4.2. Spatial Autocorrelation Patterns of the Eco-Efficiency of Tourism
The
Global Moran’s I is calculated using Stata.16 software to reveal spatial autocorrelation patterns of the eco-efficiency of provincial tourism in China from 2008 to 2017. The results are given in
Table 5.
As shown in
Table 5, (1) the eco-efficiency of provincial tourism in China shows obvious positive spatial autocorrelations, which pass the significance test at the 10% significance level in 60% of the years and even pass the test at least at the 5% significance level in 2011–2015. This finding indicates that the eco-efficiency of provincial tourism showed significant spatial autocorrelations in these years. That is, provinces with high (or low) efficiencies are obviously clustered. (2) from the characteristics of sequential variation, the positive spatial autocorrelation of the eco-efficiency of provincial tourism first increases and then decreases, with 2012 as the deflection point. From 2008 to 2012, the
Global Moran’s I was always larger than 0 and increased with fluctuations. This indicates that the positive spatial autocorrelation of the eco-efficiency constantly improved and provincial tourism showed certain cooperativity in terms of sustainable development in the period. The
Global Moran’s I, however, declined substantially after 2012 and it was even lower than 0 in 2016 while failing to pass the significance test. This finding implies that the eco-efficiency of provincial tourism gradually weakened to become unobvious during the period. This is mainly due to the fact that the sustainable development of provincial tourism was effectively driven by resource sharing among provinces, technology transfer and the trickle-down effect before 2012. Thereafter, however, the breakthrough of the previous spatial autocorrelation pattern should be achieved due to the different demands and priorities of various provinces for adjusting the industrial structure, improving management level and intensifying environmental regulation when advancing to high-quality development. As a result, the spatial autocorrelation gradually weakened.
To further study the spatial autocorrelation pattern of the eco-efficiency of provincial tourism in China, data from 2008, 2012 and 2017 were selected as cross-sectional data to calculate the
Local Moran’s I of each province. The results are shown in
Figure 2,
Figure 3 and
Figure 4. In the figures, the horizontal axis represents the standardized the regional eco-efficiency of tourism in each region and the vertical axis represents the eco-efficiency of tourism in each region by weighting spatially weighted matrixes. Moreover, the first, second, third and fourth quadrants of these figures separately represent the high-high (HH) cluster, low-high (LH) outlier, low-low (LL) cluster and high-low (HL) outlier. The HH cluster means that regions with a high eco-efficiency of tourism are surrounded by regions also with a high eco-efficiency, and others are defined following the same principle.
It can be seen from
Figure 2,
Figure 3 and
Figure 4 that the eco-efficiency of provincial tourism in China is mainly clustered in the LL cluster area, followed by the HH cluster and LH outlier areas, while only a few are distributed in the HL outlier area. To be specific, the LL areas mainly include Guangdong, Guangxi, Hainan, Gansu and Fujian, which showed a low degree of clustering in the study period. HH clustering is mainly found in Beijing, Tianjin, Henan, Shanxi, Chongqing and Shaanxi. They play a driving effect on surrounding adjoining provinces during their high-quality development of tourism, thus leading to a pattern of coordinated development of regions. The LH outlier areas mainly cover Hubei and Sichuan, which have lower eco-efficiency in tourism compared with nearby provinces, so they do not coordinate with the surrounding regions in their development. There are few provinces in the HL outlier area. From the evolution trends of clustering characteristics of the eco-efficiency of tourism in various provinces (and cities), Beijing, Tianjin, Henan, Shanxi, Shaanxi, Chongqing, Guangdong, Guangxi, Hainan, Gansu and Fujian always remained in the first and third quadrants throughout the study period. It implies that the eco-efficiency of tourism in these regions was trapped in a dilemma of the Matthew effect as follows: high-efficiency regions further promote their high-quality development of tourism by virtue of the high-efficient management, advanced technologies and resource endowment; while low-efficiency regions find it difficult to reverse the declining tendency of the low efficiency due to inefficiency of management and limitation of ecological bearing capacity. Xinjiang Uygur Autonomous Region, Jiangxi, Jilin, Hebei and Shandong turned from LL cluster and LH outlier to HL outlier and HH cluster; while Heilongjiang, Inner Mongolia Autonomous Region and Zhejiang changed from HH cluster and HL outlier to LH outlier and LL cluster. This finding indicates poor local stability of the eco-efficiency of tourism. To solve the problem, inefficient provinces and cities should take those efficient ones as their benchmark and take measures including factor flow, introducing advanced management technologies and implementing reasonable environmental regulations. By doing so, they are expected to realize an ideal situation of transition from an inefficient region to an efficient one.
4.3. Spatial Spillover Effect of the Eco-Efficiency of Tourism
The above analysis results indicate that the eco-efficiency of Chinese tourism features high spatial autocorrelations, so the spatial factor needs to be considered when studying influencing factors of the eco-efficiency of regional tourism. By using the Stata.16.0 software, the study adopts a spatial panel econometric model to carry out spatial regression analysis (spatial econometrics) on the influencing factors of the comprehensive efficiency, pure technical efficiency and scale efficiency of Chinese tourism. According to results of the Lagrange multiplier (LM) test and likelihood ratio (LR) test, statistics of the robust LM-spatial error and the robust LR-spatial error both pass the significance test at the 1% significance level, and the Hausman test results also reject the null hypothesis at the 5% level. In addition, LR test results demonstrate that the time fixed effect is superior to the spatial fixed effect and the time- and spatial-fixed effect. Therefore, the time-fixed SDM model is used to perform spatial regression analysis on the overall efficiency and decomposed efficiencies of tourism. The results are listed in
Table 6.
