Volume 13, Issue 3 (Summer 2024)                   Arch Hyg Sci 2024, 13(3): 129-138 | Back to browse issues page

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Asadi-Ghalhari M, Karimi B, Soltanzadeh A, Namdari S. Development and Evaluation of Green Industry Indicators for the Effective Implementation of Green Supply Chain Management (GSCM). Arch Hyg Sci 2024; 13 (3) :129-138
URL: http://jhygiene.muq.ac.ir/article-1-742-en.html
Development and Evaluation of Green Industry Indicators for the Effective Implementation of Green Supply Chain Management (GSCM)
Mahdi Asadi-Ghalhari1 , Behrooz Karimi2 , Ahmad Soltanzadeh3 , Saeed Namdari *4
1- Research Center for Environmental Pollutants, Qom University of Medical Sciences, Qom, Iran
2- Department of Environmental Health Engineering, Arak University of Medical Sciences, Arak, Iran
3- Department of Occupational Health & Safety Engineering, Research Center for Environmental Pollutants, Faculty of Health, Qom University of Medical Sciences, Qom, Iran
4- Department of Environmental Health Engineering, Faculty of Health, Kurdistan University of Medical Sciences, Sanandaj, Iran
Keywords: Air pollution, Controlled, Environment, Fuzzy delphi technique, Fuzzy analytic hierarchy process, Green industry, Green supply chain management (GSCM), Indoor prevention and control
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  1. Introduction
Climate change is one of the most important and urgent issues related to the Sustainable Development Goals (SDGs) with profound impacts on the future of the environment and human life [1]. The industrial sector plays a pivotal role in driving economic growth and development. However, it is also a major contributor to environmental pollution, with industrial activities often leading to contamination of air, water, and land. These pollutants pose serious risks to both the environment and human health, causing long-term consequences for public well-being and quality of life. As a result, environmental protection and pollution control have become key priorities for scientific communities, policymakers, and environmental advocates [2]. The escalating threat of global environmental degradation, fueled by unsustainable industrial practices, has raised concerns about the potential consequences of economic growth that exceeds the Earth’s ecological capacity.
To address these challenges, it is crucial to reconcile economic development with environmental sustainability. Achieving this balance requires a shift toward more sustainable industrial practices. International organizations and developed nations have introduced initiatives such as green growth, sustainable industrial transformation, and green industrial policies to encourage environmentally responsible practices [3,4]. The United Nations Industrial Development Organization highlights the importance of green development as a strategy to overcome the limitations of traditional economic models, which often fail to account for resource depletion and environmental damage [5,6]. The green industry focuses on improving energy and raw material efficiency, promoting innovation through green technologies, and adopting sustainable production methods that address both social and environmental issues.
Although the transition to a green economy is a global priority, defining and evaluating green industries, products, and jobs remains a complex task [7]. To assess the effectiveness of green industries, it is essential to establish clear standardized definitions and performance metrics. Such frameworks would allow stakeholders to monitor progress in areas such as sales, employment, and other key indicators [8]. Researchers have made significant advances in conceptualizing green supply chain management (GSCM) through various indices. These indices aim to support the implementation of greening initiatives. However, many existing indices are limited in scope and fail to account for the unique needs of different regions [9]. This gap calls for further research to refine these tools and develop new regionally adapted approaches to assessing green industry practices [10].
The present study aims to address these gaps by developing a comprehensive evaluation framework for green industries. It will focus on areas such as pollution prevention, resource conservation, and public health protection. Unlike traditional models that focus primarily on pollution output, this research proposes a more holistic approach that includes environmental education, waste management, and ecosystem preservation. Current evaluation tools, such as those used by the Environmental Protection Organization, primarily emphasize pollution output and overlook other critical aspects of green industry performance. Therefore, this study aims to introduce a broader set of indicators that can better capture the multifaceted nature of green industries, which will contribute to the advancement of GSCM practices.
Given the limitations of existing evaluation tools, there is a clear need for further research to develop a robust and reliable instrument that encompasses a full range of green industry practices.
This study is among the first to comprehensively propose green industry indicators for the implementation of GSCM in industries, providing valuable insights that could significantly contribute to the successful implementation of GSCM across industries.
  1. Materials and Methods
This study employs a descriptive-analytical research design, categorized as applied research due to its specific objectives. The statistical population consists of a panel of experts selected through snowball sampling, all of whom possess specialized knowledge in environmental health and green industries. These experts were selected based on their expertise and experience. Data collection involved a blend of library research, surveys, and expert interviews. For this purpose, questionnaires were developed using the Fuzzy Delphi Technique (FDT) and the Fuzzy Analytic Hierarchy Process (FAHP). The collected data were then analyzed using Excel and Google Docs software.
This study primarily aimed to identify and prioritize the indicators of green industries through the application of the FDT and FAHP. An extensive literature review was conducted to identify relevant indicators of greenness in the supply chain. This review guided the research process through 10 distinct stages (Figure 1).
 


