An Economic Assessment of Mountain Pine Beetle Timber Salvage in the West

Abstract

reduction treatments in the West (Barbour et al. 2008), to quantify the jobs and biomass production impacts of these treatments (Abt et al. 2011), and to evaluate whether wildfire hazard reduction treatments yield overall net benefits on timberlands of the West (Prestemon et al. 2012). Model The EBR model is a multiyear two-stage goal and spatial equilibrium program, and it was modified for this study to model the economic feasibility of salvage of dead timber on public and private lands in the West. Although details of the model, including its mathematical formulation, are provided in Prestemon et al. (2008, 2012), describing the modeling framework is important for understanding the current study. EBR can be run for a single year or multiple years, treating timberland (salvaging standing dead trees, in this study) each year according to a predefined set of objectives. After each simulated year, timber inventory data are updated, with the transition to the next year defined by stand growth, new mortality available for salvage, and the volumes removed in the previous year’s solution. The first stage of this revised version of the EBR model is a goal program that selects locations in the West to salvage timber by maximizing a goal-weighted sum of salvage volumes, subject to maximum and minimum harvest constraints, a feasible forest product market solution, and an assumed maximum amount of expenditures available to harvest and transport salvage timber to mills. The fundamental unit of information about timber volumes (salvage, nonsalvage) to which the goal weights are applied in the EBR model is the Forest Inventory and Analysis (FIA) plot. To allow for a reasonably fast solution, plot-level information is summed to a spatial and ownership aggregate. Plot-level information includes the average distance to the nearest five sawmills (which consume sawlogs) and the average distance to the two nearest pulp or pole mills (consuming the smaller diameter portions of trees in the stand). Other variables reported or calculated at the plot level include volumes by product category (merchantable live and dead sawlogs and pulpwood) and tree groups—ponderosa pine (Pinus ponderosa and P. lambertiana), lodgepole pine (P. contorta), southern pine (especially P. echinata, P. palustris, P. elliottii, P. taeda), other softwood, and hardwood; ownership (national forest, other public, private); LANDFIRE Map Zone (LANDFIRE 2010); the harvest cost for removing live or dead volumes; and, an administration cost of $200/acre for public and $100/acre for private timberland salvage. In this study, we aggregated plot-level information up to the map zone for each ownership group for each of the 12 western states in the contiguous United States. LANDFIRE map zones are generalized geographical units with similar ecological and biophysical characteristics. The 50 United States contain 79 such zones, which span state boundaries. The 12 states in this study contain 29 map zones, although the area and total standing timber volume found in these zones vary widely. Therefore, the basic modeling units, from which treatment volumes could be obtained in the first stage, are the map zone–ownership aggregates in each of the 12 western states. Depending on the simulation implemented (more information on the simulations is provided in the next section), treatment volumes selected in the first stage could be obtained from parts or all of one map zone–ownership aggregate. Harvest costs, timber volume information by species and live or dead status, and transport costs were expanded to the map zone–ownership aggregate using an area expansion factor. The result is a summary of the total area of stands of salvable timber in each map zone–ownership aggregate and for each of these the weighted average volumes per acre by species by product by live and salvable dead, weighted average transport distances to mills, and the weighted average harvest cost. Finally, the goal weights placed on map zone–ownership aggregates were the presolution net revenues of timber salvage removed; only dead standing salvable timber could be removed. The net revenue in the first stage is defined as the premarket solution value of salvaged sawlogs and pulpwood by species: the delivered volume multiplied by each product’s respective market price times a salvage discount factor minus the total stand’s harvest and transport costs per acre. As defined, net revenues can be negative. In effect, the EBR model had an ordered preference for salvage of timberlands according to their per acre net revenues. The second stage of the EBR model maximizes, subject to the salvage volumes selected in the first stage in each map zone–ownership aggregate location, the sum of timber product producer and consumer surplus minus transport costs for harvested volumes of both salvage and nonsalvage timber moving across state and international borders (Samuelson 1952, Takayama