Search

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. [ABSTRACT FROM AUTHOR]

In this paper the relative efficiency of the forest sector of 28 EU/EFTA countries during the period 2010–2015 is assessed using Data Envelopment Analysis (DEA). Three non-discretionary inputs (persons employed, forest available for wood supply and initial growing stock) are considered. The outputs are roundwood production, gross value added and final growing stock. The proposed DEA model not only computes efficiency scores but also improvement targets. The countries with the lowest efficiency scores during the period under study are Greece, Bulgaria and Italy. In the second stage, a fractional regression model is fitted and the factors that have an influence on the estimated efficiency are identified. The factors that have an influence are GDP and belonging to the NORTH Europe and CENTRAL-WEST Europe regions. Quantitative estimates of the partial effects of these factors are provided. The results can contribute in providing guidance towards the best practice in roundwood production. [ABSTRACT FROM AUTHOR]

This paper assesses the efficiency of rain-fed cereals crops grown in Southern Spain. The proposed approach uses a two-stage Data Envelopment Analysis (DEA) plus regression approach. In the first stage a Slacks-based Inefficiency (SBI) DEA model is used to project conventional and organic cropping systems onto the efficient frontier. The results of the efficiency analysis show that conventional production is more inefficient than organic production and that the main sources of inefficiency in the case of conventional production correspond to excessive input consumption and GHG emissions. In the case of organic production, the inefficiency comes from output shortfalls. It is shown that reducing inefficiency would reduce the amount of GHG emitted per unit of fresh matter yielded. This potential gain is more pronounced in the case of conventional production but also occurs for organic production. In the second stage, the crops efficiency scores are regressed against some exogenous variables using a fractional regression model. The regression results confirm that organic production significantly decreases inefficiency and they also indicate that the larger the farm, the larger the inefficiency and that growing barley is more inefficient than wheat. [ABSTRACT FROM AUTHOR]