Countries are forced to develop bio-based economic strategies to promote efficient use of renewable natural resources. The transition towards a sustainable forest bio-based economy is associated with resource efficiency optimization, adoption of innovative bio-based approaches in terms of technological improvements and cost effectiveness, and an opportunity to reach multiple societal challenges. This paper is focused on a comparative analysis of the forestry sector in the Republic of Bulgaria and the Slovak Republic by estimating the economic efficiency of four Bulgarian state-owned forest enterprises and four Slovak forest enterprises. The evaluation of economic efficiency was carried out using selected indicators of the studied enterprises over a period of five years. A data envelopment analysis (DEA) approach was used as a non-parametric linear technique for measuring the relative efficiency of a set of production decision-making units (DMUs). The Malmquist productivity index (MPI) was used to assess the pure efficiency changes (PEC) and technological changes (TCs) of the studied forest enterprises. Data for 2014–2018 were processed. The results obtained for the economic efficiency study outlined the major factors affecting the differences in efficiency scores. The long-term sustainability and increased economic efficiency of forest enterprises in both countries can be achieved by improvements in forest management and investments in research and development activities.

The ongoing transition to a low-carbon, sustainable forest-based economy, and the adoption of circular bioeconomy principles in the wood-processing industry is associated with the optimization of natural resources, application of environmentally sustainable production technologies, adoption of technological and organizational innovations, and increased economic efficiency and competitiveness. The implementation of all these measures can help to reach the biggest challenge of our time in the fight against climate change in a cost-effective and competitive way. The aim of this study was to estimate the technical efficiency of wood-processing companies in the Slovak Republic and the Republic of Bulgaria by applying data envelopment analysis (DEA) and the Malmquist productivity index (MPI), and to reveal some factors for efficiency improvements. The economic efficiency evaluation based on official data was performed using selected indices of four wood-processing companies in each country in the period 2014–2018. The study implemented an output-oriented DEA model with constant returns to scale as a nonparametric linear approach for measuring the efficiency of production decision-making units (DMUs). The results obtained revealed that the studied Slovak companies were more efficient with better management in terms of machinery planning and overhead utilization. Markedly, the Bulgarian companies achieved better materials management and current planning quality. Increased economic efficiency of wood-processing enterprises in both countries can be realized through investments in innovative technological improvements, and enhanced research and development activities.

Sustainability and optimizing waste management efficiency through circular economy principles have become significant trends in recent research. This study aims to compare the circular economy efficiency within the waste management framework in selected Central and East European countries. To achieve this, the research employs Data Envelopment Analysis (DEA) and Fractional Regression Analysis, specifically using the Slack-Based Measurement model. This model measures efficiency by referencing the inefficient Decision-Making Units (DMUs) rather than the origin of the coordinate system. Key variables such as GDP per capita, GDP growth rate, and circular economy indicators were selected and analyzed using Eurostat data from 2010 to 2022. The findings reveal varying potentials for improving the efficiency of hazardous and non-hazardous waste management in the countries investigated. Additionally, differences in the mutual influence of selected variable indicators on waste efficiency are identified using Fractional Regression Analysis. These insights emphasize the pivotal role of waste management in advancing circular economy policies.

At present, forest enterprises face many challenges in adopting innovative bio-based approaches considering global changes. Due to the specifics of forestry, the choice of financing sources is a complex issue. The aim of this study is to estimate the capital structure determinants of forest enterprises in the Czech Republic, Slovakia, and Bulgaria in the context of the relationship between leverage and the factors of its appearance. The evaluation of capital structure determinants was carried out using selected indicators for 18 forest enterprises, with 6 enterprises per country. Data were processed for the period of 2015–2019. The study methodology was based on a Panel Data Analysis with Fixed Effects and Random Effect models and Ordinary Least Squares estimation. The following specific variables were included: liquidity, leverage, return on assets, size of the enterprises, and gross domestic product. The results revealed that the forest enterprises in these three countries can be differentiated by size and form individual functional relationships with the positive influence of enterprise size on liability share. The next significant determinant was found to be liquidity, which has a negative relationship with enterprise leverage. These results will be useful for managers of forest enterprises in decision-making processes to determine the amount of debt and planning investment programme strategies.

The economic efficiency of forestry has long been a leading issue in the sector's economic research. The wide variety of policies, goals and models in forest management makes researching the factors of economic efficiency a constantly relevant issue. Since 1978 Data Envelopment Models have provided a comprehensive approach without following the particular assumptions for production function of forests. Data Envelopment Analysis (DEA) models are quite suitable to estimate the efficiency and determine the weaknesses of forests and forest-based industries with their current state and structural weaknesses. The current study estimates the relative efficiency of the Bulgarian forestry and forest-based industries comparing it to those in the EU. The methodology applied provides a specific approach for classifying the countries by the share of their sectoral GVA in comparison to the overall country GVA. The results confirm that the Bulgarian forestry and forest-based industries are efficient compared to most EU countries. The paper develops further the results that the efficiency varies based on the share of forestry and of forest-based industries in the country GVA. The results reveal that in countries where forestry is better involved than forest based industries, the efficiencies are higher. The study also suggests solutions for problems of small countries like Bulgaria in order to achieve better efficiency of their respective economic sectors.

Micro and small wood-processing enterprises represent the heart of European forest-based industries, being among the key drivers of economic growth in rural, mountainous, and poor regions. Their economic efficiency is of fundamental importance for their existence and the provision of income for the local population in rural areas. Data Envelopment Analysis (DEA) is used in the current research, which is a nonparametric, linear-programming-based approach that is commonly used to analyse the efficiency of organisational units. The main objective of this study was to investigate and evaluate the economic efficiency of micro and small wood-processing enterprises in EU countries and reveal the hidden inputs that facilitate efficiency generation. The economic efficiency evaluation was carried out on the basis of the official statistical data for the micro and small wood-processing companies in EU member states for the period 2015–2020 by performing a two-stage DEA analysis. The data used were standardised by value per employee. In addition to the first stage of DEA, a fractional regression probit and logit models with four contextual variables were used to reveal the influence of the hidden inputs in the model. The results showed that the micro and small wood-processing enterprises can be regarded as more scale-efficient than technically efficient entities. The only contextual variable affecting the economic efficiency was Investments per Person Employed, improving the efficiency by 2% per 1% increase in the investments.