Search Results (1,252)

Recirculating organic byproducts like food waste, wastewater and manure efficiently and at scale in a circular bioeconomy will be critical to ensuring future food security, energy security, climate resilience, water security and environmental health. Ultimately, we will not be able to live within the safe operating space of our planetary boundaries if we do not stop our wasteful and inefficient habits. Our food, waste, energy and water sectors are starting to transform towards circularity, driven by a diverse range of drivers, from net zero emissions targets, to food waste policies, and to rising fertiliser prices and geopolitical risks. However, these sectors are often not transforming in a coordinated manner, risking unintended consequences like competition between end-uses, technology lock-in, the prevention of scalability, or failure to achieve key sustainability targets, causing rebound effects. For example, society’s organic waste is being earmarked for the production of bioenergy, sustainable aviation fuels, biomaterials, and biofertilisers; however, it is not clear if there will be a sufficient supply of organic waste to meet these diverse demands. Phosphorus flow analyses indicate that we will need to secure almost all of the nutrients in organic waste as fertiliser raw material to produce food. There are some existing pockets of innovation within sectors related to food waste, water and wastewater, fertilisers and agriculture, and bioenergy. However, many initiatives are being driven by short-term challenges, are not operating at scale, or are not sufficiently integrated across sectors. In this paper, we provide examples of innovations and challenges from around the world, including Italy, Australia, Sri Lanka, the UK, Japan, and Malawi. This paper identifies a pathway to navigate tensions to achieve co-existing sustainability goals, including key enablers and barriers, ranging from overcoming regulatory fragmentation to a lack of capital investments. Creating a truly viable circular economy for organic byproducts requires the integration of policies, markets, technologies and people. This means engaging diverse stakeholders, from local councils and private waste contractors, farmers, and fertiliser companies to energy retailers and wastewater utilities, NGOs, informal collectors, and environmental regulators and policy-makers. Full article

Accurate prediction of dry matter intake (DMI) is essential for ration formulation, nutrient supply, and evaluation of production efficiency in lactating dairy cows. Several DMI prediction models are currently used, but most comparative studies have emphasized overall accuracy rather than whether model bias changes across biologically relevant production contexts. The objective of this study was to evaluate the context-dependent bias of widely used DMI prediction models in lactating dairy cows across classes of energy-corrected milk (ECM) and lactation stage. A literature-derived database was assembled from 135 studies consisting of 436 treatments from 6985 Holstein cows, reporting observed DMI and the variables required to implement five prediction models and evaluate their prediction error (PE): NRC2001, the Cornell Net Carbohydrate and Protein System (CNCPS), NASEM2021, Agroscope2021, and GfE2023. PE was calculated as predicted DMI minus observed DMI, such that positive values indicated overprediction and negative values indicated underprediction. Observations were classified according to ECM and days in milk (DIM). Mixed models were fitted separately for the ECM class and the lactation-stage class, with the study fitted as a random effect. PE differed among models, and the pattern of bias depended on both the ECM and the lactation-stage classes. The interaction between the ECM class and the model was significant, indicating that productive level modified model bias. The interaction between lactation-stage class and model was also significant and more pronounced, indicating marked changes in model bias across lactation stages. Across classes, NASEM2021 generally remained closest to zero, whereas GfE2023 and CNCPS showed more negative PE values in most contexts. Agroscope2021 showed a more context-sensitive pattern, and NRC2001 remained comparatively moderate across several classes. These findings indicate that the evaluation of DMI prediction models based only on global mean bias may conceal an important biological structure in PE. Context-specific evaluation, particularly across the lactation stage, may provide a more informative basis for selecting DMI prediction models for research and practical ration formulation. Full article

