Introduction

Companies need to be more productive and adaptable as globalization and competition heat up. In addition to satisfying customers, businesses should adhere to stringent economic goals and practice sustainable operations (Samal 2019). Efficient SCs are critical for producing high-quality goods and delivering them on time; thus, the sustainable development of SCs is a crucial part of running any successful business (Dai et al. 2022). The amount of levels, resources, and services within a SC all contribute to the difficulty of maintaining its operations in a sustainable way (Pal and Yasar 2020). Multiple activities, sites, and facilities in various locations (including separate nations, regions, or even different areas of the globe) make up contemporary industrial firms, as stated in Kouhizadeh et al. (2021). To guarantee SCs’ efficacy, competitiveness, sustainability, and expansion, the author contends that various SC activities, including as planning, collaboration and coordination, and response to consumer requests, must be taken into account (X. Lin et al. 2021). In tandem with the maturation of the market economy, new challenges have emerged in the green supply chain system (Q. K. Li et al. 2020). Optimizing green supply chain management (GSCM) has to pay greater attention to the concept of low carbon and environmental protection as it evolves because the supply chain network of the green economy has become an opportunity for companies to achieve rapid development). The environmental effects of suppliers and manufacturers are becoming more important to consumers as they seek to optimize green supply. In addition, people are seeking appropriate models in order to maintain harmony in their relationships and take into account a wider range of external elements (Yan et al. 2021). The vast majority of them propose green supply chain structures based on mathematical programming methods (H. Wu et al. 2022). The goals of green economics may be attained via studying business metrics that reflect environmental performance. Yu and Huo (2019) uses multi-objective analysis, supply chain structure analysis, supplier-manufacturer relationships, and the green supply chain’s environmental value as analytical criteria to find the best possible solution (Y. Wang et al. 2023).

When properly implemented, GSCM ensures that the right item gets to the right customers at the right time in the right amount at the right price (Härting et al. 2020). Short-term GSCM objectives include reducing cycle time and inventory and increasing efficiency; long-term strategic objectives include increasing revenues through market share and customer delight, which consequently increase productivity (Huo et al. 2019). The SCM literature has long acknowledged the merits of SCM and its potential advantages. Supply chain cost savings may be seen in areas such as reduced inventory, improved forecasting, faster delivery times, shorter fulfillment cycles, and higher fill rates all quantifiable advantages of SCM (Ozdemir et al. 2022). Since buying expenses account for around 80% of a company’s sales income, efficient SCM is essential small- and medium-sized company prosperity (Huang et al. 2021). Benefits of improved electronic trading include increased customer service and responsiveness, less risk, shortened product development cycle times, reduced inventory, and fewer redundant inter-organizational activities, all potential benefits of GSCM (Duong et al. 2022). Boonmee et al. (2021) discovered that using GSCM principles might result in improvements to inventory turnover, manufacturing lead times, adaptability, forecasting precision, cost savings, and resource planning precision (Z. Li et al., 2021b). Governments, businesses, and their supply chain partners are working together to decrease energy and pollution, reducing environmental risks and increasing public support as GSCM processes grow. Working together has the potential to increase everyone’s environmental knowledge (Walker et al. 2020). Cost savings (through material conservation, lower energy consumption, and lower water use), improved brand reputation, and reduced environmental responsibility have all accrued to businesses that have adopted SCM methods (Saurabh and Dey 2021). Poor environmental performance may have serious consequences for the natural world and cost businesses money via reduced stock value. According to Fang et al. (2023), corporations whose actions are beneficial to the environment see their stock prices rise, whereas companies whose actions are damaging to the environment have their stock prices fall. Therefore, businesses that care about their impact on the environment may find it easier to get funding from ethical investors.