and Judge 1964). The basic timber product market modeling unit—two levels of aggregation higher than the map zone–ownership—in which equilibrium product prices by species group are obtained, is the state. Trade restrictions that ban exports of roundwood flowing from western US federal lands are imposed. State level maximum processed volumes are determined by state-level mill capacities that act as a physical limit on the volume of timber products that can be processed within the state without new processing capacity being added. We allow these capacities to be exceeded by up to 30% to reflect the possibility of adding shifts to existing mills (Prestemon et al. 2008). The EBR model also allows for the siting of new processing capacity, although this is not implemented endogenously (as in Ince et al. 2008). The second stage optimal solution is a set of market equilibrium product prices, and the volumes by species of timber salvaged (and harvested nonsalvage) produced in each state, consumed at mills in each location, and traded across state and international borders. The result of a set of a multiyear simulations run by the EBR model is an assessment of the net revenue impacts of salvage on national forests, other public, and private lands in the 12 contiguous western US states. In this study, we further summarize the results in terms of salvage costs and salvage revenues by state and ownership group. While not reported in this study, model outputs also include prices and economic welfare changes resulting from changes in salvage. Such changes may be of interest when seeking to quantify how salvage negatively impacts the welfare of owners of nonsalvage timber (e.g., Prestemon and Holmes 2004, Prestemon et al. 2006). It is worth noting, however, that the net revenues generated from salvage on private lands are gross, before taxes. By varying an assumption on the magnitude of a government program to salvage national forest, other public, or private timber, we describe how the geographical focus of a government-subsidized or national forest salvage program might shift across states in the West. By carrying out a “what-if” scenario that tests the effects of a doubling of the total mill capacity in two states of the West that have been heavily affected by the mountain pine beetle—Montana and Colorado—we examine how efforts to encourage or subsidize the consumption of salvaged timber would affect net revenues of salvage obtained by timberland owners (public and private). Finally, by altering our assumption about the regular accumulation of additional standing volumes of 144 WEST. J. APPL. FOR. 28(4) 2013 salvage timber—from a set annual percentage increase to no more accumulation of standing dead timber—we assess how the spatial targeting of salvage efforts on national forests and other lands would be affected. Data Timber inventory data from Forest Service FIA surveys were assembled for all timberland that is open to harvest and not protected by easements or otherwise set aside for conservation purposes in 12 western US states. The survey years used for each state are reported in Table 1. Data are summarized by owner group (all owners and national forests only), by species group (ponderosa pine, lodgepole pine, other softwood, and hardwood), and by product (sawtimber—representing the cubic foot volume in the sawlog portion of the tree—and pulpwood—representing all other growing stock volume in the tree). Trees coded as standing dead had only total volume measured, so allocations to sawtimber and pulpwood were assumed to be identical to the overall share of sawtimber and pulpwood found in the live trees on the plot, if any. Forested plots without live trees were assumed to have a sawtimber share of standing volume equal to 0.8. Further information on FIA methods can be found in Bechtold and Patterson (2005). While initial interest was in modeling salvage of only MPB-killed stands, the FIA data did not offer the option to restrict the volume and acreage data for dead timber based on the cause of mortality. While some information on MPB-affected forests in the West is available from aerial detection surveys, the data produced by these surveys (e.g., Backsen and Howell 2013) were not suitable for our study (see Meddens et al. 2012). Although modeling the salvage of timber killed by all causes is not the same as modeling the salvage of MPB-killed trees, salvage operations should be indifferent to the cause of mortality. One advantage of employing FIA data is that plots are measured on a representative sample frame and, therefore, have a level of accuracy that provides greater confidence in simulated salvage programs. FIA plots have representative samples of species, sizes of trees, and site conditions, which allow for accurate assessments of both the materials that can be removed during salvage and the costs of removal of salvable timber. Salvage volume adjustment factors were applied to the standing timber, with an assumption that the net salvable volume