Search Results (262)

In the context of achieving low-carbon goals, building low-carbon energy systems is a crucial development direction and implementation pathway. Renewable energy is favored because of its clean characteristics, but the access may have an impact on the power grid. Microgrid technology provides an effective solution to this problem. Uncertainty exists in single microgrids, so multiple microgrids are introduced to improve system stability and robustness. Electric carbon trading and profit redistribution among multiple microgrids have been challenges. To promote energy commensurability among microgrids, expand the types of energy interactions, and improve the utilization rate of renewable energy, this paper proposes a cooperative operation optimization model of multi-microgrids based on the green certificate and carbon trading mechanism to promote local energy consumption and a low carbon economy. First, this paper introduces a carbon capture system (CCS) and power-to-gas (P2G) device in the microgrid and constructs a cogeneration operation model coupled with a power-to-gas carbon capture system. On this basis, a low-carbon operation model for multi-energy microgrids is proposed by combining the local carbon trading market, the stepped carbon trading mechanism, and the green certificate trading mechanism. Secondly, this paper establishes a cooperative game model for multiple microgrid electricity carbon trading based on the Nash negotiation theory after constructing the single microgrid model. Finally, the ADMM method and the asymmetric energy mapping contribution function are used for the solution. The case study uses a typical 24 h period as an example for the calculation. Case study analysis shows that, compared with the independent operation mode of microgrids, the total benefits of the entire system increased by 38,296.1 yuan and carbon emissions were reduced by 30,535 kg through the coordinated operation of electricity–carbon coupling. The arithmetic example verifies that the method proposed in this paper can effectively improve the economic benefits of each microgrid and reduce carbon emissions. Full article

Access to clean cooking remains a major challenge in rural and off-grid areas where traditional fuels are costly, harmful, or scarce. Solar cooking offers a sustainable solution, but many existing systems suffer from fixed positioning and low efficiency. This study presents a low-cost, dual-axis solar tracking parabolic dish cooker designed for such regions, featuring adjustable pot holder height and portability for ease of use. The system uses an Arduino UNO, LDR sensors, and a DC gear motor to automate sun tracking, ensuring optimal alignment throughout the day. A 0.61 m parabolic dish with ≥97% reflective silver-coated mirrors concentrates sunlight to temperatures exceeding 300 °C. Performance tests in April, June, and November showed boiling times as low as 3.37 min in high-irradiance conditions (7.66 kWh/m²/day) and 6.63 min under lower-irradiance conditions (3.86 kWh/m²/day). Compared to fixed or single-axis systems, this design achieved higher thermal efficiency and reliability, even under partially cloudy skies. Built with locally available materials, the system offers an affordable, clean, and effective cooking solution that supports energy access, health, and sustainability in underserved communities. Full article

Access to clean water is a pressing global concern and membrane technologies play a vital role in addressing this challenge. Thin-film composite membranes prepared via interfacial polymerization (IPol) using meta-phenylenediamine (MPD) and trimesoyl chloride (TMC) exhibit excellent separation performance, but face limitations such as fouling and low hydrophilicity. This study investigated the interaction between MPD and sulfonated zinc phthalocyanine, Zn(SO₂⁻)₄Pc, as a potential strategy for enhancing membrane properties. Using Density Functional Theory (DFT) and Time-Dependent DFT (TD-DFT), we analyzed the optimized geometries, electronic structures, UV–Vis absorption spectra, FT-IR vibrational spectra, and molecular electrostatic potentials of MPD, Zn(SO₂⁻)₄Pc, and their complexes. The results show that MPD/Zn(SO₂⁻)₄Pc exhibits reduced HOMO-LUMO energy gaps and enhanced charge delocalization, particularly in aqueous environments, indicating improved stability and reactivity. Spectroscopic features confirmed strong interactions via hydrogen bonding and π–π stacking, suggesting that Zn(SO₂⁻)₄Pc can act as a co-monomer or additive during IPol to improve polyamide membrane functionality. A conformational analysis of MPD/Zn(SO₂⁻)₄Pc was conducted using density functional theory (DFT) to evaluate the impact of dihedral rotation on molecular stability. The 120° conformation was identified as the most stable, due to favorable π–π interactions and intramolecular hydrogen bonding. These findings offer computational evidence for the design of high-performance membranes with enhanced antifouling, selectivity, and structural integrity for sustainable water treatment applications. Full article

