Source Of Electricity Generation Research Articles

Sustainability pathways for perovskite photovoltaics.

Solar energy is the fastest-growing source of electricity generation globally. As deployment increases, photovoltaic (PV) panels need to be produced sustainably. Therefore, the resource utilization rate and the rate at which those resources become available in the environment must be in equilibrium while maintaining the well-being of people and nature. Metal halide perovskite (MHP) semiconductors could revolutionize PV technology due to high efficiency, readily available/accessible materials and low-cost production. Here we outline how MHP-PV panels could scale a sustainable supply chain while appreciably contributing to a global renewable energy transition. We evaluate the critical material concerns, embodied energy, carbon impacts and circular supply chain processes of MHP-PVs. The research community is in an influential position to prioritize research efforts in reliability, recycling and remanufacturing to make MHP-PVs one of the most sustainable energy sources on the market.

Design and Economic Analysis of a Grid-Tied Microgrid Using Homer Software

The demand for electrical energy is increasing due to reasons such as economic growth, industrialization and electrification. The world responds to a large part of this electricity demand with fossil fuel-based production. However, the constraints on the sustainability of fossil resources and the negative effects of fossil-based production on nature have made renewable energy one of the most talked about concepts in the energy sector in recent years. After Russian – Ukrainian conflict, the effects of political crises between countries were seen in the field of energy, and many countries faced the risk of energy supply and high pricing policies. With its easy integration of renewable energy and its structure that reduces dependency in energy, Microgrids (MGs) are important for the energy systems of the future. However, the environmental dependence of renewable energy prevents it from being used as an absolute energy source in systems. In this study, a microgrid design for the city of Duquesne, USA whose main sources of electricity generation are solar and wind, has been realized and electrical and economic analyzes have been made over different scenarios as grid-tied, limited grid activation and standalone. Scenarios are evaluated on Net Present Value (NPV), Levelized Cost of Energy (LCOE), installation cost and renewable penetration. The grid-tied scenario, which reduces the LCOE by around 33% compared to the existing grid has been determined as the most economic option.

To What Extent Do Alternative Energy Sources Displace Coal and Oil in Electricity Generation? A Mean-Group Panel Analysis

This paper determines by how much alternative electricity generation sources—natural gas, nuclear, hydro, and renewables—displace electricity generation from coal and oil. It does so by employing a first-difference model and a mean-group estimator applied to a panel that spans 1985–2019 for 27 high- and 13 middle-income countries. As such, our approach avoids/addresses several statistical issues common in long-macro panel analyses—heterogeneity, nonstationarity, and cross-sectional dependence—that have largely been ignored/unaddressed in previous displacement studies. Ultimately, we find that the displacement effect is small and only marginally significant for nuclear, and is significant though less than unity for natural gas and hydro, whereas intermittent renewables (solar and wind) have unitary displacement effect. These results suggest a substantially greater displacement potential for alternative generation sources than typically found by the previous literature. In other words, increasing hydro and wind and solar are all impactful ways to decarbonize the electricity system.

Quantification and spatial distribution of the bioenergetic potential from slaughterhouses effluents biogas generation across Brazil

This study describes the estimation of biogas energy use from the anaerobic treatment system of cattle, swine, and poultry slaughter units in Brazil. The simulation includes spatial distribution of the results and geographical comparison with other energy matrices and clean sources of electricity generation. The paper is divided into four sections: 1) the database formation, which included a survey and geocoding of cattle, swine and poultry slaughter units across the country; 2) a literature review covering the period from 2000 to 2020 to obtain the average Chemical Oxygen Demand (COD) for each of the effluents; 3) a simulation of electricity generation in the slaughter units, according to their classes and origins; and, 4) the preparation of national distribution maps of slaughter units, national bioenergy potential, by state and by animal origin, comparison maps of Electric Slaughter Units (ESU) using hydroelectric, photovoltaic and wind power plants, as well as the comparison map between the biomass energy use (biogas, sugarcane bagasse and black liquor). The slaughter units bioenergetic potential presented on the maps that the density in the distribution of these units adopts the effluent treatment proposal. Moreover, biogas is considered a decentralized energy matrix, but the density of slaughter units within the work proposal opposes this concept. The results indicated the formation of a corridor with a high potential for electricity generation, coinciding with regions that experience electrical stress in Brazil. In numbers, the potential would be enough to supply a population of up to 900,000 inhabitants, emphasizing ESU as a potential alternative for electricity using biogas. This is because the Brazilian meat market is somewhat consolidated, important worldwide and continuously generates effluents, which can add up to 28% to electricity from biomass. The greatest bioenergy potential was obtained in the Midwest region (1792 MW), followed by the South (1486 MW), Southeast (1278 MW), North (923 MW) and Northeast (236 MW) regions of the country. In terms of clean energy, when comparing the distribution of slaughter units with photovoltaic, wind and hydroelectric plants, a large territorial gain is observed (more uniform distribution in the country) and in relation to the electrical use of biomass, biogas use from ESU would promote it as the second largest matrix representing about 31% of national energy.

