Despite the low local energy access rates, Africa is considered a key player in the global energy transition due to its large supply of fossil fuels and a large reserve of critical minerals essential for manufacturing renewable energy components in the energy sector and storage devices in the transportation and electronics sectors. But building a sustainable society at all levels across nations would only come when there exists a just and inclusive energy transition based on the idea of “leave no one behind”. While many African countries have embarked on ambitious and transformative transition strategies, and many energy projects classified as “clean” have economic, environmental, and social implications that jeopardize the wellbeing of those already vulnerable to the impacts of climate change. This paper explores the policy implications of the just transition to ensure that efforts to steer Africa towards a lower carbon future are supported by fair, equity, and justice considerations. Our analyses provide valuable evidence for considering a just transition in Africa that will not exacerbate the current socio-economic challenges the region is facing but will support sustained poverty reduction and the achievement of faster economic growth. Our findings show that the African continent’s multiple challenges of energy security, economic growth, and affordable access must feature in its clean energy transition. We draw conclusions that an incremental transition emphasizing low-carbon development is the most feasible and pragmatic approach to transform the region’s economy and address climate change challenges.

Exhaled breath is a body secretion, and the sampling process of this is simple and cost effective. It can be non-invasively collected for diagnostic procedures. Variations in the chemical composition of exhaled breath resulting from gaseous exchange in the extensive capillary network of the body are proposed to be associated with pathophysiological changes. In light of the foreseeable potential of exhaled breath as a diagnostic specimen, we used gas chromatography and mass spectrometry (GC-MS) to study the chemical compounds present in exhaled breath samples from patients with Alzheimer’s disease (AD), Parkinson’s disease (PD), and from healthy individuals as a control group. In addition, we also designed and developed a chemical-based exhaled breath sensor system to examine the distribution pattern in the patient and control groups. The results of our study showed that several chemical compounds, such as 1-phenantherol and ethyl 3-cyano-2,3-bis (2,5,-dimethyl-3-thienyl)-acrylate, had a higher percentage area in the AD group than in the PD and control groups. These results may indicate an association of these chemical components in exhaled breath with the progression of disease. In addition, in-house fabricated exhaled breath sensor systems, containing several types of gas sensors, showed significant differences in terms of the normalized response of the sensitivity characteristics between the patient and control groups. A subsequent clustering analysis was able to distinguish between the AD patients, PD patients, and healthy individuals using principal component analysis, Sammon’s mapping, and a combination of both methods, in particular when using the exhaled breath sensor array system A consisting of eight sensors. With this in mind, the exhaled breath sensor system could provide alternative option for diagnosis and be applied as a useful, effective tool for the screening and diagnosis of AD in the near future.

Nigeria is a nation endowed with both abundant renewable and non-renewable energy resources. Despite its vast potential, Nigeria struggles with a consistent power supply due to various systemic issues, such as inadequate funding, infrastructural decay, corruption, technical skill shortages, and macroeconomic instability. These challenges hinder the effective harnessing and distribution of energy resources, particularly renewable ones like wind, solar, biomass, and hydropower. This study assesses the existing energy policies and their efficacy in promoting sustainable energy development towards achieving universal electricity access by 2030. It highlights the necessity for a just energy transition that integrates a substantial proportion of renewable energy into the national grid, aiming to meet up to 60% of the country’s energy demands with clean sources by 2050. This transition is critical not only for energy security and reducing the environmental impact but also for fostering socioeconomic equity. Recommendations include overhauling the legal and regulatory frameworks to support renewable energy growth, particularly in off-grid areas, to ensure clean, affordable, and secure energy access. Strategic investments, enhanced infrastructure, and robust public–private partnerships are essential to overcome the current barriers and realize Nigeria’s energy potential. This paper calls for a comprehensive approach that addresses both the technical and socioeconomic dimensions of the energy crisis, laying the groundwork for a sustainable and prosperous energy future for Nigeria.

Currently, the world is actively responding to climate change problems. There is significant research interest in renewable energy generation, with focused attention on solar photovoltaic (PV) generation. Therefore, this study developed an accurate and precise solar PV generation prediction model for several solar PV power plants in various regions of South Korea to establish stable supply-and-demand power grid systems. To reflect the spatial and temporal characteristics of solar PV generation, data extracted from satellite images and numerical text data were combined and used. Experiments were conducted on solar PV power plants in Incheon, Busan, and Yeongam, and various machine learning algorithms were applied, including the SARIMAX, which is a traditional statistical time-series analysis method. Furthermore, for developing a precise solar PV generation prediction model, the SARIMAX-LSTM model was applied using a stacking ensemble technique that created one prediction model by combining the advantages of several prediction models. Consequently, an advanced multisite hybrid spatio-temporal solar PV generation prediction model with superior performance was proposed using information that could not be learned in the existing single-site solar PV generation prediction model.