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The purpose of this paper is to employ an emerging phenomenon in China concerning collaborative carbon emission reduction (CCER) to investigate: first, the coordination of suppliers and manufacturers within supply chains to reduce carbon emissions, and second, the role of governmental policy in facilitating this process.

This paper draws upon evolutionary game theory to develop an evolutionary game model for CCER for suppliers and manufacturers within supply chains. This includes a detailed analysis of the evolutionary direction and process in different areas, both with, and in the absence of, governmental subsidies.

The results demonstrate that CCER is path dependent and that its evolutionary process is influenced by the following four factors: the initial status within supply chains; the cost; the additional benefit; and the investment risk related to CCER. The research also reveals that the reward provided by manufacturers is rational over the long term, due to the excessive cost of incentives potentially preventing the implementation of CCER.

This study represents the first attempt to investigate CCER within supply chains through the application of an evolutionary game-theoretic model. The investigation of multiple factors in the model will deepen understanding of the collaborative role required for the carbon emission reduction.

Collaborative emission reduction among supply chain members has emerged as a new trend to achieve climate neutrality goals and meet consumers’ low-carbon preferences. However, carbon information asymmetry and consumer mistrust represent significant obstacles. This paper investigates the value of blockchain technology (BCT) in solving the above issues.

A low-carbon supply chain consisting of one supplier and one manufacturer is examined. This study discusses three scenarios: non-adoption BCT, adoption BCT without sharing the supplier’s carbon emission reduction (CER) information and adoption BCT with sharing the supplier’s CER information. We analyze the optimal decisions of the supplier and the manufacturer through the Stackelberg game, identify the conditions in which the supplier and manufacturer adopt BCT and share information from the perspectives of economic and environmental performance.

The results show that adopting BCT benefits supply chain members, even if they do not share CER information through BCT. Furthermore, when the supplier’s CER efficiency is low, the manufacturer prefers that the supplier share this information. Counterintuitively, the supplier will only share CER information through BCT when the CER efficiencies of both the supplier and manufacturer are comparable. This diverges from the findings of existing studies, as the CER investments of the supplier and the manufacturer in this study are interdependent. In addition, despite the high energy consumption associated with BCT, the supplier and manufacturer embrace its adoption and share CER information for the sake of environmental benefits.

The firms in low-carbon supply chains can adopt BCT to improve consumers’ trust. Furthermore, if the CER efficiencies of the firms are low, they should share CER information through BCT. Nonetheless, a lower unit usage cost of BCT is the precondition.

This paper makes the first move to discuss BCT adoption and BCT-supported information sharing for collaborative emission reduction in supply chains while considering the transparency and high consumption of BCT.

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.

From the quantum game perspective, this paper aims to study a green product optimal pricing problem of the dual-channel supply chain under the cooperation of the retailer and manufacturer to reduce carbon emissions.

The decentralized and centralized decision-making optimal prices and profits are obtained by establishing the classical and quantum game models. Then the classical game and quantum game are compared.

When the quantum entanglement is greater than 0, the selling prices of the quantum model are higher than the classical model. Through theoretical research and numerical analysis results, centralized decision-making is more economical and efficient than decentralized decision-making. Publicity and education on carbon emission reduction for consumers will help consumers accept carbon emission reduction products with slightly higher prices. When the emission reduction increases too fast, the cost of emission reduction will form a significant burden and affect the profits of manufacturers and supply chain systems.

From the perspective of the quantum game, the author explores the optimal prices of green product and compares them with the classical game.

Strict carbon-cap policy is one of the basic policies proposed by the regulatory bodies to reduce the anthropogenic greenhouse gas emission. The purpose of this paper is to examine whether it is beneficial for a company to invest in green technology or not under the strict carbon-cap policy and for that a two echelon supply chain model is developed. This paper gives insight about judicious decision about investment on green technology.