China Toward a Green Economy: Current Situation and Perspective in the Use of Different Energy Sources for Electricity Generation

China Toward a Green Economy: Current Situation and Perspective in the Use of Different Energy Sources for Electricity Generation

Emerging Trends and Challenges in Pink Hydrogen Research

Pink hydrogen is the name given to the technological variant of hydrogen generation from nuclear energy. This technology aims to address the environmental challenges associated with conventional hydrogen production, positioning itself as a more sustainable and eco-efficient alternative, while offering a viable alternative to nuclear power as a source of electricity generation. The present research analyzes the landscape of pink hydrogen research, an innovative strand of renewable energy research. The methodology included a comprehensive search of scientific databases, which revealed a steady increase in the number of publications in recent years. This increase suggests a growing interest in and recognition of the importance of pink hydrogen in the transition to cleaner and more sustainable energy sources. The results reflect the immaturity of this technology, where there is no single international strategy and where there is some diversity of research topic areas, as well as a small number of relevant topics. It is estimated that the future development of Gen IV nuclear reactors, as well as Small Modular Reactor (SMR) designs, will also favor the implementation of pink hydrogen.

Порівняльна оцінка собівартостей відпуску електроенергії з різних джерел в базовому та регулювальному режимах

The paper analyses the role and the technical and economic indicators of different electricity generation sources in the transition to "carbon-free" energy. A simplified method is proposed for estimating the minimally justified price of selling electricity from thermal power plants (TPPs), renewable energy sources (RES) and new maneuverable natural gas capacities is proposed. It is shown that the price of electricity from RES and new maneuverable capacity has a decisive influence on the amount and fixed period of return on investment in construction, as well as on the capacity utilization factor (CUF), which depends on the mode of operation of the power plants. Comparative estimates of the CUF of different electricity generation sources are presented. It has been shown that the cost of electricity from RES, new peak and maneuverable capacity is significantly higher than the cost of electricity from thermal power plants during the payback period. It is proved that at present the reserve for the reduction of the power shortage and the load regulation is available only in the thermal generation, and the conditions for the possibility of its effective use are the maintenance of the proper technical condition of the pulverized coal boiler units and the sufficient fuel base. To ensure acceptable electricity prices, RES and new control capacity should be phased in, using existing TPPs and CHPs to balance both the weighted average price and the regulation of power system load modes. Keywords: thermal power plants, coal, natural gas, gas turbine, steam-gas, gas-piston power plants, renewable energy sources, cost of electricity supply.

Optimization of leucomalachite green degradation through electron beam and persulfate oxidation using box-behnken design and life cycle assessment

This study explored the treatment of Leucomalachite Green (LMG) solutions using an electron beam and sodium persulfate (Na2S2O8), employing Box-Behnken design (BBD) to optimize operational variables such as absorbed dose, initial pH and Na2S2O8 concentration. The findings highlighted an optimal absorbed dose of 4.5 kGy, a Na2S2O8 concentration of 1.0 mM, and an initial pH of 6, leading to a remarkable 97.77% removal of LMG. The adjusted R2 for the model indicated a close match of 1.4% between predicted and actual outcomes under these optimized conditions, affirming the quadratic model's suitability for predicting the LMG removal process using combined EB and Na2S2O8. To assess the environmental impact of the LMG treatment, the study applied SimaPro 9.4 with the TRACI tool, examining ten distinct environmental impact categories. The results unveiled that deionized water and Na2S2O8 exhibited a notable impact on global warming (GW) and ecotoxicity (ET) in controlled laboratory settings. Furthermore, a comparative analysis of four scenarios shed light on the environmental implications of different energy sources. Notably, electricity generated from waste incineration demonstrated a substantial influence on all environmental indicators. In contrast, natural gas emerged as the cleanest source for electricity generation, offering a promising avenue for reducing environmental impacts. This study presents a practical method for addressing dye contaminants through the employment of EB in conjunction with Na2S2O8, with potential implications for broader applications.

