The tourism industry serves as pillar industry of the national economy and plays an irreplaceable role in promoting economic and social development and enhancing popular happiness. As is known, China has vast territory, a long history, splendid culture, diversified ethnic minorities, and extremely abundant tourism resources, which lay a solid foundation for the prosperity and development of the tourism industry in China. In 2019, the tourism industry contributed 8.9 trillion dollars to the global GDP, with a proportion of 19.3% of GDP gross, which simultaneously provided employment for 330 million people, occupying 10% of total jobs [
1]. The contribution of the tourism industry to the global GDP has increased most rapidly in the Asia-Pacific region, in which China ranks the first in terms of GDP and employment scale. In China, the overall contribution of the tourism industry to GDP is 10.94 trillion yuan, taking up 11.05% of the GDP; employment of 798.7 million people is indirectly and directly related to the tourism industry, accounting for 10.31% of the total employed population [
2]. Rapid development of China’s tourism industry has made a remarkable contribution to the global tourism growth. In 2019, China made a quarter of the contributions to the growth of the global tourism industry; the number of China’s outbound tourists and the total outbound consumption reached up to 155 million visits and USD 300 billion, respectively, which makes it the greatest tourist-generating and tourism consumption country in the world. With constant economic development and scientific progress, diversification has become the theme in China’s tourism industry development. To be specific, further refinement of tourist groups, constant innovation of tourism projects, continuous enrichment of the tourism theme, constant change of tourism types, increasingly diverse travel modes, and constantly expanded and upgraded market requirements will inevitably promote the development and growth of the tourism industry. However, China’s tourism economy shows a series of problems including unbalanced development, excess invalid input, and insufficient effective supply. It is urgent to evaluate the tourist industry efficiency for promoting balanced and high-efficiency development of the tourism economy in China.
Currently, scholars have mainly investigated tourism efficiency in some specific fields, particularly in the three pillars of modern tourism industry, i.e., travel agencies, hotels, and tourism traffic [
5,
6,
7]. Tourism agencies serve as the medium organizations for integrating tourism resources and forming tourism productions, which usually arouse much attention. Koksal et al. analyzed the operating efficiency of the international travel agencies in Turkey and found that many travel agencies have low operating efficiency in rush tourism seasons [
5]. Ramon et al. analyzed the relative efficiencies of 22 travel agencies in Alicante, Spain; proposed the corresponding improvement measures; and finally concluded that geographic position is the most important factor that affects efficiency by using the Mann–Whitney
U test [
8]. Hotels are important parts of the tourism industry, and are also the research hotspots. Assaf et al. evaluated the operating efficiency of hotels in the Asia-Pacific region and concluded that scale, ownership, and classification all significantly affect hotel efficiency [
9]. Barros et al. found that most of Portugal’s hotels were poor in efficiency and severe in terms of waste of resources, and the authors put forward some improvement measures such as enhancing productivity and attracting foreign investment [
10]. Corne et al. analyzed the technical efficiency of the hotel industry in France and pointed out that economical hotels exceed in efficiency but are poor in income, which can explain the paradox in the French hotel industry [
11]. Tourism traffic is also quite important and simultaneously raises great concern. Ripoll-Zarra et al. adopted stochastic frontier analysis for estimating the technical efficiency of Spanish airports and found that some airports are low in efficiency; the management mode, geographic position, and accommodation type can all significantly affect the airport efficiency [
12]. Lo Storto et al. measured the efficiency of Italian airports on the basis of the NSBM-DEA model and concluded that the NSBM-DEA model is more effective than the traditional DEA model; the related results can provide insightful understanding for policymakers in terms of performance improvement and management [
13]. As scholars have performed increasingly in-depth studies on tourism economy efficiency, they expanded their objectives to many aspects such as tourism attractions and tourism destinations [
14,
15,
16]. Baggio used a network analysis approach towards digital ecosystems in order to study the relationship between technological tools and physical entities in a destination and how these tools and their combinations affect the efficiency of the system at the local and global levels [
17]. Tiziana analyzed the efficiency of cultural heritage on the United Nations Educational Scientific and Cultural Organization (UNESCO) World Heritage List in Italian regions and found that UNESCO sites exert opposite effects on the performance of these sites of cultural heritage [
18]. Currently, there are two common methods for the estimation of tourism efficiency, i.e., data envelope analysis (DEA) and stochastic frontier analysis (SFA) [
19,
20,
21]. DEA is a useful and effective technique and is extensively applied in various industries owing to the use of Pareto efficiency that compares the decision units with the other decision units [
3]. For example, Sharon et al. analyzed and compared the tourism efficiencies of 105 countries on a macro level [
3], and Barros employed DEA for estimating the total-factor productivity of a state-owned chain of hotels in Portugal [
22]. By contrast, Chinese scholars have conducted related studies on multiple levels from macroscopic national tourism to microscopic tourism enterprises [
23,
24,
25], regarding diversified topics such as tourism ecological efficiency, rural tourism efficiency, poverty alleviation efficiency through tourism, and tourism management efficiency [
26,
27,
28,
29,
30]. Moreover, the research perspectives become increasingly wide and cover the mechanism of tourism efficiency, spatial–temporal difference, literature review, and the analysis of influencing factors [
31,
32,
33,
34]. With their improvement, the DEA model, super-efficiency model, three-phase DEA model, and bootstrap DEA model have now been gradually popularized in practical applications [
35,
36,
37,
38].
The existing studies on tourism efficiency are still limited to date. Firstly, in previous studies, scholars mainly focused on the estimation at a single time point or on a single department while neglecting spatial–temporal evolutions of multiple department combinations in long time series. Meanwhile, the previous studies mainly adopted traditional DEA models, whereas dynamic analysis of tourism efficiency by combining super-efficiency DEA model and the Malmquist index is especially rare. Scholars have mainly investigated the influencing factors via qualitative analysis and have not taken the effects of spatial effect into account, which cannot effectively reveal the spatial heterogeneity of the influencing factors. In addition, there is a lack of consideration of the local spatial regression model. On account of spatial flow characteristics of both tourism service and products, it is of great significance to analyze the driving factors under spatial distribution rules [
39]. For this reason, this study selected the SBM–Malmquist model to measure the tourism efficiencies of various provinces in China and explored the spatial–temporal heterogeneity among different provinces with ESDA method. By taking the spatial effect into account, we classified the influencing factors with the GWR econometric model. The research results can provide reference for other similar regions or countries with rapid development of tourism in the world.