The transition to clean energy is not inherently positive or negative, and its impacts depend on the social context, power relations, and mechanisms to include marginalized voices. India, with its ambitious climate targets and commitment to the UN SDG Agenda, is a key country for ensuring an inclusive and sustainable transition. This paper aims to understand whether India’s commitment to the SDG Agenda’s overarching principle of ‘leaving no one behind’ is reflected in the design of its domestic solar policies. It analyzes how energy justice concerns are addressed in state-level solar policies. To that end, a pragmatic framework was developed to identify key justice dimensions and indicators, linked to the SDG targets, that are essential for an inclusive transition. This research conducted a qualitative interpretive content analysis of 29 solar energy policies, using the three identified framework dimensions—income growth, enhancing inclusion, and equal opportunities. We found that the themes around energy access, employment, and skill development are reflected in policies, while those around the inclusion of the poor, women, and community remain limited. The findings indicate that the policies have focused on low-impact justice concerns, lacking structural transformation. To address these gaps, the study proposes targeted subsidies, community ownership, and gender-inclusive mechanisms. The framework offers a pragmatic tool for the evaluation of clean energy policies in the Global South, and the empirical results provide insights for the synergistic implementation of the climate and sustainable development agenda. Full article

The equitable allocation of clean energy access across towns–rural divides is a critical benchmark of modernization in developing economies. This is because it is intricately linked to the realization of strategic goals such as shared prosperity, ecological civilization advancement, and national energy security reinforcement. This research examines the impact of China’s digital village (DV) construction in reducing the urban–rural disparity in household clean energy access, evaluates the effect on towns–rural clean energy consumption inequality (CEI), explores the mediating mechanisms, and considers regional heterogeneity. It is an innovative approach to test the influence of digital village construction on clean energy consumption inequality between urban and rural areas, beyond which conventional research is limited to infrastructure investment and policy considerations. We can reach the following three results: (1) With the continuous improvement of digital village construction, CEI between towns and rural areas shows an “inverted U-shaped” change. (2) From the perspective of the intermediary mechanism, agricultural technological progress (ATP) and industrial structure upgrading (IND) can facilitate digital village construction and reduce the disparity in clean energy consumption between towns and rural regions. (3) From the perspective of heterogeneity analysis, digital village construction in areas with low urbanization levels, high terrain undulation, and non-clean energy demonstration provinces can significantly alleviate CEI. It is on this basis that the present paper proposes a policy recommendation for the Chinese government to effectively reduce the gap between towns and rural clean energy consumption in the process of digital village construction. Full article

Access to clean and sustainable energy technologies is critical for all nations, particularly developing countries in Africa. Ghana has committed to ambitious greenhouse gas emission reduction targets, aiming for 10% and 20% variable renewable energy integration by 2030 and 2070, respectively. This study explores potential pathways for Ghana to achieve its renewable energy production targets amidst a growing energy demand. An open-source energy modelling tool was used to assess four scenarios accounting for current policies and additional alternatives to pursue energy transition goals. The scenarios include Business as Usual (BAU), Government Target (GT), Renewable Energy (REW), and Net Zero (NZ). The results indicate that total power generation and installed capacity would increase across all scenarios, with natural gas accounting for approximately 60% of total generation under the BAU scenario in 2070. Total electricity generation is projected to grow between 10 and 20 times due to different electrification levels. Greenhouse gas emission reduction is achievable with nuclear energy being critical to support renewables. Alternative pathways based on clean energy production may provide cost savings of around USD 11–14 billion compared to a Business as Usual case. The findings underscore the necessity of robust policies and regulatory frameworks to support this transition, providing insights applicable to other developing countries with similar energy profiles. This study proposes a unique contextualized open-source modelling framework for a data-constrained, lower–middle-income country, offering a replicable approach for similar contexts in Sub-Saharan Africa. Its novelty also extended towards contributing to the knowledge of energy system modelling, with nuclear energy playing a crucial role in meeting future demand and achieving the country’s objectives under the Paris Agreement. Full article

