Spatial and temporal heterogeneity of factors influencing carbon emissions from energy consumption in Chinese cities

doi:10.3969/j.issn.1004-9479.2024.08.20222252

Abstract

Abstract:

Based on the constructed synthetic DMSP/OLS nighttime lighting dataset, carbon emissions from energy consumption in 286 cities in China from 2005 to 2019 were simulated and their influencing factors were analyzed from the perspective of spatial and temporal heterogeneity using the MGWR model. The results show that: ①The MGWR model is more suitable for analyzing the spatial heterogeneity of factors influencing carbon emissions in Chinese cities. ②In general, economic development and energy intensity facilitate carbon emissions from energy consumption in Chinese cities. Industrial upgrading and population density mainly show inhibiting effects, while foreign investment, population size, and green innovation show a heterogeneous impact model. ③specifically, the effects of each factor have strong spatial and temporal heterogeneity. The positive effect of economic development increases from east to west and from south to north; the energy intensity shows a positive impact with the central cities as the center and decreases in a radial pattern; the high-value area of the negative impact of industrial upgrading is mainly in the areas of Jiangsu, Zhejiang, and Shanghai, while the low-value area is in the provinces of Guangxi, Guizhou, Yunnan, and Hainan; the negative impact of population density is low in the cities of the northeast; the negative impact of foreign investment tends to increase from west to east; the impact pattern of population size changes from heterogeneous to positive, with the positive impact decreasing from the northeast to the southwest; the impact pattern of green innovation changes from negative to heterogeneous, with the positive impact area mainly in the cities of the Yangtze River Delta.

Key words: cities, carbon emissions, nighttime lighting data, MGWR, influencing factors, spatial and temporal heterogeneity

摘要：

基于构建的合成DMSP/OLS夜间灯光数据集，模拟了2005—2019年中国286个城市能源消费碳排放，并利用MGWR模型从时空异质性视角对其影响因素进行解析。结果表明：①MGWR模型更适合于分析中国城市碳排放影响因素的空间异质性。②总体上，经济发展与能源强度对中国城市能源消费碳排放具有促进作用，产业升级和人口密度主要表现为抑制作用，而外商投资、人口规模及绿色创新则呈现互异性影响模式。③具体地，各因素影响效果都具有较强的时空异质性。经济发展的正效应由东到西、由南到北依次增强；能源强度呈现出以中部地区城市为中心向周围辐散式递减的正效应；产业升级负向影响高值区主要集中在江浙沪等区域，而低值区则位于广西、贵州、云南及海南等省份；在东北地区城市，人口密度的负向影响强度偏低；外商投资负向影响呈由西到东增强趋势；人口规模影响模式由互异性影响转变为正向影响，正向影响由东北向西南地区梯度递减；绿色创新影响模式由负向影响转变为互异性影响，正向影响区域主要位于长三角地区。

关键词: 城市, 碳排放, 夜间灯光数据, MGWR, 影响因素, 时空异质性

Sufeng WANG, Jiantao HONG, Huafu LI. Spatial and temporal heterogeneity of factors influencing carbon emissions from energy consumption in Chinese cities[J]. World Regional Studies, 2024, 33(8): 102-116.

王素凤, 洪剑涛, 李化夫. 中国城市能源消费碳排放影响因素的时空异质性[J]. 世界地理研究, 2024, 33(8): 102-116.

Figures/Tables 13

Tab.1 Carbon emission factors for each type of energy

系数	原煤	焦炭	原油	汽油	煤油	柴油	燃料油	天然气	电力
换算成标准煤/（t标准煤/t）	0.714	0.971	1.429	1.471	1.471	1.457	1.429	1.330	0.345
碳排放系数 $K i$ /（万t/万t标准煤）	0.756	0.855	0.586	0.534	0.571	0.592	0.619	0.448	0.272

Tab.1 Carbon emission factors for each type of energy

系数	原煤	焦炭	原油	汽油	煤油	柴油	燃料油	天然气	电力
换算成标准煤/（t标准煤/t）	0.714	0.971	1.429	1.471	1.471	1.457	1.429	1.330	0.345
碳排放系数 $K i$ /（万t/万t标准煤）	0.756	0.855	0.586	0.534	0.571	0.592	0.619	0.448	0.272

Tab.2 Descriptive statistics

变量名称	变量内涵	单位	平均值	标准差	最小值	最大值
碳排放量	二氧化碳排放总量	万吨	177.696	1 406.273	108.955	10 023.845
经济发展	GDP	亿元	847.178	1 166.029	44.878	12 022.010
产业升级	三产与二产比值	%	0.917	0.498	0.095	5.154
外商投资	实际使用外资金额占GDP比例	%	0.019	0.020	0.000	0.199
人口规模	年末人口总数	万人	441.410	311.629	17.200	3 416.000
人口密度	单位面积人口数量	人/km²	426.535	328.504	4.700	2 648.100
绿色创新	绿色专利申请量+1	项	273.346	1 105.131	1.000	26 436.000
能源强度	单位GDP能源消耗量	吨标准煤/万元	0.806	0.512	0.049	4.784

Tab.3 Full-sample regression results

影响因素	回归系数	P值	Z值
经济发展	1.053	0.000	119.300
产业升级	－0.073	0.000	－15.770
外商投资	0.007	0.000	7.640
人口规模	0.102	0.000	6.890
人口密度	0.004	0.740	0.330
绿色创新	0.020	0.000	13.500
能源强度	1.007	0.000	192.520
常数项	－10.418	0.000	－60.140
样本量	4 290
固定效应	个体年份双向固定
拟合优度	0.973

Tab.4 Comparison of OLS, GWR and MGWR regression results

模型		2005年			2010年			2015年			2019年
模型		OLS	GWR	MGWR	OLS	GWR	MGWR	OLS	GWR	MGWR	OLS	GWR	MGWR
AICc		302	39	－3	350	134	97	436	271	226	463	213	188
R²		0.842	0.968	0.971	0.814	0.957	0.958	0.748	0.929	0.934	0.724	0.943	0.949
变量带宽	经济发展	—	62	49	—	57	58	—	58	43	—	57	43
	产业升级			46			44			43			43
	外商投资			56			56			188			51
	人口规模			56			43			58			56
	人口密度			117			283			285			56
	绿色创新			55			43			46			61
	能源强度			43			45			43			43
	常数项			117			179			43			43

Fig.1 Spatial distribution of local R2 for MGWR model

Tab.5 Means of MGWR regression coefficients

年份	2005年	2010年	2015年	2019年
经济发展	1.315	1.395	1.472	1.169
产业升级	－0.140	－0.112	－0.178	－0.291
外商投资	0.031	0.027	－0.017	0.005
人口规模	0.195	0.176	0.216	0.441
人口密度	－0.060	－0.059	－0.017	－0.132
能源强度	0.279	0.323	0.339	0.294
绿色创新	－0.345	－0.345	－0.500	－0.336
常数项	0.040	0.087	0.105	0.002

Fig.2 Spatial distribution of economic development regression coefficients

Fig.3 Spatial distribution of industrial upgrading regression coefficients

Fig.4 Spatial distribution of foreign investment regression coefficients

Fig.5 Spatial distribution of population size regression coefficients

Fig.6 Spatial distribution of population density regression coefficients

Fig.7 Spatial distribution of green innovation regression coefficients

Fig.8 Spatial distribution of energy intensity regression coefficients

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