Low temperature is one of the most significant environmental factors that threaten the survival of subtropical and tropical plant species. By conducting a study, which was arranged in a completely randomized design with three replicates, the relative freezing tolerance (FT) of four Iranian pomegranate cultivars, including ‘Alak Torsh’, ‘Tabestaneh Torsh’, ‘Poost Sefid’, and ‘Poost Syah’, as well as its correlation with some biochemical indices, were investigated. From each cultivar, pieces of one-year-old shoot samples were treated with controlled freezing temperatures (−11, −14, and −17 ℃) to determine lethal temperatures (LT₅₀) based on survival percentage, electrolyte leakage, phenolic leakage, and tetrazolium staining test (TST) methods. Results showed that FT was higher in the second year with a lower minimum temperature and a higher concentration of cryoprotectants. The stronger correlation of electrolyte leakage with survival percentage (r = 0.93***) compared to the other three indices explained that this index could be the most reliable injury index in determining the pomegranate FT to investigate freezing effects. Of all four cultivars, ‘Poost Syah’ was the hardest by presenting a higher FT than ~ −14 ℃ in mid-winter. Accordingly, this pomegranate cultivar seems to be promising to grow in regions with a higher risk of freezing and to be involved in breeding programs to develop novel commercial cultivars.

Enterprise green innovation drives sustainable development and contributes to the realization of a ‘beautiful China’. It enhances resource utilization, reduces energy consumption, and achieves economic-environmental objectives through technological advancements. This paper examines the impact of the gender composition of a company’s CEO and CFO on green innovation by empirical research method using the data of the firms listed on Chinese capital market from 2015 to 2022. Our findings indicate that: (1) Male CEOs and CFOs are more likely to promote green innovation compared to their female counterparts; (2) Leadership teams comprising opposite-sex pairs tend to weaken the promotion of green innovation. These conclusions are consistent across state-owned enterprises and within the manufacturing sector. This study provides a novel perspective on enterprise green innovation, offering insights for companies regarding their green innovation strategies and for policymakers in shaping relevant policies.

The rise of fintech in the financial sector presents a transformative shift towards digitalisation and sustainability on a global scale, leveraging technologies like AI to minimise environmental footprint. Neobanks not only challenge traditional banking models but also offer innovative solutions that align with sustainable objectives. The purpose of this paper is to analyse the impact of neobanks on global sustainability from economic, environmental, and social points of view. A comprehensive literature review of existing literature and current sustainable practices of neobanks was conducted. Results reveal that neobanks significantly positively contribute towards environmental sustainability with reduced paper use and logistics requirements of banking services. By offering more accessible and affordable banking services they importantly contribute towards higher financial inclusion, and with innovative products towards more competitive and innovative financial markets. AI-based tools they employ are increasing financial literacy and social inclusion. This article also highlights concerns regarding electronic waste management, potential high energy consumption, required digital literacy and cybersecurity risks. In conclusion, despite the mentioned risks, neobanks importantly contribute to global sustainability in many ways and will even more in the future. These findings can help neobanks shape sustainable practices and guide policymaking, as well as spread awareness of the sustainable impact of banking services.

Amidst an upsurge in the quantity of delinquent loans, the financial industry is experiencing a fundamental transformation in the approaches utilised for debt recovery. The debt collection process is presently undergoing automation and improvement through the utilisation of Artificial Intelligence (AI), an emergent technology that holds the potential to revolutionise this sector. By leveraging machine learning, natural language processing, and predictive analytics, automated debt recovery systems analyse vast quantities of data, generate forecasts regarding the likelihood of recovery, and streamline operational processes. Debt collection systems powered by AI are anticipated to be compliant, precise, and effective. On the other hand, conventional approaches are linked to increasing expenditures and inefficiencies in operations. These solutions facilitate efficient resource allocation, customised communication, and rapid data analysis, all while minimising the need for human intervention. Significant progress has been made in data analytics, predictive modelling, and decision-making through the application of artificial intelligence (AI) in debt recovery; this has the potential to revolutionize the financial sector’s approach to debt management. The findings of the research underscore the criticality of artificial intelligence (AI) in attaining efficacy and precision, in addition to the imperative of a data-centric framework to fundamentally reshape approaches to debt collection. In conclusion, artificial intelligence possesses the capacity to profoundly transform the existing approaches utilized in debt management, thereby guaranteeing financial institutions’ sustained profitability and efficacy. The application of machine learning methodologies, including predictive modelling and logistic regression, signifies the potential of the system.

Customers are displaying heightened awareness and involvement in their banking arrangements, and they are actively assessing and remembering information to make informed decisions regarding the allocation of their financial resources towards environmental protection solutions such as clean energy, sustainable construction, climate change control and social protection. Based on the current theoretical gap of factors influencing customer satisfaction and thereby encouraging continued engagement in green finance initiatives, this study aims to identify the factors influencing customer satisfaction as a means of fostering greater participation in green finance amongst customers of commercial banks in Ho Chi Minh City. Using data from a survey of 479 individuals who are customers at commercial banks in Ho Chi Minh City, this study analyses and evaluates the impact of factors influencing customer satisfaction and the role of customer satisfaction in green finance continuance behaviour. Combining basic analysis techniques in quantitative research such as statistics, evaluation of Cronbach’s alpha reliability, exploratory factor analysis (EFA), measurement models and Partial Least Squares structural equation modelling (PLS-SEM) from SPSS and SMART PLS software. the results of this research indicate that: (1) Green Banking initiative (GB), Information Support (IS) and Emotional Support (ES) positively impact Customer Satisfaction (SA); (2) Customer Satisfaction (SA) positively impacts Green Finance Continuance Behaviour (GF).

This research examines three data mining approaches employing cost management datasets from 391 Thai contractor companies to investigate the predictive modeling of construction project failure with nine parameters. Artificial neural networks, naive bayes, and decision trees with attribute selection are some of the algorithms that were explored. In comparison to artificial neural network’s (91.33%) and naive bays’ (70.01%) accuracy rates, the decision trees with attribute selection demonstrated greater classification efficiency, registering an accuracy of 98.14%. Finally, the nine parameters include: 1) planning according to the current situation; 2) the company’s cost management strategy; 3) control and coordination from employees at different levels of the organization to survive on the basis of various uncertainties; 4) the importance of labor management factors; 5) the general status of the company, which has a significant effect on the project success; 6) the cost of procurement of the field office location; 7) the operational constraints and long-term safe work procedures; 8) the implementation of the construction system system piece by piece, using prefabricated parts; 9) dealing with the COVID-19 crisis, which is crucial for preventing project failure. The results show how advanced data mining approaches can improve cost estimation and prevent project failure, as well as how computational methods can enhance sustainability in the building industry. Although the results are encouraging, they also highlight issues including data asymmetry and the potential for overfitting in the decision tree model, necessitating careful consideration.