“双碳”目标下典型地区减污、降碳与经济增长关系研究：以黄河流域城市群为例

杨帆; 甄江红

doi:10.13198/j.issn.1001-6929.2023.08.08

“双碳”目标下典型地区减污、降碳与经济增长关系研究：以黄河流域城市群为例

doi: 10.13198/j.issn.1001-6929.2023.08.08

杨帆^{1, 2,},
甄江红^1, ,

1.
内蒙古师范大学地理科学学院，内蒙古呼和浩特　010022
2.
内蒙古自治区生态环境低碳发展中心，内蒙古呼和浩特　010011

基金项目: 内蒙古自治区研究生科研创新项目(No.B20231054Z)；内蒙古师范大学基本科研业务费专项(No.2022JBXC017)；内蒙古师范大学研究生科研创新基金项目(No.CXJJB22013)

详细信息

作者简介:
杨帆（1989-），男，内蒙古包头人，工程师，博士，主要从事应对气候变化、城市地理与区域发展相关研究，yangfanbest@126.com

通讯作者:
甄江红(1970-)，女，内蒙古包头人，教授，博士，博导，主要从事城市地理与区域发展相关研究，zhenjianghong@sina.com

中图分类号: X321
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出版历程
- 收稿日期: 2023-05-13
- 修回日期: 2023-08-07
- 网络出版日期: 2023-08-18

Relationship between Pollution and Carbon Reduction and Economic Growth in Typical Regions under the ‘Dual Carbon’ Goals: A Case Study of Urban Agglomeration in the Yellow River Basin

YANG Fan^{1, 2
,},
ZHEN Jianghong^{1
, ,}

1.
College of Geographical Science, Inner Mongolia Normal University, Hohhot 010022, China
2.
Inner Mongolia Autonomous Region Ecological Environment Low-Carbon Development Center, Hohhot 010011, China

Funds: Graduate Education Innovation Program Funded Project of Inner Mongolia Autonomous Region, China (No.B20231054Z); Fundamental Research Funds for the Inner Mongolia Normal University, China (No.2022JBXC017); Graduate Students´ Research & Innovation Fund of Inner Mongolia Normal University, China (No.CXJJB22013)

