Gender Dimensions of Bioeconomy Change: Insights from Western Macedonia ^†

Abstract

Western Macedonia, the leading power-producing region in Greece, has long depended on thermoelectric plants and lignite mining. To reach climate neutrality by 2050, Greece is undergoing a delignitization process aiming to shut down all lignite plants. This structural reconstruction of the energy model will mainly affect society, the economy, the environment, and agriculture. Strengthening efforts to support lignite-dependent areas are essential for this transition. Bioeconomy could be one of the main pillars for the post-lignite era in the Western Macedonia Region (WMR). This paper explores the gender dimension in the adoption of bioeconomy practices and innovativeness among farmers in the Region of Western Macedonia. Based on 331 structured questionnaires and a Two-Step Cluster Analysis, the research identifies five farmer clusters and then correlates the clusters with Rogers’s theory of diffusion of innovations. The findings identify a dynamic group of young female farmers leading the diffusion of innovation, emphasizing their role in promoting sustainable agricultural transitions and the need for gender-responsive policies in regional bioeconomy strategies.

1. Introduction

The goal of climate neutrality by 2050 is based on bioeconomy, according to the European Green Deal [1]. Bioeconomy has become a popular topic in the policy strategies, offering opportunities based on technology and knowledge to promote sustainable economic development [2].

Western Macedonia, located in northern Greece, has been relying on lignite and thermoelectric power plants. Climate neutrality goals have initiated a process of rapid delignitization by 2028 [3,4]. The region faces many challenges to economic restructuring but also opportunities for green innovation and resilience.

Agriculture is a key factor in bioeconomy. Bioeconomy practices can be drivers of rural communities’ transformation and sustainability [5,6]. Gender issues shape innovation adoption in many ways [7]. However, gender dimensions, and more particularly the role of women farmers, remain underexplored. Research suggests that women often play leading roles innovation adoption [8], yet policy frameworks rarely address these differences.

This paper focuses on these gendered dimensions of the bioeconomy transition in Western Macedonia, seeking to understand the characteristics and motivations of farmers within this evolving policy and economic landscape. The aim of this paper is to group farmers regarding their “Adoption of bioeconomy practices”, uncover common characteristics, and attempt to relate the findings to Rogers’s theory of diffusion of innovations [9].

2. Methods

The study used a quantitative research approach, with a five-point Likert scale questionnaire, distributed to 331 farmers across the four Regional Units of the WMR (Grevena, Kastoria, Kozani, and Florina). The distribution of the sample in the 4 regional units of the WMR was formed based on the available statistical data from ELSTAT [10] on the number of farms, in order to be representative of the total population of the study area. The questionnaire covered 4 sections, demographics, personal data, bioeconomy attitudes, training on bioeconomy, and innovations.

Validity and reliability tests were performed prior to multivariate statistical analysis, using the statistical program SPSS (version 28). The a-Cronbach coefficient value was found equal to 0.920, showing a reliable scale. Data analyses, including descriptive statistics and Two-Step Cluster Analysis (TSCA), were performed to classify the farmers into groups with common characteristics [11], regarding the “Adoption of bioeconomy practices”, using several variables, and then try to correlate the clusters with the Rogers’s theory.

3. Results

3.1. Descriptive Statistics Results

In terms of gender, the majority of farmers were men (65.4%), while women accounted for 34.6%. The average farmer was 52 years old. The majority of respondents were married (or in a civil partnership or long-term relationship) (55.9%). Regarding the level of education, the majority were graduates of a Technical School (26.4%), graduates of a University (25.5%), or graduates of a High School (21.9%). The annual family income of the majority, 21.9%, is up to EUR 25,000, while 19.4% declared an income of over EUR 40,000, and 15.1% up to EUR 30,000. Finally, the sample profile is completed with the years of engagement in agriculture. The research sample is quite balanced, as there are relatively “new” farmers at a rate of 52.3% (<5 to 10 years of engagement) and farmers with more experience at a rate of 47.8% (10 to over 30 years of engagement).

Farmers’ level of knowledge towards bioeconomy, on a five-point Likert-type scale, is 2.67, indicating a medium-to-low level. Farmers apply bioeconomy practices on their farms to a relatively low extent, with a mean value of 2.46 on a five-point Likert-type scale. However, it is likely that farmers will implement bioeconomy practices on their farms in the future (mean value of 3.33 on a five-point Likert-type scale).

Financial constraints/lack of funding is the main barrier of adopting bioeconomy practices (M = 4.81), while environmental alignment with European Union’s policies (M = 3.89) and pollution reduction/waste minimization are the most important benefits. Farmers strongly believe that the promotion of bioeconomy practice adoption in the WMR will be achieved by enhancing their knowledge about innovations in the bioeconomy sector and how to implement these practices on their farms (M = 3.91).

3.2. TSCA Results

TSCA was applied to segment the population into groups of farmers with common characteristics regarding the “Adoption of bioeconomy practices”. Five clusters were created using 20 variables. According to the Silhouette measure of cohesion and separation, the clustering process is considered satisfactory.

Table 1. Results of TSCA—clusters’ characteristics.

