Models such as the "Tropical Timber Trade Model" or the "CGTM" are subject to the modification of underlying assumptions, thereby facilitating the 'testing' of various possible combinations of factors influencing the forestry sector. While data-intensive, the estimation of the underlying supply, demand and cost functions for individual countries, sub-regions or products is critical to the results or outcomes, particularly when projected over long time periods. Likewise, estimates of various elasticity's (responsiveness to price movements) is central to dynamic adjustments and comparative changes in production and consumption, and increasingly to trade flows.
The assumed objective of overall cost minimization of obtaining timber and/or products based on estimated harvesting cost, manufacturing costs, and transportation is obviously a simplification but captures the essence of comparative advantage. Because of the complexity of such models, much detail is sacrificed in terms of aggregation and generalization across countries, markets, timber types, cost structures, etc. Hence the results are subject to possible limitations where more micro-level developments are examined. But, as noted above, the underlying assumptions and relationships can be modified (within the structure of the specific model) and re-evaluated to indicate the relative sensitivity to individual or groups of variables. Drivers, such as population or GDP can also be independently estimated and utilized to determine possible influence on the simulated outcomes.