Search Results (186)

Biochar, a carbon-rich material derived from biomass, presents a sustainable solution to several pressing challenges in U.S. agriculture, including soil degradation, carbon emissions, and waste management. Despite global advancements, the U.S. biochar market remains underexplored in terms of economic viability, adoption potential, and sector-specific applications. This narrative review synthesizes two decades of literature to examine biochar’s applications, production methods, and market dynamics, with a focus on its economic and environmental role within the United States. The review identifies biochar’s multifunctional benefits: enhancing soil fertility and crop productivity, sequestering carbon, reducing greenhouse gas emissions, and improving water quality. Recent empirical studies also highlight biochar’s economic feasibility across global contexts, with yield increases of up to 294% and net returns exceeding USD 5000 per hectare in optimized systems. Economically, the global biochar market grew from USD 156.4 million in 2021 to USD 610.3 million in 2023, with U.S. production reaching ~50,000 metric tons annually and a market value of USD 203.4 million in 2022. Forecasts project U.S. market growth at a CAGR of 11.3%, reaching USD 478.5 million by 2030. California leads domestic adoption due to favorable policy and biomass availability. However, barriers such as inconsistent quality standards, limited awareness, high costs, and policy gaps constrain growth. This study goes beyond the existing literature by integrating market analysis, SWOT assessment, cost–benefit findings, and production technologies to highlight strategies for scaling biochar adoption. It concludes that with supportive legislation, investment in research, and enhanced supply chain transparency, biochar could become a pivotal tool for sustainable development in the U.S. agricultural and environmental sectors. Full article

The International Maritime Organization (IMO) has set a goal to reach net-zero greenhouse gas emissions from international shipping by or around 2050. The ship energy efficiency framework has played a positive role over the past decade in improving carbon intensity and reducing greenhouse gas emissions by employing the technical and operational energy efficiency metrics as effective appraisal tools. To quantify the ship energy efficiency performance and review the existing energy efficiency framework, this paper analyzed the data for the reporting year of 2023 extracted from the European Union (EU) monitoring, reporting, and verification (MRV) system, and investigated the operational profiles and energy efficiency for the ships calling at EU ports. The results show that the data accumulated in the EU MRV system could provide powerful support for conducting ship energy efficiency appraisals, which could facilitate the formulation of decarbonization policies for global shipping and management decisions for stakeholders. However, data quality, ship operational energy efficiency metrics, and co-existence with the IMO data collection system (DCS) remain issues to be addressed. With the improvement of IMO DCS system and the implementation of IMO Net-Zero Framework, harmonizing the two systems and avoiding duplicated regulation of shipping emissions at the EU and global levels are urgent. Full article

In response to the global imperative of reducing greenhouse gas emissions, the optimisation of renewable energy systems under regionally favourable conditions has become increasingly essential. Solar irradiance forecasting plays a pivotal role in enhancing energy planning, grid reliability, and long-term sustainability. However, in the context of Turkey, existing studies on solar radiation forecasting often rely on traditional statistical approaches and are limited to single-site analyses, with insufficient attention to regional diversity and deep learning-based modelling. To address this gap, the present study focuses on Turkey’s Mediterranean region, characterised by high solar potential and diverse climatic conditions and strategically relevant to national clean energy targets. Historical data from 2020 to 2023 were used to forecast solar irradiance patterns up to 2026. Five representative locations—Adana, Isparta, Fethiye, Ulukışla, and Yüreğir—were selected to capture spatial and temporal variability across inland, coastal, and high-altitude zones. Advanced deep learning models, including artificial neural networks (ANN), long short-term memory (LSTM), and bidirectional LSTM (BiLSTM), were developed and evaluated using standard performance metrics. Among these, BiLSTM achieved the highest accuracy, with a correlation coefficient of R = 0.95, RMSE = 0.22, and MAPE = 5.4% in Fethiye, followed by strong performance in Yüreğir (R = 0.90, RMSE = 0.12, MAPE = 7.2%). These results demonstrate BiLSTM’s superior capacity to model temporal dependencies and regional variability in solar radiation. The findings contribute to the development of location-specific forecasting frameworks and offer valuable insights for renewable energy planning and grid integration in solar-rich environments. Full article

