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/lp/de-gruyter/structural-analysis-of-the-drivers-and-barriers-to-forest-management-dSR0k2D6Ha
- Publisher
- de Gruyter
- Copyright
- Copyright © 2016 by the
- ISSN
- 0323-1046
- eISSN
- 0323-1046
- DOI
- 10.1515/forj-2016-0019
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- See Article on Publisher Site
Abstract
The paper presents an application of structural analysis in search of key drivers and barriers of forest management in two Slovak regions: Podpoanie and Kysuce. A comparison with factors identified in selected European regions is also presented. First, various relevant factors affecting forest management were selected for both regions. The selections draw on the pool of primary data (structured in-person interviews) and secondary data (qualitative analysis of national and European documents). Second, factors were grouped according to the STEEP categories (Society, Technology, Economy, Ecology, and Policy). Subsequently, factors were rigorously assessed by the regional stakeholders in participatory workshops, and their answers were analysed by structural analysis with the help of Parmenides EIDOSTM software. The results show that in both Slovak regions political, economic, and ecological factors dominated over social and technological factors. The comparison with selected European regions revealed that in the Slovak and other European regions, the Policy category dominated due to having the highest number of factors and their overall impact on forest management. In contrast, the least important societal domain was Technology in both the Slovak and other European regions. However, while stakeholders across the selected European regions perceived the Society domain as significant, stakeholders in both Slovak regions perceived the Economy and Ecology domains as more significant. Key words: STEEP categories; regional stakeholders; participatory approach; forest management Editor: Igor Stefancík 1. Introduction To address the challenges of diverse demands for ecosystem services in a future Europe requires a better knowledge of the drivers and barriers that forest management faces across various regions. These drivers and barriers influence the decisions made by forest owners, managers, and other stakeholders, which result in various approaches to forest management and the allocation of various ecosystem services (Brodrechtová et al. 2016). Knowledge of key influences and their interplay is crucial, as they play an important role in establishing consistent narratives and models for future developments in forest management in Europe (Sotirov et al. 2014). For instance, while almost a quarter of its area is either under EU and/or national nature conservation, in terms of forest management aims the provisioning services remain still crucial (Sotirov & Deuffic 2015; Brodrechtová et al. 2016). In this respect, a deeper understanding of forest management's key drivers and barriers can help eliminate existing discrepancies between social perspectives and demands that are reflected in incoherent policy aims, inconsistent instruments, and management approaches for the conservation and sustainable management of forests across Europe (e.g. Volz 2006; Adams & Jeanrenaud 2008; Adams 2009; Arnouts & Arts 2009; Winkel et al. 2009). Moreover, as the implications of various drivers and barriers directly influence different forest management strategies, more emphasis should be placed on local context next to national and European levels (Sotirov et al. 2014; Sotirov & Deuffic 2015; Brodrechtová et al. 2016). So far, these challenges of various drivers and barriers of forest management from the local perspective have not been addressed in European research (Sotirov & Deuffic 2015). The attempt has been done within an interdisciplinary INTEGRAL project1. Drawing on various schools of thought (e.g., the Advocacy Coalition Framework adapted after Sabatier & Weible 2007, the Policy Arrangement Approach adapted after Arts et al. 2006) or on politicized Institutional Analysis and Development Framework (Ostrom 2005, 2007; Clement 2010) in the case of Slovakia (Brodrechtová et al. 2016), a broad set of ecological, social, economic, technological, and political factors across local, national, and European levels has been derived. Although all these factors might be important to forest management in the future, it has not been clear which factors are the key drivers and barriers to forest management. The goal of this study is therefore twofold: first, to look at selected Slovak regions and to identify the key factors and main societal domains affecting forest management in these regions; and second, to compare the results with factors identified in selected regions across Europe that are involINTEGRAL project Future oriented integrated management of European forest landscapes FP-7 project. *Corresponding author. Rudolf Navrátil, e-mail: navratil@nlcsk.org, phone: +421 45 520 22 44 R. Navrátil et al. / Lesn. Cas. For. J. 62 (2016) 152163 ved in the INTEGRAL project. The selected European regions differ in their basic biophysical conditions and socio-economic circumstances related to forest management and conservation (Table 1). The views of forest managers, owners, and other stakeholders from each region in Slovakia as well as across Europe are grasped via participatory workshops and with the use of the future-oriented research method called structural analysis. The workshops are used as they enable to test scientific knowledge against the practical knowledge of forest managers, owners, and other stakeholders. Moreover, public participation in sustainable forestry may help to develop better informed and more widely accepted forest management outcomes (Sarvasová 2014). The structural analysis is method that can be applied within participatory workshops as it produces visible results in a time-span of a few hours. This method simulates the decision processes of policymakers, other actors, or consumers in choosing among alternatives. It allows studying relationships in order to identify the influential and dependent domain drivers, and it also enables Table 1. INTEGRAL study regions (Betuch et al. 2015). No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Region Teteven Yundola Pontenx Munich South Upper Palatinate Newmarket Western Peatlands Asiago Italy Etna Molise Netherlands Lithuania Suvalkija Zemaitija South-East Veluwe Chamusca Vale do Sousa Leiria National Forest Slovakia Kysuce Podpoanie Helgeå Vilhelmina Portugal Country Bulgaria Acreage (1000 ha) 70 5 102 60 300 75 211 47 25 42 66 38 8 75 16 11 98 21 152 850 domain participants to find a method to share ideas, and to express their views and thinking about a specific problem (Glenn & Gordon 2005). In this study, the systematic analysis is demonstrated in the Slovak regions of Podpoanie and Kysuce. The results are compared with factors identified in other European regions (Table 1). The differences and similarities found in the social, cultural, economic, and ecological contexts of forest management in Europe can generally support development of coherent policy, economic frameworks, and consistent policy instruments. 