Supply chain greening seeks a happy medium between maximizing economic output and minimizing environmental impact. Businesses have made efforts to “green” their supply chains in order to address pressing environmental concerns like as resource depletion and pollution. This implies that they have formed supply chains for the acquisition of environmentally better products or the development of standardized approaches to waste minimization and operational efficiency. In the twenty-first century, logistics management faces a new and pressing challenge: how to green the supply chain. An important issue is how to get companies to care about the environment and really do something about it inside their supply chain operations. The inbound and outbound logistics activities of China’s manufacturing firms are potential polluters; this article investigates the prevalence of GSCM methods across these firms, as well as the connection between environmental sustainability and economic development. Through its involvement in quality management and environmental management systems, the company gained insight into organizational and operational concerns (Gao et al. 2020). Because GSCM was supported by management, it gained consensus from all levels of staff and fostered cooperation across divisions (Gao et al. 2021). We demonstrate strong positive connections between environmental sustainability, economic development, and the adoption of GSCM practices after controlling for characteristics including legislation, marketing, supplier, cost constraints, industry levels of the relevant practice, and organizational size (Ding et al. 2023). Supply chain managers are under increasing pressure to enhance their company’s economic and environmental performance, prompting them to investigate and adopt GSCM strategies (Chen et al. 2023). Operations management and the supply chain have benefited greatly from the environmental and social sustainability established by sustainable development. The following is the structure of this research: sections 2 (“Literature review”), 3 (“methodology”), 4 (“empirical results”), 5 (“policy implications”), and 6 (“implications for practice”) present the study’s findings and limitations, respectively; the “Literature review” section contains a literature review of studies green supply chain management, and the environment and sustainable development; the “Methodology” section describes variables and methodology; section 4 displays empirical results; the “Results and discussion” section displays discussion and conclusion; and the “Conclusion and policy recommendations” section concludes the study with policy implications and study limitations.

Literature reviews

To successfully manage the movement of goods, money, and information to suit the needs of a company, a supply chain (SC) connects diverse parties from the client to the supplier through production and service. Scientists have been arguing for sweeping reforms to the profit-driven manner SCs have traditionally been run. The growing dangers posed by global warming and climate change have boosted the importance of efforts to green SCs. Sustainability challenges in SCM have just lately received attention from academics (K. H. Wang et al. 2021); (Y. A. Abbas et al. 2022); (Inês et al. 2020); (Salahuddin et al. 2019); (Cao 2022); (Franco et al. 2021). The sustainable economic practices are those that meet today’s demands without jeopardizing the ability of future generations to do the same. Liu and Dong (2021) recognize the importance of green and lean methods in ensuring a company’s long-term viability. The implementation of these strategies along the SC paves the way for better economic, environmental, and social outcomes (Yahman and Setyagama 2022). Reducing reductions in cost and lead time improved process flow, fulfillment of customer expectations, and environmental improvement, and boosting staff morale and dedication are just some of the advantages of aligning lean with sustainability concepts, as reported by Gnangoin et al. (2023). Sustainability can be influenced by adopting green SC practices, as evidenced by Ford’s decision to use; instead of using cardboard boxes, auto components may be sent in recyclable plastic containers, reducing transportation expenses by roughly a quarter (Gong 2022). In order for a business to endure, it must take care of more than just the bottom line, risks associated with its goods, its environmental waste, and the safety of its employees and the general public (Rehman et al. 2021). Zastempowski (2022) also take into account that the capacity to comprehend and control the economic, environmental, and social risks associated with SC is an integral part of SC sustainability. The operations area has been a focal point of severe concerns regarding environmental sustainability due to costs and SC disturbance (fragility), frequently including sustainable development that prioritizes people, earth, and profit. The development of business sustainability features an integrated framework which now includes resilience along with a focus on stakeholders, volunteers, and long-term performance view. Small- and medium-sized firms (SMEs) may find synergistic advantages that increase value production if they focus on resilience and sustainability. Thus, SC resilience is crucial to SC sustainability. These preceding reasons suggest that a sustainable SC may be developed by simultaneously deploying the lean, resilient, and green SCM paradigms. However, the literature reveals that the majority of studies have only examined one or two SCM paradigms at a time. Lean management and SC sustainability are discussed in a review in Elavarasan et al. (2020); resilient SCM and sustainability are developed in Lu et al. (2019) and Qader et al. (2022); and the intersection of green and sustainability in a SC setting is investigated in Economics and Academic (2021). The authors of a review article on the topic of SC sustainability definitions (Z. Wang and Tang 2020) note that resilience is seldom included in such documents. To meet the sustainability problem in SC, therefore, new integrative management techniques are required (Rehman et al. 2023). “Carrying products and services from suppliers, various manufacturers, and finally to customers using information flow, material flow, and monetary transactions in an environmentally friendly manner” is how the term “green supply chain” is defined (Malek and Desai 2020); (Vanhercke et al. 2021); (Abdur et al. 2022); (Tang 2022); (Bag et al. 2021);(Madni 2023) (H. Khan et al. 2023); (Kumar et al. 2021); (Gorjian et al. 2021); (D. Liu et al. 2021); (Xu et al. 2023); (Hou et al. 2019). When it comes to exports and economic development, the green supply chain—which is environmentally friendly—includes consumers, suppliers, purchases, warehouses, packing, production, and transportation, as well as the whole green design. Since exports depend on supply chain activities, there is a strong connection between the two. In particular, a green supply chain is increasingly vital to economic development because of the positive impact it has on the environment. Recent research shows a strong correlation between improvements in the supply chain and expansion of the economy, suggesting a novel strategy for fostering economic development (Hu et al. 2022).

