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This paper aims to explore how upstream supply chain companies will control the carbon emissions and price decisions of products when the government implements environmental tax policy on consumers. It provides some suggestions to control carbon emissions for the government and manufacturers.

This study establishes two-echelon Stackelberg game models with and without the implementation of environmental tax policy on consumers in a centralized scenario and a decentralized scenario. Through the comparative analysis of the four models, the optimal emission abatement and pricing strategies are obtained.

This paper concludes that implementing environmental tax policy on consumers within the market’s acceptable range is more beneficial to the retailer and the environment, as well as the overall social welfare, except for the manufacturer. Moreover, consumer’s low-carbon preference always has a broader impact on carbon abatement and corporate profits than environmental tax coefficient. Finally, the side-payment self-executing contract can effectively ensure that the supply chain members make rational decisions spontaneously while achieving a win-win solution of centralized scenario.

This paper first considers how the government’s environmental tax policy on consumers will affect the decision-making of supply chain companies, and proposes an improved side-payment self-enforcing contract to maximize environmental and economic benefits of centralized scenario. In addition, it provides a reference for the government to adopt both the carbon cap policy and the environmental tax policy.

Disruptions at ports may destroy the planned ship schedules profoundly, which is an imperative operation problem that shipping companies need to overcome. This paper attempts to help shipping companies cope with port disruptions through recovery scheduling.

This paper studies the ship coping strategies for the port disruptions caused by severe weather. A novel mixed-integer nonlinear programming model is proposed to solve the ship schedule recovery problem (SSRP). A distributionally robust mean conditional value-at-risk (CVaR) optimization model was constructed to handle the SSRP with port disruption uncertainties, for which we derive tractable counterparts under the polyhedral ambiguity sets.

The results show that the size of ambiguity set, confidence level and risk-aversion parameter can significantly affect the optimal values, decision-makers should choose a reasonable parameter combination. Besides, sailing speed adjustment and handling rate adjustment are effective strategies in SSRP but may not be sufficient to recover the schedule; therefore, port skipping and swapping are necessary when multiple or longer disruptions occur at ports.

Since the port disruption is difficult to forecast, we attempt to take the uncertainties into account to achieve more meaningful results. To the best of our knowledge, there is barely a research study focusing on the uncertain port disruptions in the SSRP. Moreover, this is the first paper that applies distributionally robust optimization (DRO) to deal with uncertain port disruptions through the equivalent counterpart of DRO with polyhedral ambiguity set, in which a robust mean-CVaR optimization formulation is adopted as the objective function for a trade-off between the expected total costs and the risk.

This paper aims to develop a bi-objective mixed integer non-linear programing model to optimize the supply chain networks consisting of raw material providers, final product manufacturers and distribution centers. Raw material substitution caused by varying raw material supply amounts, prices and carbon emissions and final product substitution due to different product prices and carbon emissions are considered.

The proposed model aims to achieve total profit maximization and total carbon emission minimization. The objective function of carbon emissions is converted into a maximization problem by changing minimum to maximum. The composite objective function is the weighted sum of the bias value of each objective function. The model is then solved using software Lingo12.

Numerical analysis results show that an increase in the number of alternate raw materials for original raw material helps improve supply chain network performance, and variation in that number causes detectable but not significant changes in downstream final product substitution results.

The major differences between this work and existing research are as follows: first, although previous research considered carbon emissions in supply chain network optimization, it has not considered the substitution effects of products or raw materials. This paper considers the substitution of both raw material and productions. Second, the item substitution considered by previous research is derived from inventory shortage or price difference of original items. However, the substitution considered in the present paper is a response to differences in purchase price, production cost and carbon emissions for items.

The objective of this paper is to investigate the impact of the information sharing of the dynamic demand on green technology innovation and profits in supply chain from a long-term perspective.

The authors consider a supply chain consisting of a manufacturer and a retailer. The retailer has access to the information of dynamic demand of the green product, whereas the manufacturer invests in green technology innovation. Differential game theory is adopted to establish three models under three different scenarios, namely (1) decentralized decision without information sharing of dynamic demand (Model N-D), (2) decentralized decision with information sharing of dynamic demand (Model S-D) and (3) centralized decision with information sharing of dynamic demand (Model S-C).

The optimal equilibrium results show that information sharing of dynamic demand can improve the green technology innovation level and increase the green technology stocks only in centralized supply chain. In the long term, the information sharing of dynamic demand can make the retailer more profitable. If the influence of green technology innovation on green technology stocks is great enough or the cost coefficient of green technology innovation is small enough, the manufacturer and decentralized supply chain can benefit from information sharing. In centralized supply chain, the value of demand information sharing is greater than that of decentralized supply chain.

The authors used game theory to investigate demand information sharing and the green technology innovation in a supply chain. Specially, the demand information is dynamic, which is a variable that changes over time. Moreover, our research is based on a long-term perspective. Thus, differential game is adopted in this paper.

This paper aims to analyze the impact of the cost coefficient of product emission reduction, coefficient of low-carbon product advertising effort cost, and sharing ratio of low-carbon product advertising effort cost on the profit of a dual-channel supply chain. After determining the best model and relevant influencing factors, the paper puts forward corresponding management inspirations and suggestions.

The paper opts for an exploratory study using Stackelberg game theory to construct a centralized decision-making (MC mode), a low carbon product advertising effort cost free sharing decentralized decision-making (SD model) and a low carbon product advertising effort cost sharing decentralized decision-making (JD model) game model. Through using optimization methods to get the equilibrium solution, the relevant management suggestions are obtained by comparison analysis.

The paper shows that the JD model is better than the SD model in terms of the profits of the manufacturer, retailer and supply chain, and the improvement of Pareto is realized. The proportion of cost sharing of low carbon product advertising effort is positively related to the wholesale price and direct influence coefficient of low carbon product advertising effort on channel, while negatively related to the retail price and the cross influence coefficient of low carbon product advertising effort on alternative channels. Under the JD model, the manufacturer can reduce advertising costs through improving the efficiency and pertinence of direct channel advertising and urging the retailer to do a better job in sales management to improve gross margin and require the retailer to increase advertising efficiency and pertinence of retail channel to reduce advertising costs of retail channel and other ways to increase their profits. The retailer can make use of its advantages closer with consumers to improve the efficiency and pertinence of advertising in the retail channel to raise the influence coefficient of advertising and reduce the advertising cost in the retail channel.

The innovations of this paper are listed as follows: First, it has considered advertising investment from both the manufacturer and the retailer simultaneously. Second, it has considered a low-carbon background to investigate cooperative advertising decision for low-carbon products. Third, it has considered the decision on the level of product emission reduction and the level of low-carbon product advertising effort investment simultaneously.