重污染企业减污降碳协同效应时空分异特征及其驱动因素分析——来自A股100家企业的证据

doi:10.16258/j.cnki.1674-5906.2024.11.001

生态环境学报 ›› 2024, Vol. 33 ›› Issue (11): 1661-1671.DOI: 10.16258/j.cnki.1674-5906.2024.11.001

• 碳循环与碳减排专栏 • 下一篇

重污染企业减污降碳协同效应时空分异特征及其驱动因素分析——来自A股100家企业的证据

田嘉莉¹^,²(), 毛靖宇¹, 彭甲超¹^,²^,³^,⁴, 姚婷婷¹^,²^,^*(), 付书科¹^,²

1.武汉工程大学法商学院（知识产权学院），湖北武汉 430205
2.武汉工程大学资源环境与经济高质量发展研究中心，湖北武汉 430205
3.商务部国际贸易经济合作研究院，北京 100710
4.浙江工商大学工商管理学院，浙江杭州 310018

收稿日期:2024-08-30 出版日期:2024-11-18 发布日期:2024-12-06
通讯作者: *姚婷婷。E-mail: yaotingting0@126.com
作者简介:田嘉莉（1990年生），女，副教授，博士，主要研究方向为环境经济。E-mail: tjl@wit.edu.cn
基金资助:
国家自然科学基金青年项目(72303174);教育部人文社会科学研究青年项目(21YJC790145);湖北省社科基金前期资助项目(22ZD077);湖北省社科基金前期资助项目(23ZD216);数字化学习技术集成与应用教育部工程研究中心创新基金重大项目(1331002);湖北省教育科学规划一般项目(2022GB035);武汉工程大学人文社会科学研究基金(R202105)

The Spatiotemporal Differentiation and Driving Factors of the Synergistic Effect of Pollution and Carbon Reduction in Heavily Polluting Enterprises: Evidence from 100 A-share Enterprises

TIAN Jiali¹^,²(), MAO Jingyu¹, PENG Jiachao¹^,²^,³^,⁴, YAO Tingting¹^,²^,^*(), FU Shuke¹^,²

1. School of Law & Business, Wuhan Institute of Technology, Wuhan 430205, P. R. China
2. Center for High Quality Collaborative Development of Resources, Environment and Economy, Wuhan Institute of Technology, Wuhan 430205, P. R. China
3. Chinese Academy of International Trade and Economic Cooperation, Beijing 100710, P. R. China
4. School of Business Administration, Zhejiang Gongshang University, Hangzhou 310018, P. R. China

Received:2024-08-30 Online:2024-11-18 Published:2024-12-06

摘要/Abstract

摘要：

重污染企业是中国污染排放和碳排放的主要来源，探究这些企业减污降碳协同效应的时空分异特征及其驱动因素，对于深入推进重污染企业减污降碳的协同治理，实现“双碳”目标和经济可持续发展至关重要。选取2008－2022年中国重污染行业100家A股上市公司为样本，利用时空地理加权回归（GTWR）模型和克里金空间插值法分析重污染企业减污降碳协同治理的时空分异特征。同时，利用双固定效应模型剖析了重污染企业减污降碳协同效应的驱动因素，并基于GTWR的回归结果进一步揭示这些因素的作用异质性。结果表明，1）从时间特征来看，自2008年以来，中国重污染细分行业减污降碳协同效应总体呈上升趋势，协同效应的均值由2008年的0.476提高到2022年的0.490。2）从空间特征看，样本期内东、中、西部地区的协同效应分别介于0.413－0.810、0.395－0.662、0.187－0.550。东部和中部地区的协同效应起初高于其他地区，但随着减污降碳协同治理的推进，各地区的差异逐渐缩小。3）能源消耗量与重污染企业减污降碳协同效应显著负相关，而全要素生产率和绿色技术创新则与协同效应显著正相关。特别是在东部地区，能源消耗量和全要素生产率的驱动作用更明显，而在西部地区，绿色技术创新的作用更突出。环境规制强度和公众环境关注度对减污降碳的驱动因素起到了调节作用。该研究不仅拓展了重污染企业减污降碳协同效应的研究视角，还为中国推进重污染企业减污降碳提供了政策启示。

