bombed cities. In the years that followed, timber shortages ensued and afforestation projects did not solve short-term shortages. In 1963 Japan started importing from the US and USSR. Import prices were more attractive than harvesting less accessible areas and by 1970 imports accounted for over half of Japanese wood consumption. Japan still relies extensively on imported softwood 8 lumber, but when prices rise and forest areas mature, Japan may look towards its domestic resources. 1.2.2 Supply Regions Global supply of softwood lumber is increasing over time due to increased harvests and developments in technology. However, as resources become less abundant and demand conditions change, countries that were net exporters could become importers. For example, India and China are satisfying their current domestic demands, but this could change depending on a variety of factors including an increase in living standards and changes in tastes. Other countries, such as Japan, have been net importers, but, as their domestic supply matures, demands for overseas' timber could lessen. Other factors that cause trade patterns to change include environmental concerns and newly emerging suppliers. Environmental concerns encompass the spotted owl controversy in the Pacific Northwest and old-growth preservation in BC. Increased supplies could come from regions such as the former USSR, and, as mentioned above, New Zealand and Chile. The ability of the former USSR developing into a large player in lumber trade is under a great amount of uncertainty due to the political situation in the area. This issue is addressed later in the study. The supply regions chosen in this paper, making up about 85 percent of softwood lumber, include Canada, the US, Scandinavia, Chile and New Zealand. A more comprehensive list of exporting nations is desirable; however, due to data and model restrictions a partial list is used. Only softwood lumber is investigated since hardwood lumber 9 does not yet appear to be a substitute. By concentrating on softwood lumber trade, the model can be used to predict the supply response of the BC industry. 1.3 PURPOSE OF THE STUDY In this study, a trade model that estimates the effects of BC policy on long-term global lumber trade is developed. The model identifies the changes in regional production, consumption, prices and trade when global welfare is maximized and a steady-state equilibrium is established. The transition from current market conditions to equilibrium conditions depends on clear assumptions and explicit modeling techniques. The provincial government's introduction of new policies in forest management is of particular interest because it affects investment decisions, land use and costs of production. By modeling lumber trade, responses to price and resource constraints can be used to estimate future demands. The forecasts act as indicators to the forest industry of long-run equilibrium conditions in global markets. These estimates aid in the development of policy and in the making of rational investment decisions—private and public. By modeling different policy scenarios, projections of future trade flows are generated and different scenarios are compared. The results are of interest for policy recommendations and for investment decisions. 1.4 OUTLINE OF STUDY 10 This study is divided into six chapters including this introductory chapter. Chapter 2 is a literature review of the various classes of trade models and their applications in forest trade. Recent research and forest trade models are then examined followed by a brief discussion of their limitations. Chapter 3 details the theoretical background of the spatial equilibrium model. A two-region case is first explained diagrammatically followed by the mathematical theory necessary to solve a multi-region case. The limitations of the model are also discussed within this chapter. Chapter 4 presents the model and clearly details the construction of the model. The constraints and assumptions necessary in model development follow. Chapter 5 presents the base-case model and relates the results to projections found in other studies. Different scenarios are then forecasted and compared with the results of the base projections. These scenarios include BC forest policy change in the size of the AAC, the effects of alternative producers supplying export markets and the effects of an energy crisis that causes transport costs to increase. Chapter 6 summarizes the findings and details the recommendations suggested by the model. 11 CHAPTER 2 LITERATURE REVIEW 2.1 LITERATURE REVIEW Research in spatial models has progressed a great deal since the early 1960s as the importance of international trade in all sectors has demanded more in-depth investigation. Inter-regional trade is increasing as restrictions such as tariff and non-tariff barriers, embargoes and technology are overcome. As a result, free-trade agreements and regional trading blocks are being formed, thus creating a demand for research in spatially optimal trade conditions. Policies, economic development and different business strategies require analysis using international trade models. The development of spatial models has come primarily from agriculture and general economic research; the forest sector did not venture into this area until the early 1970s. Spatial models can be classified as: 1) Two-region, nonspatial models; 2) Multi-region, nonspatial price equilibrium models; 3) Spatial equilibrium models; 4) Trade flow and market share models; and 5) Transportation models. Each model is explained below, including a discussion of its advantages, disadvantages and limitations. 12 2.2 TRADE MODELS 2.2.1 Two-Region, Nonspatial Models The two-region, nonspatial model divides the market into two geographical regions: the target region and the rest of the world (ROW). The target region is either a net importer or exporter and has either an excess demand or supply curve. If it is a small player in the world market, it is deemed a "small country" and has no influence on world price. If it has a significant amount of market share, it is deemed a "large country," price is endogenous, and trade prices and quantities are affected. This model is the most aggregate of all trade models and has been used very little in the forest sector. Its primary advantage is its ability to focus on a specific country or region. It is useful when only crude approximations are needed for a more detailed analysis of a domestic market. The disadvantage of this model is its high level of aggregation. The ROW category groups all bilateral trade together, thereby making supply and demand elasticities unreliable. Also, the decision of whether the region is a "small country" or "large country" is arbitrary and can lead to problems in model estimation. 2.2.2 Multi-region, Nonspatial Price Equilibrium Models The multi-region, nonspatial price equilibrium model calculates quantities traded by each region, but it does not calculate trade flows among regions. Excess demand and excess 13 supply are used to calculate a global equilibrium price that has been adjusted to include the transport costs between each region. Since this model cannot identify bilateral trade flows, it cannot be used to predict specific bilateral trade restrictions. It does, however, effectively model tariff or quota restrictions imposed on all regions by a single region. The primary disadvantage of this model is the assumption of a single global equilibrium price. 2.2.3 Spatial Equilibrium Models The spatial equilibrium (SE) model calculates prices, quantities and bilateral trade flows endogenously in the model. Transportation costs are used to quantify the spatial location of trading partners. When these costs are minimized, optimal trade conditions prevail. A partial SE model is one that only considers one commodity while assuming all other factors constant. The SE model is a desirable modeling technique when long-term analysis is required. Over time a market moves toward competitive equilibrium and trade routes maximizing individual welfare are developed. Thus, trade relationships that do not exist may very well develop over the long-term. The SE model is able to predict these new trade patterns. Adams and Haynes (1987) discuss the advantages of using the SE model and note that "...numerous geographic regions are readily accommodated...with little additional solution cost or increase in model complexity. This stands in distinct contrast to any other general modeling approaches." Also, the SE model provides the flexibility to perform changing 14 policy simulations by simply altering the objective function or constraints to imitate the proposed policy. The limitations of the SE model focus on the assumptions implicit in the model. These assumptions include perfect competition, homogeneous products and per unit transportation costs. A detailed analysis of this model is found in Chapter 3 as it is the model-type used in this study. 2.2.4 Trade flow and market share models Trade flow and market share models explain bilateral trade flows between specific regions for specific relationships. (This contrasts with the SE model where trade flows between all regions are determined.) These models attempt to explain differences in import demand characteristics depending on the region and the product. For each region and commodity, a market share relationship exists; it is a function of relative import prices and substitutes, and other derived demand shifters such as output price, economic indicators and end-use activity measures (Adams and Haynes 1987). Cardellichio and Veltkamp (1981) use this form of model to explain imports and inter-regional shipments in the softwood lumber and plywood markets. A market share relation is developed for each region to explain each supplier's preference to supply less distant markets. Another form of this model uses explicit supply and demand equations for each bilateral trade flow. For each region, a solution of equilibrium prices and flows is determined. Data requirements are extensive for these models, but trade flows are generally more accurate than in the SE model. Short-term projections are more accurate as the distributed lag of price 15 and volume achieves the appropriate inertia properties. The major limitation of these models is their inability to model trade flows that did not exist historically. Also, the models become very large and data requirements are extensive when many producers and consumers are modeled. 2.2.5 Transportation models The transportation model minimizes transportation costs of bilateral trade flows given the unit costs of shipment between each port (Koopmans 1948). This model is similar to the SE model where trade flows are transportation cost minimizing; however, the transportation model is less general than the SE model. Supply, demand and prices are set outside the model and bilateral trade flows are approximated. 2.3 FOREST SECTOR MODELS There have been major developments and extensive undertakings in forest models recently. The SE model is used most frequently and viewed advantageous for modeling trade and long-term policy effects (Adams and Haynes 1987). Studies have become more complicated and the number of regions and products have become substantial in number (Cardellichio et al. 1989; Boyd, Doroodian and Abdul-Latif 1993; Kallio, Dykstra and Binkley 1987). The methodology and results of these studies are applied in the current study. 16 The first noteworthy forest market model was the Timber Assessment Market Model (TAMM)by Adams and Haynes (1980). TAMM is a SE market model. The methodology of TAMM is still the basis of numerous models, including most of the models discussed below and the current one. TAMM models nine supply regions and six demand regions in North America for two major forest sectors (final products and stumpage) The final products (including lumber) are modeled with their individual supply and demand curves linked to the stumpage supply. Due to difficulties experienced in estimating the simple linear demand curve, the demand functions for lumber are derived using the national demand elasticity and regional demand quantities and prices.1 Regional demand elasticities are derived by relating the national US lumber demand elasticity, -0.35, to the national/regional price ratio. This is based on the assumption that the national/regional elasticities ratio is equal to the price ratio. The basic form of the demand equations is: where Dit is the quantity demanded of lumber in region / in year t; Pu is the delivered price of lumber in region i in year t; and Yo, Yi are the intercept and slope parameters, respectively. Adams and Haynes (1980) refer to their regional elasticity derivation as the hybrid approach. The above demand equation is used in the current study, with some modifications. 1 The expected size and signs of the coefficients in the demand functions are not intuitive. This phenomena was also found by Berck (1979) who had to use different price variables. 17 The product supply functions in TAMM are represented by a lagged supply term, average product price at the mill, "stump to car" transport price and an over run factor. Lumber supply elasticities with respect to price range from 0.21 in the Western Pacific Northwest to 0.79 in the Southern US. The estimate for the supply elasticity of Canada is 0.47, with an export supply elasticity estimated at 0.89. The output of TAMM consists of prices and quantities, harvest volumes and stumpage prices, and the distribution of shipments. The model is calibrated to 1978 values and achieves equilibrium for future time periods by solving for each year of the forecast. The model was validated by plotting actual values against predicted values for the ten-year period 1966 to 1976. The predicted values replicated actual values very closely, especially for lumber. US imports from Canada were overstated due to demand elasticities changing over time (Adams and Haynes 1980). Numerous simulations were then performed. The simulation of interest for the current study estimated the changes in exports when Canadian production costs increased over time. This scenario comes from the assumption that second growth timber will take longer to mature, thus creating a fall-down in supply and force firms to log the extensive margin. TAMM estimates that an increase in production costs of $6/MBF in 2010, $11/MBF in 2020 and S17/MBF in 2030 will cause a 14 percent decrease in Canadian timber production and 31% decrease in exports to the US. A similar analysis is performed in the current study, although the decrease in supply is due to provincial government policy. A SE model developed by Boyd and Krutilla (1987) is conceptually related to TAMM with some improvements. Boyd and Krutilla (1987) analyze lumber trade between 34 supply 18 regions and 39 demand regions in Canada and the United States. They pay particular attention to transportation costs, exchange rates and tariffs in the trade. Demand elasticities are derived indirectly from local construction activity and lumber prices. It is assumed that the construction component induces demand elasticities to vary across regions. The supply of US domestic lumber is assumed to be fairly inelastic since US government timber sales are unresponsive to price. They comment that since excess supply cannot be more inelastic than the domestic market (see section 3.2), Canada's exports are able to vary with price. They use TAMM's estimate of Canada's export supply elasticity and find that Canadian exporters can lose up to 7 percent of their pre-tariff welfare if the US imposed trade restrictions (a 10 percent ad valorem tariff ). If suppliers are more responsive to price changes (supply is more elastic), a tariff will be more effective in reducing exports. Changes in the foreign exchange market had little impact on the demand for Canadian lumber. Sedjo (1983) modeled the economic returns of different forest regions with an emphasis on the potential of plantation forests. This study is of interest because it establishes long-term supply potentials of plantations. Sedjo (1983) found that plantations of South America and the US South generate higher net present values (NPV) than temperate-climate wood-producing areas. Temperate regions experience negative NPV for scenarios that test high discount rates, high production costs and high transportation costs. A comparative advantage is found in areas of high yield/short rotation plantations. The implications of these findings suggest that BC's forest industry could come under pressure once its absolute advantage (in volume of timber) is minirnized. 