Figure 1. Flowchart of the Study using the Fuzzy Delphi Technique (FDT) and Fuzzy Analytic Hierarchy Process (FAHP)
 
Initially, the research team developed the evaluation tool based on a thorough review of the existing literature. The tool incorporated 12 key indicators, as follows:
Public health protection
Personnel health protection
Ecosystem protection
Water management
Sewage production management
Waste production management
Air pollution prevention
Energy consumption management
Management of raw material consumption and product production
Control of noise pollution
Prevention of pollution from accidents and disasters
Management and investment in environmental pollution control
This list was then presented to a panel of 24 experts with academic qualifications ranging from bachelor’s to Ph.D. in disciplines related to environmental health. These experts included professionals in management, environmental engineering, occupational health engineering, and the Health, Safety, and Environment (HSE) fields. They were asked to assess the importance and desirability of these indicators for evaluating green industries and to suggest any additional indicators that could be considered.
The indicators that received the highest scores were selected using the FDT. Subsequently, the weight and prioritization of these indicators were determined using the FAHP [11,12].

Fuzzy Delphi Technique (FDT)

The Fuzzy Delphi method was used to develop the green industries evaluation tool. This method is a group knowledge acquisition technique widely used in decision-making, particularly for quality-related issues. The Delphi method follows a structured process in successive rounds to gather information, aiming for consensus among participants. It assumes that the participants have extensive knowledge and understanding of the subject matter [13,14].

Fuzzy Analytic Hierarchy Process (FAHP)

Multiple criteria decision-making (MCDM) is a selective model used for evaluation of many complex decisions [15, 16]. Decision-making encompasses the process of identifying and selecting options based on evaluations conducted by decision-makers. Empirical evidence indicates that group decisions tend to exhibit higher levels of objectivity and efficacy compared to individual decisions. Consequently, group decision-making entails the integration of decisions from diverse individuals to collectively address specific issues. Central to the decision-making process is the analysis of a set of options characterized by evaluative criteria. These alternatives necessitate prioritization to determine the optimal selection or assess the relative importance of each alternative [17]. The Fuzzy Analytic Hierarchy Process, based on AHP under fuzzy environment, is one of the most robust and flexible MCDM tools in the evaluation procedure [16,18]. In 1980, a simple AHP method was introduced based on a ratio scale [16,19,20]. The method was commonly applied in previous studies with advantages such as its simple and flexible model with a wide range of usages [21,22]. The disadvantages of the AHP method include the uncertainty and ambiguity in expressing opinions, as the method depends on the decision maker’s knowledge and experiences during the decision-making process. Moreover, among other factors, the AHP method does not contain feedback loops [23]. To address these concerns, fuzzy logic principles were incorporated into AHP in this study [24].
The fuzzy set was first developed by Zadeh in 1965 (19) and combined with Saaty’s priority theory to reduce human ambiguity [25,26]. Later, the FAHP was further developed to overcome the uncertainty and ambiguity of criteria weights in deterministic and inflexible classifications [27]. Using the FAHP can provide a fuzzy number interval judgment values rather than fixed or exact values (16). This approach reduces uncertainty in assigned relative weight. As a result, the FAHP has been successfully used in many actual decision situation making [28,29].
The first task of the FAHP method is to decide on the relative importance of each pair of factors in the same hierarchy. By using triangular fuzzy numbers, via pairwise comparison, the fuzzy evaluation matrix A = (aij)n×m is constructed [30].
Buckley’s enhanced FAHP method, specifically the geometric mean method, was used to aggregate the views of multiple experts [31,32]. This approach enabled the calculation of relative weights for the main indicators and their sub-indicators, allowing for the identification of the most significant factors.

