产业结构扭曲对减污降碳的影响：基于环保重点城市的实证分析

杨瑄; 王力; 张泽阳; 谭雪; 石磊; 马中

doi:10.13198/j.issn.1001-6929.2023.09.05

产业结构扭曲对减污降碳的影响：基于环保重点城市的实证分析

doi: 10.13198/j.issn.1001-6929.2023.09.05

杨瑄^1,,
王力²,
张泽阳¹,
谭雪³,
石磊^1, ,,
马中¹

1.
中国人民大学环境学院，北京　100872
2.
生态环境部环境与经济政策研究中心，北京　100029
3.
国网能源研究院有限公司，北京　102209

基金项目: 国家重点研发计划项目(No.2018YFC0213702)

详细信息

作者简介:
杨瑄(1998-)，女，甘肃通渭人，yangxuan12@ruc.edu.cn

通讯作者:
石磊(1978-)，男，山东青岛人，教授，博士，博导，主要从事资源与环境经济学研究，shil@ruc.edu.cn

中图分类号: X196
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出版历程
- 收稿日期: 2023-03-10
- 修回日期: 2023-05-17
- 网络出版日期: 2023-09-15

Effects of Industrial Structure Distortion on Co-Control of Air Pollutants and CO₂: Empirical Study Based on Key Environmental Protection Cities

YANG Xuan^1
,,
WANG Li²,
ZHANG Zeyang¹,
TAN Xue³,
SHI Lei^{1
, ,},
MA Zhong¹

1.
School of Environment and Natural Resources, Renmin University of China, Beijing 100872, China
2.
Policy Research Center for Environment and Economy, Ministry of Ecology and Environment, Beijing 100029, China
3.
State Grid Energy Research Institute Co., Ltd., Beijing 102209, China

Funds: National Key Research and Development Program of China (No.2018YFC0213702)

摘要

摘要: 产业结构扭曲导致资源配置效率低下，严重阻碍我国实现深层次“减污降碳”. 基于2003—2017年我国112个环保重点城市(拉萨市除外)三次产业增加值份额和就业份额等年度数据，构建产业结构扭曲指数测度产业结构扭曲情况，进而运用空间杜宾双向固定效应模型探究了产业结构扭曲对“减污降碳”的空间效应. 结果表明：①案例城市整体的产业结构扭曲水平从2003年的0.145降至2017年的0.109，存在正向空间溢出效应；城市大气污染物与温室气体协同排放强度亦具有显著的空间溢出效应，本地协同减排能够显著提升附近城市的减排效果. ②产业结构扭曲会通过空间溢出效应提升周边城市的协同排放强度，分三次产业来看，第二产业的结构扭曲会显著增加城市大气污染物与温室气体协同排放强度. ③就其他影响因素而言，环境规制、外商直接投资和环境基础设施建设能力水平均能够显著促进当地和周边城市的协同减排. ④机制分析结果表明，产业结构扭曲能够通过抑制能源强度的下降，间接降低“减污降碳”效果. 因此，应进一步提高劳动力配置效率，建立区域协同排放的“联防联控”长效机制，着力于降低尤其是第二产业的结构扭曲，加大环境规制强度和城市贸易开放度，提升环境基础设施建设能力水平和能源效率.
- 产业结构扭曲 /
- 协同减排 /
- 空间溢出 /
- 环保重点城市
Abstract: The industrial structure distortion not only leads to low efficiency of resource allocation, but also seriously hinders the implementation of China′s deep co-control of air pollutants and CO₂. Against such backdrops, based on the annual employment ratio and output data of three industries in 112 key environmental protection cities (except Lhasa) from 2003 to 2017, this paper builds and measures the distortion of industrial structure, further constructs a spatial Durbin two-way fixed effect model, and empirically examines the spatial effect of industrial structure distortion on co-control of air pollutants and CO₂. The results are as follows: (1) The industrial structure distortion index of key environmental protection cities decreases from 0.145 in 2003 to 0.109 in 2017, with positive spatial spillover effect. In addition, the synergistic emission reduction of urban air pollutants and CO₂ show a significant spatial spillover effect, while local synergistic emission reduction significantly promotes the emission reduction effect of adjacent cities. (2) The industrial structure distortion can aggravate the synergistic emission of air pollutants and CO₂ in adjacent cities through the spatial spillover effect. In terms of sub-sectors, the structural distortion of the secondary industry would seriously hinder the co-control of air pollutants and CO₂. (3) In terms of other influencing factors, environmental regulation, foreign direct investment, and environmental infrastructure construction can, to a large extent, promote the coordinated emission reduction of local and adjacent cities. (4) The mechanism research results show that industrial structure distortion can also indirectly inhibit the co-control of air pollutants and CO₂ by hindering the decline of energy intensity. Therefore, it is recommended to improve the efficiency of resource allocation, promote the establishment of a long-term mechanism of regional joint prevention and co-control of air pollutants and CO₂, and reduce the structural distortion of the secondary industry. It is also advisable to increase the intensity of environmental regulation and the openness of urban trade, facilitate environmental infrastructure construction, and improve energy efficiency.
- industrial structure distortion /
- co-control of air pollutants and CO₂ /
- spatial spillover /
- key environmental protection cities

