社会—生态脆弱性相互关系的计量推断方法

doi:10.18306/dlkxjz.2016.02.008

Abstract

Abstract:

This study systematically analyzed the results of current integrated method for social-ecological vulnerability research and found that the comprehensive index method can represent the overall level of social and ecological vulnerabilities using indicators of the two particular aspects of vulnerability in certain time period. But such method cannot reflect the degree of interaction and direction of influence of indicators. Correlation analysis can indicate a quantitative relationship between indicators, but is unable to identify causality. Least square method can generate variable coefficient of explanatory indicators when the indicators have changed, but due to the subjectivity in selecting indicators as explanatory variables or explained variables, the direction of influence between variables still cannot be determined. Given these problems, this article explores the feasibility of using instrumental variable (IV) method to reveal interactions between social and ecological vulnerabilities. By application in a case study at the country level in China , the result shows that the IV method can reveal the direction of influence between indicators of social vulnerability and ecological vulnerability, which overcomes the problem identified above. From 1980s to 2000s, the impact of ecological vulnerability on social vulnerability (NPPI→SoVI) decreased, the corresponding significant impact provinces decreased from 5 to 2; the impact of social vulnerability on ecological vulnerability (SoVI→NPPI) increased, and the corresponding significant impact provinces increased from 1 to 7. A quantitative method that indicates the direction of influence is expected to explain how social vulnerability and ecological vulnerability interact and affect one another.

Key words: social vulnerability, ecological vulnerability, interaction, instrumental variable estimation

Ning LI, Zhengtao ZHANG, Xiaolin HAO. Quantitative inference method for the relationship between social and ecological vulnerabilities[J].PROGRESS IN GEOGRAPHY, 2016, 35(2): 214-222.

Figures/Tables 7

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References 20

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	主成分因子排序及命名(方差解释率大于66.95%)
社会脆弱性指数SoVI	人口年龄、教育和总人口;农村状况、城市化;经济状况;性别;粮食保障等
生态脆弱性指数NPPI	年平均降水量,年平均气温,光合有效辐射;NDVI指数;土地覆盖等

内生变量	省份	残差系数	P值
SoVI	甘肃	-1.487	0.093
NPPI	甘肃	-0.49	0.051
SoVI	青海	0.402	0.051
NPPI	河南	-3.118	0.022
SoVI	吉林	0.779	0.000
SoVI	辽宁	0.865	0.052
NPPI	贵州	1.723	0.090
NPPI	四川	1.978	0.062
NPPI	云南	-1.363	0.001
SoVI	山西	0.338	0.007
NPPI	浙江	-2.502	0.042
SoVI	山东	-1.462	0.005
NPPI	江西	-4.439	0.041
NPPI	黑龙江	3.099	0.026
SoVI	黑龙江	0.0966	0.073

内生变量	SoVI(IV: Nei, City)
Sargan 显著水平	1980s	1990s	2000s
Sargan 显著水平	2.80298 (0.0941)	1.37466 (0.2410)	2.12708 (0.1447)
内生变量	NPPI(IV: P、T)
Sargan 显著水平	1980s	1990s	2000s
Sargan 显著水平	9.33124 (0.0023)	1.51412 (0.2185)	0.957537 (0.3278)

地区	SoVI→NPPI(β₁)			NPPI→SoVI(β₂)
地区	1980s	1990s	2000s	1980s	1990s	2000s
河南	-0.00914	0.058	-0.141^*	8.089^***	2.143	2.023
吉林	0.698	-0.459^*	-0.42^***	-1.323	0.562	-1.278
青海	-0.261	-0.988	0.163^*	1.513^***	1.304^***	1.217^***
甘肃	2.122	1.013	2.429	0.81	0.399^*	0.733
辽宁	-0.463	-0.0908	0.528	-1.005	5.356	0.376^*
云南	-0.0235	0.0437	-0.101	1.054^**	0.679	0.992
贵州	0.143^**	0.16	0.0745	-2.169	-0.2	-4.274
山西	-0.122	-0.0227	-0.451^***	1.347	0.891	-0.985
四川	0.103	0.154^*	-0.0256	0.332	-1.667	0.374
山东	-0.00341	0.104	0.457^***	0.0859	1.322	0.113

Quantitative inference method for the relationship between social and ecological vulnerabilities

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