## Materials and Methods
### Supply Assessment  
#### 2.3.1 Quantity Model  
The first step in structuring a supply-demand problem is to understand the availability of the resource [36]. The supply assessment for the Nordeste was based on both the spatial distribution of forest biomass in the study area (spatial dimension) and changes in forest biomass over time in every location (temporal dimension). The biomass distribution was done based on the Land Use/Land Cover assessment COS2007 [31] data and through stochastic modelling based on statistical distributions generated from the National Forest Inventory [37]. This approach allows the integration of modelling tools of several families to accurately describe the spatial and temporal dynamics of forest resources related to forest stand growth and forest management operations such as thinning and falling. It can also to overcome a common problem in forest data sources, that is, the lack of detailed data on the spatial distribution of forest stand attributes. To build the stochastic model, we disaggregated forest \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Objective** & **Description** & **Component Directed to** & **Type** \\ \hline
1 &
\begin{tabular}{c} Assure resilience of the region to absorb \\ the operations of all industries \\ \end{tabular} & Supply & Necessary \\ \hline
2 &
\begin{tabular}{c} Assure continuity in the operations of \\ each industry throughout the \\ simulation period \\ \end{tabular} & Demand & Necessary \\ \hline
3 &
\begin{tabular}{c} Maximize volume harvest with respect \\ to control scenario \\ \end{tabular} & Demand & Priority \\ \hline
4 &
\begin{tabular}{c} Maximize resource valorisation with \\ respect to control scenario \\ \end{tabular} & Demand & Priority \\ \hline
5 &
\begin{tabular}{c} Maximize managed area in the \\ study region \\ \end{tabular} & Supply–demand Interaction & Priority \\ \hline
6 &
\begin{tabular}{c} Minimize competition among industries \\ \end{tabular} & Supply–demand Interaction & Priority \\ \hline
7 &
\begin{tabular}{c} Minimize effects of forest fires \\ \end{tabular} & Supply–demand Interaction & Priority \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Objectives established for the ranking and selection of solutions for the particular case of this research (Nordeste region, Portugal). Figure 3: Protocol for the application of the AppTitude® process/methodology. spatial data into n rows by m columns 1-ha resolution units. For each spatial unit U\({}_{\text{ij}}\) at the ith row and jth column locations of the M\({}_{\text{mm}}\) matrix, we assigned the variables Species (SP), Stand Age (t), Site Index (SI), Dominant Height (H\({}_{0}\)), Density (N), Density factor (FN), Basal Area (G), Number of previous thinnings (NT), and Age of the stand when it was last thinned (AT), based on data from the aforementioned Land Use/Land Cover database and from initialization processes based on statistical distributions established from the Portuguese National Forest Inventory [37] plot data. Initialization consisted of: (i) establishing the age of each spatial unit as a pseudorandom variable using the age probability distribution obtained from the National Forest Inventory data; (ii) initializing H\({}_{0}\) using the SI spatial distribution established using National Forest Inventory plot data; (iii) initializing G and N using the equations of [38; 39], respectively; (iv) assigning FN to each spatial unit using Land Use/Land Cover data [31]. Forest biomass available in the landscape was a function of the growth of stands and management operations applied in each spatial unit over time based on growth and yield models, projecting forest biomass at both the stand and landscape levels. For our area of study, we used previously developed dynamic models for maritime pine [38; 39; 40], after local validation [41]. The application of silvicultural practices in each spatial unit according to the supply and demand conditions simulated (no management, thinning, and felling) was based on the forest management tool FlorNExT(r)[41].