This paper aims to empirically examine the relations between energy consumption, R&D costs and capital expenditures on the profitability of manufacturing companies in the paper and allied industry. The main focus in this article is on the companies, which are operating in the manufacture of pulp from wood and the paper production industry. Multiple regression analysis was used to test if the energy consumption, R&D costs and capital expenditures significantly predict EBITDA profitability. The results of the regression analysis indicated that all used predictors explained (R2) 35.7% of the company profitability variance (R2 = 0.357, F (3; 80) = 14.82, p-value < 0.01). The performed regression analysis also shows that energy consumption has a significant contribution to the profitability of the company. The results also indicate that only energy consumption explains 12.1% of the profitability variance (R2 = 0.121, F (1; 101) = 13.86, p-value = 0.01). The results of the regression analysis show that EBITDA profitability will increase by about 3.7 · 10–7% for each 1 000 GJ energy consumed.

Climate change and efforts to mitigate it have given rise to an interest in the relationship between industry competitiveness, energy efficiency, and carbon emissions. A better understanding of this relationship can be essential for economic and environmental decision-makers. This paper presents empirical research evaluating industry competitiveness through the factors of energy efficiency and carbon emission in Europe’s most energy-intensive industries. The designed industry competitiveness measure index consists of seven components, grouped into three equally weighted sub-indexes: export performance, energy, and environmental. The export performance of the industry is described by the industry export growth rate, the share of the industry’s export, and the effects on the industry’s competitiveness of changes in a country’s export. The energy intensity of the industry and energy prices are integrated into the energy sub-index. The environmental sub-index consists of the industry’s emissions intensity, and the ratio of freely allocated allowances and verified emissions indicators. The findings indicate that countries with the highest index value also have a positive energy intensity and carbon emission indicator value. The average index value of each industry gradually reduces to zero, and the standard deviation of the index value shows a diminishing trend throughout all sectors, which implies that competitiveness in all sectors is increasing and that all countries are nearing the industry average. The ANOVA results show that: (1) the competitiveness index value was statistically significantly different in the investigated countries; (2) the competitiveness index value was statistically non-significantly different in the investigated industries; (3) there was a significant effect of the interaction between country and industry on the competitiveness index value. These results suggest that the country itself and industry/country interaction significantly affect the competitiveness index. However, it should be mentioned that industry per se does not substantially affect the competitiveness index score.

This article describe harvest prediction model for the country or for the big region on the public available data. In the article are analysed impact of main fertilizers component and environmental variables to the grain harvest The aim of the article was to create regression model, which best describes grain harvest prediction on public (free) available data. Created final regression model explain 78% (R2) of the variation in the harvest result. Presented model show, that prediction accuracy significantly increase if environmental variables are added. Prediction accuracy (RMSE) of the final regression model was 3,89. All calculation was made on the example of the Germany.