Study on the Decoupling Effect and Driving Factors of Tourism Transportation Carbon Emissions in the Yangtze River Delta Region
Abstract
:1. Introduction
- (1)
- Emission accounting method: Current approaches to quantifying tourism transportation emissions bifurcate into top-down and bottom-up methods. Top-down models, such as energy-statistics-based coefficient systems (Becken [4], 2003; Wei, Y [5], 2012) and input-output frameworks (Perch [6], 2010), offer macro-level efficiency but lack spatiotemporal granularity. Conversely, bottom-up techniques leveraging activity data and GIS trajectory tracking (Diaz Perez [7], 2019; Hu, C [8], 2022; Liu, J [9], 2024) enhance resolution but face challenges in system boundary consistency. Hybrid lifecycle assessments (Liao, H [10], 2022) have emerged to reconcile these trade-offs, yet their application remains limited to single-region studies. A notable gap lies in the absence of standardized methodologies for cross-provincial urban agglomerations, where spatial spillovers and policy coordination complexities are pronounced.
- (2)
- Decoupling effect analysis: In the study of the relationship between economic growth and environmental stress, the decoupling model represented by OECD (Lundquist [11], 2021) and Tapio frameworks plays a dominant role [12]. The Tapio model, with its dimensionless flexibility (Liu, F [13], 2022; Wang, Z [14], 2022), has been widely adopted to categorize decoupling states. However, existing studies predominantly focus on national or provincial scales, overlooking intra-regional heterogeneity within integrated urban clusters like the Yangtze River Delta. Furthermore, while IPAT-derived analyses (Lu, Z [15], 2007) evaluate environmental load–economic growth linkages, few integrate decoupling metrics with spatiotemporal evolution patterns, limiting their utility in policy formulation for dynamic regions.
- (3)
- Driving mechanism exploration: Drivers of tourism transportation emissions are increasingly analyzed through decomposition techniques (e.g., LMDI, Kaya identity) (Cansino [16], 2015; Moutinho [17], 2018; Wang, L [18], 2023) and extended STIRPAT models. Spatial heterogeneity studies (Tiwari [19], 2013; Sun, Y [20], 2020) highlight regional disparities in emission intensity reduction paths, yet fail to address cross-city synergistic mechanisms. Innovations such as panel VAR models (Li, Y [21], 2022) reveal lag effects between emissions and growth but neglect emerging factors like cultural tourism integration. (Ghazali [22], 2019; Pham [23], 2020; Su, H [24], 2024) underscore the catastrophic potential of tourism consumption–energy structure interactions, yet regulatory variables (e.g., institutional quality, cross-regional collaboration) remain underexplored.
2. Materials and Methods
- (1)
- Data integration and carbon emission accounting
- (2)
- Construction of dynamic association model
- (3)
- Drive mechanism analysis
2.1. Carbon Emission Measurement Model of Tourism Transportation
2.2. Decoupling Model
2.3. Spatial Analysis Method
2.4. Selection and Analysis of Influencing Factors
2.4.1. Index Selection
2.4.2. Kaya Identity and LMDI Decomposition Model
2.4.3. Grey Correlation Analysis Method
2.5. Source of Data
3. Results
3.1. Temporal and Spatial Evolution Characteristics
3.1.1. Temporal Evolution Characteristics of Tourism Transportation Carbon Emissions in the Yangtze River Delta Region, 2000–2022
- (1)
- Regional level
- (2)
- Provincial and municipality level
3.1.2. Spatial Evolution Characteristics of Tourism Transportation Carbon Emissions in the Yangtze River Delta Region, 2000–2022
- (1)
- In 2000, the carbon emissions of tourism transportation in the Yangtze River Delta showed a spatial distribution characteristic of “high in the east and low in the west”. The Shanghai metropolitan area, capital cities (except Hefei) and coastal cities have relatively high carbon emissions, while the western region of the Yangtze River Delta, including cities such as Hefei and Lu’an, had low carbon emissions.
