Green Supply Research Articles

Can digital inclusive finance promote agricultural green development?

Digital inclusive finance (DIF) provides new momentum for green agricultural development (AGD). This paper measured AGD with entropy weight TOPSIS in five dimensions, including resource conservation, environmental friendliness, ecological conservation, green supply, and economic growth. After that, it estimated the regional spillover effects and threshold impacts of DIF on AGD utilizing China's provincial panel data from 2011 to 2020. The paper shows that (1) DIF and AGD have such a U-shaped complex interrelationship; (2) the AGD is spatially impacted by DIF. The unique manifestation is that as DIF has increased, its effect on AGD has steadily changed from being direct to being indirect, and this effect has regional heterogeneity; and (3) in regions with higher levels of green technology innovation, better development of traditional finance, or relatively concentrated agricultural industries, DIF plays a more prominent role in promoting the AGD.

A fuzzy multi-objective decision-making model for global green supplier selection and order allocation under quantity discounts

A fuzzy multi-objective decision-making model for global green supplier selection and order allocation under quantity discounts

Emerging trends and developments in green supplier management: A bibliometric review and analysis

Green supplier management (GSM) gained significant importance in addressing environmental concerns, promoting resource efficiency, and enhancing eco-efficiency within the green supply chain system. This study presents a systematic review to provide insights into the current research status and prospects in GSM literature. Results indicate that the research about GSM is gaining consistently growing attention over the past decades. However, there exists a regional imbalance in academic research, with a substantial portion of the authors originating from developing countries in China and India. The topics of green supplier selection and evaluation have received considerable attention in academia. In addition, the multi-attribute decision-making methods, such as TOPSIS, VIKOR, and AHP, and some mathematical modeling approaches have played a crucial role in the methodology employed for GSM research. As a fundamental algorithm in the artificial intelligence area, fuzzy sets theory has also been extensively employed in supplier selection and evaluation studies, whereas other big data analysis approaches have received little attention. Considering the inherent risks and uncertainties in the business strategy environment and developing more big data and artificial intelligence techniques represent promising avenues for future research in the field.

A NOVEL MODEL PROPOSAL FOR GREEN SUPPLIER SELECTION: AN APPLICATION IN THE RETAIL INDUSTRY

Today, good relations between companies and suppliers provide companies with a competitive advantage. Determining the supplier selection criteria and rewarding the suppliers by evaluating them at certain times is of great importance in establishing successful relations with the suppliers. For these reasons, in this study, it is aimed to determine and weight the criteria to be considered in the supply of products and fabrics in companies operating in the textile sector and to present the best results to green suppliers. In line with the determined purpose, studies were carried out in a retail company located in Istanbul from the textile sector. Supplier evaluation criteria were collected from the company and other studies in the literature and analyzed in two areas as main and sub-criteria. The evaluation of 34 different sub-criteria under the main criteria of cost, quality, delivery, flexibility, environmental management, technology and social was made by 3 experts. In the solutions of the evaluations, the best-worst method was preferred. The criteria weights determined according to the Best-Worst method were evaluated with the Weighted Aggregated Sum Product Assessment method and the ranking of 9 suppliers was revealed. Excel program was used during the analysis of the data.

Modified TODIM method based on cumulative prospect theory with Type-2 neutrosophic number for green supplier selection

Today’s consumers’ awareness of environmental protection and social morality is forcing companies to implement sustainable supply chains. Choosing a green supplier can not only determine the sustainable development of the supply chain, but also protect the environment and enhance the company’s reputation, in line with the background of green development. Green supplier selection is a multi-attribute decision-making (MADM) problem. Type-2 neutrosophic number (T2NN) can be compatible with more fuzzy information, and the green supplier selection problem also has a lot of fuzzy information, which makes T2NN very suitable for supplier selection. In this paper, a new method named CPT-TODIM-CDMT is proposed for green supplier selection of T2NN set based on TODIM. A new distance measure named cosine distance measure is proposed for T2NN set, and is applied in TODIM method. In this new method, attribute weight is determined by entropy method and optimized by CPT. The gains and losses preference in the CPT is also applied to the step of calculating the dominance degree in the TODIM method. A case study is used to verify the method. Through the comparison of the results of different decision-making models and the sensitivity research of the coefficients in the model, the correctness, validity and stability of the model are verified.

