Green Supply Chain Management System Research Articles

Carbon finance, green supply chain management, and firms’ environmental innovation: the moderating role of financing structure

Many countries have introduced policies on carbon finance to promote the green development of firms and achieve a balance between the economy and the environment. This study uses institutional theory to construct a relationship model between carbon finance, green supply chain management, financing structure, and firms’ environmental innovation. It uses multiple regression analysis to examine the impact of carbon finance on firms’ environmental innovation, the mediating role of green supply chain management, and the moderating role of financing structure by selecting 1529 A-share listed companies in Shanghai and Shenzhen from 2012 to 2022 as its sample. The results show that carbon finance has a positive influence on firms’ environmental innovation, with green supply chain management playing a mediating role in this relationship. Additionally, financing structure weakens the positive effect of carbon finance on firms’ environmental innovation. Based on these findings, suggestions are made to improve the carbon trading market and trading system, build an efficient green supply chain management and supervision system, and encourage firms to optimise the financing structure.

Mechanical Design and Manufacturing and its Automation Development Strategy Based on the Concept of Green Manufacturing

Under the background of increasing global environmental pressure, mechanical design and manufacturing and its automation industry are faced with major challenges of energy conservation, emission reduction and efficient utilization of resources. Although the concept of green design is gradually popularized, it still faces problems such as technical bottlenecks and imperfect system in the implementation process. This paper proposes to strengthen the theoretical research of green design, promote the innovation of green manufacturing process, build green supply chain management system and cultivate green manufacturing talents, aiming at technological innovation and management system optimization, and realize the green transformation of the whole process of mechanical design and manufacturing.

Research on Evaluation of Green Supply Chain Management System Based on Grey System Theory

Research on Evaluation of Green Supply Chain Management System Based on Grey System Theory

On potential strategic framework for green supply chain management in the energy sector using [formula omitted]-rung picture fuzzy AHP & WASPAS decision-making model

The present communication deals with addressing the problem of handling the uncertainties in the process of green supply chain management system through an integrated approach that takes the information in the form of q-rung picture fuzzy sets and quantitatively blends it into the decision-making techniques of Analytic Hierarchy Process (AHP) and Weighted Aggregated Sum Product Assessment (WASPAS). The proposed model has been implemented in stage-wise form for outlining the potential strategic plans for green supply chain management in the energy sector. At every phase of the study, the detailed analysis has been presented in an interlinked manner for solving the underlying problem of decision-making. A brief sensitivity analysis and comparative study of the potential strategic plans with respect to the deterministic parameters have been performed for better understanding, consistency, and reliability. The findings of the propositions illustrate that while making prioritization for potential strategic plans, concentrating on utilizing customer relationship management to meet the needs of the customers is more prominent than examining the procedure for developing new services and products.

Green supply chain technology and organization performance: Moderating role of environmental dynamism and product-service innovation capability

The green supply chain management (GSCM) system is considered as an environmentally friendly approach which considers different environmental issues in supply chain flow. There are different underlying technologies used in implementing GSCM; yet, we still have limited knowledge concerning how green SCM technology can enhance organizational performance. Further, the conditions that indirectly affect organizational performance within this context remain underexplored. Drawing on this gap of knowledge, the purpose of this research work is to investigate how adoption of GSCM can improve organizational performance. We also argue that the dynamism of the environment and the product-service innovation capability of organizations will have conditioning effects on this relationship. Grounded on the dynamic capability view as a theoretical lens, a conceptual model has been developed which is explored through feedback from 307 organizations involved in green supply chain management activities. We find that environmental dynamism and product-service innovation capability have moderating impacts on adopting green supply chain technology and organizational performance. The outcomes also indicate that green supply chain technology can considerably improve business value under certain contextual factors.

Assessing the role of sports economics and green supply chain management for the coordinative and coupling development of China's green economic growth.