The following conclusions can be drawn from the regression results in
Table 6:
(1) The regression coefficient and spatial lag coefficient of the economic development level (
X1) with the comprehensive efficiency of tourism are separately −0.023 and −0.126, both of which do not pass the significance test. The regression coefficients of
X1 with pure technical efficiency and scale efficiency are 0.331 and −0.327, and their spatial lag coefficients are −0.37 and 0.217, respectively, both being significant at the 1% significance level. This indicates that the economic development level mainly has effects on pure technical efficiency and scale efficiency. The development of the social economy promotes the local energy saving and consumption reduction technologies and management level to improve, thus imposing positive effects on the pure technical efficiency of local tourism. However, as shown by Kim et al. [
38], the rapid growth of the social economy of a region also leads to the outflow of technology, talent and resource factors from adjoining regions, thereby inhibiting the pure technical efficiency in the adjoining regions. Moreover, the traditional extensive development mode also causes consumption of huge fossil energy despite its contribution to high-speed growth of the GDP, resulting in a constant rise in the clustering scale of tourism resource input in the region. This alleviates the clustering scale of tourism resource input in adjoining regions while inhibiting improvement of the scale efficiency of tourism in the region, thus promoting the scale efficiencies of adjoining regions.
(2) The industrial structure (X2) shows regression coefficients of −0.807, −0.580 and −0.472 with the comprehensive efficiency, pure technical efficiency and scale efficiency of tourism, which are all significant at the 5% significance level; its spatial lag coefficients with these efficiencies are −0.378, −1.057 and 0.450, and only the SAC coefficient with the pure technical efficiency passes the significance test. The result indicates that the industrial structure exerts an inhibition effect on the improvement of the comprehensive efficiency of tourism. A reasonable industrial structure has a positive effect on the sustainable development of Chinese tourism. In contrast, a single industrial structure may lead to excessive clustering of a single industry and impose heavy pressure on resources and the environment, which is detrimental to the sustainable development of tourism. The gradual increase in the proportion of the tertiary industry in a region also results in the outflow of tourism resources and management technologies in adjoining regions, thus inhibiting the pure technical efficiency of adjoining regions.
(3) The technical level (X3) has regression coefficients of −0.209, −0.113 and −0.128 with the comprehensive efficiency, pure technical efficiency and scale efficiency of tourism, which are all significant at the 1% significance level. Its spatial lag coefficients with the comprehensive efficiency, pure technical efficiency and scale efficiency of tourism are −0.345, −0.332 and −0.119, with the former two being significant at the 1% level and the latter failing to pass the significance test. In this study, the energy consumption per ten thousand yuan of tourism revenue is used to characterize the technical level; that is, the larger the figure is, the lower the technical level. The regression coefficients reveal that the technical level exerts adverse effects on both the comprehensive efficiency and decomposed efficiencies of tourism as follows: the higher the technical level is, the lower the energy consumption under the same tourism revenue and the higher the efficiency. From the spatial lag coefficient, the technical level exerts significant spatial spillover effects. That is, improvement of the technical level in a region can drive the sustainable development of tourism in adjoining regions and thus promote comprehensive efficiency and pure technical efficiency to improve.
(4) Traffic accessibility (X4) has regression coefficients of 0.252, 0.252 and 0.025 with the comprehensive efficiency, pure technical efficiency and scale efficiency of tourism, with the former two being significant at the 1% significance level and the latter failing to pass the significance test; its spatial lag coefficients with the comprehensive efficiency, pure technical efficiency and scale efficiency of tourism are −0.024, −0.217 and 0.222, with the former failing to pass the significance test and the latter two being significant at the 1% level. The result indicates that traffic accessibility has a promotion effect on the improvement of the eco-efficiency of tourism. Transportation is an indispensable condition for the development of tourism. Increasing the density of transportation networks can effectively improve the tourist gathering capacity of scenic spots, promote maximum utilization of tourism resources and drive scale development of tourism in adjoining regions. However, the resources and environment of scenic spots are under huge pressure with the continuously passive expansion of the scale of the tourism sector in adjoining regions, thus suppressing improvement of the pure technical efficiency in the adjoining regions.
(5) The regression coefficients of environmental regulation (X5) with the comprehensive efficiency, pure technical efficiency and scale efficiency are 0.031, 0.019 and 0.014, and only the coefficient with the comprehensive efficiency passes the significance test at the 5% significance level. The spatial lag coefficients of X5 with them are −0.020, 0.023 and −0.029, and only the coefficient with the scale efficiency passes the significance test at the 10% level. These findings indicate that environmental regulation mainly exerts a significant promotion effect on the comprehensive efficiency of a region while it does not significantly influence adjoining regions. Environmental regulation is not only a compulsory measure that forces enterprises to save energy and reduce consumption; it is also an inevitable requirement of green development. Whereas highly strict environmental regulation may lead to the race-to-the-bottom risk. That is, once environmental regulation is so strict that it exceeds the bearing capacity of enterprises, it is highly probable that it will reduce the production efficiency of enterprises and even make enterprises go bankrupt.