The evolution of smart cities represents a pivotal transformation in urban development, driven by the integration of cutting-edge technologies. Among these, blockchain and edge intelligence have emerged as pivotal forces shaping the future of smart cities. This paper presents a comprehensive review and outlook on the potential synergy between blockchain and edge intelligence, highlighting their transformative impact on sustainable smart city development. In our analysis, we delve into the key components and technologies associated with smart cities, emphasizing their goals of sustainability, efficiency, and improved quality of life. We introduce the concepts of blockchain and edge intelligence, elucidating their applications across various industries and urban domains. Moreover, we identify gaps in the existing literature and underscore the critical need for further research in the synergy of these technologies in smart cities. Our exploration extends to the significance of the study, emphasizing the timeliness of this research amidst growing interest in sustainable smart cities. We discuss the potential benefits and implications of this technological convergence for urban planning, technology adoption, and sustainability. This paper envisions smart cities that prioritize sustainability, circular economies, and data privacy, while fostering innovation and collaboration among public and private stakeholders. As we look to the future, we anticipate that this convergence will pave the way for more resilient, sustainable, and inclusive smart cities, and we outline potential areas for further research and development in this exciting field.
Read MoreDoi: https://doi.org/10.54216/JSDGT.030101
Vol. 3 Issue. 1 PP. 08-19, (2023)
In an era marked by increasing global interconnectivity and multifaceted risks, the imperative for effective risk management in international business administration has never been more pronounced. This paper presents a novel and sustainable approach to ranking risks within this dynamic landscape. Leveraging the power of the Multinomial Naive Bayes classifier, our method empowers organizations to systematically assess and prioritize risks while embracing sustainability principles. Through meticulous experimentation and analysis, we demonstrate the method's efficacy and its capacity to enhance decision-making processes for businesses operating on an international scale. Our experiments validate the method's robustness and applicability, contributing to the fields of international business administration and risk management. The findings underscores the critical importance of intelligent, data-driven risk assessment and mitigation in an interconnected world. It not only contributes to the fields of international business administration and risk management but also offers a blueprint for harmonizing economic success with environmental and social responsibility.
Read MoreDoi: https://doi.org/10.54216/JSDGT.030102
Vol. 3 Issue. 1 PP. 20-28, (2023)
As businesses expand their global footprint in an era marked by growing environmental and ethical concerns, the administration of international operations has become a focal point for fostering sustainability and responsible corporate behavior. This paper delves into the intricate realm of sustainable administration in international business, with a particular focus on strategies employed by organizations to align their practices with environmental, social, and ethical considerations. Leveraging advanced data analytical methods, including k-means clustering, and guided by the insights of the Elbow Method, our research provides a comprehensive analysis of sustainable administration strategies. Drawing from Prudential Life Insurance as a case study, we explore how multinational corporations navigate the complexities of sustainability, particularly in the face of global challenges. Through rigorous examination and empirical findings, our study offers actionable insights for businesses aiming to strike a balance between competitiveness and responsible global citizenship. In an increasingly interconnected world, this research contributes to the ongoing dialogue on sustainable business practices, underlining the significance of sustainable administration in shaping a more resilient and equitable global economy.
Read MoreDoi: https://doi.org/10.54216/JSDGT.030103
Vol. 3 Issue. 1 PP. 29-39, (2023)
It is known that the study of economic processes includes micro, macro, and an international level analysis of certain individual economic entities, that is, firms and industries (branches). While the process of studying and making decisions on the international economic behavior of specific individual subjects in order to maximize the satisfaction of unlimited needs in the context of limited resources is a research object and subject of the field of "international economy". The modern economic theories researched in the scientific article require wider use of mathematical instruments in the study of quantitative aspects of economic processes. One of the more widely used models in practice is the economic-mathematical model. An economic-mathematical model is a formalized classification of economic processes or phenomena, the composition of which is formed depending on the objective or subjective characteristics arising from the research purpose. Economic-mathematical models expressed quantitative aspects of economic processes through functions, equations, or inequalities. Modern economic theories, mathematical models, and functions (equalities or inequalities) used in the implementation of micro, macro, or international economic analysis of gross income increase in the article indicate the scientific basis, expediency, and relevance of the chosen topic.
Read MoreDoi: https://doi.org/10.54216/JSDGT.030104
Vol. 3 Issue. 1 PP. 40-46, (2023)
The integration of smart supply chain technologies has emerged as a catalyst for reshaping the landscape of sustainable business administrations. This paper presents a comprehensive investigation into the dynamic relationship between smart supply chains and sustainability, examining their intricate interplay and the transformative potential they hold for modern supply chain management. Leveraging an ensemble of three machine learning models—Decision Trees, Support Vector Machines, and Logistic Regression—we analyze extensive datasets encompassing supply chain operations. Our findings demonstrate that the strategic deployment of smart technologies enhances predictive accuracy, informs data-driven decision-making, and optimizes supply chain processes. This research underscores the pivotal role of smart supply chains in achieving sustainability objectives. By fusing predictive accuracy with data-driven decision-making, our research underscores the pivotal role of smart supply chains in achieving sustainable business practices. The insights presented herein offer not only academic contributions but also actionable guidance for businesses navigating the intricacies of modern supply chain management.
Read MoreDoi: https://doi.org/10.54216/JSDGT.030105
Vol. 3 Issue. 1 PP. 47-55, (2023)
