Cultural-tourism integration and ecological product value realization: a double machine learning approach
利用双机器学习模型分析2012-2023年中国30个省份面板数据，发现文旅融合显著促进生态产品价值实现，且数字经济与绿色金融起正向调节作用，对低消费、新兴旅游集聚等地区效果更强。
Converting inherent ecological advantages into economic assets constitutes a fundamental challenge for global sustainable development. This study utilizes a Double Machine Learning (DML) model to meticulously evaluate the causal impacts of Cultural-Tourism Integration (CT) on Ecological Product Value Realization (EPVR). Utilizing panel data from 30 Chinese provinces (2012–2023), the methodology adeptly mitigates high-dimensional confounding biases. Empirical evidence uncovers three fundamental insights: First, CT serves as a regenerative catalyst, markedly promoting EPVR across supply, regulatory, supportive, and cultural service dimensions, with the most substantial improvement noted in fundamental supporting services. Second, mechanism tests indicate that the Digital Economy and Green Finance act as beneficial moderators, substantially enhancing the ecological value conversion efficiency of CT. Third, heterogeneity analyses reveal that the driving effect of CT is deeply bounded by regional structural conditions. Specifically, CT functions as a critical “leapfrog” and “resilience” engine – exhibiting robust significance in regions with lower baseline consumption, emerging tourism agglomeration, high extreme weather exposure, and transitional green innovation capacities. This paper clarifies the rationale behind how CT converts “dormant” resources into assets, broadening theoretical perspectives on digital and financial empowerment in tourism and providing policy recommendations for attaining synergistic high-quality development and ecological preservation.