Spatio-temporal evolution characteristics of carbon emissions in Beijing-Tianjin-Hebei urban agglomeration derived from integrated data of DMSP-OLS and NPP-VIIRS
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摘要:
为探究京津冀城市群地市级以上城市尺度的碳排放时空演变特征，通过拟合最优模型，将NPP-VIIRS数据转化为DMSP-OLS尺度的夜间灯光数据，得到京津冀城市群2005—2019年的长时间序列夜间灯光数据集；再结合北京、天津、河北能源消费统计碳排放数据，构建京津冀城市群地市级以上城市尺度碳排放估算模型，模拟京津冀城市群碳排放空间分布，并结合倾向值法探究其碳排放时空演变特征。结果表明：京津冀城市群夜间灯光数据与能源消费碳排放量之间的相关性较高，且通过了1%的显著性检验。2005—2019 年，京津冀城市群13个城市的碳排放量整体逐渐增加；城市群碳排放增长速度较为缓慢，但京津唐地区增长速度较快；13个城市中已有多个城市单位国内生产总值碳排放量2019年比2005年降幅超40%。研究显示，夜间灯光数据可用于估算京津冀城市群碳排放量，且京津唐地区碳排放量较高，增速较快，应作为重点碳减排地区。
Abstract:Beijing-Tianjin-Hebei urban agglomeration(BTHUA) was taken as the study area to explore the spatio-temporal evolution characteristics of carbon emissions at the city level and above. By fitting the optimal model, NPP-VIIRS data was transformed into DMSP-OLS scale nighttime light data, and the long-time series nightlight image set of BTHUA from 2005 to 2019 was obtained. Combined with the provincial energy consumption statistical and carbon emission data, a municipal scale carbon emission estimation model at the city level and above in BTHUA was constructed. The spatial distribution of carbon emissions in BTHUA was simulated, and the temporal and spatial evolution characteristics of carbon emissions were explored in combination with the tendency value method. The results showed that: From 2005 to 2019, the correlation between nighttime light data and carbon emissions of energy consumption in BTHUA was high, and the significance test of 1% was passed. From 2005 to 2019, the carbon emissions of 13 cities in BTHUA were basically increasing gradually. Overall, the growth rate of carbon emissions in BTHUA was relatively slow from 2005 to 2019. Among them, Beijing-Tianjin-Tangshan area had a rapid growth rate. In 2019, many of the 13 cities in BTHUA reduced their carbon emissions per unit of GDP by more than 40% compared with that in 2005. The research showed that the nighttime light data could be used to estimate the carbon emissions of BTHUA, and the carbon emissions in Beijing-Tianjin-Tangshan area were high and the growth rate was fast, so it should be regarded as a key carbon emission reduction area.
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图 1 北京市、天津市碳排放量与夜间灯光DN的线性拟合
Figure 1. Linear fitting between carbon emission statistics and DN value of nighttime light in Beijing and Tianjin
图 2 河北省碳排放量与夜间灯光DN的线性拟合
Figure 2. Linear fitting between carbon emission statistics and DN of nighttime light in Hebei Province
图 3 2005—2019年京津冀城市群碳排放量的变化
Figure 3. Changes in carbon emissions of Beijing-Tianjin-Hebei urban agglomeration from 2005 to 2019
图 4 京津冀城市群各城市2005—2019年碳排放量
Figure 4. Carbon emissions of cities in Beijing-Tianjin-Hebei urban agglomeration from 2005 to 2019
图 5 2005—2019年京津冀城市群碳排放增长趋势
Figure 5. Carbon emission growth trend of Beijing-Tianjin-Hebei urban agglomeration from 2005 to 2019
表 1 2012年和2013年DMSP-OLS与NPP-VIIRS数据拟合参数
Table 1. Fitting parameters of DMSP-OLS and NPP-VIIRS in 2012 and 2013
拟合函数 a b c R2 f(x)=ax+b 4.989 33 82 138.17 0.906 5 f(x)=ax2+bx+c −0.000 103 426 9.995 12 51 872.25 0.941 2 f(x)=axb 1 225 0.514 6 0.942 2 f(x)=aln x+b 82 116 −600 651 0.920 5 
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表 2 2012年、2014—2019年与2013年的NPP-VIIRS数据拟合情况
Table 2. NPP-VIIRS data fitting in 2012, 2014-2019 and 2013
年份 拟合函数 a R2 2012 f(x)=ax 1.099 3 0.9969 2014 f(x)=ax 1.357 9 0.998 2 2015 f(x)=ax 1.476 5 0.997 2 2016 f(x)=ax 1.579 3 0.995 2 2017 f(x)=ax 1.711 1 0.994 4 2018 f(x)=ax 1.793 1 0.993 9 2019 f(x)=ax 1.948 1 0.989 6
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表 3 各能源的折标准煤换算系数和碳排放系数
Table 3. Conversion coefficient of converted standard coal and carbon emission coefficient of each energy source
能源类型 折标准煤换算系数/（t/t) 碳排放系数/（万t/万t) 煤炭 0.714 3 0.755 9 焦炭 0.971 4 0.855 0 原油 1.428 6 0.585 7 燃料油 1.428 6 0.618 5 汽油 1.471 4 0.553 8 煤油 1.471 4 0.571 4 柴油 1.457 1 0.592 1 天然气 1.330 0 0.448 3 热力 34.120 0 0.670 0 电力 0.345 0 0.272 0 注：折标准煤换算系数参照GB/T 2589—2020《综合能耗计算通则》；碳排放系数参照《IPCC国家温室气体清单指南》和《省级温室气体清单编制指南》。天然气的折标准煤换算系数单位为kg/m3；热力的折标准煤换算系数单位为kg/(106 kJ)；电力的折标准煤换算系数单位为kg/（kW·h）。
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表 4 碳排放量增长趋势等级划分标准
Table 4. Classification standard of carbon emission growth trend
缓慢增长型 较慢增长型 中速增长型 较快增长型 迅猛增长型 <$\bar C $−0.5S $\bar C $ −0.5S~
$\bar C $+0.5S$\bar C $+0.5S~
$\bar C $+S$\bar C $+S~
$\bar C $ +1.5S>$\bar C $ +1.5S 注：$ \bar C $为京津冀城市群各城市2005—2019年SLOPE平均值；S为标准差。
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表 5 京津冀城市群2019年的单位国内生产总值碳排放量相较于2005年的下降幅度
Table 5. Decline of carbon emissions per unit of GDP in Beijing-Tianjin-Hebei urban agglomeration in 2019 compared with 2005
% 北京市 天津市 保定市 沧州市 承德市 邯郸市 衡水市 廊坊市 秦皇岛市 石家庄市 唐山市 邢台市 张家口市 66 45 47 35 31 32 20 49 −5 33 37 31 38
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