Current issue: 57(1)
Under compilation: 57(2)
Forestry and forest industries are important for regional income and employment in Norway as well as in most North European countries, but few studies exist about factors affecting the timber supply at regional level. The main objective of this study is to estimate aggregated regional timber supply elasticities for six regions in Norway. Thereby we also test for regional differences, focusing on wood prices, standing stock volume and interest rate as explanatory variables. We have used three different statistical models (fixed and random effects panel models and first difference models) on regional data from the Norwegian forest inventory on standing volume and official statistics on harvested volumes, interest rate and prices of sawlogs and pulpwood for the period 1996–2016. Statistically significant different price elasticities are found in 12 out of total 15 pairs of regions. The price elasticity was lower and the volume elasticity higher in the western region compared to the other regions. The first difference models are best with respect to specification tests. The use of region specific price elasticities gives slightly better fit for the panel data models than using a uniform price parameter. The results show that the econometric specification influence the parameter values, and it is thus complicated to directly compare results in different timber supply studies. Regional differences in timber supply are important to consider.
Simulation and modeling have become more common in forest biomass studies. Dynamic simulation has been used to study the supply chain of forest biomass with numerous different models. A robust predictive multi-year model requires biomass availability data, where annual variation is included spatially and temporally. This can be done by using data from enterprises, but in some cases relevant data is not accessible. Another option is to use forest inventory data to estimate biomass availability, but this data must be processed in the correct form to be utilized in the model. This study developed a method for preparing forest inventory data for a multi-year simulation supply model using the theoretical availability of feedstock. Methods for estimating quality changes during roadside storage are also presented, including a possible parameter estimation to decrease the amount of data needed. The methods were tested case by case using the inventory database “Biomass Atlas” and weather data from a weather station in Mikkeli, Finland. The data processing method for biomass allocation produced a reasonable quantity of stands and feedstock, having a realistic annual supply with variation for the demand point. The results of the study indicate that it is possible to estimate moisture content changes using weather data. The estimations decreased the accuracy of the model and, therefore, estimations should be kept minimal. The presented data preparation method can generate a supply of forest biomass for the simulation model, but the validity of the data must be ensured for correct model behavior.