中国省际生态福利绩效测算及影响因素研究

doi:10.13249/j.cnki.sgs.2019.12.005

摘要/Abstract

摘要：

采用非期望产出SBM模型对2001~2015年中国省际生态福利绩效进行测算研究,运用空间自相关方法和空间误差模型对中国省际生态福利绩效的空间分布特征及影响因素进行检验。结果表明:① 中国生态福利绩效总体水平较高,但呈现出下降态势;各省市之间差异性明显,仅有北京、上海等6个省市实现有效,其他省市均存在不同程度的无效率状态;区域之间也存在差异性,总体呈现出东部>西部>中部的分布格局。② 中国省际生态福利绩效呈现正向空间自相关性,存在明显的集聚状态。③ 中国省际生态福利绩效的主要影响因素有技术进步、绿化程度、社会性支出、医疗水平、城镇化、产业结构和环境规制。

关键词: 生态福利绩效, SBM模型, 空间自相关, 空间误差模型

Abstract:

Based on the panel data of 30 provinces in China from 2001 to 2015, this article uses the undesired output SBM model to measure the performance of China's inter-provincial ecological well-being. Based on this, the spatial autocorrelation method and spatial error model are used to analyze spatial distribution characteristics and influencing factors of inter-provincial ecological well-being performance in China. Conclusions are as follow: 1) China's overall ecological well-being performance level is relatively high, but it shows a downward trend. There are significant differences among 30 provinces. The performance of ecological well-being in Beijing, Tianjin, Shanghai, Guangdong, Qinghai and Ningxia was effective, and the input and output reached the optimal configuration. Other provinces are inefficient with different levels, but there is still room for improvement. There are also differences between regions, showing the distribution pattern of eastern region > western region > central region. 2) China's inter-provincial ecological well-being performance presents positive spatial autocorrelation, but the distribution pattern is not stable enough and is subject to change. At the same time, China's inter-provincial ecological well-being performance has a clear agglomeration in space. Provinces that embody the ‘H-H’ agglomeration state are mainly distributed in the eastern coastal areas. Provinces that embody the "L-L agglomeration" status are mainly distributed in the central and western regions. Provinces that embody the ‘H-L’ and ‘L-H’ agglomeration status are scattered in various regions. The main influencing factors of China's inter-provincial ecological well-being performance are technological progress, greening level, social expenditure, medical level, urbanization, industrial structure and environmental regulation. Among them, technological progress, greening level, social expenditure, medical level, urbanization level and ecological welfare performance were significantly positively correlated. There is a significant negative correlation between industrial structure and environmental regulation and ecological well-being performance.

Key words: ecological well-being performance, SBM model, spatial autocorrelation, spatial error model

中图分类号:

F205/F062.2/X22

李成宇, 张士强, 张伟, 廖显春. 中国省际生态福利绩效测算及影响因素研究[J]. 地理科学, 2019, 39(12): 1875-1883.

Li Chengyu, Zhang Shiqiang, Zhang Wei, Liao Xianchun. Measurement and Influencing Factors of Inter-Provincial Ecological Well-being Performance in China[J]. SCIENTIA GEOGRAPHICA SINICA, 2019, 39(12): 1875-1883.

图/表 6

图1

表1

表2

中国省际2001~2015年生态福利绩效Moran's I检验"

年份	Moran's I	Z值	P值	年份	Moran's $I$	Z值	P值
2001	0.2201	2.1871	0.0220	2010	0.1722	1.7760	0.0410
2002	0.1873	1.8648	0.0450	2011	0.1636	1.6514	0.0680
2003	0.1495	1.5839	0.0720	2012	0.1668	1.7200	0.0530
2004	0.1947	2.0502	0.0310	2013	0.1804	1.8654	0.0470
2005	0.1720	1.7100	0.0450	2014	0.1769	1.7710	0.0500
2006	0.1617	1.7003	0.0570	2015	0.1763	1.7216	0.0530
2007	0.1505	1.6434	0.0600	“十五”	0.1851	1.8081	0.0410
2008	0.1667	1.7168	0.0560	“十一五”	0.1640	1.6595	0.0550
2009	0.1674	1.6689	0.0540	“十二五”	0.1673	1.6816	0.0670

