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The need to design buildings with due consideration for bioclimatic and passive design is central to promoting sustainability in the built environment from an energy perspective. Indeed, the energy and atmosphere considerations in building design, construction and operation have received the highest consideration in green building frameworks such as LEED and BREEAM to promote SDG 9: Industry, Innovation and Infrastructure and SDG 11: Sustainable Cities and Communities and contributing directly to support SDG 13: Climate Action. The research literature is rich of findings on the efficacy of passive measures in different climate contexts, but given that these measures are highly dependent on the prevailing weather conditions, which is constantly in evolution, disturbed by the climate change phenomenon, there is pressing need to be able to accurately predict such changes in the short (to the minute) and medium (to the hour and day) terms, where AI algorithms can be effectively applied. The dynamics of the weather patterns over seasons, but more crucially over a given season means that optimum response of building envelope elements, specifically through the passive elements, can be reaped if these passive measures can be adapted according to the ambient weather conditions. The use of representative mechatronics systems to intelligently control certain passive measures is presented, together with the potential use of artificial intelligence (AI) algorithms to capture the complex building physics involved to predict the expected effect of weather conditions on the indoor environmental conditions.

Climate change and the COVID-19 pandemic are complex and have multifaceted effects on countries in an unpredictable and unprecedented manner. While both COVID-19 and the climate crisis share similarities, they also have some notable differences. Being both systemic in nature with knock-on and cascading effects that propagate due to high connectedness of countries, COVID-19, however, presents imminent and directly visible dangers, while the risks from climate change are gradual, cumulative and often distributed dangers. Climate change has more significant medium and long-term impacts which are likely to worsen over time. There is no vaccine for climate change compared to COVID-19. In addition, those most affected by extreme climatic conditions have usually contributed the least to the root causes of the crisis. This is in fact the case of island economies. The chapter thus investigates into the vulnerability and resilience of 38 Small Islands Developing States (SIDs) to both shocks. Adopting a comprehensive conceptual framework and data on various indices from the literature and global databases, we assess the COVID-19 and climate change vulnerabilities of SIDs on multiple fronts. The results first reveal a higher vulnerability across all dimensions for the Pacific islands compared to the other islands in the sample. There is also evidence of a weak correlation between climate change risk and the COVID-19 pandemic confirming our premise that there are marked differences between these two shocks and their impacts on island communities.

Climate change places significant pressure on the tourism sector by altering environmental and socio-economic conditions that influence tourist behaviour and the attractiveness of destinations. Rising temperatures, changing precipitation patterns and the increasing frequency and severity of extreme weather events affect tourism supply and demand. On the supply side, climate change threatens tourism infrastructure, natural attractions, recreational opportunities and accessibility of destinations. Coastal destinations are particularly vulnerable to sea-level rise and coastal flooding, which can damage tourism assets. On the demand side, changing climatic conditions alter visitor comfort levels, health risks and the seasonality of destinations, influencing tourists' choice of destinations. In addition, small island destinations face unnecessary risks due to their economic dependence on climate-sensitive activities such as beach and nature tourism. Adapting the tourism sector to climate change requires reducing vulnerability through diversification, green infrastructure, ecosystem conservation, community-based adaptation and policy support. Mitigating tourism's contribution to climate change requires minimising energy use, switching to renewable energy, improving efficiency, reducing long-haul flights and promoting sustainable consumption and production. Collective and concerted efforts by all stakeholders are needed to transition to a climate-resilient and low-carbon tourism sector that continues to provide socio-economic benefits while minimising its environmental footprint.

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 study has covered many types of solar-powered air-conditioning systems that may be used as an alternative to traditional electrically powered air-conditioning systems in order to reduce energy usage. Solar adsorption air cooling is a great alternative to traditional vapor compression air-conditioning. Solar adsorption has several advantages over traditional vapor-compression systems, including being a green cooling technology which uses solar energy to drive the cycle, using pure water as an eco-friendly HFC-free refrigerant, and being mechanically simple with only the magnetic valves as moving parts. Several advancements and breakthroughs have been developed in the area of solar adsorption air-conditioners during the previous decade. However, further study is required before this technology can be put into practise. As a result, this book chapter highlights current research that adds to the understanding of solar adsorption air-conditioning technologies, with a focus on practical research. These systems have the potential to become the next iteration of air-conditioning systems, with the benefit of lowering energy usage while using plentiful solar energy supplies to supply the cooling demand.

Pakistan is one of the most climate change and natural disaster-affected countries in the globe, where the lives and livelihoods of people are repeatedly affected due to these natural disasters. Over the past few decades, the country has been impacted by numerous devastating floods, droughts, and storms. As a result, households face enormous complications, particularly those dwelling in disaster-prone areas. Therefore, this study intends to explore the status of household vulnerability and resilience practices of hazard-prone communities in Pakistan from existing literature. This study has identified the 17 most relevant documents. It argues that household vulnerability is increasing consistently with the increasing rate of disaster intensity. Frequent flooding, landslide, erosion, and crop loss are the leading causes of household vulnerability. This study reveals five types of household vulnerability components which look into several livelihood vulnerability indicators of Pakistani households. Moreover, the study unfolds that the main causes of disaster vulnerability are widespread crop loss, a lack of water, loss of soil fertility, and low socioeconomic situations. The major vulnerability components of dwellers are exposure (increasing summer duration, the rapid increase of population house build-up in the riparian areas, and increasing occurrence of hailstorms), sensitivity, low access to education facilities, human loss, diseases infestation, food insecurity, and social conflict), and less adaptive capacity (social networks, migration, poor emergency services, multiple income sources, and less access to the health facility). To address the household vulnerability, this study has also identified four key aspects of resilience, like social resilience, economic resilience, institutional resilience, and physical resilience. The findings will effectively help to understand the dynamics of household vulnerability and resilience and its measurement and management strategy from developed indicators.

