To develop an integrated, biomass-specific framework for identifying, weighting and modeling sustainable supplier selection criteria (SSSC) in biomass energy supply chains. The study aims to address gaps in existing supplier-selection research by capturing economic, social and environmental dimensions and the unique technical and seasonal challenges of biomass feedstocks.
A three-step approach was used: (1) a meta-synthesis of 56 studies to extract candidate criteria; (2) a two-round fuzzy Delphi method (12 experts from industry, academia, consultancy and policy) to validate and reduce the list to 18 final criteria and (3) application of the SISMW method to simultaneously weight criteria and model their structural interrelationships. ISM and MICMAC analyses were employed to derive hierarchical levels and driving/dependence classifications. Feedstock-specific considerations (e.g. agricultural residues, forestry residues, energy crops, municipal and industrial organic wastes) were incorporated into the framework.
The study produced 18 validated SSSC across economic, social and environmental dimensions. Top-ranked and most influential criteria are: organization and management (EC5), corporate social responsibility (SO3), environmental management system (EN4), organizational culture (SO4) and technology/equipment (EC4). SISMW yielded criterion weights and an ISM hierarchy; MICMAC classified EC4, EC5, SO3, SO4, SO6, EN1 and EN4 as primary driving (independent) criteria, while EC2, EC3, EC6, EC7, EN2 and EN5 were primarily dependent. Practical recommendations include biomass-specific certification, supplier technology development programs, carbon accounting and traceability systems and multi-tier supplier engagement.
This paper is, to our knowledge, the first application of the SISMW technique to biomass supplier selection, offering an integrated simultaneous-weighting-and-modeling approach tailored to biomass feedstock heterogeneity. The study advances theory by revealing causal links among sustainability criteria in the biomass context and provides actionable, feedstock-aware guidance for managers and policymakers.
1. Introduction
Competition among companies in global markets has been increasing since the 1990s, and in recent years, companies have been moving toward the era of the Industry 4.0 revolution (Nasrollahi et al., 2021a, b, c). Companies seek to increase their competitive power by enhancing customer satisfaction and improving the efficiency of their businesses (Kannan and Tan, 2005). In advanced economies, the shift of from production-oriented to service-oriented has become an important issue (Nasrollahi and Ramezani, 2020). The timely delivery of products to customers at a lower total cost strengthens companies’ competitive advantages (Tan et al., 2001). Companies have realized that they cannot improve their efficiency without focusing on the supply chain (Childerhouse and Towill, 2003). Achievements in supply chain management, including all activities of material flow (from raw materials to final products), information flow, and financial transactions, enhance business performance and increase companies’ competitive power in global markets (Kannan and Tan, 2005).
Generally, supply chain optimization has been emphasized in terms of economic aspects, such as cost reduction. With an increase in customer awareness of environmental and social issues and, stricter regulations by governments on these matters, attention to these concepts in supply chain modeling is increasing (Fathi et al., 2020). More specifically, social issues such as poverty, disabilities, children’s rights, women’s rights, and minorities, as well as environmental issues such as creating infrastructure to reduce water, air, and soil pollution, greenhouse gases, optimal resource utilization, and clean energy use, alongside economic issues, have become important and led to the formation of the concept of sustainability. One definition in the field of sustainable development states that sustainable development meets the needs of the present without compromising the ability of future generations to meet their needs (Butlin, 1989). Sustainability is a complex concept that encompasses various economic, environmental, and social aspects, with these three dimensions being fully interactive. Based on this, the triple bottom line (TBL) principles were developed in supply chain sustainability to strengthen this approach and form the concept of sustainable supply chain management (SSCM) (Pati et al., 2008).
In summary, SSCM involves optimizing and managing activities related to resource identification, procurement, transformation, and support in the stages before production, during production, consumption, and post-consumption in the product life cycle within a closed-loop by sharing information among companies active in the supply chain and considering economic, social, and environmental aspects to achieve a common vision (Badurdeen et al., 2009). Sustainable supply chain management means reducing long-term risks related to resource consumption, energy costs, and pollution management and, striking a proper balance among all sustainability dimensions (Nasrollahi et al., 2021a, b, c). Designing a sustainable supply chain represents a new approach to meeting current needs while considering constraints on non-renewable resources and aiming to cover economic, environmental, and social aspects (Neto et al., 2008).
