Can Inbound Tourism Improve Regional Ecological Efficiency? An Empirical Analysis from China
Abstract
:1. Introduction
2. Literature Review and Theoretical Hypotheses
3. Model Construction and Data Selection
3.1. Model Construction
3.1.1. Benchmark Regression
3.1.2. Panel Threshold Model
3.2. Indicator Construction
3.2.1. Explained Variable (Ecological Efficiency, Ecoe)
3.2.2. Explanatory Variables (Ibtr)
3.2.3. Threshold Variables
3.2.4. Control Variable
3.3. Data Sources
4. Empirical Results and Analysis
4.1. Benchmark Regression Results
4.2. Threshold Regression Results
4.2.1. From the Perspective of Green Innovation Factor Input
4.2.2. From the Perspective of Green Innovation Factor Output
4.2.3. From the Perspective of Green Innovation Efficiency
5. Conclusions and Policy Recommendations
5.1. Conclusions
- (1)
- Inbound tourism can significantly improve regional ecological efficiency: the global environment has profoundly changed since the Industrial Revolution. Ecosystems in ecologically fragile areas have poor stability, weak anti-interference, and self-recovery capabilities. Under the background of global change, natural resource supply capacity declines, land degradation, biodiversity reduction, and frequent disasters. Ecosystems face enormous risks. Therefore, countries should use inbound tourism as a driving force to improve regional ecological efficiency and ecological security.
- (2)
- With the increase in green innovation investment, the promotion effect of inbound tourism on regional ecological efficiency first increases and then decreases. Enterprises are the source of innovation. In fact, excessive R&D investment is undeniable in the business practice of enterprises, and information asymmetry will exacerbate the moral hazard of management’s opportunistic behavior. If the company’s innovation investment opportunities and investment benefits are symmetrical between shareholders and managers, managers’ over-investment or under-investment in innovative projects will be observed by shareholders, and shareholders will take measures to avoid losses to reduce agency costs.
- (3)
- With the improvement of green innovation output, the promotion effect of inbound tourism on regional ecological efficiency first decreases and then increases. It can be found that for a region, its innovation bottleneck still exists. From experience and facts, some regions are in trouble because they cannot successfully overcome some systemic bottlenecks in the process of modernization. For example, economic development cannot transform into an innovative economy, and they fall into the “middle-income trap”. This will be detrimental to the improvement of regional ecological efficiency.
- (4)
- The higher the efficiency of green innovation, the greater the promotion effect of inbound tourism on ecological efficiency. It can be found that green innovation efficiency is vital in promoting ecological efficiency. Therefore, it is necessary to speed up the improvement of urban green innovation efficiency, form a mutual promotion mechanism of innovation drive and green development, and realize the synergy and win–win of technological progress, green ecology, and economic benefits.
5.2. Policy Recommendations
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Criterion Layer | Indicator Layer | Specific Instructions |
---|---|---|
