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The consideration of alternative sources of material for construction is imperative to reduce the environmental impacts as two-fifths of the carbon footprint of materials is attributed to the construction industry. One alternative material with improved biodegradable attributes which can contribute to carbon offset is bamboo. The commercialisation of bamboo in modern infrastructures has significant potential to address few of the Sustainable Development Goals (SDGs) itemised by the United Nations, namely SDG 9 about industry, innovation and infrastructure. Other SDGs covering sustainable cities and communities, responsible consumption and production and climate action are also indirectly addressed when utilising sustainable construction materials. Being a natural material however, the full commercialisation of materials such as bamboo is constrained by a lack of durability. Besides fracture mechanisms arising from load-induced cracks and thermal modification, the durability of bamboo material is greatly impaired by biotic and abiotic factors, which equally affect its natural rate of degradation, hence fracture behaviour. In first instance, this chapter outlines the various factors leading to the durability limitations in bamboo material due to load-induced cracks and natural degradation based on recent findings in this field from the author's own work and from past literature. Secondly, part of this chapter is devoted to a new approach of processing the surge of information about the varied aspects of bamboo durability by considering the powerful technique of artificial intelligence (AI), specifically the artificial neural network (ANN) for prediction modelling. Further use of AI-enabled technologies could have an impactful outcome on the life cycle assessment of bamboo-based structures to address the growing challenges outlined by the United Nations.

The complexity of atmospheric corrosion, further compounded by the effects of climate change, makes existing models inappropriate for corrosion prediction. The commonly used kinetic model and dose-response functions are restricted in their capacity to represent the non-linear behaviour of corrosion phenomena. The application of artificial intelligence (AI)-driven machine learning algorithms to corrosion data can better represent the corrosion mechanism by considering the dynamic behaviour due to changing climatic conditions. Effective use of materials, coating systems and maintenance strategies can then be made with such a corrosivity model. Accurate corrosion prediction will help to improve climate change resilience of the social, economic and energy infrastructure in line with the UN Sustainable Development Goals (SDGs) 7 (Affordable and Clean Energy), 9 (Industry, Innovation and Infrastructure) and 13 (Climate Action). This chapter discusses atmospheric corrosion prediction in relation to the SDGs and the influence of AI in overcoming the challenges.

This chapter explores a pathway towards achieving a sustainable and inclusive future through the implementation of systems innovation, systems leadership and systems change. The author highlights the importance of understanding complex systems and identifies several models that can be employed to drive systems change, including the iceberg model and multi-level perspective. The author stresses the significance of systems leadership and innovation in creating a sustainable and inclusive future. This means that leaders and managers need to shift their mindset from reductionism to systems thinking. Reductionism views complex systems as a collection of separate parts that can be studied independently. In contrast, systems thinking acknowledges the interconnectedness of all parts and how they influence each other. When they embrace systems thinking, leaders and managers can make systems innovation and drive systems change to achieve sustainable and inclusive growth. Achieving a sustainable and inclusive future requires a collective effort from individuals, organizations and governments. It demands a comprehensive understanding of the interdependencies and interactions within complex systems, as well as a willingness to adopt new ways of thinking and leading. This chapter presents a compelling case for adopting systems innovation, systems leadership and systems change as critical components in building a sustainable and inclusive future.

The environmental performance of organizations has come under public policy limelight since the phenomenal increase of natural resource degradation and industrial waste. Thus, green concepts have been put forward, but the implementation of green practices faces many barriers and challenges. These barriers require attention as organizational practices are negatively affecting the environment leading to global warming and climate change. Therefore, this chapter systematically identifies four internal barriers including inadequate management commitment and support, insufficient technology competence and infrastructure, financial constraints, the uncertainty of economic benefits, and eight sub barriers within an organization concerning green concepts implementation. Moreover, this chapter also identifies four external barriers including lack of stakeholder's interest, inadequate environmental administrative support, scarce academic research, and lack of green collaborative practices, and eight sub barriers outside an organizational context concerning green concept implementation. The barriers in this research were identified by reviewing the existing literature on the topic. This chapter advances the green literature by identifying multiple barriers and challenges to the successful implementation of green concepts in organizations. This is of significance as if these barriers are tackled strategically, it would reduce environmental degradation problems and help make financial gains. Moreover, this research can help managers understand the key barriers to green concept implementation and provide guidance to them when attempting to implement green practices in their organizations. This research would also motivate researchers to extend further investigation on how to overcome such barriers and find out strategies to mitigate the barriers to green concept implementation to effectively address environmental issues.

