Amid intensifying ecological crises and evolving legal frameworks for the green transition, intellectual property protection (IPP) has emerged as an important institutional guarantee for promoting corporate green innovation and environmental responsibility. However, stronger green innovation incentives and market attention may encourage firms to pursue strategic patenting for institutional benefits, creating green innovation bubbles (GIBs). Using the National Intellectual Property Demonstration City Policy as a quasinatural experiment, this study employs a multiperiod DID model to examine the impact of IPP on corporate GIBs. Results reveal that IPP significantly reduces GIBs. Mechanism analysis demonstrates that this effect operates easing financing constraints. Heterogeneity analysis indicates that the effect is greater among firms with higher green media attention, legally experienced CEOs, and nonheavily polluting industries. This study has implications for improving intellectual property governance and promoting high-quality green innovation.
This study examines whether frontier-based efficiency indicators, integrated with explainable machine learning, improve corporate financial distress prediction across contrasting institutional environments in China and the United States. Using 12,000 firm-year observations from 2015–2022, we augment standard accounting ratios with sector-specific Data Envelopment Analysis scores and Malmquist productivity indices in gradient-boosting classifiers. Incorporating efficiency measures increases out-of-sample AUC from 0.79 to 0.83 in China and from 0.82 to 0.84 in the US. Under the study’s staggered predictor design, prior-year DEA scores provide distress-relevant information in capital-intensive sectors before current-year ratios are finalized. SHAP explanations exhibit greater ranking stability than LIME under structural breaks, particularly around the COVID-19 shock, and rank technical efficiency second by mean absolute SHAP value. These findings suggest that DEA-based signals can provide interpretable early-warning information for credit analysts, portfolio managers, and regulators, with greater incremental value in lower-transparency markets.
With the start of the digital economy, standardised development of data commerce has shown considerable advantages for local financial innovation. Panel data from the 31 provinces, autonomous regions and municipalities of China (excluding Hong Kong, Macao and Taiwan) from 2015 to 2024 are used in this paper for a panel regression analysis to investigate the impact of changes in data trade regulations on the level of regional financial innovation efficiency and to study the moderating effect of different regional data resource availability and digital governance capacity. Based on the above results, it can be seen that data transaction supervision will help boost the productivity of regional financial innovation and provide stable support for the development of financial innovation projects through explicit rules and factor guidance. The amount of data resources and the level of digital governance are both positively correlated with this link, and they work together. Good Digital Governance can make more effective use of the power of data assets and enhance the good effects of data transaction supervision. Heterogeneity: Due to data-transaction regulations in the East, the increase in the level of financial innovation has been more pronounced. Due to the initial constraints, there will be no joint regulations for the central and western areas. Based on the above research results, some support has been provided for the optimisation of the data-transaction supervision system and high-quality development of digital finance and all-round construction of regional financial systems has been promoted.
Corporate environmental investment captures firms’ realized use of resources for environmental projects rather than the source of financing. Using 4,139 firm-year observations from 681 Chinese A-share listed firms during 2016–2024, this study examines the association between corporate environmental investment intensity (CEII) and Huazheng ESG ratings using firm fixed-effects models. CEII is positively associated with overall ESG ratings and each E, S, and G pillar. Supplementary regressions show that CEII is associated with lower normalized borrowing costs and greater R&D investment; borrowing costs are negatively associated with ESG ratings conditional on CEII, whereas the conditional R&D coefficient is positive but insignificant. The association remains positive after pooled 1%–99% winsorization and industry-by-year fixed effects. On a common 3,297-observation sample, lagged CEII remains positively associated with subsequent ESG ratings, but with smaller coefficients than the contemporaneous specification. All results are interpreted as conditional associations rather than causal effects.
