Search Results (4,306)

This research was motivated by the urgent need to address resource shortages and high energy costs in concrete production by replacing an energy-intensive traditional curing method with a new, more sustainable solution. By exploring solar heat treatment with composite binders and THACs, the study aimed to develop sustainable, cost-effective alternatives that harness renewable energy sources and optimize natural cement hydration processes for accelerated hardening. This article explores the potential application of solar energy in the production of precast concrete products using a composite binder. The effectiveness of the composite binder in solar thermal treatment of concrete using translucent heat-accumulating coatings is tested. The results of laboratory studies are presented, and the feasibility of using concrete based on composite binder at the laboratory scale for the production of concrete and reinforced concrete products, both with steaming and with solar thermal treatment, is established. The study of the structural features and basic physical and mechanical properties of hardened concrete under various conditions indicates that, under the investigated laboratory conditions, solar-thermally treated concrete exhibits physical and mechanical properties comparable to those of normally cured concrete. Laboratory studies confirmed the effectiveness of both steaming and solar heat treatment methods under controlled experimental conditions. Within the scope of the performed laboratory tests, the structure and properties of these concretes were comparable to those of normally cured concretes and, in several aspects, superior to those obtained under conventional steam curing regimes, which indicates the effectiveness of the described method, not only from the point of view of significant savings in fuel and energy resources. When producing products based on composite binders using solar thermal treatment, the consumption of the clinker portion of the binder is reduced by 50% (composition of the composite binder itself) and the consumption of conventional fuel during heat and moisture treatment is reduced by 70–100 kg per 1 m³ of concrete (reflecting process-level comparisons), which is of significant value for external energy demand. These findings confirm the technical feasibility and environmental advantages of the proposed method at the laboratory scale and highlight its potential for broader industrial application in precast concrete production. Full article

Agricultural residues and agro-waste are increasingly recognized as valuable reinforcements for sustainable composite materials. Natural fibers derived from these biomasses offer biodegradability, low density, renewability, and potential environmental benefits. However, their performance and sustainability depend strongly on extraction, surface treatment, and processing conditions. Therefore, evaluating the environmental emissions associated with natural fiber biocomposites is essential before claiming sustainability advantages. In this research, flax, jute, kenaf, and bagasse fibers were extracted and treated using an eco-friendly sodium bicarbonate solution, then incorporated into polylactic acid (PLA) matrix to fabricate biocomposites via injection molding. A life cycle assessment (LCA) was conducted using the ReCiPe midpoint (H) method, with a functional unit defined as “per kg” of manufactured biocomposite. The results revealed that jute fiber composites generated the highest emissions across several impact categories, including climate change (1.290 × 10¹ kg CO₂-Eq), terrestrial ecotoxicity (6.327 × 10¹ kg 1,4-DCB-Eq), human toxicity: carcinogenic effects (1.923 kg 1,4-DCB-Eq), and fossil resource use (3.202 kg oil-Eq). Jute also showed a 3.6% increase in terrestrial ecotoxicity and a 19.5% increase in land compared to flax, although it exhibited a 6.5% lower impact related to bagasse. A ±20% electricity-consumption sensitivity analysis further highlighted the dependence of environmental impacts on processing energy demand. Full article

In the context of the transition of the European energy sector and economy towards sustainable systems, this study aims to investigate the influence of digital enablers on affordable and clean energy in the European Union, using an econometric approach based on panel data regression. In accordance with the literature review and the main programmatic documents that mark the sustainable transition of the energy system, as well as the role of digitalization in this process, 4 research hypotheses and 16 sub-hypotheses were developed regarding the influence of digital enablers specific to the digitalization of the population and enterprises on clean and affordable energy. To confirm the hypotheses, a panel data regression was used for the period 2016–2024 in the European Union states. From a methodological perspective, the panel data regression was carried out using estimation of fixed effects and random effects models, Hausman tests for model selection, diagnostic testing, and correction of standard errors using Driscoll–Kraay estimators. The panel data regression analysis was carried out using R software, version 4.5.1. The results obtained showed that not all independent variables that express the digitalization of the population have the same influence on the share of renewable energy. The performed analysis shows the influence of the level of digitalization of enterprises on the share of renewable energy in the final energy consumption value, but also of the digitalization of the population on the price of energy, as a synthetic expression of affordable energy. Therefore, an essential contribution of the research is represented by highlighting the differentiated impact of digital enablers on clean and affordable energy, using a dual perspective of digitalization, at both the population and enterprise levels. Full article

