Spatiotemporal Evolution and Influencing Factors of Carbon Footprint Depth of Capital Cities in the Middle Yangtze River
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摘要: 碳足迹深度指数表示区域存量资本的耗费程度，分析其时空演变及影响因素对区域差异化碳排放管控具有促进意义. 借鉴三维碳足迹改进模型计算长江中游省会城市碳足迹深度，引入夜间灯光数据拟合碳足迹深度指数，分析长江中游省会城市碳足迹深度的时空演变及分布特征，运用空间分位数模型对碳足迹深度影响因素开展研究. 结果表明：①2010—2019年，武汉市、南昌市、长沙市碳足迹深度指数均呈上升趋势. 2010年，长江中游省会城市归一化碳足迹深度指数呈现武汉市>南昌市>长沙市的特征，2015年、2019年均变为武汉市>长沙市>南昌市，各市归一化碳足迹深度高值范围均以城市的中心城区向四周扩张. ②2010—2019年，武汉市、南昌市、长沙市归一化碳足迹深度指数均在1%的显著性水平上高值聚集，由空间趋势面可知，长江中游省会城市归一化碳足迹深度指数在东西方向表现为“中间高、两边低”，而南北方向则由“北高南低”发展为“中间低、两边高”的分布格局，且北部明显高于南部. ③人口密度、工业总产值、能源总量、人均碳排放等影响因素对碳足迹深度的作用均为正向，各影响因素在碳足迹深度不同分位点的相关系数差异显著. 针对武汉市、南昌市、长沙市分别提出差异化建议：武汉市应积极发展产业转型，合理优化土地利用结构；南昌市应形成多循环工业体系，减少对生态用地的侵蚀；长沙市应加大力度发展绿色产业，打造低碳技术.Abstract: The carbon footprint depth index indicates the consumption degree of regional stock capital. Its spatiotemporal evolution and influencing factors are significance for promoting regional differentiated carbon emission control. The carbon footprint depth of the capital cities in the middle reaches of the Yangtze River was calculated using the improved three-dimensional carbon footprint model. This paper used nighttime light data to fit the carbon footprint depth index. The spatiotemporal evolution and distribution characteristics of the carbon footprint depth of the capital cities in the middle reaches of the Yangtze River were analyzed. The influencing factors of carbon footprint depth were studied using spatial quantile model. The results showed that from 2010 to 2019, the carbon footprint depth indices of Wuhan, Nanchang, and Changsha all showed an upward trend. In 2010, the normalized carbon footprint depth index of the capital cities in the middle reaches of the Yangtze River decreased from Wuhan to Nanchang and to Changsha. In 2015 and 2019, it′s decreased from Wuhan, to Changsha, and to Nanchang. The high values of normalized carbon footprint depth all expanded from the central urban area to the surrounding areas. Furthermore, from 2010 to 2019, the normalized carbon footprint depth indices of Wuhan, Nanchang, and Changsha were all at high values below the 1% significance level. The results showed that the normalized carbon footprint depth index in the study area is ‘high in the middle and low in both sides’ in the east-west direction. In the north-south direction, the distribution pattern developed from ‘high in the north and low in the south’ to ‘low in the middle and high in both sides’ and the north was significantly higher than the south. Influencing factors such as population density, total industrial output value, total energy, and per capita carbon emissions all had positive effects on carbon footprint depth. The correlation coefficients of each influencing factor were significantly different in different quantiles of carbon footprint depth. Therefore, differentiated suggestions were put forward: Wuhan should promote industrial transformation and optimize the land use structure; Nanchang should form a multi-circulating industrial system and reduce the occupation of ecological land; Changsha should develop green industries and create low-carbon technologies.
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图 1 三维碳足迹改进模型演变过程
注：在曹慧博等[13]三维生态足迹研究的基础上修改.
Figure 1. Evolution of the three-dimensional carbon footprint improvement model
图 2 武汉市、南昌市、长沙市归一化碳足迹深度指数的空间分布
Figure 2. Spatial distribution of normalized carbon footprint depth index in Wuhan, Nanchang and Changsha
图 3 武汉市、南昌市、长沙市归一化碳足迹深度指数的热点分析
Figure 3. Hot spot analysis of normalized carbon footprint depth index in Wuhan, Nanchang and Changsha
图 4 武汉市、南昌市、长沙市归一化碳足迹深度指数的趋势面分析
注：X轴表示区域经度跨度大小(西—东)，西东方向离散点趋势线见绿线；Y轴表示区域纬度跨度大小(南—北)，南北方向离散点趋势线见蓝线；Z轴表示区域归一化碳足迹深度指数的高低，见黑线.
Figure 4. Trend surface analysis of normalized carbon footprint depth index in Wuhan, Nanchang and Changsha
表 1 碳估算和夜间灯光拟合函数的模型归纳
Table 1. Models of fitting function between carbon estimation and nighttime lights
模型类别 函数表达式 线性函数 C=aD+b 指数函数 C=aebD 多项式函数 C=aD2+bD+c 对数函数 C=aln D+b 幂函数 C=aDb 注：C为碳相关估算值，D为夜间灯光值，a、b、c为各函数系数. 