Therefore, the starting point for achieving sustainability in the supply chain is the selection of suppliers based on sustainable principles. Organizations in the supply chain face complex decisions and must consider multiple criteria when evaluating and managing suppliers. Supplier management requires a delicate balance among effective evaluation criteria. Supplier diversity complicates this process, and in the literature, it is emphasized that supplier evaluation requires consideration of both tangible and intangible criteria (Sarkis and Talluri, 2002). Often, the relationships between these criteria are not clearly defined, and as a result, precise judgment is difficult. Moreover, the abstract nature of some criteria prevents traditional multi-criteria decision-making techniques from accurately measuring the qualitative judgments of experts in the evaluation process (Nasrollahi et al., 2018).
The biomass energy industry presents distinct challenges and opportunities that differentiate it from other renewable energy sectors and conventional supply chains. Unlike solar or wind energy, biomass supply involves physical material flows with significant variability in quality, availability, and characteristics. This industry operates at the intersection of agriculture, forestry, waste management, and energy production, creating unique supply chain complexities. Biomass feedstock is often bulky with low energy density, making logistics particularly challenging and environmentally significant. Furthermore, the industry faces distinct sustainability concerns related to land use competition, biodiversity impacts, and carbon debt timeframes that require specialized evaluation criteria when selecting suppliers. The seasonal nature of many biomass resources introduces supply variability that demands robust supplier management systems. Additionally, biomass supply chains frequently operate in rural areas, creating important socioeconomic considerations regarding local development and agricultural practices.
The supply chain management of Biomass is a complex process involving multiple stakeholders such as farmers, collectors, processors, and consumers. The biomass supply chain (BSC) can be considered a tool for achieving UN Sustainable Development Goals (Hiloidhari et al., 2023). Recent literature has increasingly recognized the unique characteristics of biomass supply chains and their implications for supplier selection. Unlike conventional manufacturing supply chains where supplier selection often prioritizes cost, quality, and delivery performance (Kannan and Tan, 2005), biomass energy supply chains involve additional complexities related to feedstock characteristics, seasonality, and sustainability impacts (Gold and Seuring, 2011). Creating an efficient biomass supply chain is the most important element in a successful bioenergy development project, and effective supply chain management and the selection of sustainable suppliers in the biomass industry are vital to ensure the sustainability and quality of biomass products. Biomass feedstock is sourced from various regions, processed at different facilities, and transported to numerous destinations before reaching its final consumer. As a result, supply chain managers must consider multiple factors, including supplier selection, logistics, warehousing, and inventory management, to ensure efficient and effective delivery of biomass products. In addition, supply chain sustainability is crucial because it has significant impacts on the environment, economy, and communities. These distinctive characteristics necessitate a specialized approach to sustainable supplier selection that addresses the specific technical, environmental, and socioeconomic challenges of biomass energy production.
In this paper, we have used the SISMW method (Nasrollahi et al., 2022) for simultaneous weighting and modeling criteria. The proposed framework is expected to help the biomass industry and relevant policymakers choose the best suppliers to make the biomass supply chain more sustainable. This study tried to align all the sustainable supplier selection criteria (SSSC) in the biomass industry, which should be helpful for future BSC research and innovation. The remainder of this study is organized as follows. The next section presents a literature review followed by criteria obtained from the literature analysis. The third section explains the data analysis methodology. In the fourth section, after criteria identification and validation using meta-synthesis and Fuzzy Delphi Method, weighting and modeling of the criteria influencing the selection of sustainable suppliers are carried out. Finally, the fifth section concludes discussing its contributions, limitations, and recommendations for future research.
2. Literature review
In the 1980s, the World Commission on Sustainable Development first introduced the concept of sustainable supply chain management, with the publication of the terms “sustainable development” and “sustainable use”. Sustainable development is defined by the World Commission as development that meets the needs of the present without compromising the ability of future generations to meet their needs. Organizations have found that they must take responsibility for economic, social, and environmental areas to achieve sustainable development (Tan et al., 2001). For this purpose, successful organizations develop sustainable success by adopting the green management model.
