全球价值链分工地位对于碳排放水平的影响

doi:10.18402/resci.2020.03.11

摘要/Abstract

摘要：

随着全球价值链（GVC）在全球经济与贸易中作用的提升,GVC的发展对于资源与环境的影响引起了越来越多的关注。本文以全球主要经济体为研究对象,在环境库兹涅茨曲线（EKC）模型基础上,将GVC分工地位指数和CDIAC碳排放数据库进行匹配组成面板数据,检验GVC分工地位对于碳排放水平的影响,并运用联立方程模型对实证结果进行内生性分析和稳健性检验。研究表明：与EKC模型相类似,GVC分工地位的上升对于碳排放的影响呈倒U型关系。机制分析发现,GVC分工地位可以通过提高绿色能源使用率来降低碳排放量。GVC分工地位也可以通过研究与发展（R&D）投入和创新创业水平影响碳排放,但同样也是一种倒U型关系。同时,R&D投入水平的增加和创新创业水平的提升可以提高GVC分工地位,有助于经济体更快地跨过转折点,转向更加低碳环保的发展模式。本文的结论丰富了全球价值链理论在环境方面的应用,为探索碳减排理论提供了新的思路。

关键词: 全球价值链分工地位, 碳排放, 技术进步, 绿色能源, 环境库兹涅茨曲线

Abstract:

As the global value chain (GVC) plays an increasingly more important role in the global economy and trade, the impact of GVC on resources and the environment has attracted increasingly greater attention. In order to explore the impact of GVC position on carbon emissions, we focused on major global economies and combined GVC index with the Carbon Dioxide Information Analysis Center (CDIAC) carbon emission database to form panel data for analyzing the impact of GVC position on carbon emissions, based on the environmental Kuznets curve. A structural equation model was used to perform endogenous analysis and robustness test. The empirical results show that, similar to the environmental Kuznets curve, the impact of GVC position on carbon emissions showed an inverted U-shaped relationship. Further mechanism analysis found that improved GVC position can reduce carbon emissions through increased use of green energies. Using cross-item analysis, improved GVC position can help R&D investment and innovation and entrepreneurship levels, which in turn reduces carbon emissions. Meanwhile, the increase in R&D investment levels and the improvement of innovation and entrepreneurship can improve the status of GVC positions, helping the economy to cross the turning point faster and shift to a more low-carbon and environmentally friendly development model. The research conclusions may enrich the application of global value chain theory in the environment field and provide new ideas for exploring the theory of carbon emission reduction.

Key words: global value chain position, carbon emission, technology progress, green energy, environmental Kuznets curve

徐博, 杨来科, 钱志权. 全球价值链分工地位对于碳排放水平的影响[J]. 资源科学, 2020, 42(3): 527-535.

XU Bo, YANG Laike, QIAN Zhiquan. The impact of global value chain position on carbon emissions[J]. Resources Science, 2020, 42(3): 527-535.

图/表 4

表1

表2

图1

表3

联立方程估计和机制分析"

