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This study aims to exploit the abundance of solar energy resources for socioeconomic development in the semi -arid Northeastern Brazil as a potent adaptation tool to global climate change. It points out a set of conjuncture factors that allow us to foresee a new paradigm of sustainable development for the region by transforming the sun’s radiant energy into electricity through distributed photovoltaic generation. The new paradigm, as presented in this essay, has the transformative potential to free the region from past regional development dogma, which was dependent on the scarce water resource, and the marginal and predatory use of its Caatinga Biome.

The research uses a pre ante design, following the procedures of scenario building, as an adaptation mechanism to climate change in the sector of energy generation and socioeconomic inclusion.

The scenarios of socioeconomic resilience to climate change based on the abundance of solar radiation, rather than the scarcity of water, demonstrates its potential as a global adaptation paradigm to climate change.

The developments proposed are dependent on federal legislation changes, allowing the small producer to be remunerated by the energy produced.

The proposed smart grid photovoltaic generation program increases the country's resiliency to the effect of droughts and climate change.

As proposed, the program allows for the reversion of a pattern of long term poverty in semi-arid Northeast Brazil.

The exploitation of the characteristics of abundance of the semiarid climate, i.e. its very condition of semi-aridity with abundant solar radiation, is itself an advantage factor toward adaption to unforeseen drought events. Extensive previous research has focused on weighting and monitoring drought i.e. the paradigm of scarcity. The interplay between exploiting Northeast Brazil’s abundant factors and climate change adaptation, especially at the small farmer levels constitutes a discovery never before contemplated.

This study’s fundamental objective is to assess climate change impact on reference evapotranspiration (ET_o) patterns in Egypt under the latest shared socioeconomic pathways (SSPs) of climate change scenarios. Additionally, the study considered the change in the future solar radiation and actual vapor pressure and predicted them from historical data, as these factors significantly impact changes in the ET_o.

The study utilizes data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) models to analyze reference ET_o. Six models are used, and an ArcGIS tool is created to calculate the monthly average ET_o for historical and future periods. The tool considers changes in actual vapor pressure and solar radiation, which are the primary factors influencing ET_o.

The research reveals that monthly reference ET_o in Egypt follows a distinct pattern, with the highest values concentrated in the southern region during summer and the lowest values in the northern part during winter. This disparity is primarily driven by mean air temperature, which is significantly higher in the southern areas. Looking ahead to the near future (2020–2040), the data shows that Aswan, in the south, continues to have the highest annual ET_o, while Kafr ash Shaykh, in the north, maintains the lowest. This pattern remains consistent in the subsequent period (2040–2060). Additionally, the study identifies variations in ET_o , with the most significant variability occurring in Shamal Sina under the SSP585 scenario and the least variability in Aswan under the SSP370 scenario for the 2020–2040 time frame.

This study’s originality lies in its focused analysis of climate change effects on ET_o, incorporating crucial factors like actual vapor pressure and solar radiation. Its significance becomes evident as it projects ET_o patterns into the near and distant future, providing indispensable insights for long-term planning and tailored adaptation strategies. As a result, this research serves as a valuable resource for policymakers and researchers in need of in-depth, region-specific climate change impact assessments.

The purpose of the current flow configurations is to bring to attention the thermophysical aspects of magnetohydrodynamics (MHD) Williamson nanofluid flow under the effects of Joule heating, nonlinear thermal radiation, variable thermal coefficient and activation energy past a rotating stretchable surface.

A mathematical model is examined to study the heat and mass transport analysis of steady MHD Williamson fluid flow past a rotating stretchable surface. Impact of activation energy with newly introduced variable diffusion coefficient at the mass equation is considered. The transport phenomenon is modeled by using highly nonlinear PDEs which are then reduced into dimensionless form by using similarity transformation. The resulting equations are then solved with the aid of fifth-order Fehlberg method.

The rotating fluid, heat and mass transport effects are analyzed for different values of parameters on velocity, energy and diffusion distributions. Parameters like the rotation parameter, Hartmann number and Weissenberg number control the flow field. In addition, the solar radiation, Joule heating, Prandtl number, thermal conductivity, concentration diffusion coefficient and activation energy control the temperature and concentration profiles inside the stretching surface. It can be analyzed that for higher values of thermal conductivity, Eckret number and solar radiation parameter the temperature profile increases, whereas opposite behavior is noticed for Prandtl number. Moreover, for increasing values of temperature difference parameter and thermal diffusion coefficient, the concentration profile shows reducing behavior.

This paper is useful for researchers working in mathematical and theoretical physics. Moreover, numerical results are very useful in industry and daily-use processes.

The Middle East and North Africa (MENA) region is known for its extreme weather conditions during Summer. A major determinant of the sustainability of the design of a building is its fenestrations. The purpose of this paper is to explore the problem of designing and locating windows on building facades such that a number of relevant criteria to the MENA region are optimised, including solar heat gain, privacy, daylighting and cost of installation.