Generally, a supply chain is a series that includes all activities related to the flow of goods and the transformation of materials, from raw material procurement and preparation to the delivery of the final product to the end customer. Therefore, sustainability encompasses all issues and processes involved in supply chain management: product design, product production, product development, end-of-life product management, and the recycling process at the end of the product life cycle (Tozkapan et al., 2003). The sustainability of supply chain management operations has become an innovative competitive advantage and an important tool for all businesses (Nasrollahi, 2018).
Sustainability has recently received increasing attention from researchers and practitioners. In this regard, some researchers reviewed research papers in the fields of management and operations research that examined social and environmental responsibilities in improving operational sustainability. They found that, while for many years, many studies have focused on the economic optimization of supply chain network design, with the increasing importance of sustainability, recent studies have incorporated environmental and social responsibilities as objective functions in supply chain optimization (Chaabane et al., 2012).
Recent advancements in biomass supply chain management have emphasized the critical importance of supplier selection criteria that address the unique challenges of this industry. The processes related to selecting sustainable suppliers require attention to aspects that go beyond operational decisions. With the growing emphasis on environmental and social issues in organizations and the maturity of the concept of corporate social responsibility, reviewing relationships with suppliers from a sustainability perspective (Al-Anzi and Allahverdi, 2007; Mozdgir et al., 2013). Masoomi et al. (2023) proposed a framework specifically for renewable energy supply chains that emphasizes green capabilities, highlighting how sustainability criteria in this sector extend beyond those typically applied in conventional industries.
The selection of a supplier is a critical decision taken by organizations in their supply chain. As the complexity of factors affecting supplier performance increases, problems related to evaluation increase in proportion. Various criteria can be used to evaluate comprehensive supplier performance. Managers should analyze and record important criteria and strive to convert qualitative and subjective criteria into comparable operational criteria.
As mentioned previously, most traditional supply chain management literature focuses on cost-based criteria. However, in recent literature, different aspects have also been considered in the supply chain sustainability context. In the literature, various supplier evaluation methods, including linear weighting, integer programing, hierarchical process analysis, linear programing models, matrix analysis, clustering, cost ownership analysis, statistical analysis, and neural networks, have been used (Linton et al., 2007). These methods have been developed to cover several facets of suppliers and used different methods to prioritize suppliers in the supplier selection phase. Most of these methods are based on considering multiple aspects of suppliers that have strengths and weaknesses. The absence of revealing the interrelationships between supplier selection criteria and their importance must be regarded as a deficiency of the methods mentioned above. We argue that there is a clear need to develop the methods discussed in this paper to provide a solution to this limitation.
Among the traditional research on selecting suppliers, attention to sustainability factors, especially environmental and social aspects, has been given less attention (Tozkapan et al., 2003). Managing the process of evaluating and selecting suppliers is crucial for achieving sustainability.
Recent studies have shown that selecting the right supplier and managing it, is a means to increase the competitiveness of the supply chain (Lee et al., 2001). Generally, selecting a supplier can be divided into two types (Awasthi et al., 2009); (1) selecting a supplier when there is no limitation; In other words, each supplier can independently meet a buyer’s requirements, such as quantity, quality, delivery time, etc. (2) selecting a supplier when there are limitations in the capacity of the supplier, the quality of the supplier’s product, etc. In other words, a single supplier cannot satisfy all buyers’ requirements.
In the first case, a single supplier can satisfy all buyers’ requirements (single sourcing). In this case, management only should decide which supplier is the best. In contrast, in the second case, no single supplier can meet all buyer requirements. Therefore, more than one supplier must be selected (multiple sourcing). In this case, management must make two decisions: which suppliers are the best? And how much should be purchased from each of the selected suppliers (Chaabane et al., 2012).
Many international companies have established environmental regulations for their suppliers. Also, in long-term contracts and joint investments between foreign companies and domestic suppliers, it is important to adopt strong environmental policies (Mozdgir et al., 2013; Govindan et al., 2013; Nasrollahi et al., 2021a, b, c). Therefore, organizations strive to manage environmental issues and challenges and their combination with other competitive factors, such as quality, time, and price (Nasrollahi et al., 2020a, b).
