A dynamic analysis of the global timber market under global warming: an integrated modeling approach.

• Author: Lee, Dug Man

1. Introduction

   Scientists and policymakers alike are concerned about global warming caused by the accumulation of carbon dioxide in the atmosphere. A significant number of studies have built comprehensive assessment models of carbon dioxide concentrations in the atmosphere over long time periods; however, most of these are deficient in the sense that they do not develop integrated assessment models that capture economic effects associated with global warming. In this vein, our research, as shown here, contributes to a growing body of literature that attempts to develop dynamic integrated models of ecosystem and economic system interactions that arise from predictions of global warming. We focus on the global timber market as a particular inquiry of global warming.

   As global warming forces ecosystems to migrate toward the poles, the distribution of ecosystem types and the productivity of ecosystems will be altered. The transformation and adjustment of ecosystems resulting from climate change also change the environmental conditions under which natural resources, including forest products, are extracted and regenerated. It has been discussed and predicted that changes of forest types occur along two dynamic paths: dieback and regeneration (Shugart et al. 1986; Solomon 1986; King and Neilson 1992). As climate change causes forest types to change along these dynamic paths, the global timber market will adjust as timber availability is altered.

   In this context, we have developed an integrated modeling approach that identifies the effect of global warming on the global timber market. Most literature that studied this objective have only investigated the effect of global warming on timber markets in limited regions. Binkley (1988) studied the impact of global warming on boreal forests. Joyce et al. (1995), Burton et al. (1998), and Sohngen and Mendelsohn (1998) focused only on the United States. Perez-Garcia et al. (1997) and Sohngen et al. (1997) extended the effect of global warming on the global timber market. Except for Sohngen and Mendelsohn (1998) and Sohngen et al. (1997), these studies use comparative static analysis and compare steady-state equilibria. They consider neither dynamic ecological change nor dynamic economic behavior of the timber market.

   For our integrated modeling approach, we use the Timber Supply Model (TSM) developed by Sedjo and Lyon (1990) and extend it to include additional global timber market components. BIOME 3 (Haxeltine and Prentice 1996), an equilibrium terrestrial biosphere model based on ecophysiological constraints, resource availability, and competition among plant functional types, is adopted as our steady-state ecological model and Hamburg (Claussen 1996) as our general circulation model (GCM) to investigate the change of climate variables when carbon dioxide is doubled in the atmosphere. Because there are no dynamic ecological models that span the globe, we impose linearity assumptions about ecosystem adjustment to climate change. We do this to derive a predicted time path of relevant ecological changes such as forest dieback hectares and regeneration hectares and to predict the dynamic productivity change. We modify the extended TSM (which is referred to as TSM 2000) to reflect these dynamic ecological changes. Then we simulate a non-climate change base scenario and a climate change scenario using TSM 2000 to predict the effect of global warming on the global timber market. We perform these procedures for three different timber demand scenarios to observe the sensitivity of the conclusions to the level of timber demand. These include normal timber demand growth, high timber demand growth, and very high timber demand growth. First, we specify and formulate TSM 2000. Second, we develop our procedures for estimating the relevant dynamic ecological changes caused by global warming. These include dynamic forestland area changes and productivity changes. Third, the simulation results are reported and discussed for each scenario. This includes a discussion of the sensitivity results and welfare implications.

2. Dynamic Timber Supply Model

   Alternative dynamic economic models of timber market behavior include (Berck 1979; Brazee and Mendelsohn 1990; Adams et al. 1996; Sohngen and Mendelsohn 1998; Sohngen, Mendelsohn, and Sedjo 1999) and the TSM (Sedjo and Lyon 1990, 1996). We use TSM because it has the relevant characteristics, we understand it, and we can modify it to fit the problem at hand. In general, the volume harvested in the TSM is affected by seven types of adjustments. These are (i) rotation length of age; (ii) the rate of drawdown of old growth inventories; (iii) the number of forested land classes that are utilized in the harvest; (iv) the level of regeneration input applied to the various land classes; (v) the rate at which new industrial plantations are added to the world's timber-producing regions; (vi) the rate of technical change-wood-extending, wood-growing, and wood-saving; and (vii) changes in production from nonresponsive regions of the world (Lyon and Sedjo 1992). (1) The TSM provides economically efficient solutions in the sense that it maximizes total benefit to the society as a whole, not the net income stream of an individual landowner.