Natural resource inputs | Water input | Total urban water consumption/104 cubic meters |
Energy input | Total urban electricity consumption/104 kWh | |
Land input | Urban construction land area/square kilometer | |
Input of economic factors | Labor input | Number of employees in the unit/104 people |
Capital investment | Fixed asset investment/104 yuan | |
Economic expected output | Regional GDP | Regional GDP/104 yuan |
Ecological load | Water pollution | Discharge of industrial wastewater/104 tons |
Air Pollution | Industrial sulfur dioxide emissions/104 tons |
(1) OLS | (2) FE | (3) D-K | (4) GMM | (5) Drop Variable | (6) 2SLS | |
---|---|---|---|---|---|---|
Ibtr | 0.27 *** | 0.31 *** | 0.35 *** | 0.31 *** | 0.36 *** | 0.31 *** |
(4.08) | (2.82) | (3.98) | (3.29) | (3.49) | (4.08) | |
Fdi | 0.22 *** | 0.20 *** | 0.22 *** | 0.19 *** | 0.20 *** | 0.29 *** |
(4.54) | (3.64) | (4.53) | (3.77) | (3.18) | (3.15) | |
Econ | −0.22 *** | −0.22 *** | −0.21 *** | −0.21 *** | −0.17 *** | −0.21 *** |
(−3.97) | (−4.46) | (−4.55) | (−3.40) | (−3.13) | (−2.83) | |
Ind | 1.74 *** | 2.50 *** | 1.87 *** | 2.33 *** | 2.49 *** | 1.70 *** |
(3.45) | (3.09) | (3.37) | (3.24) | (2.97) | (3.78) | |
Envi | 2.25 ** | 2.95 ** | 2.43 * | 2.96 *** | 2.70 * | 2.30 *** |
(2.32) | (2.22) | (1.97) | (1.93) | (1.77) | (2.74) | |
Urb | −0.13 | −0.09 | −0.09 | −0.13 | −0.09 | −0.11 |
(−0.93) | (−1.22) | (−1.34) | (−0.87) | (−1.30) | (−1.04) | |
Crea | 0.09 | 0.08 | 0.10 | 0.11 | 0.09 | 0.10 |
(0.36) | (0.36) | 0.43 | 0.46 | 0.37 | (0.50) | |
Time*Individual fixed effects | Control | Control | Control | Control | Control | Control |
Cons | 1.20 *** | 1.17 *** | 1.06 *** | 1.13 *** | 1.01 *** | 1.09 *** |
(3.42) | (3.32) | (4.92) | (3.72) | (4.43) | (3.97) | |
R2 | 0.6129 | 0.6251 | 0.6293 | 0.6228 | 0.5711 | 0.7014 |
Obs | 450 | 450 | 450 | 390 | 415 | 450 |
F Value | p Value | 1% Critical Value | 5% Critical Value | 10% Critical Value | |
---|---|---|---|---|---|
Single-threshold | 3.321 * | 0.093 | 7.706 | 4.932 | 3.249 |
Double-threshold | 2.359 * | 0.073 | 8.271 | 2.955 | 2.054 |
Three-thresholds | 9.062 *** | 0.000 | 3.954 | 2.252 | 1.633 |
(1) Single-Threshold | (2) Double-Threshold | (3) Three-Thresholds | ||
---|---|---|---|---|
Ibtr | Grii < δ1 | 0.25 *** | 0.27 *** | 0.30 *** |
(3.61) | (2.47) | (3.31) | ||
δ1 ≤ Grii < δ2 | 0.34 *** | 0.37 *** | 0.35 *** | |
(3.79) | (4.08) | (3.74) | ||
δ2 ≤ Grii < δ3 | 0.30 *** | 0.28 *** | ||
(3.08) | (3.22) | |||
δ3 < Grii | 0.27 *** | |||
(2.93) | ||||
Fdi | 0.19 *** | 0.28 *** | 0.26 *** | |
(4.88) | (4.64) | (4.66) | ||
Econ | −0.17 *** | −0.20 *** | −0.21 *** | |
(−3.34) | (−4.37) | (−3.29) | ||
Ind | 1.58 *** | 2.32 *** | 2.17 *** | |
(4.41) | (3.07) | (4.37) | ||
Envi | 2.65 *** | 3.01 ** | 2.35 ** | |
(2.29) | (2.59) | (2.27) | ||
Urb | −0.09 | −0.12 | −0.09 | |
(−0.98) | (−1.15) | (−1.15) | ||
Crea | 0.11 | 0.08 | 0.10 | |
(0.53) | (0.50) | (0.37) | ||
Time effects | Control | Control | Control | |
Individual effects | Control | Control | Control | |
Cons | 0.97 *** | 1.10 *** | 0.93 *** | |
(3.20) | (3.42) | (5.17) | ||
R2 | 0.6106 | 0.6202 | 0.6274 | |
Obs | 450 | 450 | 450 | |
δ1 | 0.142 | 0.171 | 0.322 | |
δ2 | 0.355 | 0.435 | ||
δ3 | 0.812 |
F Value | p Value | 1% Critical Value | 5% Critical Value | 10% Critical Value | |
---|---|---|---|---|---|
Single-threshold | 27.560 *** | 0.003 | 23.971 | 10.066 | 2.187 |