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图 1 协同排放强度和产业结构扭曲局部Moran′s I指数

Figure 1. Partial Moran′I index of ln SCpg and ed level

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图 2 112个环保重点城市产业结构扭曲程度的变化

Figure 2. The change of industrial structure distortion in key environmental protection cities

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图 3 我国东部、中部、西部地区及全国产业结构扭曲的变化

Figure 3. The change of industrial structure distortion in eastern, central and western China and the whole country

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表 1 变量描述性统计结果

Table 1. Descriptive statistical results of variables

变量名称	变量	样本数	平均值	标准差	最小值	最大值
协同排放强度综合指标	ln SCpg	1 680	0.681	0.769	−2.165	3.487
第一产业部门扭曲指数	ed₁	1 680	−0.042	0.099	−0.285	0.374
第二产业部门扭曲指数	ed₂	1 680	−0.010	0.112	−0.423	0.487
第三产业部门扭曲指数	ed₃	1 680	0.052	0.151	−0.448	0.458
产业结构扭曲指数	ed	1 680	0.193	0.112	0.003	0.624
环境规制强度	regu	1 680	0.054	0.030	0.002	0.396
经济开放程度	FDI	1 680	8 587.124	15 396.061	0.000	16.427
技术发展水平	TFP	1 680	1.622	0.763	0.042	2.991
环境基础设施建设能力水平	eic	1 680	1.152	1.260	0.010	10.664
泰尔指数	TL	1 680	0.183	0.150	0.000	0.805
能源强度	EI	1 680	0.087	0.085	0.004	0.923

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表 2 全局空间相关性Moran′I指数的检验结果

Table 2. The result of global Moran′I index

年份	ln SCpg		ed
年份	Moran′I指数	p值	Moran′I指数	p值
2003	0.149	0.000	0.049	0.000
2004	0.149	0.000	0.050	0.000
2005	0.154	0.000	0.030	0.014
2006	0.162	0.000	0.036	0.005
2007	0.163	0.000	0.019	0.074
2008	0.162	0.000	0.023	0.044
2009	0.165	0.000	0.029	0.017
2010	0.166	0.000	0.096	0.000
2011	0.178	0.000	0.013	0.165
2012	0.173	0.000	0.024	0.038
2013	0.173	0.000	0.042	0.001
2014	0.165	0.000	0.052	0.000
2015	0.158	0.000	0.081	0.000
2016	0.150	0.000	0.087	0.000
2017	0.155	0.000	0.089	0.000
注：p值为伴随概率值.

