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Background: To reduce the effects of climate change, the current fossil-based energy system must transition to a low-carbon system based largely on renewables. In both academic literature and non-academic discourse concerning the energy transition, resilience is frequently mentioned as an additional objective or requirement. Despite its frequent use, resilience is a very malleable term with different meanings in different contexts. Main text: This paper seeks to identify how resilience is understood in the field of the energy system and whether there are similar aspects in the different ways the term is understood. To this end, we review more than 130 papers for definitions of energy system resilience. In addition, we use different aspects to categorize and examine these. The results paint a diverse picture in terms of the definition and understanding of resilience in the energy system. However, a few definition archetypes can be identified. The first uses a straightforward approach, in which the energy system has one clearly defined equilibrium state. Here, resilience is defined in relation to the response of the energy system to a disturbance and its ability to quickly return to its equilibrium. The second type of resilience allows for different equilibriums, to which a resilient energy system can move after a disruption. Another type of resilience focuses more on the process and the actions of the system in response to disruption. Here, resilience is defined as the ability of the system to adapt and change. In the papers reviewed, we find that the operational definition of resilience often encompasses aspects of different archetypes. This diversity shows that resilience is a versatile concept with different elements. Conclusions: With this paper, we aim to provide insight into how the understanding of resilience for the energy system differs depending on which aspect of the energy system is studied, and which elements might be necessary for different understandings of resilience. We conclude by providing information and recommendations on the potential usage of the term energy system resilience based on our lessons learned. [ABSTRACT FROM AUTHOR]

Background: Renewable energy and climate change are vital aspects of humanity. Energy is needed to sustain life on Earth. The exploration and utilisation of traditional fossil-based energy has led to global warming. The exploration and use of fossil-based energy have significantly contributed to global warming, making the shift to renewable energy crucial for mitigating climate change. Renewable energies offer a sustainable alternative that does not harm the environment. This review aims to examine the role of machine learning (ML) in optimising renewable energy systems and enhancing climate change mitigation strategies, addressing both opportunities and challenges in this evolving field. The vital significance of renewable energy and measures to circumvent climate change in modern civilisation is first contextualised in the review. It draws attention to the difficulties encountered in these fields and describes the exciting potential of ML to solve them. Important discoveries highlight how ML can improve renewable energy technology scalability, dependability and efficiency while enabling more precise climate change projections and practical mitigation strategies. Simultaneously, issues including ethical considerations, interpretability of models and data quality demand attention. Method: This review conducted a systematic literature analysis on the application of ML in renewable energy and climate change mitigation. It involved a comprehensive search, selection, and analysis of recent studies, focusing on ML's role in energy forecasting, predictive maintenance, and climate modelling. The review synthesised key developments, challenges, and future directions, emphasising the need for ongoing transdisciplinary research to fully realise ML's potential in advancing sustainable energy solutions. Result: The review found that machine learning significantly enhances renewable energy system efficiency, scalability, and climate change mitigation through improved forecasting, predictive maintenance, and climate modelling. However, challenges like ethical concerns, model interpretability, and data quality persist. Ongoing research is essential to fully leverage ML's potential in these areas. Short conclusion: The paper highlights how machine learning can be used to revolutionise the energy and climate change mitigation industries for sustainable futures. It promotes ongoing transdisciplinary research and innovation to fully realise ML's synergistic potential and tackle urgent global issues. In the end, the review advances our knowledge of how to use ML to hasten the transition to a future that is more robust and sustainable. [ABSTRACT FROM AUTHOR]

This paper investigates the connection between climate change and energy security in Europe and provides empirical evidence that these issues are the two faces of the same coin. Using a panel of 39 European countries during the period 1980–2020, the empirical analysis presented in this paper indicates that increasing the share of nuclear, renewables, and other nonhydrocarbon energy and improving energy efficiency could lead to a significant reduction in carbon emissions and improve energy security throughout Europe. Accordingly, policies and reforms aimed at shifting away from hydrocarbons and increasing energy efficiency in distribution and consumption are key to mitigating climate change, reducing energy dependence, and minimizing exposure to energy price volatility. [ABSTRACT FROM AUTHOR]

