黄河流域能源消费碳排放时空格局演变及影响因素——基于DMSP/OLS与NPP/VIIRS夜间灯光数据

doi:10.11821/dlyj020200646

摘要/Abstract

摘要：

科学估算并动态监测长时间序列区域能源消费碳排放发展态势,是制定、实施及评估地区碳减排策略的科学依据和基础保障。基于构建的长时间序列可相互比较的DMSP/OLS与NPP/VIIRS两种夜间灯光数据集,本文模拟了2000—2018年黄河流域能源消费碳排放的时空变化特征,并从流域地理分异的角度对其影响因素进行解析。结果表明：① 2000—2018年黄河流域能源消费碳排放呈现总量不断上升但增长速率下降的态势,整体表现出收敛趋势,但还未达到碳峰值;流域内部碳排放总量呈中游>下游>上游的地理分异特征。② 以黄河干流及主要支流为串联的核心城市形成了若干规模不一的高密度碳排放中心。③ 黄河流域碳排放呈显著的正的全局空间自相关,并形成了以晋陕蒙资源型城市为依托的中上游碳排放高-高集聚,以及上游甘青宁地区为主的碳排放低-低集聚。④ 经济发展水平对碳排放空间分异的影响力始终最强,其次为城镇化水平与人口规模,“GDP+”能源结构、能源强度与产业结构所主导的交互作用是导致碳排放持续增长的主要推动力。从构建流域生命共同体的视角出发,结合黄河流域自然环境特点与经济社会特征,并统筹上下游、左右岸、干支流之间的关系,分区施策与分时施策并行,对实现以碳减排为目标的黄河流域生态保护与可持续发展意义重大。

关键词: 碳排放, DMSP/OLS, NPP/VIIRS, 时空格局, 影响因素, 黄河流域

Abstract:

Scientific estimation and dynamic monitoring for the development trend of carbon emissions from energy consumption with long time series can provide the scientific basis for formulating and implementing regional carbon reduction strategies. Based on the integrated DMSP/OLS and NPP/VIIRS nighttime light datasets, this study simulated the spatial and temporal characteristics of energy-related carbon emissions in the Yellow River Basin from 2000 to 2018, and analyzed its influencing factors from the perspective of geographical environment differentiation. The results were as follows: (1) From 2000 to 2018, the total amount of energy consumption carbon emissions in the Yellow River Basin grew continually but the growth rate tended to decrease with a convergent trend, but the carbon peak has not yet been reached; the carbon emissions within the basin presented the geographical differentiation characteristics in the order of the middle reaches > the lower reaches > the upper reaches. (2) The core cities connected by the main stream of the Yellow River and its tributaries formed several high-density carbon emissions centers of different scales from the pixel scale. (3) The carbon emissions of the basin showed a significant positive spatial autocorrelation, and formed a high-high aggregation in the middle and upper reaches of the resource-based cities in Shanxi, Shaanxi and Inner Mongolia, and a low-low aggregation in the upper reaches within Gansu, Qinghai and Ningxia. (4) The level of economic development had the greatest impact on the spatial differentiation of energy carbon emissions in the study area, followed by urbanization level and population size. The interaction of "GDP+" energy structure, energy intensity and industrial structure was the main driving force leading to the continuous growth of carbon emissions. From the perspective of building a watershed life community, combining the natural environmental and economic and social characteristics of the river basin, and coordinating the relationship between upper and lower reaches, the left and right banks, main stream and tributaries, the strategy of zoning and time-sharing is of great significance for the ecological protection and sustainable development in the Yellow River Basin with the goal of carbon emissions reduction.

Key words: carbon emissions, DMSP/OLS, NPP/VIIRS, spatio-temporal evolution, influencing factor, Yellow River Basin

杜海波, 魏伟, 张学渊, 纪学朋. 黄河流域能源消费碳排放时空格局演变及影响因素——基于DMSP/OLS与NPP/VIIRS夜间灯光数据[J]. 地理研究, 2021, 40(7): 2051-2065.

DU Haibo, WEI Wei, ZHANG Xueyuan, JI Xuepeng. Spatio-temporal evolution and influencing factors of energy-related carbon emissions in the Yellow River Basin: Based on the DMSP/OLS and NPP/VIIRS nighttime light data[J]. GEOGRAPHICAL RESEARCH, 2021, 40(7): 2051-2065.

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	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)	0.7143	0.9714	1.4286	1.4714	1.4714	1.4571	1.4286	1.3300	0.345
碳排放系数(万t碳/万t标准煤)	0.7559	0.855	0.5857	0.5538	0.5714	0.5921	0.6185	0.4483	0.272

年份	a	R²
2000—2004	1.1942	0.8914
2005—2009	1.4716	0.9618
2010—2012	1.1708	0.9848
2013—2014	1.1410	0.9780
2015—2016	1.0962	0.9583
2017—2018	0.9815	0.9502

	2000年	2005年	2010年	2015年	2018年
Moran′sI	0.137	0.194	0.254	0.375	0.379
Z-Score	2.020	2.680	3.400	4.900	4.950

影响因子	q
影响因子	2000年	2005年	2010年	2015年	2018年
经济发展水平(GDP)	0.580***	0.618***	0.727***	0.632***	0.477***
人口规模(TP)	0.233***	0.257**	0.239***	0.324***	0.303***
城镇化率(UR)	0.348***	0.487***	0.381***	0.401***	0.396***
能源结构(ES)	0.195***	0.264***	0.268***	0.289***	0.290***
能源强度(EI)	0.096	0.135*	0.175*	0.188**	0.157*
产业结构(IS)	0.189***	0.232**	0.179**	0.127*	0.154*
流域(RB)	0.160***	0.186***	0.167***	0.163***	0.163***

交互因子	2000	交互因子	2005	交互因子	2010	交互因子	2015	交互因子	2018
GDP ∩ UR	0.921	GDP ∩ UR	0.887	GDP ∩ EI	0.903	GDP ∩ ES	0.848	UR ∩ IS	0.818
TP ∩ UR	0.904	TP ∩ UR	0.841	GDP ∩ IS	0.851	GDP ∩ IS	0.806	GDP ∩ UR	0.809
GDP ∩ EI	0.834	GDP ∩ EI	0.785	TP ∩ UR	0.826	GDP ∩ UR	0.792	GDP ∩ ES	0.748
GDP ∩ ES	0.801	GDP ∩ ES	0.776	GDP ∩ RB	0.793	TP ∩ UR	0.781	TP ∩ UR	0.744
GDP ∩ TP	0.750	UR ∩ EI	0.727	GDP ∩ ES	0.791	GDP ∩ EI	0.759	UR ∩ EI	0.720
GDP ∩ IS	0.747	GDP ∩ IS	0.716	GDP ∩ TP	0.784	GDP ∩ TP	0.717	GDP ∩ IS	0.703
GDP ∩ RB	0.715	UR ∩ IS	0.692	UR ∩ IS	0.741	UR ∩ EI	0.716	GDP ∩ TP	0.702