Analyzing Temporal and Spatial Characteristics and Determinant Factors of Energy-Related CO2 Emissions of Shanghai in China Using High-Resolution Gridded Data
Abstract
:1. Introduction
2. Research Methods
2.1. Method of Processing and Updating Gridded Data
2.2. Mechanism of the Geographic Detector
3. Trends and Characteristics of Shanghai’s ffCO2 emissions
3.1. Trends of ffCO2 Emissions in Shanghai
3.2. High Spatial Clustering of CO2 Emissions in Shanghai
3.2.1. Spatial Differences in ffCO2 Emissions
3.2.2. The High Spatial Agglomeration of ffCO2 Emissions Has Not Changed over Time
3.3. CO2 Emissions Concentrated along the River and Coastal Area
3.4. Circular Layers Structure of CO2 Emissions
3.5. Differences in Grid Changes in CO2 Emissions
4. Impact Mechanisms of CO2 Emissions
4.1. Impact Mechanisms of CO2 Emissions from Large Point Sources (LPS)
4.2. Impact Mechanisms of CO2 Emissions from Area Sources
5. Conclusions and Outlook
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Top–Down Approach | Bottom–Up Approach | ||||||||
---|---|---|---|---|---|---|---|---|---|
Coal Total | Petroleum Products Total | Natural Gas | Coke Moving in from Other Provinces | Total ffCO2 | Coal Total | Petroleum Products Total | Natural Gas | Total ffCO2 | |
2010 | 130.7 | 96.3 | 9.1 | 2.6 | 238.7 | 130.5 | 72.7 | 9.3 | 212.6 |
2011 | 135.7 | 92.6 | 11.2 | 2.9 | 242.4 | 135.2 | 71.5 | 11.2 | 217.9 |
2012 | 126.4 | 96.0 | 13.0 | 1.5 | 236.8 | 126.4 | 75.1 | 12.8 | 214.3 |
2013 | 125.5 | 98.8 | 14.7 | 2.9 | 242.0 | 125.4 | 79.1 | 14.3 | 218.9 |
2014 | 108.7 | 96.7 | 14.6 | 4.9 | 224.9 | 108.7 | 77.7 | 14.2 | 200.7 |
2015 | 105.4 | 101.1 | 15.6 | 2.9 | 225.0 | 105.4 | 82.3 | 14.1 | 201.7 |
Year | Units | Top 10 1 | Top 20 | Top 50 | Top 72 (1%) 2 | Top 100 | Top 702 | Other Grids 3 | Total Emissions |
---|---|---|---|---|---|---|---|---|---|
2010 | Mt-CO2 | 105.9 | 126.5 | 151.5 | 159.3 | 163.5 | 184 | 24.9 | 208.9 |
% | 50.69 | 60.56 | 72.52 | 76.26 | 78.27 | 88.08 | 11.92 | 100.0 | |
2011 | Mt-CO2 | 115.8 | 134.1 | 156.6 | 164.1 | 167.9 | 189.5 | 28.4 | 217.9 |
% | 53.14 | 61.54 | 71.87 | 75.31 | 77.05 | 86.97 | 13.03 | 100.0 | |
2012 | Mt-CO2 | 112.5 | 129.1 | 149.6 | 156.8 | 160.4 | 182.8 | 31.5 | 214.3 |
% | 52.50 | 60.24 | 69.81 | 73.17 | 74.85 | 85.30 | 14.70 | 100.0 | |
2013 | Mt-CO2 | 115 | 131.5 | 153.9 | 161.9 | 165.4 | 187.2 | 31.7 | 218.9 |
% | 52.54 | 60.07 | 70.31 | 73.96 | 75.56 | 85.52 | 14.48 | 100.0 | |
2014 | Mt-CO2 | 98 | 113.9 | 135.3 | 143.3 | 146.7 | 168.4 | 32.3 | 200.7 |
% | 48.83 | 56.75 | 67.41 | 71.40 | 73.09 | 83.91 | 16.09 | 100.0 | |
2015 | Mt-CO2 | 97.8 | 114.8 | 136.2 | 145 | 148.4 | 169.6 | 32.1 | 201.7 |
% | 48.49 | 56.92 | 67.53 | 71.89 | 73.57 | 84.09 | 15.91 | 100.0 |
Circle | Coal-Related CO2 | Oil-Related CO2 | Natural Gas-Related CO2 | Energy-Related CO2 |
---|---|---|---|---|
Pxii | 0.01 | 5.87 | 5.66 | 2.80 |
Pdii | 0.00 | 1.01 | 0.62 | 0.45 |
Pxio | 5.09 | 8.96 | 8.67 | 6.91 |
Pdio | 2.27 | 7.99 | 5.77 | 4.85 |
Aoo | 92.63 | 76.17 | 79.29 | 84.99 |
Total | 100 | 100 | 100 | 100 |
Factor Types | Factors | Meaning | Data Resource |
---|---|---|---|
Population density | X1: Population density | Persons per grid (square kilometer) | Author mapped based on Shanghai Statistical Yearbook |
Human activity intensity | X2: Population activity intensity | Street (township) is divided into six categories according to population density at the daytime and nighttime and the ratio of daytime-to-nighttime population. | Author mapped based on the study of [50] |
