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Currently, Mauritius is adopting landfilling as the main waste management method, which makes the waste sector the second biggest emitter of greenhouse gas (GHG) in the country. This presents a challenge for the island to attain its commitments to reduce its GHG emissions to 30% by 2030 to cater for SDG 13 (Climate Action). Moreover, issues like eyesores caused by littering and overflowing of bins and low recycling rates due to low levels of waste segregation are adding to the obstacles for Mauritius to attain other SDGs like SDG 11 (Make Cities & Human Settlements Inclusive, Safe, Resilient & Sustainable) and SDG 12 (Guarantee Sustainable Consumption & Production Patterns). Therefore, together with an optimisation of waste collection, transportation and sorting processes, it is important to establish a solid waste characterisation to determine more sustainable waste management options for Mauritius to divert waste from the landfill. However, traditional waste characterisation is time consuming and costly. Thus, this chapter consists of looking at the feasibility of adopting machine learning to forecast the solid waste characteristics and to improve the solid waste management processes as per the concept of smart waste management for the island of Mauritius in line with reducing the current challenges being faced to attain SDGs 11, 12 and 13.

There is a growing interest in the use of HR-based Industry 4.0 technologies for equality, diversity, and inclusion (EDI) issues yet the emerging trends of Industry 4.0 in EDI implementations and interventions are not fully covered. This chapter investigates the emerging themes regarding EDI and Industry 4.0 interaction through Google-based big data that show the actual interest in Industry 4.0 and EDI. Drawing on a web analytics method that tracks the real click behaviours of web users through querying combined sets of keywords, the study explores the trends and interactions between Industry 4.0 technologies and EDI-related HR practices. Our search engine results page (SERP) analyses find a high volume of queries and a significant interest between EDI elements and artificial intelligence (AI) only. In contrast to the suggestions of the extant literature, no significant user interest in other Industry 4.0 applications for EDI implementations was observed. The authors suggest that other Industry 4.0 technologies such as machine learning (ML) and natural language processing (NLP) for EDI implementations are in their early stages.

While there has been great attention directed at innovation within organisations in business contexts, there has been less focus on the role of innovation at higher education institutions. A changing and turbulent environment is also placing more pressure on universities and business schools to re-imagine, inspire and design an innovation culture. This empirical case presents the innovation leadership team’s point of view on the effect of innovation leadership when integrating the ISO 56000 series-based innovation management system at the University of Ruse in Bulgaria. Several face-to-face interviews and questionnaires are conducted to uncover gaps in innovation leadership based on a comprehensive literature review and the current state of the innovation management system. These gaps hold potential for improvement of the existing management system which are discussed, and recommendations are formulated in the conclusions section of this chapter. Taking into consideration the specific context in which this case study has evolved can help readers and other innovation leaders adapt the facts, conclusions, and lessons learned for universities and business schools. This chapter presents lessons learned and best practices by the innovation leadership team, as demonstrated in several cases at the University of Ruse ‘Angel Kanchev’, Bulgaria.

Energy production and distribution is undergoing a revolutionary transition with the advent of disruptive technologies such as the Internet of Energy (IoE), 5G and artificial intelligence (AI). IoE essentially involves automating and enhancing the energy infrastructure: the power grid from grid operators to energy generators and distribution utilities. The IoE also relies on powerful connectivity networks such as 5G, big data analytics and AI to optimise its operation. By incorporating the technology that employs ubiquitous devices such as smartphones, tablets or smart electric vehicles, it will be possible to fully exploit the potential of IoE using 5G networks. 5G networks will provide high speed connections between devices such as drones, tractors and cloud networks, to transfer huge amounts of sensor data. Additionally, there are many sources of isolated data across the main energy production units (generation, transmission and distribution), and the data is increasing at phenomenal rates. By applying AI to these data, major improvements can be brought at each stage of the energy production chain. Tying renewable energy to the telecommunications sector and leveraging on the potential of data analytics is something which is gaining major attention among researchers and industry experts. This chapter therefore explores the combination of three of the most promising technologies i.e. IoE, 5G and AI for achieving affordable and clean energy, which is SDG 7 in the UN Sustainable Development Goals (SDGs).

This chapter considers aspects of ensuring competitive advantages of the banking sector of Ukraine in the context of global digital transformation. It is proved that operations that previously required a large amount of resources and time can now be carried out automatically using software solutions and artificial intelligence (AI). Companies like FinTech and BigTech that combine financial technology with their core services pose a serious threat to traditional banks. The authors noted that digital technologies make it possible to provide faster and more efficient customer service and improve the security of operations and reduce costs. The emphasis is placed on the fact that the use of AI in the banking sector will contribute to improving the efficiency and accuracy of banks, reducing risks and costs, and also contributes to the selection of personalized approaches to their customers. It has been proven that, thanks to AI, banking companies can improve the efficiency of their work, reduce costs, and improve interaction with their customers, thereby gaining competitive advantages in the market.