Comprehensive quantity discount model for dynamic green supplier selection and order allocation

We model and solve a deterministic multi-period single-product green supplier selection and order allocation problem in which the considered suppliers’ availability, cost, and green performance change from one period to another in the planning horizon. Moreover, the available suppliers may offer an all-unit or an incremental quantity discount (QD) scheme, resulting in three problem configurations. In one configuration, all suppliers offer all-unit QD. In the second, all suppliers offer incremental QD. In the third, some suppliers offer all-unit QD, and others offer incremental QD. The problem is modeled using a bi-objective integer linear programming formulation that maximizes the total green value of the purchased items from all the suppliers and minimizes their total corresponding cost, including the fixed cost, variable cost, inventory holding cost, and shortage cost. The proposed bi-objective model is scalarized and solved using the branch-and-cut algorithm and a population-based heuristic. A numerical analysis is conducted, which allows first to validate the heuristic approach using small-size instances by comparing its results with those of the exact approach. Moreover, an extensive comparison between the exact and heuristic solution approaches is carried out. The results reveal different findings. First, the economic and environmental solutions of an instance are different, and the environmental solution is independent of the suppliers’ pricing schemes. Second, the maximum difference between the heuristic approach and the exact approach in terms of the bi-objective function value is 4.72%, which makes the proposed heuristic recommended for large-size instances due to its short computation time and good accuracy. Third, there is no difference in terms of the heuristic performance between the combined model and the models with a single type of discount. Fourth, the all-unit discount scheme seems to be generally better in terms of the trade-off between the green value of purchasing and cost.

An integrated multi-criteria decision-making and multivariate analysis towards sustainable procurement with application in automotive industry

Green Supply Chain Management (GSCM) has emerged as a paramount issue in modern business organizations striving to become environmentally sustainable. Suppliers are pivotal in building a green supply chain. Green supplier selection (GSS) is a complex task involving several steps, from evaluation to final selection. This research aims to select spare parts suppliers of an automotive company based on their GSCM practices. Fourteen critical criteria are extracted from extant literature and refined through a Delphi study. The data was collected through interviews with industry experts using structured questionnaires. This study proposes integrated multi-criteria decision-making (MCDM) and multivariate analysis method with internal consistency checks. The Principal Component Analysis (PCA) is used to calculate criteria weights. A Simple Additive Weighting (SAW) method ranks the suppliers based on weighted criteria. The result shows that “collaboration with suppliers for green purchasing” is the most influential parameter for GSS. The outcome of this research may aid managers in selecting the most suitable green suppliers in the automotive industry by attaining sustainability. The proposed framework can be replicated to select suppliers in other industries.

Application of fuzzy group decision-making selecting green supplier: a case study of the manufacture of natural laurel soap

The selection of suppliers alone is one of the most important competitive challenges facing organizations today. With today's increasing awareness and facing many significant environmental pressures, the process of selecting the right suppliers for green supply chain management is even more difficult for decision-makers. The process of developing and implementing decision-making tools seek to face these challenges is rapidly evolving, especially in a fuzzy environment whose data are incomplete. The purpose of this article is to investigate prospective green chain suppliers for a laurel soap producing company based on a framework using MCDA fuzzy technologies such as Fuzzy GRA, Fuzzy TOPSIS, and Fuzzy VIKOR. During this paper, a discussion of fuzzy set was discussed with Fuzzy MCDM. Within a realistic case study, where a comparison was made between the tools, and it was noted that the results were consistent between the two tools Fuzzy TOPSIS and Fuzzy GRA, and the arrival of similar results and a slight difference with results was observed in Fuzzy VIKOR interpreted. The most detailed analyses were conducted on distance measurements, linguistic variables, assembly functions, and confusion processing. This study introduces the Fuzzy MCDM method to help researchers choose a more effective approach for green supplier selection and the conclusions and other study directions are offered toward the conclusion of the paper.