关键词: 重污染企业, 减污降碳协同, 耦合协调度, GTWR模型, 时空分异特征, 驱动因素

Abstract:

Heavily polluting enterprises are the main sources of pollution and carbon emissions in China. Investigating the spatiotemporal differentiation characteristics and driving factors of the synergistic effects of pollution and carbon reduction in these enterprises is crucial for further promoting the coordinated governance of pollution and carbon reduction in heavy-pollution enterprises, achieving the goal of "dual carbon" and sustainable economic development. For this purpose, 100 A-share listed companies in China's heavy-pollution industry from 2008 to 2022 were selected as the sample. The Spatiotemporal Geographically Weighted Regression (GTWR) model and kriging spatial interpolation were used to analyze the spatiotemporal differentiation characteristics of the coordinated governance of pollution and carbon emission reduction of heavily polluting enterprises. Additionally, the dual fixed-effects model was employed to analyze the driving factors of the synergistic effects of pollution and carbon reduction in heavy-pollution enterprises and further reveal the heterogeneity of these factors based on the regression results of the GTWR. The results showed the following. (1) From a temporal perspective, since 2008, the synergistic effects of pollution and carbon emission reductions in China's heavily polluting industries have generally exhibited an upward trend, with the mean value increasing from 0.476 in 2008 to 0.490 in 2022. (2) In terms of spatial characteristics, the synergistic effects in the eastern, central, and western regions are 0.413‒0.810, 0.395‒0.662, and 0.187‒0.550, respectively. Initially, the synergistic effect in the eastern and central regions was higher than that in other regions. However, as the collaborative governance of pollution and carbon reduction progressed, differences among the regions gradually diminished. (3) Energy consumption is significantly negatively correlated with the synergistic effect of pollution and carbon reduction in heavy-pollution enterprises, whereas total factor productivity and green technological innovation are significantly positively correlated with the synergistic effect. The driving effects of energy consumption and total factor productivity were more pronounced in the eastern region, whereas the role of green technological innovation was more prominent in the western region. The intensity of environmental regulation and public environmental attention played moderating roles in the driving factors of pollution and carbon emission reduction. This study not only expands the research perspective on the synergistic effect of pollution and carbon reduction in heavy pollution enterprises but also provides insight for China to promote the reduction in pollution and carbon emissions in these enterprises.

Key words: heavily polluting enterprises, pollution and carbon reduction synergy, coupling coordination degree, GTWR model, spatiotemporal differentiation characteristics, driving factors

中图分类号:

F426
X321

田嘉莉, 毛靖宇, 彭甲超, 姚婷婷, 付书科. 重污染企业减污降碳协同效应时空分异特征及其驱动因素分析——来自A股100家企业的证据[J]. 生态环境学报, 2024, 33(11): 1661-1671.

TIAN Jiali, MAO Jingyu, PENG Jiachao, YAO Tingting, FU Shuke. The Spatiotemporal Differentiation and Driving Factors of the Synergistic Effect of Pollution and Carbon Reduction in Heavily Polluting Enterprises: Evidence from 100 A-share Enterprises[J]. Ecology and Environment, 2024, 33(11): 1661-1671.