19 The International Institute for Applied Systems Analysis Forest Sector Project developed the Global Trade Model (HASA GTM) (Kallio, Dykstra and Binkley 1987). The HASA GTM uses a SE model to represent the global forest sector and international trade in forest products. The model estimates production, consumption, trade and prices of 16 forest products. It also projects values for the year 2030 given a variety of scenarios of structural change. These scenarios include changes in global economic growth, changes in the strength of currency exchange rates, trade liberalization, increased supply from the USSR, and environmental effects from acid rain and global warming. The basic model is a partial market equilibrium economic model with linear constraints and a non-linear objective function. The model links the components of each region's forest sectors to address the complexity of the forest industry. Timber supply, processing, demand and trade are all linked to their respective markets. There are 10 iterations of the model in a 50 year forecast where equilibrium for any time period is dependent on the previous time period. The model does not make future time periods endogenous (i.e., it is not a dynamic model). The results of the IIAS A GTM indicate trends in the forest sector that are important to the current study. The model predicts that Canada's share of softwood lumber trade will increase with a majority being shipped to Japan and the Rest of the World. The model also finds that there is not a significant increase in real prices of softwood lumber. However, when the model allows for increased supply from the USSR, the results indicate that the main losers are Canada, the US, Southeast Asia, Brazil and Chile. As in any empirical model, there are numerous assumptions and generalities that create limitations in the estimating properties of the model. The fundamental limitation in this model 20 is its inability to address inter-temporal optimization; decisions in time t+1 have no effect at decisions in time t. This causes a bias in the model that is also a problem in most trade models. Another problem of the HASA GTM is the reliability of its database (Cardellichio and Adams 1987). Data are inconsistent and unreliable for some cases and replication is problematic. Although the model has the above limitations, it does provide a framework for analysis of the forest sector trade and different trade scenarios. Sedjo and Lyons (1991) developed an optimal control model, the Timber Supply Model (TSM), that addresses the changing age and volume of forests and the changing state of the forest. Inter-temporal investment can be modeled to allow for optimal rotation length and optimal old-growth depletion. The TSM uses a partial equilibrium approach by assuming that the forest sector is a price taker with respect to macro economic parameters such as interest rates, prices of intermediate goods and factor input prices. The model calculates each region's supply and global demand; it does not predict bilateral trade flows. It is of interest for the current study because it investigates the draw down of inventories of old-growth stands and models the transition to second-growth and plantation timber. The TSM estimates that the global demand for timber will increase at 0.6 percent to 0.9 percent a year to the year 2035. These results are similar to the HASA GTM that estimates a growth rate of 1.2 percent per year to the year 2030. Sedjo and Lyons (1991) also predict that real prices of industrial roundwood will remain relatively constant over the estimated time horizon. A revised TSM (Sedjo et al. 1994) incorporates the expected decline in harvest in BC and the US West. The results show that average prices only increase about 5 percent over the original TSM base case results. Increased production from the US South, Scandinavia and 21 emerging regions offset the reduction in supply in the Pacific Northwest. When demand is assumed much stronger, prices increase 30 percent over the base case and regions such as Eastern Canada become an important alternative source. These results are consistent with Messmer and Booth (1993) who found that Ontario harvest levels are sensitive to relatively minor changes in price. Boyd, Doroodian and Adul-Latif (1993) attempt to quantify the consequences of reducing and eliminating lumber (pine and fir) tariff restrictions between Canada and the US, and the US and Mexico, in response to the North American Free Trade Agreement. They analyze trade flows, prices and welfare effects of all three countries using a SE model. Since transportation costs act like a tariff in the model, they simulate the tariff rate by raising transportation costs. Own-price elasticities are estimates from Adams, Boyd and Angles (1992) and cross-price elasticities between fir and pine are derived indirectly by relating construction activity to the input amounts of pine, fir and capital. In order to keep shipments of fir out of pine producing regions they impose exceedingly high transportation costs (US$220/m3). (This method is used in the current study to restrict trade between some regions.) The results suggest that the removal of tariffs on lumber cause consumption to increase and lumber to be allocated more efficiently. British Columbia is expected to gain over US$6 million dollars per year in a free trade scenario. The total change in welfare from the removal of tariffs is estimated as a US$35 million per year gain. Boyd, Doroodian and Adul-Latif (1993) conclude that the increase is relatively small compared to total industry revenues (about 2 percent), due in part to the lack of demand for lumber in Mexico. The 22 insignificant size of the gains from trade liberalization is a common finding of studies modeling trade liberalization. The most significant and most comprehensive forest trade model is the CINTRAFOR Global Trade Model (CGTM) developed by the Center for International Trade in Forest Products (CINTRAFOR) at the University of Washington (Cardellichio et al 1989). This is a SE model that builds on HAS A GTM. The CGTM modifies the IJASA GTM by covering less products (10 instead of 16) and substantially more regions (40 rather than 18). The CGTM reduced the number of products by combining all pulp and paper into one category and dropping fuelwood. It creates new categories to differentiate coniferous and non-coniferous products. As in the GTM the model is comprised of four components: timber supply, product supply, product demand and trade. The demand and supply curves are determined endogenously for all but 16 regions where they are determined exogenously by the user. These exogenous regions are ones that have poor data, specialized products or little trade, and include the former USSR, Eastern Europe, Africa and all of South America except for Chile. The user must provide output and consumption levels for these regions. The CGTM, as with the GTM, maximizes an objective function to determine optimal global welfare. The demand function uses a non-linear form and, for some regions, uses elasticities from other studies. The supply curves are determined in the same manner as the GTM. Also similar to the GTM, trade is banned between regions with negligible trade and regions that are not expected to trade in the future. The CGTM estimates transportation costs by simply taking the gross difference between import and export prices. This causes transport costs and other transfer costs (tariff and non-tariff barriers) to be included as one value. 23 Projections for coniferous softwood markets suggest that there will be a large increase in consumption in Western Europe, China and the US West. Production increases are projected to occur in Eastern Canada, the US South and the US North. Policy simulations with the CGTM are made through marginal increases rather than simulating extreme absolute changes since marginal policy changes are viewed more realistic. Perez-Garcia (1993) uses the CGTM to study the global impacts of a reduction in softwood from North America. The model reports considerable increases in log prices for all regions over the next 50 years and suggests price increases of 20 percent in the Pacific Northwest, 60 percent in the US South, 90 percent in Interior BC and 20 percent in Chile. In the study, Perez-Garcia (1993) estimates two scenarios: a reduction of 33 million cubic meters from Western Canada and the US West, and an increase in log exports from Siberia (with the reduction in North American supply). Under the first scenario, the results indicate further price increases and substantial welfare losses to lumber consumers. Canadian consumers lose US$141 million and US consumers lose US$970 million. Lumber producers gain US$512 million in Canada and US$754 million in the US. Globally, lumber consumers lose about US$2.5 billion dollars annually. The second scenario does not change welfare conditions much for Canada and the US, but it does reduce global losses to only US$1.5 billion. The study states that high cost forest producers are the overall winners. The preservation of one hectare by a low cost producer is offset by the harvest of 1.12 to 1.61 hectares by high cost producers. The ratio is even higher if Siberia enters into the export market. These results have global environmental implications. 24 The current study draws on the methodology, data and results of the papers reported above. Particular attention is given to the methodology of TAMM and CGTM. It is these models that the current study builds on by modifying scenarios, updating data and altering supply and demand specifications. The results of the above models are compared to the current study in the analysis that follows. 25 CHAPTER 3 THEORETICAL MODEL 3.1 CONCEPTUAL MODEL The trade model presented in this study is a spatial equilibrium model that optimizes welfare by estimating quantities traded, international prices and trade flows among regions. Under the assumptions of the SE model, countries with high costs of production or inadequate supplies look to world markets to satisfy domestic demand at lower prices. Likewise regions supply world markets at higher prices than they can otherwise acquire domestically. Regions continue to trade as long as prices, net of transportation and other transfer costs, differ from domestic prices. An iterative process is used to maximize the welfare subject to certain constraints. A spatial price equilibrium is established when the demand price is equal to the sum of the supply price and transportation costs for all regions. Consumers rearrange their consumption bundle in favour of cheaper suppliers until this equilibrium is reached. A SE model is used so bilateral trade flows can be observed. As more countries enter the world market, interactions become more complex and optimal trade flows become less obvious for each region. The SE model assumes cost minimizing (profit maximizing) behavior by the consumer (producer). Therefore, by solving for a global maximum, regional welfare is maximized. Enke (1951) gives a precise summary of the one commodity model as follows: 26 "There are three [or more] regions trading a homogeneous good. Each region constitutes a single and distinct market. The regions of each possible pair of regions are separated—but not isolated—by a transportation cost per physical unit which is independent of volume. There are no legal restrictions to limit the actions of profit-seeking traders in each region. For each region the functions which relate local price are known, and consequently the magnitude of the difference which will be exported or imported at each local price is also known. Given these trade functions and transportation costs, we wish to ascertain: 1) the net price in each region, 2) the quantity of exports or imports for each region, 3) which regions export, import, or do neither, 4) the aggregate trade in the commodity, [and] 5) the volume and direction of trade between each possible pair of regions." 3.2 BACKGROUND OF THE SPATIAL EQUILIBRIUM MODEL The partial SE, international trade model for one commodity was first developed by Samuelson (1952). Samuelson formalized Cournot's (1838) price relations problem between two spatially separate markets and related it to Enke's (1951) paper, which generalized the problem of inter-spatial markets. Enke used electric circuits to illustrate the solution, whereas Samuelson approached the problem using mathematics. Prior to Samuelson no one had 27 proposed a method to quantify the complex relationships in spatial analysis. Samuelson (1952) stated that the model is solved "by trial and error or by a systematic procedure of varying shipments in the direction of increasing social pay-off" until a unique maximum exists. In the days of Samuelson's work, complex problems were difficult to estimate due to the complicated iterative procedures; today, computers perform these calculations in a fraction of the time. 3.2.1 The Two Region Case To illustrate the concept of price equilibrium, the two region case is used. Consider Figure 2.1. It is obvious from this graph that trade will occur since equilibrium prices differ in the two regions. Without trade, price in Region A is P A and the price in Region B is PB. If a region's domestic price is greater or lower than the other region's price, net of transportation costs, it will engage in trade until an equilibrium is established. If a region cannot clear its market domestically, it becomes an exporter and its excess supply (ES) function is derived by laterally subtracting the demand curve from the supply curve at every price greater than the no-trade equilibrium in the domestic market. Likewise, the excess demand (ED) curve is derived from the importer's domestic market by laterally subtracting the supply curve from the demand curve at every price less than equilibrium in the domestic market. Notice that the trade functions (ES and ED) are more elastic than their respective functions in the domestic markets. 28 To derive Region A's excess demand elasticity, domestic prices, quantities and the slopes of the domestic supply and demand curves are required. Given that the quantity of excess demand is equal to the quantity demanded less the quantity supplied, = QD-QS, the elasticity of excess demand, €ED , is derived as follows: cP dP cP P _dQD P dP &ED ~ QED 8QD & QED & QED P QD SQs P Qs £ ED ~ SP QED QD Qo 'ED & QED QS QED (3.1) (3.2) (3.3) (3.4) where P is the domestic price, £D is the elasticity of demand and £, is the elasticity of supply. (The elasticity of the excess supply function is derived in a similar manner.) Note that if £ s andQy are greater than zero, the excess demand curve will be more elastic (more price responsive) than its domestic demand curve. Intuitively, this can be explained by analyzing the demand for domestically produced goods. As trade lowers prices in the importing region, demand is diverted away from the domestic market to the international market where prices are lower. A comparable analysis can be used to relate the supply function in the exporting region to its excess supply curve. Under conditions of zero transportation costs, a price equilibrium occurs where the excess-supply and excess-demand curves intersect. Region A imports quantity Q* from 29 Region B and world price is equal to P*. This is the solution of a nonspatial price equilibrium without transportation costs. The inclusion of transportation costs of wz per unit increases import prices, decreases export prices and reduces the amount traded. In this case Q** (< Q*) is traded and transport costs are equal to area xywz in Figure 2.1. When the area between the excess demand and excess supply curves is at a maximum, net of transportation costs, a spatial price equilibrium is calibrated. Samuelson (1952) termed this area (Axz+Byw) as the net social payoff (NSP). He explains that this area is "artificial" in magnitude since the "Invisible Hand has led us to maximization, [and] we need not necessarily attach any social welfare significance to the result." The NSP is also equivalent to the sum of aed in Region A and gkj in Region B. Constraints are imposed on the model in order to define a feasible solution space. The constraints form bounds on the solution to restrict the values of the flow parameters. The minimum constraints needed to calibrate the model include total exports equal total imports, total exports of a region are greater than or equal to the sum of all imports from that region, and the non-negativity of prices and quantities. 3.2.3 Multi-region Case The two region case is adequate for explaining the structure of the model, but it does not explain multi-lateral trade movements. By minimizing transportation costs between regions, a direction of trade matrix can be determined. The dual of this problem, maximizing 30 NSP, yields the same results. A spatial price equilibrium is found when all regions have maximized their individual welfare. The multi-region case considers n regions that supply and demand a given commodity. Each region is considered an independent market where quantity hj is supplied at price pt with a inverse supply function, Sj{hj),j=l,...,n. Quantity demanded, dt, at price % is explained with the inverse of the demand function, Di(d), i=l,...,n. It is assumed that S/hj) is a continuous monotone increasing function in hh hs >0, and that Dt(d) is a continuous decreasing monotone function in dt, dt >0. Quantities of bilateral trade, ty, are priced at c,, per unit—the transportation costs from region j to region /. Equilibrium occurs when the following condition holds for all /' and j: The NSP is derived by maximizing the sum of the area under the excess demand curves less the sum of the area under the excess supply curves and transportation costs (ED and ES curves are derived from equations 3.7 through 3.11 below). Optimal trade flows are determined by simply solving the following optimization problem: SM) + ctj -DXd,) =0if / , > 0, >0if tv = 0. (3.5) (3.6) (3.7) o j o 31 subject to: (1) Pj,*»yj,qt 0^ Vi,j, (2) 2> - z > = o , (3.8) (3.9) (3) -c7 1 + z>,,>0, (3.10) (4) yj-Zvj^0' (3.11) where qt is the quantity of imports of region /' and yt is the quantity of exports of region / The constraints form bounds on the optimal solution space. Constraint (3.8) ensures prices and quantities are positive. Constraint (3.9) ensures all markets clear. Constraint (3.10) ensures that what is supplied to region / is at least equal to what is consumed in region /'. Constraint (3.11) ensures that the supply of region j is at least as big as what region j exports. The Kuhn-Tucker conditions are equivalent to the above constraints. Since the objective function is the sum of two concave functions and a linear function, it is a concave function and the Kuhn-Tucker conditions are necessary and sufficient for equilibrium values qh yj and ttj (Florian and Los 1982). Inverse functions are used because the constraints to the problem are in terms of quantity. This framework provides the basis of the spatial equilibrium problem to be solved in this study. 32 CHAPTER 4 EMPIRCAL MODEL 4.1 THE LUMBER TRADE MODEL The object of this study is to develop a trade model that replicates current lumber trade and use it to estimate future conditions given specific policy decisions. The one-commodity SE model is used in this study because of its ability to estimate trade flows and its explicit consideration of regional price differences through transportation costs. Only softwood lumber is modeled (and not hardwood lumber) since it is of particular interest to the BC forest industry. The model is first calibrated to replicate trade flows from 1987 and then used to project trade until the year 2025. The model performs 6 iterations over a 38 year forecast period where equilibrium for each period is dependent on the supply and demand conditions of the previous period. Implicit assumptions are made on the model regarding future market conditions for each time period. Initial projections are referred to as the "base case" and are used to compare against alternative policy scenarios that are performed on the model. 4.2 OVERVIEW OF MODEL A partial equilibrium model (versus a general equilibrium model) is used in this study under the assumption that changes in the softwood lumber industry do not disturb nonforestry sectors of the economy. This implies that the softwood lumber industry acts as a price taker with respect to changes in interest rates and factor inputs. Although the SE model may not 33 produce predictions as accurate as other short-term models, it does provide information on long-term equilibrium and the competitive advantage of each region (Cardellichio et al. 1989; Adams and Haynes 1987). The model generates optimal trade patterns and predicts changes in the international demand for softwood lumber. A total of 15 regions are considered in the current study. Each region is active in the international softwood lumber market as either an importer or an exporter. Using reported supply and demand elasticities, domestic markets are modeled and trade functions are derived for the international market. A region will be represented by either an excess-demand function (if it is an importer) or an excess-supply function (if it is an exporter) in global trade. Transportation costs between regions are estimated and used to calibrate the spatial nature of international trade. By maximizing the area between the trade functions net of transportation costs and subject to a number of constraints, optimum trade flows are determined. 