The significance of the sports economy to the national economy is growing as the country develops rapidly. The term "sports economy" is used to describe the economic activities that are directly or indirectly tied to sports. This work introduces a multi-objective optimization model for a green supply chain management system, intending to reduce the economic and environmental impact of storing and transporting potentially dangerous products. This research intends to examine the impact of the sports sector on green economic growth and competitiveness in the China region. An empirical analysis is conducted to determine the relationship between sports economics and green supply chain management by employing data on 25 provinces of the China region for 2000 and 2019. To fulfill this study's objective and determine the effect of carbon emission, this study will use renewable energy, sports economics, green supply chain management, information and communications technology, and waste recycling as explanatory variables. In order to obtain the desired objectives, the current study will use the short run and long run of the cross-sectionally augmented autoregressive distributed lag and pooled mean group tests. Besides, this study uses an augmented mean group, fully modified ordinary least square, and dynamic ordinary least square estimations for the robust check. In contrast, renewable energy, green supply chain management, sports economics, information and communications technology, and waste recycling decrease CO2 emissions and, therefore, facilitate the carbon-abatement agenda of the China region.

The implementation of green supply chain management (GSCM) and environmental management system (EMS) practices and its impact on market competitiveness during COVID-19.

Despite great academic interest in global green supply chain management (GSCM) practices, its effectiveness for environmental management systems (EMS) and market competitiveness during COVID-19 remains untapped. Existing literature suggests that a fundamental link between GSCM, EMS, and market competitiveness is missing, as supply management is critical to maintain market competitiveness. To fill this gap in the literature, this study examines whether environmental management systems influence the link between GSCM practice and market competitiveness in China. We also propose the articulating role of big data analytics and artificial intelligence (BDA-AI) and environmental visibility toward these associations in the context of the COVID-19 pandemic. We evaluated the proposed model using regression-based structural equation modeling (SEM) with primary data (n = 330). This result provides empirical evidence of the impact of GSCM on EMS and market competitiveness. Moreover, the results show that the BDA-AI and the environmental visibility enhanced the positive relationship between GSCM-EMS and EMS and market competitiveness in China. Recent research shows that supply chain professionals, policymakers, managers, and researchers are turning to formal EMS, BDA-AI, and environmental visibility to help their organizations achieve the competitiveness that the market indicates they need.

The challenges of green supply chain management (GSCM) system implementation in civil construction project

Nowadays, the construction industry has negatively affected the local environment and community through the noise, air, water, and land pollution. The implementation of Green Supply Chain Management (GSCM) has been recognized as a green approach in reducing environmental issues in the construction industry. However, GSCM had no significant effect on athletic performance because not many company owners or managers had implemented green supply chain management in companies. Therefore, this study was conducted to identify the challenges to implement GSCM concepts in civil engineering construction. Through a questionnaire survey, fifty respondents from civil engineering companies at Sepang and Hulu Langat Selangor participated. The findings were analysed using SPSS software and the Relative Importance Index (RII). Different in perspective and culture, financial limitations and fear of failure were internal challenges against implementing GSCM. Meanwhile, the external challenges were a lack of support and guidelines from the government, not having an engagement of management from third parties, and not having actual environmental measurements of the GSCM system. This study is significant for apprehending the challenges and ineffectiveness of GSCM practices in Malaysia.

Influence of Green Supply Chain Management on Business Performance of Vietnamese Electronic Firms

The study’s objective is to assess the influence of green supply chain management (GSCM) on business performance of Vietnamese electronic firms by testing multivariate regression model through software SPSS. The study was completed based on survey results of 961 electronic firms and combined with interview results obtained from the directors, the deputy directors, and other positions directly involved in the supply chain management system. The results show that GSCM has a direct positive impact on business performance and has an indirect positive impact on business performance through employee job satisfaction, operational efficiency, relational efficiency. This study provides some valuable implications for electronic firms in raising their awareness and also developing their green supply chain management system. Keywords: GSCM, Green supply chain management, Business performance, Vietnamese electronic firms.

