As artificial intelligence (AI) technology becomes increasingly prevalent in the tourism sector, an in-depth exploration of its opportunities and potential risks for the United Nations Sustainable Development Goals (SDGs) is urgently needed. However, existing research falls short in constructing an integrated knowledge framework that systematically clarifies how AI can effectively advance sustainable tourism, leaving theoretical understanding and practical pathways relatively fragmented. To address this gap, this study employs a systematic literature review following the strict SPAR-4-SLR protocol and integrates a domain-based TCCM (i.e., theories, contexts, characteristics, and methods) analysis framework. A total of 177 core articles from the Scopus and Web of Science databases were rigorously analyzed. This study first examines publication trends, key journals, and the citation impact of AI in tourism. It then systematically synthesizes the theoretical foundations, research contexts, characteristics, and methodologies. Most importantly, this review delves into the antecedents, decision-making processes (including mediating and moderating variables), and outcomes of AI applications in the tourism industry. This study not only delineates a clear direction and agenda for future academic inquiry but also provides theoretical support and practical guidance for policymakers and tourism managers to design and implement AI-driven sustainable tourism strategies.

Driven by the rapid proliferation of generative artificial intelligence, the computing power industry is undergoing a paradigm shift from traditional linear supply chains toward complex, interdependent innovation ecosystems. This study investigates the evolutionary dynamics of the computing power ecosystem, specifically examining the strategic interplay between antitrust regulation and vertical integration. We construct a tripartite evolutionary game framework involving the government regulators, leading computing power incumbents, and downstream AI innovators. By deriving evolutionarily stable strategies, we analyze the underlying mechanisms of system transitions and employ numerical simulations to explore key parametric sensitivities. The theoretical analysis suggests that the evolution of the AI computing power innovation ecosystem manifests distinct stage-based progressions and threshold-driven bifurcation characteristics—potentially transitioning from an initial efficiency-based state of “natural monopoly and passive dependence” during the industry’s emergence, through transitionary states such as the “comfort zone trap” or “regulatory stalemate” during the expansion phase, and ultimately converging toward a mature configuration of “co-opetition and endogenous growth.” The model suggests that downstream AI firms may benefit from advancing vertical integration, achieving hardware–software co-optimization through self-developed domain-specific architectures, The analysis further implies that the leading computing power firm could strengthen its ecological niche by opening its underlying interfaces and software stacks to maintain its ecological niche as the industry cornerstone in integrated form. For the government, it is necessary to establish precise dynamic intervention and orderly exit mechanisms.