Stationary energy storage (SES) is increasingly needed to integrate variable renewable generation and improve consumer self-consumption, but technology choices involve associated trade-offs between cost, efficiency, and life-cycle impacts. This study evaluates the role of second-life lithium-ion (Li-ion) batteries repurposed from electric vehicles for stationary applications, compared to lead-acid (Pb-acid) batteries and power-to-hydrogen-to-power (PtH₂P) systems. We develop an optimization-based sizing and dispatch framework using measured PV–load profiles and hourly market electricity prices, and evaluate performance per 1 MWh delivered to the load over a 10-year life cycle. Economic performance is quantified through discounted cash flows equal to levelized cost of storage (LCOS), while environmental performance is assessed through life-cycle metrics with explicit representation of recycling and second-life credits. In addition to global warming potential (GWP), the analysis considers additional resource and impact metrics, as well as key operational efficiency metrics, including bidirectional consumption efficiency, autonomy, and share of self-consumption/export of photovoltaic systems. Scenario and sensitivity analyses examine the impact of policy and financial parameters, in particular feed-in tariff remuneration and discount rate, on the comparative ranking of technologies. The results highlight how circular economy pathways, especially second-life distribution for Li-ion batteries and high end-of-life recovery for lead-acid batteries, have a significant impact on the life-cycle burden for delivered energy, while market-driven conditions for dispatching and export activities shape economic outcomes. Overall, the proposed workflow provides a transparent, circularity-aware basis for selecting stationary storage technologies associated with photovoltaic systems, under realistic operational constraints. Full article

The relationship between economic development and environmental degradation in Brazil was studied over the period 1970–2022, using ecological footprint (EF) as an environmental indicator. A contribution to the scientific literature exists here because biomass energy (BIO) has been separated from other types of renewable energy sources, and environmental policy stringency (EPS) and energy depletion (END) have been simultaneously analyzed for their joint impacts on EF in Brazil. In this research, four hypotheses were formulated for the relationships of: GDP, BIO, EPS, RE, and END with EF. The ARDL method was used in this analysis due to the different orders of integration for some of the variables and sample size limitations, both of which make alternative cointegration techniques inappropriate. All four hypotheses were supported in the empirical estimates of this study. In the long run, increases in GDP will result in increased EF, decreases in BIO and EPS will decrease EF, and no long-run relationship exists between RE and EF. However, RE has a short-term rebound effect. Increases in END will increase EF, indicating the environmental costs associated with the extraction and consumption of non-renewable resources. The statistically significant error correction term also supports the idea that there will be a quick adjustment towards the long-run equilibrium. The implications of these results suggest that Brazil continues to operate within a stage of growth driven primarily by scale rather than intensity, yet well-regulated biomass energy and strict environmental regulations provide a pathway for achieving decoupling in alignment with SDG 13 and SDG 15. Full article

To mitigate environmental impact, specifically the CO₂ emissions associated with conventional thermal and nuclear facilities, renewable energy sources are increasingly being adopted as primary alternatives. However, integrating these renewable sources into the utility grid poses a significant challenge, primarily due to the stochastic and nonlinear nature of weather. Consequently, it is imperative that power systems operate under an intelligent control model to ensure energy output meets strict power quality standards. In this context, accurate forecasting is a cornerstone of smart power management, particularly in off-grid architectures, where predicting Power Quality Parameters (PQPs) is fundamental for system optimization and error correction. This study conducts a comprehensive comparative evaluation of nine different predictive architectures for estimating PQPs. The algorithms analyzed include $LSTM$, $GRU$, $DNN$, $CNN1D\text{-}LSTM$, $BiLSTM$, attention mechanisms, $DT$, $SVM$, and $XGBoost$. The central objective is to develop a reliable basis for the automated regulation and enhancement of electrical quality in isolated systems. The specific parameters investigated are power voltage (U), Voltage Total Harmonic Distortion ($TH{D}_u$), Current Total Harmonic Distortion ($TH{D}_i$), and short-term flicker severity ($P_{st}$). Data for this investigation were acquired from an experimental off-grid setup at VSB-Technical University of Ostrava (VSB-TUO), Czech Republic. To assess model performance, we utilized root mean square error ($RMSE$) as the primary accuracy metric, while simultaneously evaluating computational efficiency in terms of processing speed and memory consumption during testing. Full article

