The paper presents the results of researching the impact of export on the production of wood pellets as well as the situation on the market for this wood fuel in Serbia. Objective of the research was to produce scientifically and professionally founded conclusions and the related adequate recommendations to the decision makers in order to improve the situation on wood pellets market in Serbia and eliminate the existing problems which significantly burden and slow down this development. Special objective of the research was to observe the contributions of wood pellets to the mitigation of climate changes using Serbia as the example. Results of the conducted research show that the expansion of the consumption (demand) increase in the European Union countries in the last fifteen years and the related increase of export from Serbia are the most significant factors which have influenced the development of wood pellets production in Serbia. Parameters of econometric model of the impact of export on the increase of production show that production increase of 1.17% can be expected with the increase of export of 1%. Thus, the number of wood pellet producers has rapidly increased in the last ten years, from 2 producers in 2006 to 52 active producers in 2016. Increase of the number of producers was also accompanied by the increase of the installed capacities. At the end of 2015, total installed capacities for wood pellet production in Serbia reached 550 thousand tons, and the realized production was 229 thousand tons, or 41.6% of the installed capacity. Consumption of wood pellets in Serbia in the last four years achieved significant increase and reached the level of 89 thousand tons in 2015. However, concerning the segment of wood pellets consumption in Serbia, the situation is still unsatisfactory despite the fact that the consumption has been increasing year after year. Average price of 1 kWh of energy from wood pellets exported from Serbia was in the range 20-44 ?$, which is 2.1 to 3.2 times less than the price of 1 kWh of energy from the natural gas which was imported in Serbia in the observed period. [Projekat Ministarstva nauke Republike Srbije, br. 43007/16: Research of climate changes and their environmental impact - monitoring of impacts, adaptation and mitigation, subproject: Socioeconomic, mitigation and adaptation to climate changes]

The paper shows research results for the consumption of all types of wood fuels in the households in Bosnia and Herzegovina and its entities in 2015. The research was conducted in the period from March 15 th to August 26 th , 2016 in 109 cities/municipalities in the entire Bosnia and Herzegovina. Complex methodological approach was used for conducting the research, which required field research on the sample of 8,602 households on the entire territory of Bosnia and Herzegovina as well as adequate statistical processing of the obtained data pursuant to the high statistical standards. Research results show that in 2015, 5.4 million m 3 of firewood, 81,656 tones of wood pellets, and 6,780 tones of wood briquettes were consumed in the households in Bosnia and Herzegovina. Total expenses of households necessary for the supply of wood fuels in Bosnia and Herzegovina in 2015 were 239.8 M€, with the largest share of firewood (226.8 M€), followed by wood pellets (11.6 M€), and wood briquettes (720.9 thousand €). Average firewood consumption per household in Bosnia and Herzegovina is 6.43 m 3. Compared to the surrounding countries it is on the level of Slovenia (6.5 m 3 per household), less than in Serbia (7.3 m 3 per household) and more than in Montenegro (5.49 m 3 per household). Average consumption of energy from firewood expressed in kWh/m 2 of the heated surface was 252.7 kWh, which is significantly higher than the average in the EU. One of the reasons for such high consumption of wood energy per 1 m 2 of the heated surface is the fact that only 36.1% of the households using solid fuels have thermal insulation on their residential facilities.

This paper deals with the operational efficiency of companies engaged in the production of wooden chairs using selected statistical and DEA (Data Envelopment Analysis) methods. Indicators that typically characterise the supply chain in the production of selected companies were taken as input and output variables for the DEA method. They included three input variables: inventories, material costs and production services costs and one output variable: company’s net profit. The obtained coefficients of correlation pointed to a high degree of correlation between the variables, which justified the performance of an efficiency analysis using the DEA method. The study included 12 companies engaged in the production of wooden chairs. The results of the conducted analyses show that only one company had a relatively satisfactory operational efficiency (efficiency coefficient of 0.83) for the nine-year period. All other companies, especially micro and small enterprises, had unsatisfactorily low operational efficiency. Micro enterprises had the lowest operational efficiency, with an efficiency coefficient of only 0.14. Small enterprises reached the value of 0.3, and large companies 0.67. Medium companies had the most favourable efficiency coefficient of up to 0.83.

