Spatiotemporal Evolution and Driving Factors of the Relationship Between Land Use Carbon Emissions and Ecosystem Service Value in Beijing-Tianjin-Hebei
Abstract
1. Introduction
2. Literature Review
2.1. Accounting Methods
2.2. Spatiotemporal Patterns
2.3. Influencing Factors
3. Materials and Methods
3.1. Study Area Overview
3.2. Data Sources and Descriptions
3.3. Research Methods
3.3.1. Land Use Carbon Emission Accounting
- (1)
- Direct Calculation Method
- (2)
- Indirect Calculation Method
- (3)
- Calculation of Total Land Use Carbon Emissions
3.3.2. ESV Calculation
- (1)
- Adjustment of Economic Value per Hectare of Grain Yield
- (2)
- ESV accounting of counties in the Beijing-Tianjin-Hebei region
3.3.3. Bivariate Spatial Autocorrelation Analysis of Land Use Carbon Emissions and ESV
3.3.4. Multinomial Logistic Regression Model
4. Results
4.1. Spatiotemporal Patterns of Land Use Carbon Emissions
4.2. Spatiotemporal Patterns of ESV
4.3. Spatial Autocorrelation Analysis
4.4. Multinomial Logit Regression Analysis
5. Discussion
5.1. Comparison with Existing Studies
5.2. Limitations and Future Directions
6. Conclusions
7. Generality and Broader Implications
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
H-H | High-high agglomeration areas |
H-L | High-low agglomeration areas |
L-H | Low-high agglomeration areas |
L-L | Low-low agglomeration areas |
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Data Type | Data Source | Resolution |
---|---|---|
Land use data | Resource and Environment Science and Data Center, Chinese Academy of Sciences. (https://www.resdc.cn/, accessed on 13 January 2025) | 30 m |
Digital elevation model data | Resource and Environment Science and Data Center, Chinese Academy of Sciences. (https://www.resdc.cn/, accessed on 15 January 2025) | 500 m |
Nighttime light imagery data | Develop improved time-series DMSP-OLS-like data (1992–2019) in China by integrating DMSP-OLS and SNPP-VIIRS published by Wu et al. [45]. | 1 km |
Average precipitation data | 4 km daily gridded meteorological dataset for China (2000–2020) published by Zhang et al. [46]. | 1 km |
Average temperature data | 4 km daily gridded meteorological dataset for China (2000–2020) published by Zhang et al. [46]. | 1 km |
Normalized difference vegetation index data | MOD13A3 dataset regularly released by NASA (https://www.earthdata.nasa.gov/, accessed on 20 January 2025) | 1 km |
GDP data | Spatial distribution of GDP in kilometer grid in China published by Xu X L [47]. | 1 km |
Population data | LandScan population dataset developed by the Oak Ridge National Laboratory (ORNL), USA (https://landscan.ornl.gov/, accessed on 22 January 2025) | 1 km |
Energy consumption and socioeconomic data | Statistical materials including Beijing Statistical Yearbook, Tianjin Statistical Yearbook, Hebei Statistical Yearbook, China Statistical Yearbook, China Energy Statistical Yearbook, China Urban Statistical Yearbook, China County Statistical Yearbook, and National Compilation of Agricultural Product Cost and Benefit Data | None |
Land Use Type | Cultivated Land | Forest Land | Grassland | Water Area | Unused Land |
---|---|---|---|---|---|
Carbon Emission Coefficient | 0.4220 | −0.6125 | −0.0210 | −0.2570 | −0.0050 |
Energy Type | Standard Coal Equivalent Conversion Coefficient | Carbon Emission Coefficient |
---|---|---|
Coal | 0.7143 | 0.7559 |
Coke | 0.9714 | 0.8550 |
Crude oil | 1.4286 | 0.5857 |
Gasoline | 1.4714 | 0.5538 |