A new kind of economic growth that takes into account sociological, ecological, and social factors (Dong et al. 2022) was inspired by studies in logistics and supply chain. A country’s GDP may benefit from green supply chain management since it encourages exports. In this setting, items are exported to foreign organizations through an export exchanging organization. As part of the supply chain, these export swapping firms will manage export paperwork, logistics, and transportation. Therefore, in this procedure, supply chain management is directly tied to export logistics and transportation. Because of its positive impact on the environment, the green supply chain is also crucial. Green supply chain management was shown to significantly impact both competitiveness and economic performance in research conducted. Researchers have proposed a number of different strategies for implementing GSCM (F. Chien et al. 2021). They are also known as GSCM or activities by other researchers (Sun et al. 2021). Organizational operations, characteristics, and industry may all influence the GSCM methods. A business that opts to utilize green purchasing, green manufacturing and materials management, green distribution and marketing, and green reverse logistics are the four pillars of GSCM (F. S. Chien et al. 2022) (Fattorini and Regoli 2020). On the other side, they spoke about green logistics, which included green purchasing, green manufacturing, green distribution, and reverse logistics. Similarly, they presented GSCM’s primary components, which they identified as “green procurement,” “green manufacturing,” “green operations,” “reverse logistics,” and “waste management,” respectively. The global supply chain management (GSCM) will be investigated in this research within the categories of green buying and inbound logistics, green manufacturing, green materials management, green distribution/marketing, and reverse logistics (Khan, Hou, Irfan, Zakari and Le, 2021a, Khan, Yu and Sharif, 2021b, Li, Yang, Shi and Cai, 2023b, Li et al, 2021a, Li et al, 2023a, Li, Zhou and Huang, 2021b, Liang, Brunelli, Septian and Rezaei, 2020b).

According to the research that has been conducted, the strategic view and performance of SCM may be evaluated along a number of different dimensions. Some of these aspects include quality, services, speed, and the generation of value for end customers. In addition, utilizing these dimensions is the best way to try to grasp the business model and the solutions that can be implemented to deal with certain external environmental difficulties (Duignan et al. 2022). As a result, strategic supply chain management necessitates a pliable and adaptable strategy for reorganizing and revamping supply chain activities in response to external influences. To ensure that SCM is efficient and successful in dealing with environmental problems, several companies, like Procter & Gamble (P&G), have modified their supply chain processes (Cerný et al. 2021). Vasiliu and Dobrea made an effort by beginning SCM research which has been conducted on a number of different companies; however, the results have been ambiguous in explaining why integrated and included operations have a less sustainable effect. Vasiliu and Dobrea made an effort to research possible obstacles and problems associated with SCM in different types of businesses. The tiny sample size is another flaw in the study of Vasiliu and Dobrea which draws any firm conclusions (Alzubi and Akkerman 2022). Nonetheless, there are signs of both the strengths and challenges that supply chain management faces while trying to be more logical and strategic in the face of the ever-changing nature of the environment (Khurana et al. 2021). Strategic emphasis, processing standards, information technology support, measurements, and coordination are where the benefits and negatives (M. Abbas and Zaini 2021) reside. Strategic supply chain management has been shown to increase service levels; however, the same research indicated that it does not always succeed in reducing negative environmental impacts. Strategic SCM at SMEs is significantly influenced by factors other than SCM approaches (Jinru et al. 2021).Whether strategic SCM has a greater or lesser influence on social and environmental sustainability in developing, transitional, or established economies is unclear due to a lack of definitive research. Interestingly, it is clear that the lack of proper social sustainability suffers as a result of strategic supply chain management (Zhu et al. 2021; Pjanić 2019; Yusliza et al. 2019). Implications for societal sustainability of inadequate strategic supply chain management is mitigated by operational efficiency (L. Lin and Hong 2022).