The maritime industry remains a significant contributor to global greenhouse gas (GHG) emissions. In this article, a systematic study has been performed on the alternative fuel emissions of large cargo ships under different route scenarios and propulsion systems. For this purpose, a set of key performance indicators (KPIs) are evaluated, including total equivalent CO₂ emissions (CO_2eq), CO_2eq emissions per unit of transport mass and CO_2eq emissions per unit of transport mass per distance. The emissions analysis demonstrates that Liquified Natural Gas (LNG) paired with Marine Gas Oil (MGO) emerges as the most viable short-term solution in comparison with the conventional fuel oil propulsion. Synthetic methanol (eMeOH) paired with synthetic diesel (eDiesel) is identified as the most promising long-term fuel combination. When comparing the European Union (EU) emission calculation system (FuelEU) with the International Maritime Organization (IMO) emission metrics, a discrepancy in emissions reduction outcomes has been observed. The IMO approach appears to favor methanol (MeOH) and liquefied natural gas (LNG) over conventional fuel oil. This is attributed to the fact that the IMO metrics do not consider unburned methane emissions (methane slip) and emissions in the production of fuels (Well-to-Tank). Full article

This study introduces a deep learning (DL)-based optimal condition monitoring and control system tailored to indoor smart greenhouses, with a novel focus on maintaining symmetry—defined as a dynamic equilibrium among temperature, humidity, and CO₂ levels—critical in plant growth. A hydroponic greenhouse prototype was developed to capture real-time climate data at high temporal resolution. A custom 1D convolutional neural network (1D-CNN) optimized via a genetic algorithm (GA) was employed to predict environmental fluctuations, achieving R² scores up to 0.99 and a standard error of prediction (SEP) as low as 0.35%. The system then actuated climate control mechanisms to restore and maintain symmetry. Experimental validation revealed that plants grown under the symmetry-aware control system exhibited significantly improved growth metrics. The results underscore the potential of integrating symmetry-aware DL strategies into precision agriculture in achieving sustainable and resilient plant production systems. Full article

This study investigates the adsorption performance of biochar synthesized under varying pyrolysis and CO₂ activation conditions for the simultaneous removal of nitrogen monoxide (NO) and sulfur dioxide (SO₂), with an additional focus on its environmental impacts via life cycle assessment (LCA). Biochar was produced from Hinoki cypress using a two-stage process comprising initial pyrolysis followed by CO₂ activation, and its physicochemical properties were evaluated through pore structure analysis. Adsorption experiments were conducted under both single- and combined-gas conditions to assess the synergistic or competitive behaviors of NO and SO₂ adsorption. The results indicated that activation conditions significantly influenced the surface area and pore volume of biochar, leading to enhanced gas adsorption capacities. A trade-off between biochar yield and pollutant removal efficiency was observed, suggesting an optimal activation temperature balancing these two factors. Furthermore, the LCA approach, employing IPCC 2021 GWP 100 metrics, quantified the environmental impacts of biochar production under different thermal conditions. The findings revealed that although higher activation temperatures improved adsorption efficiency, they also resulted in increased energy consumption and associated greenhouse gas emissions. These outcomes demonstrate the necessity of optimizing activation parameters not only for functional performance but also for environmental sustainability. This work provides insight into designing efficient biochar-based gas treatment systems and supports their potential application as eco-friendly alternatives in industrial emission control strategies. Full article

The sustainable supply chain management (SSCM) literature does not directly address Scope 3 emissions despite their role as primary drivers of greenhouse gas emissions. This study aims to provide an overview of the main themes through which the SSCM literature has considered Scope 3 emissions and identify further avenues for research. A systematic literature review (SLR) was conducted. Scopus and Web of Science were the databases considered. Sixty-one papers were included in the analysis. Most papers focus on assessing and estimating Scope 3 emissions, followed by papers that discuss the reporting of Scope 3 emissions. These papers shed light on how firms may not report Scope 3 emissions if the information is negative to improve investors’ perception of the firm. The last group of papers discusses practices and strategies to manage Scope 3 emissions. The main challenge identified in establishing strategies to manage Scope 3 emissions is engagement with stakeholders, as, generally, only one or two tiers of the value chain cooperate. This study is the first to organize the literature on Scope 3 emissions under the lens of SSCM. If supply chains are to become more sustainable, focal enterprise coordination must be effective and leverage practices such as Scope 3 emissions metrics and measurement, data sharing, and green product development for all stakeholders. Full article

Urban transportation systems face increasing pressure to reduce car dependency and greenhouse gas emissions while supporting sustainable growth. This study addresses the lack of integrated modeling approaches that capture both spatial and temporal dynamics in transport planning. It develops a novel framework combining System Dynamics (SD) and Geographic Information Systems (GIS) to assess the sustainability of Transit-Oriented Development (TOD) strategies and parking policies in two brownfield redevelopment sites in Montreal. The framework embeds spatial metrics, such as proximity to transit, parking availability, and active transportation infrastructure into dynamic feedback loops. Using scenario analysis, the study compares a baseline reflecting current norms with an intervention scenario emphasizing higher density near transit, reduced parking ratios, and improved walkability and bike infrastructure. The results suggest that aligning TOD principles with targeted parking limits and investments in active mobility can substantially reduce car ownership and emissions. While primarily conceptual, the model provides a foundation for location-sensitive, feedback-driven planning tools that support sustainable urban mobility. Full article

