Gansu Province is rich in tourism resources, and it is the hometown of the “copper galloping horse”, which is the logo of China’s tourism. However, the scale and revenues of tourism in Gansu province are still at a low level. This paper first evaluated the tourism efficiency of 14 cities and prefectures of Gansu Province in China from 2011 to 2019 using the super-slack-based measure (Super-SBM) and then investigated the internal driving mechanism of the efficiency change through the Global Malmquist-Luenberger (GML) index and its decomposition, and finally analyzed the external influencing elements of tourist efficiency by the Tobit model. The results revealed that the tourism efficiency of Gansu Province had increased rapidly during the study period, especially after 2016, the rising range increased. From 2011 to 2019, the cumulative changes in GML index, technological change (TC), and efficiency change (EC) of tourism efficiency in Gansu Province were 5.053, 4.145 and 1.160, respectively, indicating that the improvement of tourism efficiency in Gansu province is mainly due to technological progress. The regression results of the Tobit model show that the status of the tourism industry, trade openness, information level, and technological innovation level can significantly promote tourism efficiency in the province. At the same time, upgrading the industrial structure and the improvement of greening coverage inhibit tourism efficiency. However, the impact of the economic development level on the tourism efficiency of Gansu Province is not apparent. According to the research results, this paper puts forward corresponding suggestions to promote the development of tourism in Gansu Province. This study is crucial for hospitality, tourism, and policy sectors to understand the underlying factors and promote the healthy development of the tourism industry in Gansu Province.

Considering China's ambitions for carbon peaking as of 2030 to ensure environmental protection and energy security, the present study is intended to explore sustainable pathways to reduce coal consumption by enhancing energy efficiency. The current article estimates coal consumption efficiency and radial super-efficiency by applying DEA-CCR and radial super-efficiency models. In the second stage, the Logarithm Mean Divisia Index (LMDI) and DEA-Malmquist models were used to explore the components of coal consumption in China's six key energy-intensive industries from 2000 to 2020. Findings indicate that (1) there is a substantial coal consumption efficiency gap among these industries, and they are working well below the production frontier. (2) Findings of DEA-Malmquist indicate that technological changes positively contributed to total productivity changes, while technical efficiency negatively impacted coal consumption growth. (3) The results of the LMDI model reveal that industrial output growth and structural changes are the key factors accelerating coal consumption. In contrast, the coal intensity had deaccelerated the coal consumption in the energy intensive industries. The current study provides several policy proposals to enhance coal conservation and consumption efficiency to achieve the aspiring goals of sustainable development.