Methodology

Table 1 shows that since the concept of green supply chain management’s impact on environmental sustainability and economic growth in the China region from 2009 to 2022 is multi-criteria, a multi-criteria decision-making method (MCDM) could be used to evaluate the relative importance of the various criteria. We propose (but not limit ourselves to) as a resource for learning more about the various MCDM approaches already in use today. Sustainable supply chain management is only one example of an area where MCDM techniques have been put to use. Securing (2013) is a great resource for learning more about the latter. Because of its novelty and the special benefits, it offers for this article the “best worst method” (BWM) which was chosen for this research. In the following paragraphs, we will go into further details about the procedure.

Table 1 Industrial applications of the best worst method (BWM)
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Best worst method

To put it simply, BWM is a pairwise comparison-based approach designed for MCDM situations (Liang et al. 2020a). BWM offers two major benefits over other MCDM strategies: the fundamental justification for choosing BWM in this research is that compared to other MCDM methods, which use a full pairwise comparison matrix, BWM (i) uses less data for pairwise comparisons and (ii) yields more consistent results. Quite a few practical issues have benefited from this technique. For instance, Wu et al. (2019) have utilized BWM to figure out the optimal packaging arrangement for shipping cargo from distribution centers to airports. The strategy was also used to choose the most effective suppliers in another research by Moslem et al. (2020). With the goal of analyzing the identified risks, Munim et al. (2020) created a methodology for risk assessment in the context of business continuity management systems. Other areas where BWM has been put to use include assessing the efficacy of university-industry PhD projects, measuring the quality of scientific output, and making decisions about transportation modes, suppliers, and the quality of scientific research.

In an MCDM, or multi-criteria decision-making, situation, the BWM method compares two criteria against one another to establish relative importance. The five key phases in using BWM as a solution approach are as follows:

Step 1: Find vitally crucial factors to consider {c1,c2,…,cn}.

Step 2: Find the criteria that are the best (B) and the worst (W).

Step 3: Rate B’s importance relative to the other criteria on a scale from 1 (not at all essential) to 9 (very important). The vector is what we get after this process AB = (aB1, aB2, …, aBj,….,aBn), where aBj is the preference of B over criterion j.

Step 4: Use a scale from 1 to 9 to figure out how much weight other criteria carry in comparison to W. Vector

Aw = (a1W, a2W, …, ajW, …., anW) is the output of this procedure, where a preference for criteria j over W is indicated by ajW

Step 5: Find the weight that's just right \(\left({w}_{1,}^{\ast }{w}_2^{\ast },\dots, {w}_n^{\ast}\right)\)

The following optimization model is used to determine how much weight each criterion should be given:

$${\displaystyle \begin{array}{c}\min \backslash \max \left\{|{w}_B-{a}_{Bj}{w}_j|,|{w}_j-{a}_{jW}{w}_W|\right\}\\ {}\textrm{j}\end{array}}$$

such that

$$\sum_{j=1}^n{w}_j=1$$
(1)

w j ≥ greatest absolute difference of 0 for every j, where the goal function minimizes {|wB − aBjwj|, |wj − ajWwW|} for all j. The first model may be used to create the

$$\operatorname{Min}\xi$$

Such that

$$\mid {w}_B-{a}_{Bj}{w}_j\mid \le \xi, \textrm{for}\ \textrm{all}j$$
(2)
$$\mid {w}_j-{a}_{jW}{w}_W\mid \le \xi, \textrm{for}\ \textrm{all}j$$
(3)
$$\sum_{j=1}^n{w}_j=1$$
(4)
$${w}_j\ge 0,\textrm{for}\ \textrm{all}j$$

Each level of the criterion hierarchy represents a local weight, which is the output of model 2 at that level. In a criterion hierarchy, the global weight of a criterion is determined by multiplying the weights of the criteria that branch off of the trunk line by themselves. Model 2’s goal function, denoted by the number, is the consistency ratio of pairwise comparisons; hence, this should be taken into account. Note that we used the program found at http://www.bestworstmethod.com to solve model 2.

