Browsing by Author "Ciano, Tiziana"

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Citation Count: 0
Evolutionary game theoretical approach for reducing carbon emissions in a complex supply chain organization
(Emerald Group Publishing Ltd, 2024) Bao, Zongke; Wang, Chengfang; Innab, Nisreen; Mouldi, Abir; Ciano, Tiziana; Ahmadian, Ali
PurposeOur research explores the intricate behavior of low-carbon supply chain organizations in an ever-evolving landscape, emphasizing the profound implications of government-mandated low-carbon policies and the growing low-carbon market. Central to our exploration is applying a combined game theory model, merging Evolutionary Game Theory (EGT) with the Shapley Value Cooperative Game Theory Approach (SVCGTA).Design/methodology/approachWe establish a two-tier supply chain featuring retailers and manufacturers within this novel framework. We leverage an integrated approach, combining strategic Evolutionary Game Theory and Cooperative Game Theory, to conduct an in-depth analysis of four distinct low-carbon strategy combinations for retailers and manufacturers.FindingsThe implications of our findings transcend theoretical boundaries and resonate with a trinity of economic, environmental and societal interests. Our research goes beyond theoretical constructs to consider real-world impacts, including the influence of changes in government low-carbon policies, the dynamics of consumer sensitivities and the strategic calibration of retailer carbon financing incentives and subsidies on the identified ESS. Notably, our work highlights that governments can effectively incentivize organizations to reduce carbon emissions by adopting a more flexible approach, such as regulating carbon prices, rather than imposing rigid carbon caps.Originality/valueOur comprehensive analysis reveals the emergence of an Evolutionary Stability Strategy (ESS) that evolves in sync with the phases of low-carbon technology development. During the initial stages, our research suggests that manufacturers or retailers adopt low-carbon behavior as the optimal approach.
Citation Count: 0
A novel deep learning approach to enhance creditworthiness evaluation and ethical lending practices in the economy
(Springer, 2024) Qian, Xiaoyan; Cai, Helen Huifen; Innab, Nisreen; Wang, Danni; Ciano, Tiziana; Ahmadian, Ali
Evaluating a borrower's creditworthiness and enabling ethical lending practices are two of the most essential functions of credit scoring, making it an integral part of the economy. Credit risk management is an essential aspect of the financial industry, with the primary goal of minimising potential losses caused by customers failing to meet their credit responsibilities, such as fails to pay and bankruptcies. This risk is inherent in lending activities, where lenders extend credit to individuals or businesses. The traditional credit scoring approaches, which rely on statistical and machine learning techniques to analyse complex data and non-linear correlations in credit data has to be improved. Because the current financial sector lacks credit scoring, a deep learning network-based credit ranking model is presented in this research. This paper applies the complicated field of deep learning known as the stacked unidirectional and bidirectional long short-term memory model in the network to resolve credit scoring issues. Since scoring is not a time sequence issue, the suggested model uses the three-layer stacked LSTM and bidirectional LSTM architecture by modelling public datasets in a new way. Our suggested models beat state-of-the-art, considerably more difficult deep learning methods, proving that we could keep complexity to a minimum. The research findings indicate that the model demonstrates high levels of accuracy across various datasets. The model obtains an accuracy of 99.5% on the Australian dataset, 99.4% on the German dataset (categorical), 99.7% on the German dataset (numerical), 99.2% on the Japanese dataset, and 99.8% on the Taiwanese dataset. These results highlight the robustness and effectiveness of the model in accurately predicting outcomes for different geographical regions.