- (2)
- In 2005, the spatial distribution shifted to “higher in the middle and lower in the west.” While high-carbon emission areas remained largely the same, some cities in Jiangsu and Zhejiang provinces, such as Xuzhou, Yancheng, and Taizhou, transitioned from high-carbon to low-carbon emission areas.
- (3)
- In 2010, the spatial distribution pattern resembled that of 2000 but at a higher emission level for most cities. By 2015, the carbon emissions from tourism transportation in the Yangtze River Delta followed a “low in the north and south, high in the middle” pattern.
- (4)
- In 2020, 36 cities in the Yangtze River Delta, excluding Shanghai and its neighboring city Suzhou, as well as the two provincial capitals and Xuzhou, were classified as low-carbon emission zones. The spatial distribution in 2022 was similar to that of 2020, except for Xuzhou, which further transitioned from a medium-carbon emission zone to a low-carbon emission zone after implementing a series of emission reduction measures.
3.2. Decoupling Effect Analysis
3.2.1. Analysis of the Relative Relationship and Decoupling Effect Between Tourism Transportation Carbon Emissions and Tourism Economy in the Yangtze River Delta Region
3.2.2. Analysis of Regional Differences in Decoupling Effect
3.2.3. Environmental Kuznets Curve (EKC) Verification
- (1)
- Results of unit root test and cointegration test
- (2)
- Granger causality test based on the VAR model
- (3)
- EKC regression verification
3.3. Analysis of Influencing Factors of Carbon Emissions from Tourism and Transportation in the Yangtze River Delta Region, 2000–2022
3.3.1. LMDI Decomposition and Analysis of Influencing Factors of Tourism and Transportation Carbon Emissions in the Yangtze River Delta Region
3.3.2. Grey Correlation Analysis of Influencing Factors
- (1)
- Tourist Scale: The tourist flow to cultural facilities is the strongest positive driving factor for carbon emissions, with a correlation degree of 0.925. The number of tourists in the Yangtze River Delta increased by 651.27% from 2000 to 2022. The scale of passenger flow in A-class scenic spots has a correlation degree of 0.876 and only slightly promoted carbon emissions due to the adoption of green transportation methods.
- (2)
- Economic Structure: The proportion of the tourism economy is the main inhibiting factor, with a correlation degree of 0.893 and a contribution value of −215.876. Tourism revenue increased by 900% from 2000 to 2022. GDP (correlation degree 0.865) and economic share of services (correlation degree 0.837) were the main contributing factors to carbon emissions.
- (3)
- Energy Transformation: The energy structure’s contribution value, with a correlation degree of 0.884, was −50.36. This contribution turned positive in the later period, indicating significant potential for optimizing the energy structure.
- (4)
- Innovation-Driven: Efficiency of educational resource allocation (correlation degree 0.881) and the contribution rate of the innovation economy (correlation degree 0.869) are positive factors, while the ratio of investment in services to output (correlation degree 0.86) serves as an inhibitory factor.
- (5)
- Consumption Demand: The per capita tourism consumption level has a contribution value of 17.11 and a correlation degree of 0.845. This factor weakly promoted carbon emissions, reflecting a balanced demand between consumption upgrading and low-carbon transformation.
4. Conclusions and Policy Recommendations
4.1. Conclusions
- (1)
- Dynamic spatiotemporal evolution law: The carbon emissions of tourism transportation in the Yangtze River Delta experience three stages of “growth, fluctuation, and decline”, and spatial differentiation is significantly affected by the economic gradient and transportation infrastructure. The radiation effect of Shanghai, as the core emission pole, must be paid attention to.
- (2)
- Nonlinear characteristics of decoupling: Weak decoupling dominance reflects the insufficient effectiveness of current emission reduction policies, and the inflection point of the inverted U-shaped curve lags behind the pace of regional economic transformation, suggesting the need to strengthen structural regulation.
- (3)
- Synergy of driving factors: The expansion of economic scale is still the main reason for the growth of carbon emissions, but the integration of culture and tourism shows significant emission reduction potential through the regulation of passenger flow (such as low-carbon diversion of cultural facilities), which confirms the feasibility of the collaborative path of “consumption upgrading—technology substitution”.
