Journal of Atmospheric and Environmental Optics, Volume. 19, Issue 4, 440(2024)
Spatio-temporal characteristics of carbon emission from energy consumption in China during 2000-2019
Figures & Tables(13)
Fig. 1. Luminous image of the research area. (a) 2000; (b) 2005; (c) 2010; (d) 2015; (e) 2019
Fig. 3. Fitting relationship between total night lighting and energy carbon emissions from 2000 to 2019.(a) 2000; (b) 2005; (c) 2010; (d) 2015; (e) 2019
Fig. 4. Simulated carbon emission distribution from 2000 to 2019. (a) 2000; (b) 2005; (c) 2010; (d) 2015; (e) 2019
Fig. 5. Carbon emission distribution map of China's provinces from 2000 to 2019. (a) 2000; (b) 2005; (c) 2010; (d) 2015; (e) 2019
Fig. 6. Local Moran′s I spatial distribution of carbon emissions from prefecture level cities from 2000 to 2019.(a) 2000; (b) 2005; (c) 2010; (d) 2015; (e) 2019
Fig. 7. Change trend of spatial distribution pattern and center of gravity migration track of carbon emissions in China from 2000 to 2019. (a) Overview map; (b) specific center of gravity transfer diagram
Fig. 8. Migration trajectory of carbon emission center of gravity in different regions from 2000 to 2019. (a) Subregional overview map; (b) Northeast China; (c) North China; (d) Eastern Chian ; (e) Central China; (f) South China; (g) West China
Table 1. Energy carbon emission coefficient
View tableView in ArticleTable 1. Energy carbon emission coefficient
Types of energy sources Raw coal Coke Crude oil Petrol Kerosene Diesel oil Fuel oil Natural gas Power Standard coal/t 0.7143 0.9714 1.4286 1.4714 1.4714 1.4571 1.4286 1.3300 1.2290 Emission coefficient 0.7559 0.8550 0.5857 0.5538 0.5714 0.5921 0.6185 0.4483 0.2720
Table 2. Classification of carbon emission types
View tableView in ArticleTable 2. Classification of carbon emission types
Carbon emission type Super heavy Heavy General type Light weight Carbon emission scale/t > 1.642 × 106 8.6 × 105-1.642 × 106 3.95 × 105-8.6 × 105 < 3.95 × 105
Table 3. Summary of univariate regression model
View tableView in ArticleTable 3. Summary of univariate regression model
Regression model R2 2000 2005 2010 2015 2019 Linear equation 0.603 0.822 0.831 0.826 0.743 Quadratic term equation 0.610 0.832 0.844 0.828 0.745 Exponential equation 0.506 0.640 0.738 0.714 0.630 Logarithmic equation 0.449 0.659 0.689 0.760 0.670 Power exponential equation 0.494 0.640 0.792 0.791 0.630
Table 4. Binomial model analysis statistics
View tableView in ArticleTable 4. Binomial model analysis statistics
Year Fitting equation R2 Sig 2000 y = 1.9 × 10-4x2 + 12.50053x + 49106.44329 0.610 0.000 2005 y = 0.00006x2 + 9.80827x + 78264.89088 0.832 0.000 2010 y = 0.00006x2 + 7.40293x + 129411.62384 0.844 0.000 2015 y = -0.00001x2 + 14.80051x + 91621311 0.828 0.000 2019 y = -8.35937 × 106x2 + 13.71627x + 105406.03210 0.745 0.000
Table 5. Standard deviation ellipse data statistics
View tableView in ArticleTable 5. Standard deviation ellipse data statistics
Year Coordinate Ellipse range/km2 Offset/km Difference value/km Direction 2000 (133°27′15″ E, 6°31′31″ N) 3469041.352 ― 216300.97 ― 2005 (133°23′06″ E, 6°14′45″ N) 3491833.578 47757.97 189651.41 Southwest 2010 (133°21′50″ E, 5°55′38″ N) 3804443.705 56974.31 161673.83 Northwest 2015 (133°36′29″ E, 4°49′20″ N) 4094436.194 141209.03 35983.23 Due west 2019 (141°17′43″ E, 33°19′34″ N) 4463968.900 3839542.93 59971.56 Northwest
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Congcong LIAO, Shuang HAO, Yuhuan CUI, Pengfei LI, Yazhou XU, Liangliang SHENG. Spatio-temporal characteristics of carbon emission from energy consumption in China during 2000-2019[J]. Journal of Atmospheric and Environmental Optics, 2024, 19(4): 440
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Received: Sep. 13, 2022
Accepted: --
Published Online: Jan. 8, 2025
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