   Description of TSM 2000

   To develop TSM 2000, we modify TSM in the following ways. First, the TSM 2000 considers the former Soviet Union to be a part of the responsive region. We postulate that the former Soviet Union will participate in the global timber market and that it will play a critical role in supplying stumpage to the global timber market since it contains approximately 25% of worldwide forest growing stock (Backman and Waggener 1991). For this research, we subdivide the former Soviet Union into three subregions: European USSR, West Siberia, and East Siberia. Also, we subdivide these three regions according to ecosystem type and the degree of accessibility for harvesting; hence, these three subregions consist of 16 land classes: eight land classes for European USSR and four land classes for West Siberia and East Siberia, respectively. These are identified more concretely later.

   Second, we include more plantation forests in the emerging region in the TSM 2000. (2) Plantation forests in India, Asia-Pacific region, and subregions in Africa except South Africa are not included in the TSM as the emerging region. According to Sedjo (1994), both tropical and subtropical regions have experienced an increase in plantation forest. Land areas in these regions, which are exploited for agricultural production or were being conserved for the future use, are now being turned into plantation forest. About six million hectares had been planted in the emerging region by 1980 (Sedjo and Lyon 1990); however, it is estimated that plantation forest acreage included in these areas were about 38 million hectares in 1990 (UNFAO 1993a, 1993b, 1995).

   Third, there has been a trend to withdraw forestland from timber harvesting and conserve it for wilderness, ecological reserves, parks, scenic corridors, and other purposes in many major timber-producing countries. Recent publications of the International Union Conservation of Nature and Natural Resources (IUCN 1990, 1994) included all the areas designated to be protected by individual governments as well as the international organizations. Yan (1996) calculated the conserved hectares of forest for seven responsive regions being included in the TSM since 1981 (1980 for Asia-Pacific region) based on publication of IUCN (1994). He designated nine scenarios of the forest conservation by combining these calculations with more information on conservation actions for each responsive region. Current trends to promote conservation of forest for environmental protection suggest that conservation patterns modeled in TSM 2000 will be an important factor affecting worldwide timber supply. In this respect, we model conservation of forest for each land class in each region by adopting Yan's (1996) scenario 5. Here we discuss the forest conservation ratios that we use for the subregions of the former Soviet Union. (3)

   Fourth, the TSM (1990, 1996) considered only 22 land classes in seven responsive regions to project the optimal time profile of important endogenous variables in the model. To meet our research objective, we consider the change of distribution of ecosystem type (vegetation pattern) and change of productivity of ecosystem type after climate change. When we examine the change of distribution of ecosystem types on the basis of BIOME 3 predictions using Hamburg as our GCM, we observe that in some regions a large portion of an ecosystem type would be transformed into other ecosystem types after climate change. This reflects the fact that some species die out from the area where they are currently standing and new species are regenerated naturally or planted by human beings for economic benefit. In this respect, we subdivide land classes in more detail in the TSM 2000 in order to include the ecological detail acquired from the BIOME 3 predictions on ecosystem change. Consequently, TSM 2000 includes 42 land classes in 10 responsive regions.

   Formulation of TSM 2000

   We now describe the model. Net surplus in the year j is defined as

   (2.1) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII],

   where [Q.sub.j] is the quantity or volume of timber for solid wood harvested in year j, [D.sup.s.sub.j]([Q.sub.j]) is the inverse demand function of industrial solid wood in year j, [[??].sub.j] is the volume of timber for pulpwood harvested in year j, [D.sup.p.sub.j]([[??].sub.j]) is the demand function of industrial pulpwood in inverse form, and [C.sub.j] is the total cost in year j. The total costs are the summation of harvest, access, transportation costs (C[H.sub.j]), and regeneration cost (C[R.sub.j]). Harvesting and transportation costs in year j depend on the total volumes harvested by land class...