This study uses the Gregory–Hansen cointegration method and the vector error correction model in the vector autoregression system to reveal how international trade contributes to economic sustainability. The Gregory–Hansen test for cointegration method reveals a permanent equilibrium relation among sustainably economic growth, exports, and imports and shows that exports facilitate GDP growth and accelerate improvements in the capability of imports in the long-run. The causality between GDP and exports is unidirectional, indicating that exports area determinant of sustainable economic growth. The bidirectional causality from imports to GDP also sheds light on the important influence of imports on economic sustainability; however, GDP growth also drives import growth. The interaction between imports and exports corresponds to their bidirectional causal relationship, which is indicative of imports contributing to export production and of export growth expanding the capacity for imports. This finding indicates that imports are both exogenous and endogenous factors for exports.

This study aims to explore the sustainable development of the regional economy from the perspective of human capital. Based on the panel data from 2005 to 2019 in Shandong Province, China, this study first analyzes the interactive coupling mechanism between human capital and sustainable economic growth, and then constructs the evaluation model of coupling coordination degree. Results reveal that Shandong Province’s human capital and sustainable economic growth gradually increased; the coupling coordination degree of human capital and sustainable economic growth changed from a state of mild imbalance to slight coordination; sustainable economic growth lagged human capital development; education scale, innovation capacity, growth level, economic openness, and investment and consumption level are the key factors affecting sustainable economic development. Through the above research, the study puts forward policy suggestions conducive to sustainable development in China.

The COVID-19 pandemic created a strong urgency for the application of artificial intelligence (AI) in the tourism and hospitality industry. This paper was set to develop a scale of AI needs in health tourism. A total of 556 valid data were collected, and both exploratory and confirmatory factor analyses were employed to analyze the data. Six constructs containing 18 items were identified, and the reliability and validity were examined to reach satisfactory levels. The measurement scale developed may serve as a foundation for future research, and shed light on tourism managers, marketers, AI designers and policymakers.