Artificial Intelligence as a Catalyst for Sustainable Tourism: A Case Study from China
Dandan Song, Hongwen Chen
Abstract
The tourism industry’s explosive growth has triggered severe carbon emission issues, making enhancing tourism carbon efficiency (TCE) a pressing concern for achieving sustainable tourism development. The widespread application of artificial intelligence (AI) in tourism presents new opportunities. This study applies the Environmental Kuznets Curve (EKC) theory to examine the pathways and mechanisms of AI’s impact on TCE, with a focus on China. The findings reveal that AI significantly enhances TCE, where improvements in tourism labor productivity, the rationalization of the tourism industry structure, and advancements in tourism technology are the key channel mechanisms. Heterogeneity tests indicate that AI substantially boosts TCE in eastern developed regions and areas with deficient tourism resource endowments. Furthermore, AI exhibits significant spatial spillover effects, enhancing both local and neighboring regions’ TCE. These insights provide crucial policy implications for utilizing AI to promote China’s sustainable tourism industry.