FAHP Process
Fuzzy Pairwise Comparison Matrix: Data were initially input into a pairwise comparison matrix, where fuzzy numbers were used to represent pairwise comparisons.
Aggregation of Expert Opinions: If multiple experts participated, their opinions were aggregated using the fuzzy geometric mean [33].
Consistency Check: After pairwise comparisons, the consistency of the matrix was verified using the Gogus and Boucher method. A consistency rate of 0.1 or lower was deemed acceptable to ensure the reliability of expert responses [20,34,35].
Initial Weight Calculation (Fuzzy Expansion): The initial weight of each element was calculated using the fuzzy geometric mean.
Normalization: The vector of geometric means was normalized by dividing each element by the sum of all elements in the vector.
Defuzzification: The fuzzy weights were defuzzified into non-fuzzy values using the formula:  
Finally, the defuzzied weights were normalized and used as the final weights to prioritize the indicators.
This process enhances the reliability of the results, and the use of fuzzy methods addresses the inherent uncertainties in expert judgment and decision-making.
  1. Results
Results of the Fuzzy Delphi Technique (FDT)
The structured questionnaire was administered to the panel of experts (n=24). Descriptive statistical analysis of the demographic characteristics revealed that 16.7% of the panel members were female, while 83.3% were male. Regarding their academic qualifications, 25% held a bachelor's degree, 37.5% held a master's degree, and the remaining 37.5% held a doctoral degree. The distribution of academic fields among the experts was as follows: 33.3% in Environmental Health Engineering, 37.5% in Occupational Health Engineering, 16.7% in HSE, and 12.5% in Environmental Studies.
To implement the FDT, a set of linguistic expressions corresponding to different levels of importance—ranging from very unimportant to very important—was employed. These expressions were represented using triangular fuzzy numbers within a 5-point Likert scale for the designed questionnaire.

Results of the Fuzzy Analytic Hierarchy Process (FAHP)
In this study, linguistic expressions based on fuzzy logic (triangular fuzzy numbers) and a 5-point Likert scale, as detailed in Table 1, were employed to apply the FAHP methodology [36].
 

Table 1. Linguistic Expressions and Corresponding Triangular Fuzzy Numbers Based on the 5-Point Likert Scale used in the Fuzzy Analytic Hierarchy Process (FAHP) Method
Language expressions triangular fuzzy numbers
L M U
Very unimportant 1 1 1
Unimportant 1 3 5
Medium importance 3 5 7
Important 5 7 9
Very important 7 9 9
 
Upon identifying the relevant indicators and constructing the hierarchical tree, pairwise comparisons between the criteria were carried out. To ensure the logical consistency of the judgments, the compatibility of the pairwise comparisons was thoroughly examined.
The outcomes of the pairwise comparison matrices, along with the definitive weights of each indicator and the consistency check, are provided in figures 2-4.
The results for determining the weight and significance of the primary indicators for evaluating green industries, based on the FAHP method, are presented in Figure 5. The inconsistency rate in the pairwise comparison matrix was found to be zero, well below the acceptable threshold of 0.1. This indicates that the pairwise comparisons are consistent, thereby permitting the continuation of the FAHP process without necessitating any revisions to the pairwise comparisons (Figure 6).
 


Figure 2. Prioritization of Sub-Indicators Based on the Fuzzy Analytic Hierarchy Process (FAHP) Method. The indicators include: (a1) Maintaining Public Health, (a2) Maintaining Personnel Health, (a3) Maintaining the Ecosystem, and (a4) Water Management

Figure 3. Prioritization of Sub-Indices Based on the Fuzzy Analytic Hierarchy Process (FAHP) Method. The indices include: (a1) Management of Wastewater Production, (a2) Management of Waste Production, (a3) Prevention of Air Pollution, and (a4) Management of Energy Consumption


Figure 4. Prioritization of Sub-Indices Based on the Fuzzy Analytic Hierarchy Process (FAHP) Method. The indices include: (a1) Management of Raw Material Consumption and Product Production, (a2) Control of Noise Pollution, (a3) Prevention of Pollution Caused by Accidents and Disasters, and (a4) Management and Total Investment to Control Environmental Pollution



Figure 5. Main indicators for evaluating green industries and the definitive weights obtained using the Fuzzy Analytic Hierarchy Process (FAHP) Method
Figure 1. Fuzzy Analytic Hierarchy Process (FAHP) structure for green industries evaluation indicators
 
