This study focuses on women's vocational training policies and systematically compares differences in policy objectives, implementation mechanisms, gender responsiveness, and social outcomes across Shanghai, the UK, and Hong Kong through textual analysis and empirical comparison. The findings reveal three key trends: Shanghai’s policies prioritize adaptability to new employment forms and migrant women’s needs; the UK faces structural challenges of gendered occupational segregation; and Hong Kong uses flexible mechanisms to address the balance between family care and employment. Based on these insights, the study proposes a women’s training policy system centered on demand precision, mechanism flexibility, and gender mainstreaming to support migrant women’s career development.

Due to the growing demand for marine resources, deep-sea mining has become one of the most important areas of global maritime policy. However, ambiguous provisions in international maritime law continue to pose a regulatory challenge. This study focuses on the application of international maritime law to the exploitation of mineral resources in the deep sea and is based on document analysis and case studies. This study examines two key questions: the specific limitations of existing international maritime law (represented by the United Nations Convention on the Law of the Sea) with regard to the regulation of deep-sea mining, and how to improve legal framework to ensure more effective governance. The results reveal several shortcomings in current international maritime law, including unclear delineation of responsibilities and inadequate regulatory mechanisms for deep-sea mining. The study ultimately proposes targeted improvement measures, such as revising treaty provisions and establishing a comprehensive regulatory platform, to meet practical requirements in the field of deep-sea mining management.

This paper explores the application and empowerment paths of Intelligent Innovation Finance in enterprise Business-Finance Integration in the era of digital economy. It elaborates on the concepts and development status of Intelligent Innovation Finance and Business-Finance Integration, analyzes their interactive relationship of mutual promotion and common development, and on this basis, proposes specific paths for Intelligent Innovation Finance to empower Business-Finance Integration. The research shows that Intelligent Innovation Finance reshapes the model of enterprise Business-Finance Integration, while Business-Finance Integration promotes the upgrading of Intelligent Innovation Finance. Enterprises should drive Intelligent Innovation Finance to empower Business-Finance Integration through five core dimensions: strategy-oriented value co-creation, technology-driven intelligent platform construction, efficiency-oriented process optimization and reengineering, data governance system based on quality and security, and organizational capability development supported by people and culture. This will enable the efficient digital transformation of enterprise financial management, thereby enhancing the value creation of enterprise financial management and the overall competitiveness of enterprises in the era of digital economy.

This article critically evaluates the paradoxical outcomes of Structural Adjustment Programmes (SAPs) in the 1980s and 1990s. While widely deemed a failure for precipitating stagnant growth, rising poverty, and social unrest in developing nations, this study engages with Ruccio’s provocative thesis that SAPs were ultimately successful in reconstituting capitalist class relations. Employing a Marxist class analysis framework, this research conducts a comparative case study of Argentina and Ghana - two prominent subjects of SAPs. It specifically examines how reforms pertaining to privatisation, fiscal austerity, and market liberalisation altered class structures through indicators such as unemployment, wage shares, inequality, and property ownership. The analysis concludes that despite their developmental shortcomings, SAPs consistently facilitated a redistribution of power and income towards capital, thereby validating Ruccio’s claim that their core, if unstated, function was the reinforcement of a capitalist class process.

This article focuses on the environmental, social and governance (ESG) dimension, exploring the monthly return and stock characteristics in different ESG groups and the corresponding investment strategies. Using monthly firm-level data, we cross-sectionally sort companies each year into five ESG groups (from high to low), compute weighted portfolio returns, and cumulative performance through compounding. We find that the low-ESG (“brown”) group delivers stronger short-term returns and has consistently outperformed the high-ESG group since 2015, with a monotonic pattern across the middle groups; however, the low-ESG advantage is subject to occasional style rotations. In cross-sectional regressions, ESG is positively related to firms’ profitability signals (ROA/ROE), while shows a significantly negative correlation with next-month stock returns---This negative relation becomes smaller at a 12-month horizon. Guided by these facts, we design a dynamic trading portfolio that long low ESG firms and short High-ESG firms. The study offers evidence on the short-run pricing of ESG and provides an implementable framework for portfolio construction under ESG grouping.

Under the background of China’s dual carbon goals and the high-quality development of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), strategic emerging industries (SEIs) have become pivotal to low-carbon transformation. However, there is lacking a targeted carbon performance evaluation system. This paper constructs a carbon performance evaluation system for GBA’s SEIs based on publicly disclosed data, adhering to the principles of validity, availability, and relevance. The system comprises three core dimensions: emission reduction efficiency, green manufacturing and innovation, and green information disclosure. Besides, the paper takes ZTE Corporation, a GBA listed SEI company, as a case study, collecting its relevant data from 2022 to 2024 to verify the applicability of the evaluation. This study enriches the theoretical framework of carbon performance evaluation for SEIs and provides a practical tool for companies’ low-carbon management.