Sustainable mineral resource management is critical amid escalating environmental concerns and growing demand for minerals in digital and clean energy technologies. While financial technology (FinTech) has been widely recognized for enhancing financial inclusion and economic efficiency, its role in environmental governance—particularly in the mining sector—remains underexplored, especially within developed economies like the United States. This study addresses this gap by examining how FinTech adoption influences mineral sustainability, using time series data from 1998 to 2023. Four FinTech proxies—mobile cellular subscriptions, Internet usage, fixed broadband access, and financial inclusion—were analyzed alongside environmental compliance and investment in sustainable mining technologies. Using the Autoregressive Distributed Lag (ARDL) model and Frequency Domain Causality (FDC) analysis, the results show that greater FinTech adoption significantly reduces mineral depletion rates, indicating improved sustainability. Internet and broadband access exhibit strong long-term impacts, while mobile connectivity and credit access show notable short- and medium-term effects. Investment in sustainable mining technologies further enhances these outcomes. Our findings suggest that FinTech serves as a multidimensional enabler of sustainability through digital inclusion, transparency, and access to green financing. This study provides empirical evidence to guide policymakers in integrating digital financial infrastructure into strategies for sustainable mineral resource governance. Full article

Power systems need to meet the ever-increasing demand for higher quality and reliability of electricity in distribution systems while remaining sustainable, secure, and economical. The globe is moving toward using renewable energy sources to provide electricity. An evaluation of the influence of artificial intelligence (AI) on the accomplishment of SDG7 (affordable and clean energy) is necessary in light of AI’s development and expanding impact across numerous sectors. Microgrids are gaining popularity due to their ability to facilitate distributed energy resources (DERs) and form critical client-centered integrated energy coordination. However, it is a difficult task to integrate, coordinate, and control multiple DERs while also managing the energy transition in this environment. To achieve low operational costs and high reliability, inverter control is critical in distributed generation (DG) microgrids, and the application of artificial neural networks (ANNs) is vital. In this paper, a power management strategy (PMS) based on Inverter Control and Artificial Neural Network (ICANN) technique is proposed for the control of DC–AC microgrids with PV-Wind hybrid systems. The proposed combined control strategy aims to improve power quality enhancement. ensuring access to affordable, reliable, sustainable, and modern energy for all. Additionally, a review of the rising role and application of AI in the use of renewable energy to achieve the SDGs is performed. MATLAB/SIMULINK is used for simulations in this study. The results from the measures of the inverter control, m, VL-L, and Vph_rms, reveal that the power generated from the hybrid microgrid is reliable and its performance is capable of providing power quality enhancement in microgrids through controlling the inverter side of the system. The technique produced satisfactory results and the PV/wind hybrid microgrid system revealed stability and outstanding performance. Full article

Electric vehicles (EVs) are central to the decarbonisation of transport systems and achievement of the Sustainable Development Goals (such as SDGs 7 and 13, affordable and clean energy and climate action, respectively). This study adopts a hybrid methodological framework, merging panel econometric models with machine learning (ML), to examine the drivers of EV adoption across Africa, China, and the European Union between 2015 and 2023. We analyse the influence of charging station density (CSD), GDP per capita, renewable energy share (RES), urbanisation, and electricity access using both first-difference and fixed-effects models for causal insight and Random Forest, XGBoost, and neural network algorithms for predictive analytics. While CSD emerges as the most significant driver across models, results reveal a paradox—GDP per capita demonstrates a negative relationship with EV adoption in econometric models yet ranks among the top predictive features in ML models. This divergence highlights the limitations of assuming linear causality in high-income settings and underscores the value of combining causal and predictive approaches. SHAP and PCA analyses further illustrate regional disparities, with Africa showing low feasibility scores due to infrastructure and grid limitations. Sub-regional case studies (Kenya, South Africa, Morocco, Nigeria) emphasise the need for tailored, integrated policies that address both energy infrastructure and transport equity. Findings highlight the value of combining interpretable models with predictive algorithms to inform inclusive and region-specific EV transition strategies. Full article

