基于空间杜宾模型（SDM）的中国低碳发展溢出和调节效应实证研究

doi:10.16381/j.cnki.issn1003-207x.2023.0671

Abstract

Abstract:

In recent years， extreme weather and severe pollution have occurred internationally， and the traditional extensive economic growth model of high investment， high consumption， and high pollution has posed serious challenges to global environmental security. China is a responsible major country， and at the Climate Ambition Summit， solemn goals and commitments of “reaching carbon peak by 2030” and “carbon neutrality by 2060” are made. To achieve the “dual carbon” goal as scheduled， it is necessary to continuously improve the green and low-carbon policy system， urge China's economy and society to achieve systematic changes as soon as possible， regard low-carbon development as an important direction for China's economic development and transformation and upgrading， and use low-carbon development as an important indicator to measure economic development. The research on factors of production， economic development， and low-carbon development has reached certain conclusions， but overall it is still relatively one-sided， lacking relevant research from the perspective of factor endowment， and even less research on the impact of factor endowment on low-carbon development from the perspective of provincial factor endowment within the same analytical framework.Based on China's provincial Panel data from 2011 to 2020， the spatial Durbin model（SDM） is used to comprehensively examine the direct and spillover effects of labor， capital， technology， data and other factors on China's low-carbon development， and the moderating effect of FDI embedding is discussed. It is found that low-carbon development has a positive spatial correlation and significant spatial spillover effects； The direct and spillover effects of labor factors are significantly positive， while the spillover effects of data factors are significantly positive. The direct effect is positive but not significant； The direct effects of capital and technological factors are significantly negative， while the spillover effects are positive； The direct and spillover effects of foreign direct investment are significantly positive， and there is a significant positive moderating effect between them and various factors. By analyzing the endowment status of provincial factors， the government can be more targeted when formulating policies for introducing foreign direct investment， and formulate low-carbon development promotion policies more scientifically. It can provide theoretical support when promoting the implementation of new development concepts.

Key words: factor endowment, low carbon development, foreign direct investment, space Dubin, regula? tory effect

CLC Number:

Feng Chen, Yanyan Yang, Ping Zhang. Empirical Study on Spillovers and Regulatory Effects of Low Carbon Development in China Based on Spatial Durbin Model (SDM)[J]. Chinese Journal of Management Science, 2025, 33(5): 45-53.

Figures/Tables 9

References 22

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变量名称	符号	测量方法	单位
碳生产率	lnLce	GDP/二氧化碳排放量的对数	万元/吨
劳动力	lnLa	就业人员平均受教育年限的对数	年
资本	lnCa	资本存量的对数	万元
技术	lnTa	绿色专利授权量的对数	项
数据	lnDa	软件业务收入的对数	万元
外商直接投资	lnFdi	实际利用外商直接投资额的对数	万元
产业结构	Str	第三产业增加值/地区生产总值	%
政府干预	lnGov	地方财政支出的对数	万元
城镇化率	Urb	城镇人口/总人口	%

变量	样本数	均值	标准差	最小值	最大值
lnLce	300	0.664	0.477	0.110	3.082
lnLa	300	2.190	0.0906	1.969	2.503
lnCa	300	10.50	0.770	8.155	12.05
lnTa	300	10.11	1.439	6.219	13.47
lnDa	300	14.96	2.214	6.968	18.87
lnFdi	300	3.581	1.713	-3.110	5.879
lnGov	300	8.375	0.586	6.559	9.766
Str	300	0.492	0.0898	0.327	0.839
Urb	300	0.573	0.123	0.350	0.896

年度	Moran ’s I	Z 值
2011	0.498	5.049
2012	0.500	5.018
2013	0.460	4.770
2014	0.481	4.909
2015	0.456	4.644
2016	0.448	4.632
2017	0.437	4.575
2018	0.437	4.566
2019	0.434	4.559
2020	0.418	4.344

检验	数值
LM-lag	47.878^***（0.000）
R-LM-lag	4.658^**（0.031）
LM-error	48.562^***（0.000）
R-LM-error	5.341^**（0.021）
LR-spatial-lag	52.74^***（0.000）
LR-spatial-error	60.41^***（0.000）
空间效应LR检验	20.6614^**
时间效应LR检验	47.2301^**
Hausman检验	60.858^***

变量	OLS	SDM Main	Wx
lnLa	0.0711^* (0.0385)	0.7191^* (0.4414)	-0.2945 (1.0021)
lnCa	-0.4364^*** (0.0646)	-0.3903^*** (0.1412)	0.5989^** (0.2959)
lnTa	0.4478^*** (0.0898)	-0.1469^*** (0.0421)	0.0815 (0.0848)
lnDa	0.0340^* (0.0177)	-0.0141 (0.0241)	0.1144^*** (0.0426)
lnFdi	0.0482^*** (0.0180)	0.0404^** (0.0206)	0.0770^* (0.0440)
控制变量	是	是	是
ρ		0.2249^** (0.0917)
N	300	300	300
R2	0.682	0.744	0.744

Empirical Study on Spillovers and Regulatory Effects of Low Carbon Development in China Based on Spatial Durbin Model (SDM)

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变量	直接效应	溢出效应	总效应
lnLa	0.2133^**	0.6931^***	0.9064^***
lnLa	(0.0839)	（0.1936）	（0.2134）
lnCa	-0.2127^*	0.4508^*	0.2382
lnCa	(0.1115)	（0.2506）	（0.2653）
lnTa	-0.1160^***	0.0733	-0.0426
lnTa	(0.0359)	（0.0805）	（0.0944）
lnDa	0.0245	0.1189^***	0.1434^***
lnDa	(0.0199)	（0.0406）	（0.0459）
lnFdi	0.1121	0.2989^**	0.4110^***
lnFdi	(0.0706)	（0.1235）	（0.1188）
lnFdi×lnLa	0.4628^***	-0.0602	0.4025^**
lnFdi×lnLa	(0.0975)	(0.1795)	(0.1946)
lnFdi×lnCa	0.0786^***	0.0829^***	0.1615^***
lnFdi×lnCa	(0.0141)	(0.0265)	(0.0287)
lnFdi×lnTa	0.0348^***	0.0489^***	0.0837^***
lnFdi×lnTa	(0.0069)	(0.0133)	(0.0147)
lnFdi×lnDa	0.0233^***	0.0442^***	0.0674^***
lnFdi×lnDa	(0.0051)	(0.0119)	(0.0142)
控制变量	是	是	是

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