**1. Introduction**

As a result of growing concerns about dependency on fossil-based energy-sources and their impact on climate change, as well as increasing awareness of and preference for sustainable production and consumption patterns, bioeconomy has become a significant solution. The European Commission (EC) defined the bioeconomy as an economy that "*encompasses the production of renewable biological resources and their conversion into food, feed, bio-based products, and bioenergy*". Agriculture, forestry, fisheries, food, pulp, and paper production, as well as some of the chemical, biotechnological and energy industries, are expected to contribute to bioeconomy activities [1,2]. Many of the strategies were further developed to improve the national economy and create job opportunities, and at the same time manage the forest sustainably. In addition to the above definitions, Winkel [3] described the forest-based bioeconomy as "*all economic activities that relate to forests and forest ecosystem services, including biomass-based value chains and the economic utilization of other types of forest ecosystem services (FES)*".

Forest-based wood production is leading the way to renewable energy sources, which are part of a long tradition in European countries [4,5]. In addition to wood forest products, forests offer valuable

forest ecosystem services and other benefits for the well-being of the people [6]. The services provided include provisioning and regulating, as well as basic, supportive and cultural services. Provisioning services cover the products obtained from ecosystems, e.g., food, water, construction and firewood, and fiber, while regulating services cover the benefits obtained from the regulation of ecosystem services, such as erosion control, climate regulation, and precipitation. Cultural services are defined as all nonmaterial benefits obtained from the forest, including spiritual, aesthetic, religious, and recreational values, all of which contribute to our well-being, social and cultural functions. Forests also provide supporting services that are necessary for the production of all other ecosystem services, e.g., nutrient and water cycling, soil formation and retention, and photosynthesis. [6–14].

In the Czech Republic, forests cover about 2.7 million ha (33.7%) of the total country area, lower than all European Union (EU) countries together (40.3%) and Austria (45%), but comparable to the German forested landscape (±31%) [15–18]. Forests are an important part of Czech history and culture. Based on the 2017 Czech forest report, forestry (forestry and logging) and the wood processing industry's shares accounted for 1.180% of the gross value added (GVA) at basic prices, not including the paper and furniture industries, which would add a contribution up to 2.018% of the GVA. The share of the forestry and wood processing industry alone was slightly lower than agriculture's share (1.713%), indicating the importance of the forest-based sector in the country [19].

Due to a growing global concern to replace fossil-based fuels with renewable energy sources, the forest-based sector has become a backbone for bioeconomy strategy. A shift in wood production from weakly regulated forests toward sustainable forest managemen<sup>t</sup> is accompanied by third-party certification, as promoted in forest strategies in EU countries like Finland, Sweden, Germany, and Austria, that has changed the demand for wood in these regions. In 2014, Sweden became the top producer of primary wood products among EU countries by approximately 70 million m3, followed by Finland (± 57 million m3) and Germany (about 54 million m3), while the Czech Republic and Austria contributed about 15 and 17 million m3, respectively [20]. The Czech Republic was also named as one of the main roundwood exporter countries in 2016 [21]. In 2017, timber production in the Czech Republic resulted in 19,387 million m3, of which roundwood production amounted to 11,488 million m3. Most of timber production, including softwood–roundwood, and pulpwood, is exported, mainly to Austria and Germany, for further processing. However, the supply for sawmills and pulp mills in some regions is still insu fficient, and this has caused the country to import from Slovakia, Germany, and Poland [19,22].

Although the bioeconomy strategy has not been mentioned in the Czech National Forest Programme (NFP) [16], bioeconomy has been mentioned in the 2018 draft strategy of the Ministry of Agriculture (MoA). In addition to timber production as one of the fundamental priorities in the Czech forest-based sector, non-wood products, like forest fruits and mushrooms, are also considered important FES [22]. Thus, it is important to provide a view of the current situation of forest products' utilization and preferences by the Czech public. The results can be used to inform policymakers and other stakeholders to o ffer better understanding, and as a baseline to make recommendations on further actions for the adoption of the forest bioeconomy strategy and the promotion of FES.

#### **2. Materials and Methods**

#### *2.1. Study Area*

The Czech forests cover about 33.7% of the total country area. In 2017, 71.9% of the total Czech forests consisted of coniferous trees, 50.3% of them being Norway spruce (*Picea Abies*). Deciduous trees, such as beech (8.4%) and oak (7.2%), covered 27% of the total forested region, and the rest (1.1%) was forested land without trees [19].

#### *2.2. Design of the Study*

The research study was part of a nationwide survey. The survey itself was part of the "Advanced research supporting the forestry and wood-processing sector's adaptation to global change and the 4th industrial revolution" and the "Diversification of the Impact of the Bioeconomy on Strategic Documents of the Forestry-Wood Sector as a Basis for State Administration and the Design of Strategic Goals" research project. The study was carried out in June 2019 in co-operation with an external market research company, REMMARK, a.s (Prague, Czech Republic). The company used the computer-assisted web interviewing (CAWI) technique to recruit the online respondents. No private information was required, and the respondents were anonymous. The online participants aged 18–65 years were recruited proportionally based on age, sex, education level, region, and village size. This technique generates emails and sent the questionnaires to the potential respondents based on the company's list through different online platforms, (e.g., Yahoo email). We have no information on the number of sent-out questionnaires. The survey was terminated after reaching the minimum required sample size. All returned questionnaires were included in the analysis (100%). The respondents were asked to answer a closed-ended questionnaire consisting of socio-demography characteristics and information on FES utilization. Additional information could be written/typed, in order to explain the answer option "others". The answers were later grouped and coded for further analysis.

#### *2.3. Data Analysis*

Descriptive data for the general characteristics of the respondents were used for single traits. Frequencies were presented by absolute numbers and their proportions. A group comparison of traits that determine the FES was made via a chi's square test or the Fischer exact test for categorical data. The age of the respondents was checked against an expected normal distribution using quantile-quantile (Q-Q) plot.

The target respondents of the survey were within a productive age. The age of the respondents was categorized as 18–24 (youth employment), while 25–54 and 55–65 years were defined as prime and mature working age, respectively [23,24]. The education levels of the respondents were categorized as elementary school, secondary school without official graduation or with vocational training (secondary school/vocational training), graduated from high school, or university level. In this article, the place of residence was grouped based on region. To fulfill further data analysis, we combined the respondents that never visited the forest with those who went one or two times per year and created a dummy variable of 0 = frequent visitors and 1= never/rarely. The frequency of forest visits is considered as one of the indicators of utilization of FES.

Two scoring systems were used to define the preferences and opinions of the respondents. The first method used five categorizations of opinions as follows:


The second method applied five degree of preferences, in which 1 (one) is most preferred and 5 (five) is least preferred.

Binary logistic regression with a forward stepwise approach was applied to identify potential predictors of the frequency of forest visits and utilization of forest products and ecosystem services. The following covariates associated with the dependent variables were included in the initial model: age, education level, and characteristics of the place of residence.

To designate the statistical significance in all analyses, a *p*-value of less than 0.05 was used. Statistical analysis was performed using IBM SPSS statistics version 25 (IBM Corp., Armonk, NY, USA).
