The Relationship between Tourism, Carbon Dioxide Emissions, and Economic Growth in the Yangtze River Delta, China
Abstract
:1. Introduction
- (i)
- Relative relationship between tourism-induced CO2 emissions and economic growth. This can be measured by the decoupling of carbon emissions to reveal the status of tourism development within a low-carbon economy approach. Decoupling index was proposed to evaluate the connection between economic change and environmental pressures [30,31], which can also be extended to determine the link between economic development and environmental changes induced by tourism growth.
- (ii)
- Separate effects of tourism on CO2 emissions to highlight the underlying factors to influence emissions, which can be investigated by the Logarithmic Mean Divisia Index (LMDI) decomposition method. This method has been preferred to explore the key factors that contribute to changes caused due to economic growth, environment pressures, and social development [29,32,33]. Collectively, these two issues provide a comprehensively quantitative basis to assess the linkage of tourism, carbon emissions, and economic growth. Furthermore, there is still very limited application in the use of decoupling analysis and LMDI decomposition within the tourism sector and subsectors [24,33].
2. Literature Review
2.1. Tourism-Induced CO2 Emissions: Scale and Method
2.2. Tourism-Induced CO2 Emissions in China
2.3. Decoupling Relation between CO2 Emissions and Economic Growth in Tourism
2.4. Decomposition of CO2 Emissions from Tourism Effects
3. Study Area
4. Materials and Methods
4.1. Bottom-Up Approach
4.1.1. Tourism Transport
4.1.2. Accommodation
4.1.3. Tourism Activities
4.2. Decoupling Index
4.3. The Kaya Identity and LMDI Decomposition Model
4.4. Data Sources
5. Results
5.1. Tourism-Induced Energy Consumption and CO2 Emissions in the YRD
5.1.1. Total Amount in YRD
5.1.2. Comparison within the YRD
5.2. Decoupling of CO2 Emissions from YRD’s Tourism Economy
5.3. Separate Effects of Tourism on CO2 Emissions
5.3.1. Industry Size Effect
5.3.2. Expenditure Size Effect
5.3.3. Energy Intensity Effect
5.3.4. Spatial Structure Effect
5.3.5. Sectorial Structure Effect
6. Discussion
7. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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State | %ΔLCO2 | %ΔL | LD | Relationship with Sustainability |
---|---|---|---|---|
Strong decoupling | <0 | >0 | <0 | The best state towards sustainability under the low-carbon tourism economy. |
Weak decoupling | >0 | >0 | 0 < LD < 1 | The state is desirable. |
Negative decoupling | >0 | >0 | >1 | The state is not favorable for sustainability. |
Recessive decoupling | <0 | <0 | >1 | The state is not favorable for sustainability. |
Weak negative decoupling | <0 | <0 | 0 < LD < 1 | The state is not favorable for sustainability. |
Strong negative decoupling | >0 | <0 | <0 | The worst state towards sustainability under the low-carbon tourism economy. |
Year | Energy Consumption (108 MJ) | CO2 Emissions (104 t) | ||||||
---|---|---|---|---|---|---|---|---|
E1 | E2 | E3 | Total | C1 | C2 | C3 | Total | |
2001 | 760.85 | 106.97 | 29.09 | 896.90 | 488.70 | 169.44 | 15.41 | 673.55 |
2002 | 856.45 | 120.24 | 33.40 | 1010.09 | 551.46 | 190.46 | 17.64 | 759.57 |
2003 | 897.12 | 122.86 | 33.52 | 1053.50 | 580.57 | 194.60 | 17.65 | 792.83 |
2004 | 1288.31 | 183.92 | 41.12 | 1513.35 | 838.94 | 291.33 | 21.57 | 1151.84 |
2005 | 1428.85 | 182.13 | 47.45 | 1658.43 | 933.48 | 288.50 | 24.88 | 1246.86 |
2006 | 1611.26 | 188.65 | 55.72 | 1855.64 | 1055.28 | 298.83 | 29.24 | 1383.35 |
2007 | 1929.21 | 201.60 | 63.77 | 2194.58 | 1268.93 | 319.34 | 33.36 | 1621.63 |
2008 | 1994.38 | 171.46 | 72.36 | 2238.20 | 1311.01 | 271.59 | 38.20 | 1620.79 |
2009 | 2100.63 | 168.30 | 85.11 | 2354.03 | 1380.84 | 266.58 | 45.32 | 1692.74 |
2010 | 2450.10 | 152.87 | 112.61 | 2715.58 | 1606.73 | 242.15 | 60.49 | 1909.37 |
