Showing papers on "Renewable energy published in 2022"

Renewable energy and climate change

Abstract: The current editorial summarized some of the scientific works presented in the Sustainable Energy and Environmental Protection (SEEP) conference-held at the University of the West of Scotland, UK, 2018. The selected work was directly related to the scope of the Renewable, Sustainable Energy Reviews (RSER) journal. During the conference activities, experts from all around the world in the subjects of: renewable energy, climate change, optimization, and economics presented and discussed the progress made in renewable energy sources, as well as the new strategies for protecting the environment from the hazards connected with fossil fuel utilization. The methods presented in the conference focused on several directions: the development of efficient energy conversion systems with low/no environmental impacts; the suggested policies to widespread renewable energies; the restriction in the emission of greenhouse gases, and the recent progresses in CO2 capture. This editorial focused on the renewable energy developments and their positive effect on the climate change, and briefly summarized the accepted manuscripts in this issue.

A comprehensive review of stationary energy storage devices for large scale renewable energy sources grid integration

Abstract: Currently, the energy grid is changing to fit the increasing energy demands but also to support the rapid penetration of renewable energy sources. As a result, energy storage devices emerge to add buffer capacity and to reinforce residential and commercial usage, as an attempt to improve the overall utilization of the available green energy. Although various research has been conducted in the field including photovoltaic and wind applications, the study on suitability identification of different storage devices for various stationary application types is still the gap observed which needs further study and verification. The review performed fills these gaps by investigating the current status and applicability of energy storage devices, and the most suitable type of storage technologies for grid support applications are identified. Moreover, various technical, economic and environmental impact evaluation criteria's are taken into consideration for the identification of their characteristics and potentials. The comprehensive review shows that, from the electrochemical storage category, the lithium-ion battery fits both low and medium-size applications with high power and energy density requirements. From the electrical storage categories, capacitors, supercapacitors, and superconductive magnetic energy storage devices are identified as appropriate for high power applications. Besides, thermal energy storage is identified as suitable in seasonal and bulk energy application areas. With proper identification of the application's requirement and based on the techno-economic, and environmental impact investigations of energy storage devices, the use of a hybrid solutions with a combination of various storage devices is found to be a viable solution in the sector. • State-of-the-art review of various energy storage technologies are provided. • A comprehensive comparative analysis of energy storage devices (ESDs) is performed. • A techno-economic and environmental impacts of different ESDs have been presented. • Feasibility of ESDs is evaluated with synthesis of technologies versus application requirements. • Hybrid solution of ESDs is proposed as feasible solution for RESs grid integration.

Environmental impact of renewable energy source based electrical power plants: Solar, wind, hydroelectric, biomass, geothermal, tidal, ocean, and osmotic

Abstract: Renewable energy source (RES) based electrical power plants are widely considered green and clean due to their contribution to decarbonizing the energy sectors. It is apparent that RESs do not produce carbon dioxide, however their significant negative impacts on the environment are still found and cannot be ignored. In this paper, the environmental impacts of RES based power plants are analyzed through a comprehensive review considering solar thermal, solar photovoltaic, wind, biomass, geothermal, hydroelectric, tidal, ocean current, oceanic wave, ocean thermal, and osmotic effects. Solar thermal power is well known as concentrated solar power. A strength, weakness, opportunity, and threat (SWOT) analysis is carried out and discussed for all RES based power plants. Comparative SWOT analyses for solar photovoltaic and concentrated solar power plants are presented. The comparative environmental impact analyses for all existing RES based power plants are tabulated for various attributes. These attributes include but are not limited to human health, noise, pollution, greenhouse gas emission, ozone layer depletion, toxification, flooding, impact on inhabitants, eutrophication, dried up rivers, and deforestation. Based on the analysis, it is found that careful selection of RES for electrical power plants is necessary because improper utilization of RES could be very harmful for the environment.

