Research on Financing Strategies, Model Construction and Numerical Analysis for Dual-Channel Tourism Supply Chain in the Low Carbon Context

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In the context of a low-carbon economy, advocating and promoting low-carbon tourism has become an inevitable trend in the development of the tourism industry. Travel agencies, as the essential link between tourists and other tourism sectors, play a pivotal role in advancing low-carbon tourism through effective publicity, organization, and communication. However, the efforts of travel agencies to adopt low-carbon practices also impose significant financial pressures. This paper focuses on numerical analysis to construct a dual-channel tourism supply chain, emphasizing the low-carbon efforts of travel agencies. It explores optimal pricing and decision-making processes related to low-carbon efforts for travel agencies facing financial constraints under two financing models: deferred payment and bank lending. The core of this study lies in its detailed numerical analysis, which compares and analyzes these two financing decisions, examining the impact of the low-carbon effort cost coefficient on the level of low-carbon efforts and the required financing amount. Additionally, it investigates the influence of low-carbon effort costs on the capital demands of travel agencies, as well as the effects of financing interest rates and available funds on optimal decisions and profitability. The research findings, derived from rigorous numerical analysis, indicate that when travel agencies with low-carbon initiatives face financial constraints, their profits are consistently higher under the deferred payment financing model compared to the bank loan model. Consequently, travel agencies are likely to prioritize deferred payment financing when feasible. This study not only considers the low-carbon behavior of travel agencies but also delves into the issue of financial constraints, offering valuable insights for the sustainable development of the tourism supply chain. The research includes detailed numerical examples to validate its conclusions and suggests several management implications.