图/表 7

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变量类型	变量名称	变量符号	数据来源及处理方法
被解释变量	耦合协调度	D	来源于EPS上市公司数据库、中国工业企业污染排放数据库、上市公司社会责任报告和国泰安数据, 并利用耦合协调度模型计算获得。对未披露碳排的企业利用排放因子法进行估算; 参照毛捷等 (2022) 的方法, 对污染当量数加总加1取对数作为企业的污染排放水平
核心解释变量	能源消耗总量	TEC	来源于上市公司企业社会责任报告、EPS上市公司数据库, 以及通过Python爬取整理的企业公开披露信息
控制变量	全要素生产率	TFP	使用LP方法 (朱沛华等, 2020) 来估算TFP, 相关变量包括产出Y (企业主营业务收入)、劳动L (职工人数)、资本K (固定资产净值)、中间投入M (购买商品、接受劳务支付的现金), 数据来源于公司年报
	环保背景高管总数	EP	来源于新浪财经网站公布的上市公司高管个人简历信息 (王辉等, 2022)
	ESG表现	ESG	来源于Wind数据库
	绿色技术创新	Green	利用中国研究数据服务平台提供的企业绿色专利申请数量 (Gong et al., 2024)
	利润总额	TP	来源于上市公司年报
	固定资产净额	Net	来源于上市公司年报，对于缺失值采用插值法处理 (李云燕等, 2023)
调节变量	环境规制强度	EI	采用重污染企业所在地区当年用于污染治理的金额占该年度工业产值的比值来表示
调节变量	公众环境关注度	BI	在百度搜索引擎中检索关键词, 采用Python爬取2008－2022年间公众对 “环境污染” 和 “雾霾” 等词条的日均搜索量, 并将其加总后加一取自然对数 (Chen et al., 2023b)

变量类型	变量名称	变量符号	数据来源及处理方法
被解释变量	耦合协调度	D	来源于EPS上市公司数据库、中国工业企业污染排放数据库、上市公司社会责任报告和国泰安数据, 并利用耦合协调度模型计算获得。对未披露碳排的企业利用排放因子法进行估算; 参照毛捷等 (2022) 的方法, 对污染当量数加总加1取对数作为企业的污染排放水平
核心解释变量	能源消耗总量	TEC	来源于上市公司企业社会责任报告、EPS上市公司数据库, 以及通过Python爬取整理的企业公开披露信息
控制变量	全要素生产率	TFP	使用LP方法 (朱沛华等, 2020) 来估算TFP, 相关变量包括产出Y (企业主营业务收入)、劳动L (职工人数)、资本K (固定资产净值)、中间投入M (购买商品、接受劳务支付的现金), 数据来源于公司年报
	环保背景高管总数	EP	来源于新浪财经网站公布的上市公司高管个人简历信息 (王辉等, 2022)
	ESG表现	ESG	来源于Wind数据库
	绿色技术创新	Green	利用中国研究数据服务平台提供的企业绿色专利申请数量 (Gong et al., 2024)
	利润总额	TP	来源于上市公司年报
	固定资产净额	Net	来源于上市公司年报，对于缺失值采用插值法处理 (李云燕等, 2023)
调节变量	环境规制强度	EI	采用重污染企业所在地区当年用于污染治理的金额占该年度工业产值的比值来表示
调节变量	公众环境关注度	BI	在百度搜索引擎中检索关键词, 采用Python爬取2008－2022年间公众对 “环境污染” 和 “雾霾” 等词条的日均搜索量, 并将其加总后加一取自然对数 (Chen et al., 2023b)

变量	D
TEC	-0.010^{*** 1)}	-0.009^***	-0.010^***
	(-21.080)	(-16.800)	(-21.080)
TFP	0.110^***	0.089^***	0.110^***
	(43.200)	(28.430)	(43.200)
EP	4.42×10^{-3** 2)}	3.30×10^{-3* 3)}	4.42×10^-3**
	(2.520)	(1.720)	(2.520)
ESG	6.4×10^-4	2.3×10^-4	6.4×10^-4
	(0.290)	(0.100)	(0.290)
Green	9.2×10^-4***	1.0×10^-4***	9.2×10^-4***
	(1.380)	(1.490)	(1.380)
TP	1.3×10^-3***	9.7×10^-4**	1.3×10^-3***
	(3.410)	(2.420)	(3.410)
Net	-1.4×10^-4	4×10^-4	-1.4×10^-4
	(-1.510)	(0.240)	(-1.510)
_cons	-0.566^***	-0.362^***	-0.566^***
	(-22.060)	(-11.390)	(-22.060)
个体效应	不控制	控制	控制
时间效应	控制	控制	控制
N	1500	1500	1500
R²	0.388	0.389	0.388