2. Material and Methods 2.1. Characteristics of INTEGRAL regions The INTEGRAL project study was conducted in 20 regions in 10 European countries (Table 1). Diversity among all the concerned areas derives not only from differences among Basic characteristics Mountainous rural area with predominance of beech forests. Dispersed rural settlements and traditional land use. Issues: wood production, biodiversity, clean water, tourism and recreation. Wooded landscape with 90 % forest cover, managed by University of Forestry in Sofia. Rich physiographic and biological diversity, broad spectrum of vegetation and natural sightseeing, richness of mushrooms and herbs. Important rural landscape with 66% forest cover primarily of maritime pine. One of the largest cultivated and privately owned (92%) region in Europe. Issues: wood production, water regulation, biodiversity, recreation, hunting. City-near forest landscape with 72% forest cover. Urban conurbation with a high economic, ecologic and recreational value. Important recreational area for urban population. Traditional forest use is in conflict with strong demand for recreational services as well as drinking water protection. Rural area with 53% forest cover. Predominance of small private forest owner (60%) and state of Bavaria (26%). Arising conflicts of the "new" forest biomass utilisation (mostly energy) with traditional forest products and conservation issues. Rural, agricultural area with 17% forest cover. State forests predominance. Conflicting objectives of timber production and biodiversity conservation. Issues: wood, fuelwood, biodiversity, clean water, soil erosion. Areas of peatlands, afforested in 1950s1960s, many considered to be `in the wrong place' with wrong objectives and systems. Important in terms of water quality, biodiversity and landscapes. High share of public (municipal) property. Asiago Plateau is a well-known tourist destination. Issues: wood production, recreational activities, biodiversity. North-western area of Mount Etna with 20% forest cover. High fragmentation of forest ownership. Active forest management is marginal and related mainly to public forests. Biodiversity conservation and recreation are of primary concern within the Natural Park area. Mountain region with forest and rural environment. About 40% of state forests, the remaining forests are private with high fragmentation problems. Biodiversity is not considered in active forest management. Issues: wood production, non-wood forest products. Region with coniferous-deciduous forests. State forests predominance. Commercial use of forests dominates with only small portion of protected areas. Medium productive mixed spruce forests on relatively unproductive agricultural land, abandoned agricultural lands, could be afforested. Predominance of private forests. Management restrictions due to the presence of National park. Conservation of natural and cultural history is the main objective while timber production is important especially for the estates. The high recreational pressure and the effects of (over) grazing are the main management concerns currently. The rural region with forests and scrublands. The main species are cork oak, eucalyptus and pines. Issues: sustainable product supply, wildfires, certification, collaboration, climate change (drought). Region with high productive potential for Eucalyptus globulus and Pinus pinaster. Irregular topography, high number of small private owners with forest estates about 1.5 ha. Issues: sustainable timber supply and revenues, wildfires, collaborative forest management. The region with largest and oldest national forests representing the state forest management system. Targeted mostly for timber production and conservation or recreation. Issues: sustainable timber supply, wind protection and recreation. Agricultural-woodland highland territory. High fragmented forest ownership structure with large proportion of non-state owners and unsettled ownership. Coniferous spruce forests affected by intensive and widespread necrosis accompanied by an incidental felling. Agricultural-forest highland landscape with varying land-uses. Dispersed rural settlements and traditional land use. A large part of the region under the nature protection. Very productive forests and rich biodiversity. State forests predominance. Heavily forested region with private ownership predomination. Conflicting interests: timber production and biodiversity conservation, rural development and urbanization. Rural area with a low population density. Boreal forest is to a great extend used for timber, bio-fuel and reindeer herding. Conflicting interests: timber production and biodiversity conservation, timber production and reindeer herding. Sweden Ireland Germany France R. Navrátil et al. / Lesn. Cas. For. J. 62 (2016) 152163 countries, but also within national conditions. Applying a uniform methodology in different regions of Europe allowed us to capture differences or similarities in the social, cultural, economic and environmental contexts of an integrated forest management in Europe. 2.2. Characteristics of Podpoanie and Kysuce regions The Podpoanie and Kysuce regions are agriculturalwoodland landscapes with more than 50% of forest cover and typical for their long tradition of agricultural and forestry use. Despite these similarities, the ownership structure and current state of the forests are remarkably different in these two areas. While in Podpoanie state forest ownership dominates, a very fragmented ownership structure is typical for Kysuce, with the ownership of large acreages of forested land unsettled due to an unfinished restitution process. Besides this, the forest stands in Podpoanie are relatively stable, productive, and healthy, while the health of spruce forests in Kysuce in the last decades has declined (e.g. Konôpka 2004; Kulla 2009; Hlásny et al. 2010), resulting in a high proportion of incidental felling. tion: state forest owners (19.5%), disputed ownership managed by state forest enterprise (33.9%), and non-state forest owners including private, communal, municipal, and church owners (46.3%) (Tucek et al. 2015). Almost half of the case study