With the global emphasis on sustainable growth and development, the depletion of natural energy reserves due to reliance on fossil fuels and non-renewable sources remains a critical concern. Despite strides in transitioning to electrical mobility, rural and agricultural communities depend heavily on liquefied petroleum gas and firewood for cooking, lacking viable, sustainable alternatives. This study focuses on community-led efforts to advance biogas adoption, providing an eco-friendly and reliable energy alternative for rural and farming households. By designing and developing balloon-type anaerobic biodigesters, this initiative provides a robust, cost-effective, and scalable method to convert farm waste into biogas for household cooking. This approach reduces reliance on traditional fuels, mitigating deforestation and improving air quality, and generates organic biofertilizer as a byproduct, enhancing agricultural productivity through organic farming. The study focuses on optimizing critical parameters, including the input feed rate, gas production patterns, holding time, biodigester health, gas quality, and liquid manure yield. Statistical tools, such as descriptive analysis, regression analysis, and ANOVA, were employed to validate and predict biogas output data based on experimental and industrial-scale data. Artificial neural networks (ANNs) were also utilized to model and predict outputs, inspired by the information processing mechanisms of biological neural systems. A comprehensive database was developed from experimental and literary data to enhance model accuracy. The results demonstrate significant improvements in cooking practices, health outcomes, economic stability, and solid waste management among beneficiaries. The integration of statistical analysis and ANN modeling validated the biodigester system’s effectiveness and scalability. This research highlights the potential to harness renewable energy to address socio-economic challenges in rural areas, paving the way for a sustainable, equitable future by fostering environmentally conscious practices, clean energy access, and enhanced agricultural productivity. Full article

Energy poverty focuses on energy affordability in developed nations but is most often used in the developing world in the context of a lack of access to electricity, clean cooking fuels, or technologies. About 1.2 billion people still lack access to electricity and nearly 40 per cent of the world’s population lacks access to clean cooking fuels. In addition, climate change mitigation strategies must be applied to a complex and diverse socio-technical landscape that varies across and within countries. Energy poverty is among the most pressing issues to be addressed within these strategies; however, due to the complexity of its causes, there is no commonly agreed upon evaluation approach or holistic set of indicators for its quantitative evaluation. In this study, a comprehensive literature review is undertaken on energy poverty measurement methods and definitions, and factors that cause energy poverty. Through this, exogenous and endogenous factors that are often overlooked in the assessment and prediction of energy poverty are identified. The need for an energy poverty prediction framework is identified, incorporating missing perspectives and elements needed to implement future energy poverty projections to enable proactive policy development. Missing perspectives included an increase in energy demand associated with the development of innovative technologies including artificial intelligence and automation, increasing fuel prices, and exogenous factors such as rising temperatures and increased acute disasters and endemic structural failures associated with climate change leading to employment impacts, all of which may be critical to the accurate prediction of energy poverty. Full article

Using the PRISMA-ScR methodology, this scoping review systematically analyzes how EU laws and regulations influence the development, adoption, and deployment of AI-driven digital solutions in energy generation, transmission, distribution, consumption, and markets. It identifies key regulatory barriers such as stringent risk assessments, cybersecurity obligations, and data access restrictions, along with enablers like regulatory sandboxes and harmonized compliance frameworks. Legal uncertainties, including AI liability and market manipulation risks, are also examined. To provide a comparative perspective, the EU regulatory approach is contrasted with AI governance models in the United States and China, highlighting global best practices and alignment challenges. The findings indicate that while the EU’s risk-based approach to AI governance provides a robust legal foundation, cross-regulatory complexity and sector-specific ambiguities necessitate further refinement. This paper proposes key recommendations, including the integration of AI-specific energy sector guidelines, acceleration of standardization efforts, promotion of privacy-preserving AI methods, and enhancement of international cooperation on AI safety and cybersecurity. These measures will help strike a balance between fostering trustworthy AI innovation and ensuring regulatory clarity, enabling AI to accelerate the clean energy transition while maintaining security, transparency, and fairness in digital energy systems. Full article

Ensuring access to clean water for drinking, agriculture, and recreational activities remains a global challenge. Groundwater, supplying approximately 50% of domestic water and 40% of agricultural irrigation, faces increasing threats from climate change, population growth, and unsustainable agricultural practices. These factors contribute to groundwater contamination, notably nitrate pollution resulting from excessive fertilizer use, which poses risks to water quality and public health. Addressing this issue demands innovative, efficient, and sustainable remediation technologies. Permeable reactive barriers (PRBs) have emerged as promising solutions for in situ groundwater treatment, using reactive media to transform contaminants into less toxic forms. PRBs offer advantages like low energy consumption and minimal maintenance. This study uses bibliometric analysis to explore the scientific production of PRBs for nitrate remediation, revealing research trends, key focus areas, and significant contributions. It included 141 articles published from 1975 to 2023. Early research focused on basic mechanisms and materials like zero-valent iron (ZVI), while recent studies emphasize sustainability and cost-effectiveness using low-cost materials such as agricultural byproducts. The findings highlight a growing focus on the circular economy and the need for more in situ studies to assess PRB performance under varying conditions. PRBs show significant potential for enhancing groundwater management and long-term water quality in agricultural contexts. Full article