Mitigating Emissions in India: Accounting for the Role of Real Income, Renewable Energy Consumption and Investment in Energy

Abstract: Accomplishing environmental sustainability has become a global initiative whilst addressing climate change and its effects. Thus, there is a necessity for innovation on part of economies as they seek energy for sustainable development. Thus, we explore the case of India a highly industrialized and heavy emitter of carbon emission. To this end, this study explores the effect of renewable energy, non-renewable, economic growth, and investment in the energy sector on CO2 emission in the Indian economy. Canonical Cointegration Regression (CCR), Fully Modified Least Squares (FMOLS) and Dynamic Least Squares (DOLS) were used to access the long-run elasticity of the variables as well as Granger Causality analysis to detect the direction of causality relationship among the highlighted variables. Empirical regression shows a negative relation between CO2 emission and renewable energy. Thus, suggesting that renewable energy serves as a panacea for sustainable development in the face of economic growth trajectory. However, there was a positive relationship between CO2 emission and both non-renewable and real GDP growth. On the Granger analysis, we observe a one-way causality among renewable energy consumption and CO2 emission, economic development, and energy investment. These outcomes have far-reaching policy direction of environmental sustainability target in Indian economy.

Role of green finance in improving energy efficiency and renewable energy development

Abstract: Deploying green energy is, directly and indirectly, related to energy- and environment-related sustainable development goals (SDGs). This study uses the stochastic impact by regression on the population, affluence, and technology (STIRPAT) model to examine the relationship between CO2 emissions, energy efficiency, green energy index (GEI), and green finance in the top ten economies that support green finance. The results show that green bonds are a suitable method to promote green energy projects and reduce CO2 emissions significantly. At the same time, there is no causal linkage between these variables in the short term. Therefore, to achieve sustainable economic growth for environmental issues, governments should implement supportive policies with a long-term approach to boost private participation in the investment of green energy projects. This policy may be applicable during and in the post the COVID-19 era when green projects have more difficulties accessing finance.

The Ten Principles of Green Sample Preparation

Abstract: The ten principles of GSP are presented with the aim of establishing a road map toward the development of overall greener analytical methodologies. Paramount aspects for greening sample preparation and their interconnections are identified and discussed. These include the use of safe solvents/reagents and materials that are renewable, recycled and reusable, minimizing waste generation and energy demand, and enabling high sample throughput, miniaturization, procedure simplification/automation, and operator's safety. Further, the importance of applying green metrics for assessing the greenness of sample preparation methods is highlighted, next to the contribution of GSP in achieving the broader goal of sustainability. Green sample preparation is sample preparation. It is not a new subdiscipline of sample preparation but a guiding principle that promotes sustainable development through the adoption of environmentally benign sample preparation procedures. • The ten principles of green sample preparation are presented. • Sustainability issues on solvents, reagents and materials are considered. • Fast, miniaturized, automated, in situ and low-energy methods are preferred. • Post-sample preparation configuration for analysis is considered. • Green metrics and the impact on sustainable development are discussed.

Do renewable and nuclear energy enhance environmental quality in France? A new EKC approach with the load capacity factor

Abstract: In this study, we test the validity of the environmental Kuznets curve (EKC) hypothesis for France from 1977 to 2017. In this context, we analyze the effects of nuclear and renewable energy on the ecological footprint, carbon dioxide (CO2) emissions, and load capacity factor using co-integration and causality tests with Fourier transforms. In addition to the traditional indicators of environmental degradation, we make an important contribution to the literature by testing for the first time the impact of nuclear energy on the load capacity factor. The results of our empirical analysis suggest that there is no inverted U-shaped relationship between CO2 emissions and income, but rather the EKC hypothesis for the load capacity factor. While nuclear energy reduces CO2 emissions and increases the load capacity factor, in other words, improves environmental quality, renewable energy has no long-term impact on environmental conditions. The findings point to the importance of nuclear energy in green sustainability.

Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

Abstract: Nanofluids have gained significant popularity in the field of sustainable and renewable energy systems. The heat transfer capacity of the working fluid has a huge impact on the efficiency of the renewable energy system. The addition of a small amount of high thermal conductivity solid nanoparticles to a base fluid improves heat transfer. Even though a large amount of research data is available in the literature, some results are contradictory. Many influencing factors, as well as nonlinearity and refutations, make nanofluid research highly challenging and obstruct its potentially valuable uses. On the other hand, data-driven machine learning techniques would be very useful in nanofluid research for forecasting thermophysical features and heat transfer rate, identifying the most influential factors, and assessing the efficiencies of different renewable energy systems. The primary aim of this review study is to look at the features and applications of different machine learning techniques employed in the nanofluid-based renewable energy system, as well as to reveal new developments in machine learning research. A variety of modern machine learning algorithms for nanofluid-based heat transfer studies in renewable and sustainable energy systems are examined, along with their advantages and disadvantages. Artificial neural networks-based model prediction using contemporary commercial software is simple to develop and the most popular. The prognostic capacity may be further improved by combining a marine predator algorithm, genetic algorithm, swarm intelligence optimization, and other intelligent optimization approaches. In addition to the well-known neural networks and fuzzy- and gene-based machine learning techniques, newer ensemble machine learning techniques such as Boosted regression techniques, K-means, K-nearest neighbor (KNN), CatBoost, and XGBoost are gaining popularity due to their improved architectures and adaptabilities to diverse data types. The regularly used neural networks and fuzzy-based algorithms are mostly black-box methods, with the user having little or no understanding of how they function. This is the reason for concern, and ethical artificial intelligence is required.

Energy transition and carbon neutrality: Exploring the non-linear impact of renewable energy development on carbon emission efficiency in developed countries

Abstract: This study aims to explore the non-linear renewables and carbon emission efficiency (CEE) nexus to optimize the energy transition path. Taking 32 developed countries that have proposed carbon neutrality targets as the research objects, the super-efficiency slacks-based measure (SE-SBM) model is first used to measure their CEE from 2000 to 2018. Then, a newly developed panel threshold model with interactive fixed effects (PTIFEs) is established to comprehensively explore the non-linear impact of renewable energy development (RED) on CEE. The results show that: (1) During the sample period, there are significant differences in CEE among countries, and most countries are inefficient. (2) On the whole, RED is conductive to CEE, but there is a significant threshold effect. Specifically, this positive effect decreases with energy consumption intensity, whereas it increases with financial development, RED, and CEE. (3) The heterogeneity analysis shows that the threshold effect persists across countries with different income levels, and the direction is consistent with the entire sample. Besides, as the incomes down, the positive correlation between RED and CEE is significantly diminished. This study provides a new perspective for optimizing the energy transition path.

Energy transition and carbon neutrality: Exploring the non-linear impact of renewable energy development on carbon emission efficiency in developed countries

Abstract: This study aims to explore the non-linear renewables and carbon emission efficiency (CEE) nexus to optimize the energy transition path. Taking 32 developed countries that have proposed carbon neutrality targets as the research objects, the super-efficiency slacks-based measure (SE-SBM) model is first used to measure their CEE from 2000 to 2018. Then, a newly developed panel threshold model with interactive fixed effects (PTIFEs) is established to comprehensively explore the non-linear impact of renewable energy development (RED) on CEE. The results show that: (1) During the sample period, there are significant differences in CEE among countries, and most countries are inefficient. (2) On the whole, RED is conductive to CEE, but there is a significant threshold effect. Specifically, this positive effect decreases with energy consumption intensity, whereas it increases with financial development, RED, and CEE. (3) The heterogeneity analysis shows that the threshold effect persists across countries with different income levels, and the direction is consistent with the entire sample. Besides, as the incomes down, the positive correlation between RED and CEE is significantly diminished. This study provides a new perspective for optimizing the energy transition path.

Assessing the energy transition in China towards carbon neutrality with a probabilistic framework

Abstract: Abstract A profound transformation of China’s energy system is required to achieve carbon neutrality. Here, we couple Monte Carlo analysis with a bottom-up energy-environment-economy model to generate 3,000 cases with different carbon peak times, technological evolution pathways and cumulative carbon budgets. The results show that if emissions peak in 2025, the carbon neutrality goal calls for a 45–62% electrification rate, 47–78% renewable energy in primary energy supply, 5.2–7.9 TW of solar and wind power, 1.5–2.7 PWh of energy storage usage and 64–1,649 MtCO 2 of negative emissions, and synergistically reducing approximately 80% of local air pollutants compared to the present level in 2050. The emission peak time and cumulative carbon budget have significant impacts on the decarbonization pathways, technology choices, and transition costs. Early peaking reduces welfare losses and prevents overreliance on carbon removal technologies. Technology breakthroughs, production and consumption pattern changes, and policy enhancement are urgently required to achieve carbon neutrality.