变量	D
TEC	-0.010^{*** 1)}	-0.009^***	-0.010^***
	(-21.080)	(-16.800)	(-21.080)
TFP	0.110^***	0.089^***	0.110^***
	(43.200)	(28.430)	(43.200)
EP	4.42×10^{-3** 2)}	3.30×10^{-3* 3)}	4.42×10^-3**
	(2.520)	(1.720)	(2.520)
ESG	6.4×10^-4	2.3×10^-4	6.4×10^-4
	(0.290)	(0.100)	(0.290)
Green	9.2×10^-4***	1.0×10^-4***	9.2×10^-4***
	(1.380)	(1.490)	(1.380)
TP	1.3×10^-3***	9.7×10^-4**	1.3×10^-3***
	(3.410)	(2.420)	(3.410)
Net	-1.4×10^-4	4×10^-4	-1.4×10^-4
	(-1.510)	(0.240)	(-1.510)
_cons	-0.566^***	-0.362^***	-0.566^***
	(-22.060)	(-11.390)	(-22.060)
个体效应	不控制	控制	控制
时间效应	控制	控制	控制
N	1500	1500	1500
R²	0.388	0.389	0.388

变量	D
TEC	-8.0×10^-3	-8.3×10^-3***	-8.4×10^-3***	-8.4×10^-3***	-8.4×10^-3***	-8.5×10^-3***
TEC	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)
TFP		0.090^***	0.091^***	0.090^***	0.090^***	0.089^***
TFP		(0.013)	(0.013)	(0.013)	(0.013)	(0.013)
EP			3.4×10^-3	3.5×10^-3	3.3×10^-3	3.3×10^-3
EP			(0.003)	(0.003)	(0.003)	(0.003)
ESG				5.0×10^-4	7.0×10^-4	2.0×10^-4
ESG				(0.005)	(0.005)	(0.005)
Green					0.001^***	0.001^***
Green					(4.0×10^-4)	(4.0×10^-4)
TP						0.001^*
TP						(0.001)
_cons	0.522^***	-0.371^***	-0.375^***	-0.376^***	-0.376^***	-0.363^***
_cons	(0.017)	(0.133)	(0.132)	(0.134)	(0.134)	(0.136)
R²	0.002	0.383	0.384	0.384	0.386	0.389
N	1500	1500	1500	1500	1500	1500

重污染企业减污降碳协同效应时空分异特征及其驱动因素分析——来自A股100家企业的证据

The Spatiotemporal Differentiation and Driving Factors of the Synergistic Effect of Pollution and Carbon Reduction in Heavily Polluting Enterprises: Evidence from 100 A-share Enterprises

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变量	D
TEC	-0.005^***	-0.005^***	-0.005^***	-0.004^***
TEC	(-7.02)	(-5.69)	(-7.19)	(-4.000)
TFP	0.004	0.004	0.005^*	0.004
TFP	(-1.540)	(-1.530)	(-1.810)	(-1.530)
Green	5.84×10^-4	5.84×10^-4	6.77×10^-4	6.14×10^-4
Green	(0.710)	(0.710)	(0.820)	(0.740)
TECEI		3.31×10^-3
TECEI		(0.020)
TECBI				-9.82×10^-5**
TECBI				(-2.290)
_cons	0.548^***	0.548^***	0.564^***	0.537^***
_cons	(16.590)	(16.290)	(17.570)	(15.790)
N	1200	1200	1200	1200
R²	0.082	0.082	0.086	0.090

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