area is part of the Kysuce Protected Landscape Area covering the northeast and northwest parts of the territory. Tree species composition in the region has been significantly changed from the past. Spruce was previously considered a very economical fast-growing tree species; therefore, spruce monocultures have been commonly established in the region. In recent years, however, the region has been affected by intensive and widespread necrosis of spruce stands (Sitková et al. 2010; Bosea et al. 2014), which also interferes with protected and valuable ecosystems within the Kysuce Protected Landscape Area. The cause of mortality is the critical health condition of spruce stands, caused by complex of abiotic, anthropogenic, and biotic harmful factors with the dominant effect of honey fungus (Armillaria) and an aggressive species of bark beetles (Hlásny & Sitková 2010). These factors are the cause of frequent calamities, so the Kysuce region belongs to the regions with the highest volume of incidental felling in Slovakia (e.g. in 2010 incidental felling represented 97% of total felling in Cadca District and 100% in Kysucké Nové Mesto District) (Vakula 2011). Podpoanie region The Podpoanie region is located in the central part of Slovakia, within Banská Bystrica Region and District Detva. The area is agricultural-forest land, with forests in the north and mainly agricultural areas in the south. Beech, fir-beech, and spruce forests are typical for the very productive upper part of the forest area, in contrast to the lower part where the Carpathian oak-hornbeam forests prevail. The northern part of the region is predominantly under nature protection, belonging to the Poana Protected Landscape Area. The area is dominated by the massif of Poana Mt., which is the highest extinct volcano in Central Europe; the entire mountain is part of the Carpathian arc. In a relatively small area mountain thermophile species of plants and animals also are present; thus, the region is known for its diversity and richness of flora and fauna. Moreover, the Podpoanie region has a specific cultural landscape characterized by dispersed rural settlements and traditional land use by small private owners (Mojses & Petrovic 2013; Bezák & Mitchley 2014). Ownership of the forest land is split between the state (84.7%) and non-state entities (e.g. communal, private, and church owners), so State Enterprise Forests of the Slovak Republic is the strongest forestry subject in the region (Tucek et al. 2015). 2.3. Data collection 2.3.1 Primary and secondary data collection Within the INTEGRAL project the various drivers and barriers were analysed at (i) local level of study regions, (ii) national and (iii) European level. Identification of various determinants of forest management in different regions was based on primary data (structured in-person interviews) and secondary data (output of qualitative analysis of national and European documents). In Slovakia primary data were obtained via 50 in-person interviews with forest owners, managers from the Podpoanie and Kysuce regions, and other relevant actors at the sub-national and national levels (Brodrechtová et al. 2016). The output of this research phase was the identification of subset of 22 drivers and barriers of forest management decision-making on regional level. More precisely, biophysical conditions and attributes of the community, including the politico-economic context, institutions, and discourses, were among the significant drivers affecting forest owners and managers, and their interactions and decisions concerning forest management in the Podpoanie and Kysuce regions. Analysis of secondary data in the form of desktop research was conducted parallel to the primary data collection. First, an extensive review of existing national documents (e.g. strategic and prognostic documents related to forestry, nature protection, and the rural economy) was conducted. This resulted in the isolation of subset of 28 drivers and barriers, including bio-physical conditions and attributes of the community, politico-economic contexts, and institutions that can potentially determine decisions concerning forest management in Slovakia. Second, European Union (EU) documents (e.g. regulations and directives concerning fore- Kysuce region The Kysuce region is located in north-western Slovakia, bordering the Czech Republic in the west and Poland in the north. It belongs to the Zilina Region and completely covers two districts: Cadca and Kysucké Nové Mesto. The region is agricultural, with forests in the north and at higher altitudes (about 56%), and agricultural land mostly in the lower hollow basin and furrows separated by hills. Forest land ownership in the Kysuce region is characterized by very high fragmenta154 R. Navrátil et al. / Lesn. Cas. For. J. 62 (2016) 152163 stry, nature protection, and energy) were subsequently systematically scrutinized, which resulted in the identification of subset of 35 potential drivers and barriers of forest management (Riemer, 2013). These factors concerning attributes of the community, including demographic and technology development, politico-economic context, institutions, and discourses provided a wide overview of possible barriers and drivers of forest management in Europe. 2.4. Data analysis 2.4.1 Participatory workshops To determine key drivers and barriers to future forest management, a structural analysis was applied. The basic idea behind the analysis was to isolate and assess the relative mutual influences of the key factors affecting forest management. Each factor was assessed according to its influence on the other factors included in the analysis and also according to how strongly it was affected by the other factors (Glenn & Gordon 2009). In order to grasp the view of regional stakeholders in Podpoanie and Kysuce, and to decrease subjectivity in factors assessment, the structural analysis was carried out via participatory workshops. In each region, seven regional stakeholders participated (from 15 invited in Podpoanie and from 11 invited in Kysuce) at a half-day workshop (Table 3). 