Spatial Spillover and Threshold Effects of High-Quality Tourism Development on Carbon Emission Efficiency of Tourism under the “Double Carbon” Target: Case Study of Jiangxi, China
Abstract
:1. Introduction
2. Literature Review
2.1. Carbon Efficiency in the Tourism Sector
2.2. The Level of High-Quality Development of Tourism
2.3. Study on the Impact of High-Quality Tourism Development on the Carbon Efficiency of the Tourism Industry
3. Study area, Methods, and Variable Selection
3.1. Study Area
3.2. Methods
3.2.1. Measurement of Carbon Efficiency in the Tourism Sector
3.2.2. Space Durbin Model
3.2.3. Threshold Model
3.3. Variable Selection
3.3.1. Explained Variable
3.3.2. Core Explanatory Variable
3.3.3. Threshold Variables
3.3.4. Control Variables
3.4. Data Sources
4. Empirical Results and Analysis
4.1. Carbon Efficiency in Tourism and the Level of Quality Tourism Development
4.2. Study on the Spatial Spillover Effect of Tourism Quality Level on Tourism Carbon Efficiency
4.2.1. Spatial Correlation Tests and Model Tests
4.2.2. Analysis of Spillover Results
- (1)
- Spatial Durbin model analysis
- (2)
- Analysis of direct effects
- (3)
- Analysis of indirect effects
4.3. Study on the Effect of the Level of Quality Tourism Development on the Carbon Efficiency Threshold of the Tourism Industry
4.3.1. Threshold Model Determination
4.3.2. Analysis of Threshold Results
5. Conclusions and Discussions
5.1. Conclusions
5.2. Discussions
6. Recommendations and Future Research
6.1. Recommendations
6.2. Future Research
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Input Indicators | Desired Output Indicators | Non-Desired Output Indicators |
---|---|---|
Tour Operators | Total Tourism Revenue | Carbon emissions from tourism |
Fixed Asset Investment in Tourism | ||
Energy consumption in the tourism sector |
System Level | Variable Layer | Indicator Layer | Properties | Weight (%) | |
---|---|---|---|---|---|
The level of high-quality development of tourism | Innovation driven | ID | Science and technology expenditure as a proportion of fiscal expenditure (%) | + | 10.474 |
Coordinated development | CD | Tourism Industry Aggregation (%) | + | 4.286 | |
Total tourism revenue as a percentage of social fixed asset investment in tourism (%) | + | 16.315 | |||
Degree of integration of tourism with the primary sector (%) | + | 1.043 | |||
Degree of integration of tourism with the secondary sector (%) | + | 1.744 | |||
Integration of tourism with the tertiary sector (%) | + | 2.100 | |||
Green development | GD | Greenery coverage in built-up areas (%) | + | 2.216 | |
Green space per capita (m2/ person) | + | 10.784 | |||
Energy consumption per unit of tourism revenue (MJ/10,000 yuan) | − | 14.880 | |||
Open development | OD | Foreign exchange earnings from tourism as a proportion of total tourism receipts (%) | + | 9.383 | |
Inbound tourist arrivals as a proportion of total tourism arrivals (%) | + | 6.621 | |||
The ratio of road miles to regional land area (km/km2) | + | 11.531 | |||
Results sharing | RS | Number of star-rated hotels (pcs) | + | 5.920 | |
Share of tertiary sector employment (%) | + | 2.703 |