摘要

摘要: 在经济高质量发展和减污降碳的双重压力下，以减污降碳协同增效为总抓手推动经济绿色转型，是碳达峰碳中和愿景下实现黄河流域生态保护和高质量发展的迫切需要. 为了分析黄河流域减污、降碳与经济增长之间的动态关系，探析其影响机制，并因地制宜提出经济社会全面绿色低碳转型政策建议，本研究基于2006—2020年黄河流域57个城市的主要环境污染物排放、碳排放和人均GDP面板数据，采用面板向量自回归模型(PAVR模型)对减污、降碳与经济增长三者的动态关系及影响机制开展实证研究. 结果表明：①黄河流域经济发展自身惯性效应显著，未来10期经济发展所占贡献比均在95%以上，经济发展与环境污染物排放增加之间存在持续性、滞后性的双向作用机制，但工业废水排放、工业SO₂排放对经济增长的反作用机制较弱，对经济增长的贡献率仅分别为0.4%、1.8%，工业烟尘排放制约着黄河流域各城市的经济增长. ②碳排放与经济增长存在双向因果关系，工业CO₂排放抑制经济增长，经济增长对工业CO₂排放的解释力度达到7.3%，互动方式呈现先促进、后阻碍、再促进的变化趋势. ③从脉冲响应与方差分析结果来看，黄河流域减污、降碳与经济发展的协调程度有待加强，相对于经济增长对减污、降碳的作用效果而言，减污、降碳对经济增长的推动力尚显不足. 因此，建议黄河流域不断促进环境保护与经济发展相协调、推动传统产业转型升级、构建绿色低碳发展新格局、充分发挥科技引领带动作用、完善协同管理体制机制.
- 经济增长 /
- 减污 /
- 降碳 /
- 面板向量自回归模型(PAVR模型) /
- 黄河流域
Abstract: In order to promote high-quality economic development as well as carbon and pollution reduction, it is urgent to promote green transformation of the economy. With a major focus on pollution and carbon reduction, the ecological protection and high-quality development of the Yellow River Basin can be achieved through carbon neutrality. To analyse the dynamic relationship between pollution and carbon reduction and economic growth in the Yellow River Basin, this study explored its potential impact mechanisms to propose policy recommendations for a comprehensive green and low-carbon transformation of the economy and society based on the local conditions. The study is based on the panel data of major environmental pollutant emissions, carbon emissions, and per capita GDP in 57 cities in the Yellow River Basin from 2006 to 2020. The panel vector autoregressive model (PAVR model) was used to conduct an empirical study on the dynamic relationship and impact mechanism between pollution and carbon reduction and economic growth. The results show that the inertia effect of economic development in the Yellow River Basin is significant, and the contribution of economic development is estimated to be more than 95% in the next 10 periods. There is a persistent and lagging two-way mechanism between economic development and the increase in pollutant emissions. However, the negative impact of industrial wastewater discharge and SO₂ discharge on economic growth is weak, and their impact on economic growth is only 0.4% and 1.8%, respectively. Industrial soot emissions restrict the economic growth of cities in the Yellow River Basin. Additionally, a two-way causal relationship exists between carbon emissions and economic growth. Industrial CO₂ emissions inhibit economic growth, and economic growth explains 7.3% of industrial CO₂ emissions. The interaction mode shows a trend of promotion and hindrance, followed again by promotion. The results of impulse response and variance analysis show that the coordination between pollution and carbon reduction and economic development in the Yellow River Basin needs to be strengthened. Compared with the effect of economic growth on pollution and carbon reduction, the driving force of pollution and carbon reduction on economic growth is still insufficient. Therefore, in the Yellow River Basin, policies should constantly focus on maintaining a balance between environmental protection and economic development, promoting the transformation and upgradation of traditional industries, building a new pattern of green and low-carbon development, realising the leading role of science and technology to its full potential, and improving the mechanism of collaborative management systems.
- economic growth /
- pollution reduction /
- carbon reduction /
- panel vector autoregressive model (PAVR model) /
- Yellow River Basin

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图 1 黄河流域地理分布范围

Figure 1. Geographical distribution of the Yellow River Basin

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图 2 黄河流域各城市碳排放空间分布

Figure 2. Spatial distribution map of carbon emissions in various cities across the Yellow River Basin

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图 3 黄河流域各城市环境污染空间分布

Figure 3. Spatial distribution map of environmental pollution in various cities across the Yellow River Basin

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图 4 经济增长与工业废水排放的脉冲响应曲线

Figure 4. Impulse response between per capita GDP and industrial wastewater discharge

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图 5 经济增长与工业SO₂排放的脉冲响应曲线

Figure 5. Impulse response between per capita GDP and industrial sulphur dioxide emissions

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图 6 经济增长与工业烟尘排放的脉冲响应曲线

Figure 6. Impulse response between per capita GDP and industrial smoke and dust emissions

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图 7 经济增长与CO₂排放的脉冲响应曲线

Figure 7. Impulse response between per capita GDP and carbon dioxide emissions

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表 1 黄河流域各城市变量描述性统计

Table 1. Descriptive statistics of various cities in the Yellow River Basin

变量	含义	平均值	标准差	最小值	最大值	观测值个数
ln[WD]	工业废水排放	8.016	1.181	3.850	10.022	798
ln[ISD]	工业SO₂排放	10.668	1.195	6.821	12.728	798
ln[SE]	工业烟尘排放	10.014	1.190	6.653	15.458	798
ln[CO₂]	CO₂排放	6.098	1.162	2.,117	8.305	798
ln[Y]	人均GDP	7.053	1.099	4.151	11.350	798