4.3 THE MODEL The model is set-up and estimated using the Microsoft Excel software package. Through an iterative approach, each region's optimal trade flow is calculated by optimizing global welfare. The Excel software package is used because of its availability and simplicity. It allows the users to try different trade scenarios, perform sensitivity analysis or impose additional constraints with little difficulty. Also, since Excel can be programmed using the Visual Basic programming language, the model can be created as a stand alone executable file to be used with the Excel spreadsheet program. 34 4.3.1 The Regions The model estimates 15 regions (detailed in table 4.1) that account for approximately 93 percent of international trade in softwood lumber. Of these regions, 8 supply lumber to the international market and 7 are demanders of international lumber. In 1992 BC and Interior Canada accounted for over three-quarters of Canada's softwood lumber exports and one-third of world exports (National Forestry Database 1993). The US West and US South primarily supply the domestic US market, but also export some softwood lumber overseas. The Scandinavian countries of Sweden and Finland form the other primary exporting region; their export share is about 16 percent of total world exports. Chile and New Zealand are emerging supply regions with growing market shares and are of interest when estimating future trade flows. The remaining exporting regions are grouped together as Rest of World Exports (ROW) and own only about 7 percent of the market share. The demand for softwood lumber is dominated by the US where almost two-fifths of all foreign exports are destined; the US imports almost all of this from Canada (FAO 1992). The US North is the largest demand region within the US, accounting for about one-third of the imports from all regions. Central Canada imports primarily from BC with some imports from different regions in the US. Western Europe imports about 32 percent and Japan accounts for about 8 percent. The remainder of the importers is included in Rest of World Imports (ROW) and competes for about 10 percent of the market. 35 4.3.2 Data The model uses price and quantity data taken from a variety of sources due to inadequate availability of some regional production and trade data. CINTRAFOR's GTM (Cardellichio et al. 1989) offers a comprehensive database of 1987 quantities and prices for most regions in the model, in particular the different US regions. This database, although somewhat dated, is used because it offers a consistent source of information for most regions included in this study. Also, by using 1987 values, validation of forecasts can be made by comparing actual and predicted values. Table 4.2 displays 1987 production data. Prices are reported in constant 1980 US dollars and quantities are reported in millions of cubic metres. All prices are prices paid in the domestic market and converted to US dollars using 1987 exchange rates. BC production and trade data are industry statistics from COFI (1993). This source is used because it reports BC shipments to Canadian provinces. The matrix of distances between regions measures the kilometres between the major port in each region (Defense Mapping Agency 1976). Freight costs are reported in 1980 US dollars per cubic metre per 100 kilometre. There is an abundance of literature reporting domestic supply and demand elasticities for Canada and especially the US (see Gaston, Cohen and Prins 1994). Elasticities for other regions are less accessible and somewhat deceiving without the price/quantity ratio of the estimation. This model uses elasticity estimates by Cardellichio et al. (1989) for all regions. Table 4.3 reports the own-price supply and demand elasticities along with 1987 production and trade data for each region. These elasticities are comparable to the elasticities used by the Timber Assessment Market Model of Adams and Haynes (1980). 36 4.4 MODEL SPECIFICATION In an ideal trade model, information on all market interactions would be included in each region's supply and demand equations. The development of a complete forest model is somewhat elusive due primarily to the lack of data. Also, by making the model more complex, the results may or may not improve over extended projection periods. This study relates prices to quantities using linear domestic supply and demand curves to estimate the international trade functions. Linear functions are used for a number of reasons. First, they are integrable and robust in determining an equilibrium. Second, elasticities are non-constant. As prices rise, price becomes more elastic and resembles market conditions more closely. Third, demand curves with constant elasticities less than 1 1 . 0 1 indicate a negative marginal revenue and therefore cannot be profit maximizing; it is impossible for marginal revenue to intersect marginal cost since marginal revenue is continuously less than zero and marginal cost is continuously greater than zero. The use of linear functions for demand and supply curves eliminate these problems associated with using non-linear functions. The supply and demand curves allow for changes in exogenous variables through an intercept shifter. By shifting the constant, a uniform increase or decrease in quantity is assumed for all price/quantity pairs. A change in slope, or a rotation about the equilibrium, may represent real world conditions more accurately, but pose a problem in solving for equilibrium values. The scenarios presented in this model can be modeled using a intercept 37 shifter since the changes in supply are assumed to cause uniform changes in price for all quantity/price ratios. 4.4.1 Consumption Function For each region, the demand function explains the relationship between domestic prices and the level of consumption. The demand function is derived using prices, quantities and elasticities from existing literature. Elasticity estimates from Cardellichio et al. (1989) are used to evaluate domestic demand curves in this study since this is also the source of price and quantity data. Given that lumber is consumed primarily by the housing market, elements outside the forest sector can influence lumber demand. Interest rates, income growth, population and technology can shift demand over time. Due to incomplete data, these factors are not explicitly included in the demand function; however, they are included as an intercept shifter. It would be desirable to have slope shifters, however due to the sensitivity of the model and time constraints, these factors are grouped together in the intercept. Given that a partial equilibrium is desired, the primary requirement for each region is that the demand function must depend on price. The final demand function for region /', i=l,..,18, and time period t is specified as follows: where: (4.1) 3 8 Q is lumber consumption in millions of cubic metres, P is the real US price of lumber per cubic metre, and a, P are the intercept and slope parameters, respectively. Changes in demand from non-price effects are measured over time by making a endogenous. Each time period is dependent on the changes of previous time periods. Let oti be defined as follows: where: rtt is the global per annum increase in demand for softwood lumber, t is the year of the current period, and t-1 is the year of the previous period. The inclusion of this effect allows the model to be used in estimating future trade flows by simulating changes in the nonforest sector of each region. It is obvious that since n,=0 in the base case, eta equals a^ui). It follows that as global demand increases, consumers will demand more at a given price, thereby shifting the intercept of the demand curve. The demand equation must be in its inverted form to be used in the algorithm of the objective function. Therefore the estimated domestic demand equation used in the model is defined as: ( 4 . 2 ) a + (",)('-('-!) . 1 (4.3) {Pi) 39 It is assumed that the slope of the demand equation remains constant over time. 4.4.2 Production Function Each domestic lumber supply function is denned as the relationship between the quantity produced and the price. Changes in the supply of lumber in domestic markets are felt in the international market. Domestic elasticities, prices and quantities are used to estimate the supply curve. As in demand elasticity estimates, there is a wide range in the values of supply elasticities. Long-run and short-run elasticities vary due to the ability to earn positive profits in the short-run. Long-run elasticities are used because they resemble a competitive market more accurately. The supply of lumber is dependent on numerous factors other than price. Technology will cause an increase in utilization rates in lumber as well as develop other sectors which may divert input materials away from lumber. Investment in enhanced silviculture and plantations cause an increase in future timber supplies. Supply is also dependent on government policy, which is currently evident in BC (Ministry of Forests 1994). These factors have not been explicitly estimated due to lack of data. Instead, they have been acknowledged in the supply function as intercept shifters for future periods. As with the demand equation, slope shifters may enhance results, however this should be addressed in a sensitivity analysis during further research. The final supply function for region /, i=l,...,18, for time period t is specified as: Yt^Cu+dtf (4.4) 40 where: Y is lumber supplied in mmm3 .P5 is the real US price of lumber per m3 c, d are the intercept and slope parameters, respectively. Parameter c is made endogenous to measure the effect that changes in non-price factors have over time. Let c, be defined as follows: «»= * 4 ( M ) + ( a ( 4 - 5 ) where: giis the rate of change in global supply due to technological developments, hi is the rate of change in regional supply due to increasing rates of harvests, t is the year of the current period, and t-1 is the year of the previous period. Forecasts of fixture lumber supply can be made by allowing these to change with expected changes in technology and harvest rates. Supply changes are easier to predict in the short-term since harvests from standing inventories can be projected over time. In the first period, cit equals cl(t.i) since gt and ht equal zero. The supply equation must be inverted so it is a function of quantity. Therefore the estimated domestic supply equation used in the model is defined as: 41 ps= c t f+(fi-frX'-('-l) , 1 y ( 4 6 ) dx d( The domestic supply curve will shift according to regional supply conditions and policy decisions. 4.4.3 Trade Functions Each region will be either a net importer, a net exporter or neither, depending on the domestic equilibrium price. The trade functions are generated by using each region's 1987 price and quantity data and estimating their respective excess demand or excess supply elasticity. If a region is a net exporter, the slope of the excess supply curve is derived from the elasticity of excess supply, the quantity traded and the domestic price. The intercept of the excess supply curve is equal to the equilibrium price in the domestic market. The excess demand curve is derived using this same method except substituting the excess supply elasticity with its excess demand elasticity. These equations are then used in the objective function to calculate optimal trade relations. The slope of the trade functions remains constant over time since the slope of the domestic supply and demand curves do not change. Changes in domestic conditions are measured in the international market through changes in the market clearing price in each region. Thus, the intercepts of the excess supply and demand curves are altered. International trade conditions are dependent on domestic conditions and the relative size of the domestic markets. It is obvious that supply changes in BC will have a more dramatic affect on price than a small volume exporter such as Chile. 4.5.4 Transportation Costs Transportation costs, used to quantify the relative distances between trading partners, play an important role in determining flows and direction of trade. Transport costs of commodities with low value-to-weight ratios, such as lumber, are even more important, especially when looking at a region's ability to compete with distant markets. BC's close proximity to the giant US market gives BC forest companies a geographic advantage over competing foreign producers. Foreign marginal producers cannot afford to trade due to their proximity to importing regions. Transportation costs can be difficult to estimate due to highly variable freight rates. Costs vary between regions and over different distances, particularly with respect to port handling fees. Long-term contracts, energy costs, port facilities, backhaul availability, commodity and length of haul are all determinants of shipping costs and account for the highly unstable transport costs. In the SE model, transport costs are assumed to be independent of volume and a function only of distance. Sedjo (1983) uses the following function to estimate freight costs as a function of distance: FR = 1.6* (16 + (4 *D)) (4.7) where: FR is the freight rate per thousand board feet (MBF); and 43 D is the distance in thousands of nautical miles. Using the above linear relationship is problematic in representing the differences between import and export prices. Since the SE model assumes that transport costs are equal to the price difference between two regions, a strategy used by Cardellichio et al. (1989) is adopted and used to model transportation and other transfer costs in this study. The price equilibrium is defined as follows: P^Pj+Tji + Cjt, (4.8) where: P is the average product price, 7" is the transportation cost, C is the value adjustment or quality differential, and /* and j are the exporting region and importing regions, respectively. The difference in price between regions, 7}, + C}i, is referred to as the transfer costs. Q, can be either positive or negative, depending on tariff and non-tariff barriers as well as the average quality of the lumber shipped compared to the average quality of the domestic market supply. Tariff rates on softwood lumber vary from region to region and, in the model, are included in the Cp term. By using this method, transportation costs are simplified and concessions can be made regarding quality differences and trade barriers between regions. 44 This study uses transportation costs derived from the functional form presented by Sedjo (1983) to equal 7},. When modeling changes in transport costs, Q, is held constant and only Tjj is changed. Transportation costs and the value adjustments are detailed in table 4.4 and 4.5, respectively. Adjustments costs less than zero simply imply that the average value of the export is greater than the average value of lumber produced in the importing region. This does not affect the solution procedure. 4.5 GENERAL ASSUMPTIONS OF THE SE MODEL The assumptions of the SE model, as stated by Enke (1951) (see Chapter 3), require further attention. The SE model assumes a perfectly competitive market where world consumption is equal to world production at any time period. Homogeneous goods, per unit transportation costs and free trade are simple rules that form the benchmark of the "normal" competitive model. Some problems arise, however, when this model is applied to real market conditions. Additional assumptions need to be imposed on the model in an attempt to create more representative conditions. 4.5.1 Homogeneity The SE model assumes that each commodity is viewed by the consumer as a homogeneous good. This implies that all lumber is of the same quality and available to all consumers at a single price. This is an unrealistic assumption as there are often large 45 differences in prices of lumber depending on the grade, size of lumber, method of processing and the species of log. Larger logs require less handling per cubic metre of output, produce more output per cubic metre and usually possess the desired characteristics of performance grade lumber (and therefore return higher profits). Quality differences arise across species as well as within species. For example, performance grade lumber fetches higher prices than structural lumber. In the construction of the post and beam style Japanese housing, structural wood is visible and therefore clear lumber is desired. Also, culturally, the Japanese view knots and defects as structural weaknesses (Sedjo 1983, p 25). Dealing with a commodity such as lumber is a difficult task when creating a forest model. Unlike pulp and paper, thousands of different products and qualities comprise the sawtimber commodity. When estimating supply and demand functions, each function must specify a particular region, a particular time period and a particular final product. Most models assume that sawtimber is a homogeneous product that is perfectly substitutable with timber from other regions and among species. Published data aggregate many heterogeneous lumber products together when evaluating lumber volumes making no attempt to define quality differences. To address the problem of homogeneity, Sedjo (1983) assumed that a representative basket of sawtimber is produced containing equal proportions of differing qualities for each region. He makes a distinction between coniferous and nonconiferous products by arbitrarily setting hardwood sawtimber prices 10 percent below those of softwood sawtimber. He also discounts plantation softwood by 10 percent to account for the higher percentage of lower quality lumber produced from this input. The CGTM makes no attempt to address the lumber 4 6 quality problem and simply regards it as a limitations of the spatial equilibrium model (Cardellichio et al. 1989). The model does, however, make a distinction between hardwoods and softwoods, and tests for their substitutability. This study does not make reference to different qualities in the base case. 4.5.2 Competitive Market Assumption In determining a spatial equilibrium it is assumed that a competitive market exists across all regions. Neo-classical long-run conditions hold so that profits equal zero, supply equals demand and average total cost equals price. The SE model maximizes the objective function to yield an efficient allocation of goods within given constraints (Pareto optimality). Efficient allocations are chosen by a firm where profits are maximized for a given price. The model used in this study assumes competitive equilibrium and includes parameters in the demand and supply functions to account for exogenous circumstances. 4.5.3 Preferences The heterogeneity of lumber makes it difficult to model changes in tastes. Consumption of high quality lumber may remain inflated even when lower quality lumber is available at a lower price. Flora, Anderson and McGuiness (1991) found that the offshore demand facing the US had a price elasticity of -1.95 for construction grade logs compared to a price elasticity of -0.80 for performance grade logs. This indicates that higher quality grades 47 are more resilient to changes in prices and more likely to show inertia in trade. It is therefore believed that Canadian softwood lumber will continue to experience high demand even at higher prices (Wallace 1987). Since tastes are driven in part by price, over time tastes will change and demand for higher priced goods will decline. The model is unable to pick up this price-quality interaction directly, and is therefore assumed that each region's exports and imports consist of a representative sample of similar quality lumber. 