Spatiotemporal Patterns and the Development Path of Land-Use Carbon Emissions from a Low-Carbon Perspective: A Case Study of Guizhou Province
Abstract
:1. Introduction
2. Overview of the Study Area and Data Sources
2.1. Overview of the Study Area
2.2. Data Sources
3. Research Methods
3.1. Dynamic Model of Land Use
3.2. Land-Use Carbon Emissions Calculation Model
4. Results
4.1. Spatiotemporal Evolution of Land Use
4.2. Spatiotemporal Pattern Analysis of Land-Use Carbon Emissions
4.3. Spatiotemporal Pattern Analysis of Land-Use Carbon Emissions Intensity
4.4. Analysis of Carbon Emissions per Capita
4.5. Analysis of Combined Spatial Difference between Emissions and Efficiency
5. Discussion
5.1. Analysis of Land-Use Change
5.2. Analysis of Low-Carbon Development Path
5.2.1. Promote the Optimization of Industrial Structure
5.2.2. Guide the Rational Distribution of Population
5.2.3. Improve Relevant Policies and Systems
6. Conclusions
- (1)
- Cultivated land and construction land in Guizhou Province have undergone the most significant changes in recent years. Construction land showed a trend of continuous expansion, and its main source was cultivated land. Guizhou Province is located in the mountainous region, with the area of forest land accounting for more than half of its total area. Forest land fulfils strong ecosystem functions and is the primary source of the carbon sink.
- (2)
- From 2009 to 2019, carbon emissions intensity in Guizhou Province continued to decline, while total carbon emissions and carbon emissions per capita maintained an upward trend. Accelerating urbanization, energy structure transformation, the immoderate utilization of land by human beings, and the increasing population density were the key causes of the significantly increasing trend in carbon emissions in Guizhou Province.
- (3)
- From the perspective of the cities in Guizhou Province, there were obvious spatial differences in carbon emissions and carbon emissions efficiency. Because of variations in land-use and economic growth patterns, Guiyang was the only high emission–high efficiency city, and Bijie was the only low emission–high efficiency city. Over time, the carbon emissions per capita and carbon emissions intensity in the different regions also exhibited agglomeration effects.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Land Type | Coefficient Value | Unit |
---|---|---|
Cultivated land | 0.0422 | kg/(m2·a) |
Forest land | −0.0578 | kg/(m2·a) |
Grassland | −0.0021 | kg/(m2·a) |
Water area | −0.0252 | kg/(m2·a) |
Unused land | −0.0005 | kg/(m2·a) |
Energy Type | Coefficient for Conversion to Standard Coal (kj·kg−1) | Average Lower Heating Value (kg·GJ−1) | Carbon Oxidation Rate (%) | Carbon Emissions Coefficient |
---|---|---|---|---|
Raw coal | 0.7143 | 20,908 | 95% | 0.5183 |
Coal | 0.7143 | 20,908 | 94% | 0.7559 |
Coke | 0.9714 | 28,435 | 93% | 0.8550 |
Cleaned coal | 0.9000 | 26,344 | 97% | 0.6225 |
Crude oil | 1.4286 | 41,816 | 98% | 0.5857 |
Gasoline | 1.4714 | 43,070 | 98% | 0.5538 |
Kerosene | 1.4714 | 43,030 | 98% | 0.5714 |
Diesel oil | 1.4571 | 42,652 | 98% | 0.5921 |
Liquefied petroleum gas | 1.7143 | 50,179 | 98% | 0.6225 |
Fuel oil | 1.4286 | 41,816 | 98% | 0.6185 |
Natural gas | 1.2143 | 35,544 | 99% | 0.4483 |
Electric power | 0.404 | 3596 | 99% | 0.7935 |
Year | Unit (km2) | Cultivated Land | Forest Land | Grassland | Water Area | Construction Land | Unused Land | Total |
---|---|---|---|---|---|---|---|---|
2009 | Area | 49,419.82 | 95,496.35 | 29,586.67 | 686.85 | 874.54 | 29.94 | 176,094.17 |
Proportion | 28.06% | 54.23% | 16.80% | 0.39% | 0.50% | 0.02% | 100.00% | |
2014 | Area | 49,087.38 | 95,423.01 | 29,529.73 | 697.75 | 1322.28 | 33 | 176,093.15 |
Proportion | 27.88% | 54.19% | 16.77% | 0.40% | 0.75% | 0.02% | 100.00% | |