表2

图2

表3

表4

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省份	2001年	2005年	2010年	2015年	均值	省份	2001年	2005年	2010年	2015年	均值
北京	1.000	1.000	1.000	1.000	1.000	湖南	0.637	0.527	0.519	0.512	0.548
天津	1.000	1.000	1.000	1.000	1.000	广东	1.000	1.000	1.000	1.000	1.000
河北	0.518	0.485	0.442	0.413	0.459	广西	0.625	0.559	0.472	0.420	0.521
山西	0.460	0.472	0.398	0.366	0.425	海南	1.000	1.000	1.000	1.000	1.000
内蒙古	1.000	0.571	0.471	0.434	0.588	重庆	0.571	0.568	0.582	0.644	0.600
辽宁	1.000	0.671	0.580	0.558	0.683	四川	0.516	0.488	0.491	0.515	0.498
吉林	0.715	0.613	0.522	0.520	0.570	贵州	0.318	0.324	0.365	0.379	0.348
黑龙江	0.906	0.681	0.651	0.548	0.672	云南	0.494	0.425	0.409	0.383	0.423
上海	1.000	1.000	1.000	1.000	1.000	陕西	0.490	0.462	0.452	0.437	0.461
江苏	0.773	0.725	0.806	1.000	0.821	甘肃	0.442	0.453	0.461	0.491	0.461
浙江	0.812	0.745	0.753	0.755	0.754	青海	1.000	1.000	1.000	1.000	1.000
安徽	0.515	0.539	0.530	0.480	0.522	宁夏	1.000	1.000	1.000	1.000	1.000
福建	1.000	0.831	0.718	0.667	0.803	新疆	0.534	0.490	0.462	0.392	0.471
江西	0.677	0.586	0.578	0.547	0.592	东部	0.886	0.819	0.807	0.819	0.828
山东	0.648	0.554	0.580	0.611	0.586	中部	0.617	0.549	0.515	0.484	0.536
河南	0.536	0.498	0.434	0.410	0.465	西部	0.636	0.576	0.561	0.554	0.579
湖北	0.492	0.475	0.484	0.489	0.494	全国	0.723	0.658	0.639	0.632	0.659

变量	系数	t统计量	变量	系数	t统计量
lnIND	0.0871	3.0030^***	lnGE	0.0882	6.8234^***
lnIS	-0.1593	-2.8394 ^***	lnDC	-0.2749	-11.1165^***
lnTP	0.0327	2.8810^***	lnFDI	0.0196	1.9685^*
lnCITY	0.3447	8.7219^***	lnSO	-0.1263	-6.4457^***
lnGZ	-0.0554	-7.1148^***
LM-lag		5.1201^**	LM-error		24.1787^***
Robust LM-lag		1.0955	Robust LM-error		20.1541^***

变量	无空间效应	空间固定效应	时间固定效应	双固定效应	随机效应
lnIND	0.1149^***	-0.0519^**	0.1761^***	0.0299	-0.0480^**
lnIS	-0.1957^***	-0.0562	-0.2689^***	-0.2715^***	-0.0944^**
lnTP	0.0344^***	0.0036	0.0447^***	0.0275^***	0.0117
lnCITY	0.3226^***	0.0298	0.2410^***	0.1096^**	0.1226^***
lnGZ	-0.0502^***	-0.0104^**	-0.0265^**	-0.0121^*	-0.0151^***
lnGE	0.0856^***	0.0148	0.0843^***	0.0264^**	0.0117
lnDC	-0.2925^***	0.0676	-0.4093^***	0.0880^*	-0.0101
lnFDI	0.0206 ^**	-0.0010	0.0105	-0.0031	0.0000
lnSO	-0.1413^**	-0.0062	-0.0976^***	0.0852^***	-0.0149
λ	0.2871^***	0.3281^***	0.0980	0.2616^***	0.3289^***
R-squared	0.7236	0.9374	0.7621	0.9455	0.9385
log-L	332.6355	671.5593	355.5121	691.8075	575.7988