4.5.4 Inertia It is a assumed that a trading agent will maximize profit by choosing the lowest cost and therefore the most profitable trade route available. This is not always the case, however, due to trade inertia. Trade inertia is the extent to which historical patterns of trade prevail over time (Kornai 1987). Once one country has established a trading relationship with another, it is more likely to continue to trade with that country than to spend the time and money in developing another trade relationship. Many factors influence a nation to carry on a trading relationship even when relative price and cost differentials indicate otherwise. Trade between regions has been driven by numerous factors including culture, geographic location, political structure and accessibility. Other factors include availability of information, preferences, costs of changing to other markets and long-term contracts. Prohibitive trade barriers also affect the inertia of trade as do tariffs, non-tariff barriers, embargoes, cartels and lack of information. As international trade becomes more efficient, inertia will play a remote 48 role. For homogeneous goods, inertia is less of a concern than with products that are more diverse in nature. Cardellichio et al. (1989) chose not to place bounds on inertia due to the artificial effect that constraints have on the model. Constraints on inertia do not allow the model to estimate an equilibrium; rather, it forces the model to equate to some preconceived level of trade. The UASA GTM accounts for inertia by placing upper and lower bounds on bilateral trade. By imposing these restrictions, the speed of adjustment is controlled. This study follows the methodology of the CGTM and places no bounds on inertia. 4.6 MODEL ASSUMPTIONS Since the SE model is unable to predict bi-directional trade flows, it is necessary to make assumptions regarding the production and consumption characteristics of some regions. It is believed that valuable results can be obtained regarding regions that are both importers and exporters. The reason why a region imports lumber even though it already has an excess supply is uncertain, however it could be a function of quality, short-term inventory deficits or location. These factors cannot be modeled in a SE model. In an attempt to gain insight regarding these areas that both import and export, these regions are divided into separate trade areas and designated as either an importer or an exporter. The US South, US West and Canada (excluding BC) are divided into smaller trading blocks. The US West is broken into two regions, the Interior and the Coast. The Interior region includes the US states of Montana, Idaho, Colorado, Nevada, Wyoming, Utah, 49 Arizona, New Mexico, and South Dakota. This region produced approximately 11.6 million cubic metres in 1987 and 7.9 million cubic metres in 1992. The Coast region is made up of Washington and Oregon and produced 31.9 million cubic metres in 1987 and 24.1 million cubic metres in 1992 (Warren 1994). Due to the size of the Interior region and comparatively low production, it is assumed that this region is an importer (importing all of the 3.5 million cubic metres destined to the Pacific Northwest (PNW)). The Coast region is assumed to be the export region because of its size and port facilities. Cardellichio et al. (1989) reported that 16.6 million cubic metres of softwood lumber were shipped from the PNW in 1987. The US South is another region that has been divided into an import and an export region for the purpose of this study. The import region is the Atlantic region and the export region is the South-Central region. The US South Atlantic is assumed an importer due to its population base. Both regions produce similar amounts of softwood lumber and have similar production capacities, however the Atlantic region has less extensive inventories (Haynes, Adams and Mills 1995). The division of the South into these two groups is undesirable since the actual quantities that are imported and exported by each region are unclear. The US South reported imports of 9.9 million cubic metres and exports of 4.5 million cubic metres in 1987 (Cardellichio et al. 1989). The creation of these two regions enables inter-state trade to be analyzed. Interior Canada and Eastern Canada are the final regions that need clarification regarding regional assumptions. Interior Canada (the import region) is comprised of the Prairies and Ontario, and Eastern Canada (the export region) includes the Maritimes and Quebec. Although Interior Canada exports softwood lumber (primarily to the US), it is 50 assumed an importer since it is Canada's largest consumer. Interior Canada produced 10.3 million cubic metres in 1987 and 9.4 million cubic metres in 1992.2 The model assumes that Interior Canada imports all shipments destined to Canada. Eastern Canada is assumed an exporter due to its quantity of production. The province of Quebec is Canada's second largest producer, next to BC. It produces over 18 percent of total Canadian production and exports over half this amount to regions outside Canada. The remainder is either consumed within the province or shipped to Interior Canada. In this model it is assumed that Eastern Canada exports all of Canada's softwood lumber, net of BC's share. Although the above assumptions are not ideal, it enables the model to predict regional trade flows. Assumptions about expected future supply conditions are detailed in table 4.6 and are discussed in Chapter 5. Less aggregate data are needed to create a set trade regions that are either importers or exporters. 4.7 MODEL VALIDATION A simulation model must be tested to see if it is a valid paradigm of the scenario being represented. Of primary concern is the models suitability in testing scenarios for which the model is developed. The current model is intended to measure, over time, the long-term effects of policy change by the BC provincial government. Without prior knowledge of the intended use of the model, validation is meaningless. Therefore to test the model's ability to predict policy changes, the model is put through a variety of tests. 2 Consumption data is unclear since it is only reported as "apparent consumption" (i.e., consumption equals production less exports plus imports). 51 First, the model is validated through simulation. In this thesis, the base year is calibrated to 1987 values using existing data. The 'base case' scenario is evaluated and trade flows are simulated in the absence of any policy intervention or exogenous supply factors. Testing the model's ability to accurately project future trade flows is an excellent indicator of its predicting capabilities. When actual raw data are compared with predicted values the model should replicate production, consumption and direction of trade with some degree of accuracy. The current model predicts 1987 production values almost exactly (table 4.2). When 1992 values are compared, short-term market fluctuations cause projected values to diverge from actual values. Second, future simulations of the model are performed and compared with research by industry, government and academia to determine their validity. It is the goal of the programmer to develop a model replicating future trade flows and it should be in agreement with expectations of future conditions. The current model performs well when compared to other studies (FAO 1991; Cardellichio etal. 1990). A third method of model validation is to perform historical simulations. To perform historical simulations spanning the same time period as performed for the future is difficult and probably of little value. There has been a great deal of structural change in the forest industry over the past 40 years. Since these changes have not been made endogenous, the model is unable to account for these technical developments. Also, the model has been calibrated using the consumer and producer preferences of 1987. Current behaviour is probably more indicative of the future rather than historical market behaviour (Adams and Haynes 1980). Therefore, a model's ability to predict historical trade relationships may be of little value in determining future behaviour. Furthermore, inaccuracies may occur due to short-term estimations. SE models are able to predict 52 long-term trade flow, but short-term fluctuations are extremely difficult, and often undesirable, to model. Due to the dependence of lumber demand on the housing market, the cyclical nature of the economy causes lumber demand to follow the peaks and valleys of the housing market. This causes instabilities and inconsistencies in short-term projections making these estimates questionable at best. The current trade model has not been tested for historical trade flows due to the above reasoning. The final test of model validation is the model's response to policy changes. The basic test is to see if trade flows, prices and quantities react in an intuitive manner when alternative scenarios are proposed. For example, if a US tariff caused BC lumber import prices to rise 10 percent, it would be expected that the model should show a decrease in BC lumber exports to the US. Further tests on policy include comparing the absolute and relative values of simulation results with actual, projected and intuitively reasonable values. Comparisons may be made with other models, studies or from industry estimates. 53 CHAPTER 5 BASE CASE RESULTS AND SIMULATIONS 5.1 BASE CASE SIMULATION This section presents the results of the trade model. The model is calibrated to 1987 price and quantity values, with forecasts generated for the years 1992, 1997, 2002, 2007, 2012 and 2025. The results of these forecasts are referred to as the base case. The base case scenario projects future quantities, prices and bilateral trade flows using expected trends and fixed policy conditions at the present time. Projections are based on a basic set of assumptions believed to be most probable in future market conditions (Sedjo and Lyons 1990; FAO 1991; Haynes, Adams and Mills 1995). The base case scenario is not deemed the most likely scenario; rather, it is an indication of how future trade could unfold given present conditions. The base case forms the basis from which all scenarios are compared and is used as a guideline to measure the impacts of a variety of changing market conditions and policy changes. By analysing how different scenarios cause trade to diverge from the base case, insight can be gained regarding the long-term behaviour of global lumber trade. The results may be interpreted and used to aid in policy decisions for industry development. The base case is developed from 1987 equilibrium conditions from which assumptions are made about current market conditions. In order to calibrate the model from actual trade values in 1987, some constraints were imposed on 1987 trade flows. Although it is optimal to use as few constraints as possible, due to imperfect world markets, it is often necessary to impose trade flow restrictions between some region as well as force trade between other regions. Although this does not yield a true competitive equilibrium solution, it will replicate real world conditions more 54 closely. Real world conditions may or may not encourage trade due to tariff and non-tariff barrier, differing product qualities and long-term contracts. Over time, however it is theorised that equilibrium conditions will prevail. From the 1987 equilibrium conditions, projections are then made for future time periods. Projections after 1987 are allowed to calibrate to equilibrium values with only the minimum constraints used in a SE model (as discussed in Chapter 3). Future projections are made in 5 year intervals until the year 2012 and then a final projection is made for the year 2025. Assumptions are made about future supply and demand conditions throughout the projection period. These assumptions are discussed below. 5.2 ASSUMPTIONS OF THE BASE CASE A set of basic conditions are assumed for all regions in the model. These assumptions are based on previous studies (Sedjo and Lyons 1990; FAO 1991; Haynes, Adams and Mills 1995), current conditions and historical trends. The assumptions made in the development of the model are as follows: 1. World demand for lumber increases at an initial rate of 1.4 percent per year until 2002 and then 1.5 percent per year afterward (FAO 1991)3; this increase is due to population growth and the rise in global economic conditions. 3 Sedjo and Lyons (1990) and the BC Ministry of Forests (1994, p. 217) forecast softwood demand to increase at 1 percent per year. 55 2. Global lumber production increases at an initial rate of 0.5 percent per year (Sedjo and Lyons 1990); this increase is due to advancements in biotechnology, harvest, milling and distribution. 3. Exchange rates remain at 1987 values. 4. Relative transportation costs are constant and, therefore, are unchanged. Using the above assumptions, the base case is generated for the different trade regions in the model. Assumptions 1 and 2 are calculated in five year intervals, using the production, consumption and price data of the previous period to determine intercept values for the current period. This process ensures non-linear changes in supply and demand (e.g., demand increases at an increasing rate due to the geometric growth in population). There is no increase in demand during the first period since historical data indicate a decline in demand during this period. It is assumed that there is a continual upward trend in the demand for softwood lumber throughout the remainder of the projection period. The only regional assumption on demand is one regarding the regions contained in the ROW. The ROW is assumed to demand less softwood lumber than the world average after the year 1992. Many regions within this group have wide access to domestic hardwood lumber supplies and it assumed that, as these supplies develop, regions within ROW will substitute softwood lumber with the hardwood variety.4 4 World hardwood consumption is predicted to grow faster than softwood consumption. Consumption is primarily from each producer's domestic market (Waggener, Schreuder and Eastin 1990). 56 Assumption (2) is used to account for increased efficiency in production over time. This assumption does not include changes in regional supply due to plantations or reforestation, but does include increases in supply due to the exploitation of existing inventories. Assumptions regarding regional supply are discussed below. Constant 1987 exchange rates are assumed throughout the model due to uncertainty in future money markets. It is unlikely that predictions of future money markets would yield better results than the status quo. Buongiomo, Chavas and Uusivuori (1988) determine that it is prices and not exchange rates that are responsible for long-run changes in imports of lumber. Transportation costs are also assumed constant. Under free trade conditions there is the free movement of knowledge, capital and labour, thereby allowing each region equal access to the same technology. Any relative cost advantages will be equated over time. 5.3 REGIONAL ASSUMPTIONS Knowledge of future supply conditions in each region makes it necessary to include assumptions for certain regions in the development of the base case. Overall global lumber production will increase in the future; however, regional output varies depending on the region. Only assumptions on production are made for individual regions (except for ROW imports) since future production levels can be inferred from standing inventory. These assumptions are simplistic and their accuracy debatable, but the recognition of these conditions is important 57 and their inclusion is necessary. A sensitivity analysis of cases where high and low values of each assumption is advisable, but time did not permit such tests for the base case. The base case follows the supply assumptions detailed in table 4.6. Each supply change is in response to current inventory assessments and current harvesting technology. This allows assumptions to be made regarding the availability of future quantities of mature supply during any particular period. Most supply assumptions come from Sedjo and Lyons (1990) and Haynes, Adams and Mills (1995) who look at timber inventories in their respective studies. Assumptions regarding individual regions are discussed below. 5.3.1 British Columbia BC continues to dominate global softwood lumber trade, accounting for almost one-third of total volume traded. The dependence on the US as the world's primary consumer creates a dependence on the US economy and its inherent business cycles. In 1993, BC exported almost three-quarters of its international exports to its southern neighbours, and these trends appear to continue into the future. Increasing supplies in the South, and other foreign supplies, fulfill the void left by the stagnant BC production. Political uncertainty and environmental concerns may force US importers to choose domestic suppliers where imports are guaranteed. Assumptions imposed on the British Columbia export region include a gradual reduction in supply due to the provincial government's reduction of harvestable timber area (Ministry of Forests 1994). Although supplies are becoming more remote and less accessible, a one-time reduction is not assumed in the base case since there are still abundant supply areas 58 to harvest. The model is forecasted assuming that the current AAC (71.6 million cubic metres) will continue into the future. In the base case, BC is assumed to continue lumber production at levels approximately equal to its current output (37 million cubic metres). Although such an assumption is probably unrealistic, it gives a basis to compare changes in trade caused by future declines in BC supply. Present day conditions are used as the base case to compare the effects of the predicted "fall-down" in lumber production. 5.3.2 Eastern Canada Since Eastern Canada is not yet harvesting at its AAC, some production growth is allowed in the base ease, but after the year 2002 production increases are modest. Although much of Eastern Canada is forested (about two-fifths of Canada's inventory), the location of the timber is often in remote areas requiring long distance transport to processing facilities. The inaccessibility of certain regions make harvesting and reforestation difficult and costly. The model assumes that supply remains relatively constant over time. 5.3.3 Pacific Northwest The US West regions are assumed to undergo radical reductions in harvestable timber in the short-term. Declining stocks on private lands and the elimination of federal forest lands have caused a worsening of timber supply conditions. Until the late 1980s, timber from government forestland supplied sawtimber for domestic sawmills in the order of about 13.9 59 million cubic metres per year. This ended in 1991 when a federal court injunction shut down most of the national forest program in Washington and Oregon to investigate the environmental impacts on the northern spotted owl. US President Bill Clinton proposed plan, referred to as Option 9, is forcing harvests from federal lands to be cut to one-quarter the 1985-1989 average (Smyth 1993). In the current model, it is assumed that there is a partial reduction in production from 1992 to 1997 and a further 20 percent reduction in the next time period. The 20 percent reduction is in response to Option 9 and the declining production of private land. This trend is predicted to stabilize due to growing inventories on private and state land, but not until 2010-2020 (Haynes, Adams and Mills 1995). 