2019 | Area | 48,437.87 | 93,082.92 | 31,327.96 | 1045.69 | 2159.25 | 30.58 | 176,084.27 |
Proportion | 27.51% | 52.86% | 17.79% | 0.59% | 1.23% | 0.02% | 100.00% | |
2009–2019 | Area change | −981.95 | −2413.43 | 1741.29 | 358.84 | 1284.71 | 0.64 | / |
Rate of area change | −1.99% | −2.53% | 5.89% | 52.24% | 146.90% | 2.14% | / |
Land Type | 2009–2014 | 2014–2019 | 2009–2019 |
---|---|---|---|
Cultivated land | −0.13% | −0.26% | −0.40% |
Forest land | −0.02% | −0.49% | −0.51% |
Grassland | −0.04% | 1.22% | 1.18% |
Water area | 0.32% | 9.97% | 10.45% |
Construction land | 10.24% | 12.66% | 29.38% |
Unused land | 2.04% | −1.46% | 0.43% |
Comprehensive dynamic degree | 0.13% | 0.60% | 0.64% |
Year | Land-Use Carbon Emissions (104 t) | Total Carbon Emissions | Carbon Absorption | Net Carbon Emissions | |||||
---|---|---|---|---|---|---|---|---|---|
Cultivated Land | Forest Land | Grassland | Water Area | Construction Land | Unused Land | ||||
2009 | 208.55 | −551.97 | −6.21 | −1.73 | 5943.7 | −0.0014 | 6152.22 | −559.91 | 5592.31 |
3.39% | 98.58% | 1.11% | 0.31% | 96.61% | 0.00% | 100.00% | 100.00% | ||
2014 | 207.15 | −551.55 | −6.2 | −1.76 | 7941.6 | −0.0016 | 8148.71 | −559.51 | 7589.2 |
2.54% | 98.58% | 1.11% | 0.31% | 97.46% | 0.00% | 100.00% | 100.00% | ||
2019 | 204.41 | −538.02 | −6.58 | −2.64 | 8756.9 | −0.0015 | 8961.34 | −546.64 | 8417.7 |
2.28% | 98.42% | 1.20% | 0.48% | 97.72% | 0.00% | 100.00% | 100.00% |
Region | GDP (CNY 108) | Carbon Emissions Intensity (t/CNY 104) | ||||
---|---|---|---|---|---|---|
2009 | 2014 | 2019 | 2009 | 2014 | 2019 | |
Guiyang | 1121.82 | 2891.16 | 3798.45 | 4.48 | 1.84 | 1.57 |
Liupanshui | 500.63 | 1201.08 | 1525.69 | 6.93 | 2.80 | 2.37 |
Zunyi | 908.76 | 2168.34 | 3000.23 | 2.99 | 1.43 | 1.28 |
Anshun | 232.9 | 625.41 | 849.4 | 5.87 | 2.65 | 2.33 |
Tongren | 293.62 | 770.89 | 1066.52 | 3.73 | 1.82 | 1.54 |
Qianxinan Autonomous Prefecture | 307.13 | 801.65 | 1163.77 | 6.35 | 3.07 | 2.33 |
Bijie | 600.85 | 1461.35 | 1921.43 | 2.56 | 1.28 | 1.11 |
Qiandongnan Autonomous Prefecture | 312.57 | 811.55 | 1026.62 | 5.13 | 2.29 | 2.07 |
Qiannan Autonomous Prefecture | 356.68 | 902.91 | 1313.46 | 5.55 | 2.35 | 1.88 |
Region | Population (104 Person) | Carbon Emissions per Capita (t·a−1·pp) | ||||
---|---|---|---|---|---|---|
2009 | 2014 | 2019 | 2009 | 2014 | 2019 | |
Guiyang | 432.93 | 462.18 | 488.19 | 11.61 | 11.5 | 12.18 |
Liupanshui | 285.43 | 288.99 | 293.73 | 12.15 | 11.63 | 12.31 |
Zunyi | 613.29 | 619.21 | 627.07 | 4.42 | 5 | 6.11 |
Anshun | 230.04 | 231.35 | 235.31 | 5.91 | 7.18 | 8.39 |
Tongren | 309.63 | 312.24 | 316.88 | 3.51 | 4.49 | 5.17 |
Qianxinan Autonomous Prefecture | 281.02 | 282.16 | 287.17 | 6.92 | 8.72 | 9.04 |
Bijie | 654.57 | 660.61 | 668.61 | 2.33 | 2.82 | 3.18 |
Qiandongnan Autonomous Prefecture | 348.52 | 348.54 | 353.83 | 4.6 | 5.33 | 6 |
Qiannan Autonomous Prefecture | 323.51 | 324.22 | 329.21 | 6.11 | 6.54 | 7.48 |
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Li, X.; Hu, S.; Jiang, L.; Han, B.; Li, J.; Wei, X. Spatiotemporal Patterns and the Development Path of Land-Use Carbon Emissions from a Low-Carbon Perspective: A Case Study of Guizhou Province. Land 2023, 12, 1875. https://doi.org/10.3390/land12101875
Li X, Hu S, Jiang L, Han B, Li J, Wei X. Spatiotemporal Patterns and the Development Path of Land-Use Carbon Emissions from a Low-Carbon Perspective: A Case Study of Guizhou Province. Land. 2023; 12(10):1875. https://doi.org/10.3390/land12101875
Chicago/Turabian StyleLi, Xiaoping, Sai Hu, Lifu Jiang, Bing Han, Jie Li, and Xuan Wei. 2023. "Spatiotemporal Patterns and the Development Path of Land-Use Carbon Emissions from a Low-Carbon Perspective: A Case Study of Guizhou Province" Land 12, no. 10: 1875. https://doi.org/10.3390/land12101875