4.2. Policy Suggestions
- (1)
- Accelerating the optimization of the energy mix: Aiming to reduce emissions through the electrification of public transport and the adoption of renewable energy sources, this measure has significant immediate mitigation potential. Considering that challenges such as the high cost of investment in new energy transportation infrastructure and financial constraints in small and medium-sized cities may arise, the government can set up a special fund for green transportation in the region in the future and implement stepped subsidies for small and medium-sized cities (such as increasing subsidies by 50% for cities with carbon intensity below 20% of the average level).
- (2)
- Establish a cross-city carbon offsetting mechanism: establish a “carbon budget-carbon quota” mechanism to incorporate emission reduction performance into official government assessments. Build a “carbon budget-cap-and-trade” platform, incorporate the inter-provincial emission gap (for example, the carbon emission intensity ratio between Shanghai and Anhui is 3.2:1) into the government performance assessment, promote the planning of high-speed rail network and direct connection lines between scenic spots, and alleviate the pressure on core hubs.
- (3)
- Cultural and tourism integration low-carbon practice: Learn from Japan’s “cultural heritage + shared transportation” model, design Jiangnan Ancient Town cycling corridor and other low-carbon cultural and tourism products, create a cycling route connecting ancient towns and cultural sites, and establish a “low-carbon cultural and tourism certification” system through digital platforms to achieve cross-district points exchange (such as “one code pass” app), guide tourists’ behavior transformation.
4.3. Research Deficiencies and Prospects
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Research Results | Railway | Highway | Civil Aviation | Waterway | |
---|---|---|---|---|---|
Global dimension | UNWTO-UNEP-WMO [6] | 27 | 133 | 137 | 66 |
Country level | China [26] | 27 | 133 | 137 | 106 |
Europe [27] | 27 | 133 | 137 | 66 | |
Regional level | Yangtze River economic belt [8] | 27 | 133 | 137 | 106 |
Five northwestern provinces [28] | 27 | 133 | 137 | - | |
Six central provinces [29] | 27 | 133 | 137 | 106 | |
Provincial and municipal level | Shanghai [20] | 27 | 133 | 137 | 106 |
Hainan [30] | 27 | 133 | 137 | 106 | |
Anhui [31] | 27 | 133 | 137 | 66 | |
Shandong [32] | 65 | 132 | 396 | 66 | |
Jiangsu [33] | 27 | 133 | 137 | 106 | |
Jiangxi [34] | 65 | 132 | 396 | 66 | |
Henan [35] | 27 | 133 | 137 | 66 |
Period | ||||
---|---|---|---|---|
Railway | Highway | Civil Aviation | Waterway | |
2000–2008 | 31.6 | 13.8 | 64.7 | 10.6 |
2009–2014 | 36.9 | 16.7 | 60.4 | 7.1 |
2015–2019 | 38.7 | 17.5 | 59.1 | 5.8 |
2020–2022 | 43.9 | 21.3 | 50.6 | 5.2 |
Index | Number of Articles | Examples | Symbols |
---|---|---|---|
GDP | 240 | [41,42] | G |
Tourism revenue | 157 | [43] | I |
Ridership | 104 | [44] | Y |
Number of visitors | 75 | [26,45] | P |
Number of A-level tourist attractions | 57 | [46] | Q |
The volume of passenger turnover | 55 | [41] | K |
Value added of tertiary industry | 52 | [47] | T |
Value added of the secondary industry | 39 | [48] | D |
Investment in fixed assets completed | 20 | [49] | A |
Number of cultural facilities | 34 | [50] | W |
Number of patents granted | 11 | [51] | Z |
Transportation energy consumption | 36 | [42] | E |
Number of college students per 10,000 people | 9 | [52] | X |
Bedding Plane | Indicator | Symbol | Variation |
---|---|---|---|
Visitor size | Per capita tourism consumption level | IP | |
Travel ratio | YP | ||
