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Food security and malnutrition are leading health and development issues in the Pacific Island region. The food system scholarship points to the need for capacity building across multiple levels of governance to improve food system outcomes in the Pacific Island Countries and Territories. This paper aims to identify the strengths and weaknesses of systemic capacity in the food system governance of the Solomon Islands and identify opportunities for capacity building. A theoretically informed, empirical policy analysis was undertaken, informed by qualitative semistructured key informant interviews. Challenges related to capacity included slow information flows, inadequate human resourcing, and skill gaps at all levels of government. Opportunities for capacity building span workload, personal, performance, supervisory, role, systems, and structural capacities. These include the improvement of coordination between food system actors through the establishment of a multisectoral food system platform or agency, and increasing the involvement of vulnerable populations in policy planning and decision making. The current food system governance of the Solomon Islands shows important strengths in systemic capacity across multiple capacity types at national, provincial, and community levels. Our analysis provides insights for future capacity building efforts that build on these strengths to improve social, environmental, and economic outcomes.

A comprehensive assessment of indoor environmental conditions is performed on a representative sample of classrooms in schools across southern Spain (Mediterranean climate) to evaluate the thermal comfort level, thermal perception and preference, and the relationship with HVAC systems, with a comparison of seasons and personal clothing. Almost fifty classrooms were studied and around one thousand pool-surveys distributed among their occupants, aged 12 to 17. These measurements were performed during spring, autumn, and winter, considered the most representative periods of use for schools. A new proposed protocol has been developed for the collection and subsequent analysis of data, applying thermal comfort indicators and using the most frequent predictive models, rational (RTC) and adaptive (ATC), for comparison. Cooling is not provided in any of the rooms and natural ventilation is found in most of the spaces during midseasons. Despite the existence of a general heating service in almost all classrooms in the cold period, the use of mechanical ventilation is limited. Heating did not usually provide standard set-point temperatures. However, this did not lead to widespread complaints, as occupants perceive the thermal environment as neutral—varying greatly between users—and show a preference for slightly colder environments. Comparison of these thermal comfort votes and the thermal comfort indicators used showed a better fit of thermal preference over thermal sensation and more reliable results when using regional ATC indicators than the ASHRAE adaptive model. This highlights the significance of inhabitants’ actual thermal perception. These findings provide useful insight for a more accurate design of this type of building, as well as a suitable tool for the improvement of existing spaces, improving the conditions for both comfort and wellbeing in these spaces, as well as providing a better fit of energy use for actual comfort conditions.

The Internet of Things (IoT) is an emerging technology and provides connectivity among physical objects with the support of 5G communication. In recent decades, there have been a lot of applications based on IoT technology for the sustainability of smart cities, such as farming, e-healthcare, education, smart homes, weather monitoring, etc. These applications communicate in a collaborative manner between embedded IoT devices and systematize daily routine tasks. In the literature, many solutions facilitate remote users to gather the observed data by accessing the stored information on the cloud network and lead to smart systems. However, most of the solutions raise significant research challenges regarding information sharing in mobile IoT networks and must be able to stabilize the performance of smart operations in terms of security and intelligence. Many solutions are based on 5G communication to support high user mobility and increase the connectivity among a huge number of IoT devices. However, such approaches lack user and data privacy against anonymous threats and incur resource costs. In this paper, we present a mobility support 5G architecture with real-time routing for sustainable smart cities that aims to decrease the loss of data against network disconnectivity and increase the reliability for 5G-based public healthcare networks. The proposed architecture firstly establishes a mutual relationship among the nodes and mobile sink with shared secret information and lightweight processing. Secondly, multi-secured levels are proposed to protect the interaction with smart transmission systems by increasing the trust threshold over the insecure channels. The conducted experiments are analyzed, and it is concluded that their performance significantly increases the information sustainability for mobile networks in terms of security and routing.

