Abstract
State Forest Management Organizations (SFMOs) play a crucial role in the European forest sector, managing almost half of the forests in the region. SFMOs are often only managed for timber production, whereas, being publicly owned, they should play an important role in providing a vast range of public goods (e.g., soil protection, biodiversity conservation). Their management goals depend on the history and current conditions of the forest sector at a national level, as well as different challenges and the potential for development. Although there is a lack of knowledge about the current performance of SFMOs, there have been recent changes to their management goals and practices in response to the new demands expressed by society (e.g., transparency, social inclusion). The main purpose of this study was to analyze the current situation of SFMOs by grouping them with the help of a Cluster Analysis according to indicators that reflect the three pillars of the common understanding of the sustainable forest management (SFM) concept. Additionally, in light of the differences in the forest practices and management priorities in each country, we used Principal Component Analysis (PCA) to group countries according to common characteristics of the forest sector at the national level. The results showed three main clusters of SFMOs in Europe. The first cluster had a rather small but commercially-oriented forestry unit together with other business activities and a strong focus on public services. The second focused on public interest, rather than commercially-oriented organizations. The third is mainly profit-seeking. The existence of diverse SFMO clusters shows the possibility of different approaches for SFM with a focus on different goals (e.g., profit gaining, public service delivery).
Highlights
State ownership appears to be a persistent characteristic of the economic forest landscape on a global scale [1]
These data were processed with Principal Components Analysis (PCA) (a statistical procedure used to analyze data by reducing the number of variables within the data to a limited number of linear combinations; each linear combination will correspond to a principal component (PC) [35]) and a cluster analysis (“clustering refers to a very broad set of techniques for finding subgroups, or clustering clusters, in a data set;when we cluster the observations of a data set, we seek to partition them into distinct groups so that the observations within each group are quite similar to each other, while observations in different groups are quite different from each other” [36] (p. 385))
The most influential variables are the economic ones related to the public forest sector: ownership of forests, Gross domestic product (GDP) per capita, Annual Working Unit (AWU) in forestry, removals from State, and Agricultural Value Added on total GDP
Summary
State ownership appears to be a persistent characteristic of the economic forest landscape on a global scale [1]. The main purpose of state ownership should be to maximize value for society through an efficient use of resources [3]. For this reason, the governance of SOEs is attracting increasing attention from citizens. Several socio-economic, political, and historical reasons explain why governments have established and maintain SOEs. One of the most common reasons for state ownership is natural monopoly. SOEs have been established to carry out nationally strategic but risky or long-term investments where private sector investors were not available. Another common argument for SOEs is externalities. Private investors do not have the incentive to invest in industries, which benefit other industries without being paid for the service
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