Using a weighted sum function that takes into account the relative importance of each criterion, an aggregate score for each provider may be determined (Eq. 5)

$${V}_i=\sum_j{u}_{ij}{w}_j\textrm{forall}i$$
(5)

Where uij represents supplier i’s score on criteria. In this case, j is the BWM-estimated global mass for criterion j.

Using the BWM’s aggregate weight, we establish the following definition for the weighted decision matrix Mn*m:

$${M}_{n\times m}={\displaystyle \begin{array}{c}\begin{array}{c}{A}_1\\ {}{A}_2\end{array}\\ {}\vdots \\ {}\begin{array}{c}{A}_i\\ {}{A}_n\end{array}\end{array}}\left[\begin{array}{ccc}\begin{array}{c}{a}_{11}{w}_1\\ {}{a}_{21}{w}_1\end{array}\kern0.5em \begin{array}{c}{a}_{12}{w}_2\\ {}{a}_{2j}{w}_2\end{array}& \cdots & \begin{array}{c}\begin{array}{cc}{a}_{1j}{w}_j& {a}_{1m}{w}_m\end{array}\\ {}\begin{array}{cc}{a}_{2j}{w}_j& {a}_{2m}{w}_m\end{array}\end{array}\\ {}\vdots & \ddots & \vdots \\ {}\begin{array}{cc}\begin{array}{c}{a}_{i1}{w}_1\\ {}{a}_{n1}{w}_1\end{array}& \begin{array}{c}{a}_{i2}{w}_2\\ {}{a}_{n2}{w}_2\end{array}\end{array}& \cdots & \begin{array}{c}\begin{array}{cc}{a}_{ij}{w}_j& {a}_{im}{w}_m\end{array}\\ {}\begin{array}{cc}{a}_{nj}{w}_j& {a}_{nm}{w}_m\end{array}\end{array}\end{array}\right],$$
(6)

where aijwj option i’s score on criterion j (aij) multiplied by criterion j’s global weight as defined by the BWM (wj). Matrix Mink’s alternatives, A1 to An, may be seen as points in a space with dimensions the choice criterion. A cloud is formed by these coordinates, with its center at the coordinates’ origin. For MCDM problems with more than three criteria, options are analyzed by projecting them onto a plane in all m dimension relationship between the alternatives and the criteria. The concept of GAIA was proposed to map out the available options from a higher-dimensional realm onto a flat surface (Sahebi et al. 2020). In two criteria that have the highest data dispersion, a PCA is built from the algebraic matrix (i.e., covariance-variance matrix, eigenvalue, and eigenvector), and the round them are selected as coordinate axes to project the information of a decision matrix onto a new plane.

Step 6: Covariance matrix computation

By calculating the covariance matrix (Cnn), we can see how the decision matrix’s many options are connected to one another. Keep in mind that the covariance matrix Cnn contains the covariance information for all possible pairings of the initial options.

$${C}_{n\times n}=\left[\begin{array}{ccc}\begin{array}{cc}\begin{array}{c} Cov\left({A}_1,{A}_1\right)\\ {} Cov\left({A}_2,{A}_1\right)\end{array}& \begin{array}{c} Cov\left({A}_1,{A}_2\right)\\ {} Cov\left({A}_2,{A}_2\right)\end{array}\end{array}& \cdots & \begin{array}{c}\begin{array}{cc} Cov\left({A}_1,{A}_i\right)& Cov\left({A}_1,{A}_n\right)\end{array}\\ {}\begin{array}{cc} Cov\left({A}_2,{A}_i\right)& Cov\left({A}_2,{A}_n\right)\end{array}\end{array}\\ {}\vdots \vdots & \ddots & \vdots \vdots \\ {}\begin{array}{cc}\begin{array}{c} Cov\left({A}_i,{A}_1\right)\\ {} Cov\left({A}_n,{A}_1\right)\end{array}& \begin{array}{c} Cov\left({A}_i,{A}_2\right)\\ {} Cov\left({A}_n,{A}_2\right)\end{array}\end{array}& \cdots & \begin{array}{c}\begin{array}{cc} Cov\left({A}_i,{A}_i\right)& Cov\left({A}_i,{A}_n\right)\end{array}\\ {}C\begin{array}{cc} ov\left({A}_n,{A}_i\right)& Cov\left({A}_n,{A}_n\right)\end{array}\end{array}\end{array}\right]$$
(7)