2011 | 2666.14 | 146.98 | 138.00 | 2951.12 | 1747.28 | 232.81 | 75.46 | 2055.54 |
2012 | 2614.91 | 173.60 | 161.63 | 2950.13 | 1705.75 | 274.98 | 88.48 | 2069.22 |
2013 | 2531.50 | 138.29 | 177.66 | 2847.45 | 1621.62 | 219.04 | 97.59 | 1938.25 |
2014 | 2685.06 | 137.55 | 239.57 | 3062.18 | 1704.57 | 217.88 | 139.54 | 2061.99 |
2015 | 2810.54 | 138.19 | 258.67 | 3207.40 | 1782.62 | 218.89 | 150.76 | 2152.27 |
Factor | Sector | 2001–2005 | 2006–2010 | 2011–2015 | 2001–2015 |
---|---|---|---|---|---|
Industry size/104 t | JT | 488.51 | 1020.59 | 757.44 | 2266.54 |
J1 | 360.77 | 824.41 | 636.63 | 1821.81 | |
J2 | 117.35 | 169.13 | 81.52 | 368.00 | |
J3 | 10.38 | 27.05 | 39.29 | 76.72 | |
Expenditure size/104 t | JT | 196.41 | 42.59 | 131.10 | 370.10 |
J1 | 145.05 | 31.36 | 110.19 | 286.61 | |
J2 | 47.18 | 10.20 | 14.11 | 71.49 | |
J3 | 4.18 | 1.03 | 6.80 | 12.00 | |
Energy intensity/104 t | JT | −272.90 | −296.50 | −382.50 | −951.90 |
J1 | −49.48 | −182.14 | −253.24 | −484.86 | |
J2 | −219.29 | −98.73 | −88.23 | −406.25 | |
J3 | −4.13 | −15.63 | −41.03 | −60.79 | |
Spatial structure/104 t | JT | −13.25 | −59.96 | −322.98 | −396.18 |
J1 | −22.58 | −63.06 | −330.15 | −415.79 | |
J2 | 8.74 | 2.77 | 0.63 | 12.14 | |
J3 | 0.59 | 0.33 | 6.55 | 7.47 | |
Sectorial structure/104 t | JT | 2.04 | −84.06 | −17.60 | −99.62 |
J1 | 6.13 | 34.76 | −59.07 | −18.18 | |
J2 | −2.73 | −136.29 | −21.95 | −160.97 | |
J3 | −1.35 | 17.47 | 63.42 | 79.53 | |
Total/104 t | JT | 400.82 | 622.66 | 165.46 | 1188.94 |
J1 | 439.90 | 645.33 | 104.36 | 1189.59 | |
J2 | −48.75 | −52.92 | −13.92 | −115.59 | |
J3 | 9.67 | 30.24 | 75.02 | 112.76 |
Year | Sub-Region | Sector | Industry Size/104 t | Expenditure Size/104 t | Energy Intensity/104 t | Spatial Structure/104 t | Sectorial Structure/104 t |
---|---|---|---|---|---|---|---|
2001–2005 | Jiangsu | J1 | 98.43 | 39.57 | −84.91 | 27.76 | −16.42 |
J2 | 42.61 | 17.13 | −36.76 | 12.02 | 35.44 | ||
J3 | 4.11 | 1.65 | −3.55 | 1.16 | 2.20 | ||
Zhejiang | J1 | 94.39 | 37.95 | −73.84 | 20.49 | −3.05 | |
J2 | 44.21 | 17.78 | −202.40 | 9.60 | 5.21 | ||
J3 | 3.25 | 1.31 | −2.55 | 0.71 | 0.86 | ||
Shanghai | J1 | 167.96 | 67.53 | 109.28 | −70.82 | 25.60 | |
J2 | 30.53 | 12.28 | 19.87 | −12.87 | −43.37 | ||
J3 | 3.02 | 1.21 | 1.96 | −1.27 | −4.41 | ||
2006–2010 | Jiangsu | J1 | 171.95 | 6.54 | −159.88 | 19.38 | 13.81 |
J2 | 67.07 | 2.55 | −62.36 | 7.56 | −61.73 | ||
J3 | 11.28 | 0.43 | −10.49 | 1.27 | 9.21 | ||
Zhejiang | J1 | 192.16 | 7.31 | −14.09 | 5.96 | 13.25 | |
J2 | 66.41 | 6.29 | −35.74 | 2.06 | −47.59 | ||
J3 | 9.34 | 0.36 | −5.03 | 0.29 | 5.00 | ||
Shanghai | J1 | 460.30 | 17.51 | −8.17 | −88.40 | 7.70 | |
J2 | 35.65 | 1.36 | −0.63 | −6.85 | −26.96 | ||
J3 | 6.42 | 0.24 | −0.11 | −1.23 | 3.26 | ||
2011–2015 | Jiangsu | J1 | 116.46 | 20.16 | −129.49 | 20.23 | −22.53 |
J2 | 28.64 | 4.96 | −31.84 | 4.97 | −14.50 | ||
J3 | 16.72 | 2.89 | −18.59 | 2.90 | 26.98 | ||
Zhejiang | J1 | 121.28 | 20.99 | −207.40 | 45.78 | −28.43 | |
J2 | 35.15 | 6.08 | −60.11 | 13.27 | −7.17 | ||
J3 | 14.15 | 2.45 | −24.20 | 5.34 | 29.57 | ||
Shanghai | J1 | 398.88 | 69.04 | 83.65 | −396.16 | −8.10 | |
J2 | 17.74 | 3.07 | 3.72 | −17.62 | −0.29 | ||
J3 | 8.41 | 1.46 | 1.76 | −1.70 | 6.87 |
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Chen, L.; Thapa, B.; Yan, W. The Relationship between Tourism, Carbon Dioxide Emissions, and Economic Growth in the Yangtze River Delta, China. Sustainability 2018, 10, 2118. https://doi.org/10.3390/su10072118
Chen L, Thapa B, Yan W. The Relationship between Tourism, Carbon Dioxide Emissions, and Economic Growth in the Yangtze River Delta, China. Sustainability. 2018; 10(7):2118. https://doi.org/10.3390/su10072118
Chicago/Turabian StyleChen, Lingling, Brijesh Thapa, and Wei Yan. 2018. "The Relationship between Tourism, Carbon Dioxide Emissions, and Economic Growth in the Yangtze River Delta, China" Sustainability 10, no. 7: 2118. https://doi.org/10.3390/su10072118