Kerosene | 1.4714 | 0.5714 |
Diesel oil | 1.4571 | 0.5921 |
Fuel oil | 1.4286 | 0.6185 |
Natural gas | 1.7143 | 0.5042 |
Liquefied petroleum gas | 1.2280 | 0.5857 |
Electricity | 0.1229 | 0.2132 |
Municipal City | Formula | R2 |
---|---|---|
Beijing | y = −0.0005x2 + 245.0611x −16,916,588.6491 | 0.8869 |
Tianjin | y = −0.0002x2 + 149.1004x −7,493,031.0201 | 0.9444 |
Chengde | y = −0.0020x2 + 259.9574x −2,148,350.7816 | 0.9988 |
Zhangjiakou | y = −0.0016x2 + 267.0448x −5,210,130.8110 | 0.9951 |
Qinhuangdao | y = −0.0018x2 + 244.0938x −2,522,723.2601 | 0.9728 |
Tangshan | y = −0.0009x2 + 429.0482x −22,497,599.4943 | 0.9936 |
Baoding | y = −0.0008x2 + 316.9759x −16,136,662.4999 | 0.9986 |
Langfang | y = 1.2083x1.3460 | 0.9269 |
Cangzhou | y = −0.0019x2 + 636.8169x −37,021,465.6978 | 0.9743 |
Shijiazhuang | y = 0.0046x + 41,811.6055 | 0.7055 |
Hengshui | y = −0.0036x2 + 485.1271x −10,015,174.2472 | 0.7302 |
Xingtai | y = −0.0017x2 + 405.5922x −14,880,042.3515 | 0.7322 |
Handan | y = −0.0029x2 + 793.7881x −39,702,839.1399 | 0.9664 |
Ecosystem Services | Cultivated Land | Forest Land | Grass-Land | Water Area | Construction Land | Unused Land | |
---|---|---|---|---|---|---|---|
Provisioning Services | Food Production | 1885.18 | 430.78 | 398.08 | 1117.46 | 0 | 8.53 |
Raw Material Production | 417.98 | 989.51 | 585.74 | 622.71 | 0 | 25.59 | |
Water Supply | −2226.39 | 511.81 | 324.15 | 9280.91 | 0 | 17.06 | |
Regulating Services | Gas Regulation | 1518.38 | 3254.29 | 2058.63 | 2277.58 | 0 | 110.89 |
Climate Regulation | 793.31 | 9737.27 | 5442.30 | 5024.31 | 0 | 85.30 | |
Environmental Purification | 230.32 | 2853.37 | 1797.04 | 7805.17 | 0 | 349.74 | |
Hydrological Regulation | 2550.54 | 6372.09 | 3986.47 | 107,882.00 | 0 | 204.73 | |
Supporting Services | Soil Conservation | 887.15 | 3962.30 | 2507.89 | 2763.80 | 0 | 127.95 |
Nutrient Cycling | 264.44 | 302.82 | 193.35 | 213.26 | 0 | 8.53 | |
Biodiversity Maintenance | 290.03 | 3608.29 | 2280.42 | 8888.51 | 0 | 119.42 | |
Cultural Services | Aesthetic Landscape | 127.95 | 1582.36 | 1006.57 | 5647.02 | 0 | 51.18 |
Total | 6738.89 | 33,604.89 | 20,580.63 | 151,522.70 | 0 | 1108.93 |
Driving Factors | NDVI | Average Precipitation | Average Temperature | Per Capita GDP | Log Population Density | Cultivated-Land Reclamation Rate |
---|---|---|---|---|---|---|
VIF | 1.601 | 4.309 | 4.344 | 1.148 | 2.602 | 1.668 |
Year | Type | Carbon Source | Carbon Sink | Net Carbon Emissions | ||||
---|---|---|---|---|---|---|---|---|
Cultivated Land | Construction Land | Forest Land | Grass-Land | Water Area | Unused Land | |||
2000 | Carbon Emissions /104/t | 460.96 | 6724.82 | −272.76 | −7.40 | −16.53 | −0.10 | 6889.00 |
Proportion/% | 6.69% | 97.62% | −3.96% | −0.11% | −0.24% | <0.01% | 100% | |
2005 | Carbon Emissions /104/t | 456.61 | 11,521.86 | −272.78 | −7.36 | −16.02 | −0.10 | 11,682.21 |
Proportion/% | 3.91% | 98.63% | −2.34% | −0.06% | −0.14% | <0.01% | 100% | |
2010 | Carbon Emissions /104/t | 454.62 | 15,051.93 | −272.74 | −7.34 | −15.94 | −0.10 | 15,210.43 |
Proportion/% | 2.99% | 98.96% | −1.79% | −0.05% | −0.10% | <0.01% | 100% | |
2015 | Carbon Emissions /104/t | 428.82 | 16,032.32 | −277.38 | −7.19 | −17.09 | −0.08 | 16,159.40 |
Proportion/% | 2.65% | 99.21% | −1.72% | −0.04% | −0.11% | <0.01% | 100% | |
2020 | Carbon Emissions /104/t | 422.36 | 15,547.50 | −277.71 | −7.10 | −18.05 | −0.08 | 15,666.91 |
Proportion/% | 2.70% | 99.24% | −1.77% | −0.05% | −0.12% | <0.01% | 100% |