When selecting a supplier, it is important to identify suitable criteria for decision-making (Ramezani et al., 2019), while the criteria for evaluating sustainable suppliers should be easily measurable. This growing body of literature underscores the need for biomass-specific supplier selection frameworks that address the unique characteristics of this industry. However, existing research has not adequately integrated the complete spectrum of sustainability dimensions into a comprehensive modeling approach specifically tailored for biomass energy supply chains. Our study addresses this gap by developing an integrated framework that captures the full range of economic, environmental, and social criteria relevant to this unique industry context.
3. Research methodology
The aim of this study is to model and weight the sustainable supplier selection criteria (SSSC) for biomass energy supply. Thus, this research is developmental and has an applied objective. This study aims to expand and develop existing models in the field of sustainable supplier selection and to consider selection criteria that have received less attention in previous research (Karimi Zarchi et al., 2019). Additionally, this study employs a survey research design because it describes and interprets existing relationships between influential criteria through data collection.
This research incorporates an expert survey, considering the structural, contextual, and environmental differences between the studied industry and other industries (Nasrollahi et al., 2020a, b). The selection of appropriate experts is crucial for the validity and reliability of this research. A total of 12 experts participated in this study, carefully chosen based on their extensive knowledge and experience in the biomass energy supply chain industry. The expert panel was composed of:
Four senior managers from biomass energy production companies with an average of 15 years of experience in biomass supply chain management. These individuals have direct responsibility for supplier selection and evaluation in their respective organizations.
Three academics specializing in sustainable supply chain management with specific research focus on renewable energy supply chains. All academic experts hold Ph.D. degrees in relevant fields and have published extensively on sustainable supplier selection.
Three environmental consultants with expertise in biomass energy systems and environmental impact assessment, each with more than 10 years of experience in evaluating the environmental performance of biomass operations.
Two policy advisors who have been involved in developing sustainability standards for the biomass energy industry at national and international levels.
The experts were selected based on the following criteria:
Minimum of 10 years of professional experience in positions related to biomass energy supply chains;
Demonstrated expertise in sustainable supplier management through publications, professional certifications, or organizational roles;
Familiarity with multi-criteria decision-making methods; and
Representation across different stakeholder perspectives.
The diversity of the expert panel was intentionally designed to capture various perspectives on sustainable supplier selection criteria, ensuring that economic, environmental, and social dimensions are adequately represented. All experts were contacted via email and provided with detailed information about the research objectives before agreeing to participate.
This research proposes a framework for sustainable supplier selection in biomass energy supply using meta-synthesis, FDM, and SISMW. This approach is implemented in three steps. In the first step, the meta-synthesis method was used to study and systematically review previous research to identify effective criteria for sustainable supplier selection in biomass energy supply. Meta-synthesis is a method that evaluates another research. Sandelowski and Barroso presented a seven-step model for this purpose, which includes setting the research question, conducting a systematic review of scientific texts, searching for relevant papers, extracting information from papers, analyzing and combining qualitative findings, performing quality control, and presenting the findings (Ludvigsen et al., 2016).
In the second step, the criteria extracted from the literature review were screened using an expert survey with the Fuzzy Delphi Method. FDM is a collaborative method to collect expert opinions in a specific field. In this method, a group of experts in a specific field are selected as panel members and present their opinions on a specific issue using fuzzy concepts (Lianto, 2023). FDM was implemented in two rounds.
Finally, in the third step, modeling and weighting of the identified criteria were conducted using SISMW. SISMW is one of the latest MCDM techniques introduced by Nasrollahi et al. (2022), and it is suitable for analyzing complex problems with hierarchical and communicative structures. Figure 1 shows the proposed approach.
The SISMW method was selected for this study due to several distinct advantages it offers over other MCDM techniques such as DEMATEL. While SISMW shares some similarities with DEMATEL in its ability to model the relationships between criteria in a complex system, it offers significant enhancements that make it particularly suitable for our research context. First, unlike DEMATEL which only focuses on the cause-effect relationships between criteria, SISMW enables simultaneous weighting and modeling of the criteria, providing a more comprehensive analysis in a single integrated framework. This is particularly important in the biomass energy supply chain where understanding both the significance (weight) and impact (relationships) of sustainability criteria is crucial for proper supplier evaluation. Second, SISMW's integrated approach reduces the complexity of the decision-making process by eliminating the need to combine multiple techniques, as is often done when using modeling techniques in conjunction with other weighting methods. This integration enhances methodological consistency and reduces potential errors from combining different approaches.