Variables	Clusters
	1	2	3	4	5
	Interested Farmers - Innovators	Following Farmers - Early Majority	Newcomer Farmers - Early Adopters	Indifferent Farmers - Laggards	Experienced Farmers - Late Majority
Knowledge about bioeconomy 1	2.53	2.60	2.39	2.76	3.10
Benefits of bioeconomy in farming 1	3.31	3.12	3.11	3.07	3.22
Lack of financial resources and financing for bioeconomy 2	4.81	4.95	4.68	4.83	4.80
High cost of bioeconomy 2	4.61	4.47	4.22	4.59	4.54
High technological level and lack of know-how 2	4.52	4.55	4.10	4.29	4.28
Lack of incentives for investment 2	4.53	4.56	4.25	4.27	4.56
Untrained research and labor personnel 2	4.50	4.71	4.04	4.17	3.98
Level of bioeconomy practices implementation 1	2.44	2.38	2.75	2.42	2.30
Interest in implementing bioeconomy practices 1	3.79	3.34	3.17	2.97	3.31
Promoting bioeconomy through training programs 2	3.55	3.76	4.07	3.93	3.80
Training to implement bioeconomy practices on the farm 2	3.73	3.48	2.96	2.47	3.00
Interest in adopting innovations 1	3.23	3.89	3.16	3.95	3.46

¹ (1 = Very low; 5 = very high); ² (1 = strongly disagree; 5 = strongly agree).

lists the mean values of the variables of each cluster.

Cluster 1—“Interested Farmers” (Innovators): Sixty-two farmers (18.7%) are classified. These farmers are young, mostly women, with a high level of education and medium-to-low annual family income. Farmers in this cluster apply bioeconomy practices to a high degree on their holdings and showed a willingness to continue in the future. Although their interest in innovations is moderate, their behavior indicates an innovative profile. Hence, they are labeled as “Interested Farmers/Innovators”.

Cluster 2—“Following Farmers” (Early Majority): This is the largest cluster, with 80 farmers (24.2%). These farmers are middle aged, mostly men, with a medium level of education and medium annual family income. They report a low bioeconomy practice adoption rate, but express a high interest in future implementation. They also express a high interest in innovations. The combination of current hesitation but future acceptance fits Rogers’s theory of Early Majority, so they are labeled as “Following farmers”.

Cluster 3—“Newcomer Farmers” (Early Adopters): This group consists of 69 farmers (20.8%). These farmers are older men, with a medium level of education and high annual family income. This group of farmers shows the highest bioeconomy practice adoption compared to the other clusters, but unwillingness to continue in the future. Their interest in innovation is low to medium. Their adoption seems motivated by practical experimentation rather than knowledge. As they tried early but risk stopping, they are categorized as “Early Adopters/Newcomers”.

Cluster 4—“Indifferent Farmers” (Laggards): This is the smallest group, including 59 farmers (17.8%). These farmers are experienced older women, with a low level of education and a high annual family income. They express a medium-to-low level of bioeconomy practice adoption, and little intention to continue in the future. Although they state that they have a very high interest in adopting innovations, their actual behavior suggests reluctance. This fact places them in Rogers’s theory as Laggards, labeled as “Indifferent Farmers”.

Cluster 5—“Experienced Farmers” (Late Majority): This cluster consists of 61 farmers (18.4%). These farmers are middle-aged women, with a medium level of education and an average annual family income. This group shows the highest knowledge level towards bioeconomy, yet the lowest current adoption. They express medium interest in innovations and a moderate willingness to adopt in the future. Their cautious stance and delayed engagement align them with Rogers’s theory as Late Majority, leading to the label “Experienced Farmers.”

4. Conclusions and Discussion

The results underline the differences between the farmers from the WMR concerning bioeconomy adoption. Gender issues stand out prominently. Women lag behind as both the over-represented innovators, Cluster 1, and the over-represented laggards, Cluster 4. Men dominate the lagging middle clusters 2 and 3, who wait and observe what others do before they are willing to adopt. Young female farmers who are innovators act as the innovation change agents, even though many do not consider themselves innovators.

The analysis revealed three distinct groups of women: (a) young female farmers who actively lead the diffusion of innovation in bioeconomy practices, (b) women forming part of the broader majority, and (c) women who appear to lack full control over their farms. As noted by Tsiaousi [12], in many cases, women assumed nominal leadership of farm, primarily because their husbands were employed in other economic sectors, outside of agriculture, in order to access relevant programs. The gendered dimension of innovation adoption processes has already been studied in depth by McGuire et al. [13], while Berdecia-Cruz et al. [14] highlighted women’s leadership role, by stating that women tend to adopt innovations with greater efficiency and effectiveness, whereas men are more inclined toward transactional leadership.

This study strongly contrasts the notion of women farmers being risk-averse. The evidence suggests that with the right information and adequate structural support, women farmers are able to lead sustainable transitions. Unfortunately, structural barriers, for example, inadequate funding, lack of incentives, and lack of motivation, risk stifling broad adoption.

The bioeconomy in Western Macedonia is somewhere in the transitional stage. It is familiar to farmers, but not yet mainstream. Part of Rogers’s diffusion theory is confirmed, with a wide gap between innovators and early adopters.

Some suggested policy actions are

• Encourage training and subsidization of female young farmers (Cluster 1) to remain in the lead as innovators.
• Develop specialized training for risk-averse adopters (Cluster 2), emphasizing economic and environmental benefits.
• Provide financial support to Newcomer Farmers (Cluster 3) with no incentives to continue.
• Generate replacement plans for experienced Indifferent Farmers (Cluster 4), encouraging farm transfer. Farmers in this cluster are unlikely to apply bioeconomy practices under current conditions on their farms, as the farmers are old and have been involved in agriculture for many years, which would be an inhibiting factor for any change.
• Facilitate outreach and awareness campaigns for Experienced Farmers (Cluster 5), highlighting bioeconomy applications in agriculture.

Finally, gender-sensitive policies are necessary to encourage more rapid adoption and inclusion and to unleash the change-making potential of the bioeconomy in the post-lignite period.