Pythagorean Fuzzy TOPSIS Method for Green Supplier Selection in the Food Industry

Pythagorean Fuzzy TOPSIS Method for Green Supplier Selection in the Food Industry

Integrating Fuzzy Rough Sets with LMAW and MABAC for Green Supplier Selection in Agribusiness

The evolving customer demands have significantly influenced the operational landscape of agricultural companies, including the transformation of their supply chains. As a response, many organizations are increasingly adopting green supply chain practices. This paper focuses on the initial step of selecting a green supplier, using the case study of the Semberka Company. The objective is to align the company with customer requirements and market trends. Expert decision making, grounded in linguistic values, was employed to facilitate the transformation of these values into fuzzy numbers and subsequently derive rough number boundaries. Ten economic-environmental criteria were identified, and six suppliers were evaluated against these criteria. The fuzzy rough LMAW (Logarithm Methodology of Additive Weights) method was employed to determine the criteria weights, with emphasis placed on the quality criterion. The fuzzy rough MABAC (Multi-Attributive Border Approximation Area Comparison) method was then utilized to rank the suppliers and identify the top performer. The validity of the results was established through validation techniques and sensitivity analysis. This research contributes a novel approach to green supplier selection, employing the powerful tool of fuzzy rough sets. The flexible nature of this approach suggests its potential application in future investigations. The limitation of this study is more complicated calculations for the decision maker. However, this approach is adapted to human thinking and minimizes ambiguity and uncertainty in decision making, and in future research, it is necessary to combine this approach with other methods of multi-criteria analysis.

How Does the Digital Capability Advantage Affect Green Supply Chain Innovation? An Inter-Organizational Learning Perspective

Green supply chain innovation has gained significant attention from academics and practitioners due to its ability to mitigate chain liability risks, meet consumer environmental demands, and create sustainable competitive advantages. Digital technology, a valuable tool for enhancing organizational information processing capabilities, plays a crucial role in promoting successful green supply chain innovation. However, existing research has a limited understanding of how digital capability advantage influences green supply chain innovation. Therefore, based on an inter-organizational learning perspective, this study aims to explore the impact of digital capability advantage on green supply chain innovation and examine the mediating role of green supply chain learning (green supplier learning and green customer learning). The survey results from 221 Chinese manufacturing firms indicate that digital capability advantages contribute directly and positively to green supply chain innovation and also indirectly enhance it through green supplier learning and green customer learning. This study establishes the positive relationship between digital capability advantages and green supply chain innovation and highlights the mediating role of green supplier learning and green customer learning. The research conclusions not only enhance our understanding of the factors and key success paths of green supply chain innovation from a digital perspective but also provide theoretical guidance for its effective implementation in manufacturing firms.

The effect of human resource strategy on green supply chain integration: the moderating role of information systems and mutual trust

PurposeThis study aims to explore the effect of human resource (HR) strategy (e.g. empowerment and teamwork) on green supply chain integration (e.g. green supplier and customer integration), which further leads to economic performance. Moreover, the authors examined the moderating effects of information systems and mutual trust on the relationship between HR strategy and green supply chain integration.Design/methodology/approachUsing the empirical data from 213 Chinese manufacturing firms, this study uses structural equation modeling and hierarchical regressions to examine the conceptual model.FindingsThe study’s findings reveal that empowerment and teamwork positively enhance green supplier and customer integration. Green supplier and customer integration are positively related to economic performance. Moreover, information systems positively moderate the relationship between empowerment and green supplier integration but negatively moderate the relationship between teamwork and green supplier/customer integration. Mutual trust positively moderates the relationship between empowerment and green supplier integration and the relationship between teamwork and green customer integration.Originality/valueThis study extends the existing understanding regarding how to enhance green supply chain integration by adopting an appropriate HR strategy in the context of different levels of information systems and mutual trust.