This study considers multiple types of biomass feedstock that are commonly used in energy production, as the type of biomass significantly impacts supplier selection criteria and their relative importance. The biomass feedstock types considered in our analysis include agricultural residues, forestry residues, energy crops, municipal organic waste, and industrial organic byproducts. The diversity of these biomass types necessitates different supplier capabilities and evaluation criteria. For example suppliers of agricultural residues must demonstrate effective collection logistics from dispersed sources and seasonal storage capabilities. Or, forestry residue suppliers must provide evidence of sustainable forestry practices and specialized transportation solutions for bulky materials. This study accounts for these variations by incorporating feedstock-specific considerations into the supplier selection framework, allowing the model to be applied across different biomass supply contexts while recognizing the unique requirements of each feedstock type.
4. Results
In order to identify effective criteria for choosing sustainable suppliers, the closest and most relevant studies were selected using a targeted approach. In the process of this research, 56 studies were selected in which the effective criteria for choosing a sustainable supplier were directly discussed. Figure 2 shows the process of removing irrelevant papers.
To check the validity of the studies used in this research, Glynn's critical tool was used, which can be used to evaluate all applied research projects. Such a tool develops the necessary skills to evaluate, read, and write articles. A tool called the Critical Assessment Skills Program (CASP) was used to evaluate primary studies. The questions of this tool are divided into 16 factors, including research objectives, method logic, research design, sampling method, data collection, reflectivity, ethical considerations, accuracy of data analysis, clear and clear statement of findings, and research value (Zeiler et al., 2022). Based on a 50-point scale, articles whose total score was less than 30 (below good) were not accepted.
After carrying out the seven-step meta-synthesis process and studying 56 previous studies, 26 effective criteria for choosing a sustainable supplier were identified in the three dimensions of economic, social, and environmental criteria. Table 1 shows the evaluation criteria of sustainable suppliers.
Although the criteria presented in Table 1 have been extracted from the literature, previous research has not taken into account the differences between the criteria of different industries. Therefore, after the initial identification of criteria, to measure and determine the final criteria among those identified, a five-option questionnaire based on the Fuzzy Delphi Method (FDM) was designed and distributed among 12 experts. In this method, each expert evaluates each criterion separately and personally. The results of the screening showed that among the 26 criteria identified in the three categories (economic, social, and environmental), 18 criteria were confirmed using the opinions of experts. A part of the FDM calculations is presented in Tables 2 and 3.
Table 4 lists the final Sustainable Supplier Selection Criteria (SSSC) for the biomass energy supply industry, and Figure 3 also presents the hierarchical model of SSSC.
In the third step, the SISMW method was used to perform simultaneous modeling and weighting of effective criteria for sustainable supplier selection. ISM only determines the relationships between criteria and cannot determine their importance. In addition to modeling, SISMW can determine the weight of criteria without the need for additional data.
Once the criteria are identified, it is necessary to determine the contextual relationships between the criteria to develop the Structural Self-Interaction Matrix (SSIM). In total, 12 experts were selected to provide their expert views. Table 5 presents the SSIM matrix.
The initial reachability matrix was derived from the SSIM (Table 6). It contains the relationships between criteria in binary form.
From the above matrix, a final reachability matrix (Table 7) is constructed, taking into account the transitivity rule, which states that if variable A is related to B and B is related to C, then A is necessarily related to C.
Table 8 presents the remaining calculations based on the SISMW process (Nasrollahi et al., 2022).
The reachability matrix was partitioned into different levels through successive iterations. For this purpose, the reachability and antecedent sets of each criterion were determined. The reachability set of a criterion consists of itself and all other criteria influenced by it, whereas the antecedent set of a criterion consists of itself and all the criteria which influence it. The intersection of these sets is derived from all the criteria. Using the procedure for partitioning the reachability matrix at different levels, the hierarchy of each criterion has been set. The ISM model presented in Figure 4 was constructed by using the final reachability matrix (Table 7) and the hierarchical level of criteria.
