The Impact of Tourism on Ecosystem Services Value: A Spatio-Temporal Analysis Based on BRT and GWR Modeling
Abstract
:1. Introduction
2. Theoretical Framework
3. Materials and Methods
3.1. Study Area
3.2. Methods
3.2.1. ESV Assessment
3.2.2. Exploratory Spatial Data Analysis
3.2.3. Geographically Weighted Regression (GWR)
3.2.4. Boosting Regression Tree (BRT)
3.2.5. Selection of Influencing Factors
- (1)
- Distance to Hotels (DH): An indicator to investigate the impact of hotel distribution on ESV. On the one hand, hotels harm the environment through energy consumption and emissions [58,76]; on the other hand, the level of hotel revenue will affect government revenue, which will affect environmental protection investment and ecological protection [77].
- (2)
- Distance to Scenic Spots (DSS): An indicator to explore the impact of scenic spots’ distribution on ESV. The construction of scenic spots limits the harmful disturbance of human activities to strictly protected areas but disrupts the balance of ecosystems in the visitor-accessible areas [78].
- (3)
- Distance to Roads (DR): An indicator to explore the impact of road distribution on ESV. The construction of roads improves the accessibility of the area. Its environmental protection effect is achieved by replacing private transport with public transport, reducing road congestion, and reducing air pollution caused by traffic congestion [79]. However, the impact of tourism traffic on the soil environment and vegetation diversity around the roads cannot be ignored [19]. Additionally, the transportation investments will also indirectly affect the expenditure of environmental protection projects, which affects the effectiveness of environmental protection [80].
- (4)
- Distance to Residential Areas (DRA): An indicator used to describe the distance from the residential areas. As the prominent place that hosts the activities of residents, the settlement becomes a tourist accommodation and catering reception area in addition to hotels. DRA is used as an indicator to explore the impact of residential areas on ESV.
- (5)
- Referring to Li et al., DEM, slope, and aspect were selected as control variables [70].
3.2.6. Data Sources and Processing
4. Results
4.1. Spatial–Temporal Variation of ESV
4.2. The Impact of Tourism Factors on ESV
4.2.1. Fitting Results of GWR
4.2.2. Fitting Results of BRT
4.3. Spatial Heterogeneity of Tourism-Influencing Factors
4.3.1. Impact of DRA on ESV
4.3.2. Impact of DSS on ESV
4.3.3. Impact of DH on ESV
4.3.4. Impact of DR on ESV
5. Discussion
5.1. ESV and Land Use Changes
5.2. Contribution of Influencing Factors Based on BRT and GWR
5.3. Spatial Heterogeneity of Influencing Factors Based on BRT and GWR
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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ESV | 2005 | 2010 | 2015 | 2018 | |||||
---|---|---|---|---|---|---|---|---|---|
ESV | Proportion/% | ESV | Proportion/% | ESV | Proportion/% | ESV | Proportion/% | ||
Provisioning services | Food production | 0.48 | 1.44 | 0.77 | 1.43 | 1.11 | 1.43 | 1.27 | 1.43 |
Raw material production | 1.00 | 2.97 | 1.59 | 2.96 | 2.30 | 2.96 | 2.64 | 2.96 | |
Water supply | 0.48 | 1.43 | 0.78 | 1.44 | 1.12 | 1.44 | 1.28 | 1.44 | |
Regulating services | Gas regulation | 3.27 | 9.74 | 5.24 | 9.73 | 7.56 | 9.73 | 8.67 | 9.73 |
Climate regulation | 9.63 | 28.67 | 15.43 | 28.65 | 22.27 | 28.65 | 25.53 | 28.65 | |