2.3.2 Selection of relevant factors The total set of 85 local, national, and EU drivers and barriers of forest management decision making as an outcome of broad diagnostic analysis represented a base for further investigation. More detailed analysis of the forest management factors with the help of structural analysis resulted in a required reduction to 15 25 relevant factors for each region (Schüll 2013). Reduction to 15 25 relevant factors was completed at a workshop attended by 10 experts from Technical University and National Forest Centre, both in Zvolen, Slovakia. From the list of 85 factors, every expert chose 20 factors considered relevant for the further development of forest management decision making in the Podpoanie and Kysuce regions. Subsequently, the experts ranked every chosen factor from most important to least important. Finally, the factors were evaluated by the frequency analysis, taking into account the frequency and ranking of the factors. The findings of the analysis were set at 20 relevant factors for each region. The factors were afterwards thematically grouped according to the STEEP categories (Table 2). STEEP is an acronym for societal fields `society', `technology', `economy', `ecology' and `politics'. The allocation of factors to these categories helped to identify a possibly unbalanced selection. Level of evaluation STEEP category Society Technology Economy Ecology Politics ECN2 Timber market ECN3 Bioenergy market ECO3 Climate change POL1 Environmental policy and legislation POL2 Forest policy and legislation POL3 Rural Development policy 2.4.2 Conducting structural analysis The task of the workshop participants was to assess the relationship among 20 relevant factors that will significantly influence forest management in their regions. First, these factors were structured into STEEP categories and explained to participants. Each factor was introduced individually, with discussion about why it was considered a factor that will influence future forest management. Second, each participant received the structural analysis matrix worksheet printed in A3 format (Fig. 1). This matrix consisted of 20 relevant factors listed in columns and rows. Finally, the participants answered two questions for each pair of factors: (1) How strongly does one factor affect the other one? and (2) To what extent can the development of one factor be explained Table 2. 20 relevant drivers and barriers of forest management for Podpoanie and Kysuce regions. Macro level Meso level SOC1 Demography development SOC3 Public opinion TEC1 Innovation and technology TEC2 Wood processing industry ECN4 Tourism Micro level SOC2 Qualified workforce SOC4 Codes of conduct ECN1 Forest owners economic situation ECN5 Forest management Costs ECO1 State and structure of forest ECO2 Abiotic and biotic harmful factors ECO4 Non-wood ecosystem Services POL4 Forest ownership Structure POL5 Subsidies and compensations Table 3. Basic characteristics of workshop participants in Podpoanie and Kysuce regions. Position Actors D 1 1 1 M Podpoanie region Education 40+ H 1 1 1 1 1 1 1 S Age 50+ 60+ Actors Position D M 1 2 1 1 1 Kysuce region Education 40+ H 1 1 2 1 1 S 1 Age 50+ 1 60+ 1 Abreviations: D Director/Chairman; M Manager; H Higher; S Secondary. State forest managers Private forest owners District government State nature protection National Forest Centre Slovak association of wood processors Non-state forest owner association Total 1 2 2 -- 1 -- 1 7 R. Navrátil et al. / Lesn. Cas. For. J. 62 (2016) 152163 by the development of the other factor? (Schüll, 2013). In order to avoid a negative group effect, everyone filled out the printed structural analysis matrix worksheet individually. The participants were given sufficient time to carefully consider their responses. An evaluation of individual factors' influence in the structural matrix was measured on a Likert scale where 0 = no influence, 1 = weak influence, 2 = medium influence, and 3 = strong influence. Subsequently, an arithmetical average of values for each matrix cell was calculated. While the resulting structural analysis matrix reflected the view of all participants, the opinion of each participant was weighted equally and not influenced by collective thought. Furthermore, the sums of rows and columns in the structural analysis matrix represented metrics for the level of mutual relationships among the considered factors. While the "row sum" represented the Active value (AV) of a factor and indicated how strongly that factor affects other factors, the "column sum" of a factor represented the Passive value (PV) of a factor and indicated how strongly that factor is influenced by other factors. In this way, every factor was evaluated according to the relationship between its Active and its Passive values (Fig. 1). 2.4.3 Conducting structural analysis with the help of Parmenides EIDOSTM The structural analysis applied via participatory workshops was carried out with the help of the program Parmenides EIDOSTM software, which is usually used in decision-making processes and strategic decisions. Its analytical and visualization functions help experts to identify key elements, especially in complicated and complex processes that require a multidisciplinary approach (Navrátil et al. 2014). Moreover, the Active and Passive values of the factors could be displayed by means of the Active/Passive Map. More precisely, the Parmenides EIDOSTM's program module Situation Analysis allows all assessed factors to be displayed in coordinate axes using the individual Active and Passive values as their x- and y-coordinates (Fig. 2). In the map four groups of factors are distinguished (Schüll 2013): (i) active or influent factors (with high AV and low PV) located in the upper left quadrant, (ii) dynamic or critical factors (with high AV and high PV) located in the upper right quadrant, (iii) excluded or lazy factors (with low both AV and PV) located in the lower left quadrant, and (iv) passive or depending factors (with low AV and high PV) located in the lower right quadrant. Fig. 1. Structural analysis matrix with 20 relevant factors used in participatory workshops. R. Navrátil et al. / Lesn. Cas. For. J. 62 (2016) 152163 For instance, active factors influence future forest development more than they are influenced by other factors (i.e., they are very influential and have a low dependency). These factors are hard to steer and control given their low PV. Dynamic factors are very influential and at the same time very dependent; thus, any action on them has consequences on the other assessed factors. Since excluded factors have little influence and, at the same time, little dependence, they are rather isolated from the rest of the assessed factors. Passive factors are influenced more strongly than they influence other factors. They are also sensitive to the development of other especially dynamic factors. In summary, the higher the position of a factor on the map, the stronger it affects the other factors. In contrast, the right-most position means that the factor is heavily affected by the other factors. Consequently, factors with high AV are factors of special importance. While the active factors can probably serve as major key drivers or barriers for the future development of forest management, dynamic factors should receive special attention, because they are more likely to change and more likely to be changed (Schüll 2013). 