5.3.4 US South The US South is expected to continue its intensive management and reforestation practices. Already the region has been able to increase production to modern day records, up 5 192 thousand cubic metres from 1990 to 1993. The region is anticipated to increase production by over 50 percent by the year 2000 (Haynes, Adams and Mills 1995). This trend is expected to continue in the Atlantic region until 2010 and until 2025 in the Central region, or until inventories and harvests start to decline on private lands. Increased demand and falling US supplies are already causing smaller timber to be harvested in the US South. Higher chip prices make the harvest of smaller timber more profitable (Smyth 1993).5 Also, as lumber prices rise, a shift in timber from pulp and paper to lumber could occur. 5 The move down from 20 centimetre chip-N-saw logs to 15 centimetre diameter in the US South has been required by more severe competition for timber, and higher stumpage and lumber prices (Smyth 1993). 60 The model acknowledges the abundant inventories of the US South by imposing 4 percent annual increases for the two US South regions. After the year 2007, the US South Atlantic only experiences growth from technological developments. 5.3.5 Chile and New Zealand The forest industries of Chile and New Zealand have highly developed plantations capable of producing high volume/short rotation timber. These resources are expected to continue to increase in the future. Although they are viewed inferior quality in current day standards, future demand conditions are changing and, therefore, the model does not make concessions for the quality differences of these products. Future harvests of Chile's plantation forests are estimated to increase by over 100 percent by the year 2005 (Cortes 1988). These projections are estimated from Chile's well stocked plantations. Inventory data indicate that 82 percent of the plantation forests are between 5 and 20 years old, on a 24 year rotation. The current model assumes a 4 percent annual increase in production from the year 1987 to 2002 and then a 3 percent increase thereafter. Similar assumption are made regarding New Zealand production. A majority of their 1.3 million ha plantation forest are less than twenty years old, assuming 25 year rotations. Exports are expected to continue to increase as well (Neilson and Smith 1993). It should be recognized that, although these regions are important in supplying the Pacific Rim, they are both only marginal suppliers in terms of volume. 61 5.4 WELFARE MEASURES To represent the overall well-being of each region, consumer and producer surplus values are calculated. These measurements calculate the area under the demand and supply curve for each importing and exporting region, respectively. It is used as an assessment of how changes in prices and quantities effect the over-all welfare of a region. For example, it is not obvious if a region's welfare increases if exports increase but price decreases. Increases in supply and demand will have diverse effects on the different regions depending on the slope of the respective supply and demand curves of each region. Consumer surplus (CS) is calculated by taking the area under the demand curve less the total cost of the amount consumed. Likewise the producer surplus (PS) is calculated by subtracting the area under the supply curve from total revenue. In figure 2.1, before trade occurs, the CS of the importing region, Region A, is equal to area faP4. It is obvious from this figure that when the region begins trading and prices remain different across regions, CS will increase. Once an optimal trade relationship is established, CS is equal to area fdP1, an increase of P^adF. The PS of Region A decreases from OaP4 to OeP*. The over-all welfare gain from trade is equal to area aed. Under different scenarios, each region will be better-off than in a non-trade case, however it is of interest to this study to see how changes in supply and demand conditions effect regional welfare. Global welfare is calculated as the sum of regional CS and PS for all regions. Comparisons of welfare measurements across studies are not analysed. Differences in the model structure and its basic set of assumptions make comparisons an irrelevant measurement. 62 5.5 THE BASE CASE FORECAST Figures 5.1 and 5.2 show the results of the base case forecasts of imports and exports. Total trade increases by almost one-third over the entire projection period. BC exports remain relatively flat whereas the US South Central experiences immense growth in exports. Chile and New Zealand also experience large increases. The US North imports increase substantially because of increased demand that is much larger than the increase in domestic production. The US South Atlantic decline in imports is due to the increased harvest of its privately owned forests. Production, consumption, trade and prices for the export and import regions are detailed in tables 5.1 and 5.2, respectively. Table 5.3 reports the direction of trade results for the base case. 5.5.1 Export Regions The model predicts that, although BC production remains constant, exports do not decline substantially. The base case predicts that BC lumber destined to the US North, currently BC's primary destination, is replaced by exports from the US South Central and Eastern Canada. Lower transportation costs and a secure supply source could be the reason for a movement away from BC lumber. BC diverts this quantity to Japan, the US West Interior and Central Canada. As the US South inventories mature, the US market becomes less important to BC as an importer as exports decline to less than 14.0 million cubic metres. By the year 2025, BC exports over half of its total 63 exports to Japan and Central Canada. Exports to the US South Atlantic have been replaced by the US South Central. The US West experiences the largest decrease in exports because of the spotted owl controversy. Although the decline is substantial in the 1990s, the base case predicts that inventories and harvests from plantations will increase production to 1987 levels by the year 2025. Also, by 2025 Japan becomes the US West Coast's most important export destination. Continued demand for high quality softwood lumber is expected in Japan. Eastern Canada increases production by almost a third over the 38 year span of the model. Technological advances and increased prices are responsible for this increase. Most of the increase continues to go to the US North region, replacing BC and the US West as suppliers. Total exports increase by approximately 3.0 million cubic metres. Production levels in the US South Central are predicted to increase to levels of 35.812 million cubic metres by the 2025,6 most of which is either consumed in its domestic market or exported to the US North. The model predicts that the US South Atlantic waits until after 2012 to start importing from the US South Central. The US South Atlantic shuts BC out of this market in favour of the US South Central. Scandinavia's increase in production is absorbed by Western Europe and also by the US North. Production levels increase by over 8 million cubic metres to 24.8 million cubic metres by the year 2012. (FAO (1991) predicts that production will be 25.4 million cubic metres by 2010.) It appears that Scandinavia attempts to develop alternative export markets in the US because of increased competition from the ROW region. 6 Haynes, Adams and Mills (1995) projected that current inventories indicate that by the year 2030 production will reach 38.0 million cubic metres. 64 Chile and New Zealand increase production by substantial amounts, although they do not make an impact on the world market. Most of the production from both regions is bound for their domestic markets or the ROW. Australia and Southeast Asia are the primary importers of the ROW group. Chile and New Zealand do enter the European market, but this seems unlikely given the distance between markets. The ROW exporters play a more significant role over time. As demand increases, more supplies are exported to Western Europe. As the former USSR develops its infrastructure, it will play a more prominent role in lumber trade. Currently the east primarily exports logs to Southeast Asia and most of the lumber comes from the west. 5.5.2 Import Regions The most substantial change in imports occurred in the US North where, as discussed above, the US South Central replaced a market currently dominated by BC and the US West Coast. The US North also increased its imports from the ROW. The remaining regions did not show any radical changes in trading partners. Japan increased imports from both BC and the US West Coast over the 38 year span. It appears that there is a preference for US West Coast lumber over BC lumber; however, restricted production causes imports from the US West to fall. Alternative suppliers—Chile, New Zealand and ROW—begin to enter the Japanese market, and appear to be replacing BC and US West lumber to a small degree. Imports from BC decline in 2025 in response to 65 increased production in the US West and alternative sources, although BC still holds over 50 percent of the market share. Western Europe increases imports from the ROW in response to increased domestic demand. Scandinavia remains its primary exporter, although its market share is declining due to cheaper imports from the ROW. The US South Central increases its exports to Western Europe over time. Exports from BC fall to zero in 1997 in response to a realignment of competitive position and non-tariff barriers, including regulations banning green lumber imports from North America.7 The US West Interior increases its imports to offset the decline in domestic production. BC continues to be supply a majority of the lumber demanded in this region. The US West Coast ships small quantities to the Interior despite higher prices in Japan. By 2025, total imports from BC approximately equal total production in the US West Interior. 5.5.3 Prices Projections from the base case indicate an increase in real prices over time. Real price changes for the base case are detailed in table 5.4. The projected softwood lumber price index is reported in table 5.5. Over time, the real price of BC softwood lumber increases at an average rate of approximately 1.0 percent a year. Most of this price increase occurs in the 7 The European Community, in 1993, placed a ban on green lumber from North America based on concern about the pinewood nematode found in some BC softwood species. Lumber is required to be kiln dried prior to export to the European Community. 66 2012-2025 period (at 1.3 percent per year) when higher domestic demand restricts exports. The changes in price are consistent with Haynes (1990). 5.6 ALTERNATIVE SCENARIOS The model's ability to represent alternative scenarios relies on each region's demand and supply curve specifications. Each alternative scenario is calculated by shifting the supply curve of a representative region and, therefore, then calculated using the underlying assumptions of the base case. The sensitivity of the model to changes in the intercept rely on the slope of the domestic demand curve in the respective market. Although shifting the constant is not an ideal method to model changes in individual markets, changes in slope cause the model to calculate large fluctuations in trade and make results unrealistic. From the base case projections, a variety of alternative conditions are predicted for the future. The base case offers one future scenario, with results strongly influenced by the economic conditions of the base year and the assumptions implicit in the model. The model's ability to analyze alternative scenarios allows the examination of a variety of different future conditions affecting domestic supply and demand conditions. This section looks at some alternative futures. The alternative conditions analyzed, along with a brief description of their importance, are as follows. 67 1. Decrease in BC Production. A 25 percent reduction in BC AAC is modelled by restricting production to 75 percent of the base case levels. The following two scenarios are examined regarding AAC reduction: a) a one-time reduction in AAC where, over time, there is a slow increase in supplies as the Forest Renewal Plan increases future yields (i.e., there is an allowable cut effect); and b) a one-time reduction in AAC with no increase in supply over time. 2. Increase in Supplies from Alternative Sources. The former USSR is of particular interest due to its immense resource base. A one-time increase in production is modelled where production from the ROW increased by 20 percent. Tables 5.1 and 5.2 summarise the production, consumption, trade and prices for the each alternative scenarios. All results are detailed by region and scenario. Direction of trade results are displayed in tables 5.6-5.8. The "winners" and "losers" of each scenario are determined through changes in consumer and producer surplus values. These measurements are found in tables 5.9-5.11. 5.6.1 Decrease in BC Production It is well documented that a fall-down in timber supply is probable in BC due to the imbalance in age classes. A reduction in BC's AAC may aid in reducing the impact of the 68 projected fall-down (Ministry of Forests 1994; Smyth 1994). Current harvesting levels cannot sustain the forest industry over the long term. In response, the provincial government is developing a new forest management strategy and assessing each region's timber supplies. This report is to be completed in December 1996; it is predicted that a 25 percent reduction in the current AAC will be needed. Other reductions in BC harvest occur from the conversion of forestlands to parks8 and slow regeneration on previously harvested areas. Scenario 1(a) illustrates the market power that BC has on world lumber trade. The one-time reduction in 1997 causes global lumber prices to increase since other producers cannot adjust quickly to the drop in global supply. Price increases are felt by all import regions in response to a redistribution of lumber trade. BC diverts supplies away from the US North and continues to supply the Canadian, Japanese and US West markets. This deficit in the US North is filled by the US South Central region. As production in BC increases to 1987 output, prices fall below the base case levels. This response is due to the increase in global production under this scenario. Scenario 1(b) projects the reduction in AAC with no recovery in output. Total exports never recover to the base case output as they did in the first scenario. Prices remain inflated in all regions and domestic production increases in regions that consume BC lumber. Exports to Japan decline over time and are replaced by the recovering US West Coast production (table 5.7). BC continues to export to Japan, however the absolute quantities are declining over time. Exports from BC to the US West and the Interior Canada remain relatively constant. The Commission on Resources and Environment (CORE) recently completed a detailed land-use plan for BC. A wide variety of interest groups were invited to present their land use demands. Among other decisions, the Vancouver Land Use Plan added 480,000 hectares to the park system (CORE 1994). 69 Figure 5.3 and 5.4 illustrate the market share in the alternative scenarios and the base case for exports and imports, respectively. With a decrease in AAC, it is evident that BC market share declines. Most notable is the increase in market share of the US South. Welfare Changes Although production decreases in BC, the province experiences a net increase in welfare in the short-run (table 5.9). In scenario 1(a) higher prices cause consumer surplus to decline, however producers are better-off through inflated revenues from higher global prices and a reallocation of exports. In the long-run, BC producers are worse off. Increased global demand and higher prices stimulate competition and technological advancements in lumber production. When BC increases production due to the ACE, world prices decline. Japan's producers feel the effect of lower BC prices through a reduction in demand from their domestic market. Imports from BC increase above the base case levels. Lower BC prices also affect the US West producers. Over time, consumers in Central Canada, BC, Japan and the US West gain from the reduction in AAC. Increased imports from BC cause the rise in CS. The biggest loser from the AAC reduction is the consumer in the US South Atlantic and the US North. Both regions experience a net loss in consumer welfare of approximately US$176.7 and US$128.4 million, respectively. This is primarily a result of higher prices in their domestic markets and in Eastern Canada. Scenario 1(b) (table 5.10) indicates that most producers will be better-off under a scenario of reduced BC production. So, even though BC experiences a drastic decline in production, the 70 net welfare of the softwood sector increases. The reduction in BC's AAC has a similar effect on other exporting regions. Higher prices and a redistribution of exports effectively increase producer surplus in all regions, except for Chile and New Zealand in 2025. Lumber producers in importing countries also gain from the reduction in BC production through higher demand for domestic lumber (due to higher import prices). Over-all, however, regional welfare in the importing regions decline; higher prices effectively reduce consumer surplus. Japan experiences the greatest reduction in consumer surplus (US$534 million) because of its dependence on BC lumber. 