Optimum Design of Stair-Climbing Robots Using Taguchi Method

Environmental issues like pollution are major threats to human health. Many systems are developed to reduce pollution. In this paper, an optimal mobile robot design to reduce pollution in Green supply chain management system. Green supply chain management involves as similating environmentally and economically feasible solutions into the supply chain life-cycle. Smartness, advanced technologies, and advanced networks are becoming pillars of a sustainable supply chain management system. At the same time, there is much change happening in the logistics industry. They are moving towards a new logistics model. In the new model, robotic logistics has a vital role. The reasons for this change are the rapid growth of the e-commerce business and the shortage of workers. The advantages of using robotic logistics are reduction in human errors, faster delivery speed, better customer satisfaction, more safety for workers, and high workforce adaptability. A robot with rocker-bogie suspension is a six-wheeled mobile platform that has a distinctive potential to keep all wheels on the ground continuously. It has been designed to traverse rough and uneven terrain by distributing the load over its wheels equally. However, there is a limitation to achieving high-speed mobility against vertical barriers. In this research, an optimal design of product delivery wheeled robots for a sustainable supply chain system is proposed to ensure higher adaptability and maximum stability during climbing staircases. The design parameters of the proposed robot are optimized using Taguchi Method. The aim is to get a smooth trajectory of the robot’s center-of-mass. The proposed approach realizes a robot with much-improved stability which can climb over heights more than the size of the wheel (i.e., 3 times the radius of wheels). The results reveal that the modified rocker-bogie system not only increases the stair-climbing capability but also thwarts instability due to overturning of a wheel of the robot.

Applying information ecological theory to analyse the green supply chain management system in universities

AbstractIn order to create higher benefits for enterprises in the university supply chain and make them meet the requirements of sustainable development, this study uses information ecology theory to study the cognitive management and transformation path of the green supply chain. Firstly, the related concepts of university supply chain management and green supply chain management are expounded, and the differences are mainly discussed. Then, the evaluation of supply chain greenness is studied. The fuzzy hierarchical comprehensive evaluation method combining quantitative and qualitative is used to establish the evaluation model. Finally, the proposed evaluation model is tested. The test results show that Suzhou University's greenness fuzzy evaluation score is 58.2 points, which is a medium level. The resource factor scored 61 points, the energy factor scored 52.9 points, the environmental factors scored 59.8 points, the economic factor scored 62.7 points, and the social factor scored 55.5 points. The company has obvious advantages in terms of resources and economy, but the level is not very high. The higher the cost synergy between enterprises and customers, the greater the environmental cost synergy. Therefore, manufacturing enterprises should strengthen communication and cooperation with customers. The higher the cost synergy control of manufacturing and waste recycling enterprises, the greater the synergy effect of environmental cost control. Therefore, increasing cooperation with waste recycling enterprises is conducive to developing environmental cost control and coordinated benefits. The proposed scheme provides some ideas for applying information ecology theory in the university supply chain.

Integration of novel uncertainty model construction of green supply chain management for small and medium-sized enterprises using artificial intelligence

In this paper to promote the coordinated growth of the economy and the environment, green supply chain management requires businesses to pay attention to environmental protection, energy conservation, and emission reduction in the closed loop of the entire life cycle, including initial research and development and design, procurement and selection of materials, production, external sales, recycling, and waste disposal. This paper also combines artificial intelligence deep learning technology to build a green supply chain management system for small and medium-sized enterprises, improving the intelligent algorithm and creating an intelligent model. These two factors together improve the effect of green supply chain management for small and medium-sized enterprises. The results of the experimental research demonstrate that the green supply chain management system for small and medium-sized businesses built in this paper, which is based on artificial intelligence deep learning technology, has good management and decision-making effects and can successfully reduce the carbon emissions of the supply chain of small and medium-sized businesses.

Green Supply Chain Management Optimization Based on Chemical Industrial Clusters

During the period after the pandemic, the chemical sector, which is a crucial component of national progress, encounters further obstacles. The industry has been affected by the pandemic, leading to a need for faster transformation and upgrading. Additionally, there is an increased demand for green and environmental protection in the chemical sector due to global climate change and the concept of sustainable development. During the time after the pandemic, there will be a continued trend of industrial clustering and scale development in the chemical sector. It is essential to prioritize optimization via green supply chain management in order to facilitate industrial upgrading. In order to achieve this objective, this study administered a questionnaire survey to gather reliable data. The collected data was then analyzed using software tools like SPSS and AMOS. The analysis aimed to investigate the influence of various factors, including regulatory compliance, green procurement, green manufacturing, green logistics, green sales, competitors, internal environmental protection, and cost control, on the awareness and implementation of green supply chain management in chemical enterprises. Additionally, a structural equation model was constructed to further examine these relationships. The findings suggest that green procurement, manufacturing, logistics, sales, and internal environmental protection and cost control have a notable and beneficial influence on the implementation of green supply chain management. Additionally, the awareness and implementation of green supply chain management also contribute positively to both economic and environmental advantages. This paper offers a theoretical framework for enhancing the efficiency of the green supply chain management system in chemical industrial clusters, hence promoting the environmentally friendly and sustainable growth of the chemical sector.