2.4.5 Comparison of results within project INTEGRAL Resulting Active and Passive values of factors from the Podpoanie and Kysuce regions were compared with Active and Passive values of factors identified in other INTEGRAL regions. In order to ensure the comparability of results, Active and Passive values from the Podpoanie and Kysuce regions were modified. First, as not all regions used the same Likert scale for their structural analysis (some used 0 3; some used 0 10), the results were adjusted to a consistent scale ranging from 0 to 10. Second, since the structural analysis across the INTEGRAL regions also differed by the number of involved factors, adjusted active and passive values of each factor were calculated using the following formulas: 2.4.4 Selection of key factors The results of the structural analysis realized within the participatory workshops were twofold: (i) key factors were isolated according to Active and Passive values, and (ii) key factors were isolated according to Active/Passive maps. The Active and Passive values were used to compare key factors from Slovak regions with factors in other INTEGRAL regions. However, the final selection of key factors in the Podpoanie and Kysuce regions was based on Active/Passive maps and subsequent discussion with stakeholders in participatory workshops. Distribution of factors in individual quadrants of maps was presented to stakeholders at the end of the workshops. The following discussions resulted in final identification of key drivers and barriers for forest management in the Podpoanie and Kysuce regions. (Avad adjusted Active value and PVad adjusted Passive value). The range of the scale used and the number of factors involved across INTEGRAL regions ensured comparability of the results (Hinterseer et al. 2014). 2.4.6 Analysis of factors according to STEEP categories The factors were also assessed by their frequencies within the STEEP categories. This gave a picture of the importance of individual societal domains for the development of forest management. However, the frequencies do not give any information about the overall influence of each STEEP category. Therefore, in both the Podpoanie and Kysuce regions, the Global Active values for each STEEP category were calculated by adding the Active values of the individual factors. Subsequently, the results of Slovak regions were compared with the factors isolated in other INTEGRAL regions. Again, the adjusted Active values were used to ensure comparability. 3. Results and discussion 3.1. Active and Passive values Podpoanie and Kysuce regions In the Podpoanie region (Table 4), the highest Active values were assigned to two policy factors (Subsidies and compensations, Forest policy and legislation), followed by the economic factor (Forest owners' economic situation). The factors with the highest Passive values, and therefore the factors most affected by other assessed factors, were Forest owners' economic situation, State and structure of forest, and Forest management costs. In the Kysuce region (Table 5), the regional stakeholders assigned the highest Active values to ecological factors (Climate change, Abiotic and biotic harmful factors, State and Fig 2. Distribution of factors in Active/Passive map. R. Navrátil et al. / Lesn. Cas. For. J. 62 (2016) 152163 structure of forest) and economic factors (Subsidies and compensations). The factor that had the highest Passive value was Forest owners' economic situation followed by the Forest management costs and Timber market. While the most influential factors with high Active values in the Podpoanie region were mainly political, in the Kysuce region, the most influential factors with high Active values were mainly ecological. This could be attributed to the regional stakeholders' perception of unfavourable health conditions of the Kysuce forests. Additionally, in both regions the factors Subsidies and compensations and Forest policy and legislation were among the top-ranked influential factors. Table 4. Active and passive values of factors in Podpoanie region. Rank 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Code POL5 POL2 ECN1 POL4 POL3 ECN2 ECO1 SOC4 ECN5 ECN3 ECO3 ECO2 ECO4 TEC1 POL1 TEC2 SOC1 ECN4 SOC2 SOC3 Factor Subsidies and compensations Forest policy and legislation Forest owners' econ. situation Forest ownership structure Rural Development policy Timber market State and structure of forest Codes of conduct Forest management costs Bioenergy market Climate change Abiotic and biotic harmful factors Non-wood ecosystem services Innovation and technology Environ. policy and legislation Wood processing industry Demography development Tourism Qualified workforce Public opinion Podpoanie region Total AV Rank 6.32 1 6.23 2 5.95 3 5.88 4 5.75 5 5.63 6 5.51 7 5.49 8 5.47 9 5.23 10 5.19 11 5.11 12 4.74 13 4.58 14 4.54 15 4.28 16 3.81 17 3.68 18 3.67 19 2.89 20 Code ECN1 ECO1 ECN5 ECN3 ECN2 POL5 TEC2 ECO4 POL3 TEC1 POL2 ECN4 SOC3 POL1 ECO2 SOC4 SOC2 POL4 ECO3 SOC1 Factor Forest owners' economic situation State and structure of forest Forest management costs Bioenergy market Timber market Subsidies and compensations Wood processing industry Non-wood ecosystem services Rural Development policy Innovation and technology Forest policy and legislation Tourism Public opinion Environ. policy and legislation Abiotic and biotic harmful factors Codes of conduct Qualified workforce Forest ownership structure Climate change Demography development Total PV 6.95 6.60 6.49 6.09 5.88 5.77 5.72 5.58 5.49 5.32 5.19 5.04 4.74 4.74 4.51 4.37 3.46 3.35 2.60 2.14 Table 5. Active and passive values of factors in Kysuce region. Rank 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Code ECO3 ECO2 POL5 ECO1 POL2 ECN5 SOC2 ECN1 ECN2 POL1 POL4 TEC1 TEC2 POL3 SOC1 SOC3 ECN3 ECN4 ECO4 SOC4 Climate change Abiotic and biotic harmful factors Subsidies and compensations State and structure of forest Forest policy and legislation Forest management costs Qualified workforce Forest owners' econ. situation Timber market Environ. policy and legislation Forest ownership structure Innovation and technology Wood processing industry Rural Development policy Demography development Public opinion Bioenergy market Tourism Non-wood ecosystem services Codes of conduct Factor Kysuce case region Total AV Rank 6.32 1 6.18 2 6.11 3 6.11 4 5.86 5 5.75 6 5.75 7 5.65 8 5.54 9 5.54 10 5.47 11 5.44 12 5.26 13 5.19 14 5.02 15 4.67 16 4.53 17 4.42 18 4.35 19 4.21 20 Code ECN1 ECN5 ECN2 ECO1 ECN3 TEC1 POL3 POL5 TEC2 POL2 SOC2 ECN4 SOC3 POL1 ECO2 ECO4 