绿色信贷政策对我国重污染企业  绿色全要素生产率的影响研究

LZUFE-KMS > 金融学院

作者	杨琴
姓名汉语拼音	YangQin
学号	2021000005023
培养单位	兰州财经大学
电话	18894031310
电子邮件	1763224430@qq.com
入学年份	2021-9
学位类别	学术硕士
培养级别	硕士研究生
学科门类	经济学
一级学科名称	应用经济学
学科方向	金融工程
学科代码	0202Z1
授予学位	硕士学位
第一导师姓名	杨世峰
第一导师姓名汉语拼音	Yang Shifeng
第一导师单位	兰州财经大学
第一导师职称	教授
题名	绿色信贷政策对我国重污染企业绿色全要素生产率的影响研究
英文题名	The Impact of Green Credit Policies on the Green Total Factor Productivity of Heavy Polluting Enterprises in China: A Study
关键词	绿色信贷政策绿色全要素生产率双重差分法 SBM-GML 指数模型
外文关键词	Green Credit Policy； ; R&D Expenditure； ; Green TFP； ; Double Differences Method； ; SBM-GML Index Mode
摘要	当前，全球环境问题日益突出，减少污染、保护环境已成为各国政府和企业的重要任务。为了推动经济可持续发展和实现环境保护的双重目标，许多国家和地区开始采取绿色金融政策，其中绿色信贷政策是一项重要举措。绿色信贷政策旨在鼓励银行等金融机构向符合环境标准的企业提供贷款，并为环境友好型项目提供贷款利率优惠、担保和其他金融支持措施。然而，重污染企业是指在生产和经营过程中产生大量污染物和环境问题的企业，这些企业通常面临着环境监管的压力，需要采取更多的措施来减少环境污染。因此，重污染企业如何提高绿色全要素生产率、实现可持续发展成为一个关键问题。在这个背景下，本文通过综述相关文献，总结学者们在绿色信贷政策和企业绿色全要素生产率方面的研究成果，探讨了绿色信贷政策对我国重污染企业绿色全要素生产率的影响。通过分析 2010 年至 2021 年我国上市公司的数据，将重污染企业和非重污染企业分别作为实验组和对照组。以 2012 年原银监会发布的《绿色信贷指引》为基础，运用 SBM 方向距离函数和 GML 指数评估了 2010-2021 年我国上市公司的绿色全要素生产率。随后，利用双重差分法（DID），研究了绿色信贷政策对我国重污染企业绿色全要素生产率的影响，并评估了其对绿色技术进步率和绿色效率的具体影响。进一步研究发现，绿色信贷政策与重污染企业绿色全要素生产率存在关系，并就所有权性质和地区差异对绿色信贷政策影响进行了探讨。此外，通过调节效应模型，详细研究了研发支出在绿色信贷政策对重污染企业绿色全要素生产率中的调节作用。最后，采用了更换测算模型、更换计量模型和安慰剂检验等方法对实验结果进行了稳健性检验，以验证结论的可靠性。通过实证研究，本文得出以下结论：（1）绿色信贷政策虽然提高了重污染企业的绿色效率，但却显著降低了其绿色技术进步率。因此，对于重污染企业的绿色全要素生产率来说，绿色信贷政策的负面影响超过了正面影响，从而产生了负面效果。（2）从异质性视角分析，绿色信贷政策明显降低了非国有、位于东部地区的重污染企业的绿色全要素生产率。（3）调节效应模型表明，研发支出减缓了绿色信贷政策对重污染企业绿色全要素生产率的抑制作用。基于研究结论，提出以下政策建议：（1）政府应增强监管力度，加强绿色信贷政策的执行力。（2）制定更具针对性的绿色信贷政策。为不同地区、不同性质的企业提供更精准的环保支持和激励措施，以此推动企业实施绿色生产，提高企业的绿色全要素生产率。（3）增加对重污染企业研发资金的支持，从根本上激发其内在转型动力，实现绿色发展。
英文摘要	Currently, global environmental issues are becoming increasingly prominent, and reducing pollution and protecting the environment have become crucial tasks for governments and businesses worldwide. In order to promote sustainable economic development and achieve the dual goals of economic growth and environmental protection, many countries and regions have started implementing green financial policies, with green credit policies being a significant measure among them.Green credit policies are designed to encourage financial institutions, such as banks, to provide loans to businesses that meet environmental standards. They also offer loan interest rate incentives, guarantees, and other financial support measures for environmentally friendly projects. However, heavily polluting enterprises, which generate a significant amount of pollutants and environmental issues in their production and operation processes, typically face environmental regulatory pressures and need to take additional measures to reduce environmental pollution.Therefore, the key question arises: How can heavily polluting enterprises improve their green total factor productivity and achieve sustainable development? This becomes a critical issue in the context of green finance policies. Against this backdrop, this study summarizes scholars' research findings on the impact of green credit policies on the overall green productivity of heavily polluting enterprises in China by reviewing relevant literature. By analyzing data from listed companies in China from 2010 to 2021, heavy polluting enterprises and non-heavy polluting enterprises were treated as the experimental and control groups, respectively. Building upon the "Green Credit Guidelines" issued by the China Banking Regulatory Commission in 2012, the SBM directional distance function and GML index were employed to evaluate the green total factor productivity of listed companies during this period.Subsequently, using the Difference-in-Differences (DID) method, the study investigated the influence of green credit policies on the green total factor productivity of heavy polluting enterprises among listed companies in China, evaluating their specific impacts on green technological progress rate and green efficiency. Further research revealed a relationship between green credit policies and the green total factor productivity of heavy polluting enterprises, exploring the effects of ownership nature and regional differences on the impact of green credit policies. Additionally, through the adjustment effect model, the study delved into the moderating role of research and development (R&D) expenditure in the impact of green credit policies on the green total factor productivity of heavy polluting enterprises. In conclusion, the empirical research led to the following findings: (1) While green credit policies increased the green efficiency of heavy polluting enterprises, they significantly reduced their green technological progress rate, resulting in an overall negative impact on the green total factor productivity. (2) From a heterogeneous perspective, green credit policies notably decreased the green overall productivity of non-state-owned heavy polluting enterprises located in the eastern regions. (3) The adjustment effect model indicated that R&D expenditure mitigated the inhibitory effect of green credit policies on the green total factor productivity of heavy polluting enterprises.Finally, the study conducted robustness tests using alternative estimation models, different econometric models, and a placebo test to validate the reliability of the conclusions.Based on the research findings, the following policy recommendations are proposed: (1) The government should strengthen regulatory oversight and enhance the enforcement of green credit policies. (2) Develop more targeted green credit policies, providing precise environmental support and incentives for enterprises of different regions and natures to promote green production and enhance overall green productivity. (3) Increase support for research and development funds for heavily polluting enterprises, fundamentally stimulating their intrinsic transformation dynamics to achieve sustainable green development.
学位类型	硕士
答辩日期	2024-05-26
学位授予地点	甘肃省兰州市
语种	中文
论文总页数	59
参考文献总数	71
馆藏号	0005754
保密级别	公开
中图分类号	F83/653
文献类型	学位论文
条目标识符	http://ir.lzufe.edu.cn/handle/39EH0E1M/37175
专题	金融学院
推荐引用方式 GB/T 7714	杨琴. 绿色信贷政策对我国重污染企业绿色全要素生产率的影响研究[D]. 甘肃省兰州市. 兰州财经大学,2024.

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