The Effect of Green Supply Chain Management, Green Intellectual Capital, and Green Information System on Environmental Performance and Financial Performance

Environmental degradation is increasing and resource depletion has become a problem. Based on the results of the preliminary survey, the majority of respondents are more concerned with financial performance than environmental performance. This study aims to examine the effect of green supply chain management (GSCM), green intellectual capital (GIC), and green information system (GIS) on environmental performance (EnvP) and financial performance (FinP). The sample in this study was 219 respondents who came from manufacturing companies in Riau Province. Data analysis used structural equation analysis with SMART PLS application. The results show that only green supply chain management has a positive effect on financial performance. Then green intellectual capital and environmental performance each have a positive effect on financial performance. This research develops a new measurement for green supply chain management and green information system variables. Companies can develop systems and information technology that can reduce waste so that their environmental and financial performance can be better.

Prediction of drivers' impact on green supply chain management using deep learning algorithm.

Nowadays, for controlling the organizations' environmental impact and attaining performance enhancement in industries, green supply chain management (GSCM) plays a significant role. This work considers four major drivers and five primary dimensions for the deployment of GSCM systems in industries. The five primary dimensions are consumer intrusiveness, restructuring quality of supply chain, risk and security consciousness, and urbanization in the region leather industry, and the four major drivers are the effectiveness, features, significance, and limitations of the restructured GSCM system. Additionally, this paper investigates the primary factors that are used for deploying the GSCM system in leather industries located in the northern part of Tamil Nadu. Initially, the survey questions are prepared based on the concept and distributed to the authorities of industries. For the survey questions provided to various industries, three leather industries properly answered all the questions. Each question is mandatory. Then, the collected data were analyzed using the deep belief network (DBN) to measure the impact of primary drivers in implementing the GSCM system in industries. The performance of the proposed system is analyzed with various existing methods in terms of accuracy, precision, f-measure, etc. The proposed framework's implementation has implications for increasing organizational efficiency, lowering costs, minimizing waste, and encouraging green culture among personnel. This study will aid in the development of policies and a better knowledge of how to implement green innovation methods.

Spherical Linear Diophantine Fuzzy TOPSIS Algorithm for Green Supply Chain Management System

Enhancing competitive pressure is one of the most significant roles of supply chain management. The competitive environment and customer perception have shifted in favour of an ecological mentality. As a result, green supplier selection (GSS) has emerged as a critical problem. The challenge of green supplier selection striving for agility, durability, ecological sensitivity, leanness, and sustainability is tackled in this paper. In terms of recycling applications, environmental applications, carbon footprint, and water consumption, the environmental parameters evaluated in GSS and traditional supplier selection differ. Because of the form of the problem, a resolution is defined, which comprises an algorithm entrenched in the spherical linear Diophantine fuzzy sets (SLDFSs) Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) technique. Before discussing the approach of the SLDF model, some background information on SLDF sets is provided. To assure the uniqueness of this robust extension, different operations on SLDFSs are described, along with some concise interpretations to help the reader comprehend these ideas. A robust TOPSIS approach has been utilized in the issue of GSS by taking into consideration the multicriteria decision-making (MCDM) technique particularly useful in several areas, like analyzing and choosing traditional and environmental conventionalities. Due to linguistic criteria and the inability to assess all criteria, the fuzzy technique must be used with the TOPSIS method to lessen the consequences of instability and ambiguity. The spherical linear Diophantine fuzzy TOPSIS approach is employed, as it simplifies the evaluation of decision-makers and criteria. The hybrid technique resulting from integrating the SLDFS and TOPSIS is extremely successful in selecting which provider is more suited among the alternatives established on the criteria set by the order of significance, and this method may also be incorporated into similar issues.