Results and discussion

Due to its complex nature, assessing environmental performance is challenging. This makes it hard for decision-makers to tell which criteria are relative to one another. Jafar Rezaei put out the BWM method for MCDM in 2015. Basic multi attribute rating, analytical network process, analytical hierarchy process, and fuzzy preference programming methodology are some of the further MCDM methodologies. These strategies compute the relative importance of each criteria using the pairwise comparison approach to zero in on the most important one. MCDM approaches are utilized in the field of sustainable development, namely the multi-criteria multi-Period Outranking Method (MUPOM). The backdrop of uncertainty and the participation of stakeholders and experts are ignored, however. Wu and Song (2022) indicate that BWM methods may be utilized to improve the speed and accuracy with which multi-criteria issues are resolved. BWM is simpler, faster, and easier to construct than other MCDM approaches since it uses less comparison data and, hence, does not require entire pairwise comparison matrices. A two- or three-criterion comparison scheme with which BWM may provide a unique solution is described in. BWM may provide numerous optimum solutions for a system that lacks consistency. Researchers have started using BWM in practical settings because to its unique properties, as seen in Table 1.

From our first set of eight indicators (Table 1), we were able to narrow it down to the final set of eight (Table 2).

Table 2 Selected environmental indicators for application purpose
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According to Table 3, the most important or best indication for ensuring supply networks’ continued commitment to environmental sustainability 1, 3, 4, 7, 8, 12, 13, 14, 17, 19, 23, 26, 27, 29, 31, 32, and 33.

Table 3 Best and worst criteria identified by experts
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Selecting the best criterion preference over other criteria

Professionals were polled to determine how crucial each factor was to them on a scale from 1 to 9. For example, the vector comparison between the best criterion and the other criteria developed by expert 18 is shown in Table 4. And scaling of other criteria over the worst criterion by expert 18 (others-to-worst) seen in Table 5.

Table 4 Scaling of the best criterion over other criteria by expert 18 (best-to-others)
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Table 5 Scaling of other criteria over the worst criterion by expert 18 (others-to-worst)
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Selecting the other criteria preference over the worst criterion

The other criteria were to be rated higher by the experts than the criterion that was rated the lowest, on a scale from 1 to 9. Table 7 displays the scaling results for respondent number 18 out of the whole pool of 34.

MATLAB was then used to solve the BWM model. Table 6 displays expert 18’s preferred allocation of importance to several sustainability indicators and goal functions.

Table 6 Optimal weights of the criteria based on the scaling of expert 18
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Using Eq. (2), we used MATLAB to calculate the appropriate weights for each indicator in BWM models for the remaining experts. Table 7 shows the average (arithmetic mean) ideal weights obtained by averaging the indicators’ weights as selected by 34 experts. This investigation demonstrated a very stable and efficient system, with L* values very near to zero across all simulations. As was previously said, Bangladesh’s garment sector is a major economic driver. Chinese supply chains may benefit from greater environmental sustainability if the country’s textile sector generated less trash. Many Chinese factories and tanneries create waste that is harmful to people and the environment. The next two most significant indicators were resource use (EC3) and reuse and recycling (EC7), each of which was given a weight of 0.1276%. For this reason, after the implementation of sustainable waste management systems, businesses should prioritize the reuse and recycling of their byproducts and surplus goods. By making the most of what we have, we can keep our planet habitable and our economy booming. Researchers in China discovered that ISO certification was the weakest indication supply chain environmental performance. This might be due to the fact that Chinese companies have little experience with ISO certification. Maintaining ISO criteria across the Chinese supply chain is difficult because of a lack of awareness on the part of vendors. But we think the top indicators offer the greatest promise for boosting China’s environmental sustainability. Tabulated in Table 7, the combined weight of EC6 (land pollution) and EC8 (lead, arsenic, and toxic chemicals) is 0.0754. Both the carbon footprint (EC4) and the use of renewable energy (EC2) were given a weight of 0.0564, placing them in the fourth and fifth positions, respectively, among the most important indicators. Therefore, these indicators should be included into environmental management strategies, considering the priority placed carbon footprint was not as heavily considered as it should have been by governments and international organizations working to reduce carbon emissions. This is because there are not many heavy metal or chemical businesses in China, two largest sources of carbon pollution.

