This paper examines the spillover effects of China’s Carbon Emissions Trading Scheme (CETS) on non-regulated firms’ bank loans. Using a sample of Chinese A-share listed firms and a staggered difference-in-differences design, we find that suppliers experience a significant decline in bank loans when their customers are included in the CETS. This effect is driven by reductions in firms’ cash flow and customer concentration. The negative effect of downstream CETS on suppliers’ bank loans is attenuated for suppliers with better environmental performance, more comprehensive carbon disclosure, and closer geographic proximity to customers. We also find that, in response to reduced bank credit, firms rely more heavily on trade credit. Overall, this study sheds new light on the unintended financial consequences of CETS policy on non-regulated firms.

We use a sample of China’s listed firms and employ a naïve Bayesian machine learning algorithm to reveal that environmental information intangibility superficially promotes green innovation. We demonstrate that this effect is channelled through the acquisition of institutional resources, including bank loans and government subsidies. The impact of environmental information intangibility on green innovation is most pronounced within state-owned enterprises, large firms, and politically connected firms. Furthermore, we confirm that environmental information intangibility does not lead to improvements in innovation efficiency or quality. This implies that green innovation may serve as a symbolic environmental activity. Our findings contribute to the understanding of the consequences of environmental information intangibility, greenwashing behaviour, and their relationship to green innovation.

We study how bank liberalization affects analyst forecast accuracy using two interest rate deregulations in China—the removal of the cap on bank lending rates in 2004 and the removal of the floor in 2013—as quasi-natural experiments. Our results show that the analyst forecast accuracy for high-risk firms decreases significantly after the removal of the lending rate cap, whereas analyst forecast accuracy for low-risk firms increases significantly after the removal of the lending rate floor. Moreover, interest rate liberalization affects forecast accuracy through operational risk and information asymmetry channels. Furthermore, the impact was concentrated on firms whose actual performance fell short of performance expectations and those that received more bank loans. Our findings imply that interest rate liberalization policies may have unintended consequences for analyst forecasts.

Utilizing the removal of the bank lending interest rate floor (IRFD) in China as an exogenous shock of banking liberalization, we find that IRFD leads to a significant rise in firms’ cost of equity capital, which is consistent with the prediction from the MM theory. The identified effects are more pronounced among firms with weaker ex-ante corporate governance and more severe ex-ante agency problems. We also find that IRFD witnesses an increase in the amount of acquired bank loans, a decrease in the average interest rate, and an increase in free cash flow. Further evidence also suggests IRFD provokes a drop in firms’ investment quality. Overall, our findings highlight an unexplored role of banking sector deregulation on firms’ cost of equity capital.

This study examines the impact of climate-transition risks on trade credits for Chinese listed companies from 2007-2017. We develop an index of county-level climate-transition risks faced by Chinese-listed companies using data on local carbon emissions and carbon sequestration when moving towards net zero carbon emissions. Our two-way fixed effects OLS regression results find that local firms facing greater climate-transition risks significantly reduce their trade credit financing. Specifically, a one standard deviation of increase in Risk leads to a 0.73% decrease in trade credit. This reduction is more pronounced for state-owned enterprises (SOEs), firms operating in less competitive industries, and those headquartered in regions without carbon trading markets. Our main finding is robust to a battery of sensitivity tests including the use of alternative measures and lagged independent variables. Results on an Instrumental Variable (IV) method and a differences-in-difference (DiD) analysis suggest a causal relationship between climate-transition risks on trade credit. Further analyses reveal two plausible channels for the effect: increased financial distress risk and enhanced access to bank credit.

Using a proprietary bank-loan-level dataset, we find a surprising negative relation between loan spreads and minimum wage. We propose a stylized model to explain the relation: banks filter out the low-quality borrowers after the wage shocks, resulting in a separating equilibrium. Our evidence is consistent with the model’s predictions: (1) city-level and firm-level evidence shows that an increase in minimum wage is negatively associated with the likelihood of obtaining bank loans, especially for labor-intensive borrowers, (2) deal-level evidence shows that both the average default rate and loan spreads decrease when minimum wage rises, and (3) subsequently, labor intensive firms that are still able to obtain bank loans when minimum wage rises outperform their peers. Our findings suggest that as more credit resources are allocated to better quality firms and leave other firms far more behind, the existence of such credit reallocation effects can exacerbate the divergence between higher and lower quality firms induced by an increase in minimum wages.

Using plant-level pollution data from Environmental Survey and Reporting Database in China, we analyze how the government's 2007 environmental policy aiming to curb pollution from two-high industries (i.e., high-polluting and high energy-consuming) affect real firm pollution activities across the supply chain. We employ the Differences-in-Differences approach and find that following the 2007 environmental policy, firms in two-high industries that are mostly in the upstream of the supply chain indeed reduce emissions of air and water pollutions significantly (e.g., sulfur dioxide (SO2), chemical oxygen demand (COD), and wastewater). Such reductions are mainly due to higher pollution removal and the increased investments in treatment facilities that are funded by subsidized bank loans such as the China Development Bank (CDB). By contrast, following the 2007 policy, firms in the downstream of two-high industries increase their production levels without any increases in their pollution treatment facilities and capabilities, which leads to increased pollution levels such as SO2 emissions and coal consumption. Furthermore, such adverse spillover effects in downstream industries can be alleviated by CDB loans, which help finance the investments in pollution treatments for these downstream firms. Our findings about direct and unintended spillover effects of the environmental policy suggest that policymakers should carefully consider all potential impacts across the supply chain when designing the package of environmental policies to mitigate unintended adverse consequences.

This paper studies the effect of top management team (TMT) network centrality on bank loan contracts. We show that firms with high TMT network centrality obtain bank loans with lower loan spreads, larger loan size, longer maturity, and fewer collateral requirements. From the mediating effect analysis, we find that TMT interlocking networks affect loan pricing by reducing agency costs, improving the quality of accounting information, expanding resource channels, and enhancing the credibility of companies. In addition to easing financial constraints, TMT network centrality is also beneficial to investment efficiency and innovation output of corporates, but it will decrease firm performance.

We study how government intervention in the bank loan granting process affects firms’ leverage dynamics. We exploit the setup of administrative approval centers (AACs) in China, a program aiming to reduce bureaucracy in business activities, as a quasi-natural experiment. On average, AACs help to shorten the leverage rebalancing period by as much as a quarter. This acceleration pattern persists in under-leveraged firms, which issue more debt to rebalance accordingly. Cross-sectional analyses show that the positive effect of AACs on leverage adjustment is more pronounced for firms that are in poorer legal environment, with more financial constraints, or less politically connected. ics.

This paper uses a loan transactions dataset from China to identify whether shadow loans cost more than formal bank loans even with collateral. This motivates us to explore the reasons as to why a listed firm would opt for such loans. Using propensity-score matched data, we find that privately-owned firms with shadow loans are forced to obtain these loans since they are politically discriminated following a regulation change in 2009 that favoured state-owned firms. However, state-owned firms obtain shadow loans due to their inferior firm characteristics. Further, we employ a Difference-in-Differences methodology to uncover that privately-owned firms experience a decline in their performance, investment growth and an increase in default probability following their high dependence on shadow loans when they are excluded from the formal loan market. The above results survive various robustness checks, including doubly-robust inverse-probability weighted Difference-in-Differences regressions.