The maritime sector accounts for approximately 3% of global greenhouse gas (GHG) emissions and faces binding decarbonization obligations under the International Maritime Organization’s (IMO) Net-Zero Framework and the FuelEU Maritime Regulation. Conventional marine fuels, including very low sulphur fuel oil (VLSFO) and liquefied natural gas (LNG), are insufficient to meet long-term regulatory intensity targets on a well-to-wake (WtW) lifecycle basis, creating an urgent need for credible fuel alternatives. This study investigates ethanol as a primary fuel for marine dual-fuel propulsion systems, assessed across four distinct production pathways, sugar beet, corn, sugarcane, and wheat straw, to determine its full decarbonization potential relative to VLSFO and LNG benchmarks. A simulation-based multi-dimensional evaluation framework is developed and applied, integrating dynamic operational simulation, energy analysis, environmental lifecycle modelling, and regulatory compliance assessment. The framework is calibrated against a high-resolution dataset from an active container ship, with scenario-specific engine data. While ethanol requires 39.1% more fuel mass than VLSFO due to its lower energy density, all four ethanol pathways deliver substantially superior WtW GHG reductions: from 50.2% (corn) to 76.9% (wheat straw), compared with 20.6% for LNG. All ethanol scenarios satisfy FuelEU compliance limits across the 2026–2045 horizon, with wheat straw ethanol achieving a GFI of 22.52 gCO₂e/MJ, compliant marginally with the 2040 IMO target. These findings demonstrate that bio-based ethanol, particularly from lignocellulosic feedstocks, is a technically viable and regulatorily superior alternative to LNG for maritime decarbonization, warranting accelerated research into production scale-up and bunkering infrastructure development. Full article

The escalating global demand for renewable energy has positioned solar photovoltaics (PV) as a critical technology for achieving net-zero emissions. However, PV efficiency is strictly limited by thermal degradation, where elevated operating temperatures significantly reduce power output and accelerate material aging. This review systematically evaluates the integration of advanced phase change materials (PCMs) as a passive thermal management solution. We analyze the transition from material-level innovations—including nano-enhanced PCMs, 3D conductive frameworks, and shape-stabilization—to system-level hybrid architectures such as liquid—PCM, heat pipe-fin, and thermoelectric generator (TEG) integrations. Synthesis of recent empirical data (2024–2026) demonstrates that optimized PCM composites can achieve PV temperature reductions of up to 32 °C and electrical efficiency enhancements exceeding 19%. Furthermore, techno-economic assessments reveal that these systems can reduce the levelized cost of energy (LCOE) by 5–15% and achieve energy payback times as short as 1.5 years. Finally, this paper identifies critical research gaps in long-term outdoor durability, AI-driven predictive modeling, and sustainable bio-based encapsulation, providing a strategic roadmap for the commercialization of next-generation solar thermal management systems. Full article

The increasing worldwide demand for sustainable energy and effective waste management has heightened interest in solutions. Microbial fuel cells (MFCs) represent a potential category of bioelectrochemical systems that directly transform the chemical energy contained in organic waste into electrical energy via the metabolic processes of electroactive microorganisms. In the last twenty years, significant advancements have occurred in the comprehension of extracellular electron transfer (EET) mechanisms, biofilm formation, microbial community dynamics, electrode material engineering, and reactor design, resulting in marked enhancements in power density and wastewater treatment efficacy. Despite these breakthroughs, the extensive deployment and commercialization of MFC technology are constrained by various hurdles, including inadequate energy recovery, elevated material and fabrication expenses, operational instability, and the intricacies of system scale-up. This cutting-edge analysis offers a thorough evaluation of recent advancements in MFCs and their incorporation with sophisticated technology for waste management and energy generation. Focus is directed towards essential bioelectrochemical principles, microbial and biofilm engineering techniques, sophisticated electrode and membrane materials, reactor designs, and hybrid MFC systems integrated with anaerobic digestion, microbial electrolysis, and advanced oxidation methods. Ultimately, emerging trends, significant knowledge deficiencies, and future research goals are defined to inform the advancement of next-generation MFC systems that support circular economy and net-zero energy initiatives. Full article

Against the backdrop of the global aviation industry’s commitment to achieving the “Net Zero Carbon Emissions by 2050” goal, the issue of superimposed peak loads on distribution networks—arising from the large-scale transition from fossil-fueled to electric Ground Service Equipment (GSE) at civil airports—has become increasingly prominent, emerging as a critical constraint on green airport development. Focusing on the high-value airside area, this paper presents the first systematic review of how Vehicle-to-Grid (V2G) technology can transform electric Ground Service Equipment (e-GSE) from mere “charging loads” into “dispatchable energy storage resources.” The study proposes that, through bidirectional DC charging/discharging and intelligent aggregation technologies, e-GSE fleets operating on predictable schedules can be integrated as flexible regulation units within airport microgrids. To realize this pathway, the study comprehensively examines the core technological framework, encompassing wide-power-range bidirectional charging infrastructure, grid-forming power conversion topologies, standardized communication and grid interconnection interfaces, flight-schedule-based potential assessment and dispatch algorithms, and photovoltaic storage–charging hybrid system integration schemes. The review demonstrates that this technology can not only enhance grid resilience and promote renewable energy accommodation through peak shaving, valley filling, and ancillary services but also yields significant economic benefits. Finally, the study identifies the technical, standardization, and business model barriers hindering large-scale deployment, thereby providing a theoretical reference and a technology roadmap for the energy system planning and construction of future “zero-carbon smart airports”. Full article

