Spatiotemporal Variations of Carbon Emissions and Their Driving Factors in the Yellow River Basin
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Sources and Processing
2.3. Methods
2.3.1. Carbon-Emission Measurement
2.3.2. Spatial Autocorrelation Analysis
2.3.3. Explanatory Variables
2.3.4. Construction of Influencing Factor Model
- (1)
- Analysis framework of influencing mechanism
- (2)
- Selection of explanatory variables
- (3)
- Geographical detector
3. Results
3.1. Temporal Variations of Carbon Emissions in the YRB
3.1.1. Temporal Variations of Carbon Emissions in the Whole Basin
3.1.2. Temporal Variations of Carbon Emission in Different Reaches
3.2. Spatial Pattern Evolution of Carbon Emissions in the YRB
3.2.1. Overall Characteristics of Carbon Emissions
3.2.2. Spatial Agglomeration Characteristics of Carbon Emissions
3.3. Driving Factors of Spatiotemporal Variations in Carbon Emissions in the YRB
3.3.1. Driving Factor Detection
- (1)
- Natural environmental factors
- (2)
- Socioeconomic factors
- (3)
- Policy factors
3.3.2. Results of Interaction Detection
4. Discussion
4.1. Spatiotemporal Variation of Carbon Emissions in the YRB
4.2. Driving Factors of Carbon Emissions in the YRB
4.3. Limitations and Prospects
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Data Type | Data Sources |
---|---|
Carbon emissions data | China Carbon Accounting Database (https://www.ceads.net/), (accessed on 1 May 2022) |
Defense Meteorological Program/Operational Line-Scan System (DMSP/OLS; 2000–2013) | Resource and Environmental Science and Data Center (https://www.resdc.cn/), (accessed on 1 May 2022) |
Suomi National Polar-Orbiting Partnership/Visible Infrared Imaging Radiometer Suite (Suomi NPP/VIRRS; 2013–2020) nighttime lighting data | National Geophysical Data Center (http://www.geodata.cn/), (accessed on 1 May 2022) |
Meteorological data | China Meteorological Science Data Sharing Service Network (http://cdc.cma.gov.cn/), (accessed on 1 May 2022) |
Socioeconomic statistics | Statistical Yearbooks, Statistical Communiques, and China County Statistical Yearbooks of the provinces and regions in the YRB |
Driving Factor | Explanatory Variable | Impact Factor | Interpretation |
---|---|---|---|
Natural environmental factors | Climate condition | X1 Annual average temperature (°C) | Value of each unit obtained by the method of Kring with ArcGIS software |
X2 Annual average precipitation (mm) | Value of each unit obtained by the method of Kring with ArcGIS software | ||
Topographic condition | X3 Elevation (m) | Digital elevation map (DEM) data of all counties (cities, districts and flags) in the Yellow River Basin (YRB) obtained by analysis tool with ArcGIS software | |
X4 Slope (°) | Average slope of each regional unit extracted based on DEM data | ||
Socioeconomic factors | Population size | X5 Population density (people/km2) | Total population divided by the total regional area |
X6 Population urbanization rate (%) | Proportion of nonagricultural population in all regions | ||
Economic level | X7 Economic density (×109 RMB/km2) | Gross domestic product divided by the total regional area | |
X8 Average fixed-asset investment (×108 RMB/km2) | Fixed-asset investment divided by the total regional area | ||
X9 Second industry ratio (%) | Proportion of secondary industries | ||
X10 Tertiary industry ratio (%) | Proportion of tertiary industries | ||
X11 Disposable income of urban residents (RMB) | Disposable income of urban residents in each regional unit | ||
