All around the world, inequalities persist in the complex web of social, economic, and ecological factors that mediate food security outcomes at different human and institutional scales. There have been rapid and continuous improvements in agricultural productivity and better food security in many regions of the world during the past 50 years due to an expansion in crop area, irrigation, and supportive policy and institutional initiatives. However, in Sub-Saharan Africa, the situation is inverted. Statistics show that food insecurity has risen since 2015 in Sub-Saharan African countries, and the situation has worsened owing to the Ukraine conflict and the ongoing implications of the COVID-19 threat. This review looks into multidimensional challenges to achieving the SDG2 goal of “End hunger, achieve food security and improved nutrition, and promote sustainable agriculture” in Sub-Saharan Africa and the prosper policy recommendations for action. Findings indicate that weak economic growth, gender inequality, high inflation, low crop productivity, low investment in irrigated agriculture and research, climate change, high population growth, poor policy frameworks, weak infrastructural development, and corruption are the major hurdles in the sustaining food security in Sub-Saharan Africa. Promoting investments in agricultural infrastructure and extension services together with implementing policies targeted at enhancing the households’ purchasing power, especially those in rural regions, appear to be essential drivers for improving both food availability and food access.

In economies, cleaner technology, increased demand for renewable energy, and more efficient use of natural resources contribute to meeting environmental sustainability targets. The Chinese economy is no exception in its attempts to conserve economic and natural resources via collaborative efforts to embrace cleaner technology, green energy sources, and resource conservation management to preserve resources for future generations. This research examines the influence of cleaner technologies, green energy sources, and natural resource management on reducing greenhouse gas emissions using quarterly data for the Chinese economy from 2000Q1 to 2020Q4. The findings demonstrate that increasing demand for green energy reduces greenhouse gas emissions, hence substantiating the premise of ‘green is clean’ energy development. Additionally, optimum resource usage enhances environmental quality, corroborating the ‘resource cleaner blessing’ hypothesis. The positive link between inward foreign direct investment and greenhouse gas emissions substantiates the ‘pollution haven’ concept, according to which inward foreign direct investment uses unsustainable technology in manufacturing processes, hence degrading air quality indicators. Inadequate access to clean cooking technology and increased population density has a detrimental effect on the country’s environmental sustainability agenda, which must be corrected via sustainable regulations. The causality estimates show the feedback relationship between renewable energy demand (and economic growth) and cleaner technology, between economic growth and green energy (and inbound foreign direct investment), and between population density and economic growth (and green energy). The Impulse Response function estimates suggested that economic growth and population density would likely increase GHG emissions. In contrast, cleaner technology, green energy demand, natural resource management, and inbound foreign direct investment would likely decrease greenhouse gas emissions for the next ten-year time period. The sustainability of the environment and natural resources in China is bolstered by developing cleaner technologies, a greater reliance on renewable energy sources, and better management of natural resources.

The development of tourist destinations has been determined by sustainable energy management and the advancement of traditional methods. The rate of development must adapt to technological innovations while also considering future generations. The present study aims to determine to what extent tourism personnel are aware of the skills, knowledge, and methods for sustainable energy management, and also to analyze the Internet of Things as a technological solution for sustainable energy management at tourism destinations in India with the help of the Servqual method. It is important to implement modern technologies such as internal things and develop a sustainable attitude toward tourism. Findings suggested that over each attribute of Servqual model, reliability, assurance, tangibles, empathy and responsiveness of IoT as sustainable energy management solutions at tourism destinations in India, tourism stakeholders have higher level of expectations (23.41, 19.86, 18.45, 23.60 and 24.73) and perceptions (18.34, 16.50, 14.97, 18.17 and 19.20) followed by tourists expectations (22.10, 17.36, 16.01, 22.62 and 21.87) and perceptions (19.32, 11.75, 09.46, 15.06 and 17.43) and local residents expectations (20.17, 14.61, 14.87, 19.46 and 18.81) and perceptions (13.48, 08.85, 07.73, 13.54 and 12.94), respectively. Results also showed that older generations and traditional tourism destinations are unable to cope with the modern advanced terminology, tools, and management strategies, which makes the present study the most significant about changing the traditional way of energy management and developing tourism destinations as sustainable and responsible.

Understanding and predicting the relationship between big data analytics (BDA) and knowledge management (KM) and how they complement each other is still an ambiguous and strategically crucial issue. This study aims to investigate the interrelationships between BDA and KM in fostering green innovation practices (GIP) and organizational performance and, in addition, to explore the mediation effect of KM and how it can boost the impact of BDA on GIP. Based upon the underpinning theories that cover the study’s research model, several hypotheses were proposed and then examined. Using a cross-sectional research design, 174 questionnaires were collected from medium and large Algerian manufacturing firms. The study applied smart PLS-SEM for data analysis. The sorted results show that BDA positively impacts KM and GIP. The results also indicate that GIP positively influences and increases firm performance. In addition, the findings reveal that government support plays a significant part in leveling up firms’ GIP. Furthermore, the study emphasizes the critical impact of KM to polish the impact of BDA on GIP. This study highlights the necessity of improving the technological and managerial aspects of BDA by determining valuable capabilities, such as KM processes, to enhance organizational performance. Tracking business opportunities and predicting their relevant threats has become a severe concern to knowledge-extensive firms. Therefore, BDA can enable managers to absorb a large amount of data to increase the efficiency of a business decision to ensure survival and advancement. The study discusses both practical and theoretical implications.

This study work is among the few attempts to understand the significance of AI and its implementation barriers in the healthcare systems in developing countries. Moreover, it examines the breadth of applications of AI in healthcare and medicine. AI is a promising solution for the healthcare industry, but due to a lack of research, the understanding and potential of this technology is unexplored. This study aims to determine the crucial AI implementation barriers in public healthcare from the viewpoint of the society, the economy, and the infrastructure. The study used MCDM techniques to structure the multiple-level analysis of the AI implementation. The research outcomes contribute to the understanding of the various implementation barriers and provide insights for the decision makers for their future actions. The results show that there are a few critical implementation barriers at the tactical, operational, and strategic levels. The findings contribute to the understanding of the various implementation issues related to the governance, scalability, and privacy of AI and provide insights for decision makers for their future actions. These AI implementation barriers are encountered due to the wider range of system-oriented, legal, technical, and operational implementations and the scale of the usage of AI for public healthcare.