李振声、吴小平 | 1月5日金融学院科研团队第三期Seminar预告

时间:2024-01-02浏览:465

 间:202415日(周五)上午9:00-11:00

 点:博文楼508

 

 

报告一:Environmental regulation, intelligent manufacturing and corporate investment & financing: Evidence from industrial robot investment

主讲人:李振声

 

内容简介

Industrial robots, as a core technology and essential tool in intelligent manufacturing, have brought about a new transformation in industrial production methods. China is at a critical juncture in its green and low-carbon transition and urgently needs to increase investments in industrial robots. Therefore, the escalating environmental policies warrant further research into their impact on investments in industrial robots. Based on the policy shock provided by the implementation of the Clean Air Action in 2013, we matched industrial robot data and Chinese listed enterprise data and then constructed a quasi-natural experiment to assess the environmental regulation impact on investments in industrial robots. The results indicate that the stringent environmental regulation significantly inhibited heavily polluting enterprises from adopting industrial robots. Mechanism analysis reveals that environmental regulation has influenced the transformation and upgrading of polluting enterprises through investment and financing channels: Under the pressure the environmental regulation, highly polluting manufacturing firms have relatively reduced investment, thereby inhibiting the installation of industrial robots. In terms of corporate financing, debt financing constraints are the main channel of inhibiting investments. Furthermore, factors such as corporate ownership and industry concentration also play a moderating role in the policy effect. The findings of this paper provide insights for the government in formulating and implementing relevant environmental policies as well as complementary industrial policies.

 

【主讲人简介】

李振声,博士,安徽财经大学“龙湖学者”,金融学院讲师。主要研究方向为资源与环境经济学、碳中和及碳金融研究。

 

报告:含内生性的截尾分位数回归的识别和估计

主讲人:吴小平

 

内容简介

分位数回归具有异常值稳健、比基于均值回归需要更少的假设、能反映更多维度的信息和在异质性分析上具有优势等良好的性质,而截尾数据和内生性具有一定的普遍性,因此,本文研究含内生性截尾分位数回归的识别和估计,文章先估计和识别了不含内生性的模型,进一步分析了含有内生性的情形通过蒙特卡洛模拟得出模型的估计结果,并进行了一致性证明

 

【主讲人简介】

吴小平,博士,安徽财经大学金融学院讲师。主要研究方向为微观计量经济学金融计量经济学;环境经济学;教育经济学。在Operations Research LettersFinance Research Letters知名期刊上发表论文多篇。


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