Traditional inflation research has mainly focused on the macro transmission of the inflation channel, but has paid insufficient attention to the micro interaction characteristics among economic agents. This study believes there exists a dynamic coupling relating among corporate pricing strategy and household inflation expectations. By using the empirical data of Bank of Canada's Household Survey and US Consumer Confidence Index, this study examines their relationship and operational mechanisms. Households differ significantly in their requested price increases based on education and income levels. This divergence results in large differences in consumption patterns, which similarly affects the setting of price levels, thus forming a closed-loop phenomenon of "expectations-pricing-revisited expectations", and the effects of policy intervention are increasingly difficult to predict. In this context, it is necessary to account for individual differences and improve public trust through two-way communication. This enhances resource allocation efficiency and strengthens macroeconomic stability on a broader scale. By exploring inflation formation based on micro-interactions, this study aims to establish a new theoretical and empirical foundation for inflation governance.

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Environmental, Social and Governance( ESG) investment has attracted more and more attention in the field of green finance. However, The nonlinear and heavy tail nature of the risk associated with ESG assets is not well represented by traditional asset risk measures such as mean–variance model. The paper fills an important void in current research regarding the reason for underestimating the risk of ESG assets using standard models. A new composite model based on exponential finance theory and actuarial analysis is developed here.We achieve this by combining the methods of dynamic sensitivity analysis and probabilistic, time-consistent valuation. Methodologically, we extend a single-index model with an explicit ESG risk factor, and we construct an ESG Adjusted Conditional Value at Risk (ESG-CVaR) metric. Using historical data of constituents of the S&P100 and green industry classifications, our formulation is empirically validated using a portfolio optimization with realistic constraints.The limits are the ESG score and leverage ratio. Empirical results reveal that the ESG risk factor is significant on some levels. The green stocks have negative skewness and fat tail, which can not be captured in the classic model. The CVaR with ESG always has larger value of risks than the classic VaR.stressing on the materiality of extreme ESG events.The optimized portfolio built with respect to the proposed model is more resilient, having a smaller maximum draw down in case of market stress related to the sustainability issue. To conclude, Applying actuarial methods to financial risk modelling provides us with an intuitive, powerful way of addressing the issue of ESG risk.

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With the rise of digital technologies such as big data, artificial intelligence, and cloud computing, digital trade has increasingly transcended the temporal and spatial constraints inherent in traditional trade, emerging as a vital engine for global economic growth. In the digital economy context, traditional tax governance models confront difficulties in regulating tax avoidance practices by digital enterprises that decouple the location of user value creation from that of revenue generation. This has led to a significant imbalance between the profits digital enterprises earn in market countries and their corresponding tax contributions. This paper analyzes the current state of digital taxation in China, further examines the benefits and drawbacks of digital tax collection for different stakeholders, and explores a balanced approach to taxation that accommodates multiple interests. The discussion covers the following aspects: differences between the digital economy and the physical economy, current tax avoidance methods in the digital economy, and inequality issues arising from the tax framework. The paper concludes that a dual and relatively high-threshold regulatory mechanism should be established, and developing countries should be permitted to implement differentiated policies.

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Mainstream accounting automation technologies are mainly divided into robotic process automation (RPA) and generative artificial intelligence (GenAI). RPA is a relatively mature technology that has been widely used, especially for standardized and repetitive tasks, whereas GenAI is an emerging technology that has demonstrated strong potential and remarkable capabilities. This paper employs a structured literature review approach, primarily covering relevant literature from 2020 to 2025, and compares the application performance of RPA and GenAI in accounts receivable and expense reimbursement processes. Regarding accounts receivable, efficiency, accuracy, and reliability are used as key evaluation criteria. For expense reimbursement, the comparison emphasizes flexibility, error-handling capabilities, and user interaction. The findings indicate that RPA demonstrates superior consistency and stability in standardized, repetitive task processes, remaining dominant in tasks such as invoice matching and cash receipts. GenAI significantly outperforms RPA in ambiguous scenarios that involve unstructured receipts, policy interpretation, and anomaly detection. The study concludes that a hybrid adoption strategy based on task characteristics and data structure is more effective than relying on either technology alone. This research provides empirical guidance for accounting practitioners in technology selection and highlights the importance of developing RPA-GenAI hybrid architecture in future accounting functions.

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