Driven by renewable energy, the operating temperatures of alkaline water electrolyzers (AWEs) exhibit significant dynamic variations. Conventional control strategies rely on fixed startup parameters, causing dispatch plans to deviate from actual physical states, which leads to transient over-temperature or startup failures. To address this issue, this paper proposes a dual-layer optimization strategy for multi-electrolyzer systems based on temperature–power adaptation. First, a thermo-electro-hydrogen coupling model is established to quantitatively reveal the dynamic relationship among the initial temperature, startup power, and transition time. This relationship is utilized to construct a dynamic startup boundary, overcoming the limitations of traditional static constraints. Within the proposed framework, the upper layer utilizes a Mixed-Integer Linear Programming (MILP) model to formulate state-switching and baseline power allocation plans derived from short-term forecasts. Concurrently, the lower layer employs the Mongoose Optimization Algorithm (MOA) for real-time rolling optimization, enabling the system to actively perceive temperature variations and adaptively schedule power allocation. Simulations across typical seasonal scenarios validate the strategy’s superiority. In a typical spring scenario, compared to the traditional Daisy Chain and Rotation Control strategies, as well as the Equal Allocation strategy, the proposed approach reduces total startup time and energy consumption by 59.2% and 54.6%, respectively. Furthermore, it increases wind power accommodation rates by 17.7% and 14.2%, and total hydrogen production by 20.0% and 14.9%, respectively. These superior renewable energy utilization and production efficiencies are robustly maintained across typical seasonal scenarios. By actively perceiving actual temperatures for adaptive scheduling, the proposed strategy ultimately ensures synergy and reliability between the control strategy and actual operational constraints under fluctuating conditions. Full article

This paper presents a detailed life cycle assessment (LCA) of a Dynamic Sand Filter continuous upflow sand filtration system used in municipal and industrial wastewater treatment. This study evaluates the environmental impacts associated with raw material extraction, manufacturing, transportation, operation, and end-of-life disposal. Using the TRACI methodology and following ISO 14040/44 standards, the results indicate that operational energy use, primarily electricity consumption, is the dominant environmental contributor, accounting for over 99% of total Global Warming Potential (GWP). Comparative analyses demonstrate that Dynamic Sand Filter outperforms conventional Dynamic Sand Filtration Methods (DSFMs) in both energy and emission efficiency across multiple wastewater treatment scenarios. The findings emphasize the environmental advantages of continuous filtration systems and highlight opportunities for further optimization through renewable energy integration and eco-design improvements, with the technology presenting savings of over 40 percent lower CO₂ per cubic meter in operational emissions and over 25 percent lower overall energy consumption. Additionally, this research includes further comparison with conventional Rapid Sand Filters and simulated suggestions to enhance ecological footprint. Full article

This study develops econometric models to examine greenhouse gas emissions associated with coal and natural gas consumption in Poland between 2015 and 2023. Poland has one of the most carbon-intensive energy systems in Europe. Three complementary log–log econometric models were estimated: a model explaining total CO₂ emissions, a model assessing emission intensity (CO₂ per unit of GDP), and a model capturing short-term variations in emission intensity. The results demonstrate that coal consumption remains the dominant determinant of absolute emissions, whereas the expansion of renewable energy significantly contributes to lowering the carbon intensity of economic growth. However, short-term fluctuations in emission intensity are still largely influenced by changes in fossil fuel consumption patterns. The findings highlight the gradual and sequential character of Poland’s energy transition, where gains in environmental efficiency precede a consistent reduction in total emissions. The proposed modeling framework offers an empirical basis for evaluating the effectiveness of climate and energy policies and can support the formulation of decarbonization strategies in economies heavily reliant on fossil fuels. Full article

