Green innovation has attracted worldwide attention in the past decades. In this paper, we develop series of game models to address the effects of green loans and government subsidies on green innovation activities of enterprises. We derive a threshold value for loaning interest rate. If the interest rate of the green loan is lower than this threshold value, then the enterprises are willing to accept the loan from the bank and implement green innovation. By defining a measure for environmental effect of productive activities, we obtain a threshold value for the loaning scale. If the loaning scale is larger than this threshold value, then it meets the purpose of green loan to improve the environmental quality. We prove the effectiveness of government subsidies as an intervention way in supporting green innovation and environmental protection.

In this paper, we argue that obtaining government R&D subsidies has a certification effect and is used by innovative entrepreneurial firms as a legitimation strategy to access bank finance. We extend the extant literature on the certification effect by combining legitimacy theory with information asymmetry to build our theoretical framework. We test our theoretical model under China's unique institutional setting, in particular, the weak intellectual property rights (IPR) protection. Using 549 listed and 192 unlisted Chinese innovative entrepreneurial firms from 2009 to 2013, we find a positive certification effect on the acquisition of bank loans for all those sample firms. This positive effect is more profound in unlisted firms in our sample than the listed ones. We further find that regional variation of IPR protection has a moderating effect on the effectiveness of the certification. The certification effect is more significant in those regions where IPR protection is weaker.

Grounded in gestalt insight learning theory and organizational learning theory, we collected data from 280 middle and top-level managers to investigate the impact of each big data characteristic (i.e., data volume, data velocity, data variety, and data veracity) on firm innovation competency (i.e., exploitation competency and exploration competency), mediated through data-driven insight generation (i.e., descriptive insight, predictive insight, and prescriptive insight). Findings show that while data velocity, variety, and veracity enhance data-driven insight generation, data volume does not impact it. Additionally, results of the post hoc analysis indicate that while descriptive and predictive insights improve innovation competency, prescriptive insight does not affect it. These results provide interesting and unique theoretical and practical insights.

When studying an outcome Y that is weakly positive but can equal zero (e.g., earnings), researchers frequently estimate an average treatment effect (ATE) for a “log-like” transformation that behaves like log (Y) for large Y but is defined at zero (e.g., log (1 + Y), $\\operatorname{arcsinh}(Y)$). We argue that ATEs for log-like transformations should not be interpreted as approximating percentage effects, since unlike a percentage, they depend on the units of the outcome. In fact, we show that if the treatment affects the extensive margin, one can obtain a treatment effect of any magnitude simply by rescaling the units of Y before taking the log-like transformation. This arbitrary unit dependence arises because an individual-level percentage effect is not well-defined for individuals whose outcome changes from zero to nonzero when receiving treatment, and the units of the outcome implicitly determine how much weight the ATE for a log-like transformation places on the extensive margin. We further establish a trilemma: when the outcome can equal zero, there is no treatment effect parameter that is an average of individual-level treatment effects, unit invariant, and point identified. We discuss several alternative approaches that may be sensible in settings with an intensive and extensive margin, including (i) expressing the ATE in levels as a percentage (e.g., using Poisson regression), (ii) explicitly calibrating the value placed on the intensive and extensive margins, and (iii) estimating separate effects for the two margins (e.g., using Lee bounds). We illustrate these approaches in three empirical applications.

本文利用上市企业董事兼任和绿色专利引用数据构建企业关系网络指标,进而基于上市公司数据检验企业数字化及其网络地位对污染减排的影响。研究发现,数字化转型能够显著降低企业的污染排放水平,且企业所处网络位置越优越,越有助于发挥数字化转型的污染减排效应;与社会关系网络相比,绿色技术关联网络对企业数字化转型的作用效果具有更为明显的提升作用。进一步研究发现,企业数字化转型的污染减排效应主要通过绿色技术创新效应、规模经济效应以及要素配置效应来实现,且其作用效果随着“宽带中国”战略和国家级大数据综合试验区的实施而得到明显增强。本文不仅从企业关系网络视角拓展了数字化转型对污染减排的影响机理,而且也为数字经济下企业走好绿色转型发展之路提供了有益借鉴。

This research paper employs the concurrent directorship and green patent citation data of listed enterprises to establish an enterprise relationship network index. It then investigates the impact of enterprise digitalization and its network status on pollution reduction, utilizing panel data from listed manufacturing enterprises. The findings suggest that the implementation of enterprise digitalization can significantly reduce the pollutant emission levels of enterprises, and they indicate that the more advantageous an enterprise network location is, the more conducive it is to facilitating the pollution-reducing effects of digital transformation. Compared to social relationship networks, green technology linkage networks contribute more significantly to enterprise digital transformation. Additional research reveals that the pollution-reducing effect of enterprise digitalization is primarily achieved through green technology innovation, scale economy and factor allocation effects, and its influence is significantly enhanced with the implementation of national big data comprehensive pilot zones and the “Broadband China” strategy. This paper not only examines the impact mechanism of digital transformation on pollution reduction from an enterprise relationship network perspective, but also offers valuable insights for enterprises seeking to embrace green transformation and development in the digital economy.