Classification | Definition | Calculation Method | Symbols | Average Value | Standard Deviation | Minimum Value | Maximum Value |
---|---|---|---|---|---|---|---|
Explained variable | Carbon efficiency in the tourism sector | Carbon Emission Efficiency Evaluation Index System for Tourism | lnTCEP | −0.313 | 0.679 | −1.543 | 1.106 |
Core explanatory variable | The level of high-quality development of tourism | Tourism quality development level evaluation index system | lnHQDT | 2.075 | 0.199 | 1.265 | 2.495 |
Threshold variables | Tourism economy scale | Tourism revenue as a share of GDP (%) | TES | 16.966 | 14.093 | 2.331 | 77.591 |
Tourism Industry Structure | Tourism revenue as a share of the tertiary sector (%) | TIS | 44.626 | 31.407 | 7.389 | 158.796 | |
Tourism Investment Structure | Amount of fixed asset investment in tourism as a percentage of GDP (%) | TISS | 0.001 | 0.001 | 0.000 | 0.008 | |
Tourism Carbon Emission Intensity | Tourism carbon emissions per unit of tourism revenue (kg/yuan) | TCEI | 0.021 | 0.019 | 0.002 | 0.132 | |
Control variables | Level of economic development | GDP per capita (yuan/One person) | lnEDL | 9.904 | 0.954 | 7.972 | 11.579 |
Environmental regulation | Investment in fixed assets in water, environment, and public facilities management (10,000 yuan) | lnER | 12.840 | 1.590 | 8.389 | 15.833 | |
Government intervention | Fiscal expenditure as a percentage of GDP (%) | lnGI | −3.141 | 0.883 | −5.565 | −1.146 | |
Open to the public | The actual amount of foreign capital used in the year (USD million) | lnOPEN | 10.303 | 1.431 | 6.271 | 13.614 | |
Spatial weighting matrix | Economic Geography Weighting Matrix | The inverse of the difference between the annual average GDP of the two regions | W |
Year | I-Value | Z-Value | Year | I-Value | Z-Value |
---|---|---|---|---|---|
2000 | −0.178 | −1.114 | 2011 | −0.249 ** | −2.114 |
2001 | −0.263 ** | −2.306 | 2012 | −0.249 ** | −2.171 |
2002 | −0.218 | −1.636 | 2013 | −0.252 ** | −2.293 |
2003 | −0.201 | −1.407 | 2014 | −0.260 | −2.352 |
2004 | −0.244 ** | −2.023 | 2015 | −0.242 ** | −2.054 |
2005 | −0.272 ** | −2.481 | 2016 | −0.192 | −1.384 |
2006 | −0.257 ** | −2.230 | 2017 | −0.179 | −1.095 |
2007 | −0.241 ** | −2.027 | 2018 | −0.171 | −0.994 |
2008 | −0.238 * | −1.911 | 2019 | −0.178 | −1.089 |
2009 | −0.238 * | −1.893 | 2020 | −0.103 | −0.039 |
2010 | −0.253 ** | −2.198 |
Test Methods | Model | Eigenvalue |
---|---|---|
LR test | Spatial lag | 14.410 *** |
Spatial error | 9.454 *** | |
Wald test | Spatial lag | 82.870 *** |
Spatial error | 69.570 *** | |
Hausman test | 412.72 *** |
Variables | SDM | Direct Effects | Indirect Effects | Total Effect |
---|---|---|---|---|
ln(ID) | 0.028 (0.800) | 0.022 (0.680) | 0.050 (0.510) | 0.072 (0.680) |
ln(CD) | 0.866 *** (6.450) | 0.620 *** (5.330) | 1.707 *** (4.150) | 2.327 *** (5.080) |
ln(GD) | 0.343 *** (3.480) | 0.478 *** (4.490) | −0.887 * (−1.820) | −0.409 (−0.900) |
ln(OD) | 0.116 (1.640) | 0.185 ** (2.580) | −0.485 *** (−3.470) | −0.300 ** (−2.190) |
ln(AS) | −0.190 * (−1.860) | −0.127 (−1.540) | −0.440 (−1.390) | −0.568 * (−1.680) |
