China Journal of Accounting Research最新文献

In this study, we examine the peer effect on climate risk information disclosure by analyzing A-share listed companies in China. We find that industry peers influence target firms’ climate risk information disclosure through active (passive) imitation resulting from cost–benefit considerations (institutional pressures). Leader companies are more likely to be emulated by within-industry follower companies and target firms prefer to learn from similar within-industry firms. Executive overconfidence and performance pressure negatively affect target firms’ willingness to emulate their peers. Finally, the peer effect of climate risk information disclosure demonstrates a regional aspect. Our findings have implications for reasonable climate risk information disclosure at the micro level and effective regulation to move toward achieving carbon peak/neutrality at the macro level.

The application of big data technology to global tax management is becoming increasingly widespread. China has been implementing increasingly mature technologies for tax governance using big data systems in recent years. By collecting data through web scraping on the earliest implementation times of big data tax administration in various provinces of China, we explore the relationship between big data tax administration and corporate bank credit in emerging markets. Our results show that big data tax administration enhances firms’ ability to obtain bank loans. Mechanism tests indicate that big data tax administration improves the quality of corporate information disclosure, facilitating access to bank credit loans. We find that big data tax administration improves the corporate financing environment, enhancing the efficiency of resource allocation in the credit market.

To improve the usefulness of audit opinions, on 23 March 2021, the China Securities Regulatory Commission mandated that auditors disclose overall quantitative materiality of consolidated financial statements in special explanations of modified audit opinions. This paper selects Chinese A-share companies issued with modified audit opinions for the period of 2020–2022 as the research sample and analyzes the assessment of materiality in audit practice and the informativeness of audit materiality. Our findings are as follows. (1) The most commonly used bases for materiality by auditors are profit and income, with considerable differences in the percentages applied to the different bases and variations even within the same base. (2) The higher the materiality amount, the poorer the audit quality. This negative correlation is mainly observed in scenarios where the audited companies engage in downward earnings management and where the competency of audit firms or auditors is relatively low. (3) Companies that disclose quantitative materiality in the special explanations of modified audit opinions have a lower earnings response coefficient than companies that do not disclose audit materiality. This research sheds light on the “black box” of the audit process and verifies the information value of audit materiality. The conclusions are of significant value to auditing standard-setters, investors and regulators.

The deep integration of artificial intelligence (AI) into enterprises presents both opportunities and challenges, making it a focal point of current research. This study explores the impact of AI on corporate risk-taking, using data spanning 2010–2019 from A-share listed companies in China. Our findings suggest that AI significantly heightens companies’ level of risk-taking. Furthermore, financing constraints can amplify the relationship between AI and risk-taking, enhancing their sensitivity correlation. AI also significantly improves firms’ investment efficiency and mitigates their underinvestment issues. Finally, mediation tests indicate that AI enhances risk-taking by diminishing firms’ risk perception. Overall, we offer valuable insights into and references for accelerating the deep integration of AI into enterprises.

In this study, we take a machine learning-based approach to measure institutional investor attention to corporate social responsibility (CSR) issues when communicating with firms during site visits. We find that institutional investors can effectively enhance CSR performance through CSR-related communication. This effect remains robust to various checks and is more pronounced for non-state-owned enterprises and firms with lower levels of institutional ownership and in periods following the issuance of Green Investment Guidelines. We also identify information asymmetry and financing constraints as the two mechanisms underlying this effect. Overall, our findings highlight the importance of private interactions between management and institutional investors in promoting CSR.

This study investigates whether and how customer firms’ environmental, social and governance (ESG) performance impacts suppliers’ green innovation quality using a sample of Chinese A-share listed companies from 2009 to 2022. We find that customers’ ESG performance facilitates suppliers’ green innovation quality through green learning and corporate competition. Additional tests indicate that customers with stickier customer–supplier relationships and a more central position in the supply chain network than peers enhance suppliers’ green innovation quality. After categorizing whether customers engage in greenwashing, we determine that those adherence to green principles, genuinely promote suppliers’ green innovation quality. Finally, we find the above effect ultimately enhances suppliers’ environmental performance. This study provides valuable insights for supply chain companies into collaboratively achieving sustainable development.

This research contributes to understanding the spillover effect of customer digital transformation along the supply chain. We take a supply chain relationship perspective to explore the influence of customers’ digital transformation on suppliers’ audit fees and find a significant reduction in such fees when customers undergo digital transformation. An economic mechanism analysis reveals that this transformation reduces audit fees by lowering the risks and costs encountered by auditors. This is achieved by mitigating suppliers’ business risks and improving earnings quality. Heterogeneity analysis reveals that the impact of customers’ digital transformation on suppliers’ audit fees is more pronounced when the supply chain is geographically distant, suppliers with more specific investments and with high levels of market competition.

This study investigates the valuation and real effects of the mandatory disclosure of greenhouse gas (GHG) emission costs from the perspective of “double materiality.” We consider a firm with a Cobb-Douglas production function that combines GHG-related and non-GHG-related investments to produce short-term and long-term returns. In particular, the GHG-related investment entails short-term and long-term social costs of GHG emissions, including corporate costs and negative externalities. We demonstrate how the mandatory disclosure of the long-term costs of GHG emissions affects capital market valuations and corporate investment decisions relative to a non-disclosure regime. The social welfare in an accounting regime hinges on three parameters: the persistence of the short-term investment return, the ratio of the productivity of GHG-related investment to that of non-GHG-related investment, and the social cost parameter for GHG emissions. Our findings suggest that disclosing the long-term costs of GHG emissions may be detrimental to social welfare. Specifically, the non-disclosure regime results in higher social welfare than the disclosure regime for high values of these parameters.