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The authors examine the effect of climate risk on accounting conservatism for a sample of listed companies operating in 26 developing countries.

The authors employ the Climate Risk Index (CRI) developed by Germanwatch to capture the severity of losses due to extreme weather events at the country level. The authors use different approaches to measure firm-level accounting conservatism.

The authors find that greater climate risk leads to a lower level of accounting conservatism. The results hold even after using different estimation methods.

Although the authors' analysis is limited to the period 2007–2016, it could be helpful for standard setters such as International Accounting Standards Board (IASB) and International Sustainable Standards Board (ISSB) as they may consider the potential effect of climate risk in their international standards.

The negative impacts of climate risk on the quality of financial reporting as proxied by accounting conservatism could trigger regulators and standard setters to require disclosure of information relating to climate risks and to incorporate climate-related risks in their risk management systems. In addition, for policymakers, incorporating accounting conservatism as a financial quality reporting standard could help promote greater transparency, accuracy and reliability in financial reporting in the context of climate risk.

The authors add to the literature on international differences in accounting conservatism by showing that climate risk significantly affects unconditional and conditional conservatism. The authors' results provide fresh evidence of the dark side of climate change. That is, climate risk is shown to decrease financial reporting quality.

This paper aims to present a dynamic model for strategic and personalized decision-making in human resources (HR), using data mining techniques to enhance corporate social sustainability (CSS). The focus is on the interconnectedness of employee engagement (EE), enablement and the quality of work life.

The proposed model integrates various HR data, including demographic information, job specifications, payment and rewards, attendance and absence, alongside employees’ perceptions of their work-life quality, engagement and enablement. Data mining processes are applied to generate meaningful insights for senior and middle managers.

The study implemented the model within a production organization, revealing that factors influencing EE and enablement differ based on gender, marital status and occupational group. Performance-based rewards play a significant role in enhancing engagement, regardless of the reward amount. Factors such as “being recognized for competency” influence engagement for women, while payment has a greater impact on men. Engagement does not directly influence the quality of work life, but subcomponents like perceived transparency and the organization’s processes, particularly the “employee performance evaluation system,” improve work-life quality.

The findings are specific to the studied organization, limiting generalizability. Future research should explore the model’s effectiveness in different cultural and organizational settings.

The proposed model provides practical implications for organizations that enhance CSS. Organizations can gain insights into factors influencing EE and enablement by using data mining techniques, enabling informed decision-making and tailored human resource management practices.

This research addresses the societal concern regarding the impact of business activities on sustainability. Organizations can contribute to a more socially responsible and sustainable business environment by focusing on work-life quality and EE.

This paper offers a dynamic model using data mining and machine learning techniques for sustainable human resource management. It emphasizes the importance of customization to align practices with the unique needs of the workforce.