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表 2 面板单位根检验结果

Table 2. Panel unit root inspection results

变量	LLC检验	IPS检验	ADF检验	PP检验	检验结果
ln[WD]	−8.528^***(0.001)	−1.636^*(0.051)	140.530^**(0.047)	156.947^***(0.005)	不平稳
△ln[WD]	−19.926^***(0.000)	−17.381^***(0.000)	277.612^***(0.000)	564.574^***(0.000)	平稳
ln[ISD]	−7.223^***(0.000)	−0.873(0.809)	131.443(0.126)	192.614^***(0.000)	不平稳
△ln[ISD]	−20.873^***(0.000)	−18.879^***(0.000)	324.429^***(0.000)	713.948^***(0.000)	平稳
ln[SE]	−8.964^***(0.000)	−0.631(0.264)	130.268(0.142)	130.819(0.134)	不平稳
△ln[SE]	−22.958^***(0.000)	−19.991^***(0.000)	356.396^***(0.000)	743.834^***(0.000)	平稳
ln[CO₂]	−10.680^***(0.000)	2.896^***(0.002)	311.434^***(0.000)	251.328^***(0.000)	平稳
△ln[CO₂]	−20.210^***(0.000)	−19.056^***(0.000)	378.589^***(0.000)	744.170^***(0.000)	平稳
ln[Y]	−6.571^***(0.000)	3.216(0.999)	114.082(0.480)	70.198(0.999)	不平稳
△ln[Y]	−15.027^***(0.000)	−9.480^***(0.000)	205.028^***(0.000)	430.095^***(0.000)	平稳
注：*、、*分别表示在1%、5%、10%的置信水平上显著，括号内数据为对应的P值. 下同.

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表 3 面板协整检验结果

Table 3. Panel co-integration inspection results

检验方法	经济发展与工业废水排放	经济发展与工业SO₂排放	经济发展与工业烟尘排放	经济发展与CO₂排放
Kao检验	3.054^***(0.002)	2.573^***(0.005)	3.311^***(0.001)	2.841^***(0.006)
Pedroni检验	6.083^***(0.000)	5.768^***(0.000)	5.506^***(0.000)	5.735^***(0.000)
Westerlund检验	8.558^***(0.000)	7.894^***(0.000)	10.805^***(0.000)	4.436^***(0.000)

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表 4 PVAR模型最优滞后期

Table 4. Optimal hysteresis period of PVAR model

变量	lag	AIC	BIC	HQIC	变量	lag	AIC	BIC	HQIC
ln[Y]-ln[WD]	1	−1.243¹⁾	0.462¹⁾	−0.941¹⁾	ln[Y]-ln[SE]	1	−0.203¹⁾	0.578¹⁾	0.099¹⁾
	2	−0.125	0.739	0.211		2	−0.125	0.739	0.211
	3	−0.853	0.875	−0.289		3	−0.085	0.875	0.289
	4	−0.073	1.002	0.348		4	−0.072	1.002	0.348
	5	−0.075	1.136	0.402		5	−0.075	1.136	0.402
ln[Y]-ln[ISD]	1	−0.584¹⁾	0.197¹⁾	−0.281¹⁾	ln[Y]-ln[CO₂]	1	−1.979	−1.198	−1.677
	2	−0.504	0.359	−0.168		2	−1.987	−1.123	−1.652
	3	−0.460	0.500	−0.086		3	−2.088	−1.127	−1.713
	4	−0.564	0.511	−0.143		4	−2.310	−1.235¹⁾	−1.888¹⁾
	5	−0.529	0.682	−0.051		5	−2.317¹⁾	−1.106	−1.840
注：lag为滞后期数；AIC为赤池信息准则；BIC为贝叶斯信息准则；HQIC为汉南-奎因信息准则，其值越小，模型拟合度越好，用于模型选择. 1)表示对应准则选择的最优滞后阶数.