The built environment influences and promotes cycling that has now become a challenge for sustainable urban mobility in many cities where this mode of transport carries little weight. This is the case for Granada (Spain), a medium-sized city in southern Europe, which as a university city and with lots of green areas, could find potential supportive factors to promote cycling. Website-apps with a Global Positioning System (GPS), such as Ciclogreen that encourage active accessibility try to promote cycling and are supported by the University of Granada. The aim of this work is to assess the capacity of green areas and some influential factors of their built environment to attract cycling routes. To this end, a spatial analysis was made and interpreted by a statistical model to check the correlation between these factors and a high number of cycling routes through or near the green areas. The results show a high number of cycling routes within urban surroundings that include green areas, cycle lanes, university facilities, and public car parks in proximity relationships. Identifying synergies among these urban factors and the information and incentive coming from a digital catalyst in shape on an app could be useful in urban planning for cycling.

The current literature on sustainable practices shows that social missions and economic achievement can be compatible. However, very few studies have offered a quantitative perspective on this relationship. This study presents the first empirical evidence of the effects of the creating shared value (CSV) principle on the performance of companies. Using a sample of Spanish hotels, the results suggest that companies that apply CSV achieve higher financial performance. These findings represent a substantial contribution to the literature and shed light on how to efficiently implement CSV practices to simultaneously achieve economic and social benefits.

The building and construction sector in the Kingdom of Saudi Arabia (KSA), experiencing rapid growth, is in the early stages of embracing sustainability measures. In the years ahead, the booming building sector in business-as-usual scenarios may pose serious energy and environmental challenges for the Kingdom. This situation will require the Saudi building sector to adopt robust sustainability measures. Embedding life cycle assessment (LCA) as a standard practice can be a useful strategy for improving the energy and environmental footprint of buildings. This paper proposes a theoretical framework within which to promote LCA in the Saudi Arabian construction industry. This framework comprises three pillars: policy, social, and technical. The framework covers the role of the Saudi building sector’s stakeholders such as policy makers, building industry professionals, representatives of relevant governmental bodies, and academics. Adaptation of this LCA framework can help substantially improve the energy and environmental performance of buildings. The proposed LCA framework is aligned with the international as well as the Saudi government’s drive for sustainability in the building sector.

South Asia remains the region with the highest prevalence of undernourishment with India accounting for 255 million food insecure people. A worsening of child nutritional outcomes has been observed in many Indian states recently and children in rural areas have poorer nutrition compared to those in urban areas. This paper investigates the relationship between land ownership, non-farm livelihoods, food security, and child nutrition in rural India, using the Young Lives Survey. The survey covers the same rural households and children over the period 2002–2013 in the states of Andhra Pradesh and Telangana. Our empirical analysis uses a sample of 1209 children (and their households) who were aged around 1 year in 2002. Our results show that large agricultural land ownership is significantly associated with better child nutrition (measured using height-for-age and stunting) and household food security. A transition from farm to non-farm work improves child nutrition, but only among landless households. While access to land is still critical for improving household food and nutrition security among rural households, there is a trend towards greater non-farm livelihoods, and a decline in reliance on farming, particularly among landless and marginal farmers.

Air pollution is a major issue all over the world because of its impacts on the environment and human beings. The present review discussed the sources and impacts of pollutants on environmental and human health and the current research status on environmental pollution forecasting techniques in detail; this study presents a detailed discussion of the Artificial Intelligence methodologies and Machine learning (ML) algorithms used in environmental pollution forecasting and early-warning systems; moreover, the present work emphasizes more on Artificial Intelligence techniques (particularly Hybrid models) used for forecasting various major pollutants (e.g., PM2.5, PM10, O3, CO, SO2, NO2, CO2) in detail; moreover, focus is given to AI and ML techniques in predicting chronic airway diseases and the prediction of climate changes and heat waves. The hybrid model has better performance than single AI models and it has greater accuracy in prediction and warning systems. The performance evaluation error indexes like R2, RMSE, MAE and MAPE were highlighted in this study based on the performance of various AI models.