ln(EDL) | −0.449 (−1.350) | −0.575 ** (−2.110) | 0.902 (1.440) | 0.327 (0.410) |
ln(ER) | 0.060 (0.830) | 0.026 (0.410) | 0.238 (1.070) | 0.264 (1.070) |
ln(GI) | −0.160 *** (−2.750) | −0.059 (−1.120) | −0.729 *** (−2.830) | −0.788 *** (−3.030) |
ln(OPEN) | 0.226 *** (3.040) | 0.124 * (1.840) | 0.765 *** (3.810) | 0.889 *** (4.050) |
W × ln(ID) | 0.103 (0.550) | |||
W × ln(CD) | 3.720 *** (5.520) | |||
W × ln(GD) | −1.204 (−1.510) | |||
W × ln(OD) | −0.689 *** (−2.710) | |||
W × ln(AS) | −0.938 (−1.610) | |||
W × ln(EDL) | 1.117 (0.800) | |||
W × ln(ER) | 0.446 (1.100) | |||
W × ln(GI) | −1.404 *** (−3.350) | |||
W × ln(OPEN) | 1.504 *** (4.330) | |||
Spatial | −0.969 *** (−4.750) |
Model | Types of Thresholds | F-Value | p-Value | Threshold Estimates | 95% Confidence Interval | Number of BS |
---|---|---|---|---|---|---|
TES | Single threshold | 22.700 *** | 0.000 | 5.496 | [5.388, 5.5243] | 300 |
Double Threshold | 12.460 | 0.333 | 300 | |||
Three-fold threshold | 5.920 | 0.667 | 300 | |||
TIS | Single threshold | 35.840 *** | 0.000 | 14.938 | [14.736, 15.154] | 300 |
Double Threshold | 16.230 *** | 0.000 | 42.317 | [38.581, 42.340] | 300 | |
Three-fold threshold | 6.940 | 0.667 | 300 | |||
TISS | Single threshold | 14.930 *** | 0.000 | 0.003 | [0.003, 0.004] | 300 |
Double Threshold | 7.390 | 0.333 | 300 | |||
Three-fold threshold | 8.180 | 1.000 | 300 | |||
TCEI | Single threshold | 25.460 *** | 0.000 | 0.025 | [0.024, 0.026] | 300 |
Double Threshold | 12.590 | 0.667 | 300 | |||
Three-fold threshold | 8.690 | 0.667 | 300 |
Variable | TES | TIS | TISS | TCEI |
---|---|---|---|---|
HODT(Stage 1) | 0.702 *** | 0.647 *** | 0.957 *** | 0.850 *** |
(2.990) | (3.120) | (4.430) | (4.000) | |
HODT(Stage 2) | 0.867 *** | 0.923 *** | 0.741 *** | 0.638 *** |
(3.950) | (4.700) | (3.150) | (2.820) | |
HODT(Stage 3) | 1.032 *** | |||
(5.220) | ||||
ln(EDL) | −0.156 *** | −0.165 ** | −0.107 | −0.218 *** |
(−2.010) | (−2.280) | (−1.350) | (−2.820) | |
ln(ER) | 0.022 | −0.020 | 0.006 | 0.012 |
(0.540) | (−0.510) | (0.140) | (0.300) | |
ln(GI) | −0.042 | −0.034 | −0.023 | −0.041 |
(−0.920) | (−0.790) | (−0.510) | (−0.920) | |
ln(OPEN) | 0.044 | 0.025 | 0.002 | 0.016 |
(1.340) | (0.820) | (0.050) | (0.490) |
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Wang, L.; Jia, G. Spatial Spillover and Threshold Effects of High-Quality Tourism Development on Carbon Emission Efficiency of Tourism under the “Double Carbon” Target: Case Study of Jiangxi, China. Sustainability 2023, 15, 4797. https://doi.org/10.3390/su15064797
Wang L, Jia G. Spatial Spillover and Threshold Effects of High-Quality Tourism Development on Carbon Emission Efficiency of Tourism under the “Double Carbon” Target: Case Study of Jiangxi, China. Sustainability. 2023; 15(6):4797. https://doi.org/10.3390/su15064797
Chicago/Turabian StyleWang, Liguo, and Guodong Jia. 2023. "Spatial Spillover and Threshold Effects of High-Quality Tourism Development on Carbon Emission Efficiency of Tourism under the “Double Carbon” Target: Case Study of Jiangxi, China" Sustainability 15, no. 6: 4797. https://doi.org/10.3390/su15064797