The PELPS framework offers a number of options that have not been used to date in the Global Forest Products Model. This is by design, to keep the model as simple as possible. Any change should be made cautiously, though it is relatively easy to add further shifters of supply or demand, to introduce by-products and to make other changes. A good way to learn about all the capabilities of the PELPS system is to study the descriptions for data entry at the top of each sheet in the master input files.
Changes in the data are readily implemented by following the general guidelines for data entry. For example, dividing industrial roundwood into sawlogs and pulpwood would require using different codes for these wood types; making the appropriate price, quantity, elasticity, and bound entries in the supply sheet; making any changes needed in the manufacturing sheet to reflect the role of these log types in the manufacturing process; and making further entries to simulate trade inertia.
Trends in the global forest sector environment are captured in the Exogenous Change sheet in the input data file. The default treatment of this sheet is that, for each year t, the parameters governing year t-1 continue to apply unless they are overridden by a new entry in the Exogenous Change sheet. That is, if no entry has been made in the Exogenous Change sheet, PELPS continues to use parameters from the previous period. For example, in the current version of the GFPM, changes in trade inertia constraints appear only in Period 1, and PELPS therefore assumes that the same constraints apply in every future period. A complete list of all the types of exogenous trends that may be introduced in the model is available in the descriptions at the top of the Exogenous Change sheet.
The model currently has the capability to represent endogenous capacity expansion in two different ways. The accelerator model currently used is simpler and less data intensive. The Tobin Q model representation would require data on the marginal cost of capacity expansion.
The current version of the GFPM uses only derived data on manufacturing costs, calculated as the residual of product price less input costs. Including cost data from external sources is simply a matter of typing in new numbers. Similarly, product prices could be derived from sources other than international trade data. The main consideration in pursuing this path is how it might affect the relative profitability of manufacturing in some countries (i.e. how it might affect the relative shadow price of capacity), and how this in turn might reallocate the distribution of changes in manufacturing capacity across countries.
A related issue is the use of bilateral trade flows in the model. It is conceptually straightforward to include bilateral (country-to-country) trade flows, however, this seems practical only for models with a small number of countries (Zhang et al. 1997).