Governance is often treated as a slow-moving background condition in energy transition research, even though institutional reform and implementation capacity shape outcomes over long horizons. This study adopts a time–frequency perspective to examine how institutional quality aligns with energy-system and carbon-efficiency transition dynamics using multivariate wavelet coherence. Unlike mean-based regression approaches, the multivariate design allows assessment of whether governance aligns with carbon efficiency through three distinct systems—external integration, energy transition with resource rents, and governance coherence—using carbon intensity of GDP (CIGDP) as a common anchor. Using annual data for a comparative sample of GCC economies and non-GCC emerging economies over the period 1996–2022, we examine the evolution of coherence among governance indicators, energy use, renewable energy consumption, external economic exposure, and carbon efficiency, with emissions-related measures explicitly incorporated into the wavelet systems. Environmental implications are therefore interpreted only for systems that directly include carbon-efficiency indicators. The results indicate that institutional quality is most strongly associated with transition dynamics at low frequencies, pointing to persistent long-run alignment rather than short-run adjustment. Across GCC economies, low-frequency coherence is stronger and more continuous, while medium-term weakening appears as time-specific episodes that do not disrupt the underlying long-run structure. In non-GCC emerging economies, long-run coherence remains evident but is less continuous, and medium-horizon fragmentation is more frequent and more prolonged. At high frequencies, coherence is generally weak across countries, suggesting that short-run variation appears more closely associated with external shocks and market conditions than with structural or institutional alignment. Overall, the findings position institutional quality as a stabilising and conditioning factor in energy and carbon-efficiency transitions, operating primarily through long-run coherence and resilience. Systematic differences across governance regimes reflect variation in the continuity and stability of alignment across time horizons, rather than differences in the relevance of governance itself. Full article

Decarbonising greenhouse food production requires improvements in thermal management, energy efficiency, and system integration. Greenhouse energy demand is shaped by coupled heat and mass transfer processes, particularly envelope performance, ventilation, and latent heat associated with humidity control. This article synthesises recent advances in greenhouse microclimate control with emphasis on heat transfer, low-carbon heating and cooling, thermal storage, renewable and waste heat integration, and advanced modelling and control approaches. The review shows that humidity control and latent load management are primary drivers of winter energy use, as moisture removal through ventilation and dehumidification directly increases the sensible heating required to maintain indoor temperature setpoints. When assessed using realistic psychrometric relationships, ventilation and dehumidification can dominate peak heating demand and seasonal consumption. The performance of heat pumps, storage systems, semi-closed greenhouse concepts, and renewable heat pathways depends on how thermal loads are defined, how system boundaries are set, and how technologies are integrated in operation. Digital twins, predictive control, and hybrid physics-data models are increasingly used to manage variability in weather, energy prices, and infrastructure constraints. Greenhouse decarbonisation cannot be treated as a simple substitution of energy sources. System performance depends on coordinated design and operation, including heat recovery, moisture removal, and integration of supply technologies. Semi-closed and heat recovery-based configurations can reduce the ventilation–heating penalty and lower primary energy demand compared with vent-to-dry approaches. Long-term market projections suggest that the commercial greenhouse sector could expand substantially by 2050 under plausible growth scenarios, reflecting increased capital investment rather than a proportional rise in global food output. Net-zero greenhouse production is achievable through combined improvements in thermal management, electrification, and renewable energy integration. However, large-scale deployment depends on consistent modelling assumptions, credible economic assessment, and alignment with heat and CO₂ supply infrastructure. The transition is therefore shaped by system integration and planning as much as by individual technologies. Full article

Energy efficiency improvement represents a central strategic objective of the European Union (EU), essential for mitigating climate change and facilitating the transition toward a sustainable energy system. In 2023, renewable energy sources generated approximately 46% of the electricity produced in the EU, becoming the dominant component of the regional energy mix. This progress has been supported by coherent public policies, dedicated investment programs, and regulatory mechanisms aimed at accelerating the adoption of sustainable technologies. However, the existing literature highlights a research gap regarding the relationship between the dynamics of the European energy transition, the operational challenges generated by the rapid increase in the share of renewable energy sources, and the potential for energy savings in the residential sector through non-technological interventions. This paper analyzes the structural transformations of the European energy mix, the limitations of energy systems in the context of accelerated renewable energy integration, and the role of behavioral interventions in supporting the stability of the energy system. The study examines the dynamics of residential energy consumption, behavioral determinants of energy use, and the effectiveness of instruments such as information campaigns, real-time feedback, dynamic pricing, and demand response programs. The results indicate that these interventions can reduce peak loads, increase consumption flexibility, and alleviate pressure on energy networks under conditions of variable renewable energy generation. The integration of energy storage systems and the implementation of low-cost behavioral measures can act as complementary instruments for maintaining the dynamic stability of the energy system and for achieving the EU’s sustainability and climate neutrality objectives. Full article