  1. Discussion
The findings from the FDT highlight the systematic and rigorous process undertaken by the study team in developing a comprehensive set of indicators. The approach included 12 primary indicators and 120 sub-indicators, all of which were carefully reviewed by 24 experts. This led to substantial revisions, ultimately resulting in a set of indicators that met the required thresholds for inclusion in the final model. Two additional sub-indicators were incorporated into the model after this review. The FAHP was then employed to prioritize these indicators. In addition, the threshold value in the FDT was considered to be 0.65.
In decision-making contexts, FAHP plays a crucial role in synthesizing expert knowledge and addressing the complexities of prioritizing competing criteria. It has been widely recognized for its flexibility, high content validity, and interdisciplinary applicability, particularly in environmental decision-making. The FAHP is an ideal tool for evaluating green industry practices, especially for assessing their environmental impact. However, despite its strengths, the method must be applied carefully, as expert judgment can lead to inconsistent rankings, particularly when dealing with complex and multifaceted issues such as green industry development. The subjectivity inherent in the process underscores the need for further refinement and validation of results, ensuring that the final model reflects the best possible consensus among experts.
Public health is a key focus area in green industry practices. The prioritization of sub-indicators in this category revealed that maintaining an appropriate distance between industrial sites and residential areas is one of the most significant factors. This underscores the importance of spatial planning and industrial zoning in reducing health risks to nearby communities. Urbanization and industrial expansion often pose challenges in maintaining such distances, particularly in rapidly growing cities where land use is contested. Additionally, the study revised the sub-indicator regarding health attachment programs to improve clarity, ensuring that preventive health measures are integrated effectively before and during industrial development. This revision aligns with best practices for public health protection. However, balancing the need for industrial growth with optimal health protection strategies remains a significant challenge, especially in urban environments experiencing accelerated development.
The study also identified personnel health as a critical area. The highest-ranked sub-indicator in this category was “attention to the physical work environment,” emphasizing the importance of creating safe, health-promoting workplaces. This aligns with the concept of green human resource management, which has gained significant attention in recent years. The study further advanced this concept by introducing 13 sub-indicators that cover all dimensions of personnel health, making it one of the most comprehensive assessments of this issue. While the sub-indicators addressed physical health concerns, they did not fully account for psychological and emotional health, which is an increasingly important factor in today’s workplace environments. The inclusion of mental health considerations would strengthen the comprehensiveness of the assessment, reflecting the growing recognition of the psychological aspects of employee well-being.
In the area of ecosystem preservation, the study highlighted the importance of conducting environmental impact assessments (EIAs) before and after industrial construction. This is critical for evaluating the environmental consequences of industrial projects and ensuring the long-term health of ecosystems. However, the implementation of EIAs can be challenging due to varying regulatory standards across regions and industries [37]. The study also emphasized the importance of maintaining adequate distances between industrial sites and water sources, particularly in water-scarce regions. Such measures are essential for protecting vital water resources, but economic considerations often complicate their implementation. Industries facing pressure to expand may view these measures as a barrier to growth, despite their clear environmental benefits [38].
In wastewater management, the highest-priority sub-indicator was compliance with national and local environmental regulations. Adherence to these regulatory standards helps minimize wastewater production and supports broader resource conservation goals, especially in areas experiencing water shortages. However, compliance can be inconsistent across industries, particularly in regions with less stringent enforcement. Inconsistent application of regulations can undermine efforts to reduce wastewater pollution, highlighting the need for stronger regulatory oversight and enforcement mechanisms to ensure consistent industry practices.
Waste management emerged as another priority, particularly the adoption of safe and reliable disposal methods to prevent environmental harm. The study also recommended reducing waste production by utilizing eco-friendly raw materials and promoting recycling. This recommendation aligns with global sustainability goals, as minimizing waste production is critical for reducing environmental impact. However, the transition to such practices may face resistance due to the higher initial costs associated with eco-friendly materials and technologies, which can pose challenges, particularly for smaller industries operating on tight budgets.
Air pollution control was identified as a critical issue in industrial green practices. The study recommended the implementation of advanced pollution control technologies, such as switching to cleaner fuels and adopting cleaner production processes. These measures are essential for preventing industrial emissions from exceeding the environment’s natural self-purification capacity. Nevertheless, the adoption of such technologies requires significant financial investment and technical expertise, which may be difficult for smaller companies or industries in developing regions to afford. This underscores the need for targeted financial and technical support to help industries transition to cleaner technologies.
Energy consumption management was another significant focus area. The study emphasized the importance of transitioning to renewable energy sources and adopting energy-efficient process designs. These measures are crucial for reducing the environmental footprint of industrial operations and enhancing their sustainability. However, the shift to renewable energy can be hindered by a lack of infrastructure and financial resources, particularly in regions where renewable energy markets are still developing. Industries may require additional support to make this transition, particularly in regions where renewable energy infrastructure is limited.