MICMAC analysis was used to classify the criteria based on their driving power and dependence. The criteria are classified into four clusters namely autonomous, dependent, linkage, and independent, as shown in Figure 5. Autonomous variables have weak dependence and driving power. They are relatively isolated from the system. Criteria EC1, SO1, SO2, SO5, and EN3 form this cluster. The variables in the dependent cluster have weak driving power but are strongly dependent on other factors to drive them. Criteria EC2, EC3, EC6, EC7, EN2, and EN5 form this cluster. Linkage variables have high dependence and high driving power. This cluster contains no variables. Finally, the variables in the independent cluster have strong drive power and weak dependence. Criteria EC4, EC5, SO3, SO4, SO6, EN1, and EN4 fall into this cluster.
5. Discussion and implications
This study aimed to identify, model, and weigh the criteria for selecting sustainable suppliers in the biomass energy supply chain. Through a comprehensive methodology combining meta-synthesis, fuzzy Delphi method, and SISMW, we identified 18 final criteria across economic, social, and environmental dimensions and analyzed their interrelationships and relative importance.
The results indicate that five criteria are of paramount importance in selecting sustainable suppliers in the biomass energy industry: organization and management (EC5), corporate social responsibility (SO3), environmental management system (EN4), organizational culture (SO4), and technology/equipment (EC4). These findings demonstrate that beyond traditional economic considerations, social and environmental criteria play crucial roles in sustainable supplier selection for biomass energy supply chains.
One of the study's most significant insights is the interactive and overlapping effects among the three dimensions of sustainability. The complex interrelationships revealed through our SISMW analysis show that sustainability dimensions cannot be considered in isolation. For instance, suppliers with strong environmental management systems typically demonstrate better long-term economic performance through resource efficiency and risk reduction. Similarly, suppliers with robust corporate social responsibility programs often exhibit stronger organizational management capabilities that enhance overall operational performance.
The ISM model (Figure 4) visually represents these complex relationships, positioning foundational criteria such as organization and management (EC5), corporate social responsibility (SO3), and environmental management system (EN4) at the lower level. These criteria exert causal influence on other aspects of supplier performance, serving as building blocks for sustainable supply chain management in the biomass energy sector.
The MICMAC analysis (Figure 5) provides further insights by classifying criteria based on their dependency and driving powers. Most criteria fall into either independent or dependent clusters. Independent criteria such as EC5, SO3, SO4, and EN4 possess high influence with low dependency, making them strategic leverage points for decision-makers. These criteria can trigger cascading improvements across the supplier evaluation framework. Conversely, dependent criteria like EC2, EC3, EC6, and EC7 are heavily influenced by other factors and should be viewed as outcomes rather than drivers of sustainable performance.
A comparison of this study’s results with previous research indicates that the importance of social and environmental criteria in selecting sustainable suppliers in the biomass energy industry is greater than in other industries. For example, in the study by Memari et al. (2019), economic criteria such as quality and delivery were given higher priority, whereas in this study, social and environmental criteria are more important. This difference may be due to the specific nature of the biomass energy industry and its greater emphasis on sustainability. Another noteworthy finding of this study is the high importance placed on the organizational culture criterion (SO4), which is ranked fourth. This result is consistent with Naibor and Moronge (2018), who emphasized the importance of cultural compatibility between buyers and suppliers. In the biomass energy industry, cultural alignment can improve cooperation and communication between companies and lead to better supply chain performance. The positioning of the technology/equipment criterion (EC4) in fifth place is another significant finding, highlighting the importance of innovation and the use of advanced technology in this industry. This finding aligns with the results of Hiloidhari et al. (2023), who emphasized the role of technology in improving the efficiency and sustainability of biomass supply chain.
Our findings offer several practical applications for managers and decision-makers in the biomass energy industry. First, organizations can develop prioritized supplier evaluation frameworks that give greater weight to the high-impact criteria identified in our study. For instance, the significant importance of environmental management systems suggests that biomass companies should establish clear minimum requirements in this area during initial supplier screening.