5.6.2 Increased Production in the Former USSR Over time new supply regions develop and compete with existing export countries. It is reported that the former USSR has a forest area of over 800 million ha of which 52 percent of the world's coniferous forests are located (Neilson 1994). Growth rates are very low (0.5-1.5 cubic metres per ha per year) and quality is often inadequate for harvest. However, the absolute size of the resource creates an alternative source to many regions. In 1983 Japanese log imports totalled 6.4 million cubic metres, but has since fallen to about half that level due to political, social and physical access to the resource. This simply emphasises the instability and unpredictability of this region. Inadequate infrastructure and political turmoil have slowed growth in this region. In the base case, the former USSR is grouped within the ROW exporters. ROW production is assumed to increase by over 50 percent (40 million cubic metres) from 1992-2025. World prices are projected to fall and global production and exports increase. Western Europe decreases its domestic production in favour of cheaper imports from the ROW. BC reduces its 71 total production in response to lower prices and diverts trade away from the US West Interior in favour of Japan, Interior Canada and the US North. Figure 5.3 and figure 5.4 illustrates the changes in market share when exports increase from the former USSR Again, BC market share declines, however BC does capture more of Japan's market for softwood lumber. Welfare Changes By the year 2025, an increase in output from the former USSR causes world welfare to increase (see table 5.11). The most significant beneficiary is Western Europe. CS increases by over US$1.3 billion at the expense of a reduction of US$500 million in PS. All other consumers (except for the Rest of the World importers and small losses in Canada) gain from lower prices brought on by the increased production. Producers are net losers due to lower prices. Scandinavia is the worst off since its exports are replaced by the former USSR. The BC market is relatively unaffected by the increase in production, and the redistribution of trade does, in fact, positively affect BC producers. 72 CHAPTER 6 SUMMARY AND CONCLUSIONS 6.1 SUMMARY AND CONCLUSIONS The purpose of this study is to examine the effects of potential scenarios on global lumber trade. By using a spatial equilibrium model, a long-term equilibrium is established that projects an efficient distribution of lumber. Trade flows between regions are calculated for a base period using a set of assumptions regarding future supply and demand conditions. Assumptions are made using the results of previous studies and current inventory assessments. The model is dependent on price and quantity data, and domestic elasticity estimates. Transportation costs are assumed to equal the difference in prices between any two trading nations. Production, consumption, trade and prices statistics are calculated using this model. The results of the base case indicate that global production increases to 472.8 million cubic metres in 2012 and to 541.8 million cubic metres in 2025. FAO (1991) project that the total lumber demand for softwood and hardwood lumber will be 742.0 million cubic metres in 2010. (Currently hardwoods make-up about 30 percent of all lumber.) Production results of the current study and the CGTM (Cardellichio et al. 1989) are reasonably close for the year 2002 and 2000.9 Direct comparisons, however, are difficult due to a different set of assumptions regarding supply and demand conditions in the base case. From the base case projections, alternative scenarios are calculated and then analyzed. The model reacted intuitively to changes in market conditions. The results of the above 9 The CGTM only reports results up to the year 2000. 73 scenarios are significant when considering policy judgments and future investment decision. The results of the current study indicate that BC is indeed a "large country" in terms of softwood lumber trade. This is evident in the projected price increases when a reduction in BC's AAC is modeled. Another conclusion that can be drawn from the study is one regarding BC's export destinations. BC will increase exports to Japan and decrease exports to the US. Perez-Garcia (1993) reaches the same conclusion when he modeled a reduction in production. As the US South increases production, BC increases exports to Japan and Interior Canada. Increased production in Chile and New Zealand is found to have little effect on traditional suppliers. Increased production in the US South, distance from market and lower quality are reasons why they are unable to capture more market share. The results from the model suggest that even when supply is low, the US South Atlantic is more likely to increase production than to import from Chile and New Zealand. The results from the welfare analysis reveal important findings. The study calculates an increase in welfare for BC with a sustained decrease in the AAC. This is especially important since 94 percent of BC forests are publicly owned. Forest companies can increase PS by reducing harvests. The results indicate that the provincial government can charge higher stumpage prices while still allowing forest companies to earn comparable PS. It is of interest to note that when BC increases its production after a reduction in AAC (Scenario 1(a)), the over-all welfare of the softwood lumber sector in BC is lower. This suggests that at current production levels higher stumpage prices can be demanded from BC's producers to force lower rates of harvest. In fact, in the short term, a reduction in AAC causes a large increase in PS at little expense to its domestic consumers. 74 Another important finding of the study is the limited effects of the former USSR on BC; producers in BC actually experience an increase in revenues over time. The former USSR, on the other hand, has a decline in PS due to lower prices in the region; CS, however, increases drastically and the Rest of the World has a net gain in welfare. These results suggest that the former USSR must be careful in the extraction of its domestic resources. Since the former USSR can play a significant role as a supplier, market saturation is an important consideration in regards to the timing and quantity of additional production. By looking at the changes on welfare, it is evident that a decline in production does not have a negative effect on the province's welfare. BC lumber trade influences world prices enough to cause net revenues for BC forest companies to increase when faced with lower harvests; forest companies' concerns about AAC reduction may very well be ill-founded. The reduction in AAC, however, will cause a reduction in jobs unless labour intensive forest practices are enforced. 6.2 MODEL LIMITATIONS The spatial equilibrium model has inherent limitations that must be considered when results are being analyzed. These limitations include assumptions of homogeneous goods and perfect competition. The variability in transportation costs is another limitation that must be considered. Also, the SE model is unable to calibrate an equilibrium solution when one region is both an importer and an exporter. Some of these limitations can be overcome by less aggregate data and a further breakdown of regions, but such statistics are difficult or even impossible to obtain. 75 Elasticity estimates are another questionable area. Often point elasticities are reported without their respective price/quantity ratios. This creates some questions regarding the validity of the estimate. The current model uses elasticity estimates and price/quantity data from the same study, the CGTM (Cardellichio et al. 1989). To some extent, therefore, the results rely on the accuracy of the CGTM estimates. However, significant changes have been made to make the current model more robust. In the current study numerous assumptions are made regarding the regional breakdown in lumber trade. For a few regions (the US South and the US West) these assumptions are made because of lack of additional data. To solve this problem, the model must have either a less complex trade matrix or a more complete data set. Even though the model possesses the above limitations, the forecasts and simulations provide a starting point for future policy and trade considerations. The model tracked 1987 trade flows with a high degree of precision, thereby creating a basis for long-term equilibrium conditions. As addressed above, the results are not intended to provide an exact forecast of the future, rather it is designed to assist in the development of future trade relations. 6.3 SUGGESTIONS FOR FURTHER STUDY The model presented in this study offers a number of different opportunities for policy analysis. First, however, a more detailed data set is required to model inter-regional trade. Furthermore, the domestic supply and demand curves should be estimated for each region rather than relying on elasticity estimates from previous studies. This, however, could prove 76 to be extremely difficult and the model may not improve its predicting ability. Additional variables should be included in the supply and demand specifications so the model more accurately represents the softwood lumber market. For further analysis of lumber trade, projections should be performed by changing the slope of the domestic supply and demand curves in response to changes in market conditions. Intercept shifters may or may not accurately model these changes. However sensitivity analysis should be performed when performing such a test due to the sensitivity of the model to small changes in the slope. 77 Table 4.1 Exporting Regions Importing Regions British Columbia Eastern Canadab US West Coast0 US South Central0 Scandinaviah Chile New Zealand Rest of the World Exports (ROW) Central Canada8 US West Interiord US South Atlantic1 US North8 W. Europe1 Japan Rest of the World Imports (ROW) Notes: a) Alberta, Saskatchewan, Manitoba and Ontario. b) Quebec and Atlantic provinces. c) Washington and Oregon. d) Montana, Idaho, Colorado, Nevada, Wyoming, Utah, Arizona, New Mexico and South Dakot e) Kentuky, Alabama, Tennessee, Arkansas, Louisiana, Oklahoma and Texas. f) Virginia, North Carolina, South Carolina, Florida and Georgia. g) The Notheast and North Central US States. h) Finland and Sweden. i) All Western Block countries (excluding Finland and Sweden). Table 4.2 Total Quantity Produced millions of cubic metres Actual Predicted Export Regions 1987 1992 1987 1992 British Columbia 37.6* 33.373 a 36.912 34.499 Eastern Canada 14.955 * 11.893 a 14.817 14.471 US West Coast 31.24* 24.242 b 29.660 28.084 US South Central 10.3 15.340 c 10.263 10.887 Scandinavia 18.7 * 18.815 d 18.605 19.277 Chile 2.311 * 2.582 d 2.674 2.358 New Zealand 1.844* 2.465 d 2.177 2.029 ROW 76.759 * n.a. 77.599 79.184 Actual Predicted Import Regions 1987 1992 1987 1992 Interior Canada 9.443 * 9 339 a 9.311 9.078 US West Interior 10.31 * 9.440 b 10.092 9.488 US South Atlantic 10.3 ** 13.452 c 10.119 11.022 US North 3.1* 3.540 c 2.957 2.869 Western Europe 33.9* 32.920 d 33.728 35.195 Japan 26.204 * 24.423 d 26.127 26.055 ROW 93.143 * n.a. 94.090 96.037 n.a. not available * Cardellichio et al. (1989) ** assumed from Cardellichio et al. (1989) ' COFI 1992 b Warren 1994 e 1990 values (Haynes, Adams and Mills 1995) 4 FAO 1994 Table 4.3 Lumber Supply and Demand Elasticities Region Own-price Own-price Elasticity of Elasticity of Supply Demand British Columbia 1.0 -0.3 Central Canada 1.0 -0.3 Eastern Canada 1.0 -0.3 US West 1.0 -0.3 US South 1.0 -0.3 US North 1.4 -0.3 Scandinavia 1.0 -0.3 Western Europe 1.0 -0.3 Chile 2.8 -0.3 New Zealand 2.2 -0.45 Japan 0.9 -0.67 Rest of the World 1.0 -0.3 Source: Cardellichio et al. (1989) 8 0 Table 4.4 Transportation Costs in 1980 US$/cubic metre Export/Import Int. Canada US West Int. US South Atl. US North W. Europe Japan ROW British Columbia $18.44 $11.93 $19.53 $18.98 $35.53 $22.51 $36.88 Eastern Canada $10.85 $18.44 $13.56 $12.20 $19.53 $40.41 $38.78 US West Coast $18.44 $10.85 $19.53 $18.98 $35.25 $22.51 $40.14 US South Central $13.56 $19.53 $10.85 $13.56 $22.51 $37.15 $45.56 Scandinavia $20.88 $35.80 $24.41 $23.86 $12.47 $38.51 $29.83 Chile $26.58 $25.49 $20.88 $22.78 $31.73 $37.42 $34.17 New Zealand $38.78 $28.47 $35.53 $36.61 $45.29 $24.41 $27.66 ROW $38.78 $40.14 $45.56 $46.64 $20.34 $27.12 $10.85 Source Sedjo and Lyons 1983 Table 4.5 Value Differential in 1980 US$/cubic metre Export/Import Int. Canada US West Int. US South Atl. US North W. Europe Japan ROW British Columbia $2.56 $31.07 $28.47 $39.02 $47.47 $109.49 $36.12 Eastern Canada -$8.85 $5.56 $15.44 $26.80 $44.47 $72.59 $15.22 US West Coast -$35.44 -$5.85 -$9.53 $1.02 $9.75 $71.49 -$5.14 US South Central -$31.56 -$15.53 -$1.85 $5.44 $21.49 $55.85 -$11.56 Scandinavia -$58.88 -$51.80 -$35.41 -$24.86 $11.53 $34.49 -$15.83 Chile -$3.58 $19.51 $29.12 $37.22 $53.27 $96.58 $40.83 New Zealand -$75.78 -$43.47 -$45.53 -$36.61 -$20.29 $49.59 -$12.66 ROW -$70.78 -$50.14 -$50.56 -$41.64 $9.66 $51.88 $9.15 (Value differential =Import Price - Export Price - Transportation Costs) Table 4.6 Regional Assumptions of Future Supply Conditions - Base Case (percent change per annum) 1992-1997 1997-2002 2002-2007 2007-2025 BC -0.75 -0.75 -1 -1 Eastern Canada +0.5 +0.5 no growth no growth US West -0.75 +4.0 no growth no growth US Sout Atlantic +2.0 +4.0 +5.0 no growth US South Central +2.0 +4.0 +5.0 +1.0 Chile +4.0 +4.0 +4.0 +4.0 New Zealand +4.0 +4.0 +4.0 +4.0 Source: Haynes, Adams and Mills (1995) 82 Table 5.1 Simulated Production, Consumption, Exports and Prices (Quantities Reported in millions of cubic metres; prices reported in 1980 US$/cubic metre) British Columbia Eastern Canada Reduced AAC in BC Increased Reduced AAC in BC Increased Base Scenario Scenario Production in Base Scenario Scenario Production in Year Case a) b) Former USSR Year Case a) b) Former USSR Production Production 1987 36.912 36.912 36.912 36.912 1987 14.817 14.817 14.817 14.817 1992 34.499 34.499 34.499 34.499 1992 14.471 14.471 14.471 14.471 1997 38.626 25.751 25.751 37.569 1997 16.237 17.466 17.466 15.860 2002 37.492 25.489 25.401 37.686 2002 16.901 17.842 17.842 16.922 2007 36.360 28.896 23.896 35.260 2007 17.660 17.479 18.028 17.101 2012 37.660 31.231 26.609 37.548 2012 17.601 18.959 18.948 17.531 2025 37.036 36.905 27.600 37.421 2025 19.597 19.630 20.572 19.903 Consumption Consumption 1987 4.430 4.430 4.430 4.430 1987 4.143 4.143 4.143 4.143 1992 4.499 4.499 4.499 4.499 1992 4.221 4.221 4.221 4.221 1997 4.789 4.681 4.681 4.826 1997 4.488 4.352 4.352 4.529 2002 5.062 5.040 4.953 5.061 2002 4.729 4.615 4.615 4.729 2007 5.383 5.366 5.310 5.426 2007 4.999 5.001 4.941 5.064 2012 5.760 5.813 5.626 5.772 2012 5.381 5.213 5.210 5.396 2025 6.647 6.676 6.506 6.644 2025 6.210 6.156 6.046 6.187 Exports Exports 1987 32.482 32.482 32.482 32.482 1987 10.674 10.674 10;674 10.674 1992 30.000 30.000 30.000 30.000 1992 10.250 10.250 10.250 10.250 1997 33.837 21.070 21.070 32.742 1997 11.749 13.114 13.114 11.331 2002 32.430 20.448 20.448 32.625 2002 12.172 13.227 13.227 12.193 2007 30.977 23.530 18.585 29.833 2007 12.660 12.477 13.087 12.036 2012 31.900 25.418 20.983 31.776 2012 12.220 13.746 13.738 12.135 2025 30.389 30.229 21.094 30.777 2025 13.387 13.474 14.526 13.716 Prices Prices 1987 $95 $95 $95 $95 1987 $115 $115 $115 $115 1992 $90 $90 $90 $90 1992 $109 $109 $109 $109 1997 $102 $110 $110 $99 1997 $120 $130 $130 $117 2002 $106 $108 $114 $107 2002 $126 $133 $133 $126 2007 $111 $111 $115 $108 2007 $131 $130 $134 $127 2012 $113 $108 $121 $112 2012 $131 $141 $141 $130 2025 $130 $128 $137 $131 2025 $146 $147 $154 $149 83 Table 5.1 (continued) Simulated Production, Consumption, Exports and Prices (Quantities Reported in millions of cubic metres; prices reported in 1980 US$/cubic metre) US West Coast US South Central Reduced AAC in BC Increased Reduced AAC in BC Increased Base Scenario Scenario Production in Base Scenario Scenario Production in Year Case ai b) Former USSR Year Case a) b) Former USSR Production Production 1987 29.660 29.660 29.660 29.660 1987 10.263 10.263 10.263 10.263 1992 28.084 28.084 28.084 28.084 1992 10.887 10.887 10.887 10.887 1997 25.003 26.149 26.149 24.135 1997 14.395 14.661 14.661 14.215 2002 22.631 23.839 23.839 22.632 2002 18.652 19.174 19.174 18.543 2007 24.149 23.624 24.425 23.001 2007 23.246 23.378 23.736 22.854 2012 24.001 25.542 25.515 24.071 2012 26.196 27.195 27.205 26.112 2025 29.642 28.985 31.640 29.758 2025 36.030 36.563 36.931 35.749 Consumption 1987 13.446 13.446 13.446 13.446 1992 13.609 13.609 13.609 13.609 1997 14.583 14.429 14.429 14.699 2002 15.503 15.311 15.311 15.526 2007 16.462 16.488 16.381 16.640 2012 17.717 17.468 17.464 17.745 2025 20.625 20.635 20.270 20.653 Consumption 1987 5.806 5.806 5.806 5.806 1992 5.909 5.909 5.909 5.909 1997 6.321 6.276 6.276 6.351 2002 6.713 6.634 6.634 6.725 2007 7.149 7.150 7.090 7.206 2012 7.678 7.535 7.537 7.679 2025 8.952 8.915 8.857 8.982 Exports 1987 16.214 16.214 16.214 16.214 1992 14.475 14.475 14.475 14.475 1997 10.420 11.720 11.720 9.435 2002 7.128 8.527 8.527 7.107 2007 7.686 7.135 8.044 6.361 2012 6.284 8.074 8.051 6.326 2025 9.017 8.350 11.370 9.105 Exports 1987 4.457 4.457 4.457 4.457 1992 4.978 4.978 4.978 4.978 1997 8.074 8.385 8.385 7.864 2002 11.939 12.541 12.541 11.818 2007 16.097 16.227 16.646 15.648 2012 18.518 19.660 19.668 18.433 2025 27.078 27.647 28.075 26.768 Prices Prices 1987 $133 $133 $133 $133 1987 $136 $136 $136 $136 1992 $128 $128 $128 $128 1992 $127 $127 $127 $127 1997 $141 $146 $146 $137 1997 $141 $145 $145 $139 2002 $144 $150 $150 $144 2002 $145 $151 $151 $145 2007 $151 $149 $153 $146 2007 $151 $150 $155 $146 2012 $151 $158 $158 $150 2012 $151 $162 $161 $152 2025 $169 $166 $178 $169 2025 $169 $169 $173 $167 84 Table 5.1 (continued) Simulated Production, Consumption, Exports and Prices (Quantities Reported in millions of cubic metres; prices reported in 1980 US$/cubic metre) Scandinavia Chile Reduced AAC in BC Increased Reduced AAC in BC Increased Base Scenario Scenario Production in Base Scenario Scenario Production in Year Case a) b) Former USSR Year Case a) b) Former USSR Production Production 1987 18.605 18.605 18.605 18.605 1987 2.674 2.674 2.674 2.674 1992 19.277 19.277 19.277 19.277 1992 2.358 2.358 2.358 2.358 1997 20.191 20.412 20.412 19.443 1997 3.218 3.446 3.446 2.908 2002 21.177 21.599 21.599 20.179 2002 4.312 4.370 4.370 4.199 2007 22.384 22.626 22.641 21.618 2007 5.253 5.581 5.655 4.760 2012 23.687 23.473 23.691 22.090 2012 6.607 6.735 6.754 6.225 2025 26.850 26.949 27.034 25.150 2025 10.072 10.235 10.223 9.598 Consumption Consumption 1987 6.410 6.410 6.410 6.410 1987 1.277 1.277 1.277 1.277 1992 6.387 6.387 6.387 6.387 1992 1.304 1.304 1.304 1.304 1997 6.981 6.958 6.958 7.058 1997 1.415 1.401 1.401 1.434 2002 7.420 -7.376 7.376 7.526 2002 1.491 1.490 1.490 1.495 2007 7.907 7.879 7.877 7.995 2007 1.592 1.575 1.570 1.618 2012 8.424 8.441 8.419 8.601 2012 1.685 1.682 1.681 1.701 2025 9.900 9.887 9.875 10.112 2025 1.986 1.984 1.985 2.000 Exports 1987 12.196 12.196 12.196 12.196 1992 12.891 12.891 12.891 12.891 1997 13.210 13.454 13.454 12.385 2002 13.757 14.224 14.224 12.653 2007 14.477 14.748 14.763 13.623 2012 15.263 15.032 15.272 13.488 2025 16.950 17.062 17.159 15.038 Exports 1987 1.397 1.397 1.397 1.397 1992 1.053 1.053 1.053 1.053 1997 1.803 2.046 2.046 1.475 2002 2.821 2.880 2.880 2.704 2007 3.661 4.007 4.084 3.142 2012 4.922 5.052 5.073 4.524 2025 8.087 8.251 8.237 7.598 Prices Prices 1987 $155 $155 $155 $155 1987 $101 $101 $101 $101 1992 $157 $157 $157 $157 1992 $94 $94 $94 $94 1997 $161 $163 $163 $154 1997 $99 $102 $102 $94 2002 $165 $168 $168 $157 2002 $104 $104 $104 $104 2007 $170 $172 $172 $164 2007 $107 $111 $112 $101 2012 $177 $175 $176 $164 2012 $113 $114 $114 $110 2025 $190 $191 $191 $177 2025 $120 $120 $120 $118 85 Table 5.1 (continued) Simulated Production, Consumption, Exports and Prices (Quantities Reported in millions of cubic metres; prices reported in 1980 US$/cubic metre) New Zealand Rest of the World Reduced AAC in BC Increased Reduced AAC in BC Increased Base Scenario