Renewable energy has emerged as a transformative and essential alternative in the global energy sector. Many countries are striving to achieve the Sustainable Development Goals (SDGs) established by the United Nations for 2030, particularly the goal of ensuring that all individuals have access to clean and affordable energy. This paper re-examines the impact of economic growth (EG), trade openness (TO), exchange rates (ER), foreign direct investment (FDI), green finance (GF), and oil prices (OL) on renewable energy consumption (REC) across 14 Southern African countries: South Africa, Botswana, Lesotho, Namibia, Tanzania, Madagascar, Mauritius, Kenya, the Comoros, Zambia, Eswatini, Rwanda, Angola, and Mozambique, during the period of 2000 to 2022. This study employed cointegration and unit root tests, as well as the RALS-EG and MMQR models, to estimate the long-run relationships among the variables. The results reveal that renewable energy consumption is positively and directly related to economic growth, trade openness, exchange rates, green finance, and foreign direct investment across all quantiles (q05–q95), with no evidence of asymmetric effects. These findings suggest that economic growth, green finance, and foreign direct investment are crucial for fostering renewable energy innovation in Southern African countries. Policymakers are encouraged to prioritize strategies that enhance these factors as a foundation for achieving sustainable energy solutions. Full article

Arsenic contamination poses a severe health risk in regions with high water vulnerability and limited treatment infrastructure. This study evaluates a photovoltaic-powered water treatment system for arsenic removal in La Yarada Los Palos District, Tacna, Peru, where arsenic concentrations reached up to 0.0417 mg/L, significantly surpassing the World Health Organization (WHO) limit of 10 µg/L (0.01 mg/L) for drinking water. The system integrates a natural sedimentation pretreatment stage in a geomembrane-lined reservoir, followed by oxidation with sodium hypochlorite, coagulation, and adsorption. Arsenic removal efficiencies ranged from 99.72% to 99.85%, reducing residual concentrations below WHO guidelines. Pretreatment significantly improved performance, reducing turbidity by up to 66.67% and TSS by up to 70.37%, optimizing subsequent treatment stages. Operationally, pretreatment decreased cleaning frequency from six to four cleanings per month, while backwashing energy consumption dropped by 33% (from 45.72 kWh to 30.48 kWh). The photovoltaic system leveraged the region’s high solar radiation, achieving an average daily generation of 20.31 kWh and an energy surplus of 33.08%. The system’s performance was evaluated within the context of existing arsenic removal technologies, demonstrating that the integration of natural sedimentation and renewable energy constitutes a viable operational alternative. Given the regulatory framework in Peru, where arsenic limits align with WHO standards, conventional water treatment systems are normatively and technically unfeasible under national legislation. Furthermore, La Yarada Los Palos District faces challenges due to its limited infrastructure for conventional electrification via power grid, as identified in national reports on rural electrification and gaps in access to basic services. Beyond its performance in the study area, the system’s modular design allows adaptation to diverse water sources with varying arsenic concentrations, turbidity levels, and other physicochemical characteristics. In remote regions with limited access to the power grid, such as the study site, photovoltaic energy provides a self-sustaining and replicable alternative, particularly in arid and semi-arid areas with high solar radiation. These conditions are not exclusive to Latin America but are also prevalent in remote regions of Africa, the Middle East, Asia, and Oceania, where groundwater arsenic contamination is a significant issue and renewable energy availability can enhance water treatment sustainability. These findings underscore the potential of using sustainable energy solutions to address water contamination challenges in remote areas. The modular and scalable design of this system enables its replication in regions with adverse hydrogeological conditions, integrating renewable energy and pretreatment strategies to enhance water treatment performance. The framework presented in this study offers a replicable and efficient approach for implementing eco-friendly water treatment systems in regions with similar environmental and resource constraints. Full article