Year | Type | Cultivated Land | Forest Land | Grass-Land | Water Area | Construction Land | Unused Land | Total Value |
---|---|---|---|---|---|---|---|---|
2000 | Value/106 CNY | 73,611.14 | 149,651.01 | 72,477.95 | 97,450.44 | 0.00 | 230.16 | 393,420.71 |
Proportion/% | 18.71 | 38.04 | 18.42 | 24.77 | 0.00 | 0.06 | 100 | |
2005 | Value/106 CNY | 72,915.93 | 149,659.13 | 72,115.02 | 94,463.83 | 0.00 | 223.74 | 389,377.65 |
Proportion/% | 18.73 | 38.43 | 18.52 | 24.26 | 0.00 | 0.06 | 100 | |
2010 | Value/106 CNY | 72,597.51 | 149,638.60 | 71,928.71 | 93,981.81 | 0.00 | 219.56 | 388,366.19 |
Proportion/% | 18.70 | 38.53 | 18.52 | 24.20 | 0.00 | 0.06 | 100 | |
2015 | Value/106 CNY | 68,477.77 | 152,183.23 | 70,457.48 | 100,750.60 | 0.00 | 183.53 | 392,052.60 |
Proportion/% | 17.47 | 38.82 | 17.97 | 25.70 | 0.00 | 0.05 | 100 | |
2020 | Value/106 CNY | 67,445.78 | 152,365.56 | 69,589.45 | 106,427.25 | 0.00 | 185.85 | 396,013.90 |
Proportion/% | 17.03 | 38.47 | 17.57 | 26.88 | 0.00 | 0.05 | 100 |
Spatial Statistic | 2000 | 2005 | 2010 | 2015 | 2020 |
---|---|---|---|---|---|
Global Moran’s I | −0.100 | −0.101 | −0.048 | −0.051 | −0.052 |
p | 0.001 | 0.001 | 0.046 | 0.042 | 0.031 |
Z | −3.126 | −3.290 | −1.987 | −1.995 | −2.011 |
Variable | Type | H-H | H-L | L-H | L-L |
---|---|---|---|---|---|
NDVI | Coefficient | −0.220 | 0.548 ** | 0.062 | 0.624 |
Standard error | 0.302 | 0.273 | 0.454 | 0.389 | |
Average Precipitation | Coefficient | −0.345 | 0.043 | −2.033 | 1.849 *** |
Standard error | 0.946 | 1.359 | 2.098 | 0.523 | |
Average Temperature | Coefficient | 0.011 | −0.134 | 1.699 | −1.807 *** |
Standard error | 0.637 | 0.712 | 1.054 | 0.416 | |
Per Capita GDP | Coefficient | −0.532 | −0.432 ** | 0.082 | −0.606 * |
Standard error | 0.452 | 0.207 | 0.259 | 0.309 | |
Log Population Density | Coefficient | −1.249 ** | 2.187 *** | 3.247 *** | −1.546 *** |
Standard error | 0.520 | 0.395 | 0.690 | 0.459 | |
Cultivated-Land Reclamation Rate | Coefficient | −1.802 *** | 1.073 *** | 2.580 *** | −2.221 *** |
Standard error | 0.526 | 0.377 | 0.737 | 0.436 | |
Constant | Coefficient | −5.271 *** | −3.087 *** | −5.425 *** | −4.998 *** |
Standard error | 0.735 | 0.380 | 0.776 | 0.621 | |
Training Accuracy | 0.768 | ||||
Test Accuracy | 0.778 | ||||
Pseudo R2 | 0.343 | ||||
Wald test | Wald x2 = 179.87, df = 24, p < 0.001 | ||||
Likelihood Ratio p-value | <0.001 |
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Li, A.; Yin, X.; Wei, H. Spatiotemporal Evolution and Driving Factors of the Relationship Between Land Use Carbon Emissions and Ecosystem Service Value in Beijing-Tianjin-Hebei. Land 2025, 14, 1698. https://doi.org/10.3390/land14081698
Li A, Yin X, Wei H. Spatiotemporal Evolution and Driving Factors of the Relationship Between Land Use Carbon Emissions and Ecosystem Service Value in Beijing-Tianjin-Hebei. Land. 2025; 14(8):1698. https://doi.org/10.3390/land14081698
Chicago/Turabian StyleLi, Anjia, Xu Yin, and Hui Wei. 2025. "Spatiotemporal Evolution and Driving Factors of the Relationship Between Land Use Carbon Emissions and Ecosystem Service Value in Beijing-Tianjin-Hebei" Land 14, no. 8: 1698. https://doi.org/10.3390/land14081698
APA StyleLi, A., Yin, X., & Wei, H. (2025). Spatiotemporal Evolution and Driving Factors of the Relationship Between Land Use Carbon Emissions and Ecosystem Service Value in Beijing-Tianjin-Hebei. Land, 14(8), 1698. https://doi.org/10.3390/land14081698