5.1. The Spatial and Temporal Evolution Characteristics of Urban Tourism Development and Green Development Efficiency
The Super-EBM model and entropy-weighted method were respectively used to calculate the GDE and TD of 41 cities in the Yangtze River Delta region from 2000 to 2018, and then the Theil index was used to calculate the regional differences (
Figure 2). As shown in
Figure 2, from 2000 to 2018, the tourism development level in the Yangtze River Delta region generally showed fluctuating upward trend. The difference in regional tourism development showed an inverted U-shaped evolution trend. The green development efficiency and its regional difference exhibited a staggered evolution trend of high efficiency and low difference or low efficiency and high difference. Specifically, the evolution process could be divided into the following three stages. The first stage (2000–2004) was the initial rise of green growth efficiency. The green development efficiency increased from 1.025 in 2000 to 1.082 in 2004, with an average annual growth rate of 5.56%. The second stage (2004–2015) was the ebb and flow of green development efficiency. There presented a downward-upward W-shaped fluctuation trend from 2004 to 2010 and a downward-upward-downward inverted N-shaped fluctuation trend from 2010 to 2015. While the regional differences showed an inverted V-shape decrease pattern from 2004 to 2012 and an N-shaped increase pattern from 2010 to 2015. In the third stage (2015–2018), the green development efficiency presented an inverted V-shaped upward trend, and the Theil index of green development efficiency showed a downward trend.
In order to depict the spatial differentiation characteristics of urban tourism development, the interquartile spatial visualization was carried out using 0.1, 0.3, and 0.5 as critical values to classify tourism development into four types, namely, low, medium, high, and higher levels (
Figure 3a). As shown in
Figure 3a, most cities had achieved a significant increase in the level of tourism development since 2000, with an increase in the number of cities with high and higher levels of tourism development increasing and a decrease in the number of cities with low levels of tourism development. There were large spatial differences in the development of urban tourism economy, showing a pattern of “high in the southeast and low in the northwest”. Shanghai had always kept a higher level of tourism economy, far ahead of other cities. Hangzhou had always focused on the construction of a livable and touristy city, and the level of tourism economy had stabilized at a high-level echelon. The tourism development level of Suzhou, Huaibei, Bozhou, Huainan, Fuyang, and Tongling in Anhui Province had been low for a long time, and it was difficult to jump because of the relative lack of tourism resources and the lack of momentum of capital introduction. Hefei, Nanjing, Suzhou, Huangshan, Wuxi, and other cities had risen to a high level of tourism development echelon due to their developed economy levels and rich tourism resources. In terms of provincial scale, Shanghai, Zhejiang, and Jiangsu all achieved medium and above levels of tourism development in 2018, with only some cities in Anhui Province still at a low level of tourism development.
Similarly, the critical values of 0.3, 0.5, and 0.8 were used to classify green development efficiency into four grades, namely, low, medium, high, and higher efficiency (
Figure 3b). As shown in
Figure 3b, urban green development efficiency in the Yangtze River Delta region was generally not high and tended to decrease. Specifically, the number of cities with high and higher green development efficiency was 10, 9, and 4 in 2000, 2009, and 2018, respectively. The number of cities with low and medium green development efficiency accounted for 75.61%, 78.05%, and 90.24% in 2000, 2009, and 2018, respectively. Overall, the gradual decrease in the number of cities with high and higher green development efficiency implied that the current economic development mode in the Yangtze River Delta region still relied heavily on the consumption of resources and environment, and there was still much room for improving urban green development efficiency. Besides, urban green development efficiency showed a distribution pattern of “high in the south and low in the north”. The cities with high and higher green development efficiency in 2000 were concentrated in southern Zhejiang Province and southwestern Anhui Province and scattered in Shanghai and Huai’an, while the low-value areas were clustered in the northwestern delta and northern Zhejiang Province. In 2009, the high-value areas of green development efficiency were more dispersed, and the low-value areas were still mainly distributed in the northern and central delta. In 2018, cities with high and higher green development efficiency shrank to Huangshan, Shanghai, Hangzhou, and Quzhou, while cities with low green development efficiency tended to be more concentrated in the northern delta.
5.3. The Influencing Effects of Tourism Development on Green Development Efficiency
According to existing studies, green development efficiency was also influenced by many other socioeconomic factors, so it was necessary to further identify the impact of TD on GDE while controlling for other relevant variables (
Table 5). Thereinto, (1) the level of economic development, as an important component of desirable output, is an important driver of green development and directly affects the efficiency of green development. However, inevitably, the process of economic development also increases the level of resource consumption and environmental pollution [
47,
48]. Per capita GDP was used to characterize urban economic development level (
ED). (2) Different industries have significant differences in the scale and efficiency of economic and environmental output, while industrial development has the heaviest impact on resource consumption and environmental pollution and is an important part of the secondary industry [
49,
50]. The ratio of the industrial-added output value to GDP (%) was used to characterize industrial structure (
IS). (3) Improvements in advanced production and management technologies can directly contribute to green technological progress and reduce energy consumption and environmental pollution, thus promoting green development efficiency [
51]. The energy consumption of 10,000 Yuan GDP was used to represent urban innovation ability (
IA). (4) There are generally two opposite views about foreign direct investment. On the one hand, economically developed countries transfer highly polluting and energy-intensive industries to underdeveloped regions for production at a lower environmental cost, which exacerbates local resource consumption and environmental pollution and creates a “pollution refuge” effect. On the other hand, foreign direct investment may also bring advanced technology and management concepts, generate technological spillover effects, and stimulate the transformation of local economic development [
52,
53]. As an important foreign trade agglomeration, the Yangtze River Delta region accounts for one-fourth of the country’s foreign direct investment, thus the ratio of foreign direct investment to regional GDP was used to characterize the level of regional openness to the outside world. (5) Environmental regulation can directly reduce environmental pollution emissions and resource waste through the control and treatment of pollutant sources and production processes, so that economic efficiency can be improved. However, environmental regulation may increase firms’ production costs and induce fluctuations in green development efficiency [
54,
55]. The ratio of total environmental investment to GDP was selected to measure the intensity of environmental regulation (
ER). (6) Government policy regulation is an important means to promote energy conservation and emission reduction in the whole society. By formulating corresponding policies of command and control, economic incentives, and public participation, it not only influences the institutional prospect of green technology development, but also effectively promotes enterprises to innovate production technology, reduce pollutant emissions and form a public participatory and conservation-oriented society [
56,
57]. The proportion of fiscal expenditure to regional GDP was selected to reflect the influence of urban government intervention (
GI).
