黄河流域县域碳排放的时空格局演变及空间效应机制

doi:10.13249/j.cnki.sgs.2021.08.003

摘要/Abstract

摘要：

利用空间面板模型、空间自相关分析和以区域背景与最近邻状况为空间滞后的空间马尔科夫链对2000—2017年黄河流域县域碳排放时空格局与空间效应进行分析，结果表明：① 2000年以来黄河流域碳排放量激增，由山东全域和陕甘宁蒙交界的高值区向外圈层与轴向扩张，形成东高西低碳排放格局；② 存在“俱乐部趋同”现象，高碳排放县集聚于山东全域和陕甘宁蒙交界，低碳排放县集聚于西南部；2000年与2017年对比发现县域碳排放类型稳定性强，较高碳排放变为较低碳排放的县集中在东南部区域，而相反方向转变的县集中在内蒙古；③ 高碳溢出效应与低碳锁定效应是塑造时空格局的重要作用力，前者作用力更强；区域背景增强了“俱乐部趋同”与被包围异常值趋同，作用力强于最近邻状况，不显著区域内碳排放类型转变概率提高。④ 空间面板模型结果显示年轻人口结构、大经济规模、二产为主产业结构、高生活水平和高公共支出促进了碳排放量增加与空间效应作用，其中经济规模与产业结构是重要驱动因素。

关键词: 碳排放, 黄河流域, 空间马尔科夫链, 空间面板模型

Abstract:

Carbon emission control is the main problem and measure of ecological protection and high-quality development in the Yellow River Basin. Carbon emission at county level research can provide more accurate theoretical support for collaborative governance and sustainable development of the Yellow River Basin. Spatial panel model, spatial autocorrelation analysis and spatial Markov chain with regional background and nearest neighbor as spatial lags were used to analyze the spatiotemporal pattern and spatial effect of carbon emissions in counties of the Yellow River Basin from 2000 to 2017, the results showed that: 1) the carbon emission in the Yellow River basin has increased dramatically since 2000; the high carbon emissions areas, Shandong province and the boundary between Shaanxi, Gansu, Ningxia and Inner Mongolia, expands to the outer circle layer and the axial direction, forming the spatial pattern of high in the east and low in the west; 2) there is a phenomenon of “club convergence”; the high carbon emission counties converge in Shandong province and the boundary between Shaanxi, Gansu, Ningxia and Inner Mongolia; the low carbon emission counties converge in the southwest; the comparison between 2000 and 2017 shows that county carbon emission type has strong stability; counties which tranfered from higher carbon emission type to lower carbon emission type are concentrated in the southeast region, while counties that change in the opposite direction are concentrated in Inner Mongolia. 3) high carbon spillover effect and low carbon locking effect are important forces to shape the spatiotemporal pattern and the former is stronger; the regional background enhances “club convergence” and the convergence of surrounded outliers and its acting force was stronger than the nearest neighbor; the probability of carbon emission type transition in insignificant regions increased; 4) the spatial panel model shows that increase of carbon emissions and its spatial effect are promoted by young population structure, large economy, industrial structure dominated by the secondary industry, high living standard and high public expenditure; economy and industrial structure are important driving factors.

Key words: carbon emission, the Yellow River Basin, spatial Markov chain, spatial panel model

中图分类号:

F129.9

莫惠斌, 王少剑. 黄河流域县域碳排放的时空格局演变及空间效应机制[J]. 地理科学, 2021, 41(8): 1324-1335.

Mo Huibin, Wang Shaojian. Spatio-temporal Evolution and Spatial Effect Mechanism of Carbon Emission at County Level in the Yellow River Basin[J]. SCIENTIA GEOGRAPHICA SINICA, 2021, 41(8): 1324-1335.

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t\t+1	n	1	2	3	4
1	3077	97.69%	2.31%	0%	0%
2	3060	2.32%	93.56%	4.08%	0.03%
3	3086	0%	4.05%	91.28%	4.67%
4	3068	0%	0.03%	4.69%	95.27%

区域背景	t	n	t+1				最近邻状况	t	n	t+1
区域背景	t	n	1	2	3	4	最近邻状况	t	n	1	2	3	4
低低集聚（LL_t）	1	2150	98.56%	1.44%	0%	0%	1	1	399	97.74%	2.26%	0%	0%
	2	974	2.46%	95.89%	1.64%	0%		2	696	1.58%	96.41%	2.01%	0%
	3	173	0%	10.40%	89.60%	0%		3	547	0%	3.29%	91.41%	5.30%
	4	0						4	541	0%	0%	6.10%	93.90%
低高集聚（LH_t）	1	136	92.65%	7.35%	0%	0%	2	1	1085	97.51%	2.49%	0%	0%
	2	517	1.55%	88.97%	9.48%	0%		2	1060	2.83%	92.17%	4.91%	0.09%
	3	390	0%	12.82%	87.18%	0%		3	1099	0%	4.09%	91.72%	4.19%
	4	0						4	821	0%	0.12%	4.87%	95.01%
高低集聚（HL_t）	1	0					3	1	888	97.75%	2.25%	0%	0%
	2	0						2	563	2.31%	92.36%	5.33%	0%
	3	181	0%	0%	93.92%	6.08%		3	657	0%	4.57%	90.56%	4.87%
	4	189	0%	0%	6.88%	93.12%		4	612	0%	0%	4.41%	95.59%
高高集聚（HH_t）	1	0					4	1	705	97.87%	2.13%	0%	0%
	2	0						2	741	2.29%	93.79%	3.91%	0%
	3	815	0%	0%	89.57%	10.43%		3	783	0%	4.09%	91.19%	4.73%
	4	1890	0%	0%	4.23%	95.77%		4	1094	0%	0%	4.02%	95.98%
不显著	1	791	96.21%	3.79%	0%	0%
	2	1569	2.49%	93.63%	3.82%	0.06%
	3	1527	0%	3.73%	93.12%	3.14%
	4	989	0%	0.10%	5.16%	94.74%

	W_S	W_E	W_M
无空间滞后	1609.006^***	3443.012^***	3863.890^***
无空间滞后（稳健性检验）	1792.133^***	885.931^***	1886.104^***
无空间误差	3.667^*	2752.739^***	2005.268^***
无空间误差（稳健性检验）	186.794^***	195.657^***	27.482^***
Hausman（SDM）	37.08（RE）	17.38（FE）	27.46（RE）

	VIF	主函数			空间滞后项
	VIF	W_S（RE）	W_E（FE,BOTH）	W_M（RE）	W_S（RE）	W_E（FE,BOTH）	W_M（RE）
人口规模POP	1.403	−0.065	−0.313^***	−0.187^***	−0.045	−0.006	0.341^***
人口结构PS	1.188	0.673^***	0.239^***	0.271^***	−0.716^***	0.349^***	0.353^***
经济规模GDP	5.133	−0.143^***	0.077^**	0.126^***	1.779^***	−0.451^***	−0.671^***
产业结构SIND	1.177	0.263^***	0.115^***	0.066^***	0.702^***	0.887^***	1.410^***
生活水平LS	5.516	0.408^***	0.191^***	0.290^***	−0.401^***	0.510^***	0.435^***
公共支出PE	3.885	0.540^***	0.073^**	0.305^***	−1.671^***	0.125^*	−0.403^***
常数项		−11.598^***		−8.594^***
空间滞后系数		0.182^**	0.408^***	0.828^***