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This paper aims to introduce corporate social responsibility into the green supply chain and analyse the impact of different decision makers’ decision-making schemes on carbon emission reduction in the supply chain.

This study uses a two-stage low-carbon supply chain composed of a manufacturer and retailer as the research object. It uses the Stackelberg game model to analyse optimal carbon emission reduction and its influence under different decision-making modes.

Increased consumer green preferences and trust can improve the manufacturing enterprises’ carbon emission reduction rate. The carbon emission reduction rate decreases with increased green innovation costs. When green technology innovation costs remain constant, the greater the market capacity, the higher the carbon emission reduction rate. Market capacity has the most significant impact on the optimal carbon emission reduction rate without considering social responsibility decisions and has the least impact on the optimal carbon emission reduction rate while fully considering the social responsibility decision. To achieve decarbonisation production, the market capacity must be small, and when green innovation costs are high, it is the optimal choice without considering social responsibility. To achieve a higher level of carbon emission reduction, when the market capacity is low and the research and development cost is high or when the market capacity is large, it is the optimal choice.

The results provide scientific policy decisions and management significance for governments and enterprises in low-carbon subsidies and supply chain management. The findings also provide a basis for future theoretical research and enterprise practice.

In acknowledging the reality of climate change, large firms have set internal and external (supplier oriented) targets to reduce their greenhouse gas emissions. This study aims to explore the complex processes behind the evolution and diffusion of carbon reduction strategies in supply networks.

The research uses complex adaptive systems (CASs) as a theoretical framework and presents a single case study of a focal buying firm and its supply network in the food sector. A longitudinal and multilevel analysis is used to discuss the dynamics between the focal firm, the supply network and external environment.

Rather than being a linear and controlled process of adoption implementation outcomes, the transition to reduce carbon in a supply network is much more dynamic, emerging as a result of a number of factors at the individual, organisational, supply network and environmental levels.

The research considers the emergence of a carbon reduction strategy in the food sector, driven by a dominant buying firm. Future research should seek to investigate the diffusion of environmental strategies more broadly and in other contexts.

Findings from the research reveal the limits of the control that a buying firm can exert over behaviours in its network and show the positive influence of consortia initiatives on transitioning to sustainability in supply networks.

CAS is a fairly novel theoretical lens for researching environmental supply network dynamics. The paper offers fresh multilevel insights into the emergent and systemic nature of the diffusion of environmental practices in supply networks.

To explore how the blockchain affects the pricing and financing decisions in a low-carbon platform supply chain.

Considering the dual roles of the e-commerce platform as a seller and an initiator, a typical game-theoretical method is applied to analyze the behavior of supply chain decision-makers and the impact of key variables on equilibriums.

When loan interest rates are symmetric, whether blockchain is used or not, the e-commerce platform financing mode will always produce higher wholesale price and unit carbon emission reduction, while the retail price is the opposite. Higher unit additional income brought by the blockchain can bring higher economic and environmental performances, and the e-commerce platform financing mode is superior to bank financing mode. The application of blockchain may cause the manufacturer to change his/her financing choice. For bank financing, with the increase of loan interest rates, the advantages brought by blockchain will gradually disappear, but this situation will not occur under e-commerce platform financing.

Blockchain is known for its information transparency properties and its ability to enhance user trust. However, the impacts of applying blockchain in a low-carbon platform supply chain with different financing options are not clear. The authors examine the manufacturer's strategic choices for platform financing and bank financing, whether to adopt blockchain, and the impact of these decisions on carbon emissions reduction, consumer surplus and social welfare. The research conclusion can provide reference for the operation and financing decisions of platform supply chain under the carbon reduction target in the digital economy era.

The authors consider a dynamic emission-reduction technology investment decision-making problem for an emission-dependent dyadic supply chain consists of a manufacturer and a retailer under subsidy policy for carbon emission reduction. The consumers are assumed to prefer to low-carbon products and formulate a supply chain optimal control problem.

The authors adopt differential game to analyze investment strategies of cost subsidy coefficient with respect to vertical incentive of a manufacturer and a retailer. A comparison analysis under four different decision-making situations, including decentralized decision-making, centralized decision-making, maximizing social welfare, is obtained.