Symmetric and asymmetric impact of economic growth, capital formation, renewable and non-renewable energy consumption on environment in OECD countries

Abstract: This study examines the symmetric (linear) and asymmetric (nonlinear) impact of economic growth (EG), capital formation (CF), renewable and non-renewable energy (NRE) consumption on CO2 emissions and ecological footprint (EF) of seventeen OECD countries spanning data from 1970 to 2016. The autoregressive distributed lag (ARDL) model is used to examine the symmetric impact and the nonlinear autoregressive distributed lag (NARDL) model is employed to explore the asymmetric impact of the variables on the environment. The results indicate that economic growth and gross capital formation dampens environmental quality in the OECD region over the sampled period. Our estimation using the ARDL model shows that a 1% increase in renewable energy (RE) is projected to reduce CO2 emission by 0.2% and a 1% increase in NRE is estimated to increase CO2 emission by 1.08%. Similarly, a 1% rise in EG and NRE is expected to increase ecological footprint (EF) by 0.10% and 0.53%, respectively. Estimation using NARDL decomposed EG with positive (negative) shocks shows that a 1% increase (decrease) in EG is expected to reduce CO2 emissions by 0.4% (0.16%). Similarly, 1% positive (negative) shock in RE is expected to decrease CO2 emission by 0.5%. The findings indicate that conventional energy obtained from fossil fuels is observed to worsen the environment. Interestingly, renewable energy consumption enhances environmental quality for both fitted models with CO2 emission and ecological footprint. This is insightful for stakeholders and government administrators in the region. This demonstrates the importance of a paradigm shift towards renewable energy consumption in the OECD countries to improve economic growth and productive capital stock. This finding also aligns with the non-linear investigation of the pivotal role of renewable energy consumption for a cleaner environment.

The role of interaction effect between renewable energy consumption and real income in carbon emissions: Evidence from low-income countries

Abstract: Even though the existing studies have extensively investigated the impacts of renewable energy and real income on carbon emissions, the literature overlooks the role of their interaction effect in the level of emissions. In addition, the studies have usually chosen high-income and middle-income countries as focused group. To fill these gaps in the existing body of energy-environment literature, this study investigates the impacts of real income, renewable energy consumption and their interaction effect on carbon emissions in low-income countries by employing empirical estimations that control different econometric and economic issues such as heterogeneity and cross-sectional dependence. The results reveal that renewable energy mitigates emissions; however, the interaction effect stays positive. The marginal effect of renewable energy on emissions varies with the levels of real income. Policymakers in these economies should implement policies and regulations to promote the adoption and use of renewable energy to mitigate carbon emissions. Besides, this study emphasizes that the levels of renewable energy and real income are not the only panacea to abating pollution, but the interaction effect should be considered in ensuring environmental sustainability.

The role of interaction effect between renewable energy consumption and real income in carbon emissions: Evidence from low-income countries

Abstract: Even though the existing studies have extensively investigated the impacts of renewable energy and real income on carbon emissions, the literature overlooks the role of their interaction effect in the level of emissions. In addition, the studies have usually chosen high-income and middle-income countries as focused group. To fill these gaps in the existing body of energy-environment literature, this study investigates the impacts of real income, renewable energy consumption and their interaction effect on carbon emissions in low-income countries by employing empirical estimations that control different econometric and economic issues such as heterogeneity and cross-sectional dependence. The results reveal that renewable energy mitigates emissions; however, the interaction effect stays positive. The marginal effect of renewable energy on emissions varies with the levels of real income. Policymakers in these economies should implement policies and regulations to promote the adoption and use of renewable energy to mitigate carbon emissions. Besides, this study emphasizes that the levels of renewable energy and real income are not the only panacea to abating pollution, but the interaction effect should be considered in ensuring environmental sustainability.