This research is based on creating regression models as follows: 1. Total carbon sequestration, 2. Total carbon dioxide (CO₂) sequestration and carbon credit (CO₂e) generation, 3. Annual carbon sequestration and 4. Annual CO₂ sequestration and annual carbon credit generation (CO₂e). The research was carried out in plantations of the species Populus x euramericana (Dode) Guinier clone I-214. In addition to the field research, a modeling framework for quantifying carbon sequestration in forest ecosystems CO₂FIX var 3.1 was used to calculate stored carbon. Analysis of collected samples of branches and leaves was performed using CHN Vario EL III analyzer. The results of the research indicated that the total sequestration of carbon (C) for a thirty-year production cycle was 78.58 tC ha^-1, while the average value for all years of a thirty-year production cycle was 44.02 tC ha^-1. The average annual sequestration of carbon for all years of a thirty-year production cycle was 2.62 tC ha^-1yr^-1, while the average annual sequestration of carbon dioxide, or average annual changes in CO₂ stocks for all years of a thirty-year production cycle was 9.60 tCO₂ ha^-1yr^-1.

Background and Purpose: The fact that new organizational concepts require comparison and ranking of some business entities, implies the analogy that, in forestry, ranking should create the basis for differentiation of Forest Estates (FE) (seen as profit centers) according to their capability to allocate funds from rent for the utilization of forests and forest land. In this sense, it was necessary to determine the basic criteria and variables, and then to create the model for FE ranking on the basis of ecological and production potentials, and business results (economic indicators). The main idea was to create a model that can be used primarily by forest owners (which are, in certain countries such as Bosnia and Herzegovina, Croatia, Serbia, and Montenegro, mainly governments) and by public forest enterprises. The proposed models may serve to all other scientific, professional, research and other institutions, as the starting point for further research and as suggestions for possible improvements of the proposed solutions. Materials and Methods: The research was carried out within the project "Differential rent in the Republic of Srpska forestry". Total sample for the survey was 44 interviewed parties, with 118 questionnaires filled in. The methods of classification, content analysis, desk research, analysis, synthesis and comparison were used. In the concrete application of the Forest Estates/Organisational Units Ranking Model (hereinafter MRG Model; Model rangiranja šumskih gazdinstava, in Bosnian), the following methods were used: brainstorming, focus groups, survey, desk research method, Pareto analysis, modelling and induction. The statistical methods used were descriptive statistics and rank correlation. By using these methods and by combining them, a new model for forest estates ranking was created. Different input data and variables that refer to economic and natural indicators were used for ranking, all in accordance with the values for areas for which the ranking was carried out. Results: The main results are used for defining and proposal of the new model for forests estates ranking, i.e. the MRG Model. This model includes the following steps: (1) Survey, (2) Selection and scoring of specific variables, (3) Determining the intervals for specific variables, (4) Ranking of forest estates, and (5) Validation and rank correlation. This paper presented the algorithm of implementation of specific steps within the MRG Model, together with all activities that need to be implemented in order to perform forest estates ranking. It is necessary to emphasize that forest estates ranking was performed in accordance with the following three ranks: (1) for all analyzed variables, (2) for economic variables, and (3) for natural variables. Additionally, three modules for the calculation of scores for individual forest estates are the result of this research. Conclusions: The MRG Model is based on FE ranking according to deviation from the average value of the selected variables. The quality of the model lies in the fact that it is relatively simple (there are no complex statistical or other methods, necessary data can be collected easily), and that it can be applied again for similar surveys. Implementation of the MRG Model involves 5 basic steps with 7 phases to be performed in the order specified in this paper. The selection of variables which will be part of the MRG Model is crucial. The survey sample must include representatives that are directly or indirectly involved in the forestry sector. Although it might seem that all selected variables are significant, it is always necessary to give each variable the importance in accordance with the survey results. It is necessary to validate the defined model, data and final ranks on a pilot sample. Since there are three ranks, it is necessary to consider their mutual correlation, by performing statistical analysis rank correlation.