中国城市能源消费碳排放影响因素的时空异质性

doi:10.3969/j.issn.1004-9479.2024.08.20222252

摘要/Abstract

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

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

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.

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.

图/表 13

表1 各类能源碳排放系数

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

表1 各类能源碳排放系数

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

表2 描述性统计

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

表3 全样本回归结果

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

表4 OLS、GWR和MGWR回归结果比较

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

图1 MGWR模型的局部R2 空间分布注：该图基于国家测绘地理信息局审图号为GS（2020）4630号的标准地图制作，底图无修改，以下地图同。

Fig.1 Spatial distribution of local R2 for MGWR model

表5 MGWR回归系数均值

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

图2 经济发展回归系数空间分布

Fig.2 Spatial distribution of economic development regression coefficients

图3 产业升级回归系数空间分布

Fig.3 Spatial distribution of industrial upgrading regression coefficients

图4 外商投资回归系数空间分布

Fig.4 Spatial distribution of foreign investment regression coefficients

图5 人口规模回归系数空间分布

Fig.5 Spatial distribution of population size regression coefficients

图6 人口密度回归系数空间分布

Fig.6 Spatial distribution of population density regression coefficients

图7 绿色创新回归系数空间分布

Fig.7 Spatial distribution of green innovation regression coefficients

图8 能源强度回归系数空间分布

Fig.8 Spatial distribution of energy intensity regression coefficients

参考文献 32

1	王少剑,苏泳娴,赵亚博.中国城市能源消费碳排放的区域差异的空间溢出效应及影响因素.地理学报,2018,73(3):414-428.
	WANG S, SU Y, ZHAO Y. Regional inequality, spatial spillover effects and influencing factors of China's city-level energy-related carbon emissions. Acta Geographica Sinica,2018,73(3):414-428.
2	潘家华.中国碳中和的时间进程与战略路径.财经智库,2021,6(4):42-66.
	PAN J. On the time course and strategic path of carbon neutralization in China. Financial Minds, 2021,6(4):42-66.
3	王瑛,何艳芬.中国省域二氧化碳排放的时空格局及影响因素.世界地理研究,2020,29(3):512-522.
	WANG Y, HE Y. Spatiotemporal dynamics and influencing factors of provincial carbon emissions in China. World Regional Studies,2020,29(3):512-522.
4	王长建,张小雷,张虹鸥,等.基于IO-SDA模型的新疆能源消费碳排放影响机理分析.地理学报,2016,71(7):1105-1118.
	WANG C, ZHANG X, ZHANG H, et al. Influencing mechanism of energy-related carbon emission in Xinjiang based on IO-SDA Model. Acta Geographica Sinica,2016,71(7):1105-1118.
5	CHO S, CHAE C. A study on life cycle CO₂ emissions of low-carbon building in South Korea. Sustainability, 2016, 8(6): 579.
6	HU C, HUANG X. Characteristics of carbon emission in China and analysis on its cause. China Population, Resources and Environment, 2008, 18(3): 38-42.
7	唐赛,付杰文,武俊丽.中国典型城市碳排放影响因素分析.统计与决策,2021,37(23):59-63.
	TANG S, FU J, WU J. Analysis of factors influencing carbon emissions in typical Chinese cities. Statistics & Decision,2021,37(23):59-63.
8	黄蕊,王铮,丁冠群,等.基于STIRPAT模型的江苏省能源消费碳排放影响因素分析及趋势预测.地理研究,2016,35(4):781-789.
	HUANG R, WANG Z, DING G, et al. Trend prediction and analysis of influencing factors of carbon emissions from energy consumption in Jiangsu Province based on STIRPAT Model. Geographical Research,2016,35(4):781-789.
9	苏泳娴,陈修治,叶玉瑶,等.基于夜间灯光数据的中国能源消费碳排放特征及机理.地理学报,2013,68(11):1513-1526.
	SU Y, CHEN X, YE Y, et al. The characteristics and mechanisms of carbon emissions from energy consumption in China using DMSP/OLS night light imageries. Acta Geographica Sinica,2013,68(11):1513-1526.
10	ZHAO J, JI G, YUE Y, et al. Spatio-temporal dynamics of urban residential CO₂ emissions and their driving forces in China using the integrated two nighttime light datasets. Applied Energy, 2019, 235: 612-624.
11	ELVIDGE C, IMHOFF M, BAUGH K, et al. Night-time lights of the world: 1994-1995. ISPRS Journal of Photogrammetry and Remote Sensing, 2001, 56(2): 81-99.
12	SHI K, CHEN Y, YU B, et al. Modeling spatiotemporal CO₂ (carbon dioxide) emission dynamics in China from DMSP-OLS nighttime stable light data using panel data analysis. Applied Energy, 2016, 168: 523-533.
13	王艳军,王孟杰,李少春,等.由NPP-VIIRS影像估算广东省碳排放的尺度效应分析.测绘通报,2021(11):25-30.
	WANG Y, WANG M, LI S, et al. Scale effect analysis of carbon emission simulation based on NPP-VIIRS images in Guangdong Province. Bulletin of Surveying and Mapping,2021(11):25-30.