POL4 SOC4 ECO3 SOC1 Factor Forest owners' economic situation Forest management costs Timber market State and structure of forest Bioenergy market Innovation and technology Rural Development policy Subsidies and compensations Wood processing industry Forest policy and legislation Qualified workforce Tourism Public opinion Environ. policy and legislation Abiotic and biotic harmful factors Non-wood ecosystem services Forest ownership structure Codes of conduct Climate change Demography development Total PV 7.37 6.91 6.63 6.14 5.96 5.89 5.79 5.72 5.72 5.51 5.47 5.40 5.33 5.09 5.05 4.91 4.81 4.21 2.91 2.53 Table 6. Active and passive values of factors from other INTEGRAL regions (Hinterseer et al. 2014). Rank 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Factor Policies, laws and regulations Ownership structure Timber market Population Bioenergy market Climate change Subsidies Non-wood ecosystem services Management plans Owner`s norms, values & objectives Owner`s economic situation Forest calamities Public opinion Timber processing industry Forest structure Rural Development (Plans/Activities) Technology Forest services and functions Forestry paradigms Certification INTEGRAL regions Total AV Rank 104.42 1 88.24 2 79.16 3 60.48 4 55.03 5 54.27 6 48.51 7 48.50 8 46.88 9 40.96 10 38.38 11 36.31 12 36.14 13 35.34 14 34.26 15 34.11 16 31.34 17 29.06 18 27.43 19 25.79 20 Factor Policies, laws and regulations Timber market Ownership structure Bioenergy market Subsidies Owner`s norms, values & objectives Management plans Non-wood ecosystem services Owner`s economic situation Population Public opinion Timber processing industry Forest structure Climate change Rural Development (Plans/Activities) Technology Forest road network Type of silviculture Forest services and functions Management costs Total PV 91.17 73.23 68.70 53.91 49.96 49.72 48.77 44.12 44.12 42.07 40.16 39.21 38.72 36.33 36.31 34.24 32.59 31.11 29.91 26.70 R. Navrátil et al. / Lesn. Cas. For. J. 62 (2016) 152163 Comparison of the Passive values in Podpoanie and Kysuce revealed that the top five influenced factors (Forest owners' economic situation, Forest management costs, State and structure of forest, Timber market, and Bioenergy market) were similar. According to their Active and Passive values, six factors (Forest owners' economic situation, Subsidies and compensations, State and structure of forest, Forest management costs, Timber market, and Forest policy and legislation) were therefore identified as key factors for future forest management of both the Podpoanie and Kysuce regions. 3.2. Active and passive values other INTEGRAL regions Comparison across INTEGRAL regions (Table 6) revealed that Policies; laws and regulations; Timber market; Ownership structure; Bioenergy market; Subsidies; and Owner`s norms, values, and objectives were important for future forest management. Moreover, the first three factors have both the highest active and passive values, considerably higher than other factors. Accordingly, based on the perception of regional stakeholders across Europe, the Policies, laws and regulations, Timber market, and Ownership structure factors were crucial for future forest management in Europe. If compared to the Slovak regions, the Policies, laws and regulations, Timber market, and Subsidies factors were universal for both Slovak and other European regions. However, for stakeholders from the Podpoanie and Kysuce regions, State and structure of forest, Forest management costs, and Forest owners' economic situation were also important factors. impact, with a huge gap between it and other categories in ranking. In sum, according to the Global Active values the STEEP categories Policy, Economy, and Ecology were considered societal domains with the highest importance for future forest management in the regions of Podpoanie and Kysuce. From the European perspective, the comparison of STEEP categories by Global Active values revealed both similarities and differences with the Slovak regions. The Policy category was ranked the highest, whereas Technology had the lowest ranking (Table 8). In contrast, in the Slovak regions the Economy category was the second most important; in INTEGRAL regions the second most important category was Society. Thus, there were apparent dissimilarities between stakeholders' perceptions of societal factors in Slovak and in other European regions involved in INTEGRAL. Table 8. STEEP categories and their Global Active values. Rank 1. 2. 3. 4. 5. Podpoanie region Kysuce region *INTEGRAL regions STEEP Global STEEP Global STEEP Global Rank Rank Category AV Category AV category AV Policy 28.72 1. Policy 28.18 1. Policy 426.86 Economy 25.96 2. Economy 25.89 2. Society 344.72 Ecology 20.54 3. Ecology 22.95 3. Economy 319.28 Society 15.86 4. Society 19.65 4. Ecology 241.95 Technology 8.86 5. Technology 10.7 5. Technology 163.29 * Hinterseer et al. 2014. 3.4. Active/Passive maps Podpoanie and Kysuce Active/Passive Maps were used for easier and transparent visualization of the results during participatory workshops and subsequent discussion of findings with regional stakeholders. The resulting distribution of factors in maps showed at a glance how strongly each factor acted on all other factors and how strongly it was affected by other factors (Fig. 3). In Podpoanie the distribution of factors and their concentration in the right upper quadrant demonstrates their dynamics as well as their strong connection among each other. Policy factors Subsidies and compensations (POL5), Forest policy and legislation (POL2), and Rural development policy (POL3) clearly formed the group of most dynamic factors. Similarly, the majority of economic and ecological factors were dynamic factors. Factors located in the upper left corner, Ownership structure (POL1), Climate change (ECO3), and Demography development (SOC15), were determined to be active; thus, they were very influential and uncontrollable factors. Public opinion (SOC3) fell into the lower right quadrant as the only passive factor. No one factor was determined to be an excluded factor. The factors in Kysuce were more dispersed. The current unfavourable state of forests in the region was reflected in the highest positions of the ecological factors Climate change (ECO3), State and structure of forest (ECO1), and Abiotic and biotic harmful factors (ECO2). The latter two factors formed the group of dynamic factors along with Subsidies and compensations (POL5), Forest policy and legislation (POL2), Forest management costs (ECN5), Qualified workforce (SOC2), Forest owners' economic situation (ECN1), and Timber market (ECN2). Climate change (ECO3) and Forest ownership structure (SOC4) were