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The purpose of this study is to investigate the main effect of top executive smog risk perception on green innovation and to examine the mediating role of corporate social responsibility (CSR) in the positive relationship between smog risk perception and green innovation along with the moderating role of smog knowledge.

A theoretical framework is developed based on the upper echelons theory to argue that top executive smog risk perception can be closely related to green innovation. Hierarchical analysis is conducted using a sample of eight firms in China.

Hypothesis testing indicates that physical health risk perception and mental health risk perception positively affect green innovation, and that these effects are positively mediated by CSR. In addition, smog knowledge moderates the relationship between physical health risk perception and green innovation.

The findings extend current studies on green innovation by highlighting the role of top executives’ perceptions beyond studying top executives’ attributes. The findings suggest that top executives should actively respond to smog pollution and fulfill CSR.

Previous studies have suggested that top executives’ demographic characteristics are the determining factors of green innovation. This empirical paper fills a gap in the literature by exploring the impact of top executive smog risk perception on green innovation within the framework of the upper echelons theory.

In recent years, domestic smog has become increasingly frequent and the adverse effects of smog have increasingly become the focus of public attention. It is a way to analyze such problems and provide solutions by mathematical methods.

This paper establishes a new gray model (GM) (1,N) prediction model based on the new kernel and degree of grayness sequences under the case that the interval gray number distribution information is known. First, the new kernel and degree of grayness sequences of the interval gray number sequence are calculated using the reconstruction definition of the kernel and degree of grayness. Then, the GM(1,N) model is formed based on the above new sequences to simulate and predict the kernel and degree of the grayness of the interval gray number sequence. Finally, the upper and lower bounds of the interval gray number are deduced based on the calculation formulas of the kernel and degree of grayness.

To verify further the practical significance of the model proposed in this paper, the authors apply the model to the simulation and prediction of smog. Compared with the traditional GM(1,N) model, the new GM(1,N) prediction model established in this paper has better prediction effect and accuracy.

This paper improves the traditional GM(1,N) prediction model and establishes a new GM(1,N) prediction model in the case of the known distribution information of the interval gray number of the smog pollutants concentrations data.

Smog seriously affects the ecological environment and poses a threat to public health. Therefore, smog control has become a key task in China, which requires reliable prediction.

This paper establishes a novel time-lag GM(1,N) model based on interval grey number sequences. Firstly, calculating kernel and degree of greyness of the interval grey number sequence respectively. Then, establishing the time-lag GM(1,N) model of kernel and degree of greyness sequences respectively to obtain their values after determining the time-lag parameters of two models. Finally, the upper and lower bounds of interval grey number sequences are obtained by restoring the values of kernel and degree of greyness.

In order to verify the validity and practicability of the model, the monthly concentrations of PM2.5, SO2 and NO2 in Beijing during August 2017 to September 2018 are selected to establish the time-lag GM(1,3) model for kernel and degree of greyness sequences respectively. Compared with three existing models, the proposed model in this paper has better simulation accuracy. Therefore, the novel model is applied to forecast monthly PM2.5 concentration for October to December 2018 in Beijing and provides a reference basis for the government to formulate smog control policies.

The proposed model can simulate and forecast system characteristic data with the time-lag effect more accurately, which shows that the time-lag GM(1,N) model proposed in this paper is practical and effective.

Based on interval grey number sequences, the traditional GM(1,N) model neglects the time-lag effect of driving terms, hence this paper introduces the time-lag parameters into driving terms of the traditional GM(1,N) model and proposes a novel time-lag GM(1,N) model.

While existing research explores the impact of audit market competition on audit fees and audit quality, there is limited investigation into how competition in the audit market influences auditors' writing style. This study examines the relationship between audit market competition and the readability of audit reports in Iran, where competition is particularly intense, especially among private audit firms.

The sample comprises 1,050 firm-year observations in Iran from 2012 to 2018. Readability measures, including the Fog index, Flesch-Reading-Ease (FRE) and Simple Measure of Gobbledygook (SMOG), are employed to assess the readability of auditors' reports. The Herfindahl–Hirschman Index (HHI) is utilized to measure audit market competition, with lower index values indicating higher auditor competition. The concentration measure is multiplied by −1 to obtain the competition measure (AudComp). Alternative readability measures, such as the Flesch–Kincaid (FK) and Automated Readability Index (ARI) are used in additional robustness tests. Data on textual features of audit reports, auditor characteristics and other control variables are manually collected from annual reports of firms listed on the Tehran Stock Exchange (TSE).

The regression analysis results indicate a significant and positive association between audit market competition and audit report readability. Furthermore, a stronger positive and significant association is observed among private audit firms, where competition is more intense compared to state audit firms. These findings remain robust when using alternative readability measures and other sensitivity checks. Additional analysis reveals that the positive effect of competition on audit report readability is more pronounced in situations where the auditor remains unchanged and the audit market size is small.

This paper expands the existing literature by examining the impact of audit market competition on audit report readability. It focuses on a unique audit market (Iran), where competition among audit firms is more intense than in developed countries due to the liberalization of the Iranian audit market in 2001 and the establishment of numerous private audit firms.

The purpose of this study is to explore whether pension plan reporting readability affects earnings volatility. Moreover, as SFAS 158 requires firms to fully recognize their funded status on the balance sheet, the firms’ pension liabilities (inside debts) and financing ability have the corresponding change. This study further investigates whether pension plan reporting readability affects earnings volatility from the SFAS 158 and funded status perspectives.

This study follows Li (2008), Lehavy et al. (2011) and Rennekamp (2012) to use the FOG and SMOG variables as the readability proxies and investigates whether pension plan reporting readability affects earnings volatility from the perspectives of inside debts and SFAS 158 by using a sample of 3,077 American firms from the year 2006 to 2009.

Empirical results of this study show that firms with low readability of pension plan reporting have high earnings volatility, revealing that less readable pension plan reporting increases the assessed variance of a firm’s inside debts, financing flexibility, investment ability and therefore profitability. In addition, the implement of SFAS 158 enhances the effect of pension plan reporting readability on earnings volatility. Moreover, the authors also find that the funded status plays a moderating role for the effect of pension plan reporting readability on earnings volatility. Finally, the results are robust to endogeneity issue.

Earnings stability measures how consistently earnings have been generated over time, and its importance has been acknowledged by most firms. For example, prior literature has documented that manipulating financial reporting to smooth earnings is becoming a business common practice (Burgstahler et al., 2006; Liu and Espahbodi, 2014). The empirical results suggest that pension plan reporting readability is a significant determinant of earnings volatility.

As a practical implication, this study points out that manipulations of the pension reporting readability are not costless. It incurs the costs of earnings instability.

This study indicates that the issuance of SFAS 158 makes firms more likely to engage in pension plan readability manipulation. As a result, it has policy implication that the regulator should consider how the policy change alters the firm financial reporting behavior.

The empirical results suggest that firms may be more likely to engage in obfuscating pension plan disclosure after FASB’s issuance of SFAS 158. This would further increase outside investors’ assessed variance for inside debts and earnings volatilities. When policymakers require firms to recognize their funded status in statement of financial position, they should consider the costs or benefits that the firm manager face and, therefore, how this policy change alter the firm financial reporting behavior.