关键词: 全渠道零售, 碳政策, 损失厌恶, 最小最大后悔值

Abstract: Omni-channel retailing, as a new type of business model and industrial form that integrates the development of online and offline, has gradually become a key path for the high-quality development of China’s retail industry. “Buy online and pick up in store” (BOPS) is an important strategy for traditional dual-channel retailers to transform to omni-channel. Dual-channel retailers that implement BOPS fulfil online orders through offline physical shops, and need to stock a sufficient number of products in their physical shops. Adequate inventory can avoid losses due to stock-outs, but uncertain market demand tends to lead to inventory backlogs. Therefore, it is necessary to explore effective ordering strategies to cope with the inventory pressure caused by BOPS. In practice, limited market demand information has been a bottleneck restricting the transformation and development of retail enterprises due to seasonal changes, promotional activities and market competition. This dilemma is rooted in the fact that retailers cannot accurately know the number of consumers in each channel under the limited demand information, which makes them prone to inter-channel supply and demand mismatch, and increases the risk of business management. In addition, the loss aversion of individual risk decision-making process often drives retailers to deviate from the optimization path of rational expectation theory and adopt more conservative ordering strategies. This not only directly leads to the damage of economic efficiency, but also indirectly increases the cost of warehousing and logistics and environmental burden due to excessive storage.
Motivated by the real challenges mentioned above, this paper considers the limited information of market demand and the irrational behavior of decision makers, and investigates the impact of the implementation of BOPS on the ordering strategy of brick-and-mortar shops by omni-channel retailers. Combining prospect theory and the minimum-maximum regret value criterion, this paper constructs robust optimization models for loss averse retailers under the mandatory carbon emission capacity and carbon cap-and-trade regulations, respectively. With the objective of minimizing the retailer’s maximum regret value, we solve the robust ordering quantity of the two models, and analyze the impacts of loss aversion, carbon quota and carbon trading price on the retailer’s ordering quantity, regret value and carbon emission from the theoretical and numerical aspects.
The paper has the following results. (1)Under the mandatory carbon emission capacity regulation, retailers’ robust ordering strategies need to be flexibly adjusted in accordance with the carbon quota set by the government, taking into account the impact of loss aversion. When the government-set carbon quota is lenient, the robust ordering strategy will not be restricted by the carbon regulation, but the loss aversion characteristic may lead to over-conservative ordering and increase the likelihood of retailer’s regret. At this point, retailers should adjust their strategies to balance environmental requirements and economic benefits to reduce the risk of regret. On the contrary, if the carbon quota imposed by the government is more stringent, the impact of loss aversion on ordering volume and regret value will be weakened. The retailer should pay more attention to factors such as product price, inventory cost and cross-selling profit to optimize the ordering strategy. (2)The robust ordering strategy for retailers under the cap-and-trade regulation is independent of government mandated cap. Retailers should consider a variety of factors when developing their ordering strategies. These include not only economic factors such as market demand range, selling price per unit of product and inventory cost, but also environmental factors such as carbon emission. (3)Compared with the mandatory carbon emissions capacity regulation, the cap-and-trade regulation not only effectively mitigates retailers’ regrets due to non-optimal decision-making, but also ensures that they operate with lower carbon emission at the same time, which has the dual advantages of risk management and environmental protection. However, in the process of implementing the regulation, setting strict quota may cause resistance from enterprises and affect the effective implementation of the regulation. Therefore, the government needs to take into account both the emission reduction target and the adaptability of enterprises when determining carbon emission quota.
This paper focuses on the ordering strategy of the loss-averse retailer when the demand information is located in a certain range. Future research directions can consider the case where only the mean and variance of the demand distribution are known. This model can also be extended to consider the supply chain system.

Key words: omni-channel retail, carbon regulation, loss aversion, min-max regret