Photovoltaic systems (PV) have been extensively used worldwide as a reliable and effective renewable energy resource due to their environmental and economic merits. However, PV systems are prone to several environmental and weather conditions that impact their performance. Amongst these conditions is dust accumulation, which has a significant adversative impact on the solar cells’ performance, especially in hot and arid regions. This study provides a comprehensive review of 278 articles focused on the impact of dust on PV panels’ performance along with other associated environmental factors, such as temperature, humidity, and wind speed. The review highlights the importance of modelling dust accumulation along with other ecological factors due to their interactive nature, and the differences between cleaning techniques and schedules effectiveness. Moreover, the study provides a review of statistical and artificial intelligence models used to predict PV performance and its prediction accuracies in terms of data size and complexity. Finally, the study draws attention to several research gaps that warrant further investigation. Among these gaps is the need for proper dynamic optimisation models for cleaning schedules and a more advanced machine and deep learning models to predict dust accumulation while considering environmental and ageing factors.

The shift toward renewable energy resources, and photovoltaic systems specifically, has gained a huge focus in the past two decades. This study aimed to assess several environmental and economic impacts of a photovoltaic system that installed on the rooftop of an industrial facility in Dubai, United Arab Emirates (UAE). The life cycle assessment method was employed to study all the flows and evaluate the environmental impacts, while several economic indicators were calculated to assess the feasibility and profitability of this photovoltaic system. The results showed that the production processes contributed the most to the environmental impacts, where the total primary energy demand was 1152 MWh for the whole photovoltaic system, the total global warming potential was 6.83 × 10^–2 kg CO₂-eq, the energy payback time was 2.15 years, the carbon dioxide payback time was 1.87 years, the acidification potential was 2.87 × 10^–4 kg SO₂-eq, eutrophication potential was 2.45 × 10^–5 kg PO₄³-eq, the ozone layer depletion potential was 4.685 × 10^–9 kgCFC-11-eq, the photochemical ozone creation potential was 3.81 × 10^–5 kg C₂H₄-eq, and the human toxicity potential was 2.38 × 10^–2 kg1,4-DB-eq for the defined function unit of the photovoltaic system, while the economic impact indicators for the whole system resulted in a 3.5 year payback period, the benefit to cost ratio of 11.8, and 0.142 AED/kWh levelized cost of electricity. This was the first study to comprehensively consider all of these impact indicators together. These findings are beneficial inputs for policy- and decision-makers, photovoltaic panel manufacturers, and photovoltaic contractors to enhance the sustainability of their processes and improve the environment.

The residential sector is multi-faceted by nature. Although evidence shows that the UAE is among the countries in the world that take sustainability seriously, there is a lack of information about the perception of sustainability by occupants in the residential sector in the UAE. The aim of this paper is to assess the perception of sustainability of the residential sector in the UAE, which is achieved by following a methodological framework using the relevant literature review and experts’ knowledge. An online survey was distributed to the targeted population, followed by a statistical analysis to fulfill the aim of the paper. Results confirm the correlation between social, economic, and environmental aspects of sustainability. Additionally, structural equation modeling reveals that the perception of sustainability is significantly influenced by economic and environmental aspects in the residential sector in the UAE. Comparative analysis shows a statistical difference in the perception of sustainability among gender, educational level, employment status, and monthly income. Finally, a predictive classification model is built to classify the perception of occupants based on their attributes using decision tree algorithms. The outcomes of this study would be beneficial to policy and decision makers, developers, contractors, designers, and facility management entities to enhance overall sustainability in the residential sector.

Transportation is fundamental, granting access to goods, services, and economic opportunities. Ensuring sustainable transportation, especially in vehicular modes, is crucial for the pillars of social, economic, and environmental sustainability. High-traffic countries, like the United Arab Emirates (UAE), grapple with significant challenges to this end. This study delves into the repercussions of traffic-related incidents on UAE road users and their intricate links to the social and economic dimensions of sustainability. To achieve this, this work examines the influential demographic factors contributing to incidents, utilizing artificial neural network models to predict the likelihood of individuals experiencing traffic tickets and accidents. Findings reveal associations between gender, driving frequency, age, nationality, and reported incident frequency. Men experience more accidents and tickets than women. Age exhibits a negative linear relationship with incident occurrence, while driving experience shows a positive linear relationship. Nationalities and cultural backgrounds influence road users’ adherence to traffic rules. The predictive models in this study demonstrate their high accuracy, with 93.7% precision in predicting tickets and 95.8% in predicting accidents. These insights offer valuable information for stakeholders, including government entities, road users, contractors, and designers, contributing to the enhancement of the social and economic aspects of road sustainability.

Photovoltaic (PV) systems are important components of the global shift towards sustainable energy resources, utilizing solar energy to generate electricity. However, the efficiency and performance of PV systems heavily rely on cleanliness, as dust accumulation can significantly obstruct their effectiveness over time. This study undertook a comprehensive literature review and carried out multiple interviews with experts in the PV systems field to propose a map for selecting the optimal PV cleaning method for PV systems within MENA region. These factors, covering meteorological conditions, the local environment, PV system design, module characteristics, dust deposition attributes, exposure time to dust, and socio-economic and environmental considerations, were employed as criteria in a Multi-Criteria Decision-Making (MCDM) model, specifically, an Analytic Network Process (ANP). The results indicate that partially automated cleaning is the most suitable method for existing utility-scale PV projects in the MENA region. The findings provide robust guidelines for PV system stakeholders, aiding informed decision-making and enhancing the sustainability of PV cleaning processes.