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表 3 地理距离空间权重矩阵下的协同排放强度和产业结构扭曲的LISA集聚特征

Table 3. LISA clustering characteristics of ln SCpg and ed under geographic distance matrix

城市	协同排放强度空间集聚特征		产业结构扭曲空间集聚特征		城市	协同排放强度空间集聚特征		产业结构扭曲空间集聚特征
城市	2003年	2017年	2003年	2017年	城市	2003年	2017年	2003年	2017年
合肥市		低-低			无锡市		低-低	高-高	高-高
马鞍山市		高-低	高-高	高-高	徐州市
芜湖市		低-低		高-高	扬州市		低-低	高-高	高-高
北京市	低-高	低-高			九江市
福州市	低-低	低-低			南昌市
泉州市	低-低	低-低			鞍山市
厦门市	低-低	低-低			本溪市
金昌市		高-高	高-低		大连市
兰州市		高-高			抚顺市
佛山市	高-低	低-低			锦州市
广州市		低-低			沈阳市
汕头市	高-低	低-低			包头市	高-高
韶关市	高-低	低-低			赤峰市
深圳市	低-低	低-低			呼和浩特市	高-高
湛江市	高-低				石嘴山市	高-高	高-高
中山市	高-低	低-低			银川市	高-高	高-高
珠海市	高-低	低-低			西宁市		高-高	高-低
北海市	高-低				济南市	高-高

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续表
城市	协同排放强度空间集聚特征		产业结构扭曲空间集聚特征		城市	协同排放强度空间集聚特征		产业结构扭曲空间集聚特征
城市	2003年	2017年	2003年	2017年	城市	2003年	2017年	2003年	2017年
桂林市					济宁市	高-高		高-高
柳州市					青岛市	高-高	低-低	高-高	高-高
南宁市					日照市	高-高		高-高	高-高
贵阳市					泰安市	高-高
遵义市					威海市	低-高	低-低	高-高
海口市	低-低				潍坊市	高-高	高-低	高-高	高-高
三亚市	高-低		高-低	高-低	烟台市		低-低		高-高
保定市	高-高				枣庄市	高-高		高-高
邯郸市	高-高	高-高			淄博市			低-高
秦皇岛市	高-高				大同市	高-高
石家庄市	高-高	高-高	高-高		临汾市	高-高
唐山市	高-高				太原市	高-高
安阳市					阳泉市	高-高		低-高
焦作市	高-高	低-高			长治市	高-高
开封市					宝鸡市		高-高
洛阳市	高-高	低-高			铜川市	高-高
平顶山市					西安市		低-高
郑州市	高-高				咸阳市	高-高	高-高
大庆市					延安市	高-高	高-高
哈尔滨市					上海市		低-低	高-高	高-高
牡丹江市					成都市
齐齐哈尔市					泸州市
荆州市				高-低	绵阳市
武汉市				低-低	攀枝花市
宜昌市					宜宾市
常德市					天津市	高-高
湘潭市					克拉玛依市			高-低	高-低
岳阳市					乌鲁木齐市	高-低		高-低
张家界市					昆明市		低-高
长沙市					曲靖市
株洲市					杭州市		低-低	高-高	高-高
吉林市					湖州市		高-低	高-高	高-高
长春市					嘉兴市		低-低	高-高	高-高
常州市		低-低	高-高	高-高	宁波市		低-低	高-高	高-高
连云港市		高-低	高-高		绍兴市		低-低	高-高	高-高
南京市		低-低	高-高	高-高	台州市		低-低	高-高	高-高
南通市		低-低	高-高	高-高	温州市		低-低	高-高	高-高
苏州市		低-低	高-高	高-高	重庆市

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表 4 模型选择检验结果

Table 4. Test results of model selection

检验	检验结果
SDM-FE vs SAR-FE (原假设H₀：空间杜宾模型可以简化为空间滞后模型)	33.95^***
SDM-FE vs SEM-FE(原假设H₀：空间杜宾模型可以简化为空间误差模型)	35.23^***
LMLAG检验	1 804.927^***
稳健的LMLAG检验	36.871^***
LMERR检验	3 339.813^***
稳健的LMERR检验	1 571.757^***
Wald检验	34.25^***
Hausman检验	64.98^***
LR检验(原假设H₀：不存在时间固定效应)	3 955.81^***
LR检验(原假设H₀：不存在个体固定效应)	37.23^***
注：***表示p<0.01.