14	ZHENG X, LU Y, YUAN J, et al. Drivers of change in China's energy-related CO₂ emissions. Proceedings of the National Academy of Sciences, 2020, 117(1): 29-36.
15	GUAN D, MENG J, REINER D, et al. Structural decline in China's CO₂ emissions through transitions in industry and energy systems. Nature Geoscience, 2018, 11(8): 551-555.
16	孙秀锋,施开放,吴健平.县级尺度的重庆市碳排放时空格局动态.环境科学,2018,39(6):2971-2981.
	SUN X, SHI K, WU J. Spatiotemporal dynamics of CO₂ emission in Chongqing: An empirical analysis at the county level. Environmental Science,2018,39(6):2971-2981.
17	ZHANG W, XU H. Effects of land urbanization and land finance on carbon emissions: A panel data analysis for Chinese provinces. Land Use Policy, 2017, 63: 493-500.
18	CHANG K, DU Z, CHEN G, et al. Panel estimation for the impact factors on carbon dioxide emissions: A new regional classification perspective in China. Journal of Cleaner Production, 2021, 279: 123637.
19	刘贤赵,高长春,张勇,等.中国省域碳强度空间依赖格局及其影响因素的空间异质性研究.地理科学,2018,38(5):681-690.
	LIU X, GAO C, ZHANG Y, et al. Spatial dependence pattern of Carbon emission intensity in China's provinces and spatial heterogeneity of its influencing factors. Scientia Geographica Sinica,2018,38(5):681-690.
20	沈体雁,于瀚辰,周麟,等.北京市二手住宅价格影响机制:基于多尺度地理加权回归模型(MGWR)的研究.经济地理,2020,40(3):75-83.
	SHEN T, YU H, ZHOU L, et al. On hedonic price of second-hand houses in Beijing based on multi-scale geographically weighted regression: Scale law of spatial heterogeneity. Economic Geography,2020,40(3):75-83
21	LIU Z, HE C, ZHANG Q, et al. Extracting the dynamics of urban expansion in China using DMSP-OLS nighttime light data from 1992 to 2008. Landscape and Urban Planning, 2012, 106(1): 62-72.
22	WU K, WANG X. Aligning pixel values of DMSP and VIIRS nighttime light images to evaluate urban dynamics. Remote Sensing, 2019, 11(12): 1463.
23	LI X, LI D, XU H, et al. Intercalibration between DMSP/OLS and VIIRS night-time light images to evaluate city light dynamics of Syria's major human settlement during Syrian Civil War. International Journal of Remote Sensing, 2017, 38(21): 5934-5951.
24	王少剑,谢紫寒,王泽宏.中国县域碳排放的时空演变及影响因素.地理学报,2021,76(12):3103-3118.
	WANG S, XIE Z, WANG Z. The spatiotemporal pattern evolution and influencing factors of CO₂ emissions at the county level of China. Acta Geographica Sinica,2021,76(12):3103-3118.
25	王正,樊杰.能源消费碳排放的影响因素特征及研究展望.地理研究,2022,41(10):2587-2599.
	WANG Z, FAN J. The characteristics and prospect of influencing factors of energy-related carbon emissions: Based on literature review. Geographical Research,2022,41(10):2587-2599.
26	张雪华,董会忠."2+26"城市碳排放时空演变特征及其驱动因素研究.资源开发与市场,2021,37(12):1448-1456.
	ZHANG X, DONG H. Study on the spatial-temporal evolution of carbon emissions in "2+26" cities and its driving factors. Resource Development & Market,2021,37(12):1448-1456.
27	干春晖,郑若谷,余典范.中国产业结构变迁对经济增长和波动的影响.经济研究,2011,46(5):4-16.
	GAN C, ZHENG R, YU D. An empirical study on the effects of industrial structure on economic growth and fluctuations in China. Economic Research Journal,2011,46(5):4-16.
28	李建豹,黄贤金,揣小伟,等.长三角地区碳排放效率时空特征及影响因素分析.长江流域资源与环境,2020,29(7):1486-1496.
	LI J, HUANG X, TU X, et al. Spatio-temporal characteristics and influencing factors of carbon emission efficiency in the Yangtze River delta region. Resources and Environment in the Yangtze Basin,2020,29(7):1486-1496.
29	王少剑,黄永源.中国城市碳排放强度的空间溢出效应及驱动因素.地理学报,2019,74(06):1131-1148.
	WANG S, HUANG Y. Spatial spillover effect and driving factors of carbon emission intensity at city level in China. Acta Geographica Sinica,2019,74(6):1131-1148.
30	苏涛永,郁雨竹,潘俊汐.低碳城市和创新型城市双试点的碳减排效应——基于绿色创新与产业升级的协同视角.科学学与科学技术管理,2022,43(1):21-37.
	SU T, YU Y, PAN J. Carbon emission reduction effect of low-carbon cities and innovative cities: Based on the synergic perspective of green innovation and industrial upgrading. Science of Science and Management of S.& T,2022,43(1):21-37.
31	赵玉焕,钱之凌,徐鑫.碳达峰和碳中和背景下中国产业结构升级对碳排放的影响研究.经济问题探索,2022(3):87-105.
	ZHAO Y, QIAN Z, XU X. Study on the impact of industrial structure upgrading on carbon emissions in China in the context of carbon peaking and carbon neutrality. Inquiry into Economic Issues,2022(3):87-105.
32	刘华军,石印,郭立祥,等.新时代的中国能源革命:历程、成就与展望.管理世界,2022,38(7):6-24.
	LIU H, SHI Y, GUO L, et al. China's energy reform in the new era: Process, achievements and prospects. Journal of Management World,2022,38(7):6-24.