Impact of turbine characteristics on low temperature organic Rankine cycles operating with zeotropic and azeotropic mixtures

The urgent need to mitigate the environmental impacts of fossil fuel combustion has spurred interest in renewable energy sources for electricity generation, particularly solar organic Rankine cycles (ORCs). This study investigates the potential of fluid mixtures in low-temperature ORCs, focusing on a radial turbine. While fluid mixtures have shown promise for heat recovery in low-temperature applications, previous research often overlooked turbine characteristics by assuming constant efficiencies. This study set out to examine the impact of turbine characteristics on the ORC performance when adapting fluid mixtures by coupling a cycle-turbine model incorporated with real gas analysis. The outcomes of the turbine model are exported to the cycle model to evaluate its performance. Results reveal significant variations in cycle performance due to turbine power and efficiency, outweighing the effects of temperature glide in fluid mixtures. Although fluid mixtures enhance thermal compatibility with heat sources, certain pure fluids exhibit superior cycle performance owing to better turbine characteristics. Moreover, the assumption of constant turbine efficiency compromises result accuracy by up to 45 % compared to dynamic turbine efficiency. Notably, the isobutane/isopentane mixture (0.7/0.3) emerges as the most favourable, achieving a turbine power of 48.90 kW, turbine efficiency of 71 %, and thermal efficiency of 12.29 %.

Reinvestigating the EKC hypothesis: Does renewable energy in power generation reduce carbon emissions and ecological footprint?

For a developing nation such as Malaysia with a significant reliance on fossil fuels for electricity, understanding the environmental consequences of this dependency is crucial. Thus, this study utilizes the Autoregressive Distributed Lag (ARDL) method to explore the impact of electricity intensity (EINT), renewable energy in electricity generation (ERE), and gross domestic product (GDP) on Malaysia's environmental quality, from 1985 to 2020. By using carbon emissions (CO2 emissions) and ecological footprint (EF) as proxies, the study finds a significant long-run impact of these factors on environmental degradation. Notably, the study also observed an inverted U-shaped relationship between GDP and environmental degradation, validating the existence of Environmental Kuznets Curve (EKC) hypothesis. The findings also imply that while electricity intensity is associated with increased emissions, the use of renewable energy (RE) sources for electricity generation may contribute to emission reduction. But the results for both variables show reversal signs on EF. The study's adoption of the fully ordinary least square (FMOLS) method reinforces the same results, thus, justifying the cointegrating relationship between studied variables. Drawing from these outcomes, the study advocates for a comprehensive approach to renewable energy management, emphasizing both its utilization and waste in order to improve ecological footprint. Moreover, the pressing need to enact legislation on energy efficiency and conservation is crucial in ensuring decoupling and securing sustainable development in Malaysia.

Geothermal resource potentials estimation from the interpretation of aeromagnetic data over parts of Southwestern Nigeria

In a bid to explore unconventional electricity generation sources and reduce effects of fossil fuels, this study evaluates geothermal resource potentials over parts of Southwestern Nigeria, precisely depth to the bottom of magnetic source (DBMS), heat flow, geothermal gradient and their relationships. Regional-residual anomaly separation was conducted, and spectral analysis was applied on the residual anomaly component of 14 aeromagnetic data sheets. Depth to the bottom of magnetic source varies between 1.87 and 6.26 km, with average value of 3.50 km, heat flow varies between 23.18 and 77.38 mWm−2, with average value of 43.79 mWm−2, while geothermal gradient varies between 9.27 and 30.95 oC/km with average value of 17.52 oC/km. Northcentral region has the highest heat flow (77.4 mWm−2), followed by Northeast (68.3 mWm−2), Southwest has the least (< 40.0 mWm−2) while Southeast has values in between the extremes. A comparison between the average heat flow and that of ‘thermally stable’ continental regions of the world (60 or 65) mW/m2 gives 72.98 or 67.37% respectively. The estimated heat is probably from mantle plumes, radioactive sources or heat generated from pressures within basements that are overlaid by thick thermally insulated sediments, hence hot magmatic fluid flows into fractured basement and cause hydrothermal alterations of surrounding rocks. Since most geodynamic operations depends on thermal structure of the earth’s crust, the result of this study has undoubtedly contributed significantly to the body of existing thermal knowledge and closed the information gap regarding crustal temperature distribution at depth in Southwest part of Nigeria and Nigeria in general.