Performance evaluation model and algorithm of green supply chain management based on sustainable computing

Abstract How to facilitate collaborative development between the enterprise and the environment under the dual constraints of resources and the environment is the focus of today's green supply chain management system research. Through the performance evaluation of the green supply chain, we can understand the operation of the whole supply chain and its shortcomings, provide a basis for improving related processes, and have important practical significance for improving the competitiveness and protection of its products. First of all, by summarising and analysing the research status of sustainable supply chain management in different countries, the research idea and overall background of this paper are proposed. It discusses the theory of sustainable supply chain management and the performance evaluation system and calculation types of sustainable supply chain management. Finally, the relative weight of each index is determined based on the sustainability calculation method, and then the decentralisation degree of the index is constructed. During this period, the fuzzy comprehensive evaluation method is used to evaluate the performance of sustainable supply chain, conduct case analysis and summary, and evaluate the performance of green supply chain component in economic, social, environmental and other aspects. In this paper, representative companies are selected as examples to evaluate their green supply chain management performance, and the evaluation algorithm is studied based on sustainable calculation method. The results show that a reasonable and effective evaluation of the enterprise performance of green supply chain management and a sustainable algorithm study can effectively identify potential problems in the operation of the company and improve the overall operation of the company at this stage.

Modeling and Simulation of a Multi-agent Green Supply Chain Management System for Retailers

In recent years, the agent technology has been successfully applied in supply chains, thanks to its excellent interactivity, proactivity, and autonomy. However, the existing research on multi-agent green supply chain (GSC) stops on the strategic and tactical levels, failing to implement the relevant supply chain models. To overcome the limitation, this paper designs a multi-agent GSC management system for retailers, with the aim to obtain scientific collaboration strategies among multiple agents and to make effective logistics decisions for the supply chain. Firstly, a 3-layer hierarchical evaluation index system (EIS) was established under the framework of the multi-agent GSC management system. Next, the authors modeled the supply-demand relationship and dynamic game of multi-agent GSC, and discussed the cooperation and negotiation models among multiple agents. Experimental results show that the proposed multi-agent GSC management model is highly effective. The research findings provide a reference for the application of multi-agent technology in other types of supply chain enterprises.

Role of technological dimensions of green supply chain management practices on firm performance

PurposeThe research study aims to investigate green supply chain management (GSCM) elements as part of a complete system. It aims to understand the special properties of the GSCM system under the moderating effects of product complexity and purchasing structure.Design/methodology/approachA thorough literature review led to the building of the conceptual framework. Six constructs were identified using systems theory. These constructs include green supply chain technological dimensions (particularly, Artificial Intelligence (AI) based), green supply chain strategy, green supply chain process, product complexity, purchasing structure, and firm performance. The instrument was scientifically developed for gathering survey responses using complete design test methods. The conceptual model was eventually tested based on survey data collected from 250 automotive components and allied manufacturers in the emerging economy of South Africa.FindingsThe results indicate that GSCM technological dimensions (AI-based) positively influence GSCM strategy. Further, GSCM strategy was found to positively influence the GSCM process. The GSCM processes have significant effects on environmental performance, social performance, and financial performance. The product complexity has a significant moderation effect on the paths GSCM strategy and GSCM process.Originality/valueThe findings from multivariate data analysis provide a better understanding of GSCM system dynamics and are helpful to key decision-makers. This unique model has elevated GSCM theory to a new level. There are limited studies available in the existing GSCM literature using systems theory. This study will offer an advanced/comprehensive understanding to readers in this relatively new concept.

Green Supply Chain Management Optimization Based On NSGA II Method

Green Supply Chain Management (GSCM) is the adopted by many companies due to the government policies of various countries. The optimization technique can be applied in the GSCM to increase the profit of the company. In this research, Non-dominated Sorting Genetic Algorithm-II (NSGA-II) technique is applied for the optimization of GSCM to increase the performance. The NSGA-II method has the advantage of choosing the solution closer to the pareto-solution and uses the elitist technique to preserve the best solution in the next generation. Mathematical model of the GSCM system is established and data is provided as input to the mathematical mode. Data is generated in three types, small scale, medium scale and large scale. The proposed NSGA-II method has high performance in the optimization technique compared to existing method. The proposed NSGA-II method has the Number of Pareto Solution (NPS) metrics of 17 for large scale data, while existing method has 14.