长江经济带不同等级城市碳排放的时空演变及其影响因素

王兆峰; 李竹; 吴卫

doi:10.13198/j.issn.1001-6929.2022.02.29

长江经济带不同等级城市碳排放的时空演变及其影响因素

doi: 10.13198/j.issn.1001-6929.2022.02.29

湖南师范大学旅游学院，湖南长沙　410081

基金项目: 国家自然科学基金项目（No.41771162，41971188）；湖南省国内一流培育学科建设项目（No.5010002）

详细信息

作者简介:
王兆峰（1965-），男，湖南桑植人，教授，博士，博导，主要从事旅游地理研究，jdwzf@126.com

中图分类号: X321
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出版历程
- 收稿日期: 2021-10-07
- 修回日期: 2022-01-20
- 网络出版日期: 2022-10-11

Spatio-Temporal Evolution and Influencing Factors of Carbon Emissions in Different Grade Cities in the Yangtze River Economic Belt

College of Tourism, Hunan Normal University, Changsha 410081, China

Funds: National Natural Science Foundation of China (No.41771162, 41971188)；Construction Program for First Class Disciplines of Hunan Province, China (No.5010002)

摘要

摘要: 厘清区域不同等级城市碳排放对实施差异化的城市碳减排行动方案具有重要的指导意义. 采用2000—2019年DMSP_OLS和NPP_VIIRS夜间灯光数据模拟长江经济带城市碳排放，运用空间自相关分析和空间面板杜宾模型分别探讨长江经济带整体和各等级城市碳排放的时空演变及其影响因素. 结果表明：①研究期间，长江经济带整体和各等级城市碳排放量均呈波动上升趋势，其中各等级城市碳排放量呈大型城市>中型城市>小型城市的特征，整体和各等级城市碳排放量的年均增长率均有所降低. ②除个别年份外，整体和各等级城市碳排放的全局Moran′s I值均大于0，分别在5%和10%水平下显著，高-高聚集区主要分布在上海市、江苏省和浙江省等东部地区的城市，高-低聚集区主要分布在重庆市，低-低聚集区主要分布在乐山市等城市. ③人口增长、城镇化率和经济增长等因素对整体碳排放有显著的直接正向影响，而城镇生活污水处理率和生活垃圾无害化处理率对整体碳排放有显著的直接负向影响；人口增长、经济增长及环境规制等因素对各等级城市碳排放的影响有明显差异. 研究显示，长江经济带整体和各等级城市碳排放的时空演变及其影响因素有显著差异，城市减碳行动方案的制定和实施需要注重差异性.
- 不同等级城市碳排放 /
- 空间面板杜宾模型 /
- 影响因素 /
- 减碳政策 /
- 长江经济带
Abstract: It is importance for implementing differentiated urban carbon emission reduction action plan to clarify the carbon emissions of different grade cities. In this study, the DMSP_OLS and NPP_VIIRS nighttime light data from 2000 to 2019 were used to simulate the urban carbon emissions in the Yangtze River Economic Belt. Additionally, spatial autocorrelation analysis and spatial panel Durbin model were used to analyze the spatio-temporal evolution, and the influencing factors of carbon emissions in the Yangtze River Economic Belt as a whole and in different grade cities. The results show that: Firstly, the overall carbon emissions of the Yangtze River Economic Belt and those of different grade cities showed a fluctuating upward trend during the study period. The carbon emissions of the different grade cities were large cities>medium cities>small cities. The average annual growth rate of carbon emissions in the overall and different grade cities decreased. Secondly, except for a few years, the values of Moran's I regarding carbon emissions in the overall and different grade cities were greater than 0, which was significant at 5% and 10% levels, respectively. Moreover, its High-High aggregation areas were mainly distributed in Shanghai City, Jiangsu Province, Zhejiang Province and other cities in the eastern region. High-Low aggregation areas were mainly distributed in Chongqing City. Low-Low aggregation areas were mainly located in Leshan City and other cities. Thirdly, population growth, urbanization rate, economic growth and other factors can exert significant, direct and positive impacts on overall carbon emissions. Moreover, the treatment rate of urban sewage and the harmless treatment rate of domestic garbage had significant, direct and negative impacts on overall carbon emissions. In addition, population growth, economic development, environmental regulation, and other factors had significantly different influences on carbon emissions from different grade cities. The research showed that there were significant differences in the spatio-temporal evolution and influencing factors of carbon emissions from overall and different grade cities in the Yangtze River Economic Belt. The formulation and implementation of urban carbon reduction action plans should pay attention to the differences.
- carbon emissions of different grade cities /
- spatial panel Durbin model /
- influencing factors /
- carbon reduction policies /
- Yangtze River Economic Belt