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表 5 空间面板杜宾模型参数的估计结果

Table 5. Estimation results of spatial panel Durbin model

变量	回归系数
变量	模型1	模型2	模型3
ed	−0.203^***	−0.210^***	−0.193^***
ed	[0.072]	[0.071]	[0.072]
regu		0.044	0.020
regu		[0.144]	[0.145]
FDI		−0.023^***	−0.023^***
FDI		[0.006]	[0.006]
TFP		−0.014	−0.011
TFP		[0.013]	[0.014]
eic		−0.028^**	−0.032^***
eic		[0.011]	[0.011]
W×ed	0.978^***	0.978^***	1.007^***
W×ed	[0.268]	[0.293]	[0.376]
W×regu		−1.892^***	−2.309^**
W×regu		[0.538]	[1.120]
W×FDI		−0.131^***	−0.131^**
W×FDI		[0.045]	[0.058]
W×TFP		−0.018	0.075
W×TFP		[0.015]	[0.116]
W×eic		−0.159^***	−0.313^***
W×eic		[0.058]	[0.093]
City_FE	Yes	Yes	Yes
Year_FE	No	No	Yes
N	1 680	1 680	1 680
注：*表示p<0.01，表示p<0.05；中括号内为稳健标准误；City_FE为个体固定效应，Year_FE为时间固定效应；N为样本量，下同.

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表 6 产业结构扭曲对协同排放强度的影响效应分解

Table 6. The decomposition of the effect of the ed on lnSCpg

变量	直接效应			间接效应			总效应
ed	−0.066	−0.176^**	−0.180^**	14.161^***	3.128^***	1.510^**	14.095^***	2.952^***	1.330^**
ed	[0.078]	[0.073]	[0.073]	[4.054]	[0.977]	[0.599]	[4.082]	[0.979]	[0.592]
regu		−0.010	0.009		−6.996^***	−3.714^*		−7.006^***	−3.705^*
regu		[0.139]	[0.139]		[1.838]	[1.908]		[1.861]	[1.929]
FDI		−0.028^***	−0.025^***		−0.539^***	−0.214^**		−0.568^***	−0.239^**
FDI		[0.006]	[0.006]		[0.163]	[0.101]		[0.166]	[0.102]
TFP		−0.015	−0.010		−0.104^***	0.120		−0.118^***	0.110
TFP		[0.013]	[0.013]		[0.029]	[0.189]		[0.025]	[0.192]
eic		−0.035^***	−0.036^***		−0.666^***	−0.518^***		−0.701^***	−0.554^***
eic		[0.011]	[0.011]		[0.201]	[0.181]		[0.204]	[0.185]
City_FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Year_FE	No	No	Yes	No	No	Yes	No	No	Yes
N	1 680	1 680	1 680	1 680	1 680	1 680	1 680	1 680	1 680

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表 7 分部门产业结构扭曲对协同排放强度的影响效应分解

Table 7. The decomposition of the effect of the sub-sector ed on ln SCpg

变量	直接效应	间接效应	总效应
ed₁	0.167	−8.243^***	−8.076^***
ed₁	[0.145]	[1.601]	[1.583]
ed₂	0.020	1.190^*	1.210^*
ed₂	[0.071]	[0.691]	[0.702]
ed₃	0.014	0.989	1.003
ed₃	[0.074]	[0.702]	[0.713]
City_FE	Yes
Year_FE	Yes
N	1 680