Mathematical modeling approach has been adopted to understand the effect of investment on green technology. All the cost and emissions parameters have been derived and the total cost (TC) and total emission equations have been formulated mathematically. Two constrained mixed-integer nonlinear programming (MINLP) problems have been formulated and solved considering with or without green investment. Further, supply chain cost is optimized without carbon constraint to understand the effect of carbon constraint.

The investment in green technology can reduce the total supply chain cost. The study reveals that handling different parameters optimally can reduce both cost and emissions.

This paper tries to assess the effectiveness of green investment on technology under strict carbon-cap policy on a supply chain and, thereby, added value to the existing work. It examines the role played by various parameters under strict carbon-cap policy to draw insights, which will be beneficial for the academic community and managers.

Promoting low-carbon in the construction industry is important for achieving the overall low-carbon goals. Public–private partnership is very popular in public infrastructure projects. However, different perceptions of low-carbon and behaviors of public and private sectors can hinder the realization of low-carbon in these projects. In order to analyze the willingness of each stakeholder to cooperate towards low-carbon goals, an evolutionary game model is constructed.

An evolutionary game model that considers the opportunistic behavior of the participants is developed. The evolutionary stable strategies (ESSs) under different scenarios are examined, and the factors that influence the willingness to cooperate between the government and private investors are investigated.

The results illustrate that a well-designed system of profit distribution and subsidies can enhance collaboration. Excessive subsidies have negative impact on cooperation between the two sides, because these two sides can weaken income distribution and lead to the free-riding behavior of the government. Under the situation of two ESSs, there is also an optimal revenue distribution coefficient that maximizes the probability of cooperation. With the introduction of supervision and punishment mechanism, the opportunistic behavior of private investors is effectively constrained.

An evolutionary game model is developed to explore the cooperation between the public sector and the private sector in the field of low-carbon construction. Based on the analysis of the model, this paper summarizes the conditions and strategies that can enable the two sectors to cooperate.

The purpose of this paper is to examine the impact of carbon permit allocation rules (grandfathering mechanism and benchmarking mechanism) on incentive contracts provided by the retailer to encourage the manufacturer to invest more in reducing carbon emissions.

The authors consider a two-echelon supply chain in which the retailer offers three contracts (wholesale price contract, cost-sharing contract and revenue-sharing contract) to the manufacturer. Based on the two carbon permit allocation rules, i.e. grandfathering mechanism and benchmarking mechanism, six scenarios are examined. The optimal price and carbon emission reduction decisions and members’ equilibrium profits under six scenarios are analyzed and compared.

The results suggest that the revenue-sharing contract can more effectively stimulate the manufacturer to reduce carbon emissions compared to the cost-sharing contract. The cost-sharing contract can help to achieve the highest environmental performance, whereas the implementation of revenue-sharing contract can attain the highest social welfare. The benchmarking mechanism is more effective for the government to prompt the manufacturer to produce low-carbon products than the grandfathering mechanism. Although a loose carbon policy can expand the total emissions, it can improve the social welfare.

These results can provide operational insights for the retailer in how to use incentive contract to encourage the manufacturer to curb carbon emissions and offer managerial insights for the government to make policy decisions on carbon permit allocation rules.

This paper contributes to the literature regarding to firm’s carbon emissions reduction decisions under cap-and-trade policy and highlights the importance of carbon permit allocation methods in curbing carbon emissions.

This research explores mitigating carbon emissions and integrating sustainability in e-commerce logistics by optimizing the multi-depot pollution routing problem with time windows (MDPRPTW). A proposed model contrasts non-collaborative and collaborative decision-making for order assignment among logistics service providers (LSPs), incorporating low-carbon considerations.

The model is substantiated using improved adaptive large neighborhood search (IALNS), tabu search (TS) and oriented ant colony algorithm (OACA) within the context of e-commerce logistics. For model validation, a normal distribution is employed to generate random demand and inputs, derived from the location and requirements files of LSPs.

