中国县域碳排放的时空演变及影响因素

doi:10.11821/dlxb202112016

摘要/Abstract

摘要：

县域是实现新型城镇化的重要平台和关键地域单元,揭示县域碳排放的时空格局演变及其驱动因素对于完善中国新型城镇化战略和促进生态文明建设、绿色转型发展具有重要意义。本文使用2000—2017年中国县域碳排放数据,分析了县域人均碳排放的总体变化、区域差异、时空格局及集聚特征,并在STIRPAT模型和环境库茨涅茨曲线（EKC）假说下,运用面板分位数回归解释社会经济发展对县域人均碳排放的动态影响。结果表明：① 中国县域人均碳排放呈现先急后缓的增长趋势。人均碳排放水平差异加大,且呈上升趋势,西部地区县域人均碳排放差距悬殊。② 县域人均碳排放总体上呈现“北高南低”的空间格局,经济发达地区的人均碳排放远高于其他地区,空间极化效应明显。③ 县域人均碳排放具有显著的空间正相关性,高—高集聚的区县数量逐渐增多且分布重心向西北移动,而低—低集聚的区县数量不断减少,主要集中于中南地区,县域人均碳排放集聚类型具有空间锁定效应。④ 人口密度、政府财政支出对县域人均碳排放具有抑制作用,第二产业产值规模、碳排放强度则存在显著的正相关性,中低碳排放水平区县的经济发展和人均碳排放之间呈现倒“N”型曲线关系,社会经济发展结构的调整是实现整体碳减排的关键。因此,政府减排策略的落实应考虑区县碳排放的阶段性差异,实现落后地区发展和转型“两手抓”的同时发挥重点城市群、都市圈在碳减排中的先导作用。此外,通过技术创新提高能源利用效率应作为现阶段县域碳减排的主要手段。

关键词: 县域人均碳排放, 时空格局, 区域差异, 影响因素, 面板分位数回归

Abstract:

County is the pivotal platform and region unit to realize the new-type urbanization. The study of county-level CO₂ emissions is of great significance to improve China's urbanization strategy, accelerate the achievement of ecological civilization and low-carbon transformation. Based on the data of China's county-level CO₂ emissions from 2000 to 2017, this paper analyzed the overall tendency, regional differences, spatiotemporal pattern and agglomeration characteristics of per capita CO₂ emissions. Meanwhile, under the STIRPAT model and EKC hypothesis, this study employed the panel quantile regressions to explain the dynamic impact of socio-economic development on per capita CO₂ emissions. The main conclusions show that: (1) China's county-level CO₂ emissions show an increasing trend of rapid growth followed by slow growth. The regional disparity of per capita CO₂ emissions is distinct and shows a more uneven trend. (2) On the whole, China's county-level CO₂ emissions present a spatial pattern of "high in the north and low in the south". The per capita CO₂ emissions level in economically developed areas is much higher than that in other areas, thus brings about an obvious spatial polarization effect. (3) There is a significant positive spatial correlation of per capita CO₂ emissions within counties. The number of counties with High-High concentration gradually increases and the distribution center gradually moves to Northwest China, while the number of Low-Low concentration counties decreases continuously and they were mainly distributed in the central and southern regions. The agglomeration type of county-level per capita CO₂ emissions presents a spatial locking effect. (4) Population density and government expenditure have an inhibitory effect on county-level per capita CO₂ emissions, while the scale of secondary industry output value and carbon emission intensity have significant promotive influence. And there is an inverted "N-shaped" relationship between economic development and per capita CO₂ emissions in the counties with low- and middle-level emissions. The adjustment of socio-economic development structure plays a critical role in achieving China's total CO₂ emission reduction target. Therefore, the policy makers of emission reduction strategy should consider the regional disparity to realize the development and transformation of backward counties. And the key urban agglomerations should play a leading role in carbon emission reduction simultaneously. In addition, improving energy use efficiency through technological innovation should be the key way to the reduction of carbon emissions in China's counties at the present stage.

Key words: per capita CO₂ emissions at county level, spatiotemporal pattern, regional disparity, influencing factor, panel quantile regression

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[J]. 地理学报, 2021, 76(12): 3103-3118.

WANG Shaojian, XIE Zihan, WANG Zehong. The spatiotemporal pattern evolution and influencing factors of CO₂ emissions at the county level of China[J]. Acta Geographica Sinica, 2021, 76(12): 3103-3118.