Increasing the adoption of energy-efficiency measures (EEM) by households is one of the practical strategies to increase energy efficiency and reduce greenhouse gas emissions. For the 3.24 million low-income households in Australia, partly due to their potentially disproportionately large waste of usable energy, there is a gap between the potential for energy efficiency through existing EEM and the actual energy-efficiency level. Many measures increase energy efficiency in buildings; however, barriers persist for such households to adopt these measures, of which limited research has yet to be conducted. In response, this study examines the relationships involved by a questionnaire survey of 212 low-income households in Australia, showing that financial barriers have the greatest influence followed by split incentive barriers, with the provision of information having no significant role to play. This paper contributes to filling the gap of limited energy-efficiency research involving low-income households, revealing the effect of energy-efficiency barriers on their adoption of EEM. [ABSTRACT FROM AUTHOR]

Energy has certainly played a robust role in economic development. However, the damages it has caused to the environment and climate change are innumerable and difficult to identify. Therefore, the new challenge facing humanity is no longer the rise in energy prices or resources scarcity, but rather the climate crisis. Against this background, our paper aims to analyzing the long-run relationship between the energy sector and climate change phenomena for a panel of 14 Mediterranean countries spanning the period 1990–2019. The analysis was based on different econometric techniques, containing panel unit root tests, panel cointegration tests, and panel regression estimators, i.e., Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS) estimators. The different findings revealed that energy consumption and various air quality indicators such as carbon dioxide emissions (CO2) and nitrous oxide emissions (N2O) have contributed to increasing greenhouse gas emissions (GHGs). This empirical investigation suggests that to minimize the spillovers of global warming and climate shift, the energy path in the Mediterranean should be adjusted by the implementation of energy efficiency measures and the advancement of renewable energy sources for reducing carbon emissions in order to preserve the environment for future generations. [ABSTRACT FROM AUTHOR]

Solar Irradiance depicts the light energy produced by the Sun that hits the Earth. This energy is important for renewable energy generation and is intrinsically fluctuating. Forecasting solar irradiance is crucial for efficient solar energy generation and management. Work in the literature focused on the short-term prediction of solar irradiance, using meteorological data to forecast the irradiance for the next hours, days, or weeks. Facing climate change and the continuous increase in greenhouse gas emissions, particularly from the use of fossil fuels, the reliance on renewable energy sources, such as solar energy, is expanding. Consequently, governments and practitioners are calling for efficient long-term energy generation plans, which could enable 100% renewable-based electricity systems to match energy demand. In this paper, we aim to perform the long-term prediction of daily solar irradiance, by leveraging the downscaled climate simulations of Global Circulation Models (GCMs). We propose a novel Bayesian deep learning framework, named DeepSI (denoting Deep Solar Irradiance), that employs bidirectional long short-term memory autoencoders, prefixed to a transformer, with an uncertainty quantification component based on the Monte Carlo dropout sampling technique. We use DeepSI to predict daily solar irradiance for three different locations within the United States. These locations include the Solar Star power station in California, Medford in New Jersey, and Farmers Branch in Texas. Experimental results showcase the suitability of DeepSI for predicting daily solar irradiance from the simulated climate data, its superiority over related machine learning methods, and its ability to reproduce the daily variability. We further use DeepSI with future climate simulations to produce long-term projections of daily solar irradiance, up to year 2099. [ABSTRACT FROM AUTHOR]

Faced with rapidly rising energy demand in industrialised societies and widespread global concern, countries are actively promoting the transition from conventional to renewable energy systems. The goal is to invest in renewable energy in the most efficient way to meet rising energy demand and reduce the challenges posed by climate change. However, decision makers must carefully weigh various factors when selecting the most appropriate renewable energy investment projects. This paper presents a novel method for Multi-Attribute Decision Making(MADM) that uses the Bipolar Complex Fuzzy(BCF) to convey the vagueness and uncertainty of decision makers, so that the result obtained better reflects the actual scenario and the subjective biases of decision makers. We defined BCF Einstein Weighted Averaging (BCFEWA) operator and BCF Einstein Ordered Weighted Averaging (BCFEOWA) operator to aggregate evaluation information. Then we discussed some properties of the proposed aggregation operators. Additionally, we present an integrated MADM technique grounded in the BCF framework that combines the CRiteria Importance Through Intercriteria Correlation (CRITIC) and ELECTRE III methods. Specifically, the CRITIC method determines attribute weights, and the ELECTRE III method ranking the alternatives to determine the best renewable energy investment projects. After analysing the results and comparisons, it can be inferred that the suggested methodology offers an effective evaluation process. [ABSTRACT FROM AUTHOR]