Marginal effect of electricity generation on CO2 emissions: Disaggregated level evidence from China by KRLS method and high-frequency daily data

The effect of energy use on carbon dioxide (CO2) emissions has been frequently studied in the literature. These studies have mainly concluded that countries should decline (increase) the use of fossil fuel (clean) energy. However, the literature suffers from a significant shortcoming as it does not focus on the marginal effect of a 1 % increase for each energy generation source on sectoral CO2 emissions. Therefore, a detailed analysis is conducted in this study to examine the relationship between electricity generation (EG) and CO2 emissions at a disaggregated level. The focus is on China, the world's leading country in terms of CO2 emissions and energy use. Thus, the study considers source-based EG and sector-based CO2 emissions, uses high-frequency daily data between January 1, 2019, and December 31, 2022, and applies the kernel-based regularized least squares (KRLS) method. The outcomes show that (i) the effects of EG sources on sectoral CO2 emissions follow a nonlinear structure, suggesting that the marginal effect varies by sector, EG sources, and estimation models (either incremental or degressive). Therefore, there are certain externalities among alternative EG sources for the effects of CO2 emissions in the sectors; (ii) the statistically significant effects of EG sources on CO2 emissions vary by sector and constructed models, showing that some EG sources are much more important for CO2 emissions in some sectors. For this reason, not all EG sources have the same importance for sectoral CO2 emissions; (iii) the KRLS method has a higher estimation ability of CO2 emissions, reaching ∼99.8 %, which provides novel outcomes and allows researchers to argue various policy options based on the obtained results. The study thus highlights varying marginal impacts of EG sources on sectoral CO2 emissions. The changing marginal influence is a crucial point that should be considered by Chinese policymakers when formulating energy-related environmental policies.

Dependence and sustainability: a disaggregated analysis of electricity generation in ASEAN countries

Purpose This study aims to explore the diverse sources of electricity generation (coal, natural gas, oil and hydroelectricity) and their respective associations with economic growth and environmental quality. Design/methodology/approach This study uses static panel data analysis with a random effects model for six selected ASEAN countries (Indonesia, Malaysia, Filipina, Thailand, Vietnam and Myanmar) from 1994 to 2014. Findings This study reveals that economic growth in six selected ASEAN countries is enhanced by electricity generation from all sources, while the contribution of electricity production from hydroelectricity remains the largest and strongest. There is no environmental impact of electricity production from hydroelectric, whereas fossil fuel-based electricity production emits carbon dioxide, with coal sources being the largest contributor, followed by natural gas and oil. Practical implications Based on the results, these six ASEAN countries should invest more in hydropower projects, reduce the coal mix in power generation and promote clean coal technology to improve economic efficiency and environmental sustainability. Originality/value To the best of the authors’ knowledge, no research has examined the relationship between electricity production, environmental quality and economic growth in Southeast Asian nations. Therefore, the outcome of this study is expected to provide insightful results to supplement the framing and implementation of national and collective regional strategies for sustainable electricity generation in ASEAN countries.

Environmental Life Cycle Assessment of Two Types of Flexible Plastic Packaging under a Sustainable Circular Economy Approach

While it is of great importance to evaluate plastic waste within the framework of a circular economy today, it is also of great importance to evaluate flexible plastic packaging, which is increasingly used in order to prevent environmental problems. To avoid the disadvantages experienced in recycling due to its multilayer nature, in this study, a life cycle assessment was made for flexible packages consisting of PET/metallized PET/PE and PP/metallized PP/PP with the data provided by the R&D Center of Elif Plastik Ambalaj Sanayi ve Tic. A.Ş.-Huhtamaki Flexibles Istanbul. Within the scope of this evaluation, two types of flexible packaging were analyzed, and an optimal flexible packaging structure for Türkiye was revealed by comparing different scenarios based on different methods in SimaPro 8.1.1.16. LCA was performed for both packages with cumulative energy demand (CED) and CML-IA methods. Four scenarios with different amounts of recycled raw materials were compared against the existing system and a fifth scenario, where electricity is assumed to be obtained from solar energy. Overall, we found that the largest environmental impact was in the existing system. However, despite being a renewable energy source, we observed that the solar energy scenario had almost as significant an impact as the existing system. When scenarios involving recycled raw materials were examined, we clearly observed that as the amount of recycled raw materials increased, the environmental impact decreased. Therefore, it emerged that the scenario with the highest amount of recycled raw materials is the most optimal scenario in many respects. There are clear differences in the results due to differences in plastic types. This study, conducted with real data, is highly important for the flexible packaging literature. A table has been provided for changing the type of plastic, changing the source of electricity generation, and reducing waste by using recycled raw materials in order to make flexible packaging more environmentally beneficial.

