• Energy Research

<p>Climate change necessitates an immediate and sustained global effort to reduce greenhouse gas emissions while adapting to the increased climate risks caused by historical emissions. This broader climate transition will involve mass global interventions including renewable energy deployment, coastal protection and retreat, and enhanced space cooling, which will result in CO<sub>2</sub> emissions from energy and materials use. Yet, the magnitude of these emissions remains largely unconstrained, leaving open the potential for under-accounting of emissions and conflicts or synergies between mitigation and adaptation goals. Here, we use a suite of models to estimate the CO<sub>2</sub> emissions embedded in the broader climate transition. For a pathway limiting warming to 2°C, we estimate that selected adaptations will emit ~1.5GtCO<sub>2</sub> through 2100. Emissions from energy used to deploy renewable capacity are much larger at ~95GtCO<sub>2</sub>, equivalent to over two years of current global emissions and ~8% of the remaining carbon budget for 2°C. These embedded transition emissions are reduced by 80% to 20GtCO<sub>2</sub> under a rapid decarbonization scenario limiting warming to 1.5°C. However, they roughly double to 185GtCO<sub>2</sub> under a low-ambition transition consistent with current policies (2.7°C warming by 2100), mainly because a slower transition relies more on fossil fuels. Under this status-quo, the emissions embedded in the transition total nearly half the remaining carbon budget for 1.5°C. Our results provide the first holistic assessment of the carbon emissions embedded in the transition itself, and suggest that these emissions can be largely minimized through rapid energy decarbonization, an underappreciated benefit of enhanced climate ambition.  </p>

Saudi Arabia fully depends on fossil fuels such as oil and natural gas to generate its electricity. Fossil fuels may have limited life and a history of fluctuating costs, which will lead to multiple issues that can affect the energy security of this country in the long-term. Critical Infrastructure Protection (CIP) is a concept different to “energy security”, which must consider the solar and wind energy as basic sources of energy supplies in Saudi Arabia. Monte Carlo Simulation (MCS) and Brownian Motion (BM) approaches were employed to predict the future behaviour of solar and wind energy, along with long-term temperature performance, based on 69 years of historical daily data. MCS and BM were employed to provide a wide range of options for future prediction results. A validation exercise showed that the north-western region was the most highly recommended region for deployment of solar and wind energy applications due to an abundance of solar and wind energy resources with low temperature supported by a clearer sky during the year. This is followed by the southern region, which exhibited good solar and wind energy resources. This study can be considered as a roadmap to meet the climate and sustainability goals by providing a long-term overview of solar energy, wind energy, and temperature performance in some countries that have a lack of long-term future prediction analysis such as Saudi Arabia.

Climate change mitigation is one of the most critical challenges of this century. The unprecedented global effects of climate change are wide-ranging, including changing weather patterns that threaten food production, increased risk of catastrophic floods, and rising sea levels. Adapting to these impacts will be more difficult and costly in the future if radical changes are not made now. This review paper evaluates the Gulf Cooperation Council (GCC) countries’ potential for solar and wind energy resources to meet climate change mitigation requirements and assesses the ability of the GCC region to shift towards low-carbon technologies. The review demonstrates that the GCC region is characterized by abundant solar energy resources. The northwestern, southeastern, and western mountains of the region are highlighted as locations for solar energy application. Oman displays the highest onshore wind speed range, 3–6.3 m s⁻1, and has the highest annual solar radiation of up to 2500 kWh/m2. Kuwait has the second highest onshore wind speed range of 4.5–5.5 m s⁻1. The western mountains and northwestern Saudi Arabia have a wind speed range of 3–6 m s⁻1. The United Arab Emirates (UAE) has the second highest annual solar radiation, 2285 kWh/m2, while Saudi Arabia and the state of Kuwait have equal annual solar radiation at 2200 kWh/m2. This review demonstrates that abundant offshore wind energy resources were observed along the coastal areas of the Arabian Gulf, as well as a potential opportunity for wind energy resource development in the Red Sea, which was characterized by high performance. In addition, the GCC countries will not be able to control and address the interrelated issues of climate change in the future if they do not eliminate fossil fuel consumption, adhere to the Paris Agreement, and implement plans to utilize their natural resources to meet these challenges.