Scenario Production in Base Scenario Scenario Production in Year Case a) Former USSR Year Case a) W Former USSR Production Production 1987 2.177 2.177 2.177 2.177 1987 77.599 77.599 77.599 77.599 1992 2.029 2.029 2.029 2.029 1992 79.184 79.184 79.184 79.184 1997 2.870 2.897 2.897 2.699 1997 82.425 84.146 84.146 96.134 2002 3.580 3.600 3.600 3.562 2002 87.249 87.828 87.828 99.324 2007 4.307 4.417 4.449 4.164 2007 91.365 93.217 93.267 105.542 2012 5.224 5.300 5.320 5.063 2012 96.940 97.272 97.315 109.205 2025 7.812 7.875 7.884 7.661 2025 111.339 111.344 111.115 122.252 Consumption 1987 1.687 1:687 1.687 1.687 1992 1.729 1.729 1.729 1.729 1997 1.838 1.834 1.834 1.867 2002 1.956 1.954 1.954 1.955 2007 2.086 2.069 2.064 2,105 2012 2.216 2.206 2.203 2.235 2025 2.613 2.609 2.609 2.622 Consumption 1987 64.169 64.169 64.169 64.169 1992 64.238 64.238 64.238 64.238 1997 70.345 69.912 69.912 70.880 2002 74.574 74.409 74.409 75.644 2007 79.680 79.186 79.173 80.377 2012 84.828 84.691 84.679 86.148 2025 99.332 99.255 99.311 101.450 Exports 1987 0.490 0.490 0.490 0.490 1992 0.300 0.300 0.300 0.300 1997 1.032 1.063 1.063 0.833 2002 1.624 1.646 1.646 1.607 2007 2.221 2.347 2.385 2.058 2012 3.008 3.093 3.117 2.828 2025 5.199 5.266 5.275 5.039 Exports 1987 13.430 13.430 13.430 13.430 1992 14.945 14.945 14.945 14.945 1997 12.081 14.234 14.234 25.254 2002 12.676 13.419 13.419 23.680 2007 11.685 14.031 14.094 25.165 2012 12.112 12.581 12.635 23.058 2025 12.007 12.089 11.804 20.802 Prices 1987 $158 $158 $158 $158 1992 $149 $149 $149 $149 1997 $162 $163 $163 $156 2002 $164 $165 $165 $165 2007 $168 $171 $172 $164 2012 $173 $175 $175 $170 2025 $180 $180 $180 $180 Prices 1987 151.641 151.641 151.641 151.641 1992 151.099 151.099 151.099 151.099 1997 153.565 156.928 156.928 149.406 2002 158.966 160.012 160.012 150.943 2007 162.745 166.252 166.351 158.242 2012 169.176 169.621 169.703 160.245 2025 185.001 184.766 184.310 171.869 Table 5.1 (continued) Simulated Production, Consumption, Exports and Prices (Quantities Reported in millions of cubic metres; prices reported in 1980 US$/cubic metre) Global Total Reduced AAC in BC Increased Base Scenario Scenario Production in Year Case a) b) Former USSR Production 1987 192.707 192 707 192.707 192.707 1992 190.790 190 790 190.790 190.790 1997 202.967 194 929 194.929 212.963 2002 211.995 203 740 203.653 223.048 2007 224.723 219 217 216.096 234.299 2012 237.915 235 706 231.356 247.844 2025 278.378 278 486 272.999 287.492 Consumption 1987 101.368 101 368 101.368 101.368 1992 101.896 101 896 101.896 101.896 1997 110.760 109 843 109.843 111.645 2002 117.448 116 829 116.741 118.661 2007 125.260 124 715 124.407 126.433 2012 133.689 133 050 132.819 135.277 2025 156.264 156 117 155.460 158.649 Exports 1987 91.339 91 339 91.339 91.339 1992 88.894 88 894 88.894 88.894 1997 92.207 85 086 85.086 101.318 2002 94.547 86 911 86.911 104.387 2007 99.464 94 503 91.690 107.866 2012 104.227 102 656 98.537 112.567 2025 122.113 122 368 117.539 128.843 Prices 1987 134.059 134 059 134.059 134.059 1992 132.049 132 049 132.049 132.049 1997 138.630 145 649 145.649 135.873 2002 143.809 148 882 149.700 140.156 2007 148.931 151 878 154.527 145.481 2012 152.829 156 047 158.673 148.275 2025 169.158 168 580 173.130 162.918 87 Table 5.2 Simulated Production, Consumption, Imports and Prices Interior Canada Reduced AAC in BC Increased Base Scenario Scenario Production in Year Case ai hi Former USSR Production 1987 9.311 9.311 9.311 9.311 1992 9.078 9.078 9.078 9.078 1997 10.354 10.963 10.963 9.972 2002 10.645 11.185 11.185 10.641 2007 10.918 11.065 11.374 10.722 2012 11.227 11.163 12.113 11.487 2025 12.799 12.616 13.271 12.799 US West Interior Reduced AAC in BC Increased Base Scenario Scenario Production in Year Case a) b) Former USSR Production 1987 10.092 10.092 10.092 10.092 1992 9.488 9.488 9.488 9.488 1997 8.558 9.114 9.114 8.291 2002 7.787 8.399 8.399 7.827 2007 8.080 8.076 8.311 7.945 2012 8.410 7.567 8.948 8.399 2025 10.369 9.967 10.779 10.228 Consumption 1987 15.508 15.508 15.508 15.508 1992 15.732 15.732 15.732 15.732 1997 16.790 16.491 16.491 16.978 2002 17.823 17.537 17.537 17.838 2007 19.026 18.911 18.759 19.136 2012 20.421 20.404 19.930 20.314 2025 23.632 23.669 23.247 23.632 Consumption 1987 13.883 13.883 13.883 13.883 1992 14.078 14.078 14.078 14.078 1997 15.049 14.826 14.826 15.156 2002 15.982 15.693 15.693 15.987 2007 17.063 16.999 16.905 17.139 2012 18.283 18.552 17.992 18.314 2025 21.281 21.403 20.998 21.369 Imports 1987 6.196 6.196 6.196 6.196 1992 6.653 6.653 6.653 6.653 1997 6.436 5.528 5.528 7.006 2002 7.178 6.352 6.352 7.197 2007 8.108 7.846 7.385 8.413 2012 9.194 9.241 7.817 8.828 2025 10.834 11.053 9.976 10.832 Imports 1987 3.791 3.791 3.791 3.791 1992 4.590 4.590 4.590 4.590 1997 6.491 5.712 5.712 6.865 2002 8.195 7.293 7.293 8.160 2007 8.983 8.923 8.593 9.194 2012 9.874 10.986 9.044 9.915 2025 10.911 11.436 10.219 11.141 Prices Prices 1987 $116 $116 $116 $116 1987 $137 $137 $137 $137 1992 $111 $111 $111 $111 1992 $131 $131 $131 $131 1997 $124 $131 $131 $119 1997 $145 $153 $153 $142 2002 $127 $134 $134 $127 2002 $149 $159 $159 $150 2007 $131 $133 $137 $128 2007 $153 $154 $158 $151 2012 $132 $131 $143 $135 2012 $155 $145 $164 $155 2025 $151 $149 $157 $151 2025 $175 $171 $181 $172 Table 5.2 (continued) Simulated Production, Consumption, Imports and Prices (Quantities Reported in millions of cubic metres; prices reported in 1980 US$/cubic metre) US South Atlantic US North Reduced AAC in BC Increased Reduced AAC in BC Increased Base Scenario Scenario Production in Base Scenario Scenario Production in Year Case a) b) Former USSR Year Case a) b) Former USSR Production Production 1987 10.119 10.119 10.119 10.119 1987 2.957 2.957 2.957 2.957 1992 11.022 11.022 11.022 11.022 1992 2.869 2.869 2.869 2.869 1997 14.410 14.794 14.794 14.146 1997 3.290 3.589 3.589 3.148 2002 18.620 19.890 19.890 18.579 2002 3.481 3.679 3.679 3.440 2007 21.297 21.591 21.768 21.077 2007 3.716 3.706 3.824 3.598 2012 22.973 23.772 23.821 22.882 2012 3.864 4.042 4.094 3.829 2025 25.669 26.158 26.294 25.597 2025 4.527 4.552 4.698 4.527 Consumption Consumption 1987 20.307 20.307 20.307 20.307 1987 28.487 28.487 28.487 28.487 1992 20.490 20.490 20.490 20.490 1992 28.810 28.810 28.810 28.810 1997 22.004 21.778 21.778 22.160 1997 30.982 30.376 30.376 31.269 2002 23.399 22.699 22.699 23.392 2002 32.931 32.501 32.501 33.027 2007 24.949 24.863 24.759 25.042 2007 35.101 35.071 34.831 35.358 2012 26.773 26.397 26.368 26.788 2012 37.621 37.208 37.090 37.723 2025 31.286 31.049 30.966 31.290 2025 44.122 43.962 43.636 44.168 Imports Imports 1987 10.188 10.188 10.188 10.188 1987 25.529 25.529 25.529 25.529 1992 9.468 9.468 9.468 9.468 1992 25.942 25.942 25.942 25.942 1997 7.594 6.984 6.984 8.014 1997 27.692 26.787 26.787 28.121 2002 4.779 2.809 2.809 4.813 2002 29.451 28.822 28.822 29.587 2007 3.652 3.272 2.991 3.966 2007 31.385 31.365 31.007 31.761 2012 3.800 2.624 2.547 3.906 2012 33.757 33.167 32.996 33.894 2025 5.618 4.891 4.671 5.693 2025 39.595 39.410 38.938 39.641 Prices Prices 1987 $142 $142 $142 $142 1987 $153 $153 $153 $153 1992 $138 $138 $138 $138 1992 $148 $148 $148 $148 1997 $151 $156 $156 $147 1997 $160 $171 $171 $155 2002 $154 $171 $171 $155 2002 $164 $171 $171 $163 2007 $159 $160 $162 $157 2007 $170 $169 $173 $166 2012 $160 $168 $168 $160 2012 $172 $178 $180 $171 2025 $177 $179 $181 $178 2025 $187 $187 $192 $188 89 Table 5.2 (continued) Simulated Production, Consumption, Imports and Prices (Quantities Reported in millions of cubic metres; prices reported in 1980 USS/cubic metre) Western Europe Japan Reduced AAC in BC Increased Reduced AAC in BC Increased Year Base Case Scenario a) Scenario Production in b) Former USSR Year Base Case Scenario ai Scenario b) Production in Former USSR Production Production 1987 33.728 33.728 33.728 33.728 1987 26.127 26.127 26.127 26.127 1992 35.195 35.195 35.195 35.195 1992 26.055 26.055 26.055 26.055 1997 36.754 36.859 36.859 35.420 1997 28.038 28.815 28.815 27.626 2002 38.154 38.728 38.728 36.820 2002 29.141 29.900 29.900 29.129 2007 40.059 40.554 40.498 38.999 2007 30.325 30.387 30.858 29.925 2012 42.303 42.284 42.169 39.713 2012 31.217 31.729 32.158 31.147 2025 47.799 47.786 47.626 45.135 2025 35.205 34.893 36.067 35.065 Consumption 1987 53.315 53.315 53.315 53.315 1992 52.492 52.492 52.492 52.492 1997 56.890 56.758 56.758 58.561 2002 60.269 59.544 59.544 62.017 2007 63.598 62.935 63.005 65.091 2012 66.810 66.758 66.906 70.299 2025 76.397 76.325 76.549 80.447 Imports 1987 19.586 19.586 19.586 19.586 1992 17.297 17.297 17.297 17.297 1997 20.137 19.898 19.898 23.141 2002 22.114 20.817 20.817 25.197 2007 23.539 22.382 22.507 26.092 2012 24.507 24.474 24.737 30.586 2025 28.599 28.540 28.923 35.312 Consumption 1987 33.068 33.068 33.068 33.068 1992 33.724 33.724 33.724 33.724 1997 35.847 35.120 35.120 36.234 2002 37.981 37.236 37.236 38.009 2007 40.401 40.273 39.831 40.796 2012 43.306 42.747 42.323 43.411 2025 49.914 50.050 48.870 50.101 Imports 1987 6.941 6.941 6.941 6.941 1992 7.670 7.670 7.670 7.670 1997 7.809 6.305 6.305 8.608 2002 8.840 7.335 7.335 8.881 2007 10.076 9.886 8.974 10.871 2012 12.089 11.018 10.165 12.264 2025 14.710 15.157 12.803 15.036 Prices Prices 1987 $179 $179 $179 $179 1987 $228 $228 $228 $228 1992 $183 $183 $183 $183 1992 $221 $221 $221 $221 1997 $186 $187 $187 $179 1997 $234 $242 $242 $230 2002 $189 $192 $192 $182 2002 $238 $246 $246 $238 2007 $194 $196 $196 $189 2007 $243 $243 $248 $239 2012 $200 $200 $200 $187 2012 $244 $249 $253 $244 2025 $215 $215 $214 $202 2025 $263 $259 $270 $262 90 Table 5.2 (continued) Simulated Production, Consumption, Imports and Prices (Quantities Reported in millions of cubic metres; prices reported in 1980 US$/cubic metre) Rest of the World Global Total Reduced AAC in BC Increased Reduced AAC in BC Increased Base Scenario Scenario Production in Base Scenario Scenario Production in Year Case a) b) Former USSR Year Case a) b) Former USSR Production Production 1987 94.090 94.090 94.090 94.090 1987 186.425 186.425 186.425 186.425 1992 96.037 96.037 96.037 96.037 1992 189.743 189.743 189.743 189.743 1997 100.063 101.659 101.659 97.491 1997 201.470 205.794 205.794 196.095 2002 105.219 105.586 105.586 100.421 2002 213.046 217.367 217.367 206.855 2007 109.612 111.723 112.160 106.821 2007 224.006 227.102 228.793 219.085 2012 115.946 115.828 115.764 114.396 2012 235.940 236.384 239.066 231.854 2025 127.016 126.975 126.881 127.557 2025 263.382 262.947 265.617 260.909 Consumption Consumption 1987 113.197 113.197 113.197 113.197 1987 277.764 277.764 277.764 277.764 1992 113.310 113.310 113.310 113.310 1992 278.636 278.636 278.636 278.636 1997 116.115 115.532 115.532 117.056 1997 293.677 290.879 290.879 297.414 2002 119.209 119.069 119.069 120.973 2002 307.593 304.278 304.278 311.242 2007 123.333 122.553 122.393 124.389 2007 323.470 321.604 320.483 326.952 2012 126.952 126.975 126.996 127.571 2012 340.167 339.041 337.603 344.420 2025 138.863 138.857 138.890 138.745 2025 385.496 385.315 383.156 389.752 Imports 1987 19.107 19.107 19.107 19.107 1992 17.273 17.273 17.273 17.273 1997 16.049 13.872 13.872 19.565 2002 13.990 13.483 13.483 20.552 2007 13.721 10.830 10.233 17.569 2012 11.006 11.146 11.232 13.175 2025 11.847 11.882 12.008 11.188 Prices 1987 $172 $172 $172 $172 1992 $171 $171 $171 $171 1997 $174 $177 $177 $169 2002 $179 $180 $180 $170 2007 $182 $186 $187 $177 2012 $189 $188 $188 $186 2025 $195 $195 $195 $197 Imports 1987 91.339 91.339 91.339 91.339 1992 88.894 88.894 88.894 88.894 1997 92.207 85.086 85.086 101.318 2002 94.547 86.911 86.911 104.387 2007 99.464 94.503 91.690 107.866 2012 104.227 102.656 98.537 112.567 2025 122.113 122.368 117.539 128.843 Prices 1987 174.472 174.472 174.472 174.472 1992 172.988 172.988 172.988 172.988 1997 179.024 182.779 182.779 174.201 2002 182.793 186.724 186.724 177.435 2007 186.536 188.939 190.472 182.320 2012 191.207 192.173 193.497 187.685 2025 203.029 202.291 204.614 201.283 Table 5.3 Direction of Trade - Base Case 1987 (in millions of cubic metres) Export/Import Int Canada US West Int US South AtL US North W.Europe Japan ROWTotal Export British Columbia 4.33 2.81 6.99 9.88 3.39 3.50 1.59 32.48 Eastern Canada 0.34 0.07 1.09 6.55 1.24 0.00 1.39 10.67 US West Coast 1.30 0.72 2.09 7.33 0.00 3.27 1.50 16.21 US South Central 0.23 0.19 0.02 1.73 1.06 0.00 1.23 4.46 Scandinavia 0.00 0.00 0.00 0.05 12.11 0.00 0.04 12.20 Chile 0.00 0.00 0.00 0.00 0.81 0.17 0.42 1.40 New Zealand 0.00 0.00 0.00 0.00 0.00 0.00 0.49 0.49 ROW 0.00 0.00 0.00 0.00 0.98 0.00 12.45 13.43 Total Imports 6.20 3.79 10.19 25.53 19.59 6.94 19.11 91.34 1992 Export/Import Int Canada US West Int US South AU. US North W.Europe Japan ROWTotal Export British Colombia S.148 3.544 2.485 14.821 0.330 2.487 1.186 30.000 Eastern Canada 0.339 0.069 0.975 7.686 0.000 0.001 1.180 10.250 US West Coast 0.926 0.767 5.987 0.719 0.000 5.028 1.048 14.475 US South Central 0.239 0.190 0.021 2.715 0.940 0.001 0.871 4.978 Scandinavia 0.000 0.000 0.000 0.000 12.890 0.000 0.000 12.891 Chile 0.000 0.019 0.000 0.000 0J54 0.153 0.728 1.053 New Zealand 0.000 0.000 0.000 0.000 0.199 0.000 0.101 0.300 ROW 0.000 0.002 0.000 0.000 2.784 0.000 12.160 14.945 Total Imports 6.653 4.590 9.468 25.942 17.297 7.670 17.273 88.894 1997 Export/Import Int Canada US West Int US South AtL US North W.Europe Japan ROWTotal Export British Columbia 5.130 5.253 2.451 14.497 0.000 4.886 1.620 33.837 Eastern Canada 0.346 0.061 1.217 8.346 0.000 0.000 1.779 11.749 US West Coast 0.510 0.975 3.892 0.733 0.000 2.905 1.404 10.420 US South Central 0.448 0.195 0.022 4.087 1.786 0.000 1.536 8.074 Scandinavia 0.000 0.000 0.000 0.000 13.210 0.000 0.000 13.210 Chile 0.000 0.003 0.001 0.000 0.074 0.001 1.724 1.803 New Zealand 0.000 0.000 0.000 0.000 0.079 0.000 0.952 1.032 ROW 0.000 0.002 0.012 0.028 4.987 0.017 7.034 12.081 Total Imports 6.436 6.491 7.594 27.692 20.137 7.809 16.049 92.207 2002 Export/Import Int Canada US West Int US South AtL US North W.Europe Japan ROW Total Export British Columbia 5.46 6.19 2.18 10.53 0.00 7.79 0.27 32.43 Eastern Canada 0.35 0.07 1.64 9.77 0.00 0.00 0.34 12.17 US West Coast 0.90 1.46 0.91 0.69 0.00 0.97 2.19 7.13 US South Central 0.27 0.29 0.02 8.43 2.45 0.00 0.47 11.94 Scandinavia 0.00 0.00 0.00 0.01 13.74 0.00 0.00 13.76 Chile 0.02 0.02 0.00 0.00 0.01 0.02 2.76 2.82 New Zealand 0.16 0.16 0.00 0.00 0.32 0.04 0.94 1.62 ROW 0.03 0.00 0.02 0.01 5.59 0.02 7.01 12.68 Total Imports 7.18 8.20 4.78 29.45 22.11 8.84 13.99 94.55 92 Table 5.3 (continued) Direction of Trade - Base Case 2007 (in millions of cubic metres) Export/Import Int Canada US West Int US South AtL US North W.Europe Japan ROW Total Exports BC 6.505 7.505 1.600 7.229 0.000 7.079 1.058 30.977 Quebec and East Coast 0.352 0.066 1.061 10.038 0.000 0.000 1.143 12.660 US West Coast 0.501 0.987 0.493 2.191 0.000 2.588 0.927 7.686 US South Central 0.371 0.213 0.034 11.489 2.867 0.000 1.122 16.097 Scandinavia 0.000 0.000 0.001 0.001 14.473 0.001 0.000 14.477 Chile 0.167 0.152 0.185 0.127 0.162 0.186 2.682 3.661 New Zealand 0.058 0.058 0.076 0.051 0.546 0.012 1.418 2.221 ROW 0.153 0.001 0.201 0.257 5.491 0.210 5.371 11.685 Total Imports 8.108 8.983 3.652 31.385 23.539 10.076 13.721 99.464 2012 Export/Import Int Canada US West Int US South AtL US North W.Europe Japan ROW Total Exports British Columbia 7.633 8.245 2.659 5.345 0.000 8.011 0.005 31.900 Eastern Canada 0.346 0.071 0.962 10.765 0.000 0.000 0.076 12.220 US West Coast 0.756 1.171 0.021 0.637 0.000 3.666 0.033 6.284 US South Central 0.456 0.301 0.023 16.789 0.830 0.000 0.119 18.518 Scandinavia 0.000 0.000 0.000 0.000 15.263 0.000 0.000 15.263 Chile 0.000 0.056 0.000 0.112 0.281 0.098 4.376 4.922 New Zealand 0.003 0.028 0.075 0.003 0.247 0.168 2.485 3.008 ROW 0.000 0.002 0.060 0.106 7.886 0.146 3.912 12.112 Total Imports 9.194 9.874 3.800 33.757 24.507 12.089 11.006 104.227 2025 Export/Import Int Canada US West Int US South AtL US North W.Europe Japan ROW Total Exports British Columbia 8.660 9.516 0.000 4.464 0.000 7.744 0.005 30.389 Eastern Canada 0.084 0.059 0.083 13.105 0.000 0.000 0.055 13.387 US West Coast 0.843 0.596 0.000 1.157 0.000 6.421 0.000 9.017 US South Central 1.206 0.646 5.408 16.972 2.680 0.000 0.166 27.078 Scandinavia 0.003 0.000 0.025 1.818 15.093 0.011 0.000 16.950 Chile 0.018 0.062 0.034 0.222 0.454 0.118 7.179 8.087 New Zealand 0.003 0.031 0.037 0.003 0.421 0.262 4.442 5.199 ROW 0.017 0.002 0.031 1.853 9.951 0.154 0.000 12.007 Total Imports 10.834 10.911 5.618 39.595 28.599 14.710 11.847 122.113 Table 5.4 Projected Real Softwood Lumber Prices (1980 US$ per cubic metre) Actual Predicted Export Regions 1987 1987 1992 1997 2002 2007 2012 2025 British Columbia 97 95.23 90.14 101.90 106.45 110.78 112.73 130.07 Eastern Canada 116 114.93 109.49 120.38 125.53 131.41 130.96 146.44 US West Coast 135 133.47 127.98 140.97 144.36 151.19 150.52 168.89 US South Central 136 135.51 127.49 141.47 145.40 150.65 151.24 168.67 Scandinavia 156 155.21 157.09 160.70 164.71 170.36 176.56 190.10 Chile 95 100.52 93.96 98.82 104.29 107.03 113.44 120.21 New Zealand 155 157.58 149.13 162.02 164.17 167.53 173.01 180.08 ROW 150 151.64 151.10 153.57 158.97 162.75 169.18 185.00 Actual Predicted Import Regions 1987 1987 1992 1997 2002 2007 2012 2025 Interior Canada 118 116.35 110.65 123.76 127.39 130.80 131.60 151.23 US West Interior 140 137.18 130.60 145.37 149.43 153.42 155.43 174.63 US South Atlantic 145 142.45 138.06 150.86 154.34 159.25 160.37 177.30 US North 155 153.42 147.53 160.45 164.43 169.90 171.94 187.14 Western Europe 180 179.09 182.58 186.18 188.74 193.79 200.39 214.97 Japan 229 228.29 221.50 234.44 238.33 242.76 244.06 263.08 ROW 170 171.73 171.16 174.13 178.97 182.19 188.75 195.20 Table 5.5 Projected Softwood Lumber Price Index (1987=100) Export Regions 1987 1992 1997 2002 2007 2012 2025 British Columbia 1.00 0.95 1.07 1.12 1.16 1.18 1.37 Eastern Canada 1.00 0.95 1.05 1.09 1.14 1.14 1.27 US West Coast 1.00 0.96 1.06 1.08 1.13 1.13 1.27 US South Central 1.00 0.94 •1.04 1.07 1.11 1.12 1.24 Scandinavia 1.00 1.01 1,04 1.06 1.10 1.14 1.22 Chile 1.00 0.93 0.98 1.04 1.06 1.13 1.20 New Zealand 1.00 0.95 1.03 1.04 1.06 1.10 1.14 ROW 1.00 1.00 1.01 1.05 1.07 1.12 1.22 Import Regions 1987 1992 1997 2002 2007 2012 2025 Interior Canada 1.00 0.95 1.06 1.09 1.12 1.13 1.30 US West Interior 1.00 0.95 1.06 1.09 1.12 1.13 1.27 US South Atlantic 1.00 0.97 1.06 1.08 1.12 1.13 1.24 US North 1.00 0.96 1.05 1.07 1.11 1.12 1.22 Western Europe 1.00 1.02 1.04 1.05 1.08 1.12 1.20 Japan 1.00 0.97 1.03 1.04 1.06 1.07 1.15 ROW 1.00 1.00 1.01 1.04 1.06 1.10 1.14 94 Table 5.6 Direction of Trade - Reduced AAC in BC (Scenario a) (in millions of cubic metres) 1987 Export/Import Int Canada US West Int US South Ad. US North W. Europe Japan ROW Total Exports British Columbia 4.33 2.81 6.99 9.88 339 3.50 1.59 32.48 Eastern Canada 0.34 0.07 1.09 6.55 124 0.00 139 10.67 US West Coast 130 0.72 2.09 733 0.00 3.27 1.50 1621 US South Central 0.23 0.19 0.02 1.73 1.06 0.00 123 4.46 Scandinavia 0.00 0.00 0.00 0.05 12.11 0.00 0.04 1270 Chile 0.00 0.00 0.00 0.00 0.81 0.17 0.42 1.40 New Zealand 0.00 0.00 0.00 0.00 0.00 0.00 0.49 0.49 ROW 0.00 0.00 0.00 0.00 0.98 0.00 12.45 13.43 Total Imports 620 3.79 10.19 25.53 19.59 6.94 19.11 9134 1992 Export/Import Int Canada US West Int US South Ad. US North W.Europe