Hydrogen (H₂) is becoming a meaningful way to store energy for long-term use and support thorough decarbonization in systems that use renewable energy. Underground hydrogen storage (UHS) has strategic benefits over above-ground systems because it can hold large volumes, is contained by geology, and is cheap to operate in cycles. This review compares four key geological formations for underground hydrogen storage (UHS): salt caverns, lined rock caverns, depleted hydrocarbon reservoirs, and saline aquifers. Each system is evaluated based on storage mechanisms, efficiency, safety, technological maturity, and economic feasibility. This review also introduces a unified cross-media evaluation framework, a TRL-risk matrix, a technology development roadmap, and novel insights into AI-based monitoring, offering prescriptive guidance for large-scale UHS implementation. Salt caverns have high injectivity, maintain their purity, and undergo 6 to 12 cycles per year at pressures of 60 to 180 bar; however, they are only found in certain places. Lined rock caverns can be built anywhere, but sealing and economic issues make them difficult to use. Depleted hydrocarbon reservoirs with TWh-scale capacity and already built infrastructure. Saline aquifers, on the other hand, have the most potential in the world but need enhanced management of microbiological responses and cushion gas optimization. A synthesis of current studies highlights key research gaps in cyclic geomechanics, hydrogen–rock–microbe interactions, and liner performance for high-pressure storage. The review concludes with techno-economic and safety considerations and identifies future directions for deploying geological UHS as a critical component of a net-zero hydrogen economy. Full article

Buildings are the primary energy consumption layer of Renewable Energy Communities (RECs) and a key target for net-zero policy under the EPBD recast. This scoping review applies the PRISMA-ScR framework to map Digital Twin (DT) architectures for building-scale and community-scale energy management in REC configurations. A Scopus search yielded a final analytical corpus of 102 studies, coded through an eight-dimensional thematic matrix covering lifecycle phases, digitalization objectives, enabling technologies, DT capability dimensions, and data realism. DT is the dominant enabling technology (55.9%), followed by IoT (23.5%) and machine learning (22.5%). Research is concentrated in the Planning and Design phase (77.5%) and markedly underrepresented in Implementation and Commissioning (16.7%). Notably, only 10.8% of studies integrate real-time operational data, exposing a significant gap between simulation-based research and the deployment conditions required under current EPBD mandates. The evidence base supports building energy monitoring, demand forecasting, and flexible grid operation but remains limited for retrofit verification, standardized net-zero KPIs, and operational workflows in existing stock. Critical DT capability gaps persist in Data Services (7.8%) and User Experience (18.6%). Overall, DT architectures show genuine potential for grid-interactive, net-zero building management, yet the field presents unresolved structural challenges for large-scale real-world deployment. Full article

Wind turbine blade (WTB) end-of-life waste is projected to increase significantly, yet no sustainable recycling solution with a clear energy, economic, and environmental (3E) assessment exists. This paper presents a validated 3E model of a WTB thermal recycling pilot (1 t/day) to benchmark recycled glass fibre (rGF) against virgin glass fibre (vGF) and identifies the throughput at which rGF becomes competitive. This subsequently leads to a projection of 3E performance at 5000 t/y plant capacity, at which rGF achieves approximately 46% lower specific primary thermal energy, 92% of the CO₂ emissions of vGF, and a selling price of 80% of vGF for a financial break-even. Building on this baseline, a novel combined material, heat, and power system is proposed and simulated, integrating the WTB recycling pilot with a 20 kWₑₗ/130 kWₜₕ organic Rankine cycle to serve residential buildings. Results show that coupling the pilot with 3000 m² of apartments yields a near net-zero CO₂ and energy-cost residential complex, with overall CO₂ emissions falling below those of standalone residential buildings combined with vGF production when more than 25 apartments are integrated. Full article