HTML全文

图 1 长江经济带整体和各等级城市碳排放的时序演变

Figure 1. Temporal evolution of carbon emissions from overall, and different grade cities in the Yangtze River Economic Belt

下载: 全尺寸图片幻灯片

图 2 长江经济带整体和各等级城市碳排放的空间集聚情况

Figure 2. Spatial agglomeration of carbon emissions from overall, and different grade cities in the Yangtze River Economic Belt

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表 1 2000—2019年碳排放模拟回归结果

Table 1. The results of simulation regarding carbon emission from 2000 to 2019

2000—2009年			2010—2013年			2014—2019年
t检验	R²	k	t检验	R²	k	t检验	R²	k
27.41^***	0.87	0.05	17.01^***	0.87	0.06	23.23^***	0.89	0.03
注：k为拟合系数. 表示在10%水平(双侧)上相关显著，表示在5%水平(双侧)上相关显著，**表示在1%水平(双侧)上相关显著. 下同.

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表 2 LM、Robust-LM、Wald、LR、AIC和Hausman检验结果

Table 2. The results of LM, Robust-LM, Wald, LR, AIC and Hausman test

统计量		空间邻接矩阵				空间距离矩阵				经济距离矩阵
统计量		长江经济带整体	大型城市	中型城市	小型城市	长江经济带整体	大型城市	中型城市	小型城市	长江经济带整体	大型城市	中型城市	小型城市
LM-spatial lag		530.58^***	—	—	—	468.85^***	137.87^***	410.49^***	320.38^***	11.43^***	246.17^***	70.88^***	159.57^***
Robust LM-spatial lag		31.34^***	—	—	—	32.00^***	16.65^***	16.59^***	4.75^**	0.99	55.18^***	0.31	11.17^***
LM-spatial error		857.51^***	—	—	—	749.97^***	212.30^***	574.65^***	501.09^***	46.61^***	377.38^***	130.63^***	277.76^***
Robust LM-spatial error		358.27^***	—	—	—	313.12^***	91.08^***	180.75^***	185.45^***	36.17^***	186.38^***	60.07^***	129.36^***
Wald-spatial lag		19.08^**	—	—	—	19.24^**	42.15^***	48.72^***	21.61^***	31.17^***	52.89^***	66.09^***	26.37^***
Wald-spatial error		17.97^**	—	—	—	21.68^***	36.85^***	53.76^***	21.38^***	28.50^***	54.76^***	65.37^***	26.01^***
LR-spatial lag		18.97^**	—	—	—	19.17^**	40.35^***	46.86^***	21.37^***	31.45^***	50.51^***	63.15^***	25.86^***
LR-spatial error		17.90^**	—	—	—	21.57^***	35.72^***	51.15^***	21.13^***	29.87^***	52.62^***	62.98^***	25.33^***
AIC		−1654.20	—	—	—	−1 644.60	−643.70	−777.70	−636.60	−1 633.60	−636.20	−780.10	−626.00
Hausman 检验	双固定效应	40.69^***	—	—	—	—	142.80^***	57.89^***	28.20^**	—	—	—	—
	个体固定效应	56.41^***	—	—	—	—	29.60^***	28.61^***	20.54^**	—	—	—	—
	时点固定效应	244.07^***	—	—	—	—	94.18^***	30.39^***	129.39^***	—	—	—	—
注：LM-spatial lag表示空间滞后模型拉格朗日乘数检验；LM-spatial error表示空间误差模型拉格朗日乘数检验；Robust LM-spatial lag表示稳健性空间滞后模型拉格朗日乘数检验；Robust LM-spatial error表示稳健性空间误差模型拉格朗日乘数检验；Wald-spatial lag表示空间滞后模型沃尔德检验；Wald-spatial errorr表示空间误差模型沃尔德检验；LR-spatial lag表示空间滞后模型似然比检验；LR-spatial errorr表示空间误差模型似然比检验；AIC表示赤池信息准则.