Table 7 Final optimal weights of the selected criteria
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The rankings of the remaining variables were not substantially altered by the sensitivity analysis. Table 8 ranks sensitivity analysis iterations based on their impact on environmental sustainability metrics. In addition, both charts show the typical relative importance and order of the criteria. Table 7 shows that throughout the course of nine iterations of sensitivity testing, EC1’s weight shifted from 0.1 to 0.9. Table 7 depicts the relative importance of the various indicators throughout the course of each run. Consequently, there was a major shift in how the other criteria were weighted when the priority indicator shifted. On the other hand, as shown in Table 7, there was little variation in the relative indicator rankings throughout the experiment. In Table 7, the outermost line represents the position of EC5 for a range of EC1 weights. At the 0.6 weight, when EC5 was ranked 6 and where all the other lines were equally significant, the line turned concave. Since the other weights shifted in tandem with the weight of EC1’s shift, the trend in both sets of numbers is understandable, and see Table 8 ranking of environmental sustainability indicators during sensitivity analysis.

Table 8 Ranking of environmental sustainability indicators during sensitivity analysis
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Research shows that GSCM and sustainability performance are intrinsically linked within the SCM framework. As was previously mentioned, the vast majority of research (C. Wang et al. 2020) (Mubarik et al. 2021) have shown a statistically significant correlation between the two variables in question. Involving suppliers in green product design and incorporating environmental principles into operations are two examples of unexplored areas. Taking this into account, this research proposes environmental cooperation as a buffer between green supply chain management methods and sustainability outcomes. It is anticipated that the existence of environmental cooperation would simplify the adoption of GSCM processes. Sustainable development and logistics are brought together in green supply chain management. Business strategy and conduct are also influenced by definition of sustainable development “the integration of economic, social, and environmental goals.” Despite the fact that GSCM’s purview is novel, the field’s popularity has skyrocketed in recent years (Umar et al. 2022). It is widely acknowledged that GSCM is a transparent integration and attainment of social, environmental, and economic objectives via the methodical coordination of important inter-organizational business activities to enhance long-term economic performance. This study’s findings, along with those from others, suggest that green supply chain activities focused on the outside world, such as green buying and reverse logistics, do not improve an organization’s bottom line. This might mean that the projects’ advantages will go to other parties rather than the company itself. In green buying, for instance, a company’s attention is directed on the enhancement of its suppliers’ environmental performance. Green materials and other inputs may help the company in the long run, but the initiative’s immediate impact is on the suppliers. Khokhar et al. (2020) supports this view by showing that there is no correlation between PSR and cost savings. However, he does find a correlation between PSR and cost savings through the mediation of supplier performance. This suggests that the direct benefit of green purchasing reflects first on suppliers and then on firm performance.

Conclusion and policy recommendations

Expanding the supply of green buildings which are being implemented commercially and on a large scale is considered contingent upon the existence of a green supply chain. The green building supply chain has the potential to provide environmental, social, and economic benefits that extend well beyond the walls of the actual green building. Competitive advantage and new economies of scale and economies of scale may be brought to the firm and the area via a more resource-efficient strategy, and a change in materials and technology (Chinese Commission). New employment, economic development, and a green, low-carbon economy may be fostered through acquiring the necessary new skills and instituting organizational transformation. Sustainable supply chain management in the construction industry not only lessens the strain on natural resources during building projects, but it also significantly lessens the overall environmental harm that is incurred throughout construction, constructing the fundamental model for green supply chain optimization. The supply chain model is predicated on either a single-objective producer who solely cares about profit and the environment, or a dual-objective manufacturer and retailer who both care about profit and the environment. We examine the relationship between the manufacturer’s and retailer’s level of environmental preference and GSCM’s ideal decision-making process across all three models. The findings indicate the following:

(1)Manufacturing and retail businesses that prioritize environmental sustainability boost product greenness, company-wide environmental friendliness, and overall demand. The greenness and environmental friendliness of products have been on the rise as manufacturers and retailers have become more environmentally conscious. In fact, when both manufacturers and retailers prioritize eco-friendliness, product demand is at its highest. Obviously, aiming for environmental friendliness will greatly increase businesses’ environmental protection levels and give them a leg up in the green consumer market

(2)The more the manufacturers’ preference for environmental friendliness, the greater the retailer’s profit and the lesser the manufacturer’s profit when both are considered simultaneously. Manufacturers have a lower environmental preference and retailers have a higher preference when both profit and environmentally friendly goals are considered, while retailers have a higher preference and properly consider environmental objectives, which is beneficial to manufacturers’ profits. Supply chain profits rise initially, but fall as manufacturers and consumers become more environmentally conscious. In other words, if businesses operate under the assumption that consumers have a preference for environmentally friendly options, then doing so will improve the green degree of products, boost supply chain profits, and create a win-win situation for both business profits and ecological sustainability. Investment in green environmental protection without considering the financial impact might be disastrous for businesses.

(3)Both wholesale and retail prices go up first as companies become more environmentally conscious, and then they go down when companies become equally concerned with profit and environmental friendliness. When businesses first start thinking about environmental goals, merchants and manufacturers alike may optimize profits by adopting a high-price approach that increases revenue per unit. However, when environmental preference is strong, the price of green research and development is high, and the whole supply chain adopts the price reduction strategy to support the growth in product demand, the overall optimization condition of “small profits but quick turnover” is realized. This research provided a framework for business leaders to implement environmentally responsible practices across their supply chains. There has been no research done on this subject in relation to Chinese supply networks. Our research helped business leaders choose which metrics need their attention first. The findings of this research have real-world applications, especially for China’s manufacturing sector. In addition, the study’s findings made it possible to rate sectors according to their supplier chain’s effectiveness in promoting environmental sustainability. The current research provides a framework for selecting the most and least important criteria against which to evaluate this performance. China’s garment, small manufacturing, production, and tannery sectors all contributed data to our investigation. This research is significant because it may help industrial managers in other developing nations think about how they may improve the environmental sustainability of their supply chains. Implications for industrial managers in other developing countries assessing the environmental sustainability of their supply networks are a key contribution of this study. The results of this research may be used in a number of different ways to aid decision-makers. Success in implementing environmental GSCM principles depends on a wide range of technical, policy, and strategic considerations. To encourage green transport and logistics, the government could provide rebates and tax breaks for cars with eco-friendly designs. Regulatory oversight and government initiatives encouraging environmentally responsible industrial expansion certification schemes that support and promote a sustainable agenda are the result of collaboration between the logistics industry and government regulatory bodies. To discourage non-green logistics activities, regulatory authorities should implement high taxes and import levies, and other financial penalties on polluting logistics systems. The government should provide low-interest loans to businesses interested in using renewable and environmentally friendly energy sources.

Research limitations and future directions

Although every attempt was made to get the study started in a thorough manner, it could always be done better. There are caveats to this research, just as there are to any other. The study’s primary shortcoming is its exclusive emphasis on China; future research should expand its scope to include other emerging nations. Due to the cross-sectional nature of the study, participants were surveyed just once over the specified time period. In light of the possibility of variation or additional confirmation with the passage of time, future researchers should think about panel research that follows the same people throughout time to ensure consistent results delays. Therefore, this layout would provide more hard proof. In addition, in-depth interviews with industry leaders on panels would be useful. However, although the quantitative approach has provided some truth (with a focus on numerical depiction of connection), the qualitative approach would provide more nuanced nuances of the truth that is really valuable. That is to say, there is a good likelihood of uncovering previously unknown relationships between the research variables and qualitatively focused patterns and characteristics. However, this research may aid new sectors in establishing supply networks that minimize environmental impact. Superior environmental performance may provide a competitive edge in today’s economy. As both consumers and governments place a greater emphasis on protecting the environment, the results of this study will be invaluable to researchers and business leaders alike.