The study also highlighted the reduction of material consumption and waste production as an important strategy for promoting sustainability. Using high-quality, environmentally friendly raw materials and equipment is essential for reducing waste and conserving resources. While this practice is vital for resource conservation, sourcing such materials at scale can be challenging, particularly in developing regions where access to sustainable materials is limited. This challenge must be addressed to ensure that industries can adopt sustainable practices without sacrificing economic viability.
Noise pollution control was identified as a critical area in industrial settings. The study stressed the importance of addressing noise at its source through engineering design and proper placement of sound-producing devices. Effective noise management can significantly reduce health risks associated with prolonged exposure to industrial noise, such as hearing loss and stress-related conditions. However, noise reduction technologies and zoning laws can be difficult to enforce consistently, particularly in areas with high industrial activity and limited regulatory oversight.
Pollution prevention in industrial accidents and disasters was another key focus of the study. The study emphasized the importance of training programs, emergency preparedness, and rapid response strategies to minimize the environmental and health impacts of industrial accidents. However, effective implementation of such programs can be constrained by budgetary limitations and lack of awareness in certain industries, highlighting the need for better industry-wide education and investment in safety and disaster preparedness.
Environmental management and investment were also prioritized in the study, with a strong focus on the need for managerial commitment and financial investment to implement environmental protection measures. The study also recommended reducing transportation costs through eco-friendly methods to further conserve resources. However, balancing environmental goals with economic constraints remains a significant challenge, especially in industries with tight profit margins. Financial incentives and support mechanisms are crucial to ensuring that green practices are adopted without compromising economic viability.
The overall prioritization of green industry indicators revealed that preventing air pollution and managing water resources were the highest-priority indicators, followed closely by preserving public health and protecting ecosystems. These priorities reflect the most urgent environmental challenges faced by industrializing regions, where air and water pollution remain significant concerns. However, the implementation of these priorities may be complicated by regional disparities in industry practices, economic conditions, and regulatory environments.
This study represents one of the first comprehensive efforts to assess and prioritize green industry indicators, offering valuable insights for industries seeking to adopt greener practices. The findings provide a solid framework for industries to reduce environmental impacts, enhance public health protection, and contribute to SDGs. However, the study also highlights the need for further refinement and contextual adjustments to ensure that its recommendations are applicable across different industrial and geographical contexts. This further emphasizes the importance of tailored approaches to green industry development that consider local environmental, economic, and social conditions.
  1. Conclusion
This study aimed to identify and develop evaluation indicators for green industries through an extensive review of the literature and structured interviews with experts and specialists. These indicators are intended to facilitate the effective implementation of GSCM, a critical strategy for industries aiming to achieve a competitive advantage in the increasingly eco-conscious market. However, successful implementation of GSCM requires a collaborative, company-wide effort, where each member contributes to an interconnected and cohesive system.
The findings substantiate the robustness and reliability of the proposed indicators. All indicators and sub-indicators, derived through the application of the FDT, demonstrated crisp values exceeding the threshold of 0.65, confirming their relevance for guiding GSCM practices. Additionally, the FAHP results showed that all pairwise comparison matrices had consistency ratios below the accepted threshold of 0.1, demonstrating a high level of consistency and credibility. These indicators were validated by all expert participants, reflecting the research team’s rigorous and iterative approach. Experts from academia, industry, and government provided feedback through multiple rounds of refinement, leading to the development of a robust and comprehensive questionnaire.
A review of the existing literature highlights a significant gap in the development of comprehensive and systematic green industry indicator frameworks. Although some studies have addressed various aspects of green industry practices, few have proposed integrated and comprehensive systems for GSCM [39]. Moreover, the absence of a standardized framework for GSCM presents challenges for regulatory bodies responsible for formulating policies that balance environmental, social, and economic concerns [40]. This underscores the need for further research to broaden the scope and applicability of green industry indicator systems.
This study contributes to addressing this gap by providing one of the first comprehensive sets of green industry indicators designed specifically for the implementation of GSCM. The insights gained from this research provide a solid foundation for organizations seeking to adopt sustainable and environmentally responsible practices within their supply chains. Given the increasing pressure on industries to meet environmental sustainability goals, the framework developed in this study provides a critical tool for facilitating the transition to more sustainable industrial operations, thereby contributing to the broader objectives of sustainable development.

Acknowledgments
The present work was a student thesis supported by Qom University of Medical Sciences, and we would like to express our gratitude to all those who helped us in this research.

Authors’ Contribution
Mahdi Asadi-Ghalhari: Project administration, Conceptualization, Methodology, Writing Review & Editing. Behrooz Karimi: Investigation, Formal analysis, Software, Data Curation. Ahmad Soltanzadeh: Validation, Formal analysis. Saeed Namdari: Software, Formal analysis, Data Curation, Investigation, Resources, Writing the original draft.

Competing Interests
The authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Funding
No funding was received for conducting this study.
Ethical Approval
The present work was a student thesis supported by Qom University of Medical Sciences (number: IR.MUQ.REC.1400.194).

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Type of Study: Original Article | Subject: Environmental Health
Received: 2025/07/14 | Accepted: 2025/07/19 | Published: 2024/10/3
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