The need for renewable energy sources is recently necessitated by attaining sustainability and climate change mitigation. Accordingly, the use of renewable energy sources has been growing rapidly during the last two decades. Yet, the potentials of renewable energy sources are generally influenced by several climatic factors that either determine the source of energy such as wind speed in the case of wind power or affect the performance of system such as the reduction in solar PV power production due to temperature increase. This highlights the need for assessing climate change impacts on renewable energy sources in the future to ensure their reliability and sustainability. This paper is intended to assess impacts of climate change on wind and solar potential energy in Egypt by the year 2065 under RCP 8.5 scenario. For this purpose, a GIS-based methodology of three main steps was applied. The results revealed that solar energy potential in Egypt is expected to be relatively less vulnerable to climate change compared to wind energy. In this respect, it was found that while wind energy potential was estimated to range ± 12%. By the year 2065 under RCP 8.5 scenario, PV module power is expected to decrease by about 1.3% on average. Such assessment can assist in developing more sustainable and flexible renewable energy policy in Egypt. [ABSTRACT FROM AUTHOR]

This paper examines the linkages between information and communication technology (ICT) diffusion, renewable energy consumption (REC), and carbon (CO2) emissions across three distinct regions: the South Asian Association for Regional Cooperation (SAARC), Middle East and North Africa (MENA), and Organization for Economic Cooperation and Development (OECD) nations. We explore the causal pathways linking these variables and perform a comparative assessment of region-specific patterns with their policy implications. Utilizing a panel dataset of 28 countries from 1998 to 2019, we employ panel ARDL, FMOLS, DOLS, and D-H causality tests. The comprehensive analysis of the sample reveals that ICT development enhances CO2 emissions, whereas REC reduces emissions. However, the comparative analysis suggests a positive linkage between ICT diffusion and CO2 emissions in MENA and OECD countries but a negative linkage in SAARC countries. Further, REC substantially reduces emissions in MENA and OECD countries but has an insignificant effect in SAARC countries. Our findings corroborate the first-order effect of ICT (negative environmental impact) in MENA and OECD countries, with contextual variations attributed to economic structure, financial sector growth, trade openness, and industrialization. These findings offer pivotal insights for policymakers to leverage ICT capabilities and REC to attain sustainable development and mitigate climate change consequences. [ABSTRACT FROM AUTHOR]

The availability of refined and efficient energy resources has played a decisive role in the advancement of societies, especially since the Industrial Revolution of the mid eighteenth to nineteenth centuries. In the twentieth century, the international energy scenario is experiencing a profound transition in terms of energy resources and their utilization. The energy transition is in response to the challenges the global energy landscape faces such as rapidly growing demand, depleting fossil fuel reserves, surging energy prices, risks associated with the security of supplies, and above all climate change. Nuclear power is an important form of energy making a significant contribution to the electricity mix around the world, especially in developed countries. One of the major advantages of nuclear power is its minimal greenhouse gas emissions as compared to fossil fuels. The paper examines the prospects of nuclear power in the energy transition considering both the trend of phase-out that the technology has experienced primarily in several European countries since the 1980s, as well as the growing interest it has received more recently as a low carbon energy solution toward addressing climate change. It also examines the significance of nuclear power toward energy transition with the help of multi-criteria decision analysis (MCDA) based on expert opinion from developed as well as developing countries. [ABSTRACT FROM AUTHOR]

This study introduces an advanced mathematical methodology for predicting energy generation and consumption based on temperature variations in regions with diverse climatic conditions and increasing energy demands. Using a comprehensive dataset of monthly energy production, consumption, and temperature readings spanning ten years (2010–2020), we applied polynomial, sinusoidal, and hybrid modeling techniques to capture the non-linear and cyclical relationships between temperature and energy metrics. The hybrid model, which combines sinusoidal and polynomial functions, achieved an accuracy of 79.15% in estimating energy consumption using temperature as a predictor variable. This model effectively captures the seasonal and non-linear consumption patterns, demonstrating a significant improvement over conventional models. In contrast, the polynomial model for energy production, while yielding partial accuracy (R² = 0.65), highlights the need for more advanced techniques to fully capture the temperature-dependent nature of energy production. The results indicate that temperature variations significantly affect energy consumption, with higher temperatures driving increased energy demand for cooling, while lower temperatures affect production efficiency, particularly in systems like hydropower. These findings underscore the necessity for integrating sophisticated models into energy planning to ensure resilience in energy systems amidst climate variability. The study offers critical insights for policymakers to optimize energy generation and distribution in response to changing climatic conditions. [ABSTRACT FROM AUTHOR]