Double-threshold | 8.659 *** | 0.000 | 4.728 | 2.791 | 1.936 |
Three-thresholds | 24.743 *** | 0.007 | 19.746 | 6.338 | 0.059 |
(1) Single-Threshold | (2) Double-Threshold | (3) Three-Thresholds | ||
---|---|---|---|---|
Ibtr | Grii < δ1 | 0.31 *** | 0.31 *** | 0.36 *** |
(3.85) | (2.87) | (3.50) | ||
δ1 ≤ Grii < δ2 | 0.28 *** | 0.30 *** | 0.20 *** | |
(3.23) | (3.25) | (3.42) | ||
δ2 ≤ Grii < δ3 | 0.33 *** | 0.32 *** | ||
(2.82) | (4.10) | |||
δ3 < Grii | 0.33 ** | |||
(2.46) | ||||
Fdi | 0.18 *** | 0.23 *** | 0.23 *** | |
(3.23) | (4.84) | (4.67) | ||
Econ | −0.19 *** | −0.14 *** | −0.22 *** | |
(−4.33) | (−2.93) | (−3.52) | ||
Ind | 1.67 *** | 1.90 *** | 2.52 *** | |
(4.18) | (2.82) | (3.93) | ||
Envi | 2.94 ** | 2.08 ** | 2.79 ** | |
(2.41) | (2.29) | (2.33) | ||
Urb | −0.11 | −0.13 | −0.09 | |
(−1.17) | (−1.14) | (−1.16) | ||
Crea | 0.10 | 0.07 | 0.10 | |
(0.50) | (0.34) | (0.48) | ||
Time effects | Control | Control | Control | |
Individual effects | Control | Control | Control | |
Cons | 1.24 *** | 1.23 *** | 0.87 *** | |
(4.56) | (4.92) | (3.86) | ||
R2 | 0.5629 | 0.5927 | 0.5998 | |
Obs | 450 | 450 | 450 | |
δ1 | 0.468 | 0.468 | 0.261 | |
δ2 | 0.632 | 0.621 | ||
δ3 | 0.897 |
F Value | p Value | 1% Critical Value | 5% Critical Value | 10% Critical Value | |
---|---|---|---|---|---|
Single-threshold | 9.124 *** | 0.000 | 4.329 | 2.367 | 1.717 |
Double-threshold | 22.719 *** | 0.000 | 17.085 | 3.815 | 0.472 |
Three-thresholds | 34.704 *** | 0.000 | 19.827 | 13.444 | 9.024 |
(1) Single-Threshold | (2) Double-Threshold | (3) Three-Thresholds | ||
---|---|---|---|---|
Ibtr | Grii < δ1 | 0.22 *** | 0.26 ** | 0.22 *** |
(3.25) | (2.52) | (4.05) | ||
δ1 ≤ Grii < δ2 | 0.42 *** | 0.33 *** | 0.36 *** | |
(2.69) | (2.67) | (3.20) | ||
δ2 ≤ Grii < δ3 | 0.54 *** | 0.43 *** | ||
(2.92) | (3.83) | |||
δ3 < Grii | 0.56 *** | |||
(3.15) | ||||
Fdi | 0.22 *** | 0.19 *** | 0.21 *** | |
(3.40) | (3.97) | (3.36) | ||
Econ | −0.20 *** | −0.19 *** | −0.15 *** | |
(−2.89) | (−3.19) | (−4.20) | ||
Ind | 2.43 *** | 2.40 *** | 2.27 *** | |
(3.14) | (4.03) | (4.48) | ||
Envi | 3.04 ** | 3.07 *** | 3.03 *** | |
(2.29) | (2.98) | (3.10) | ||
Urb | −0.11 | −0.12 | −0.11 | |
(−1.13) | (−1.21) | (−0.89) | ||
Crea | 0.07 | 0.09 | 0.11 | |
(0.38) | (0.39) | (0.45) | ||
Time effects | Control | Control | Control | |
Individual effects | Control | Control | Control | |
Cons | 1.41 *** | 0.91 *** | 1.08 *** | |
(4.14) | (4.74) | (3.11) | ||
R2 | 0.6710 | 0.6722 | 0.6771 | |
Obs | 450 | 450 | 450 | |
δ1 | 0.277 | 0.336 | 0.351 | |
δ2 | 0.473 | 0.688 | ||
δ3 | 0.864 |
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Zhao, L.; Xu, L.; Li, L.; Hu, J.; Mu, L. Can Inbound Tourism Improve Regional Ecological Efficiency? An Empirical Analysis from China. Int. J. Environ. Res. Public Health 2022, 19, 12282. https://doi.org/10.3390/ijerph191912282
Zhao L, Xu L, Li L, Hu J, Mu L. Can Inbound Tourism Improve Regional Ecological Efficiency? An Empirical Analysis from China. International Journal of Environmental Research and Public Health. 2022; 19(19):12282. https://doi.org/10.3390/ijerph191912282
Chicago/Turabian StyleZhao, Liang, Lifei Xu, Ling Li, Jing Hu, and Lin Mu. 2022. "Can Inbound Tourism Improve Regional Ecological Efficiency? An Empirical Analysis from China" International Journal of Environmental Research and Public Health 19, no. 19: 12282. https://doi.org/10.3390/ijerph191912282