deter159 3.3. Evaluation of factors according to STEEP categories In terms of frequency of factors within STEEP categories in the Podpoanie and Kysuce regions, the Policy category had the highest number of factors (Table 7). Comparison across INTEGRAL regions showed similar results. There was a considerable gap between Policy and other categories such as Economy, Society and Ecology. Across all INTEGRAL regions, the Technology category was far behind all other categories. Table 7. Frequency of factors grouped by STEEP categories. STEEP category Society Technology Economy Ecology Policy Frequency Podpoanie and Kysuce regions INTEGRAL regions 4 85 2 46 4 88 4 71 5 102 In both Slovak regions, the ranking of STEEP categories according to Global Active values showed similar findings in the number of factors in each category (Table 8). Specifically, in Podpoanie and Kysuce, the Policy category had not only the highest frequency of identified key factors, but it also had the highest overall impact. The Global Active values of the Economy, Ecology, and Society categories indicated differences in their relevance despite the same number of factors. In both regions, the Technology category had the least R. Navrátil et al. / Lesn. Cas. For. J. 62 (2016) 152163 Fig 3. Active/Passive Maps for Podpoanie and Kysuce regions (Society yellow, Technology grey, Economy red, Ecology green, Policy orange). mined to be active factors. The lower left quadrant contained the passive factors Public opinion (SOC3), Bioenergy market (ECN3), Tourism (ECN4), and Non-wood ecosystem services (ECO4). Two societal factors, Demography development (SOC1) and Codes of conduct (SOC4), formed the group of excluded factors. nance in Podpoanie; highly fragmented ownership structure in Kysuce), stakeholders in both regions expressed the opinion that forest ownership significantly affects forest management, but it is hard to control its arrangement. Thus, Forest ownership structure was regarded by stakeholders as a stable factor, although stakeholders from Kysuce especially stressed the need for an adjustment of fragmented ownership structure. With the exception of Tourism (ECN4) and Bioenergy market (ECN3, in Kysuce), in both maps all other economic factors were very influential and at the same time very dependent. The dynamic factors Forest owners' economic situation (ECN1), Timber market (ECN2), and Forest management costs (ECN5) were in close proximity, situated furthest to the right of all factors. This illustrates the stakeholders' perception that those factors significantly affect forest management in their regions but, at the same time, could be seriously influenced by other factors. Thus, stakeholders from both regions stated during discussions that these factors might be classified as crucial factors. While the Bioenergy market (ECN3) factor was classified as dynamic factors in Podpoanie, surprisingly, it was classified as a passive factor in Kysuce. Stakeholders from Kysuce have mentioned that they still consider the Timber market factor more important than the Bioenergy market factor. In both regions, the factor Non-wood ecosystem services (ECO4) had the least importance of all ecological factors that were considered; even in Kysuce the factor was located on the boundary between excluded and passive factors. In both maps, the most affected (rightmost) ecological factor was State and structure of forest (ECO1). Contrariwise, the leftmost ecological factor Climate change (ECO3) illustrates the stakeholders' perception that one of the major ecological drivers can be influenced very little. The distribution of societal factors in both maps showed that in each region just one factor was determined to be dynamic. While in Podpoanie the Codes of conduct (SOC4) was 3.5. Selection of key factors according to Active/ Passive maps and discussion in participatory workshops The visualization of results via the Active/Passive maps (Fig. 3) supported discussion with workshops participants, who helped to detect the subtle differences between the Podpoanie and Kysuce regions, which in turn aided the final selection of key factors for future forest management in both regions. Comparison of the Active/Passive maps of Podpoanie and Kysuce revealed apparent dissimilarities as well as commonalities between the regions. The most dynamic factors in Podpoanie were political and in Kysuce were ecological. In other words, in Kysuce the position of factors State and structure of forest (ECO1), and Abiotic and biotic harmful factors (ECO2), mirrored the current poor health of forest stands and excessive incidental felling, which was confirmed by Kysuce's stakeholders in the workshop discussion. Looking at the distribution of policy factors (Fig. 3), it is evident that in both regions all factors are in the right upper quadrant or on its border. While the Rural development policy (POL3) was classified by stakeholders in Podpoanie as a clearly dynamic factor, in Kysuce it was located on the border of passive factors. In both maps, the leftmost policy factor Forest ownership structure (POL4) influenced the remaining factors much more strongly than it was influenced. Despite the diametrically different situation in the ownership structure between these two regions (state ownership predomi160 R. Navrátil et al. / Lesn. Cas. For. J. 62 (2016) 152163 rated as very influential and at the same time a very dependent social factor, conversely, in Kysuce this factor was determined to be the least influential factor of all 20 assessed factors. It seems that in Podpoanie stakeholders perceived the significance of informal institutions, while stakeholders in Kysuce were more worried about different problematic issues. In subsequent discussions, stakeholders in Podpoanie confirmed the impact of informal institutions (e.g., clientelism, lobbying, and networking) and expressed their concerns about the influence of political or financial groups. Although stakeholders in Kysuce also perceived these types of influences, they did not consider it as serious a problem. Kysuce's stakeholders considered fragmented ownership structure the main problem. As the size of state owned forests in Kysuce (19.5%) is smaller than that of Podpoanie (84.7%), the influence of the political and financial groups is less pronounced. On the other hand, the lack of skilled labour in Kysuce was reflected in the position of Qualified workforce (SOC2), which was classified as a dynamic factor. Stakeholders in Kysuce confirmed in discussion the concerns about shortages of skilled workers in the near future. Public opinion (SOC3) was placed in both maps more among passive factors than active ones. Even in