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表 5 GMM估计结果

Table 5. GMM estimation results

被解释变量	解释变量	GMM估计系数	被解释变量	解释变量	GMM估计系数
h_ln[WD]	h_ln[WD]	0.121^*(0.083)	h_ln[Y]	h_ln[WD]	−0.135^***(0.001)
L1.	h_[lnY]	−0.620^***(0.000)	L1.	h_ln[Y]	0.639^***(0.000)
h_ln[ISD]	h_ln[ISD]	−0.166(0.123)	h_ln[Y]	h_ln[ISD]	−0.004(0.594)
L1.	h_ln[Y]	1.444^**(0.000)	L1.	h_ln[Y]	0.640^***(0.000)
h_ln[SE]	h_ln[SE]	−0.173^**(0.045)	h_ln[Y]	h_ln[SE]	−0.004(0.223)
L1.	h_ln[Y]	0.583^**(0.014)	L1.	h_ln[Y]	0.648^***(0.000)
h_ln[CO₂]	h_ln[CO₂]	−0.121(0.414)	h_ln[CO₂]	h_ln[CO₂]	−0.024(0.258)
L1.	h_ln[Y]	0.435^***(0.002)	L1.	h_ln[Y]	0.550^***(0.000)
h_ln[CO₂]	h_ln[CO₂]	−0.108(0.103)	h_ln[CO₂]	h_ln[CO₂]	−0.007(0.607)
L2.	h_ln[Y]	0.381^***(0.006)	L2.	h_ln[Y]	−0.085^**(0.050)
h_ln[CO₂]	h_ln[CO₂]	−0.065(0.181)	h_ln[CO₂]	h_ln[CO₂]	−0.255^**(0.013)
L3.	h_ln[Y]	0.130(0.363)	L3.	h_ln[Y]	0.133^***(0.001)
h_ln[CO₂]	h_ln[CO₂]	−0.268(0.560)	h_ln[CO₂]	h_ln[CO₂]	0.007(0.443)
L4.	h_ln[Y]	−0.345^**(0.021)	L4.	h_ln[Y]	0.044(0.299)
注：“h_”表示为消除固定效应，经Helmert转变的形式；“L1.”表示滞后1阶，其他以此类推；*、、*分别表示在1%、5%、10%的置信水平上显著，括号内的数据为对应的P值.

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表 6 方差分解分析

Table 6. Variance decomposition

滞后期数	经济增长与工业废水排放				经济增长与工业SO₂排放
	ln[Y]的方差分解		ln[WD]的方差分解		ln[Y]的方差分解		ln[ISD]的方差分解
	ln[Y]	ln[WD]	ln[Y]	ln[WD]	ln[Y]	ln[ISD]	Ln[Y]	ln[ISD]
1	1.000	0.000	0.000	1.000	0.986	0.014	0.000	1.000
2	0.997	0.003	0.015	0.985	0.983	0.017	0.041	0.959
3	0.997	0.003	0.019	0.981	0.982	0.018	0.050	0.950
4	0.996	0.004	0.021	0.979	0.982	0.018	0.054	0.946
5	0.996	0.004	0.022	0.978	0.982	0.018	0.056	0.944
6	0.996	0.004	0.022	0.978	0.982	0.018	0.057	0.943
7	0.996	0.004	0.022	0.978	0.982	0.018	0.057	0.943
8	0.996	0.004	0.022	0.978	0.982	0.018	0.057	0.943
9	0.996	0.004	0.022	0.978	0.982	0.018	0.057	0.943
10	0.996	0.004	0.022	0.978	0.982	0.018	0.057	0.943

滞后期数	经济增长与工业烟尘排放				经济增长与CO₂排放
	ln[Y]的方差分解		ln[SE]的方差分解		ln[Y]的方差分解		ln[CO₂]的方差分解
	ln[Y]	ln[SE]	ln[Y]	ln[SE]	ln[Y]	ln[CO₂]	ln[Y]	ln[CO₂]
1	1.000	0.000	0.000	1.000	1.000	0.000	0.027	0.973
2	0.999	0.001	0.005	0.095	0.995	0.005	0.039	0.961
3	0.999	0.001	0.006	0.994	0.993	0.007	0.061	0.939
4	0.999	0.001	0.007	0.993	0.987	0.013	0.068	0.932
5	0.999	0.001	0.007	0.993	0.987	0.013	0.073	0.927
6	0.999	0.001	0.007	0.993	0.987	0.013	0.073	0.927
7	0.999	0.001	0.007	0.993	0.987	0.013	0.073	0.927
8	0.999	0.001	0.007	0.993	0.987	0.013	0.073	0.927
9	0.999	0.001	0.007	0.993	0.987	0.013	0.073	0.927
10	0.999	0.001	0.007	0.993	0.987	0.013	0.073	0.927

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