[1]	陈宇奇, 蒋雪中. 长江南京以下港口城市可持续效率演变与影响因素分析[J]. 世界地理研究, 2024, 33(7): 116-127.
[2]	俞路, 张昕昀. 中国高铁站和城际铁路站的空间溢出效应及异质性研究[J]. 世界地理研究, 2024, 33(7): 86-98.
[3]	宋关东, 唐承丽, 周国华. 基于县域尺度的乡村韧性测量及其影响因素分析[J]. 世界地理研究, 2024, 33(6): 128-140.
[4]	蒋文嘉, 傅英姿. 城市夜生活对感知维度社会融入的影响研究[J]. 世界地理研究, 2024, 33(6): 154-166.
[5]	胡彬, 刘军. 肯尼亚人口分布空间格局演化及影响因素研究[J]. 世界地理研究, 2024, 33(6): 64-75.
[6]	甄延临, 郭文超, 周子航. 新区域主义背景下的城市营销、品牌流动与跨界治理[J]. 世界地理研究, 2024, 33(5): 150-162.
[7]	邬林果, 武荣伟, 杨德刚. 2000—2020年中国人口老龄化时空演变及影响因素研究[J]. 世界地理研究, 2024, 33(5): 163-176.
[8]	刘海朦, 田小波, 曹婷婷. 基于百度指数的乡村旅游需求时空特征及其影响因素研究[J]. 世界地理研究, 2024, 33(5): 177-188.
[9]	姜炎鹏, 林栋, 李静宜. 伦敦成长为全球城市的转型过程：产业与发展战略视角[J]. 世界地理研究, 2024, 33(5): 98-110.
[10]	彭红艳, 丁志伟. 中国淘宝村“增长-消失”的时空特征及影响因素分析[J]. 世界地理研究, 2024, 33(4): 103-116.
[11]	张圣忠, 孟迪, 柴廷熠. 关中平原城市群物流企业空间格局演化及影响因素研究[J]. 世界地理研究, 2024, 33(4): 117-129.
[12]	李晓梅, 黄俊. 中国省域物流碳排放效率的演化特征及收敛性分析[J]. 世界地理研究, 2024, 33(4): 155-166.
[13]	刘清春, 宗文丽, 赵培雄, 孙亚男, 马交国. 基于上市企业联系的山东省城市网络结构演变特征及影响因素研究[J]. 世界地理研究, 2024, 33(4): 88-102.
[14]	陈鑫, 周加贝, 廖远涛, 王少剑. 长三角地区碳排放-城市高质量发展水平耦合协调演变及影响因素分析[J]. 世界地理研究, 2024, 33(3): 103-115.
[15]	李培庆, 赵新正, 姜永青, 郁星, 张得康. 长三角多尺度城市网络联系特征及其对城市高质量发展的影响[J]. 世界地理研究, 2024, 33(3): 147-160.