Coupling Coordination Relationship and Driving Force Analysis between Gross Ecosystem Product and Regional Economic System in the Qinling Mountains, China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Research Framework
2.3. Data Sources
- (1)
- Daily climate data of national meteorological stations in the QMs were downloaded from the China Meteorological Data Service Centre, and the meteorological elements included precipitation, temperature, etc. Then, with the meteorological elements, the local runoff and evaporation data were obtained using the hydrological formula;
- (2)
- Environmental data included air quality and water environment quality data, collected from Shaanxi Province air quality monitoring station and Shaanxi Provincial Department of Ecology and Environment, respectively;
- (3)
- Remote sensing data mainly included land use, soil, DEM, NPP, and NDVI. Since there were many types of remote sensing data involved, and the spatial resolutions of different data were significantly different, the remote sensing data were resampled to a resolution of 1000 m in the data processing process, and the data output resolution of ES and GEP assessment results were also set to 1000 m;
- (4)
- Social economic data, including GDP, population, water price, agricultural product price, tourist income, etc., were obtained from Shaanxi provincial statistical yearbook, Shaanxi provincial tourism development statistical bulletin, Shaanxi water conservancy statistical yearbook, etc. The statistical scale of social economic data is district-county scale, so the data output resolution of ES value evaluation results is also unified to district-county scale. Therefore, the total GEP results are also read with the same scale. In view of the fact that some districts and counties are not fully included in the scope of the QMs [40], the social economic statistics of these districts and counties are assigned within the coverage area by an area-weighted method.
2.4. Methods
2.4.1. GEP Accounting Methodology
2.4.2. Coupling Coordination Degree (CCD) Model
- Data standardization processing
- 2.
- Coupling Degree (CD) model
- 3.
- Coupling Coordination Degree (CCD) model
- 4.
- Relative Development Degree (RDD) model
2.4.3. Geographic Detector Model
- Factor detection
- 2.
- Interaction detector
- 3.
- The selection and processing of indicators
3. Results
3.1. Spatial Variations of ESs and GEP
3.1.1. Variations of PES from 2010–2020
3.1.2. Variations of RES during 2010–2020
3.1.3. Variations of CES during 2010–2020
3.1.4. Variations of GEP during 2010–2020
3.2. Spatiotemporal Variations of the Coupling Relationship between GEP and GDP
3.2.1. Evolution Characteristics between CD and CCD
3.2.2. Evolution Characteristics of RDD and CDD Type
3.3. Driving Factors Affecting the Coupling Coordination Degree between GEP and GDP
3.3.1. Factor Detection Analysis
3.3.2. Interaction Detection Analysis
4. Discussion
4.1. Implications of GEP Assessment
4.2. Interaction between GEP and GDP
4.3. Policy Recommendations
4.4. Limitations and Future Research
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Category | Index | Time Resolution | Spatial Resolution | Data Sources |
---|---|---|---|---|
Climate data | Precipitation, Temperature, etc. | 2010–2020 | Meteorological station | China Meteorological Data Service Centre (http://data.cma.cn, accessed on 20 June 2021) |
Runoff | Calculated from meteorological data | |||
Evaporation | ||||
Environmental data | Air quality | Observation station | Shaanxi Province air quality monitoring station, accessed on 20 September 2021 | |
Water environment quality | Shaanxi Provincial Department of Ecology and Environment, accessed on 20 September 2021 | |||
Remote sensing data | Land use | 2010, 2015, 2020 | 30 m | China Multi-Period Land Use Remote Sensing Monitoring Dataset (CNLUCC) (https://www.resdc.cn, accessed on 6 September 2021) |
Soil | 2017 | 250 m | Global gridded soil information (https://www.isric.org/explore/soilgrids, accessed on 17 August 2021) | |
DEM | 2011 | 12.5 m | NASA EARTHDATA Advanced Land Observing Satellite data (https://search.earthdata.nasa.gov/search, accessed on 15 August 2021) | |
NPP | 2010, 2015, 2020 | 500 m | NASA’s Land Processes Distributed Active Archive Center (https://e4ftl01.cr.usgs.gov/, accessed on 23 October 2021) | |
NDVI | 2010, 2015, 2020 | 1000 m | MODIS/Terra Vegetation Indices Monthly L3 Global 1 km SIN Grid V006, NASA EOSDIS Land Processes DAAC (https://search.earthdata.nasa.gov/search, accessed on 28 April 2022) | |
