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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.

This chapter compares the current biodiversity practices of higher education institutions (HEIs) and their learning effects of the Global North and South. It particularly explores the HEIs’ strategies targeting biodiversity and ecosystem services preservation. In order to answer the research question, a qualitative content analysis of published sustainability reports of the systematically selected HEIs was performed. The Times Higher Education (THE) was used to select HEIs. The results show that biodiversity reporting and management is still in its early stages in HEIs from both the Global North and South and could benefit from further research and suggestions for improvement. One implication for the HEIs is that they could increase public awareness and knowledge of biodiversity through the integration of this topic into their curricula, more research projects on biodiversity, and operations on and off campus.

The current chapter proposes a theoretical framework to assess the sustainability of economic growth in the Gulf Cooperation Council (GCC) States. The authors integrate insights from endogenous growth models and consider the unique socioeconomic characteristics of the GCC region to provide a comprehensive and tailored approach to understanding the determinants of economic growth and formulating effective policy measures to foster sustainable development and growth. This chapter highlights the environmental challenges faced by GCC; based on this, the authors suggested indicators to construct a theoretical framework (Economic Growth, Climatic Indicators, Energy Indicators, Social Indicators, and Economic Resources Indicators). The authors propose that policymakers and researchers in GCC States should take these factors into account when devising policies or conducting research aimed at fostering sustainable economic growth. Overall, this chapter presents significant insights for policymakers, researchers, and stakeholders involved in promoting the sustainable economic advancement of the GCC States.

Although healthcare and healthy living are integral to the Sustainable Development Goals (SDGs) for 2030, the coronavirus epidemic has dealt a devastating blow to these efforts. As governments and policymakers were compelled to shift their focus to lockdowns, sustenance, procurement, and distribution of vaccines, the momentum for health initiatives slowed, and the already fragile health systems of emerging markets were subjected to additional shocks. However, in many underserved regions of the globe, the introduction of technology has greatly facilitated the distribution and adoption of healthcare services.

This chapter highlights mini-cases from four emerging nations: Bangladesh, Nigeria, Vietnam, and the Philippines. Although the countries are emerging, each one of them are in a distinct stage of development and face a unique set of healthcare-related challenges. The chapter showcases how four different organizations based in these countries leveraged the use of technology to take healthcare services to underserved populations. In doing so, they addressed the key challenges of imparting healthcare: geographic accessibility, availability, financial accessibility, and acceptability.

This chapter concludes with a discussion of the implications of expanding healthcare industries leading to increased healthcare waste. To prevent mass population exposure to hazardous substances, the emergence of intelligent healthcare waste collection and disposal systems will be an absolute necessity. Hence, with the development of healthcare services, governments and policymakers need to mechanize smart waste management systems to safeguard humans, animals, and the environment.

Nuclear energy investments contribute significantly to the energy independence of countries. This situation helps countries to develop both economically and socially. Therefore, countries need to develop strategies to increase their nuclear energy investments. However, radioactive waste generated in nuclear reactors is one of the most important disadvantages of this type of energy. Due to this situation, the wastes generated must be disposed of effectively for nuclear energy investments to be effective. In this study, it is aimed to create optimal strategies for the effective waste managements generated in nuclear power plants. Within this framework, four different balanced scorecard perspectives are considered as the criteria. In the analysis process, Analytical Hierarchy Process (AHP) methodology is taken into consideration. It is identified that learning and development is the most critical factor for the effective radioactive waste management created in nuclear power plants. On the other side, internal factors are the second most important criterion in this framework. Hence, it is understood that nuclear energy investors should give priority to the technological development for effective waste management in nuclear power plants. With the help of these technological improvements, the wastes can be disposed more successfully. As a result, environmental sustainability can be provided much easily.

The CEEQUAL certification system promotes sustainable development (SD) principles in infrastructure construction, with 12 categories of criteria and indicators. ENVISION assigns 60 points to five categories, while Green Guide is divided into seven classifications. SD certification requires 11 requirements, including an ecological assessment procedure, life-cycle costing assessment, quality control plan, noise mitigation plan, waste management plan, pollution prevention plan, low impact development, pavement management system, site maintenance plan, and educational outreach. SD certification technique provides 8 critical criteria, 153 standards, 17 stringent prerequisites, and 8 categories of points. Culp (2011) developed a grading system to incorporate SD principles into infrastructure projects; Shen et al. (2011) explored significant assessment parameters for the SD of infrastructure projects using the fuzzy set method; Lui and Cui (2012) studied SD frameworks for building projects, Montgomery et al. (2014) and Lim (2009) identified 77 major SD indicators based on a comprehensive review of the relevant literature, and the Transport Division of New South Wales established a Transportation Project Sustainability Framework to ensure the long-term viability of their transportation network and the continuous improvement of their environmental and sustainability performance. Assah Amiril (2014) revealed a global network of 27 SD criteria produced by professionals, for-profit and non-profit organizations, divided into five groups: ecological, financial, sociological, engineering, asset use, and project management. The most important details in this study are that 57 indicators were categorized into eight types, weighted according to their respective impacts, and the influence of each component on SD was evaluated using a 100-dimensional scale. This study investigated the eight criteria and the impact of three additional criteria (public participation, climate change response, and stakeholder management). This chapter examined the criteria and indicators of SD criteria and indicators for road infrastructure development in developing countries. The most important findings are that the CEEQUAL, Ugwu, Haupt, and Lim models are the most comprehensive theoretical models of SD requirements for infrastructure design, and that all frameworks and models promote the sharing, promotion, and acknowledgement of SD concepts.