IoT-enabled Smart Weather Stations: Innovations, Challenges, and Future Directions

The evolution of the Internet of Things (IoT) has ushered in innovative approaches facilitated by breakthrough technologies. This paper presents a comprehensive review of recent advancements in smart weather stations, focusing on IoT-enabled solutions. The integration of Internet of Things (IoT) technologies has revolutionized traditional weather monitoring systems, enabling seamless data collection, analysis, and dissemination. Commercially available automated weather stations offer cost-effective solutions for comprehensive meteorological data collection. However, challenges such as limited local deployment and reliance on expensive options persist, hindering comprehensive monitoring efforts. The critical need for improved data collection methods is underscored to enhance the accuracy of weather forecasts and address evolving climatic conditions. Climate change impacts, including shifts in weather patterns and rising temperatures, highlight the importance of effective weather monitoring for agriculture, infrastructure, and national security. Additionally, the dependence on non-renewable energy sources for electricity generation emphasizes the environmental and economic implications of energy production. In response to these challenges, numerous IoT based smart weather station systems have been proposed by earlier researchers. The introduction of IoT-enabled smart weather stations represents a significant advancement in weather monitoring technology. These stations leverage IoT technologies to collect, analyse, and visualize meteorological data in real time. In order to identify the challenges and prospects in this area of technology, the purpose of this study is to present a thorough analysis of the suggested designs as well as to compare, evaluate, and assess the outcomes, contributing to the development of robust and efficient weather monitoring systems.

Dynamic effect of disaggregated level electricity generation on residential carbon emissions: Daily inference from the largest EU economies

This study examines the dynamic effects of electricity generation (EG) on CO2 emissions from the residential sector. The study focuses on the EU-4 countries (Germany, Spain, France, and Italy), considers residential CO2 emissions as the dependent variable, and includes disaggregated level fossil and renewable EG as explanatory variables. In this context, the study runs nonlinear quantile-on-quantile (QQ) regression and Granger causality in quantiles (GQ) as the main models with daily data from January 2, 2019, to March 10, 2023, while quantile regression (QR) is used for robustness check. The findings present that in terms of CO2 emissions: (i) EG from coal, natural gas, and oil has a stimulating effect at higher quantiles in all countries; (ii) EG from hydro has an increasing effect at higher quantiles, while it has a decreasing effect at lower and middle quantiles in all countries except France; (iii) EG from solar has a dampening effect at higher quantiles in all countries except France; (iv) EG from wind has a declining effect at higher quantiles in both Spain and France; (v) both fossil and renewable energy EG have a causal effect on residential sector CO2 emissions at the disaggregated level except at some quantiles. Overall, the effect size and the causal effect of EG on CO2 emissions change for quantiles, countries, and EG sources. Therefore, the study proposes to rely on the specific EG sources for Germany and Italy (solar energy), Spain, and France (wind energy) to mitigate climate change by reducing residential CO2 emissions.

Effectiveness of green bonds on carbon neutrality and clean electricity generation: Comprehensive evidence from the leading emitting country by disaggregated level analysis

Considering increasing public interest in environment-related problems and the carbon-neutrality aims of countries, this study focuses on the effect of green bonds on enabling carbon neutrality and supporting clean electricity in China, which is the leading top carbon-emitting and energy-using country in the world. In this context, the study makes a disaggregated level empirical analysis by considering sectoral emissions and source-based electricity generation by using quantile-based approaches from January 2, 2019, to December 31, 2023. The outcomes show that (i) green bonds decrease mainly emissions in transport and international aviation sectors; (ii) green bonds have mixed effects on the remaining sectoral emissions; (iii) green bonds increase electricity generation from all sources at higher quantiles, whereas they decrease electricity generation at lower quantiles. Thus, the effect of green bonds varies across sectors, electricity generation sources, and quantiles. Accordingly, a set of policy endeavors, such as dealing with firstly such critical sectors as power and industry, supporting the allocated green bond issuance to specified sectors, and focusing on some clean electricity generation sources like solar and wind, are argued for China.