Podpoanie, this factor received the lowest active sum of all the assessed factors; thus, according to stakeholders, it was influenced more strongly by other factors than it acted on them. In both cases, the leftmost factor was clearly Demography development (SOC1). Despite its higher active value compared to other social factors, Public opinion, Qualified workforce (in Podpoanie), and Codes of conduct (in Kysuce), stakeholders perceived this social factor in both case study areas as a societal driver that was influenced very little by other factors. In both maps, the technological factors Innovation and technology (TEC1) and Wood processing industry (TEC2) were in close proximity to each other. Although they were in the upper right quadrant, their position near the boundary of passive factors indicates that they had less influence because they were affected by other factors. Subsequent discussions confirmed stakeholders' perception that innovations and the renewal of vehicle fleet are influenced mainly by availability of finances. Table 9. Key strategic drivers and barriers of forest management in Podpoanie and Kysuce regions. Rank 1. 2. 3. 4. 5. 6. 7. 8. 9. Podpoanie region Isolated key factor POL5 Subsidies and compensations POL2 Forest policy and legislation ECN1 Forest owners' economic situation POL3 Rural development policy ECN2 Timber market ECO1 State and structure of forest ECN5 Forest management costs ECN3 Bioenergy market SOC4 Codes of conduct Rank 1. 2. 3. 4. 5. 6. 7. 8. Kysuce region Isolated key factor ECO1 State and structure of forest ECO2 Abiotic and biotic harmful factors POL5 Subsidies and compensations POL2 Forest policy and legislation ECN5 Forest management costs ECN1 Forest owners' economic situation SOC2 Qualified workforce ECN2 Timber market finally isolated (Table 9). The highest ranked were policy factors (Subsidies and compensations, Forest policy and legislation, and Rural development policy) and the economic factor Forest owners' economic situation. Ecological and societal factors were represented by State and structure of forest and Codes of conduct. In the Kysuce region eight key factors were selected that will affect forest management in the future (Table 9). The most important were two ecological factors (State and structure of forest, Abiotic and biotic harmful factors), followed by policy, economic factors, and the societal factor Qualified workforce. 4. Conclusions Findings of structural analysis showed similarities as well as differences between the Podpoanie and Kysuce regions. Although in both regions, political factors were among the dominant factors, stakeholders in the Kysuce region also very strongly perceived the adverse impact of harmful factors on the current state of their forests, and thus on the entire forest management in Kysuce. Results in Podpoanie indicated that if the forests are relatively healthy and in good condition, the main societal domains driving forest management are Policy and Economy. The Active/Passive maps which resulted and subsequent discussions during workshops also revealed a slightly different perception of societal factors in both regions. In Podpoanie, the prevalence of state owned forests is an obvious influence of the informal institution of forest management. In contrast, in Kysuce, with its highly fragmented ownership structure and prevalence of non-state owners, this impact is not so significant. Commonalities and dissimilarities were also revealed between Slovak and other European regions involved in the INTEGRAL project. The STEEP category Policy seems to be the main societal domain affecting forestry and forest management across Europe. In Slovak and INTEGRAL regions, the Policy category dominated by influencing a number of factors and also through its overall impact. In general, forest, environmental or rural development policies, and laws and regulations were perceived by stakeholders from other European regions as the factors with the highest importance for forest management. Additionally, the technological factors were considered to follow forest development rather than drive it. Differences in results concerned perception of social factors. While the STEEP category Society was ranked high in INTEGRAL regions, stakeholders in Slovak regions preferred economic and ecological factors. This study's contribution was on a theoretical level as well as on a methodological level. The future research method of structural analysis proved to be a suitable instrument for comparing a mixture of qualitative and quantitative factors influencing forest management. Since the analysis compares the relevant factors based on their mutual influence, it is essential to properly select the relevant factors to be analysed. It is important to include all relevant factors that affect the analysed system, which in this case is forest management. As the structural analysis allows comparison at first glance of seemingly incomparable qualitative (e.g., climate change) and quantitative (e.g., timber market) factors, it appears to be a suitable method of application in forestry. In summary, according to the Active/Passive map and discussion with stakeholders, in the Podpoanie region the set of nine key drivers and barriers to forest management was R. Navrátil et al. / Lesn. Cas. For. J. 62 (2016) 152163 Using Active/Passive values was particularly appropriate for evaluation of the overall impact of individual STEEP categories. Active/Passive maps allowed more detailed comparison of the various factors that were considered. The Parmenides EidosTM analytical tool allowed visualization of the individual stakeholder's perception of a factor's importance. Distribution of factors in the map's quadrants is much more readable and transparent as simply Active or Passive values. The application of the structural analysis in participatory manner proved to be very suitable for identification of drivers and barriers to forest management. For correct implementation of the method it was necessary that the workshop participants represented different interest groups of regional stakeholders. Our results confirmed other researches that stakeholder's participation in forestry decision-making might provide regional expertise, increase the legitimacy of final outcome (e.g. Beckley et al. 2005) and strength the involvement of local stakeholders in policy-making processes (e.g. Sarvasová 2014; Marzano et al. 2015; Sarvasová 2016). Acknowledgments This research study has received funding from the European Union's Seventh Programme for research, technological development and demonstration under grant agreement No. 282887.
Journal
Forestry Journal – de Gruyter
Published: Sep 1, 2016