The transition from non-renewable to renewable energy sources has emerged as a pressing global issue, driven by concerns over climate change, resource depletion, and the need for sustainable development. This study compares Canada, an energy-rich nation, and Bangladesh, an energy-scarce country, to understand the structural, institutional, and market factors driving their respective renewable energy transitions. Using univariate time-series models (ARIMA, ETS, and Prophet) for energy demand forecasting and extensive literature-based policy evaluation, the paper examines trends in energy production, consumption, and trade from 1990 to 2024. Our analysis indicates that Canada’s vast reserves of both renewable and non-renewable energy sources, its diversified energy portfolio, and carbon-pricing framework support a stable decarbonization pathway, with renewables projected to account for more than 20% of total supply by 2030. However, regional disparities and political resistance from the established energy sector continue to delay transition outcomes. On the other hand, Bangladesh has limited renewable and non-renewable energy sources, with its primary energy resource being natural gas reserves. Consequently, its heavy reliance on imports (over 75% of primary energy) and institutional bottlenecks expose its energy system to commodity-price volatility, undermining energy security and slowing renewable investment. Despite these challenges, targeted solar programs and concessional financing have modestly increased the penetration of renewable energy. The analysis highlights that commodity market fluctuations, technological innovations (such as smart grids and energy storage), and market-based policy instruments critically shape each country’s transition trajectory. A coordinated policy linking market stabilization, innovation investment, and social inclusion is essential for achieving a just and secure low-carbon transition in both countries. Full article

Ports play a pivotal role in global trade but are also associated with significant environmental and social challenges. Despite growing research on green ports, existing studies remain fragmented, with limited integration between technological, environmental, and governance perspectives within the blue economy framework. This review examines the transition from green port initiatives toward integrated blue-economy-oriented port systems by synthesizing recent advances in sustainable maritime infrastructure, smart port technologies, renewable energy integration, and policy frameworks. The analysis reveals three major findings. First, ports are increasingly evolving into energy-integrated hubs, with leading examples adopting shore power systems, renewable energy microgrids, and hydrogen-based infrastructure, thereby contributing to emissions reductions. Second, digitalization through artificial intelligence, IoT, and data-driven logistics significantly enhances operational efficiency, reduces energy consumption, and improves real-time decision-making. Third, effective governance frameworks that combine regulatory measures and incentive-based instruments are critical to accelerating sustainability transitions while ensuring economic competitiveness. In addition, the review highlights the growing integration of biodiversity conservation, marine pollution mitigation, and community engagement into port management strategies, reflecting a shift toward ecosystem-based approaches. Overall, the findings demonstrate that ports are transitioning from conventional logistics hubs into integrated socio-technical systems that enable low-carbon maritime transport while supporting inclusive and resilient coastal development. Full article

Decarbonizing the transportation sector requires quick adoption of low-carbon energy carriers, with green hydrogen becoming a promising option for zero/low-emission mobility. Hydrogen refueling stations powered by renewable energy sources present a practical way to cut down lifecycle greenhouse gases and ease grid congestion. Nonetheless, most existing photovoltaic (PV)-based hydrogen production systems focus solely on electrical aspects, overlooking thermal energy flows and temperature effects that greatly impact PV and Electrolyzer performance. This study provides a thorough techno-economic evaluation of a hybrid PV/photovoltaic-thermal (PVT) green hydrogen system for refueling stations. The simulation framework models the combined electrical, thermal, and hydrogen subsystems under realistic conditions, incorporating rooftop PV/PVT collectors, battery storage, a water Electrolyzer, and hydrogen storage. Thermal energy from the PVT is used to pre-heat Electrolyzer feedwater, lowering electricity demand for hydrogen production and boosting PV efficiency via active cooling. Hydrogen production follows a demand-driven control strategy based on randomly generated stochastic daily refueling events. Three configurations are compared: (i) grid-only electrolysis, (ii) PV-only assisted electrolysis, and (iii) fully integrated PV/PVT-assisted electrolysis. The results show that the integrated PV/PVT setup significantly increases self-consumption, autarky rate, and overall efficiency, while lowering reliance on grid electricity and hydrogen production costs. Developed case studies highlight the economic feasibility and real-world viability of PV/PVT-assisted (decentralized) hydrogen refueling infrastructure. Full article