The need for global energy conservation has become more urgent because of the negative effects of excessive energy use, such as higher fuel consumption, greater environmental pollution, and depletion of the ozone layer. There has been a significant increase in the demand for central and high-capacity household air conditioning systems in Muscat in recent years. The need for this is influenced by factors such as arid climate, increasing temperatures, air pollution, and population increase. As a result, there has been a significant increase in electricity use, putting a strain on power resources. To tackle this difficulty, the incorporation of solar collectors as supplementary thermal compressors in air conditioning systems offers a chance to utilise renewable energy sources. The objective of this hybrid technique is to enhance the effectiveness of cooling systems, hence minimising the need for electricity and lowering the release of environmental pollutants.

The interest in smart grids and new technologies is growing around the world. Countries are investing in the development of new technologies that will help achieve environmental goals, energy supply efficiency, improve energy efficiency and increase consumer involvement in the energy generation. One of such technology is a blockchain. It is believed that the blockchain, combined with a smart grid, provides an opportunity to integrate the activities of all stakeholders, including: generators, distributors and consumers of electricity. The aim of the article is to identify the key research areas discussed by the researchers of both the smart grid and the blockchain issues. An analysis of the Scopus database from 2015 to 2023 was conducted. Using a created bibliometric query, a systematic literature review was conducted. 476 scientific publications relating to the issues addressed were identified. Using the VOSviewer software, a bibliometric analysis was performed using the author’s keywords. The bibliometric maps obtained allowed for the identification of key research areas. The article also presents potential future directions of scientific considerations, which should be focused on the issue of green smart grid and green blockchain. The results presented in the article can inspire researchers looking for research gaps or describing the current state of knowledge in the field of the smart grid and the blockchain issues.

Global warming is a thermodynamic problem. When excess heat is added to the climate system, the land warms more quickly than the oceans due to the land’s reduced heat capacity. The oceans have a greater heat capacity because of their higher specific heat and the heat mixing in the upper layer of the ocean. Thermodynamic Geoengineering (TG) is a global cooling method that, when deployed at scale, would generate 1.6 times the world’s current supply of primary energy and remove carbon dioxide (CO₂) from the atmosphere. The cooling would mirror the ostensible 2008–2013 global warming hiatus. At scale, 31,000 1-gigawatt (GW) ocean thermal energy conversion (OTEC) plants are estimated to be able to: a) displace about 0.8 watts per square meter (W/m²) of average global surface heat from the surface of the ocean to deep water that could be recycled in 226-year cycles, b) produce 31 terawatts (TW) (relative to 2019 global use of 19.2 TW); c) absorb about 4.3 Gt CO₂ per year from the atmosphere by cooling the surface. The estimated cost of these plants is $2.1 trillion per year, or 30 years to ramp up to 31,000 plants, which are replaced as needed thereafter. For example, the cost of world oil consumption in 2019 was $2.3 trillion for 11.6 TW. The cost of the energy generated is estimated at $0.008/KWh.

This paper is devoted to the discussion of dynamical properties of anisotropic dark energy cosmological model of the universe in a Bianchi type-V space time in the framework of scale covariant theory of gravitation formulated by Canuto et al.(phys.Rev.Lett.39:429,1977).A  dark energy cosmological model is presented by solving the field equations of this theory by using some physically viable conditions. The dynamics of the model is  studied  by computing the cosmological parameters, dark energy density, equation of state(EoS) parameter, skewness parameters, deceleration parameter and the jerk parameter. This being a scalar field model gives us the quintessence model of the universe which describes a significant dark energy candidate of our accelerating universe. All the physical quantities discussed are in agreement with the recent cosmological observations.

Credit policies for clean and renewable energy businesses play a crucial role in supporting carbon neutrality efforts to combat climate change. Clustering the credit capacity of these companies to prioritize lending is essential given the limited capital available. Support Vector Machine (SVM) and Artificial Neural Network (ANN) are two robust machine learning algorithms for addressing complex clustering problems. Additionally, hyperparameter selection within these models is effectively enhanced through the support of a robust heuristic optimization algorithm, Particle Swarm Optimization (PSO). To leverage the strength of these advanced machine learning techniques, this paper aims to develop SVM and ANN models, optimized with the PSO, for the clustering problem of green credit capacity in the renewable energy industry. The results show low Mean Square Error (MSE) values for both models, indicating high clustering accuracy. The credit capabilities of wind energy, clean fuel, and biomass pellet companies are illustrated in quadrant charts, providing stakeholders with a clear view to adjust their credit strategies. This helps ensure the efficient operation of banking green credit policies.