Understanding the risks posed to humankind, the environment, and overall growth requires a deep exploration of the profound impact of greenhouse gas (GHG) emissions, especially carbon dioxide (CO2), on global climate change. This study explores the complex relationships among economic extension, energy utilization, financial progress, natural resource rents, and CO2 emissions in Bangladesh. By analyzing annual data from 1974 to 2022, our analysis reveals a long-term connection between the variables, as evidenced by the autoregressive distributed lag (ARDL) bounds test. We found that for every 1% rise in economic growth, energy use, financial progress, and natural resource rents, CO2 emissions rise by 0.13%, 1.50%, 0.39%, and 0.04%, respectively. This is based on the dynamic ordinary least squares (DOLS) method. The outcomes are further supported by the canonical cointegrating regression (CCR), followed by fully modified ordinary least squares (FMOLS). By illuminating the causes of Bangladesh's environmental degradation, this study offers actionable policy suggestions aimed at fostering sustainable growth and enforcing strict regulations that effectively mitigate environmental harm. Highlights: • This study explored the environmental effects of the economy, energy, finance, and natural resources. • Bangladesh's annual time series data were utilized for econometric assessment. • The results show that rising carbon emissions are connected to economic evolution, energy usage, financial development, and natural resource rents. • The results suggest policies toward CO2 emission reduction and sustainable development in Bangladesh. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO2), whose atmospheric volume has been increasing, is the principal greenhouse gas (GHG) that causes global warming and climate change. Climate change from increasing greenhouse gases (GHGs) has broad health and environmental influences. Human-emitted GHGs constitute a significant cause of global warming. The main objective of this article is to assess the influence of technological innovation, economic growth, fossil energy use, renewable energy consumption, and globalisation on CO2 emissions (CO2Es) in South Africa over the observation period 1990–2020. The Dynamic Ordinary Least Squares (DOLS) estimator was implemented to examine the relationship between the constituents. The study results indicate that economic growth, fossil energy use, technological innovation, and globalisation boost CO2Es, whereas renewable energy consumption reduces CO2Es. The output of the canonical cointegrating regression and fully modified least squares estimators also supports the output of the DOLS estimator. We concluded that, in South Africa, a rise in economic growth, fossil energy use, technological innovation, and globalisation results in an upsurge in CO2Es. In contrast, an increase in renewable energy consumption leads to a reduction in CO2Es. This research work contributes to the discourse on CO2Es in the South African context. The study recommends that policymakers in South Africa should design policies that foster the formation of a low-carbon country, the consumption of renewable energy, and the implementation of environment-friendly technological innovations that reduce the use of fossil energy. Highlights: • This study assessed the impact of economic growth, technological innovation, fossil energy use, renewable energy consumption, and globalisation on CO2Es in South Africa. • The analysis revealed that economic growth, fossil energy use, technological innovation, and globalisation boost CO2Es, whereas renewable energy consumption reduces CO2Es. • There is a need to incorporate policies that promote the development of a low-carbon economy. [ABSTRACT FROM AUTHOR]

Water-scarce regions, like the Mediterranean, face worsening conditions due to climate change, intensifying pressure on key economic sectors such as hydropower. Additionally, environmental conservation policies, particularly the implementation of environmental flows, present challenges for hydropower systems. Certainty regarding the impact of these factors on future hydropower production is crucial for informed decision-making in the transition to sustainable energy. This study introduces S + HydPower, a tool coupled with SWAT+ to assess climate change and watershed management effects on small hydropower plant (SHP) systems. In this study, we used this tool to investigate the consequences of implementing environmental flows and climate change on run-of-river SHPs in the Catalan River Basin District (CRBD), in Catalonia. The results show that applying environmental flows would lead to a significant 27% reduction in SHP production. However, this reduction would represent only 0.25% of the region's current energy demand. Furthermore, the study reveals a potential 38% to 73% reduction in SHP production by the end of the twenty-first century due to the combined effects of environmental flows and climate change. This suggests a substantial decline in run-of-river SHP's contribution to the CRBD's electricity supply. These findings emphasize the need to explore alternative and sustainable energy sources to ensure the long-term reliability and resilience of the region's energy supply. [ABSTRACT FROM AUTHOR]