	模型7	模型8	模型9	模型10	模型11	模型12
碳排放量
GVC	4.296***	3.632***	2.801***	1.121*	3.676***	3.068***
	(11.026)	(8.633)	(5.493)	(1.883)	(7.642)	(5.791)
GVC²	-2.948***	0.315	-2.333**	-2.939***	-2.278**	-2.255**
	(-3.695)	(0.324)	(-2.434)	(-3.179)	(-2.557)	(-2.563)
Reenergy	-0.021***	-0.012***	-0.021***	-0.021***	-0.020***	-0.020***
	(-13.462)	(-4.268)	(-9.466)	(-9.773)	(-11.185)	(-11.485)
GVC $×$ Reenergy		-0.049***
		(-4.189)
RD			-0.041	-0.015
			(-0.711)	(-0.279)
GVC $×$ RD			0.595**	2.731***
			(2.476)	(5.254)
GVC $×$ RD²				-0.544***
				(-4.530)
Entre					0.047***	0.041***
					(3.174)	(2.756)
GVC $×$ Entre					-0.006	0.236**
					(-0.123)	(2.176)
GVC $×$ Entre²						-0.016**
						(-2.538)
其他控制变量	YES	YES	YES	YES	YES	YES
GVC分工地位
C	0.165***	0.127***	0.124***	0.124***	0.137***	0.137***
	(15.318)	(12.338)	(11.801)	(11.722)	(13.308)	(13.348)
RD	0.052***	0.046***	0.045***	0.046***	0.052***	0.054***
	(7.696)	(6.261)	(5.811)	(5.841)	(7.370)	(7.628)
Entre	0.008***	0.006**	0.007***	0.007***	0.001	0.001
	(3.187)	(2.486)	(2.798)	(2.708)	(0.342)	(0.409)
其他控制变量	YES	YES	YES	YES	YES	YES
观测值	233	233	233	233	233	233

表3

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变量名	均值	标准差	最小值	最大值	变量定义及单位
C	166.470	376.522	0.563	2654.360	碳排放量/百万t
Population	1.2E+08	2.8E+08	3.8E+05	1.3E+09	人口/人
Pergdp	2.3E+04	1.9E+04	451.218	1.1E+05	实际人均GDP/美元,以1999年为基期
GVC	0.126	0.143	-0.216	0.562	GVC分工地位
Reenergy	15.196	13.167	0.155	52.633	可再生能源占能源消费比重/%
Export	2.6E+05	3.5E+05	2.6E+03	2.1E+06	出口额/百万美元
FDI	3.60E+10	6.80E+10	2.00E+06	7.30E+11	FDI净流入/美元
RD	1.392	0.893	0.048	3.914	研发投入支出占GDP比重/%
Entre	4.347	2.412	0.450	14.470	18~64岁创业人口比重/%
Rail	2.20E+04	3.70E+04	275	2.30E+05	铁路里程/km
Labor	3.429	0.354	1.792	3.947	高技能劳动报酬占总劳动报酬比重/%

	总碳排放量				人均碳排放
	模型1	模型2	模型3		模型4	模型5	模型6
GVC		0.242**	0.271**			0.247**	0.258**
GVC		(2.139)	(2.292)			(2.167)	(2.178)
GVC²		-0.631**	-0.690**			-0.658**	-0.759**
GVC²		(-2.050)	(-2.154)				(-2.126)	(-2.386)
Pergdp	0.254***		0.248***		0.308***		0.279***
Pergdp	(3.128)		(3.308)		(3.854)			(3.839)
Pergdp²	-0.011**		-0.015***		-0.014***		-0.016***
Pergdp²	(-2.386)		(-3.495)		(-2.896)			(-3.773)
Population	0.691***	0.751***	0.820***
Population	(6.275)	(7.034)	(7.237)
Reenergy	-0.029***	-0.029***	-0.027***		-0.026***	-0.028***	-0.026***
Reenergy	(-16.567)	(-18.736)	(-15.749)		(-17.074)		(-19.156)	(-16.478)
Export	0.142***	0.198***	0.180***		0.155***	0.219***	0.180***
Export	(5.112)	(9.882)	(6.019)		(5.598)		(12.196)	(6.023)
FDI	-0.004	-0.003	-0.004	-0.005	-0.003	-0.004
FDI	(-1.061)	(-1.012)	(-1.145)	(-1.233)		(-0.907)	(-1.145)
常数项	-10.416***	-10.703***	-12.697***	-11.447***	-10.536***	-11.322***
常数项	(-5.125)	(-5.579)	(-6.173)	(-35.668)		(-51.904)	(-38.519)
观测值	450	395	384	450	395	384
调整后R²	0.708	0.734	0.744	0.669	0.689	0.705
个体固定效应	YES	YES	YES	YES	YES	YES
时间固定效应	YES	YES	YES	YES	YES	YES