Under the new development model, the digital economy has become a new engine to promote the green development of the economy and realize the goal of “double carbon”. Based on the panel data of 30 provinces in China from 2010 to 2020, this paper empirically investigates the impact of the development of the digital economy on energy and carbon emissions using a series of econometric models such as baseline regression, a mechanism test, and the spatial Durbin model, etc. Common prosperity plays an intermediary role between digital economy development and carbon emissions; digital economic development optimizes resource allocation, effectively solves the problem of uneven resource distribution, and reduces energy and carbon emissions while achieving common prosperity. In addition, green innovation, industrial structure, urbanization level, R&D intensity, and the degree of marketization also have different degrees of influence on energy and carbon emissions. Therefore, the government should accelerate the construction of new digital infrastructure and implement the digital economy development strategy according to local conditions, so as to promote the digital economy to produce a more significant carbon emission reduction effect.

In the face of increasingly severe global environmental challenges, corporate environmental information disclosure (CEID) has become a critical link connecting national ecological governance goals with firms’ green development practices. From the perspective of green signaling, this study examines whether government environmental concerns (GEC) in China incentivize CEID and the mechanisms underlying this effect. We theoretically elaborate the transmission pathways and moderating effects of GEC, and measure GEC and CEID indicators using text analysis of local government work reports and corporate annual reports. Based on a series of empirical tests on Chinese A-share listed firms from 2008 to 2023, we find that: (1) GEC can significantly enhance CEID by attracting green investors and fostering greater media scrutiny. (2) Green technological innovation exhibits a masking effect, which reveals a counterintuitive mechanism whereby stringent environmental regulation may divert innovation resources toward pollution control investments. (3) The impact of GEC is positively moderated by external volatility such as climate policy and market uncertainty and internal capabilities such as firms’ digital transformation. (4) Further heterogeneity analysis shows that GEC has a more significant impact on non-state-owned enterprises, enterprises in heavily polluting industries, and those in the mature or declining stage. This study provides a new theoretical lens for understanding the dynamic interplay between institutional pressure and corporate behavioral responses, and offers empirical insights for calibrating the intensity of GEC to maximize incentives for firms to engage in sustainable practices.

Purpose: Green credit has emerged as a crucial economic tool for achieving a green and low-carbon transition. However, the relationship between green credit and bank credit risk, particularly in China, remains underexplored. In this regard, this study investigates how green credit balance (GCB) exacerbates the non-performing loan ratio (NPLR) in Chinese commercial banks. Design/methodology/approach: Based on the data of 30 listed commercial banks (6 state-owned large commercial banks, 11 joint-stock commercial banks, 10 urban commercial banks and 3 rural commercial banks) in China from 2012 to 2022, this study constructs two-way fixed regression model, threshold effect model, and moderating effect model to investigate the non-linear correlation between green credit and commercial bank credit risk and its influence mechanism. Findings: The study finds: (1) There is a substantial inverted U-shaped relationship between GCB and NPLR, and China is currently in the rising stage of NPLR. (2) The leverage ratio of the bank (LEV) plays a significant double-threshold effect. As the LEV escalates, the amplifying impact of GCB on NPLR exhibits a dynamic progression, characterized by an initial surge in potency, culminating in a gradual attenuation over time. (3) The M2 growth ratio (M2R) plays an important moderating effect. When M2R increases, the inverted U-shaped effect of GCB on NPLR is weakened, and the volatility of NPLR is reduced. (4) Banks with larger assets and higher profitability are more effective in managing the credit risk of green loans. Practical implications: We make the following policy recommendations: (1) Commercial banks should continue to smoothly expand the scale of green credit, actively optimize their asset-liability structure, and strengthen industry cooperation. (2) The central bank should play the role of macro-control and make timely adjustments to the money supply. (3) Financial regulators should play a supervisory function to ensure that banks operate in compliance and avoid systemic risks. Originality/value: The marginal contributions of this study mainly include: (1) This study collected and collated the data of 30 listed commercial banks in China and updated the research time to 2022, which solved the limitations of previous studies such as older sample years, unupdated data and insufficient consideration of bank stock types. (2) This study breaks through the traditional research on the linear impact of green credit, innovatively constructs a bidirectional fixed model with quadratic terms, and finds that there is a significant inverted U-shaped relationship between green credit and credit risk. This exploration has greatly changed the risk management strategy of banks. (3) This study is not limited to elaborating on the development of green credit from a theoretical perspective, but further explores the influence mechanism between the two using econometric models to provide a reference for other relevant empirical evidence on green finance.