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表 8 稳健性检验回归结果

Table 8. Regression results of robustness test

变量	直接效应			间接效应			总效应
变量	经济距离矩阵	地理经济嵌套矩阵	变更解释变量	经济距离矩阵	地理经济嵌套矩阵	变更解释变量	经济距离矩阵	地理经济嵌套矩阵	变更解释变量
ed	−0.142^**	−0.191^***	−0.097	0.465^***	0.725^***	1.768^***	0.323^**	0.534^***	1.671^***
ed	[0.069]	[0.070]	[0.068]	[0.140]	[0.136]	[0.637]	[0.154]	[0.142]	[0.640]
regu	−0.028	0.010	−0.008	−0.470	−0.819^***	−3.563^*	−0.498	−0.809^**	−3.571^*
regu	[0.143]	[0.142]	[0.143]	[0.327]	[0.311]	[1.908]	[0.339]	[0.322]	[1.938]
FDI	−0.025^***	−0.000^***	−0.026^***	0.032^***	0.000^*	−0.196^**	0.007	−0.0001	−0.222^**
FDI	[0.005]	[0.000]	[0.006]	[0.011]	[0.000]	[0.098]	[0.012]	[0.0002]	[0.100]
TFP	−0.013	−0.011	−0.011	0.003	−0.030	0.116	−0.010	−0.041	0.105
TFP	[0.013]	[0.013]	[0.013]	[0.031]	[0.030]	[0.194]	[0.034]	[0.032]	[0.199]
eic	−0.025^**	−0.029^***	−0.031^***	−0.022	−0.010	−0.468^***	−0.046^*	−0.039	−0.500^***
eic	[0.011]	[0.011]	[0.011]	[0.026]	[0.022]	[0.161]	[0.028]	[0.025]	[0.163]
City_FE	Yes
Year_FE	Yes
N	1 680

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表 9 产业结构扭曲抑制减污降碳的机制检验

Table 9. The mechanism test of industrial structure distortion inhibiting co-control of air pollutants and CO₂

变量	直接效应			间接效应			总效应
变量	ln SCpg 式(6)	EI 式(7)	ln SCpg 式(8)	ln SCpg 式(6)	EI 式(7)	ln SCpg 式(8)	ln SCpg 式(6)	EI 式(7)	ln SCpg 式(8)
ed	−0.170	−0.007	−0.182^**	3.251^**	0.244^*	1.288^**	3.081^**	0.236^*	1.106^*
ed	[0.204]	[0.018]	[0.073]	[1.457]	[0.134]	[0.626]	[1.448]	[0.132]	[0.621]
regu	−0.020	−0.147^***	0.019	−6.561^**	1.270^***	−4.371^**	−6.581^**	1.123^**	−4.352^**
regu	[0.308]	[0.035]	[0.143]	[3.058]	[0.437]	[1.997]	[3.079]	[0.442]	[2.020]
FDI	−0.028^***	−0.001	−0.025^***	−0.546^*	0.029	−0.241^**	−0.574^**	0.029	−0.266^***
FDI	[0.008]	[0.001]	[0.005]	[0.284]	[0.022]	[0.099]	[0.288]	[0.022]	[0.101]
TFP	−0.015	−0.004	−0.010	−0.102^***	0.035	0.092	−0.118^***	0.030	0.083
TFP	[0.013]	[0.003]	[0.013]	[0.029]	[0.046]	[0.181]	[0.028]	[0.047]	[0.184]
eic	−0.032	0.006^**	−0.035^***	−0.702^*	−0.052	−0.499^***	−0.734^**	−0.046	−0.534^***
eic	[0.021]	[0.003]	[0.011]	[0.370]	[0.035]	[0.167]	[0.371]	[0.035]	[0.169]
EI			0.364^***			2.297			2.661^*
EI			[0.098]			[1.423]			[1.442]
City_FE	Yes
Year_FE	Yes
N	1 680

下载: 导出CSV

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