X3: Constructed land development intensity | Constructed land area accounts for the proportion of the total area in each grid. | Author calculated | |
Land-use types | X4: Residential land | The total area of residential land in each grid. | Author interpreted from high-resolution remote sensing image. |
X5: Traffic land | The total area of traffic land in each grid. | ||
X6: Public building land | The total area of public building land in each grid. | ||
X7: Industrial land | The total area of industrial land in each grid. | ||
Energy efficiency | X8: Energy consumption intensity | Energy consumption per 10000 RMB GDP output value of above-designated size enterprise. | Shanghai Statistical Yearbook on Energy |
Urban planning | X9: City-level types | Including the central city, key new town, Chengguan town, key towns, and rural areas. | Shanghai Urban Plan (1999–2000) |
X10: Types of urban circle layers | Including areas within the Inner Ring road, areas between Inner Ring road and Outer Ring road, and areas outside the Outer Ring road. | Shanghai Urban Plan (1999–2000) |
Year | Value | X1 | X2 | X3 | X4 | X5 | X6 | X7 | X8 | X9 | X10 |
---|---|---|---|---|---|---|---|---|---|---|---|
2010 | q statistic | 0.30 | 0.28 | 0.21 | 0.11 | 0.23 | 0.16 | 0.07 | 0.11 | 0.19 | 0.32 |
p value | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | |
2015 | q statistic | 0.42 | 0.36 | 0.24 | 0.14 | 0.26 | 0.18 | 0.06 | 0.28 | 0.25 | 0.42 |
p value | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
Circle | X1 | X2 | X3 | X4 | X5 | X6 | X7 | X8 | X9 | |
---|---|---|---|---|---|---|---|---|---|---|
Inner Ring | q statistic | 0.32 | 0.39 | 0.08 | 0.06 | 0.09 | 0.05 | 0.02 | 0.45 | -- |
p value | 0.00 | 0.00 | 0.22 | 0.60 | 0.45 | 0.58 | 0.84 | 0.00 | -- | |
Middle Ring | q statistic | 0.19 | 0.17 | 0.03 | 0.07 | 0.16 | 0.10 | 0.03 | 0.30 | 0.03 |
p value | 0.00 | 0.00 | 0.07 | 0.00 | 0.00 | 0.00 | 0.03 | 0.00 | 0.04 | |
Outer Ring | q statistic | 0.11 | 0.08 | 0.17 | 0.07 | 0.16 | 0.10 | 0.14 | 0.08 | 0.02 |
p value | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
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Zhu, H.; Pan, K.; Liu, Y.; Chang, Z.; Jiang, P.; Li, Y. Analyzing Temporal and Spatial Characteristics and Determinant Factors of Energy-Related CO2 Emissions of Shanghai in China Using High-Resolution Gridded Data. Sustainability 2019, 11, 4766. https://doi.org/10.3390/su11174766
Zhu H, Pan K, Liu Y, Chang Z, Jiang P, Li Y. Analyzing Temporal and Spatial Characteristics and Determinant Factors of Energy-Related CO2 Emissions of Shanghai in China Using High-Resolution Gridded Data. Sustainability. 2019; 11(17):4766. https://doi.org/10.3390/su11174766
Chicago/Turabian StyleZhu, Hanxiong, Kexi Pan, Yong Liu, Zheng Chang, Ping Jiang, and Yongfu Li. 2019. "Analyzing Temporal and Spatial Characteristics and Determinant Factors of Energy-Related CO2 Emissions of Shanghai in China Using High-Resolution Gridded Data" Sustainability 11, no. 17: 4766. https://doi.org/10.3390/su11174766
APA StyleZhu, H., Pan, K., Liu, Y., Chang, Z., Jiang, P., & Li, Y. (2019). Analyzing Temporal and Spatial Characteristics and Determinant Factors of Energy-Related CO2 Emissions of Shanghai in China Using High-Resolution Gridded Data. Sustainability, 11(17), 4766. https://doi.org/10.3390/su11174766