Assessment and Improvement Strategies for Sustainable Development in China’s Cultural and Tourism Sector

Against the backdrop of sustainable development and from a macro perspective, this paper focuses on the cultural and tourism industry, measures its sustainable development efficiency, analyzes influencing factors, and systematically explores improvement paths. Based on the theoretical perspective of sustainable development, this study has constructed an evaluation index system for measuring the sustainable development level of the cultural and tourism industry across four dimensions, as follows: cultural and tourism economic construction, cultural and tourism basic resources, social basic support, and ecological environment quality. The range entropy value was adopted to measure the sustainable development level of the cultural and tourism industry in 31 provinces of China from 2006 to 2023. The results show that the sustainable development level of China’s cultural and tourism industry is generally low, but shows an increasing trend. In terms of the annual growth rate of regional scores, the trend is as follows: North China (7.05%) > Central South (6.00%) > East China (5.97%) > Southwest (5.03%) > Northwest (4.56%) > Northeast (2.94%). This indicates considerable room for improvement in the future. Furthermore, this study used kernel density estimation to analyze the distribution dynamics and evolution trends of the sustainable development level of the cultural and tourism industry and its scores at all levels, revealing differences in development levels among provinces and regions. Finally, this study has innovatively adopted the fsQCA method to explore improvement paths for the sustainable development level of the cultural and tourism industry, and identified three implementation paths: “openness–human resources–consumption–environment-driven”, “human resources–consumption–environment-driven”, and “openness–environment-driven”. By constructing a multi-condition combination model, this study breaks through the limitations of traditional single-factor analysis and reveals multiple concurrent causal relationships in complex situations. This approach showcases the differentiated development models of each province under the interacting effects of multi-dimensional factors, and provides policymakers with a basis for precise policy implementation “tailored to local conditions and multi-dimensional collaboration”.