## 4 Discussion
### Final Considerations  
#### 4.2.1 Behaviour of the Modelling Framework  
The approach we followed was able to generate near infinite combinations of variables that were not possible to ponder in this study. For example, the number and location of industries in the region, the uses of forest biomass, the variability in forest variable distribution and the possible uncertainty in environmental, social and economic aspects are factors that had to be constrained in order to formalize the problem in a viable way. Here, we tried to simplify processes and reduce uncertainty based on a value model, price and possible trends in the short term.  
The heuristic framework of AppTitude(r) and the respective protocol applied in this research allowed the selection of viable and sustainable industrial scenario hypotheses regarding supply/demand of forest resources, location and dimensioning. The initialization process was considered satisfactory in overcoming the problem of lack of spatial data on forest stands (Appendix A). The value model resulted in a spatial distribution of a suitability index reflecting the point of view of industries over a period of 20 years (Appendix D). Similarly, the price model, that consists of a direct relationship between value and maximum and minimum prices, was revealed to be a useful approach, as indicated by the spatial distribution of prices obtained (Appendix E).  
Forest management comprises a high level of uncertainty resulting from several biotic and abiotic hazards that are potentially harmful to trees, stands and the forest landscape as a whole. These hazards  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Obj. 1**} & \multicolumn{2}{c}{**Obj. 2**} & \multicolumn{2}{c}{**Obj. 3**} & \multicolumn{2}{c}{**Obj. 4**} & \multicolumn{2}{c}{**Obj. 5**} & \multicolumn{2}{c}{**Obj. 6**} & \multicolumn{2}{c}{**Obj.7**} \\ \hline
**Scenario** & **R 1** & **R 2** & **R 3** & **R 4** & **R 5** & **R 6** & **R 7** & **R 8** & **R 9** & **R 10** \\ \hline
2 & 0.24 & 0.16 & Yes & 2.20 & 1.23 & 1.02 & 0.69 & 0.0005 & 0.98 & 0.82 \\ \hline
8 & 0.14 & 0.09 & Yes & 2.49 & 1.19 & 1.28 & 1.00 & 0.0349 & 0.98 & 0.77 \\ \hline
9 & 0.39 & 0.41 & Yes & 2.08 & 1.00 & 1.43 & 0.90 & 0.0020 & 1.01 & 0.84 \\ \hline
10 & 0.60 & 0.59 & Yes & 1.72 & 1.23 & 1.43 & 1.04 & 0.0054 & 1.00 & 0.91 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ratios between indicators in Table 5 relative to scenarios 2, 8, 9 and 10, and scenario 1 except for the ratio relative to Id 8 (R 8), calculated as the ratio of Id 8 (volume loss due to competition) and Id 4 (volume harvested.  
are often taken into account in forest management due to their potentially high economic and ecological impact trough the integration of risk management into forest management processes and tools [63]. In the Nordeste region, the major forest hazard is fire. Wildfires occur with relatively high frequency, although fire indicators here are not as dramatic as in other regions in Portugal. Nevertheless, fire is a threat to forest products and ecosystem services [32] and burned area and fire intensity tend to increase in the future due to changes in climate and in the landscape composition and configuration. The solution found for integrating fire risk into our model as a random process under a certain probability distribution [42] is apparently a good solution, in particular if combined with an ignition probability model [43] since, on average, 98.7% of fires are human-caused [64], because this process is sensitive to social and biophysical variables and, most importantly, to dendrometric variables affected by forest management related to the several scenarios tested in this study. Other factors affecting fire ignition and behaviour, such as climate change, were not considered in this study given the short (20-y) temporal scale considered.