The literature on green finance has grown rapidly in recent years with the inclusion of global dual-carbon targets, and policy signals are making significant contributions to the field and play an essential institutional driving force in the era of green capital markets. Green finance policies not only serve to regulate corporate investments and environmental disclosures but can also affect auditors' risk perception and judgment. The current study takes data from China's total A-share listed companies from 2013 to 2024 to set up the green finance policy signal intensity index and adopt event studies and dynamic panel regression methods to examine dynamic reactions to policy signal intensity in external audit quality. The results show that there is substantial positive correlation between policy signal intensity and audit quality with stronger correlations in heavily polluted industries and state-owned enterprises, thereby demonstrating that policy pressure promotes auditors' independence and prudence in terms of market expectations and risk identification mechanisms. The current study explains the dynamic interaction between policy signals and market disciplines in auditors' reactions and provides empirical evidence for formulating green audit standards and green regulation implementation and management guidance for related regulators in green finance policy implementation and management.

This study examines the relationship between firm age and initial public offering (IPO) underpricing in the Australian market, using a sample of 35 companies listed on the Australian Securities Exchange between 2018 and 2022. The central objective of this study is to contribute to the ongoing discussion by rigorously testing the hypothesis that there is a negative correlation between the age of the company listing and the degree of underpricing experienced. The main test variable of this study is the natural logarithm of the company’s age, measured by the number of years between the company’s establishment and listing date. This model acknowledges the multifactorial nature of IPO pricing and introduces several control variables to isolate the impact of company age. These controls include Tobin’s Q, acting as a proxy for growth opportunities and market valuation; the natural logarithm of total assets, representing firm size; a binary variable indicating whether the company is audited by the Big Four accounting firms (BigN), reflecting audit quality and credibility; and the natural logarithm of board size, indicating the corporate governance structure. The empirical results are consistent with the main hypothesis: the underpricing experienced by older companies tends to be less severe. However, this relationship does not reach statistical significance at any conventional level. This study finds no statistically significant evidence to support the view that company age is a key determinant of IPO underpricing in the recent Australian market landscape. The low explanatory power of the model highlights the limitations of the study. Conversely, these limitations point to directions for future research, which should aim to use larger cross-border datasets and integrate a more comprehensive set of explanatory factors to better reveal the complex dynamics of IPO underpricing.

The study presents a semiparametric ARIMA model that combines high-frequency payment data with inflation nowcasting to enhance real-time macroeconomic tracking. The model combines the linear ARIMA model to capture the deterministics of inflation with respect to time with the nonlinear model to capture payment data-driven shocks and regimes in which the models present different nonlinear relationships. The study applies the semiparametric ARIMA model to the structurally simulated data environment from January 2016 to June 2024, with the data being daily digital payment transactions in the consumption sectors of four different sectors, yielding high precision with good interpretability. The model shows improvement in the out-of-sample test with the reduction in Root Mean Square Error of 23.5% ± 2.8% and mean directional accuracy of 12% with the harmonic average-function-adjusted lead of 4.1 ± 1.0 days over ARIMA models with comparable performance on turning points in inflation data. The model properly extracts volatility in the periods in which there was the pandemic and policy tightening periods while exhibiting consistent seasonality in the deterministics model of ARIMA in tracking inflation data in real time.

In response to the recurrent financial crises and persistent asset price fluctuations, policy makers have increasingly questioned the sufficiency of traditional monetary policy rules, such as the Taylor Rule, in safeguarding both asset price and financial stability. This paper explores how the basic Taylor rule, which focuses on the inflation gap and output gap, ignores the significance of U.S. financial cycles including credit expansion, housing market fluctuations and stock market volatility. It proposes an augmented Taylor rule that adds some key financial cycle indicators as an analytical tool for enhancing macroprudential stability. The paper aims to provide the Federal Reserve with a policy rule suggestion that strikes a balance between maintaining price stability and enhancing financial system robustness. Empirical results show that the augmented rule, which includes a financial cycle gap, significantly improves model fit (R² = 0.822) and reveals a significant negative policy response to financial stress. Counterfactual simulations indicate that adherence to this rule could have prompted pre-emptive interest rate hikes before the 2008 crisis and deeper cuts thereafter. Welfare analysis demonstrates a 63.6% reduction in total social loss compared to the baseline Taylor rule. It concludes that systematically incorporating financial cycle indicators enhances macroprudential stability and provides a more robust framework for monetary policy, though effectiveness may be constrained at the zero lower bound.