Social economic data | Density of road network | 2019 | 1000 m | A dataset of 1 km Grid Road network density in China (2019) (https://cstr.cn/31253.11.sciencedb.02938, accessed on 12 October 2023) [41] |
GDP, Population, Water price, Agricultural product price, Tourist income, etc. | 2011, 2016, 2021 | District- county scale | Shaanxi provincial statistical yearbook, Shaanxi provincial tourism development statistical Bulletin, Shaanxi water conservancy statistical yearbook |
Categoriy | Accounting ES | Material Quantity Method | Monetary Value Method |
---|---|---|---|
Provisioning ecosystem service | Agricultural products | Statistical survey method | market value |
Forestry products | |||
Animal husbandry products | |||
Fishery products | |||
Water resources | |||
Regulating ecosystem service | Water conservation service | water balance equation | shadow project |
Water purification service | empirical method | replacement cost | |
Flood regulating service | empirical method | shadow project | |
Carbon sequestration service | Vegetation photosynthesis model | market value | |
Oxygen release service | |||
Air purification service | empirical method | replacement cost | |
Climate regulating service | Ecosystem Evapotranspiration Model | replacement cost | |
Soil conservation service | RUSLE model | replacement cost | |
Cultural ecosystem service | Ecological tourism service | Statistical survey method | replacement cost |
Gross Ecosystem Product | Total value of 14 ES types | - | accumulation |
Level | Classification | CCD | Relative Development Degree (RDD) | CCD Features | Type |
---|---|---|---|---|---|
1 | Severe unbalance | 0 < D ≤ 0.2 | 0 < RDD ≤ 0.9 | Severe unbalance—GEP lag | SU—GEP lag |
0.9 < RDD ≤ 1.1 | Severe unbalance | SU—Balance | |||
1.1 < RDD | Severe unbalance—GDP lag | SU—GDP lag | |||
2 | Moderate unbalance | 0.2 < D ≤ 0.4 | 0 < RDD ≤ 0.9 | Moderate unbalance—GEP lag | MU—GEP lag |
0.9 < RDD ≤ 1.1 | Moderate unbalance | MU—Balance | |||
1.1 < RDD | Moderate unbalance—GDP lag | MU—GDP lag | |||
3 | Slight coordination | 0.4 < D ≤ 0.6 | 0 < RDD ≤ 0.9 | Slight coordination—GEP lag | SC—GEP lag |
0.9 < RDD ≤ 1.1 | Slight coordination | SC—Balance | |||
1.1 < RDD | Slight coordination—GDP lag | SC—GDP lag | |||
4 | Moderate coordination | 0.6 < D ≤ 0.8 | 0 < RDD ≤ 0.9 | Moderate coordination—GEP lag | MC—GEP lag |
0.9 < RDD ≤ 1.1 | Moderate coordination | MC—Balance | |||
1.1 < RDD | Moderate coordination—GDP lag | MC—GDP lag | |||
5 | High coordination | 0.8 < D ≤ 1 | 0 < RDD ≤ 0.9 | High coordination—GEP lag | HC—GEP lag |
0.9 < RDD ≤ 1.1 | High coordination | HC—Balance | |||
1.1 < RDD | High coordination—GDP lag | HC—GDP lag |
Type | Factor | Abbreviation | Code | Unit |
---|---|---|---|---|
Geographical conditions | Temperature | TEM | X1 | °C |
Precipitation | PRE | X2 | mm | |
Altitude | Altitude | X3 | m | |
Slope | Slope | X4 | ° | |
NDVI | NDVI | X5 | - | |
Landscape features | Aggregation index | AGI | X6 | % |
Landscape shape index | LSI | X7 | - | |
Shannon diversity index | SDI | X8 | - | |
Shannon evenness index | SEI | X9 | - | |
Contagion index | CAI | X10 | % | |
Social economic features | Road network density | RND | X11 | km/100 km2 |
Population density | POD | X12 | persons/km2 |
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Wang, P.; Chen, Y.; Liu, K.; Li, X.; Zhang, L.; Chen, L.; Shao, T.; Li, P.; Yang, G.; Wang, H.; et al. Coupling Coordination Relationship and Driving Force Analysis between Gross Ecosystem Product and Regional Economic System in the Qinling Mountains, China. Land 2024, 13, 234. https://doi.org/10.3390/land13020234
Wang P, Chen Y, Liu K, Li X, Zhang L, Chen L, Shao T, Li P, Yang G, Wang H, et al. Coupling Coordination Relationship and Driving Force Analysis between Gross Ecosystem Product and Regional Economic System in the Qinling Mountains, China. Land. 2024; 13(2):234. https://doi.org/10.3390/land13020234
Chicago/Turabian StyleWang, Pengtao, Yuxuan Chen, Kang Liu, Xupu Li, Liwei Zhang, Le Chen, Tianjie Shao, Peilin Li, Guoqing Yang, Hui Wang, and et al. 2024. "Coupling Coordination Relationship and Driving Force Analysis between Gross Ecosystem Product and Regional Economic System in the Qinling Mountains, China" Land 13, no. 2: 234. https://doi.org/10.3390/land13020234
APA StyleWang, P., Chen, Y., Liu, K., Li, X., Zhang, L., Chen, L., Shao, T., Li, P., Yang, G., Wang, H., Gao, S., & Yan, J. (2024). Coupling Coordination Relationship and Driving Force Analysis between Gross Ecosystem Product and Regional Economic System in the Qinling Mountains, China. Land, 13(2), 234. https://doi.org/10.3390/land13020234