Green supplier governance and firm performance: a comprehensive understanding of three governance approaches

PurposeThis study aims to develop a comprehensive green supplier governance (GSG) concept and explore whether specific GSG approaches (green supplier assessment, green supplier assistance and green strategic partnership with suppliers (GSPS)) bring environmental and economic performance. Moreover, this study aims to reveal a synergistic effect of three GSG approaches on performance improvement.Design/methodology/approachUsing data collected from 200 Chinese manufacturing firms, regression analysis was employed to reveal the relationship between specific GSG approaches and firm performance. Further, cluster analysis was used to identify groupings of firms regarding implementation levels of three GSG approaches and compare the performance of the firm groups.FindingsGreen supplier assessment (GSA) can bring environmental performance, but GSA is not associated with economic performance. Green supplier assistance is positively associated with economic performance, while green supplier assistance cannot improve environmental performance. Only GSPS leads to improvement for both environmental and economic performance. Furthermore, firms with high implementation levels of GSA and GSPS (whether with high or low implementation levels of GSAS) can achieve the best environmental and financial performance.Practical implicationsThis study provides implications for firms to more strategically and comprehensively implement GSG approaches, which can be more effective in bringing environmental and economic performance.Originality/valueThe authors' study extends the GSG concept with two approaches by subdividing the collaborative approach into green supplier assistance and GSPS based on the collaboration levels. This study also sheds light on how to improve firm performance by different GSG approaches and reveals a synergistic effect of three GSG approaches on performance.

Enhanced TODIM based on VIKOR method for multi-attribute decision making with Type-2 neutrosophic number and applications to green supplier selection

Enhanced TODIM based on VIKOR method for multi-attribute decision making with Type-2 neutrosophic number and applications to green supplier selection

A Fuzzy–Rough MCDM Approach for Selecting Green Suppliers in the Furniture Manufacturing Industry: A Case Study of Eco-Friendly Material Production

Green supplier selection is always one of the most important challenges in all of supply chain management, especially for production companies. The purpose is to have reliable suppliers which can fulfill all requests and be flexible in any supply chain stage. The aim of this paper is to create an adequate and strong MCDM (multicriteria decision making) model for the evaluation and selection of suppliers in a real environment. The main contribution of this study is proposing a novel fuzzy–rough MCDM model containing extension stepwise weight assessment ratio analysis (SWARA) and additive ratio assessment (ARAS) methods with fuzzy–rough numbers (FRN). The integrated FRN SWARA–FRN ARAS model was implemented in a case study of eco-friendly material production. The FRN SWARA method was used to calculate the weights of 10 green criteria, while using FRN ARAS, 6 suppliers were evaluated. The results of the applied model show that supplier S3 received the highest ranking, followed by supplier S2, while supplier S5 performed the poorest. In order to verify the strengths of the developed fuzzy–rough approach, we created a comparative analysis, sensitivity analysis, and dynamic matrix, which confirm the robustness of our model.

A Life Cycle Risk Management Framework for Green Building Project Stakeholders

The construction industry compromises the sustainability of the built environment because of its adverse impacts on the environment, economy, and society. In response, green building (GB) projects advocate for targeted sustainability goals while some particular risks in GB projects could cause several unfavorable outcomes. This study aims to assess the life cycle risks of GB projects and emphasize the role of stakeholders in the management of corresponding risks in a four-step analysis procedure. In the first two steps, a comprehensive literature review and focus-group discussion were employed to construct the decision-making framework suitable to the GB life cycle. A fuzzy analytical hierarchy process (AHP) was used to prioritize the identified risks based on expert opinions. The fuzzy technique for order preference by similarity to ideal solution (TOPSIS) was used to associate stakeholders’ role with the management of GB risks that might arise throughout GB projects. Findings indicate that lack of experienced green construction staff, limited availability and reliability of green subcontractors and suppliers for the specific requirements of GB, and inflation of green materials’ prices are ranked as the most significant risks. A generic life cycle risk management framework for green buildings was developed to analyze the most significant risks and corresponding stakeholders. By addressing the gap in the literature, this study is expected to engage practitioners and GB project stakeholders for more effective and robust risk management frameworks.