This research validates the efficacy of e-commerce logistics optimization and IALNS, TS and OACA algorithms, especially when demand follows a normal distribution. It establishes that cooperation among LSPs can substantially reduce carbon emissions and costs, emphasizing the importance of integrating sustainability in e-commerce logistics optimization.

This paper proposes a meta-heuristic algorithm to solve the NP-hard problem. Methodologies such as reinforcement learning can be investigated in future work.

This research can help logistics managers understand the status of sustainable and cost-effective logistics operations and provide a basis for optimal decision-making.

This paper describes the complexity of the MDPRPTW model, which addresses both carbon emissions and cost reduction. Detailed information about the algorithm, methodology and computational studies is investigated. The research problem encompasses various practical aspects related to routing optimization in e-commerce logistics, aiming for sustainable development.

The purpose of this paper is to review the relevant literature on low carbon supply chain management (LCSCM) and classify it on contextual base. It also aims at identifying key decision-making issues in LCSCM. This paper also highlights some of the future challenges and scope of research in this domain.

A content analysis is carried out by systematically collecting the literature from major academic sources over a period of 18 years (2000-2017), identifying structural dimensions and classifying it on contextual base.

There is an increasing trend of research on LCSCM, but this research is still in a nascent stage. All supply chain functions such as supplier selection, inventory planning, network design and logistic decisions have been redefined by integrating emissions-related issues.

Limitation of this study is inherent in its unit of analysis. Only peer-reviewed journal articles published in English language have been considered in this study.

Findings of prior studies on low carbon inventory control, transportation planning, facility allocation, location selection and supply chain coordination have been highlighted in this study. This will help supply chain practitioners in decision making.

Though there are an increasing number of studies about carbon emission-related issues in supply chain management, the present literature lacks to provide a review of the overarching publications. This paper addresses this gap by providing a comprehensive review of literature on emissions-related issues in supply chain management.

This paper aims to promote urban–rural synergy in carbon reduction and achieve the dual carbon goal, reconstruct the low-carbon urban–rural spatial pattern and explore planning strategies for carbon mitigation in urban agglomerations.

The authors propose the idea of land governance zoning based on low-carbon scenario simulation, using the Beijing–Tianjin–Hebei (BTH) urban agglomeration as the empirical research area. Specifically, the authors analyze its spatiotemporal evolution characteristics of carbon balance over the past two decades and simulate the land use pattern under the scenario of low-carbon emission in 2030. Furthermore, the authors create spatial zoning rules combined with land use transition characteristics to classify the urban agglomeration into carbon sink restoration zone, carbon sink protection zone, carbon control development zone and carbon transition agriculture zone and put forward corresponding targeted governance principals.

The study findings classify the BTH urban agglomeration into carbon sink restoration zone, carbon sink protection zone, carbon control development zone and carbon transition agriculture zone, which account for 28.1%, 17.2%, 20.1% and 34.6% of the total area, respectively. The carbon sink restoration zone and carbon sink protection zone are mainly distributed in the northern and western parts and Bohai Rim region. The carbon transition agriculture zone and carbon control development zone are mainly distributed in the southeastern plain and Zhangjiakou.

The authors suggest restoring and rebuilding ecosystems mainly in the northwest and east parts to increase the number of carbon sinks and the stability of the ecosystem. Besides, measures should be taken to promote collaborative emission reduction work between cities and optimize industrial and energy structures within cities such as Beijing, Langfang, Tianjin and Baoding. Furthermore, the authors recommend promoting sustainable intensification of agriculture and carefully balance between both economic development and ecological protection in Zhangjiakou and plain area.

The authors propose a zoning method based on the optimization of land use towards low-carbon development by combining “top-down” and “bottom-up” strategies and provide targeted governance suggestions for each region. This study provides policy implications to implement the regional low-carbon economic transition under the “double carbon” target in urban agglomerations in China.