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表 3 长江经济带整体和各等级城市碳排放空间面板杜宾模型基本回归结果

Table 3. Basic regression results of the space panel Durbin mode for carbon emissions from overall, and different grade cities in the Yangtze River Economic Belt

变量	长江经济带整体	大型城市	中型城市	小型城市
Main-ln POP	0.30^***	0.72^***	0.64^***	0.04
Main-ln GDP	0.27^***	0.16^***	0.26^***	0.35^***
Main-ln IS	−0.04^*	0.09^***	−0.06	−0.02
Main-ln CU	0.06^***	−0.02	0.26^***	0.11^***
Main-ln SD	0.02^***	0.09^***	0.01	0.003
Main-ln SC₁	−0.02^**	−0.04^*	0.008	−0.04^**
Main-ln SC₂	−0.09^***	−0.11^***	−0.05^**	−0.07^***
Main-ln SC₃	0.04^**	0.15^***	0.16^***	0.004
Wx-ln POP	−0.04	−0.23	0.20	0.34^**
Wx-ln GDP	0.09	0.13	−0.07	0.06
Wx-ln IS	0.05	0.03	−0.16^**	0.19^***
Wx-ln CU	−0.05	0.46^***	0.29^**	0.02
Wx-ln SD	0.001	0.11^***	0.11^***	0.008
Wx-ln SC₁	0.05^**	−0.03	−0.05	0.08^**
Wx-ln SC₂	0.01	−0.01	0.18^***	−0.04
Wx-ln SC₃	−0.07^**	0.13	−0.21^***	−0.02
Spatial rho	0.19^***	−0.30^***	−0.30^***	0.18^***
sigma2_e	0.03^***	0.01^***	0.02^***	0.03^***
N	2 120	460	740	920
注：Main-X、Wx-X、Spatial rho、sigma2_e分别表示解释变量的基本、滞后项、空间效应特异误差和个体效应特异误差的估计结果. N表示样本量. 下同.

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表 4 长江经济带整体和各等级城市碳排放空间面板杜宾模型直接、间接和总效应回归结果

Table 4. Regression results of direct, indirect, and total effect estimation of the space panel Durbin model for carbon emissions from overall, and different grade cities in the Yangtze River Economic Belt

变量	长江经济带整体	大型城市	中型城市	小型城市	变量	长江经济带整体	大型城市	中型城市	小型城市	变量	长江经济带整体	大型城市	中型城市	小型城市
D-ln POP	0.30^***	0.75^***	0.64^***	0.05	IND-ln POP	0.04	−0.35	0.02	0.42^**	T-ln POP	0.34^**	0.40	0.66^***	0.47^**
D-ln GDP	0.27^***	0.15^***	0.26^***	0.36^***	IND-ln GDP	0.17^**	0.07	−0.11^*	0.15	T-ln GDP	0.45^***	0.22^*	0.15^**	0.50^***
D-ln IS	−0.03^*	0.10^***	−0.05	−0.01	IND-ln IS	0.05	0.002	−0.12^**	0.22^**	T-ln IS	0.01	0.10^**	−0.17^**	0.21^**
D-ln CU	0.06^***	−0.06	0.24^***	0.11^***	IND-ln CU	−0.04	0.40^***	0.19^*	0.06	T-ln CU	0.03	0.35^***	0.43^***	0.17^*
D-ln SD	0.02^***	0.09^***	0.007	0.003	IND-ln SD	0.005	0.07^**	0.08^***	0.01	T-ln SD	0.02	0.16^***	0.09^***	0.01
D-ln SC₁	−0.02^*	−0.03^*	0.01	−0.03^**	IND-ln SC₁	0.06^**	−0.01	−0.04	0.09^**	T-ln SC₁	0.04	−0.05	−0.03	0.06
D-ln SC₂	−0.09^***	−0.11^***	−0.06^***	−0.08^***	IND-ln SC₂	−0.009	0.01	0.15^***	−0.07	T-ln SC₂	−0.10^***	−0.09^**	0.10^**	−0.14^**
D-ln SC₃	0.04^**	0.14^***	0.17^***	0.003	IND-ln SC₃	−0.07^*	0.07	−0.21^***	−0.02	T-ln SC₃	−0.03	0.21^***	−0.04	−0.02
注：D-ln X、IND-ln X和T-ln X分别表示解释变量的直接、间接和总效应估计结果.

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