Carbon capture and storage (CCS) for fossil-fuel power plants is perceived as a critical technology for climate mitigation. Nevertheless, limited installed capacity to date raises concerns about the ability of CCS to scale sufficiently. Conversely, scalable renewable electricity installations—solar and wind—are already deployed at scale and have demonstrated a rapid expansion potential. Here we show that power-sector CO2 emission reductions accomplished by investing in renewable technologies generally provide a better energetic return than CCS. We estimate the electrical energy return on energy invested ratio of CCS projects, accounting for their operational and infrastructural energy penalties, to range between 6.6:1 and 21.3:1 for 90% capture ratio and 85% capacity factor. These values compare unfavourably with dispatchable scalable renewable electricity with storage, which ranges from 9:1 to 30+:1 under realistic configurations. Therefore, renewables plus storage provide a more energetically effective approach to climate mitigation than constructing CCS fossil-fuel power stations. Carbon capture and storage can help reduce fossil-fuel power-plant emissions. Here the authors show that the energy return on input of thermal plants with carbon capture is in general lower than the energy return of most types of renewable energy even when combined with energy storage.

This Special Issue focuses on proposing and analyzing systemic interdisciplinary approaches to support collaborative strategies and agreed-upon global sustainability policies toward addressing the challenges that lie ahead for our planet’s future. The contributions target applications in system dynamics, systems thinking, discrete event simulation, agent-based modelling, and hybrid approaches and provide valuable qualitative and quantitative insights to guide the collaborative efforts of governments, institutions, organizations in general, and even the financial sector toward the next Conference of Parties (COP28).

The rapid growth of wind and solar energy penetration has created critical issues, such as fluctuation, uncertainty, and intermittence, that influence the power system stability, grid operation, and the balance of the power supply. Improving the reliability and accuracy of wind and solar energy predictions can enhance the power system stability. This study aims to contribute to the issues of wind and solar energy fluctuation and intermittence by proposing a high-quality prediction model based on neural networks (NNs). The most efficient technology for analyzing the future performance of wind speed and solar irradiance is recurrent neural networks (RNNs). Bidirectional RNNs (BRNNs) have the advantages of manipulating the information in two opposing directions and providing feedback to the same outputs via two different hidden layers. A BRNN’s output layer concurrently receives information from both the backward layers and the forward layers. The bidirectional long short-term memory (BI-LSTM) prediction model was designed to predict wind speed, solar irradiance, and ambient temperature for the next 169 h. The solar irradiance data include global horizontal irradiance (GHI), direct normal irradiance (DNI), and diffuse horizontal irradiance (DHI). The historical data collected from Dumat al-Jandal City covers the period from 1 January 1985 to 26 June 2021, as hourly intervals. The findings demonstrate that the BI-LSTM model has promising performance in terms of evaluation, with considerable accuracy for all five types of historical data, particularly for wind speed and ambient temperature values. The model can handle different sizes of sequential data and generates low error metrics.

Time series modeling is an effective approach for studying and analyzing the future performance of the power sector based on historical data. This study proposes a forecasting framework that applies a seasonal autoregressive integrated moving average with exogenous factors (SARIMAX) model to forecast the long-term performance of the electricity sector (electricity consumption, generation, peak load, and installed capacity). In this study, the model was used to forecast the aforementioned factors in Saudi Arabia for 30 years from 2021 to 2050. The historical data that were inputted into the model were collected from Saudi Arabia at quarterly intervals across a 40-year period (1980−2020). The SARIMAX technique applies a time series approach with seasonal and exogenous influencing factors, which helps reduce the error values and improve the overall model accuracy, even in the case of close input and output dataset lengths. The experimental findings indicated that the SARIMAX model has promising performance in terms of categorization and consideration, as it has significantly improved forecasting accuracy compared with the simpler autoregressive integrated moving average-based techniques. Furthermore, the model is capable of coping with different-sized sequential datasets. Finally, the model aims to help address the issue of a lack of future planning and analyses of power performance and intermittency, and it provides a reliable forecasting technique, which is a prerequisite for modern energy systems.