Evaluation and Selection of a Green and Sustainable Supplier by Using a Fuzzy ARAS Mathematical Modeling

In the present scenario, the main aim of each and every organization is to be competitive, eco-friendly, and sustainable in order to run effectively and efficiently. Therefore, to meet and fulfill the above objectives, each organization must identify and choose the appropriate and best green and sustainable supplier, as a green supplier considers green technology, green products, green packaging, etc., while a sustainable supplier considers all three characteristics of sustainability, namely social, economic, and environmental. The problem of the evaluation and selection of green and sustainable suppliers is an example of a multi-criteria decision-making (MCDM) problem. In this research paper, to handle the uncertainty hidden in the green supply chain, the extended version of a MCDM technique named fuzzy additive ratio assessment (F-ARAS) is implemented for the evaluation of the relative weights of the green and sustainable supplier selection criteria, namely social, economic, and environmental, and also to determine the best green and sustainable supplier through fuzzy theory by using linguistic terms, which includes the application of interval-valued triangular fuzzy numbers (IVTFNs) in an organization. Finally, the technique, F-ARAS, has performed well and enables the organization’s managers to attain their required solution and final decision.

Changes in energy consumption and CO2 emissions in the Lithuanian household sector caused by environmental awareness and climate change policy

The increasing frequency of extreme weather events forced policymakers to take stricter commitments to mitigate climate change. The household sector, which is responsible for one-third of greenhouse gas (CO2) emissions is also important when mitigating climate change. However, there is still a lack of research which analyzes the changes in electricity and energy for heating consumption and CO2 emissions caused. Furthermore, the aim of this paper is to analyze the determinants such as: environmental awareness (knowledge about the environment, attitudes towards energy behavior, and pro-environmental behavior), climate change policy (renovations, choice of green electricity supplier) and social-demographic variables of electricity, energy for heating, electricity consumption and CO2 pollution caused in 2022 in Lithuania. The results showed that energy for heating consumption and CO2 emissions caused decreased significantly from 2017 to 2022. Meanwhile, electricity consumption increased but CO2 pollution caused changed insignificantly. From the main determinants, only education level significantly and negatively affected electricity consumption and its carbon dioxide emissions. Environmental knowledge and pro-environmental behavior significantly and positively influenced electricity consumption and its pollution. Energy consumption for heating significantly and negatively depended on education level, environmental knowledge, and renovation level. Meanwhile, renovation and choice of green electricity supplier significantly and negatively determined carbon dioxide emissions caused by energy consumption for heating. Thus, these results confirmed the importance of climate change policy implementation, particularly the promotion of renovation processes, and renewable energy consumption.

GREEN SUPPLIER ASSESSMENT USING A FUZZY MULTI-CRITERIA DECISION-MAKING APPROACH

This study presents a fuzzy multi-criteria decision making (MCDM) approach to support the evaluation of green suppliers. The method utilizes linguistic terms represented by triangular fuzzy numbers to assess the ratings of different green suppliers based on various criteria, as well as to determine the weights assigned to each criterion. The arithmetic of fuzzy numbers is employed to derive a weighted fuzzy decision matrix. In order to simplify and enhance practicality, the weighted ratings are converted into crisp values using the widely-used centroid index ranking approach. The proposed fuzzy MCDM method is then applied to a green supplier assessment problem, showcasing its effectiveness and computational process.
 Keywords: Green Supplier Assessment, MCDM, Fuzzy Numbers, TOPSIS.

Green supplier selection and environmental performance of firms in the Chinese manufacturing industry: the roles of behavior and outcome controls

PurposeGreen supplier selection (GSS) is acknowledged as important governance in green supply chain management (GSCM). However, this paper argues that GSS is not a stand-alone GSCM governance mode that determines manufacturers' environmental performance but rather one that needs to be aligned with contractual governance, particularly contractual control and adaptation, to promote environmental performance effects. This paper adopts GSS as ex ante governance and introduces behavior and outcome controls as ex post contractual control and adaptation, respectively. Thus, this paper addresses how GSS affects environmental performance directly and indirectly through behavior and outcome controls within transaction cost economics (TCE) theory.Design/methodology/approachThis research model was tested on 300 Chinese manufacturing firms, and multiple regression analysis was used to validate our hypotheses.FindingsA direct relationship was observed between GSS and environmental performance. This direct relationship is positively mediated by behavior and outcome controls.Originality/valueThis paper develops and elucidates an integrative green supply chain process proceeding from the implementation of ex ante GSS and ex post contractual governance to the realization of environmental performance. Furthermore, this paper considers two different forms of contractual governance, specifically contractual control and adaptation, and explains how they can be implemented using behavior and outcome controls from the perspective of TCE theory.