中国城市绿色金融及其影响因素——以绿色债券为例

doi:10.31497/zrzyxb.20211217

摘要/Abstract

摘要：

绿色金融是金融创新的重点,旨在调控资本供给,实现环境向好发展。以绿色债券为例,基于2016—2019年中国142个地级以上城市,使用面板数据模型和空间杜宾模型探讨政策激励、金融基础、环境污染对绿色金融的影响。结果显示：（1）中西部地区引入绿色金融的城市数目较多,东部地区是绿色金融规模增长的承载区域。（2）政策激励和金融基础显著提升绿色金融规模,而环境污染起到抑制作用。（3）不同区域的政策措施都能与金融基础协同,促成绿色金融发展。仅在东部地区,政策激励能够缓和环境污染的负面影响。研究表明,绿色金融的发展模式存在区域差异,东部地区是在政策激励和金融基础推动下的顺势转型,中西部地区则需应对环境污染造成的阻力。

关键词: 绿色金融, 绿色债券, 环境污染, 空间格局

Abstract:

Green finance, which aims to regulate social capital flows, optimize the industrial structure, and realize green growth, has been a promising area of financial innovation. Since 2015, both the Chinese central and local governments have issued various policy incentives to benefit green finance. As a result, many cities in China have introduced and practiced green finance, which led to a boom. Previous research focuses on the concept and development of green finance, enterprises' and financial institutions' performance, but largely neglecting its regional disparity and influencing factors. This paper takes green bonds issued from 2016 to 2019 in 142 Chinese prefecture-level cities as a case. Based on the panel data model and spatial Durbin model, we explore the influence of policy incentives, financial development, and environmental pollution on the green bond issuance scale in different regions. Meanwhile, the interaction between variables has been explored to reveal the regional heterogeneity of green finance development. The results reveal that green bonds are mainly distributed in cities with a robust financial basement and a high administrative level. Although quite a few cities in the central and western regions started to issue green bonds after 2015, the growth of green bonds mainly took place in the eastern region. Policy incentives and financial development have significantly promoted the growth of green bonds, while environmental pollution has impeded the issuance. Policy incentives provide a low-risk context and stimulate the local financial sectors to innovate in green finance across China. However, the obstacle brought by environmental pollution can only be alleviated by the policy in the eastern region. Environmental pollution, generally caused by polluting industries, is a challenge for the central and western regions to develop green finance. With applicable policy incentives and robust financial industries, the eastern region can easily follow the green economic transformation trend and realize green finance innovation. For the central and western regions, the conflict between economic and environmental goals impeded the development of green finance. This paper suggests that more attention should be paid to regions with weak financial foundations and severe pollution. A regional cooperation platform should be established to improve the inclusion of green finance.

Key words: green finance, green bond, environmental pollution, spatial pattern

朱向东, 周心怡, 朱晟君, 黄海峰. 中国城市绿色金融及其影响因素——以绿色债券为例[J]. 自然资源学报, 2021, 36(12): 3247-3260.

ZHU Xiang-dong, ZHOU Xin-yi, ZHU Sheng-jun, HUANG Hai-feng. The development of Chinese urban green finance and its influencing factors: An empirical analysis based on green bond[J]. JOURNAL OF NATURAL RESOURCES, 2021, 36(12): 3247-3260.

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维度	指标	含义	单位
服务可得性（Access）	每万人银行网点数	城市银行网点数/城市人口	个/万人
服务可得性（Access）	单位面积银行网点数	城市银行网点数/城市面积	个/km²
地理渗透性（Penetration）	人均存款余额	存款余额/城市人口	亿元/万人
地理渗透性（Penetration）	人均贷款余额	贷款余额/城市人口	亿元/万人
使用效用（Usage）	存款余额占GDP比例	存款余额/城市GDP	%
使用效用（Usage）	贷款余额占GDP比例	贷款余额/城市GDP	%

变量	观测数/个	均值	标准差	最小值	最大值	单位
GB	568	34.96	292.44	0.00	5804.97	亿元
FI	568	0.00	1.78	-0.76	6.49	—
Policy	568	2.66	8.05	0.00	47.00	个
PM	568	47.60	15.88	13.36	105.46	—
GDPPC	568	6.31	3.43	1.39	25.60	万元
Popu	568	489.12	332.80	31.00	3391.00	万人

变量	空间杜宾模型				交叉项
	（1）				（2）	（3）
	主回归	直接效应	间接效应	总效应	全样本	全样本
Policy	0.943^***	0.930^***	-0.452	0.478	0.909^***	1.837^***
FI	0.734^***	0.737^***	0.163	0.900^***	0.282^*	0.744^***
PM	-2.356^***	-2.303^***	2.122^***	-0.181	-2.368^***	-2.225^***
Policy×FI					0.560^***
Policy×PM						0.215^**
POPU	0.953^***	0.951^***	0.116^*	1.067^***	0.880^***	0.919^***
GDPPC	0.521^**	0.532^**	0.0628	0.595^**	0.500^**	0.458^**
W×Policy	-0.524^*				-0.383	-0.480^*
W×FI	0.0783				0.0786	0.0548
W×PM	2.194^***				2.178^***	2.219^***
常数项	-5.115^***				-4.642^***	-5.509^***
Rho	0.106^*				0.101^*	0.0983^*
观测数	568	568	568	568	568	568
可决系数	0.368	0.368	0.368	0.368	0.379	0.366
城市数	142	142	142	142	142	142

变量	面板数据模型						空间杜宾模型
	东部地区			中西部地区			东部地区		中西部地区
	（1）	（2）	（3）	（4）	（5）	（6）	（7）	（8）	（9）	（10）
Policy	0.573^*	0.611^**	1.944^***	0.942^***	0.909^***	-0.0182	0.939^***	1.118^***	0.929^***	0.779^***
FI	0.742^***	-0.0828	0.742^***	0.647^***	0.298^*	0.637^***	0.635^***	0.750^***	0.631^***	0.740^***
PM	-1.445^***	-1.462^***	-1.247^***	-0.855^**	-0.962^***	-1.038	-2.315^***	-2.371^***	-2.474^***	-2.400^***
R×Policy×FI		0.850^*			0.579^***		0.179		0.401^***
R×Policy×PM			0.306^***			-0.242		0.115^**		-0.0625
POPU	1.280^***	1.190^***	1.246^***	0.687^***	0.609^***	0.693^***	0.933^***	0.993^***	0.946^***	0.984^***
GDPPC	0.943^**	1.012^**	0.898^**	0.525^**	0.442^*	0.527^**	0.512^**	0.591^***	0.524^**	0.578^**
W×Policy							-0.500^*	-0.477^*	-0.479^*	-0.511^*
W×FI							0.0684	0.114	0.102	0.105
W×PM							2.158^***	2.226^***	2.283^***	2.202^***
常数项	-3.009	-2.585	-3.806^*	-0.905	0.115	-0.214	-5.020^***	-5.628^***	-5.006^***	-5.279^***
Rho							0.102^*	0.109^*	0.113^*	0.110^*
观测数	240	240	240	328	328	328	568	568	568	568
可决系数	0.421	0.424	0.424	0.282	0.304	0.282	0.369	0.372	0.372	0.371
城市数	60	60	60	82	82	82	142	142	142	142