Japan ROW Total Exports British Columbia 5.148 3.544 2.485 14.821 0.330 2.487 1.186 30.000 Eastern Canada 0339 0.069 0.975 7.686 0.000 0.001 1.180 10550 US West Coast 0.926 0.767 5.987 0.719 0.000 5.028 1.048 14.475 US South Central 0739 0.190 0.021 2.715 0.940 0.001 0.871 4.978 Scandinavia 0.000 0.000 0.000 0.000 12.890 0.000 0.000 12.891 Chile 0.000 0.019 0.000 0.000 0.154 0.153 0.728 1.053 New Zealand 0.000 0.000 0.000 0.000 0.199 0.000 0.101 0.300 ROW 0.000 0.002 0.000 0.000 2.784 0.000 12.160 14.945 Total Imports 6.653 4.590 9.468 25.942 17297 7.670 17273 88.894 1997 Export/Import Int Canada US West Int US South Ad. US North W.Europe Japan ROW Total Exports British Columbia 4.154 4215 1.316 8368 0.000 2.455 0.563 21.070 Eastern Canada 0.337 0.061 1.010 11.025 0.000 0.000 0.680 13.114 USWestCoast 0.568 1202 4.387 0.857 0.000 3.756 0.950 11.720 US South Central 0.467 0.199 0.022 5.953 0.920 0.000 0.824 8385 Scandinavia 0.000 0.000 0235 0290 12.928 0.000 0.000 13.454 Chile 0.000 0.003 0.001 0212 0.074 0.001 1.754 2.046 NewZealand 0.000 0.029 0.001 0.051 0.078 0.075 0.829 1.063 ROW 0.000 0.002 0.012 0.030 5.898 0.018 8273 14234 Total Imports 5.528 5.712 6.984 26.787 19.898 6.305 13.872 85.086 2002 Export/Import Int Canada US West Int US South Ad. US North W.Europe Japan ROW Total Exports British Columbia 429 4.44 0.73 4.85 0.00 5.90 023 20.45 Eastern Canada 035 0.07 1.01 11.51 0.00 0.00 029 1323 USWestCoast 122 2.30 1.02 0.87 0.00 1.36 1.77 8.53 US South Central 027 030 0.02 11.57 0.00 0.00 0.38 12.54 Scandinavia 0.00 0.00 0.00 0.01 1421 0.00 0.00 1422 Chile 0.02 0.02 0.00 0.00 0.01 0.02 2.81 2.88 NewZealand 0.18 0.17 0.00 0.00 032 0.04 0.93 1.65 ROW 0.03 0.00 0.02 0.01 627 0.02 7.07 13.42 Total Imports 635 729 2.81 28.82 20.82 7.34 13.48 86.91 95 Table 5.6 (continued) Direction of Trade - Reduced AAC in BC (Scenario a) (in millions of cubic metres) 2007 Export/Import Int Canada US West Int US South Ad. US North W.Europe Japan ROW Total Exports British Columbia 5.717 7.592 1.341 0.842 0.000 7.687 0.352 23.530 Eastern Canada 0.313 0.065 0.880 11.146 0.000 0.000 0.074 12.477 US West Coast 0.473 0.841 0.497 3.331 0.000 1.753 0542 7.135 US South Central 0.329 0500 0.034 15.533 0.019 0.000 0.112 16527 Scandinavia 0.609 0.000 0.001 0.001 14.135 0.001 0.000 14.748 Chile 0.181 0.164 0510 0.143 0.165 0504 2.940 4.007 New Zealand 0.061 0.060 0.081 0.054 0.579 0.013 1.501 2.347 ROW 0.165 0.001 0529 0.315 7.484 0528 5.609 14.031 Total Imports 7.846 8.923 3572 31.365 22.382 9.886 10.830 94.503 2012 Export/Import Int Canada US West Int US South Ad. US North W. Europe Japan ROW Total Exports British Columbia 7.358 9.147 1.504 2.042 0.000 5.362 0.005 25.418 Eastern Canada 0.358 0.071 0.922 12.320 0.000 0.000 0.076 13.746 US West Coast 0.833 1371 0.021 0.656 0.000 5.161 0.033 8.074 US South Central 0.476 0310 0.023 17.924 0.809 0.000 0.118 19.660 Scandinavia 0.094 0.000 0.012 0.002 14.845 0.078 0.000 15.032 Chile 0.063 0.056 0.008 0.114 0586 0.098 4.427 5.052 New Zealand 0.003 0.029 0.075 0.003 0550 0.170 2.565 3.093 ROW 0.057 0.002 0.060 0.107 8584 0.149 3.922 12.581 Total Imports 9.241 10.986 2.624 33.167 24.474 11.018 11.146 102.656 2025 Export/Import Int Canada US West Int US South Ad. US North W. Europe Japan ROW Total Exports British Columbia 9.480 10546 0.315 1.895 0.000 8588 0.005 30529 Eastern Canada 0.083 0.058 0.083 13.195 0.000 0.000 0.054 13.474 US West Coast 0.599 0.476 0.000 0.996 0.000 6578 0.000 8.350 US South Central 0.849 0.559 4.367 19536 2.479 0.000 0.157 27.647 Scandinavia 0.003 0.000 0.025 1.902 15.122 0.011 0.000 17.062 Chile 0.018 0.063 0.034 0539 0.522 0.125 7550 8551 New Zealand 0.003 0.031 0.037 0.003 0.479 0598 4.415 5566 ROW 0.017 0.002 0.031 1.943 9.938 0.158 0.000 12.089 Total Imports 11.053 11.436 4.891 39.410 28.540 15.157 11.882 122.368 96 Table 5.7 Direction of Trade - Reduced AAC in BC (Scenario b) 1987 (in millions of cubic metres) Export/Import Int Canada US West Int US South All US North W.Europe Japan ROW Total Exports British Columbia 4.33 281 6.99 9.88 3.39 3.50 1.59 3Z48 Eastern Canada 0.34 0.07 1.09 6.55 1.24 0.00 1.39 10.67 USWestCoast 130 0.72 209 7.33 0.00 3.27 1.50 16.21 US South Central 0.23 0.19 0.02 1.73 1.06 0.00 1.23 446 Scandinavia 0.00 0.00 0.00 0.05 121 0.00 0.04 1220 Chile 0.00 0.00 0.00 0.00 0.81 0.17 0.42 1.49 NewZealand 0.00 0.00 0.00 0.00 0.00 0.00 0.49 0.49 ROW 0.00 0.00 0.00 0.00 0.98 0.00 1245 13.43 Total unpens 6.20 3.79 10.19 25.53 19.59 6.94 19.1 9134 1991 Export/Import Int Canada US West Int US South Ait US North W.Europe Japan ROW Total Exports British Columbia 5.148 3.544 2485 14.821 0.330 2487 1.186 30.000 Eastern Canada 0.339 0.069 0.975 7.686 0.000 0.001 1.180 10.250 USWestCoast 0.926 0.767 5.987 0.719 0.000 5.028 1.048 14.475 US South Central 0.239 0.190 0.021 2715 0.940 0.001 0.871 4978 Scandinavia 0.000 0.000 0.000 0.000 12890 0.000 0.000 12891 Chile 0.000 0.019 0.000 0.000 0.154 0.153 0.728 1.053 NewZealand 0.000 0.000 0.000 0.000 0.199 0.000 0.101 0.300 ROW 0.000 0.002 0.000 0.000 2784 0.000 12160 14945 Total Imparts 6.653 4.590 9.468 25.942 17.297 7.670 17.273 88.894 1997 Export/Import InL Canada US West Int US South AtL US North W.Europe Japan ROW Total Exports British Columbia 4.154 4.215 1.316 8.368 0.000 2455 0.563 21.070 Eastern Canada 0.337 0.061 1.010 11.025 0.000 0.000 0.680 13.11 USWestCoast 0.568 1.202 4.387 0.857 0.000 3.756 0.950 11.720 US South Central 0.467 0.199 0.022 5.953 0.920 0.000 0.824 8385 Scandinavia 0.000 0.000 0.235 0.290 12928 0.000 0.000 13.454 Chile 0.000 0.003 0.001 0.212 0.074 0.001 1.754 2046 NewZealand 0.000 0.029 0.001 0.051 0.078 0.075 0.829 1.063 ROW 0.000 0.002 0.012 0.030 5.898 0.018 8.273 14.234 Total Imports 5.528 5.712 6.984 26.787 19.898 6.305 13.872 85.086 2002 Export/Import Int Canada US West Int US South AO. US North W.Europe Japan ROW Total Exports British Columbia 429 4.44 0.73 485 0.00 5.90 0.23 20.45 Eastern Canada 0.35 0.07 1.0 11.5 0.00 0.00 0.29 13.23 USWestCoast 1.22 230 1.02 0.87 0.00 1.36 1.77 8.53 US South Central 0.27 0.30 0.02 11.57 0.00 0.00 0.38 1254 Scandinavia 0.00 0.00 0.00 0.01 14.21 0.00 0.00 14.22 Chile 0.02 0.02 0.00 0.00 0.01 0.02 281 288 NewZealand 0.18 0.17 0.00 0.00 0.32 0.04 0.93 1.65 ROW 0.03 0.00 0.02 0.01 6.27 0.02 7.07 13.42 Total Imports 6.35 7.29 281 28.82 20.82 7.34 13.48 86.91 97 Table 5.7 (continued) Direction of Trade - Reduced AAC in BC (Scenario b) (in millions of cubic metres) 2007 Export/Import Int Canada US West Int US South AtL US North W. Europe Japan ROW Total Exports British Columbia 4.848 6.943 1.001 0.658 0.000 5.136 0.000 18.585 Eastern Canada 0.365 0.067 0.957 11.625 0.000 0.000 0.074 13.087 US West Coast 0.530 1.126 0.473 2324 0.000 3.356 0.234 8.044 US South Central 0.388 0.220 0.034 15.891 0.000 0.000 0.114 16646 Scandinavia 0.826 0.000 0.001 0.001 13.933 0.001 0.000 14.763 Chile 0.194 0.175 0.211 0.141 0.165 0.219 2978 4.084 New Zealand 0.062 0.061 0.081 0.054 0.586 0.013 1.529 2385 ROW 0.175 0.001 0.232 0.313 7.821 0.249 5.304 14.094 Total Imports 7.385 8.593 2991 31.007 22507 8.974 10.233 91.690 2012 Export/Import Int Canada US West Int US South AtL US North W.Europe Japan ROW Total Exports British Columbia 5.933 7.201 1.424 1.886 0.000 4533 0.005 20.983 Eastern Canada 0.358 0.071 0.925 12308 0.000 0.000 0.076 13.738 US West Coast 0.833 1.374 0.021 0.654 0.000 5.136 0.033 8.051 US South Central 0.476 0310 0.023 17.924 0.816 0.000 0.118 19.668 Scandinavia 0.094 0.000 0.012 0.000 15.087 0.078 0.000 15.272 Chile 0.063 0.056 0.008 0.113 0.286 0.098 4.448 5.073 New Zealand 0.003 0.029 0.075 0.003 0.250 0.170 2588 3.117 ROW 0.057 0.002 0.060 0.107 8.298 0.149 3.963 12635 Total Imparts 7.817 9.044 2547 32995 24.737 10.165 11.232 98.537 2025 Export/Import Int Canada US West Int US South AtL US North W.Europe Japan ROW Total Exports British Columbia 7.508 8.716 0.000 1320 0.000 3.545 0.005 21.094 Eastern Canada 0.085 0.060 0.083 14.243 0.000 0.000 0.055 14.526 US West Coast 0.922 0.637 0.000 1.11 0.000 8.698 0.000 11370 US South Central 1.421 0.711 4.462 18.477 2841 0.000 0.164 28.075 Scandinavia 0.003 0.000 0.025 1.765 15.355 0.011 0.000 17.159 Chile 0.018 0.063 0.034 0.227 0.477 0.120 7.299 8.237 New Zealand 0.003 0.031 0.037 0.003 0.441 0.275 4485 5.275 ROW 0.017 0.002 0.031 1.789 9.810 0.155 0.000 11.804 Total Imports 9.976 10.219 4.671 38.938 28.923 12803 12008 117.539 s Table 5.8 Direction of Trade - Increased Production in the Former USSR (in millions of cubic metres) 1987 Export/Import Int. Canada US West IntUS South Atl. US North W. Europe Japan ROW Total Exports British Columbia 4.33 2.81 6.99 9.88 3.39 3.50 1.59 32.48 Eastern Canada 0.34 0.07 1.09 6.55 1.24 0.00 1.39 10.67 US West Coast 1.30 0.72 2.09 7.33 0.00 3.27 1.50 16.21 US South Central 0.23 0.19 0.02 1.73 1.06 0.00 1.23 4.46 Scandinavia 0.00 0.00 0.00 0.05 12.11 0.00 0.04 12.20 Chile 0.00 0.00 0.00 0.00 0.81 0.17 0.42 1.40 New Zealand 0.00 0.00 0.00 0.00 0.00 0.00 0.49 0.49 ROW 0.00 0.00 0.00 0.00 0.98 0.00 12.45 13.43 Total Imports 6.20 3.79 10.19 25.53 19.59 6.94 19.11 91.34 1992 Export/Import Int. Canada US West IntUS South Atl. US North W. Europe Japan ROW Total Exports British Columbia 5.148 3.544 2.485 14.821 0.330 2.487 1.186 30.000 Eastern Canada 0.339 0.069 0.975 7.686 0.000 0.001 1.180 10.250 US West Coast 0.926 0.767 5.987 0.719 0.000 5.028 1.048 14.475 US South Central 0.239 0.190 0.021 2.715 0.940 0.001 0.871 4.978 Scandinavia 0.000 0.000 0.000 0.000 12.890 0.000 0.000 12.891 Chile 0.000 0.019 0.000 0.000 0.154 0.153 0.728 1.053 New Zealand 0.000 0.000 0.000 0.000 0.199 0.000 0.101 0.300 ROW 0.000 0.002 0.000 0.000 2.784 0.000 12.160 14.945 Total Imports 6.653 4.590 9.468 25.942 17.297 7.670 17.273 88.894 1997 Exportflmport Int. Canada US West Int.US South Atl. US North W. Europe Japan ROWTotal Exports British Columbia 5.678 5.640 2.612 12.749 0.000 5.464 0.600 32.742 Eastern Canada 0.351 0.061 1.284 9.029 0.000 0.000 0.607 11.331 US West Coast 0.508 0.959 3.829 0.712 0.000 2.849 0.577 9.435 US South Central 0.468 0.199 0.022 5.266 1.104 0.000 0.805 7.864 Scandinavia 0.000 0.000 0.255 0.201 11.647 0.234 0.047 12.385 Chile 0.000 0.003 0.001 0.092 0.074 0.001 1.304 1.475 New Zealand 0.000 0.000 0.000 0.044 0.090 0.041 0.658 0.833 ROW 0.000 0.003 0.012 0.028 10.226 0.018 14.966 25.254 Total Imports 7.006 6.865 8.014 28.121 23.141 8.608 19.565 101.318 2002 Export/Import Int. Canada US West IntUS South Atl. US North W.Europe Japan ROW Total Exports British Columbia 5.47 6.16 2.20 10.69 0.00 7.83 0.27 32.62 Eastern Canada 0.35 0.07 1.64 9.79 0.00 0.00 0.34 12.19 US West Coast 0.90 1.47 0.92 0.69 0.00 0.98 2.16 7.11 US South Central 0.27 0.29 0.02 8.39 2.37 0.00 0.47 11.82 Scandinavia 0.00 0.00 0.00 0.01 12.64 0.00 0.00 12.65 Chile 0.02 0.02 0.00 0.00 0.01 0.02 2.64 2.70 New Zealand 0.16 0.16 0.00 0.00 0.32 0.04 0.93 1.61 ROW 0.03 0.00 0.02 0.01 9.85 0.02 13.75 23.68 Total Imports 7.20 8.16 4.81 29.59 25.20 8.88 20.55 104.39 99 Table 5.8 (continued) Direction of Trade - Increased Production in the Former USSR (in millions of cubic metres) 2007 Export/Import Int Canada USWestlnt US South Ad. US North W.Europe Japan ROW Total Exports BC 6.637 7.771 1.754 5216 0.000 7.812 0.645 29.833 Quebec and East Coast 0.357 0.066 1.169 9.737 0.000 0.000 0.707 12.036 USWestCoast 0.485 0.925 0.491 1.679 0.000 2230 0.551 6361 US South Central 0.380 0216 0.034 . 14.101 0.186 0.000 0.731 15.648 Scandinavia 0.165 0.000 0.001 0.000 13.456 0.001 0.000 13.623 Chile 0.171 0.157 0.190 0.124 0.162 0.107 2231 3.142 NewZealand 0.059 0.058 0.077 0.045 0.509 0.009 1.301 2.058 ROW 0.162 0.001 0250 0.858 11.780 0.712 11.403 25.165 Total Imports 8.413 9.194 3.966 31.761 26.092 10.871 17.569 107.866 2012 Ejqxift/Import Int Canada USWestlnt US South Ad. US North W.Europe Japan ROW Total Exports British Columbia 7.126 8284 2.735 5.563 0.000 8.063 0.005 31.776 Eastern Canada 0.344 0.071 0.965 10.680 0.000 0.000 0.076 12.135 USWestCoast 0.752 1.175 0.021 0.641 0.000 3.705 0.033 6326 US South Central 0.453 0300 0.023 16.703 0.835 0.000 0.119 18.433 Scandinavia 0.026 0.000 0.028 0.000 13.377 0.025 0.031 13.488 Chile 0.000 0.056 0.000 0.111 0277 0.097 3.982 4.524 NewZealand 0.003 0.028 0.074 0.003 0244 0.169 2.306 2.828 ROW 0.124 0.002 0.060 0.193 15.852 0204 6.622 23.058 Total Imports 8.828 9.915 3.906 33.894 30.586 12264 13.175 112.567 2025 Export/Import Int Canada USWestlnt US South Ad. US North W.Europe Japan ROW Total Exports British Columbia 8.656 9.743 0.000 4.400 0.000 7.972 0.005 30.777 Eastern Canada 0.084 0.059 0.083 13.434 0.000 0.000 0.055 13.716 USWestCoast 0.842 0.5% 0.000 1.151 0.000 6.516 0.000 9.105 US South Central 1.208 0.648 5.483 16.541 2.722 0.000 0.165 26.768 Scandinavia 0.003 0.000 0.025 1.767 13233 0.011 0.000 15.038 Chile 0.018 0.062 0.034 0223 0.458 0.118 6.686 7.598 NewZealand 0.003 0.031 0.037 0.003 0.425 0264 4277 5.039 ROW 0.017 0.002 0.031 2.123 18.473 0.156 0.000 20.802 Total Imports 10.832 11.141 5.693 39.641 35.312 15.036 11.188 128.843 100 Table 5.9 Change in Producer and Consumer Scenario: Change in AACa in millions 1980 US$ tw 2002 Change in Welfare 2025 Export Regions cs PS CS PS CS PS British Columbia 720.14 i m « -8.17 423.99 13.99 -152.74 Eastern Canada 602,9$ -37.22 121.57 -23.54 4.79 US West Coast 3035.56 1979.31 -99.21 259.66 7.31 -122.10 US South Central 1317.51 $9537 -41.25 66.70 -25.83 0.16 Scandinavia 1669 05 -26.69 69.40 -10.49 17.41 Chile 198.72 379.39 -0.48 1.69 -0.99 -1.74 New Zealand 2S8.40 447,62 -0.99 2.57 -2.00 0.14 ROW 15989.51 588X59 -95.47 85.39 -58.84 -22.19 Total 2SS2L8Z ms9.11 -309.48 1030.97 -100.39 -276.27 1987 2002 2025 Import Regions CS CS PS CS PS Central Canada 3062 61 541.69 -128.86 70.70 22.09 -27.86 US West Interior 3259.08 692.21 -155.19 136.65 88.06 -49.03 US South Atlantic 4933 40 720.67 -386.29 189.12 -176.69 26.88 US North 73S1 47 42331 -255.95 38.49 -128.42 -2.75 W. Europe 6022.21 3020,20 -183.85 108.56 -23.30 -8.82 Japan 5662 85 268331 -290.19 179.08 70.17 -100.58 ROW 31974.58 -83.14 58.54 -3.88 -26.25 Total 62296.20 1616035 -1483.47 781.13 -151.96 -188.40 Note: CS refers to Consumer Surplus and PS refers to Producer 101 Table 5.10 Change in Producer and Consumer Surplus Scenario: Change in AACb in millions 1980 US$ Base Year Change in Welfare 190 2002 2025 Export Regions CS PS CS PS CS PS British Columbia 720,14 1757A9 -40.24 748.43 -68.36 287.05 Eastern Canada 602.98 851*47 -37.22 121.57 -70.36 146.48 USWestCoast StiSSJft 1979.31 -99.21 259.66 -243.32 430.11 US South Central 1317,51 £95.37 -41.25 66.70 -66.43 54.93 Scandinavia 1669.05 1443,8)3 -26.69 69.40 -19.88 30.83 Chile 198.72 mM -0.48 1.69 -0.23 -5.77 NewZealand 28140 447.62 -0.99 2.57 -1.93 -1.04 ROW 1598931 $883.59 -95.47 85.39 -16.02 -65.25 Total 2382L88 I343$M -341.55 1355.41 -486.54 877.33 1987 2002 2025 Import Regions CS P S CS PS CS PS Central Canada 3062,61 541,69 -128.86 70.70 -229.89 71.27 US West Interior 3259,08 692,21 -155.19 136.65 -202.01 95.10 US South Atlantic 4933.40 720.67 -386.29 189.12 -238.91 48.48 US North 73*1.47 423.31 -255.95 38.49 -387.48 25.65 W.Europe 6022.21 3020.20 -183.85 108.56 49.02 -41.20 Japan 5662.85 2683,31 -290.19 179.08 -534.12 199.90 ROW 31974.58 S073.9S -83.14 58.54 18.70 -45.84 Total miHM tMMM -N83.47 781.13 -1524.69 353.35 102 Table 5.11 Change in Producer and Consumer Surplus Scenario: Increased Production in Former USSR in millions 1980 US$ Base Tear 1987 2002 Change in Welfare 2025 Export Regions CS PS CS PS CS PS British Columbia 720.14 1757.49 -0.52 7.08 -1.50 49.97 Eastern Canada 602.98 85J.47 0.17 2.74 -9.97 45.10 US West Coast 3035.56 1979,31 11.76 -21.94 19.67 -6.63 US South Central 1317.51 695.37 6.36 -8.57 20.65 -21.88 Scandinavia 1669.05 1443,88 64.31 -157.27 172.28 -281.66 Chile 198.72 379.39 1.44 -7.68 6.90 -14.79 New Zealand 288.40 447,62 -0.56 5.45 4.62 -1.88 ROW 15989.51 588159 624.44 -636.15 1651.57 -1199.02 Total 2382188 13438.11 707.39 -816.35 1864.21 -1430.80 1987 2002 2025 Import Regions CS PS CS PS CS PS Central Canada 3062.61 541.69 6.77 -0.46 -0.53 0.79 US West Interior 3259.08 692.21 2.58 0.67 63.56 -37.54 US South Atlantic 4933.40 720.67 -4.17 4.82 2.58 5.09 US North 7381.47 423.31 57.51 -7.26 37.09 3.32 W. Europe 6022.21 3020.20 452.83 -241.15 1345.74 -500.95 Japan 5662,85 2683.31 11.27 -0.40 96.65 -30.06 ROW 31974 58 8078.95 1057.37 -826.81 -81.37 175.63 Total W296.m umjs 1584.16 -1070.59 1463.73 -383.73 Note: CS refers to Consumer Surplus and PS refers to Producer 103 Figure 1.1 Comparative Forest Productivity MAI (m3/ha/year) m'/ha/yr Source: FRDA(1992) Figure 1. 2 British Columbia Lumber Shipments 1983-92 (million board feet) 16000 14000 12000 10000 8000 6000 4000 2000 o o o-_J 1_ -1 I I I 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 year -Total Production —•—Canada -A—USA — - U K —d—Japan Source: COFI (1993) Figure 5.1 Projected Exports 1987-2025 35.000 T 0.000 I I 1 1 1 1 1 1— 1987 1992 1997 2002 2007 2012 2017 2022 year —•— British Columbia —a—Eastern Canada —A—US West Coast X US South Central M Scandinavia • Chile —I—NewZealand ROW Figure 5. 2 Projected Imports 1987-2025 107 40.000 T 35.000 4-30.000 25.000 T E u 3 20.000 i ID e o E 15.000 + 10.000 5.000 0.000 H 1 1 1 1 h -+-1987 1992 1997 2002 2007 2012 2017 2022 year - Central Canada -W. Europe -US West riterior - Japan - US South Atlantic •ROW -US North Figure 5. 3 World Softwood Lumber Exports - Market Share 108 100% I I I I I I +-2002 Scenario -+-or % 3 • "3 2 0C. 2025 BROW •New Zealand 0 Chile • Scandinavia • US South Central • US West Coast • Eastern Canada D British Columbia 109 Figure 5. 4 World Softwood Lumber Imports - Market Share IDROW BJapan •Western Europe • US North • US South Atlantic BUS West Interior £3 Interior Canada 1087 2002 2025 Scenario 110 REFERENCES Adams, D.M., R. Boyd and J. Angle. 1992. "Evaluating the stability of softwood lumber demand elasticity by end-use sector: A stochastic parameter approach." Forest Science 38 (4). Adams, D.M. and R.W. Haynes. 1980. "The 1980 Softwood Timber Assessment Market Model: Structure, Projections, and Land Policy Simulations." Forest Science, Monograph 22, Supplement to Forest Science 26 (3). Adams, D.M. and R.W. Haynes. 1987. "Interregional Modeling." In M. Kallio, D.P. Dykstra and C. 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"Pent-up US housing demand and the timber supply crisis collide in 1994." IWA-Canada Working Paper, Research Department, Vancouver, BC. Thompson, W.A., P.H. Pearse, G.C. van Kooten and I. Vertinsky. 1990. "Rehabilitating the Backlog of Unstocked Forest Lands in British Columbia: A Preliminary Simulation Analysis of Alternative Strategies," FEPA Working Paper #147, University of British Columbia, Vancouver, BC. 114 Waggener, T.R., G.F. Schreuder and IX. Eastin. 1990. "Production and Trade in Tropical Hardwoods: An Asian-Pacific Case Study." CINTRAFOR Working Paper, #29, University of Washington, Seattle, Washington. Wallace, P. 1987. Southern pine dealt setback by costlier Canadian Lumber." Wall Street Journal, 27 January, 1987:37. Warren, D.D. 1994. "Production, Prices, Employment and Trade in Northwest Forest Industries, First Quarter 1994." United States Department of Agriculture, Forest Service Research Paper PNW-RP-204, November. Widman. 1994. "TheUS South." 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