The results show that the economic benefit and environmental pressure have a win–win performance in centralized decision-making. In four different game models, equilibrium strategies, profits and social welfare show changing diversity and have a consistent development trend as time goes on.

The authors estimate the demand function is a linear function in this paper. According to the consumers’ preference to low-carbon products, consumer’s awareness meets the law of diminishing marginal utility like advertising goodwill accumulation. The carbon-sensitive coefficient might be a quadratic expression, which will complicate the problem and be consistent with reality.

It captures that there is a necessity to strengthen cooperation and exchange of carbon emission technology among the enterprises by simulation of different decision-makings when government granted cost subsidy.

The results provide significant guidelines for the supply chain to make decision-makings of emission-reduction technology investment and relevant government departments to determine emission subsidies costs.

An endogenous subsidies coefficient is produced by the social welfare function. Distinguished from previous study, it also considered the influences of carbon emission trade policy and consumer preference.

Although supply chain finance (SCF) aims to optimize capital flows in the supply chain process, its effectiveness in improving cost performance remains controversial. From the perspective of efficiency motives, this study aims to explore how the combinations of SCF solutions and traditional financing instruments lead to supply chain cost reduction.

A mixed-method approach is used in this study. First, using the fuzzy-set qualitative comparative analysis (fsQCA), the authors analyze 405 survey data across four industries in China and identify the configurations of financing instruments for supply chain cost reduction. Second, to better understand the reasons behind each configuration, the authors conduct the content analysis on the interview data composed of 24 Chinese companies.

The authors find that the effectiveness of SCF solutions for supply chain cost reduction is related to the focal company's use of traditional financing instruments. Moreover, compared with guaranteed financing, companies that use credit financing are more likely to adopt SCF solutions to achieve supply chain cost reduction. Finally, the effectiveness of SCF solutions in reducing supply chain costs varies greatly across industries.

The study’s findings provide insights for policymakers and SCF practitioners in the aspects of simplifying the SCF application.

This study contributes to the current literature by addressing the theory–practice gap related to SCF. The study also provides new understandings of factors related to supply chain cost reduction, as well as factors that influence SCF adoption.

This paper aims to find optimal emission reduction investment strategies for the manufacturer and examine the effects of carbon cap-and-trade policy and uncertain low-carbon preferences on emission reduction investment strategies.

This paper studied a supply chain consisting of one manufacturer and one retailer, in which the manufacturer is responsible for emission reduction investment. The manufacturer has two emission reduction investment strategies: (1) invest in traditional emission reduction technologies only in the production process and (2) increase investment in smart supply chain technologies in the use process. Then, three different Stackelberg game models are developed to explore the benefits of the manufacturer in different cases. Finally, this paper coordinates between the manufacturer and the retailer by developing a revenue-sharing contract.

The manufacturer's optimal emission reduction strategy is dynamic. When consumers' low-carbon preferences are low and the government implements a carbon cap-and-trade policy, the manufacturer can obtain the highest profit by increasing the emission reduction investment in the use process. The carbon cap-and-trade policy can encourage the manufacturer to reduce emissions only when the initial carbon emission is low. The emission reduction, order quantity and the manufacturer's profit increase with the consumers' low-carbon preferences. And the manufacturer can adjust the emission reduction investment according to the emission reduction cost coefficient in two processes.

This paper considers the investment of emission reduction technologies in different processes and provides theoretical guidance for manufacturers to make a low-carbon transformation. Furthermore, the paper provides suggestions for governments to effectively implement carbon cap-and-trade policy.

The purpose of this paper is to focus on tracing GHG emissions across the supply chain industries associated with the US residential, commercial and industrial building stock and provides optimized GHG reduction policy plans for sustainable development.

A two-step hierarchical approach is developed. First, Economic Input-Output-based Life Cycle Assessment (EIO-LCA) is utilized to quantify the GHG emissions associated with the US residential, commercial and industrial building stock. Second, a mixed integer linear programming (MILP) based optimization framework is developed to identify the optimal GHG emissions’ reduction (percent) for each industry across the supply chain network of the US economy.

