This study assesses the impact of China’s Green Credit Guidelines (GCG) 2012 on the quality of firms’ green innovation and their financial asset allocations. While examining patent applications and grants, our findings reveal that, although the GCG 2012 led to a significant increase in green patent applications, its influence on granted patents, especially in the invention category, was minimal. This highlights a discrepancy between innovation intent and quality, suggesting that highpolluting enterprises (HPEs) prioritize rapid policy compliance rather than substantial environmental improvements. However, HPEs seem to prioritize liquidity over long-term financialization, potentially indicating enhanced credit allocation efficiency.

This paper examines the spillover effect of environmental regulations on firms’ green technological innovation, from the perspective of supply chain relationships. Analyzing data from Chinese listed companies, we find that the average environmental regulatory pressure faced by the client firms of a supplier firm enhances the green patent applications filed by the supplier firm, indicating that environmental regulatory pressure from clients spills over to suppliers. When the industries of suppliers are more competitive or the proportion of their sales from the largest client is higher, suppliers feel more pressured to engage in green innovation, resulting in more green patent applications. Thus, via their negotiation power, client firms can prompt supplier firms to innovate to meet their demand for green technologies. Finally, we show that this effect is particularly pronounced when supplier firms are located in highly marketized regions, receive low R&D government subsidies, or have high ESG ratings.

We (re)evaluate the general-equilibrium effects of (environmental) policies from the perspectives of input-output networks and firm dynamics and heterogeneity. Using China’s carbon emission trading system (ETS) as an example, we find that ETS leads to more patent applications, especially the ones associated with low-carbon technologies in the targeted sectors. The effects are muted at the firm level due to selection effects, whereby only larger firms are significantly and positively affected. Meanwhile, larger firms occupy a small share in number but a large share of aggregate outcomes, contributing to the discrepancy between the effects of ETS at the individual firm and aggregate sector levels. The effects also diffuse in input-output networks, leading to more patents in upstream/downstream sectors. We build and estimate the first firm dynamics model with input-output linkages and regulatory policies in the literature and conduct policy experiments. ETS’s effects are amplified given input-output networks.

This study examines the impact of ESG rating events on innovation in China's new energy vehicle industry. The baseline Difference-in-Differences (DID) analysis demonstrates that ESG ratings promote innovation among new energy vehicle companies. A series of robustness tests, including parallel trend analysis and placebo tests, support the baseline results. The channel tests in the mechanism analyses indicate that ESG ratings promote innovation in new energy vehicle enterprises by increasing R&D investment and the number of R&D personnel. Other mechanism analyses suggest that ESG ratings also enhance innovation output and quality by stimulating green patent applications, encouraging joint patent applications, and increasing the number of high-quality patents.

In the process of green transformation of China's economy, it is of great practical significance to study the impact of green finance in supporting the development of the real economy, especially the impact of green credit on enterprise innovation, in order to promote the green transformation of enterprises, industrial structure upgrading and sustainable economic development. This paper takes green credit as a perspective and introduces it into the analytical framework of the impact of environmental regulation on corporate green innovation, through theoretical mechanism analysis and empirical testing, in order to reveal the impact and mechanism of green credit on corporate green innovation. It is found that green credit can effectively promote green innovation in heavy polluting enterprises, and it is mainly reflected in the increase of green utility model patent applications with a low degree of inventiveness. The promotion effect of green credit on green innovation is more obvious in regions with lower levels of economic development. Further mechanism analysis shows that green credit policy promotes green innovation of heavy polluting enterprises mainly through the incentive effect brought by changing financing environment and the pressure effect brought by increasing market competition. The findings of this paper can provide references for policy-making departments, banks and enterprises.

Large technology firms have substantial advantages in data, a key non-rival input for developing AI technology. We argue that investments by large technology firms stimulate innovation by AI startups through the sharing of data, bringing more than money to the startups. We assemble a unique dataset containing (nearly) the universe of AI-inventing firms in China to examine the innovation effects of these investments. Our difference-in-differences estimation shows that, after receiving investments from large technology firms, AI startups increase the number of AI patent applications by 62% and the number of software products by 56%, relative to their mean values prior to the investments. Using a triple-differences strategy, we further find that the innovation impact of investments by large technology firms is stronger than that of investments by other firms without data advantages. We confirm these findings using an instrumental variables approach based on recent investments by large technology firms in peer startups. Finally, we provide novel evidence that the innovation effect works mainly through sharing non-rival data by leveraging our rich information on non-AI data-related patent applications and data-related online job postings.

Government subsidies are often used to stimulate environment-friendly investment. We find that Chinese firms reduce green investment as the uncertainty of subsidies rises. This effect is identified from weather-driven fluctuations in air pollution that lead to fluctuations in subsidy allocations: Firms in cities where weather-driven subsidy uncertainty is high engage in less green R&D investment, patent applications, and research staff. Industries that are heavy emitters and those focused on environmental technologies are more affected. The results suggest that policy uncertainty may originate not only from political and macroeconomic shocks but from behavioral mechanisms that link policy to salient recent conditions.

We examine the impact of mixed ownership on the performance of venture capital (VC) firms in China. We use successful/unsuccessful exits from VC-financed entrepreneurial companies and number of patent applications by VC-financed companies as proxies for VC firms’ performance. Consistent with existing research on the inferior performance of SOEs relative to non-SOEs, we find that on average government-controlled VC firms (GVCs) underperform domestic private investors-controlled VC firms (PVCs). More importantly, we find that introducing minority private investors (i.e., mixed ownership) helps improve the performance of GVCs. However, we find no evidence that introducing minority government investors (i.e., mixed ownership) helps improve the performance of PVCs. Our results provide relevant information to the ongoing debate on the role of the government investors and private investors in developing the VC industry in emerging markets.