The transportation sector is an important stakeholder in greenhouse gas emissions. Sustainable transportation systems come to the forefront against this problem, with the solutions within the scope of micro-mobility especially attracting attention for their environmentally friendly structures. While micro-mobility vehicles reduce the carbon footprint in transportation, their widespread use remains limited due to various security concerns. In this paper, an image processing-based process was carried out on vehicle and safety equipment usage to provide solutions to the security concerns of micro-mobility users. The effectiveness of frequently used data augmentation techniques was also examined to detect the presence of micro-mobility users and equipment usage with higher accuracy. In this direction, two different datasets (D1_Micro-mobility and D2_Helmet detection) and a total of 46 models were established and the effects of data augmentation techniques on YOLOv12 model performance outputs were evaluated with Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), one of the Multi-Criteria Decision-Making (MCDM) methods. In addition, the determination of Multiple Model Ensemble (MME), consisting of multiple data augmentation techniques, was also carried out through the K-means clustering–Elbow method. For D1_Micro-mobility datasets, it is observed that MME improves the model performance by 19.7% in F1-Score and 18.54% in mAP performance metric. For D2_Helmet detection datasets, it is observed that MME improves the model performance by 2.36% only in the Precision metric. The results show that, in general, data augmentation techniques increase model performance in a multidimensional manner. Full article

In the context of mounting concerns over carbon emissions and the need to accelerate the energy transition, green hydrogen has emerged as a strategic solution for decarbonizing hard-to-abate sectors. This paper introduces a methodological innovation by proposing the Green Hydrogen Efficiency Index (GHEI), a unified and quantitative framework that integrates multiple stages of the hydrogen value chain into a single comparative metric. The index encompasses six core criteria: electricity source, water treatment, electrolysis efficiency, compression, end-use conversion, and associated greenhouse gas emissions. Each are normalized and weighted to reflect different performance priorities. Two weighting profiles are adopted: a first profile, which assigns equal importance to all criteria, referred to as the balanced profile, and a second profile, derived using the analytic hierarchy process (AHP) based on structured expert judgment, named the AHP profile. The methodology was developed through a systematic literature review and was applied to four representative case studies sourced from the academic literature, covering diverse configurations and geographies. The results demonstrate the GHEI’s capacity to distinguish the energy performance of different green hydrogen routes and support strategic decisions related to technology selection, site planning, and logistics optimization. The results highlight the potential of the index to contribute to more sustainable hydrogen value chains and advance decarbonization goals by identifying pathways that minimize energy losses and maximize system efficiency. Full article

The mounting global population and the challenges posed by climate change underline the need for sustainable food production systems. This review synthesizes evidence for a dual-track bioeconomy, green (terrestrial plants and insects) and blue (aquatic algae), as complementary pathways toward sustainable nutrition. A comprehensive review of the extant literature, technical reports, and policy documents published between 2015 and 2025 was conducted, with a particular focus on environmental, nutritional, and techno-economic metrics. In addition, precision agriculture datasets, gene-editing breakthroughs, and circular biorefinery case studies were extracted and compared. As demonstrated in this study, the use of green resources, such as legumes, oilseeds, and edible insects, results in a significant reduction in greenhouse gas emissions, land use, and water footprints compared with conventional livestock production. In addition, these alternative protein sources offer substantial benefits in terms of bioactive lipids. Blue resources, centered on micro- and macroalgae, furnish additional proteins, long-chain polyunsaturated fatty acids, and antioxidant pigments and sequester carbon on non-arable or wastewater substrates. The transition to bio-based resources is facilitated by technological innovations, such as gene editing and advanced extraction methods, which promote the efficient valorization of agricultural residues. In conclusion, the study strongly suggests that policy support be expedited and that research into bioeconomy technologies be increased to ensure the sustainable meeting of future food demands. Full article