The optimal lag period 2 of GDE was determined as the instrumental variable to estimate the SGMM model. To illustrate the validity and robustness of the estimation results, the mixed OLS model and the panel fixed effect model were also estimated by using Stata16.0 software. Moreover, in order to avoid collinearity, the stepwise regression method was used, and relevant variables were successively introduced into 7 groups for regression (
Table 6). As shown in
Table 6, the coefficient of the lag term of the independent variable was between the coefficient values of the OLS model and the fixed effect model, which indicates that the SGMM model was reasonably set.
The coefficient of tourism development was 0.305 and passed the 1% significance level test, and the coefficient of the quadratic term of tourism development was −0.010 and also passed the 1% significance level test, which indicated that there existed an inverted U-shape relationship between tourism development and green development efficiency. This was due to the fact that the rapid development of the tourism industry played a certain multiplier effect on urban economic growth and job creation, and this positive effect offset the external negative effect, thus significantly promoting green development efficiency. However, with the advent of mass tourism, tourism resources were continuously being developed and the scale of tourism continued to expand, and the negative impact of tourism development on resource consumption, environmental pollution, and local culture gradually appeared. Tourism was no longer a “smokeless” industry, and due to its heavy dependence on oil, coal, and other energy sources, tourism had become a major carbon emitter, which made tourism development produce a negative impact on urban green development efficiency.
The elasticity coefficient of economic development on green development efficiency was 0.055 and passed the 1% significance level test, which indicated that economic development level had a significant positive driving effect on green development efficiency. This was due to the fact that economic development not only promoted the rationalization and advancement of industrial structures and provided a solid material foundation for green growth, but also improved residents’ environmental awareness and demands, thus exerting a positive impact on green development efficiency. However, the economic development mode relying on the input and consumption of natural resources should be avoided, which may increase ecological environment pollution and undesirable outputs and hinder the sustainable and rapid improvement of green growth.
The elasticity coefficient of the proportion of secondary industry was significantly negative, indicating that the industrialization level had a negative driving effect on green development efficiency in the Yangtze River Delta region. This was because the secondary industry had always been a major sector with high resource consumption and high environmental pollution in national economic development. The higher the industrialization rate, the lower the green development efficiency. It could be anticipated that with the development of the social economy, regional cities gradually entering into the post-industrial era, the degree of industrialization would tend to decrease, and industrial production would be cleaner and more efficient, which contributed to improving green development efficiency.
At the 1% significance level, the influence coefficient of innovation capacity on urban green development efficiency was −0.037. The innovation capacity characterized by energy consumption per ten thousand yuan of GDP was an inverse indicator, the higher energy consumption per ten thousand yuan of GDP, the weaker innovation ability, and vice versa, which implied that the stronger the innovation capacity, the higher green development efficiency. This was because technological innovation could effectively promote social development, improve economic production efficiency, optimize industrial structures, transform economic growth mode, and thus improve urban green development efficiency.
The elasticity coefficient of foreign direct investment on urban green development efficiency was −0.02, which passed the 1% level of the statistical significance test. This was because the Yangtze River Delta region was highly open to the outside world and has frequent foreign economic and trade exchanges. Due to the relatively looser environmental access policies, a large amount of global logistics, information flow, and capital flow were concentrated in the Yangtze River Delta region, which accounted for a quarter of China’s foreign investment. A large amount of foreign direct investment not only promoted rapid urban economic development, but also consumed a large amount of natural resources and increased the level of environmental pollution. However, the “pollution halo” effect was offset by the “pollution haven” effect, which would restrict the improvement of green development efficiency.
The influence coefficient of environmental regulation on green development efficiency was 0.003, but not significant. This was due to that environmental regulation could reduce and restrain environmental pollution emissions and resource waste through the long-term investment of capital and technology, and finally improved green development efficiency. However, the enhancement of environmental regulation intensity would lead to the increase in pollution cost of enterprises, inhibit the investment of enterprises in cleaner production technology, and affect the production efficiency and direct economic benefits of enterprises, which was insignificantly conducible to improving green development efficiency in the short term. With the continuous implementation of environmental regulations, the scale and proportion of investment in environmental pollution control would significantly increase, which would lead to the reduction in resource consumption and environmental pollutant emissions and promote the improvement of green development efficiency.
At the 1% level, government intervention had a significant positive impact on green development efficiency. Every 1% increase in the degree of government intervention would improve the green development efficiency of the Yangtze River Delta urban agglomeration by 0.002%. This was because that government regulation was an important driving force to promote social energy conservation and emission reduction. It could effectively promote enterprises to innovate production technology, reduce pollutant emissions, and thus improve green development efficiency by integrating command and control with economic incentive policies.