The global renewable energy sector now represents the world’s fastest-growing sector, with growth projected to more than double by 2030 and expected to exceed 4600 GW between 2025 and 2030. This is driven by falling costs, increasing consumer awareness, sustainable energy production models, and national and international climate commitments. This review study aims to discuss the transformation initiatives in the renewable energy sector with net-zero targets. A total of 89 studies published between 2020 and 2026 were identified for this literature review. The results indicate that digital transformation has the potential to significantly optimize the performance of the renewable energy sector by resolving its sustainability issues. This study discusses the waste types and waste management strategies in the renewable energy sector. It also highlights the indicators, barriers, and drivers of sustainable performance in the renewable energy sector by integrating advanced technological solutions in manufacturing, supply chain management, maintenance, monitoring, and the management of renewable energy equipment. The study findings demand global commitment and policy coordination in achieving the goals of decarbonization. The literature insights highlight future core research fields and can guide international organizations, industrial policymakers, and academic scholars towards a better and more sustainable future. Full article

Manufacturing systems are undergoing a fundamental transition as efficiency-driven optimisation paradigms prove increasingly inadequate for meeting net-zero, resource-efficiency, and resilience objectives. Digital twins have emerged as a central enabler of this transition, offering continuously coupled physical–digital representations capable of real-time monitoring, prediction, and control. Recent advances in artificial intelligence have accelerated this evolution, transforming digital twins from static simulation artefacts into adaptive, learning-enabled systems embedded within cyber–physical manufacturing environments. However, this shift has also exposed critical challenges related to trust, interpretability, scalability, and sustainability alignment. This review provides a critical synthesis of AI-enabled digital twin research with a specific focus on manufacturing and additive manufacturing systems. It examines the progression from physics-based and data-driven twins toward hybrid AI–physics architectures that balance predictive performance with physical consistency and explainability. Beyond technical performance, the review reframes digital twins as decision-making infrastructures whose value depends on how effectively they integrate energy consumption, material efficiency, carbon intensity, and lifecycle impacts into optimisation and control logic. Particular attention is given to real-time optimisation, predictive maintenance, and intelligent asset management, highlighting persistent gaps in uncertainty propagation, cross-scale coordination, and sustainability-aware governance. The review further identifies structural barriers to large-scale industrial adoption, including data interoperability fragmentation, platform lock-in, organisational resistance, and regulatory ambiguity surrounding AI-driven decisions. Synthesising insights across domains, it argues that many current digital twin implementations remain technically sophisticated yet strategically conservative, reinforcing throughput-centred objectives rather than enabling systemic decarbonisation and circularity. The paper concludes by outlining future research directions and policy-relevant opportunities, emphasising the need for digital twins that reason across timescales, objectives, and lifecycle boundaries. By aligning manufacturing intelligence with measurable sustainability outcomes, AI-enabled digital twins can move from incremental efficiency gains toward transformative impact in net-zero and circular manufacturing systems. Full article

Renewable energy is a central component of global sustainable energy development, with solar energy experiencing substantial growth over recent decades. Solar power is widely regarded as one of the most accessible routes to clean energy generation. However, high upfront costs remain a major barrier to adoption. Many potential users are reluctant to invest in solar photovoltaic (PV) systems because of the longer payback period. To address this financial constraint, a range of business models has been developed. This study used a systematic literature review to examine existing and emerging business models for promoting Solar PV solutions. The review included peer-reviewed journal articles published in English from 2020 to 2026. In total, 39 articles were critically evaluated considering their characteristics. Nine potential business models were identified, several of which are commonly used internationally and have shown positive results that could also be applied in the UK. Importantly, Community Energy Models have shown success in Europe, Sub-Saharan and Asian regions. This has been widely supported by the government due to sustainability and climate change targets. The UK has set their target to achieve net-zero in greenhouse gas emissions by 2050. Beyond financial barriers, reliance on weather conditions and the mismatch between energy demand and supply remain substantial barriers to wider solar PV deployment. Full article