Reducing income gap and enhancing welfare of people can be achieved through improved performance regarding socio-economic indicators, which can support sustainable development. People living in villages tend to migrate to the cities with the hope of enhancing their income. People living in cities and income gap among the people in the cities are expected to increase further. Enhanced living conditions in villages can reduce the need for this migration and increase welfare of the people in the villages. This chapter aims to examine drivers for sustainable smart villages and ways for enhancing and supporting their effectiveness in socio-economic development, in enhancing welfare and living conditions of people living in villages, and in reducing income gap between urban and rural people. This chapter investigates potential and roles of sustainable and smart villages in enhancing welfare and living conditions of people living in villages. Furthermore, this chapter emphasises the villages’ roles in sustainable development as well as importance of transformation of villages into the sustainable and smart ones and establishment of sustainable and smart villages so that convergence, social inclusion and socio-economic indicators can be supported. This chapter explains drivers for transformation of the villages into the sustainable and smart ones as well as establishment of new sustainable and smart villages. This chapter provides recommendations for sustainable and smart villages effective in contributing to sustainable and socio-economic development. This chapter can be useful to urban planners, construction industry stakeholders, policy makers and researchers.

The seasonality in the behavior of travelers is something that goes back to the origin of the trips themselves. This seasonality is due to multiple factors, some easy to counteract and others difficult to solve. But, regardless of the causes, it is a phenomenon that generates significant negative impacts on society and the environment in which the phenomenon of tourist seasonality occurs. All tourist destinations have seasonality, but in some cases, it is very high and in others it has a minimal incidence. The objective of this chapter is to ponder the impacts and consequences of seasonality in regions with a strong tourism development, allowing to put into context the aspects of society impacted by this phenomenon and the positive implications that the reduction of seasonality would have. For this, an analysis of a theoretical model with two regions in opposite situations is carried out, raising the possible effects of a high seasonality. The cases of the Balearic Islands and the Canary Islands are also reviewed, as real examples of the regional typologies taken into consideration in the theoretical model. This seeks to ponder the problems attributable to seasonality. As a final reflection, the enormous typology of negative impacts generated and the need to continue analyzing the seasonality and its impacts are emphasized.

There is an urgent need to develop climate-smart agrosystems capable of mitigating climate change and adapting to its effects. Conventional agricultural practices prevail in Mauritius, whereby synthetic chemical fertilizers, pesticides and insecticides are used. It should be noted that Mauritius remains a net-food importing developing country of staple food such as cereals and products, roots and tubers, pulses, oil crops, vegetables, fruits and meat (FAO, 2011). In Mauritius, the agricultural sector faces extreme weather conditions like drought or heavy rainfall. Moreover, to increase the crop yields, farmers tend to use 2.5 times the prescribed amount of fertilizers in their fields. These excess fertilizers are washed away during heavy rainfall and contaminate lakes and river waters. By using smart irrigation and fertilization system, a better management of soil water reserves for improved agricultural production can be implemented. Soil Nitrogen, Phosphorus and Potassium (NPK) content, humidity, pH, conductivity and moisture data can be monitored through the cloud platform. The data will be processed at the level of the cloud and an appropriate mix of NPK and irrigation will be used to optimise the growth of the crops. Machine learning algorithms will be used for the control of the land drainage, fertilization and irrigation systems and real time data will be available through a mobile application for the whole system. This will contribute towards the Sustainable Development Goals (SDGs): 2 (Zero Hunger), 11 (Sustainable cities and communities), 12 (Responsible consumption and production) and 15 (Life on Land). With this project, the yield of crops will be boosted, thus reducing the hunger rate (SDG 2). On top of that, this will encourage farmers to collect the waters and reduce fertilizer consumption thereafter sustaining the quality of the soil on which they are cultivating the crops, thereby increasing their yields (SDG 15).

This chapter aims to estimate the impact of the use of an innovative cultivation method on the social, economic and environmental aspects in the French region Aix-en-Provence, by using the survey data for 200 heterogeneous vegetable producers (organic and conventional). It distinguishes three types of producers in the French region Aix-en-Provence. First, conventional producers (n = 100) who used a high level of mechanization, better access to water, high yield, high labor costs. Second, certified organic producers (n = 70) who used organic technologies such as biotechnology and rotation, low yield, high organic product price compared to conventional products, a family workforce and high transport. Third, noncertified organic producers (n = 30) have used the same technologies as certified organic producers, while they sell their products at the same price as conventional products. Labor is the member of the family. These noncertified farms are marked by high operating and transport costs and low yield compared to conventional producers or certified organic producers. The results show that this cultivation method has a positive effect on the environmental aspect, however a negative one on the social and economic aspect.