Long Term Forecasting of Peak Demand and Annual Electricity Consumption of the West African Power Pool Interconnected Network by 2032

The uneven distribution of primary sources of electric power generation in Economic Community of West African States (ECOWAS) compelled the heads of states to create the West African Power Pool (WAPP). The vision of this system is to set up a common electrical energy market to satisfy the balance between supply and demand at an affordable price using the interconnected network. Forecasting maximum power demand and energy consumption is essential for planning and the coordination of new power plant and transmission lines building. This work consists of predicting maximum power demand and total energy that must transit through the WAPP interconnected network by the year 2032. We compare the performances of three time series models namely the Long Short-Term Memory (LSTM), Auto-Regressive Integrated Moving Average (ARIMA) and Fb Facebook Prophet. Electric power and energy data used for training the systems comes from the WAPP authorties. The results show that, for monthly peaks, the Facebook (Fb) Prophet model is the best, with a MAPE (mean absolute error percentage) of 3.1% and a low RMSE (root mean square error) of 1.225 GW. For energy prediction, ARIMA performances are the best compared to others with (RMSE 1.20 TWh, MAPE 1.00%). Thus, the forecast for total annual energy consumption and annual peak demand will be, respectively, 96.85TWh and 13.6 GW in 2032.

Deep Learning Method for Improving Rate of Penetration Prediction in Drilling

Summary The urgent global need to reduce CO2 emissions necessitates the development of sustainable power generation sources. Geothermal power emerges as a renewable and dependable energy option, harnessing the Earth’s natural heat sources for electricity generation. Unlike other renewables, geothermal energy offers uninterrupted power, immune to weather conditions. However, its efficiency hinges on technological innovation, particularly in the challenging realm of geothermal drilling. Rate of penetration (ROP) is a crucial drilling performance metric, and this study explores how deep learning models, particularly transformers, can optimize ROP prediction. Leveraging data from Utah Frontier Observatory for Research in Geothermal Energy (FORGE), we analyze the relationship between drilling parameters and ROP. Traditional drilling optimization methods face limitations, as drilling dysfunctions can disrupt the linear relationship between ROP and weight on bit (WOB). We propose a dynamic approach that allows adapting drilling parameters in real time to optimize ROP. Our experiments investigate optimal sampling intervals and forecast horizons for ROP prediction. We find that a 60-second sampling interval maximizes the transformer model’s forecasting accuracy. Additionally, we explore retraining to fine-tune models for specific wells, improving forecasting performance. Our transformer-based ROP forecaster outperforms deep learning models, achieving a low overall 5.22% symmetrical mean average percentage error (SMAPE) over a forecast horizon of 10 minutes. This model offers opportunities for cost-effective drilling optimization, with real-time accuracy, speed, and scalability. Future work will focus on larger data sets and integration with drilling automation systems to further enhance the model’s practicality and effectiveness in the field.

How are electricity generation effective on carbon neutrality in the global south? Evidence from sectoral CO2 emissions by daily data

In light of the efforts to ensure carbon neutrality by combating climate-related problems, the study investigates the effectiveness of electricity generation (EG) from the main renewable sources (hydro-HEG, solar-SEG, and wind-WEG). In this context, the study examines the countries of the Global South (i.e., Brazil, China, and India), considers EG at a disaggregated level and sectoral CO2 emissions, applies nonlinear methods, and uses daily data between January 2, 2019 and December 31, 2022. The results demonstrate that (i) disaggregated EG sources have a stronger (weaker) time and frequency dependency on sectoral CO2 emissions in China (Brazil and India); (ii) HEG has a stimulating impact on sectoral CO2 emissions in all countries; (iii) SEG has an increasing impact on sectoral CO2 emissions in Brazil and China, while it provides a decrease in sectoral CO2 emissions in India; (iv) WEG upsurges sectoral CO2 emissions in China, while it achieves a CO2 reduction in Brazil and India; (v) disaggregated level EG has a causal impact on sectoral CO2 emissions across all quantiles except some lower, middle, and higher quantiles. The study adds scientific value to existing knowledge by analyzing for the first time which EG sources are effective in reducing daily CO2 emissions in the Global South. Based on the outcomes, the study demonstrates that WEG is the best EG source for Brazil, that SEG and WEG are optimal EG sources for India, and that China cannot benefit from the EG sources considered. In this way, the study provides fresh insights for the countries of the Global South and underlines the crucial role of renewable EG in ensuring carbon neutrality.