Achieving deep decarbonization of the residential building sector is essential for meeting Canada’s climate commitments and Net Zero targets. However, large-scale residential retrofit planning is often constrained by the time, cost, and expertise required for detailed building energy modelling. This study evaluates the applicability of a typology-based retrofit planning tool developed by Homes to Zero (HTZ) as a simplified alternative to conventional simulation-based analysis. Two representative Canadian residential archetypes—a detached bungalow and a two-storey semi-detached home located in Toronto—were analyzed using both the HTZ platform and detailed hourly energy simulations conducted in eQuest (DOE-2.2 engine). Baseline energy consumption and greenhouse gas (GHG) emissions were first compared across the two modelling approaches. Results show strong agreement for the bungalow case, with differences of less than 1% for electricity and natural gas consumption and approximately 4% for total emissions. For the two-storey dwelling, baseline electricity estimates were identical while natural gas consumption differed by approximately 17%, highlighting the sensitivity of physics-based simulations to envelope and operational assumptions. Retrofit scenarios were then compared using single-measure GHG reductions derived from HTZ and incremental simulation results from eQuest. While both tools identified electrification through air-source heat pumps as the dominant emission-reduction strategy, differences were observed in the magnitude of savings for envelope upgrades and secondary measures. The HTZ platform also provides approximate retrofit cost estimates, enabling order-of-magnitude budgeting, whereas eQuest requires separate costing analysis. This study is framed as a screening-level benchmark rather than a full validation exercise, highlighting the trade-off between scalability and modelling fidelity in residential retrofit planning. The results suggest that typology-based tools can provide credible screening-level guidance for residential retrofit planning and large-scale policy analysis, while detailed simulation remains valuable for evaluating integrated retrofit packages and design-level decisions. Full article

In this study, we developed a leakage-free time-series machine learning framework to improve the accuracy of short-term (10 min ahead) wind speed forecasting. The measurements were obtained from real operational data collected at the Bandırma/Balıkesir wind power plant in Türkiye. The framework incorporates chronological train validation test splitting, causal missing data imputation, leakage-free feature engineering, and supervised lag-based modeling. Such a leak-proof design is crucial to avoid future information influencing the training and testing process of models, thus making the forecasting process more realistic and reliable in practice. We tested several models, including persistence, Support Vector Regression (SVR), Least-Squares Gradient Boosting (LSBoost), Random Forest (RF), Elastic Net (ELASTIC), and a stacking ensemble, and evaluated their performance using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), R-Squared (R²), bias measures, and skill scores, complemented by diagnostic analyses including residual distribution, autocorrelation, regime-based evaluation, Bland–Altman plots, and Quantile Quantile (Q-Q) plots. Our analyses showed that the Elastic Net model achieved balanced and statistically consistent performance, with a test RMSE of 0.6325 m/s, R² = 0.977, and negligible bias. Residual analysis indicated that errors were centered around zero, exhibited weak temporal dependence, and followed an approximately normal distribution in the central quantiles. Regime-based evaluation revealed that the model performed strongly in medium- and high-wind-speed conditions, while accuracy decreased under low wind speeds due to measurement uncertainty and low signal-to-noise ratios. Feature importance analysis indicated that previous wind speed was the dominant predictor, with solar irradiation and air temperature also contributing significantly. Forecast error decomposition showed that most prediction errors arose from natural atmospheric variability, with minimal systematic bias. The Diebold–Mariano test confirmed that ELASTIC statistically outperformed conventional machine learning models such as SVR and Random Forest. The proposed framework demonstrates statistically consistent short-term forecasting behavior that may support operational wind energy management and grid balancing applications. Full article

Higher education institutions (HEIs), engaged in education, research and community services, play an important role in promoting sustainable development. This study investigated the relationships between carbon emission-related variables—Knowledge, Attitude, and Practice (KAP)—and the amount of carbon emissions in HEIs. A cross-sectional design was adopted, and data were collected via an online questionnaire from September to December 2024. The participants were students from eight colleges and universities in central Taiwan, yielding 293 valid responses. The average daily per capita carbon emissions were calculated based on activity categories and emission coefficients. Carbon emissions from daily life contributed 87.7%, followed by transportation and academic activities (9.7% and 2.6%). The average carbon emission was 11.82 kg CO₂e/day/person. Statistical analysis showed that living arrangements and household size exhibited significant differences (p < 0.05). Regarding the KAP analysis, attitude and practice showed a significant positive correlation (r = 0.59, p < 0.01), while practice and individual-level Scope 3 emissions were negatively correlated (r = −0.12, p < 0.01), indicating that carbon reduction behaviors can effectively decrease individual carbon emissions. This study quantified the carbon emission in HEIs and addressed a research gap by linking individual-level energy behaviors with carbon emission estimates in Taiwan. The findings provide a basis for policy-making and promoting low-carbon behaviors. Future campus initiatives should focus on equipment upgrades, environmental education, and low-carbon actions to achieve sustainability and net-zero carbon goals. Full article