On-site conversion of organic waste into biogas to satisfy consumer energy demand has the potential to realize energy equality and mitigate climate change reliably. However, existing methods ignore either real-time full supply or methane escape when supply and demand are mismatched. Here, we show an improved design of community biogas production and distribution system to overcome these and achieve full co-benefits in developing economies. We take five existing systems as empirical examples. Mechanisms of synergistic adjusting out-of-step biogas flow rates on both the plant-side and user-side are defined to obtain consumption-to-production ratios of close to 1, such that biogas demand of rural inhabitants can be met. Furthermore, carbon mitigation and its viability under universal prevailing climates are illustrated. Coupled with manure management optimization, Chinese national deployment of the proposed system would contribute a 3.77% reduction towards meeting its global 1.5 °C target. Additionally, fulfilling others' energy demands has considerable decarbonization potential. Shen and colleagues demonstrate an improved design for community biogas production and distribution system to overcome common obstacles and achieve full co-benefits in developing economies. [ABSTRACT FROM AUTHOR]

Rapid urbanization, climate change, and aging infrastructure pose significant challenges to achieving sustainability and resilience goals in urban building energy use. Although retrofitting offers a viable solution to mitigate building energy use, there has been limited analysis of its effects under various weather conditions associated with climate change in urban building energy use simulations. Moreover, certain parameters in energy simulations necessitate extensive auditing or survey work, which is often impractical. This research proposes a framework that integrates various datasets, including building footprints, Lidar data, property appraisals, and street view images, to conduct neighborhood-scale building energy use analysis using the Urban Modeling Interface (UMI), an Urban Building Energy Model (UBEM), in a coastal neighborhood in Galveston, Texas. Seven retrofit plans and three weather conditions are considered in the scenarios of building energy use. The results show that decreasing the U-value of building envelopes helps reduce energy use, while increasing the U-value leads to higher energy consumption in the Galveston neighborhood. This finding provides direction for coastal Texas cities, like Galveston, to update building standards and implement retrofit measures. [ABSTRACT FROM AUTHOR]

This research takes a methodical look at how rising incomes and climate change affect energy use in six different divisions of Bangladesh. To investigate the indirect mechanism of income influence on the consumption of energy, this study employs indicators of industrial structure upgrading and urbanization in a novel way using the fixed effects model which has not been used so far in this kind of study. The results show that income affects energy use in two ways: directly and indirectly. The influence of income on the consumption of energy is inverted U-shaped and may be readily observed. Furthermore, by encouraging urbanization and upgrading of industrial structure, income can indirectly lower energy use. While energy consumption is negatively impacted by climate change, it is less severe than the effect on earnings. Furthermore, there are substantial geographical and temporal variations in the effect of wealth on energy use. Energy use decreases significantly as income rises over time. Income has a detrimental effect on the consumption of energy in the developed southern area. Energy usage is positively affected by income in the undeveloped northern area. In light of Bangladesh's unique the consumption of energy profile, we must reject the "one size fits all" approach and instead concentrate on reducing wasteful spending in areas like income growth, industrial structure and urbanization, and while simultaneously increasing efficiency and precision in our aiming. This study aims to provide policymakers with fresh insights to inform decisions on energy production and consumption policies considering urbanization and industrial growth. [ABSTRACT FROM AUTHOR]

The human use of energy is inherently understood and experienced through socially constructed frameworks. However, the degree of engagement with this topic on the part of humanities and the social sciences has until recently been uneven at best. This seems strange given current upheavals experienced in Europe and across the globe as the climate and biodiversity crises deepen. At the centre of all these crises is the energy system. Energy flows through various forms of natural and social circuitry (from production, to distribution and consumption) and these energyscapes are sited at the local, national, and transnational scales. The correlation between the (meta)physical flows taken by the various forms of energy we depend on—and the transitory social, cultural, economic, and political relationships that frame them—require much deeper study if we are to achieve the types of sustainable communities envisaged by the United Nations as part of its sustainable development goals (SDGs) for 2030. Arising from a review of current literature, this article presents recent research into the forming of citizen energy communities in Europe and the governance structures designed to facilitate their development. It also highlights the key drivers and barriers to citizen engagement with emergent, novel energetic communities. [ABSTRACT FROM AUTHOR]