Environment depuration | 2.84 | 8.45 | 4.55 | 8.45 | 6.57 | 8.45 | 7.53 | 8.45 | |
Hydrological adjusting | 6.43 | 19.15 | 10.35 | 19.22 | 14.94 | 19.22 | 17.12 | 19.21 | |
Soil conservation | 3.98 | 11.85 | 6.37 | 11.84 | 9.20 | 11.83 | 10.55 | 11.84 | |
Supporting services | Nutrients cycle maintenance | 0.30 | 0.90 | 0.48 | 0.90 | 0.70 | 0.90 | 0.80 | 0.90 |
Biodiversity | 3.59 | 10.69 | 5.76 | 10.69 | 8.31 | 10.69 | 9.52 | 10.69 | |
Cultural services | Aesthetic landscape | 1.58 | 4.69 | 2.53 | 4.70 | 3.65 | 4.70 | 4.18 | 4.70 |
Total | 33.58 | 100 | 53.85 | 100 | 77.74 | 100 | 89.10 | 100 |
Factors | 2010 | 2015 | 2018 | ||||||
---|---|---|---|---|---|---|---|---|---|
VIF | Tolerance | CI | VIF | Tolerance | CI | VIF | Tolerance | CI | |
DH | 2.95 | 0.34 | 3.35 | 3.31 | 0.30 | 3.40 | 2.79 | 0.36 | 3.38 |
DSS | 2.17 | 0.46 | 3.94 | 2.56 | 0.39 | 4.12 | 2.74 | 0.37 | 4.34 |
DR | 1.04 | 0.96 | 5.16 | 1.08 | 0.93 | 5.13 | 1.27 | 0.79 | 5.04 |
DRA | 2.09 | 0.48 | 5.82 | 1.97 | 0.51 | 5.73 | 1.23 | 0.81 | 5.37 |
DEM | 1.35 | 0.74 | 7.39 | 1.29 | 0.78 | 7.61 | 1.39 | 0.72 | 6.97 |
Slope | 1.03 | 0.97 | 10.63 | 1.02 | 0.98 | 10.07 | 1.01 | 0.99 | 8.34 |
Aspect | 1.01 | 0.99 | 17.71 | 1.01 | 0.99 | 17.25 | 1.01 | 0.99 | 17.77 |
Explanatory Variables | 2010 | 2015 | 2018 | ||||||
---|---|---|---|---|---|---|---|---|---|
Average | Positive Numbers | Negative Numbers | Average | Positive Numbers | Negative Numbers | Average | Positive Numbers | Negative Numbers | |
DH | 5.13 | 23.25% | 76.75% | 7.05 | 26.44% | 73.56% | 6.06 | 47.35% | 52.65% |
DSS | 8.10 | 92.60% | 7.40% | 10.43 | 89.27% | 10.73% | 8.81 | 80.32% | 19.68% |
DR | 3.98 | 81.16% | 18.84% | 7.43 | 77.45% | 22.55% | 6.85 | 85.58% | 14.42% |
DRA | 6.00 | 16.09% | 83.91% | 8.01 | 9.89% | 90.11% | 12.07 | 23.08% | 76.92% |
DEM | 7.83 | 98.27% | 1.73% | 12.05 | 98.63% | 1.37% | 10.51 | 94.50% | 5.50% |
Slope | 2.63 | 96.23% | 3.77% | 3.44 | 92.63% | 7.37% | 3.82 | 92.66% | 7.34% |
Aspect | 1.61 | 92.13% | 7.87% | 2.45 | 84.21% | 15.79% | 3.04 | 59.40% | 40.60% |
Best Bandwidth | 28,299 | 19,829 | 14,580 | ||||||
R2 | 0.62 | 0.63 | 0.43 | ||||||
Adjusted R2 | 0.50 | 0.51 | 0.36 |
Explanatory Variables | 2005–2010 | 2010–2015 | 2015–2018 | |||
---|---|---|---|---|---|---|
Contribution | Rankings | Contribution | Rankings | Contribution | Rankings | |
DRA | 20.1% | 1 | 23.3% | 1 | 19.6% | 1 |
DSS | 19.9% | 2 | 23.1% | 2 | 15.1% | 3 |
DH | 19.9% | 3 | 14.5% | 3 | 13.8% | 5 |
DR | 16.2% | 4 | 4.3% | 7 | 10.5% | 6 |
Slope | 10.2% | 5 | 13.4% | 4 | 14.8% | 4 |
DEM | 7.6% | 6 | 10.7% | 6 | 8.8% | 7 |
Aspect | 6.1% | 7 | 10.8% | 5 | 17.3% | 2 |
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Liu, J.; Yue, M.; Liu, Y.; Wen, D.; Tong, Y. The Impact of Tourism on Ecosystem Services Value: A Spatio-Temporal Analysis Based on BRT and GWR Modeling. Sustainability 2022, 14, 2587. https://doi.org/10.3390/su14052587
Liu J, Yue M, Liu Y, Wen D, Tong Y. The Impact of Tourism on Ecosystem Services Value: A Spatio-Temporal Analysis Based on BRT and GWR Modeling. Sustainability. 2022; 14(5):2587. https://doi.org/10.3390/su14052587
Chicago/Turabian StyleLiu, Jun, Mengting Yue, Yiming Liu, Ding Wen, and Yun Tong. 2022. "The Impact of Tourism on Ecosystem Services Value: A Spatio-Temporal Analysis Based on BRT and GWR Modeling" Sustainability 14, no. 5: 2587. https://doi.org/10.3390/su14052587