The scale of passenger flow in A-class scenic spots | PJ | ||
The scale of tourist flow of cultural facilities | PW | ||
Level of economic development | Gross Domestic Product | G | |
Proportion of tourism economy | IG | ||
Economic share of services | TG | ||
Passenger turnover economic intensity ratio | KG | ||
Value-added tourism contribution ratio of secondary industry | DI | ||
Energy intensity | Passenger-turnover energy ratio | EK | |
Energy mix | CE | ||
New quality productivity | Efficiency of educational resource allocation | GX | |
Production education ratio index | XD | ||
Investment-output ratio | GZ | ||
The contribution rate of the Innovation economy | ZD | ||
Value-added ratio of production and investment | DA | ||
Investment service-output ratio | AT | ||
Infrastructure | Utilization rate of cultural facilities | WY | |
Yield ratio | JI |
Test Sequence | Unit Root Test | Cointegration Test | |||||
---|---|---|---|---|---|---|---|
t | p-Value | Conclusion | p-Value | Statistics | Result | ||
Original time -series data | −1.888 | 0.3377 | Uneven | _ | |||
−2.115 | 0.2385 | Uneven | |||||
−1.877 | 0.343 | Uneven | |||||
−1.684 | 0.4395 | Uneven | |||||
First difference sequence number | −3.331 | 0.0136 | Smooth | 0.0104 | −3.416 | Smooth (5%) | |
−3.072 | 0.0287 | Smooth | |||||
−3.063 | 0.0294 | Smooth | |||||
−3.045 | 0.0309 | Smooth |
Optimal Lag Order | ||||||
---|---|---|---|---|---|---|
Chi-Square Statistics | p-Value | Results | Chi-Square Statistics | p-Value | Results | |
1 | 4.4227 | 0.035 | Reject null hypothesis | 7.2952 | 0.007 | Reject null hypothesis |
Evaluation Items | Symbols | Relevancy | Ranking |
---|---|---|---|
The scale of tourist flow of cultural facilities | PW | 0.925 | 1 |
Proportion of tourism economy | IG | 0.893 | 2 |
Energy mix | CE | 0.884 | 3 |
Efficiency of educational resource allocation | GX | 0.881 | 4 |
The scale of passenger flow in A-class scenic spots | PJ | 0.876 | 5 |
The contribution rate of the innovation economy | ZD | 0.869 | 6 |
Gross Domestic Product | G | 0.865 | 7 |
Investment service-output ratio | AT | 0.86 | 8 |
Per capita tourism consumption level | IP | 0.845 | 9 |
Economic share of services | TG | 0.837 | 10 |
Passenger-turnover energy ratio | EK | 0.797 | 11 |
Value-added ratio of production and investment | DA | 0.786 | 12 |
Yield ratio | JI | 0.783 | 13 |
Value-added tourism contribution ratio of secondary industry | DI | 0.778 | 14 |
Utilization rate of cultural facilities | WY | 0.771 | 15 |
Production education ratio index | XD | 0.751 | 16 |
Passenger turnover economic intensity ratio | KG | 0.734 | 17 |
Investment-output ratio | GZ | 0.73 | 18 |
Travel ratio | YP | 0.689 | 19 |
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Feng, D.; Li, C.; Deng, S. Study on the Decoupling Effect and Driving Factors of Tourism Transportation Carbon Emissions in the Yangtze River Delta Region. Sustainability 2025, 17, 3056. https://doi.org/10.3390/su17073056
Feng D, Li C, Deng S. Study on the Decoupling Effect and Driving Factors of Tourism Transportation Carbon Emissions in the Yangtze River Delta Region. Sustainability. 2025; 17(7):3056. https://doi.org/10.3390/su17073056
Chicago/Turabian StyleFeng, Dongni, Cheng Li, and Shiguo Deng. 2025. "Study on the Decoupling Effect and Driving Factors of Tourism Transportation Carbon Emissions in the Yangtze River Delta Region" Sustainability 17, no. 7: 3056. https://doi.org/10.3390/su17073056
APA StyleFeng, D., Li, C., & Deng, S. (2025). Study on the Decoupling Effect and Driving Factors of Tourism Transportation Carbon Emissions in the Yangtze River Delta Region. Sustainability, 17(7), 3056. https://doi.org/10.3390/su17073056