The results indicated that “ready-mix concrete manufacturing”, “electric power generation, transmission and distribution” and “lighting fixture manufacturing” sectors were found to be the main culprits in the GHG emissions’ stock. Additionally, the majorly responsible industries in the supply chains of each building construction categories were also highlighted as the hot-spots in the supply chains with respect to the GHG emission reduction (percent) requirements.

The decision making in terms of construction-related expenses and energy use options have considerable impacts across the supply chains. Therefore, regulations and actions should be re-organized around the systematic understanding considering the principles of “circular economy” within the context of sustainable development.

Although the literature is abundant with works that address quantifying environmental impacts of building structures, environmental life cycle impact-based optimization methods are scarce. This paper successfully fills this gap by integrating EIO-LCA and MILP frameworks to identify the most pollutant industries in the supply chains of building structures.

The purpose of this study is to examine the impacts of different forecasting methods (judgmental, quantitative and mixed forecasting) on firms' supply chains and competitive performance.

Working with three groups of manufacturing companies, we explore the consequences of judgmental, quantitative and mixed forecasting methods on firms' competitive performance in supply chains. The validity of constructs and path relationships was examined using structural equation modeling (SEM).

Our findings indicate that supply chain efficiency influences both cost reduction and customer satisfaction. In addition, the three dimensions of supply chain performance are shown to be direct antecedents of competitive performance. Our empirical results reveal that although all studied forecasting methods meaningfully influence supply chain performance, the mixed method, compared to the other two methods, has greater capabilities to enhance supply chain performance.

This research provides originality and insight into supply chain practices through forecasting methods to improve competitive performance.

Platform-based enterprises, as micro-entities in the platform economy, have the potential to effectively promote the low-carbon development of both supply and demand sides in the supply chain. Therefore, this paper aims to provide a multi-criteria decision-making method in a probabilistic hesitant fuzzy environment to assist platform-type companies in selecting cooperative suppliers for carbon reduction in green supply chains.

This paper combines the advantages of probabilistic hesitant fuzzy sets (PHFS) to address uncertainty issues and proposes an improved multi-criteria decision-making method called PHFS-DNMEREC-MABAC for aiding platform-based enterprises in selecting carbon emission reduction collaboration suppliers in green supply chains. Within this decision-making method, we enhance the standardization process of both the DNMEREC and MABAC methods by directly standardizing probabilistic hesitant fuzzy elements. Additionally, a probability splitting algorithm is introduced to handle probabilistic hesitant fuzzy elements of varying lengths, mitigating information bias that traditional approaches tend to introduce when adding values based on risk preferences.

In this paper, we apply the proposed method to a case study involving the selection of carbon emission reduction collaboration suppliers for Tmall Mart and compare it with the latest existing decision-making methods. The results demonstrate the applicability of the proposed method and the effectiveness of the introduced probability splitting algorithm in avoiding information bias.

Firstly, this paper proposes a new multi-criteria decision making method for aiding platform-based enterprises in selecting carbon emission reduction collaboration suppliers in green supply chains. Secondly, in this method, we provided a new standard method to process probability hesitant fuzzy decision making information. Finally, the probability splitting algorithm was introduced to avoid information bias in the process of dealing with inconsistent lengths of probabilistic hesitant fuzzy elements.

The purpose of this paper is to empirically examine the relationships between low carbon supply chain practices and their relationships with environmental sustainability (ES) and the economic performances (EP) of firms. The study also includes an examination of the low carbon supply chain practices that are utilized by Indian manufacturing firms.

Through a questionnaire-based survey, the data received from 83 Indian manufacturing firms was analyzed using a variance-based structural equation modeling technique to test the proposed hypotheses.

The study indicates that carbon governance is a strategic imperative for the adoption of low carbon supply chain practices. Similarly, low carbon product and process design (LCPPD), manufacturing and logistics lead to improved ES. In addition, low carbon purchasing is positively related to the adoption of LCPPD, manufacturing and logistics. No significant relationship was found between the adoption of low carbon supply chain practices and the EP of a firm.

The findings of this study may assist manufacturing managers in prioritizing operational practices for the reduction of emissions.

This study provides two major contributions to green supply chain management. First, it provides comprehensive empirical evidence on low carbon supply chain practices that are being followed by Indian manufacturing firms. Second, this study also empirically validated a structural model of low carbon supply chain practices.