Second, our identification of causal criteria with high influence on other aspects of sustainability enables the design of targeted supplier development initiatives. By focusing improvement efforts on high-leverage criteria such as technology and equipment, organizations can achieve cascading benefits across multiple sustainability dimensions. This approach optimizes resource allocation by concentrating development efforts where they will generate the greatest system-wide improvements.
Third, the interconnected nature of criteria revealed in our model enables more sophisticated risk assessment. Understanding that organizational management practices significantly influence several other criteria allows companies to assess upstream risks in their supply chain with greater precision. For example, weak organizational management at the supplier level can signal potential future issues across operational, environmental, and social dimensions.
Fourth, our results provide guidance for designing comprehensive key performance indicators (KPIs) for ongoing supplier monitoring. The weighted criteria can be translated into a balanced scorecard approach that appropriately emphasizes the most impactful sustainability factors, enabling more effective performance management throughout the supplier relationship lifecycle.
Based on these applications, we propose several specific measures for biomass energy companies:
Implementation of biomass-specific certification requirements tailored to address the unique environmental impacts of different feedstock types, such as agricultural residues, forestry biomass, or energy crops.
Development of collaborative technology advancement programs with suppliers to improve feedstock quality consistency, reduce moisture content variability, and enhance preprocessing capabilities through technologies such as torrefaction, pelletization, or advanced moisture monitoring.
Establishment of local economic development initiatives that enhance community benefits from biomass supply chains, including processing hubs in rural areas, technical training programs, and long-term contracts that provide income stability for biomass producers.
Creation of transparent carbon accounting systems that track emissions throughout the supply chain, addressing both direct emissions from transportation and processing and indirect impacts related to land use change and carbon stock dynamics.
Development of integrated quality-sustainability assessment protocols that simultaneously evaluate physical biomass characteristics alongside sustainability parameters, helping suppliers understand how quality improvements can enhance overall sustainability performance.
Implementation of digital traceability systems using technologies such as blockchain to create immutable records of sustainability practices from biomass production through conversion, addressing the complex interactions between criteria identified in our model.
Establishment of multi-tier supplier engagement programs that look beyond immediate suppliers to understand sustainability impacts throughout the supply network, including landowners, harvesting contractors, and transportation providers.
While this research provides valuable insights, several limitations should be acknowledged. First, our expert panel, while diverse, had stronger representation from certain geographic regions, potentially introducing bias toward specific regulatory contexts. This geographic scope limitation may affect the generalizability of our findings to all biomass supply chain contexts globally. Future research should include expert panels specifically designed to represent diverse geographic regions, particularly including developing economies where biomass supply chains may face different sustainability challenges.
Second, while our study considered multiple biomass feedstock types, some specific feedstock types may have unique sustainability considerations not fully captured in our model. The breadth of potential biomass sources means that specialized criteria relevant to emerging feedstocks such as algae or novel energy crops might be underrepresented. Future studies should develop feedstock-specific adaptations of our model with targeted expert panels for each major biomass category.
Third, the static nature of our modeling approach does not fully capture how criteria weights and relationships may evolve over time as technologies, regulations, and market conditions change. Longitudinal studies tracking the evolution of sustainability criteria importance would provide valuable insights into the dynamic nature of supplier selection in this rapidly evolving field.
Fourth, while our model is based on expert judgments, it lacks empirical validation against quantitative performance data from actual biomass supply chains. Future research should conduct case studies applying our model in real-world supplier selection scenarios, measuring the actual sustainability outcomes achieved when suppliers are selected based on our prioritized criteria.
Finally, all MCDM approaches have inherent limitations in capturing the full complexity of real-world decision-making contexts. Future studies employing mixed-method approaches combining MCDM techniques with qualitative case studies, simulation modeling, or data-driven approaches could provide more comprehensive insights into sustainable supplier selection for biomass energy supply chains.
Overall, this research contributes to both theory and practice by providing a comprehensive framework for sustainable supplier selection in the biomass energy supply chain. By identifying, modeling, and weighting the most critical criteria, we offer guidance for industry practitioners seeking to enhance the sustainability of their supplier relationships while providing a foundation for future research in this important and evolving field.