X12 Disposable income of rural residents (RMB) | Disposable income of rural residents in each regional unit | ||
Regional policy factors | Vegetation coverage | X13 Normalized Differentiation Vegetation Index (NDVI) | Normalized vegetation index obtained by spatial interpolation method with ArcGIS |
Policy of Grain for Green | X14 Area of returning cultivated land (km2) | Conversion area extracted from cultivated land to ecological land (forest land, grassland, water area) with ArcGIS |
Interaction Types | Condition |
---|---|
nonlinear weakening | q(X1∩X2) < Min(q(X1),q(X2)) |
single-factor nonlinear weakening | Min(q(X1),q(X2)) < q(X1∩X2) < Max(q(X1),q(X2)) |
double-factor enhancement | q(X1∩X2) > Max(q(X1),q(X2)) |
independent | q(X1∩X2) = q(X1)+q(X2) |
nonlinear enhancement | q(X1∩X2) > q(X1)+q(X2) |
Region | Carbon Emissions in 2000/Million Tons | Carbon Emissions in 2010/Million Tons | Carbon Emissions in 2020/Million Tons | 2000–2020 | |
---|---|---|---|---|---|
Variation/Million Tons | Change Rate/% | ||||
Whole basin | 495.65 | 1023.03 | 1628.87 | 1133.22 | 228.64 |
Upper reaches | 147.89 | 324.93 | 645.08 | 497.19 | 336.18 |
Middle reaches | 190.45 | 395.24 | 582.30 | 391.58 | 205.60 |
Lower reaches | 157.30 | 302.87 | 401.76 | 244.46 | 155.41 |
Year | Global Moran’s I | E(Gi*) | Z(Gi*) | p |
---|---|---|---|---|
2000 | 0.249 | –0.004 | 8.952 | 0.000 |
2010 | 0.239 | –0.004 | 9.090 | 0.000 |
2020 | 0.210 | –0.004 | 8.818 | 0.000 |
Impact Factor | 2000 | 2010 | 2020 | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Whole Basin | Upper Reaches | Middle Reaches | Lower Reaches | Whole Basin | Upper Reaches | Middle Reaches | Lower Reaches | Whole Basin | Upper Reaches | Middle Reaches | Lower Reaches | |
X1 | 0.140 *** | 0.189 *** | 0.017 | 0.222 *** | 0.111 *** | 0.162 *** | 0.022 | 0.179 ** | 0.123 *** | 0.132 *** | 0.056 * | 0.147 ** |
X2 | 0.067 *** | 0.192 *** | 0.031 | 0.212 *** | 0.109 *** | 0.432 *** | 0.091 | 0.077 ** | 0.087 *** | 0.180 *** | 0.103 *** | 0.036 |
X3 | 0.188 *** | 0.344 *** | 0.039 | 0.005 | 0.173 *** | 0.381 *** | 0.014 | 0.001 | 0.130 *** | 0.111 *** | 0.017 | 0.002 |
X4 | 0.171 *** | 0.233 *** | 0.092 * | 0.063 | 0.181 *** | 0.326 *** | 0.074 | 0.063 | 0.180 *** | 0.120 *** | 0.060 | 0.044 |
X5 | 0.212 *** | 0.133 *** | 0.246 *** | 0.222 * | 0.197 *** | 0.098 | 0.221 *** | 0.336 *** | 0.133 *** | 0.159 ** | 0.185 *** | 0.268 *** |
X6 | 0.009 | 0.212 *** | 0.017 | 0.131 | 0.068 *** | 0.244 *** | 0.133 *** | 0.041 | 0.121 *** | 0.087 *** | 0.137 *** | 0.177 ** |
X7 | 0.099 | 0.007 | 0.001 | 0.305 | 0.430 *** | 0.539 *** | 0.296 *** | 0.396 *** | 0.297 *** | 0.591 *** | 0.292 *** | 0.368 ** |
X8 | 0.010 | 0.001 | 0.052 | 0.026 | 0.387 *** | 0.550 *** | 0.219 *** | 0.497 *** | 0.113 *** | 0.334 *** | 0.084 | 0.252 * |
X9 | 0.055 | 0.004 | 0.007 | 0.161 | 0.365 *** | 0.532 *** | 0.271 *** | 0.118 | 0.193 *** | 0.428 *** | 0.189 ** | 0.228 |
X10 | 0.063 * | 0.001 | 0.013 | 0.164 * | 0.348 *** | 0.424 *** | 0.171 * | 0.266 * | 0.214 *** | 0.452 *** | 0.100 * | 0.290 * |
X11 | 0.001 | 0.049 | 0.001 | 0.019 | 0.025 ** | 0.068 ** | 0.031 | 0.005 | 0.160 *** | 0.448 *** | 0.088 ** | 0.092 |
X12 | 0.001 | 0.001 | 0.005 | 0.019 | 0.181 *** | 0.205 *** | 0.235 *** | 0.139 ** | 0.163 *** | 0.370 *** | 0.139 *** | 0.180 ** |
X13 | 0.013 | 0.211 *** | 0.087 * | 0.147 | 0.060 ** | 0.255 *** | 0.104* | 0.047 | 0.104 *** | 0.053 *** | 0.077 | 0.125 |
X14 | 0.050 *** | 0.057 | 0.004 | 0.035 | 0.016 | 0.090 ** | 0.011 | 0.222 ** | 0.015 | 0.049 | 0.001 | 0.110 ** |