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表 3 武汉市、南昌市、长沙市各区县归一化碳足迹深度指数
Table 3. Normalized carbon footprint depth index of each district and county in Wuhan, Nanchang and Changsha
武汉市 南昌市 长沙市 辖区 2010年 2015年 2019年 辖区 2010年 2015年 2019年 辖区 2010年 2015年 2019年 江岸区 0.978 2 1.000 0 1.000 0 青云谱区 0.950 5 1.000 0 1.000 0 芙蓉区 0.975 3 1.000 0 1.000 0 江汉区 1.000 0 1.000 0 1.000 0 西湖区 0.989 6 1.000 0 1.000 0 天心区 0.750 8 0.883 0 0.967 3 硚口区 1.000 0 1.000 0 1.000 0 东湖区 0.996 5 1.000 0 1.000 0 岳麓区 0.412 9 0.513 2 0.660 1 汉阳区 0.984 4 1.000 0 1.000 0 进贤县 0.042 8 0.053 5 0.073 2 开福区 0.656 0 0.871 9 0.949 0 武昌区 0.998 9 1.000 0 1.000 0 新建区 0.135 3 0.172 9 0.240 3 雨花区 0.474 4 0.562 1 0.660 9 青山区 0.986 9 1.000 0 1.000 0 青山湖区 0.737 5 0.903 5 0.964 1 望城区 0.256 4 0.463 8 0.619 5 洪山区 0.705 3 0.960 3 0.994 1 湾里区 0.190 8 0.198 4 0.275 6 长沙县 0.158 3 0.246 9 0.326 2 东西湖区 0.409 9 0.692 5 0.799 4 南昌县 0.154 8 0.222 3 0.357 2 浏阳市 0.050 0 0.043 1 0.077 8 汉南区 0.136 6 0.226 9 0.448 5 安义县 0.067 9 0.061 1 0.113 8 宁乡市 0.053 2 0.080 9 0.125 1 蔡甸区 0.295 7 0.443 3 0.519 1 南昌市 0.474 0 0.512 4 0.558 2 长沙市 0.420 8 0.518 3 0.598 4 江夏区 0.206 7 0.285 9 0.350 7 黄陂区 0.170 2 0.224 8 0.310 8 新洲区 0.147 0 0.166 4 0.261 7 武汉市 0.616 9 0.692 3 0.744 9
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表 4 武汉市、南昌市、长沙市归一化碳足迹深度指数的全局自相关性
Table 4. Autocorrelation results of normalized carbon footprint depth index in Wuhan, Nanchang and Changsha
年份 武汉市 南昌市 长沙市 Moran's I Z值 Moran's I Z值 Moran's I Z值 2010 0.974 5*** 98.583 9 0.962 2*** 75.907 6 0.973 6*** 112.243 4 2015 0.979 2*** 99.051 3 0.960 9*** 75.790 9 0.973 7*** 112.228 3 2019 0.984 9*** 99.629 9 0.966 9*** 76.253 5 0.975 0*** 112.371 7 注：***表示通过了1%的显著性水平检验.
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表 5 空间分位数模型回归结果
Table 5. Results of spatial quantile model regression
变量 线性回归 分位点 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 ln (PD) 3.992 7***
(0.402 1)3.895 8**
(1.531 7)4.009 3***
(1.244 9)4.490 8***
(1.026 1)4.503 7***
(0.886 8)4.928 3***
(0.877 8)4.059 0***
(0.829 6)4.129 6***
(0.795 9)3.775 7***
(0.756 8)3.778 7***
(0.764 1)ln (GIOV) 0.260 5***
(0.060 9)0.325 9*
(0.169 5)0.291 5*
(0.142 1)0.315 2**
(0.119 7)0.304 6**
(0.112 7)0.351 1***
(0.123 9)0.307 5**
(0.122 8)0.323 4**
(0.123 1)0.420 9***
(0.129 0)0.409 5***
(0.141 9)TE 1.829 7***
(0.272 4)1.763 5***
(0.609 2)1.671 9***
(0.562 0)1.548 5***
(0.513 5)1.531 5***
(0.502 9)1.422 5**
(0.548 4)1.655 0***
(0.546 8)1.631 4***
(0.565 7)1.283 1*
(0.630 6)1.380 8*
(0.677 5)PCCE 2.945 9***
(0.461 2)3.391 7***
(1.135 7)3.180 6***
(1.004 5)3.047 4***
(0.924 5)3.007 6***
(0.886 0)3.072 8***
(0.944 7)3.461 9***
(0.987 8)3.613 9***
(1.084 9)4.862 0***
(1.198 3)4.731 2***
(1.350 3)常数 −31.444 1***
(3.426 5)−32.324 7**
(11.945 5)−32.287 1***
(9.760 1)−35.669 2***
(8.009 2)−35.534 3***
(7.107 8)−39.067 6***
(7.393 5)−32.921 1***
(7.043 4)−33.715 4***
(6.731 5)−33.704 7***
(6.469 5)−33.451 7***
(6.677 0)R2 0.987 6 0.875 1 0.891 6 0.906 5 0.903 0 0.902 0 0.908 3 0.907 4 0.893 6 0.888 3 注：***、**、*分别表示通过了1%、5%、10%的显著性水平检验；括号内数值表示通过400次bootstrap抽样得到的标准误差.
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