In recent years, there has been widespread concern regarding the carbon footprint (CF) of food waste due to the key impact of CF on climate change, particularly as China’s food waste is rising with its economic development. China has the largest scale of higher education in the world, and the amount of food waste in university canteens is considerable and cannot be ignored. This study attempts to assess the carbon footprint (CF) of food waste at Chinese universities for the first time based on a national survey. It is estimated that 1.55 million tons of food were wasted in Chinese university canteens in 2018, based on 9,192 samples covering 29 provinces in China. The associated CF was 2.51 Mt CO2eq. The top two food categories contributing to the total CF were meat and grains, accounting for 46.28% and 36.52%, respectively. Furthermore, the location of the university was significantly associated with the CF of plate waste. It also indicated that household income, meal satisfaction, sex, education, meal days, and food-saving campaigns were important factors influencing the CF of food waste. This study highlights areas that can help reduce the environmental impact of plate waste. It also provides targeted measures to reduce the associated CF of food waste in Chinese universities.

Risk management has been a topic of great interest to Michael McAleer. Even as recent as 2020, his paper on risk management for COVID-19 was published. In his memory, this article is focused on bankruptcy risk in financial firms. For financial institutions in particular, banks are considered special, given that they perform risk management functions that are unique. Risks in banking arise from both internal and external factors. The GFC underlined the need for comprehensive risk management, and researchers since then have been working towards fulfilling that need. Similarly, the central banks across the world have begun periodic stress-testing of banks’ ability to withstand shocks. This paper investigates the machine-learning and statistical techniques used in the literature on bank failure prediction. The study finds that though considerable progress has been made using advanced statistical and computational techniques, given the complex nature of banking risk, the ability of statistical techniques to predict bank failures is limited. Machine-learning-based models are increasingly becoming popular due to their significant predictive ability. The paper also suggests the directions for future research.

The transformative impact of AI technologies on the financial sector has been a topic of increasing interest. This study investigates ChatGPT’s applications in financial reasoning and analysis and evaluates ChatGPT-4o’s effectiveness and limitations in conducting both basic and complex financial analysis tasks. By designing a series of multi-step, advanced reasoning tasks and establishing task-specific evaluation metrics, we assessed ChatGPT-4o’s performance compared to human analysts. Results indicate that while ChatGPT-4o demonstrates proficiency in basic and some complex financial tasks, it struggles with deep analytical and critical thinking tasks, especially in specialized finance areas. This study underscores the need for meticulous task formulation and robust evaluation in AI financial applications. While ChatGPT enhances efficiency, integrating it with human expertise is crucial for effective decision-making. Our findings highlight both the potential and limitations of ChatGPT-4o in financial analysis, providing valuable insights for future AI integration in the finance sector.

This study develops and tests several bonding theory-derived hypotheses using a panel dataset covering the Chinese firms cross-listed in the US, Hong Kong, Singapore and London over the period 2001–2012. Our empirical results challenge the general postulate of bonding theory that cross-listing in international stock markets helps to improve the corporate and financial performance of firms whose home countries have weaker institutions and lower-level legal enforcement to protect minor investors. We show that the bonding theory only holds partially for Chinese firms cross-listed in the United States markets, whereas it is not evidenced for the rest of share markets in the sample. It is argued that the general bonding theory needs to contextualise endogenous characteristics such as firms’ listing locations.