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Purpose This paper aims to investigate whether and how air pollution affects auditor behavior and audit quality. Specifically, the authors draw from studies of behavioral economics and psychology to develop a new prediction that air pollution-induced negative mood causes pessimistic bias in auditors’ risk assessments of client firms, which motivates them to put more effort into achieving higher audit quality. Design/methodology/approach This study uses a sample of Chinese public firms for the period 2013 to 2018 and an ordinary least squares model to examine the effects of air pollution on audit quality. Findings The results suggest that auditors exposed to higher levels of air pollution are more likely to put more effort into their audits, resulting in higher audit quality. Furthermore, the impacts of air pollution on audit quality are more pronounced when an auditor has a higher level of education, a major in accounting or a related subject and a position as a partner. A series of identification tests and sensitivity tests further support the main findings. Practical implications This study provides deeper insight into how air pollution affects auditors’ decision-making through its effect on mood. Social implications The findings have broad potential implications for auditing and other high-skill professions. Because air pollution-induced negative mood is a common occurrence and numerous psychological experiments have demonstrated the potentially adaptive and beneficial role of negative mood in decision-making for professions like auditing that need a more conservative, alert and detail-oriented cognitive style, negative mood may to some extent facilitate decision-making. Professionals may benefit from paying closer attention to the adaptive benefits of different moods. Originality/value Few studies empirically discuss the effects of auditors’ psychology on audit outcomes. This study responds to this research gap with analyzes of how air pollution-induced negative mood can affect auditors’ professional judgment and audit outcomes. Further, this study adds to the growing literature that examines how air pollution affects various aspects of the economy and enriches the literature on behavioral economics, providing empirical evidence from a large sample of the effects of an environmental stressor on individual auditors’ professional judgment.

In this paper, we propose an improved partial bundle method for solving linearly constrained minimax problems. In order to reduce the number of component function evaluations, we utilize a partial cutting-planes model to substitute for the traditional one. At each iteration, only one quadratic programming subproblem needs to be solved to obtain a new trial point. An improved descent test criterion is introduced to simplify the algorithm. The method produces a sequence of feasible trial points, and ensures that the objective function is monotonically decreasing on the sequence of stability centers. Global convergence of the algorithm is established. Moreover, we utilize the subgradient aggregation strategy to control the size of the bundle and therefore overcome the difficulty of computation and storage. Finally, some preliminary numerical results show that the proposed method is effective.

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