A multi-objective optimisation problem is proposed with the focus on capturing the requirements of residential dwellings in the MENA region. Since the problem contains conflicting objectives that need to be optimised, a lexicographic approach is adopted. In order to display the Pareto curve, a bi-objective analysis based on the ε-constraint method is utilised.

The conflicting nature of the proposed problem is indicated via the Pareto optimal solutions yielded. Depending on the preference of criteria adopted in lexicographic optimisation, the location of the windows on the building façade tends to change. The bi-objective analysis indicates the importance of balancing out the daylight factor against each of privacy, solar heat gain and installation cost criteria. Furthermore, an analysis conducted in three major cities in the MENA region highlights the discrepancy in design alternatives generated depending on the local climatic condition.

This work proposes a novel mathematical optimisation model which focuses on producing a sustainable design and layout for windows on the facades of residential dwellings located in the MENA region. The proposed model provides designers with guidance through an automated support tool that yields optimised window designs and layout to ensure the sustainability of their designed buildings.

Solar power forecasting will have a significant impact on the future of large-scale renewable energy plants. Predicting photovoltaic power generation depends heavily on climate conditions, which fluctuate over time. In this research, we propose a hybrid model that combines machine-learning methods with Theta statistical method for more accurate prediction of future solar power generation from renewable energy plants. The machine learning models include long short-term memory (LSTM), gate recurrent unit (GRU), AutoEncoder LSTM (Auto-LSTM) and a newly proposed Auto-GRU. To enhance the accuracy of the proposed Machine learning and Statistical Hybrid Model (MLSHM), we employ two diversity techniques, i.e. structural diversity and data diversity. To combine the prediction of the ensemble members in the proposed MLSHM, we exploit four combining methods: simple averaging approach, weighted averaging using linear approach and using non-linear approach, and combination through variance using inverse approach. The proposed MLSHM scheme was validated on two real-time series datasets, that sre Shagaya in Kuwait and Cocoa in the USA. The experiments show that the proposed MLSHM, using all the combination methods, achieved higher accuracy compared to the prediction of the traditional individual models. Results demonstrate that a hybrid model combining machine-learning methods with statistical method outperformed a hybrid model that only combines machine-learning models without statistical method.

Purpose – The purpose of this study was to determine the efficacy value of national street lighting on energy conservation and carbon dioxide (CO₂) emission reduction.

Design/Methodology/Approach – The methods used are the measurement of electrical parameters (low voltage network), the national road illumination level with SON lamp specification, 400 W, 180 W, and 110 Lumen/W, the simulation of energy conservation calculation, and the CO₂ emission reduction obtained by utilizing panel solar cells as a source of energy and LED lights for illumination.

Finding – The results show the efficacy of a 100-W light bulb at an altitude of 8 m for the following specification of light bulbs: LED, 130 Lumens/W, SON, 110 Lumen/W, and MBF, 53 Lumen/W gives the illumination level respectively 13,913 Lux, 11,773 Lux, and 5,672 Lux. By replacing the 180 W SON lamp with an LED, 100 W, of energy conservation by 3.171 GW h is obtained, which is equivalent to a CO₂ emission reduction of 3.641 kTon CO₂.

Originality/Value – This study is a continuation of a study of energy conservation with the utilization of solar cells as an electrical power source for an LED bulb that replaces low-voltage networks as a power source for the bulb type SON.

The Gulf Cooperation Council (GCC) of countries has some of the highest electricity consumptions and carbon dioxide emissions per capita in the world. This poses a direct challenge to the GCC government’s ability to meet their CO₂ reduction targets. In this review paper the current household electricity consumption situation in the GCC is reviewed.

Three scenarios for reducing energy consumption and CO₂ emissions are proposed and evaluated using strengths, weaknesses, opportunities and threats (SWOT) as well as the political, economic, social, technical, legal and environmental (PESTLE) frameworks.

The first scenario found that using solar Photovoltaic (PV) or hybrid solar PV and wind system to power household lighting could save significant amounts of energy, based on lighting making up between 8% to 30% of electricity consumption in GCC households. The second scenario considers replacement of conventional appliances with energy-efficient ones that use around 20% less energy. The third scenario looks at influencing consumer behavior towards sustainable energy consumption.

Pilot trials of these scenarios are recommended for a number of households. Then the results and feedback could be used to launch the schemes GCC-wide.

The proposed scenarios are designed to encourage responsible electricity consumption and production within households (SDG12).

All three proposals are found viable for policymakers to implement. However, to ensure successful implementation GCC Governments are recommended to review all the opportunities and challenges associated with these schemes as laid out in this paper.

Research to find new energy source is still an intensive work by researchers in this field. One of the energy sources with no negative impact to environment is solar energy. Solar cell is used to convert solar energy to electrical energy. The electrically powered solar cell in direct current (DC) power is not suitable for our daily office equipment since they need the alternating current (AC) power. This research has succeeded in realizing a solar cell automation tool based on Arduino Uno with input from solar energy, from which output AC voltage can be used for the needs of household appliances and office equipments. Output power of this tool is approximately 700 W, which can turn on the lights, charge the hand phones, laptops, and so forth.