Unfired bricks offer a sustainable alternative to traditional fired bricks by enabling the large-scale reuse of industrial, construction, and municipal wastes while significantly reducing energy consumption and greenhouse gas emissions. This review contributes to eliminating knowledge fragmentation by systematically organising stabiliser technologies, performance metrics, and sustainability indicators across a wide variety of unfired brick systems. It thus provides a coherent reference framework to support further development and industrial translation. Emphasis is placed on the role of stabilisers—including cement, lime, geopolymers, and microbial or bio-based stabilisers—in improving mechanical strength, moisture resistance, and durability. Performance data are analysed in relation to compressive strength, water absorption, drying shrinkage, thermal conductivity, and resistance to freeze–thaw and wet–dry cycles. The findings indicate that properly stabilised unfired bricks can achieve compressive strengths above 20 MPa and water absorption rates below 10%, with notable improvements in insulation and acoustic properties. Additionally, life-cycle comparisons reveal up to 90% reductions in CO₂ emissions and energy use relative to fired clay bricks. Despite technical and environmental advantages, broader adoption remains limited due to standardisation gaps and market unfamiliarity. The paper concludes by highlighting the importance of hybrid stabiliser systems, targeted certification frameworks, and waste valorisation policies to support the transition toward low-carbon, resource-efficient construction practices. Full article

Engineered wood products (EWPs) and timber buildings are increasingly recognised for their potential to reduce greenhouse gas emissions by storing biogenic carbon and replacing emission-intensive materials. This article systematically evaluates the carbon footprint (CF) of EWPs and timber buildings during the production stage (A1–A3), identifies key sources of variability, and extracts quantitative emission reduction metrics. Based on a review of 63 peer-reviewed studies, CF values vary widely, from −40 to 1050 kg CO₂eq m⁻² for buildings and 12 to 759 kg CO₂eq m⁻³ for EWPs, due to inconsistent system boundaries, functional units, and emission factor assumptions. Median CFs were 165.5 kg CO₂eq m⁻² and 169.3 kg CO₂eq m⁻³, respectively. Raw material extraction (50.7%), manufacturing (37.1%), and transport (12.2%) were the dominant contributors. A mitigation matrix was developed, showing potential reductions: 20% via transport optimisation, 24–28% through low-density timber, 76% from renewable energy, 11% via sawmill efficiency, 75% through air drying, and up to 92% with reclaimed timber. The geographic skew toward Europe and North America underscores the need for region-specific data. The findings provide actionable benchmarks and strategies to support carbon accounting, emissions modelling, and climate policy for more sustainable construction. Full article

Sustainability in food production is a pressing priority due to environmental and political crises, the need for long-term food security, and feeding the populace. Food producers need to increasingly adopt sustainable practices to reduce negative environmental impacts and food waste. The ocean is a source for sustainable food systems; deforestation, water scarcity, and greenhouse gas emissions burden traditional, terrestrial resources. Our oceans contain the largest unexploited resource in the world in the form of mesopelagic fish species, with an estimated biomass of 10 billion metric tons. This resource is largely untapped due in part to the difficulties in harvesting these species. To ensure sustainability of this resource, management of fish stocks and fish processing practices must be optimised. Generation of fish protein hydrolysates from by-catch/underutilised species creates high-value, functional ingredients while also reducing waste. Marine hydrolysates offer a renewable source of nutrition and align with the principles of the circular economy, where waste is minimised and resources are reused efficiently. Ocean-derived solutions demand fewer inputs, generate less pollution, and have a smaller carbon footprint compared to traditional agriculture. This review collates clearly and succinctly the current and potential uses of FPHs for different market sectors and highlights the advantages of their use in terms of the scientifically validated health benefits for humans and animals and fish, and the protection and crop yield benefits that are documented to date from scientific studies. Full article

The rapid expansion of international maritime transportation has led to rising greenhouse gas emissions, exacerbating climate change and environmental sustainability concerns. According to the International Maritime Organization, carbon dioxide (CO₂) emissions from vessels are projected to increase by over 17% by 2050. Traditional emission estimation methods are prone to inaccuracies due to uncertainties in emission factors, and inconsistencies in fuel consumption data. This study proposes deep learning-based CO₂ emission prediction models leveraging engine operation data. Unlike previous approaches that primarily relied on fuel consumption, this model incorporates multiple parameters capturing the relationship between combustion characteristics and emissions to enhance predictive accuracy. We developed and evaluated individual models—convolutional neural network (CNN), long short-term memory (LSTM), and temporal convolutional network (TCN)—as well as hybrid model (TCN–LSTM). The hybrid model achieved the highest predictive performance, with a coefficient of determination of 0.9726, outperforming other models across multiple quantitative metrics. These findings demonstrate the potential of deep learning for vessel emission assessment, providing a scientific basis for carbon management strategies and policy development in the international shipping industry. This study thus holds major academic and industrial value, advancing the field of deep learning-based emission prediction and extending its applicability to diverse operational scenarios. Full article