Year | * Whole Basin | Upper Reaches | Middle Reaches | Lower Reaches | ||||
---|---|---|---|---|---|---|---|---|
Interactive Factors | Interactive Value | Interactive Factors | Interactive Value | Interactive Factors | Interactive Value | Interactive Factors | Interactive Value | |
2000a | X1∩X5 | 0.359 * | X3∩X14 | 0.484 * | X4∩X5 | 0.456 * | X4∩X5 | 0.622 * |
X4∩X5 | 0.351 ** | X4∩X6 | 0.471 ** | X13∩X5 | 0.422 * | X1∩X5 | 0.562 * | |
X4∩X3 | 0.347 ** | X2∩X3 | 0.463 ** | X1∩X5 | 0.406 * | X13∩X5 | 0.557 * | |
X1∩X3 | 0.340 * | X13∩X3 | 0.441 ** | X2∩X5 | 0.369 * | X13∩X4 | 0.538 * | |
X13∩X5 | 0.336 * | X1∩X3 | 0.440 ** | X6∩X5 | 0.362 * | X7∩X5 | 0.536 ** | |
X2∩X5 | 0.324 * | X13∩X6 | 0.423 ** | X3∩X5 | 0.329 * | X6∩X5 | 0.531 * | |
X3∩X5 | 0.312 ** | X3∩X5 | 0.403 ** | X8∩X5 | 0.265 ** | X9∩X13 | 0.510 * | |
X2∩X3 | 0.300 * | X1∩X6 | 0.398 ** | X12∩X5 | 0.265 * | X9∩X5 | 0.502 * | |
X1∩X2 | 0.295 * | X6∩X14 | 0.392 * | X2∩X6 | 0.253 * | X13∩X7 | 0.496 * | |
X1∩X4 | 0.291 ** | X13∩X4 | 0.386 ** | X11∩X5 | 0.252 ** | X10∩X5 | 0.490 * | |
2010a | X1∩X7 | 0.550 * | X9∩X6 | 0.725 ** | X6∩X5 | 0.555 * | X13∩X8 | 0.695 * |
X2∩X7 | 0.548 * | X2∩X7 | 0.724 ** | X6∩X7 | 0.532 * | X13∩X7 | 0.653 * | |
X6∩X7 | 0.541 ** | X13∩X7 | 0.721 ** | X6∩X8 | 0.507 * | X8∩X5 | 0.652 ** | |
X4∩X7 | 0.536 ** | X6∩X7 | 0.718 ** | X9∩X5 | 0.503 ** | X10∩X7 | 0.639 ** | |
X10∩X7 | 0.532 ** | X14∩X7 | 0.707 * | X12∩X7 | 0.485 ** | X8∩X7 | 0.604 ** | |
X13∩X7 | 0.528 * | X4∩X7 | 0.705 ** | X12∩X5 | 0.468 ** | X9∩X8 | 0.584 ** | |
X9∩X8 | 0.522 ** | X14∩X9 | 0.703 * | X9∩X8 | 0.455 ** | X10∩X8 | 0.582 ** | |
X7∩X5 | 0.521 ** | X4∩X8 | 0.702 ** | X7∩X5 | 0.453 ** | X6∩X7 | 0.577 * | |
X10∩X8 | 0.521 ** | X9∩X4 | 0.701 ** | X9∩X13 | 0.451 * | X6∩X5 | 0.569 * | |
X3∩X7 | 0.518 ** | X9∩X3 | 0.692 ** | X9∩X6 | 0.445 * | X2∩X8 | 0.567 ** | |
2020a | X2∩X7 | 0.592 * | X4∩X7 | 0.777 ** | X2∩X7 | 0.531* | X8∩X5 | 0.726 * |
X1∩X7 | 0.519 * | X11∩X4 | 0.734 ** | X9∩X8 | 0.529 * | X10∩X5 | 0.657 * | |
X7∩X5 | 0.492 * | X7∩X5 | 0.732 * | X9∩X5 | 0.519 * | X13∩X7 | 0.640 * | |
X3∩X7 | 0.476 * | X10∩X13 | 0.712 * | X9∩X2 | 0.519 * | X6∩X5 | 0.634 * | |
X10∩X7 | 0.473 ** | X11∩X9 | 0.712 ** | X9∩X7 | 0.484 ** | X8∩X7 | 0.633 ** | |
X8∩X7 | 0.462 * | X10∩X7 | 0.711 ** | X9∩X6 | 0.477 * | X10∩X6 | 0.633 * | |
X13∩X7 | 0.460 * | X11∩X7 | 0.711* * | X2∩X5 | 0.462 * | X10∩X8 | 0.628 * | |
X9∩X2 | 0.449 * | X13∩X7 | 0.710 ** | X1∩X7 | 0.454 * | X6∩X7 | 0.619 * | |
X11∩X7 | 0.448 ** | X9∩X7 | 0.707 ** | X9∩X13 | 0.453 * | X10∩X13 | 0.609 * | |
X4∩X7 | 0.446 ** | X8∩X7 | 0.706 ** | X4∩X7 | 0.443 * | X9∩X8 | 0.592 * |
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Wang, S.; Sun, P.; Sun, H.; Liu, Q.; Liu, S.; Lu, D. Spatiotemporal Variations of Carbon Emissions and Their Driving Factors in the Yellow River Basin. Int. J. Environ. Res. Public Health 2022, 19, 12884. https://doi.org/10.3390/ijerph191912884
Wang S, Sun P, Sun H, Liu Q, Liu S, Lu D. Spatiotemporal Variations of Carbon Emissions and Their Driving Factors in the Yellow River Basin. International Journal of Environmental Research and Public Health. 2022; 19(19):12884. https://doi.org/10.3390/ijerph191912884
Chicago/Turabian StyleWang, Shiqing, Piling Sun, Huiying Sun, Qingguo Liu, Shuo Liu, and Da Lu. 2022. "Spatiotemporal Variations of Carbon Emissions and Their Driving Factors in the Yellow River Basin" International Journal of Environmental Research and Public Health 19, no. 19: 12884. https://doi.org/10.3390/ijerph191912884