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本文以我国绿色金融改革政策试点为外生冲击，构建了含ESG因子的多因子风险资产组合的理论模型，并通过多期DID模型实证检验了绿色金融改革政策对试点城市上市公司ESG风险溢价的影响。结果显示：在绿色金融试点城市中，披露ESG的公司和高ESG表现的公司，存在更高的额外资本回报。异质性检验的结果显示随着绿色政策试点城市的增加，将带来更显著的ESG的风险溢价，并在政策的持续推进中，有效提高了ESG风险溢价波动调整速度，降低了市场的过度反应。交叉效应结果显示各种绿色政策的引导存在间接为负的交叉效应，市场对更多的规制所导致的高ESG表现具有更大的不信任感。上述结果通过一系列稳健性检验后仍然成立。本研究为我国借助绿色金融改革引导上市公司有效提高绿色价值回报、发展绿色经济，提供了基础理论依据。
Abstract：This article employs China's green finance reform policy pilot as an exogenous shock for constructing a theoretical model of multi-factor risk asset portfolios that incorporate ESG factors. The empirical investigation utilizes a multi-period DID model to examine the impact of green finance reform policies on the ESG risk premium of listed companies in pilot cities. The findings indicate that firms which disclose ESG information and exhibit high ESG performance tend to generate higher additional capital returns within green finance pilot cities. Notably, these outcomes remain robust even after subjecting the results to a series of rigorous tests for validity. The results of the heterogeneity test show that with the continuity of the green financial reform pilot policy, policy guidance will become more significant as the number of pilot cities increases, resulting in a more significant risk premium for high ESG performance companies in the short term, and an increase in the market's response speed to ESG risk premiums in the long term. The cross-effect results show that there is an indirect negative cross-effect of guidance from various green policies, and the market shows greater distrust of high ESG performance information caused by more regulations. Overall, this study offers a foundational theoretical framework for China to leverage green finance reform as a mechanism for guiding listed companies towards enhancing green value returns and fostering the development of a green economy.
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(1)政策全文来源于中华人民共和国生态环境部,《关于构建绿色金融体系的指导意见》,索引号000014672/2016-01336,https://www.mee.gov.cn/gkml/hbb/gwy/201611/t20161124_368163.htm。
(2)来源:浙江衢州市政府信息公开,2021-09-16,http://www.qz.gov.cn/art/2021/9/16/art_1229037105_4732649.html。
(3)来源:雪球,2021-08-19https://xueqiu.com/6287486907/194724573。
(4)来源:第一财经,2021-10-13,https://baijiahao.baidu.com/s?id=1713487122949323885andwfr=spiderandfor=pc。
(5)删除未有ESG评分的上市公司,最终得到1363家公司的9009个“公司-年度”观测值。参考表1的统计方式,将ESG评分按照C-CCC、CCC-BB、BBB-AAA对ESG进行分组,并分别对市值和账户价值比与ESG因子构建2×3和3×3组合,并分别计算组合的因子收益率差额,衡量不同资产票组合下不同ESG表现所带来的超额收益。
(6)假设评分机构对ESG的评分,仅基于企业可被公开搜集、已披露的ESG信息,则在评分机构评分为零的公司为未有可披露的ESG信息的公司,该部分公司数量为N-S。
(7)数据以万德数据库的行业分类为主,包含在证监会第一类别中未列明为金融业的其他类别中的金融公司,比如保险公司、证券公司。
(8)由于目前其他ESG披露数据如和讯网、Wind、彭博社、商道绿盟数据披露缺失值较多,故本文选取华证ESG作为实证分析的主要数据来源。
(9)根据华证官方信息披露,年度上市公司ESG评级的面板数据自2009年开始2021年截止,覆盖A股上市公司个数高达80%,共计51936个样本。其中非空样本共33299个,面板数据覆盖率64.11%。在实证分析中参考本章第四部分样本预处理规则,留存2010—2021年面板数据共25867个样本,其中被华证评级,非空样本共16832个,覆盖率65.07%,比例与整体华证ESG数据库样本比例差异较小,实证样本的选择较为合理。
(10)参考债券市场对评分的通用规则,将评分中低于CCC(含)的公司划分在低ESG表现组(L),评分高于BBB(含)的公司划分在高ESG表现组(H),其他在BB和B评分的公司皆在中ESG表现组(M)中,在整体样本中,中ESG表现组的公司占比61.66%,较ESG评分的均分更为合理。
(11)■。(17)
(12)结合了连续变量的平均绝对差与分类变量的广义匹配系数的方式计算距离,具体而言,先计算匹配企业与实验企业之间的加总距离,之后分别计算表示连续变量的平均绝对差和分类变量的广义匹配系数。
(13)城市分别为吉林市、徐州市、绍兴市、金华市、马鞍山市、芜湖市、泉州市、龙岩市、潍坊市、东营市、株洲市、衡阳市、佛山市、东莞市、玉溪市、拉萨市、汉中市。
(14)从第一批试点城市的处理组企业,在接受绿色政策干预的前一期(2016年)开始匹配,去掉无法寻找到匹配组的35个样本,并为进入控制变量匹配半径的71个样本公司,匹配1410个具有相似财务指标的样本,平衡性检验结果显示所有匹配变量在均值上均无显著性差异。
(15)对第二批2019年试点城市的处理组进行多期灵活条件的DID匹配分析,并为进入匹配半径的48个样本公司,匹配453个上市公司,平衡性检验结果显示所有匹配变量在均值上均无显著性差异。
基本信息:
DOI：
中图分类号:F832.51;X196
引用信息:
[1]金缦,凌士显.绿色金融改革政策对ESG风险溢价的影响研究——来自我国A股市场的经验证据[J].经济学报,2024,11(04):183-216.
基金信息:
国家社科基金一般项目,“双循环”新格局下金融支持技术创新的机制和路径优化研究(21BJY146); 安徽省科协决策咨询研究“表达权争夺情境下安徽新能源汽车产业吸引国际绿色投资机制研究”(KX2024A22); 安徽社会科学创新发展研究攻关项目“澳门回归治理实践对安徽省台湾事务产业协同发展实践研究”(2023CX86)的资助