Environmental degradation is a global concern that results from massive economic activities. Carbon dioxide (CO₂) emissions are one of the environmental degradation indicators. This study investigates the impact of population, oil consumption, international tourist arrival, and corruption on CO₂ emissions in ASEAN’s five developing countries of Malaysia, Indonesia, Thailand, Philippines, and Vietnam from 1998 to 2017. This study employed panel Fixed-effect (FE) regression to estimate the panel data generated by British Petroleum and World Bank. The result revealed that the population has a significant positive effect on CO₂ emissions. Furthermore, oil consumption has a significant positive effect on CO₂ emissions. Meanwhile, the effect of tourism and the corruption perception index (CPI) as a proxy of corruption on CO₂ emissions was positive but not significant. Authorities should construct such policies to reduce CO₂ emissions by applying low-carbon technologies, green mass transport, and creating less corrupt behavior.

It is now well-established that good water quality is associated with economic prosperity, reduced incidence on public health and the good functioning of the various ecosystems found in our environment. Water contamination is mostly related to both diffused (agricultural lands and geologic rock degradations) and point sources of pollution. Mauritius has many water resources which depend solely on precipitation for their replenishment. Water parameters which are of relevance include total dissolved solids (TDS), temperature, pH, electrical conductivity, turbidity, dissolved oxygen, dissolved and particulate organic carbon and major cations and anions. The traditional methods of analysis for these parameters are mostly using electrical and optical methods (probes and sensors in the field), while chemical titrations, Flame AAS and High-Performance Liquid Chromatography techniques are carried out in the laboratory. Image Classification techniques using neural networks can also be used to detect the presence of contaminants in water. In addition to basic water quality parameters, the field sensors range have been extended to cover important major ions and can now be integrated with Artificial Intelligence (AI)-based models for the prediction of variations in water quality to better protect human health and the environment, reduce operation costs of water and wastewater treatment plant unit processes.

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.

The present world is captivated by the global challenges of climate change, environmental and ecological degradation, challenges related to migration, inequality, conflict and unrest. The development process failed to appreciate and signify the role of non-value, non-materialistic and altruistic value of both living and non-living natural entities. Thus, a significant disconnect or gap between public policy and human well-being has been observed. The departure of human beings from their true self has also distanced humans from the rest of the species and ecosystem. The essence of altruistic value and its recognition is plausible only through the upliftment and evolution of the inner self. The interconnectivity of the inner and outer world can be perceived through self-evolution. A public policy that is driven by the force of self-enhancement and realization, connecting and synthesizing human and non-human interactions selflessly, is required for revolutionizing the development paradigm. Against this backdrop, Gandhian philosophy that values the individual and his relationship with his true self becomes instrumental in ethical transformation. The self-transformation, manifestation and realization of truth and love are deeply embedded in Gandhian Revolution. Gandhian philosophy is a phenomenal illustration of self-transformation based on self–realization and soul force (Satyagraha) that acts as a driving force for a paradigm shift. Gandhian thoughts recognize the otherness of others (either human beings or nature), embedded in the notion of ‘Sarvodaya’. Based on spiritual inquiry and ethical value judgement, and cosmic interdependence, the Gandhian notion constitutes a building block for a non-violent socioeconomic system guided by empathy and the law of non-possession. The twin models, ‘The trusteeship model’ and ‘Economy of Permanence’, advocate a spiritual inquiry and ethical value judgement in economics and governance. Addressing the economic problem of scarcity and choice, the attributes of decentralization, self-sufficiency and self-reliance envisage that individuals are satisfied with minimum consumption. In addition, non-violence, truth and love create a subsequent balance in ecology and the environment. Ethical responsibility is not just limited to socio-economic and political systems but also integrates environmental and ecological sustainability.