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变量维度	变量名称	缩写	单位	区县数量(个)	来源
环境压力(I)	人均碳排放量	PCO₂	t/人	1908	中国碳核算数据库
城镇化水平(P)	人口密度	PopDen	人/km²	1908	人口规模/城市行政面积
经济发展(A)	人均地区生产总值	PGDP	元	1908	《中国县域统计年鉴》
财政投入(A)	一般公共预算支出	PFE	万元	1908	《中国县域统计年鉴》
产业结构(T)	第二产业产值规模	IS	万元	1908	《中国县域统计年鉴》
技术进步(T)	碳排放强度	CI	-	1908	碳排放量/地区生产总值
变量对数	最小值	最大值	均值	标准差	样本量
lnPCO₂	-15.7614	5.2985	1.2207	1.1359	34344
lnCI	-15.0003	11.4358	1.0121	0.9231	34344
lnPopDen	-2.2965	9.0840	5.1626	1.3639	34344
lnPGDP	6.4935	13.0268	9.0633	0.7902	34344
lnIS	4.6052	16.0759	11.6039	1.5056	34344
lnPFE	6.1070	15.9838	10.4047	0.9487	34344

年份	空间关联模式	HH_t₊₁	HL_t₊₁	LL_t₊₁	LH_t₊₁
2000—2005	HH_t	Ⅳ(147)	Ⅱ(0)	Ⅲ(0)	Ⅰ(0)
	HL_t	Ⅱ(2)	Ⅳ(7)	Ⅰ(1)	Ⅲ(0)
	LL_t	Ⅲ(0)	Ⅰ(1)	Ⅳ(227)	Ⅱ(0)
	LH_t	Ⅰ(0)	Ⅲ(0)	Ⅱ(0)	Ⅳ(17)
2005—2010	HH_t	Ⅳ(136)	Ⅱ(0)	Ⅲ(0)	Ⅰ(0)
	HL_t	Ⅱ(0)	Ⅳ(10)	Ⅰ(0)	Ⅲ(0)
	LL_t	Ⅲ(0)	Ⅰ(0)	Ⅳ(207)	Ⅱ(0)
	LH_t	Ⅰ(0)	Ⅲ(0)	Ⅱ(0)	Ⅳ(13)
2010—2017	HH_t	Ⅳ(110)	Ⅱ(0)	Ⅲ(0)	Ⅰ(0)
	HL_t	Ⅱ(0)	Ⅳ(5)	Ⅰ(0)	Ⅲ(0)
	LL_t	Ⅲ(0)	Ⅰ(0)	Ⅳ(163)	Ⅱ(0)
	LH_t	Ⅰ(0)	Ⅲ(0)	Ⅱ(0)	Ⅳ(5)

影响因素	分位数					固定效应
影响因素	10^th	25^th	50^th	75^th	90^th	固定效应
lnPopDen	-0.18218^*** (-28.555)	-0.15187^*** (-21.685)	-0.17282^*** (-27.010)	-0.24991^*** (-54.692 )	-0.29087^*** (-39.553)	-0.20069^*** (-76.88)
lnCI	0.90664^*** (158.487)	0.90433^*** (171.892)	0.89386^*** (205.112)	0.85964^*** (153.908)	0.83368^*** (113.389)	0.95526^*** (342.30)
lnIS	0.26706^*** (25.618)	0.16169^*** (19.225)	0.14599^*** (18.188)	0.21041^*** (30.355)	0.25208^*** (34.522)	0.30189^*** (78.71)
lnPFE	0.05451^*** (-10.758)	0.08925^*** (26.564)	0.15641^*** (24.127)	0.18372^*** (29.201)	0.15736^*** (13.609)	-0.05577^*** (-9.77)
lnPGDP	-5.78257^*** (12.350)	-4.82953^*** (-3.245)	-2.84177 ^*** (-4.162)	0.65888 (0.555)	2.23077 (1.620)	1.48360^*** (3.06)
lnPGDP²	0.74913^*** (-12.793)	0.65329^*** (3.775)	0.43963 ^*** (5.697)	0.06170 (0.456)	-0.12378 (-0.788)	-0.10288 (-1.95)
lnPGDP³	-0.02913^*** (6.010)	-0.02523^*** (-3.775)	-0.01738 ^*** (-5.991)	-0.00433 (-0.847)	0.00265 (0.447)	0.00335 (1.76)