Search Results (10,824)

Under the “dual carbon” strategic framework, the low-carbon transition of the logistics sector—a major source of carbon emissions in the national economy—has become imperative for achieving green development. The adoption of new-energy vehicles (NEVs) represents a critical pathway for decarbonizing logistics operations. Initiated in 2009, China’s “Ten Cities, Thousand Vehicles” Demonstration Project served as a pioneering policy to accelerate NEV deployment, offering a valuable use case for reducing emissions in urban logistics. Using this initiative as a quasi-natural experiment, we employ a multi-period difference-in-differences (DID) approach and panel data from 275 Chinese prefecture-level cities (2000–2021) to evaluate the causal effect of the policy on urban logistics CO₂ emissions. The robustness of the findings is confirmed through parallel trend tests, placebo tests with reassigned treatment timing, alternative dependent variable construction, and instrumental variable estimation. Mechanism and heterogeneity analyses are further conducted to uncover underlying channels and contextual variations. The results indicate a statistically significant reduction in logistics carbon emissions in pilot cities, which remains consistent across multiple robustness checks. Mediation analysis reveals that the policy effect is partially transmitted through increased NEV stock. Moreover, the emission reduction effect is more pronounced in cities with lower logistics dependency and non-consumer-oriented economic structures, while it is weaker in consumer and highly logistics-dependent cities. These findings confirm the sustainable contribution of early NEV policies through advancing the transition to low-carbon logistics and supporting dual carbon goals, fill the empirical gap in developing countries’ freight decarbonization, and offer actionable insights for targeted regional sustainable logistics strategies. Full article

Nano–bio hybrid catalysts have emerged as a promising platform for sustainable energy conversion by integrating the high selectivity of enzymes with the structural robustness and conductivity of nanomaterials. In recent years, the growing demand for clean energy technologies has driven the development of biohybrid systems capable of efficient electron transfer, enhanced catalytic activity, and improved operational stability. This review comprehensively discusses the design principles, mechanistic foundations, and performance metrics of enzyme–nanomaterial interfaces for energy-related applications. We first outline the fundamentals of enzymatic redox catalysis and the limitations of free enzymes in practical systems. Subsequently, we examine the functional roles of nanomaterials including carbon-based materials, metal and metal oxide nanoparticles, and two-dimensional platforms such as MXenes in facilitating enzyme immobilization and promoting direct or mediated electron transfer. Special emphasis is placed on engineering strategies at the bio–nano interface, including immobilization techniques, surface functionalization, and structural tuning to optimize catalytic efficiency. The review further highlights representative hybrid systems based on laccase, glucose oxidase, peroxidase, and hydrogenase enzymes, and evaluates their applications in biofuel cells, solar–bio hybrid systems, green oxidation reactions, and self-powered biosystems. Stability challenges, deactivation mechanisms, and enhancement strategies such as polymer coatings, cross-linking, and nanoconfinement are critically analyzed. Finally, emerging directions including artificial enzymes, AI-guided catalyst design, and self-healing bioelectrodes are discussed to provide a forward-looking perspective on next-generation sustainable bioelectrocatalytic systems. Full article

This research addresses the effects of moisture content, particle size, and compression pressure on the quality of fuel briquettes produced from groundnut shells. The objective is to optimize the briquetting process for quality attributes. Experiments were performed using a prototype briquetting machine with a 3 × 3 × 2 factorial design. The experiments considered moisture (6.48%, 9.63%, and 12.17%), particle sizes (3 mm, 5 mm, and 7 mm), and compression pressure (70 MPa, 140 MPa). Quality attributes measured included density, breaking index, rolling index, water resistance, moisture resistance, and compressive strength. Findings showed optimal processing variables for quality with lower particle sizes (3 mm) and higher levels of pressure (140 MPa). The lowest level of moisture (6.48%) showed optimal mechanical properties, represented by maximum density of 1301.81 kg m⁻³ with maximum mechanical strength of 97.01% fracture resistance and 91.12% rolling resistance. Maximum water resistance of 13.25% was obtained with intermediate values of moisture (9.63%). The data obtained showed that increasing particle size and moisture content negatively affected briquette quality. Groundnut shells possess promise for the production of superior-quality fuel briquettes. This research validates the criticality of optimizing processing variables. This research poses critical challenges regarding scale and environmental effects. Full article

The rally to mitigate growing carbon emissions and climate change necessitates decarbonization strategies, with hydrogen emerging as a key candidate option across multiple sectors. This review examines the current state of the hydrogen economy, including production, implementation, and associated risks. Hydrogen’s versatility in industry, transportation, and energy storage is highlighted, alongside the challenges of transitioning from fossil fuel-based production. It explores the current state of hydrogen technologies, differentiating between green, blue, and gray hydrogen production methods, and highlights advancements in production techniques like thermochemical water splitting. Key findings show that while green hydrogen offers the cleanest pathway, high production costs and infrastructure limitations remain significant barriers to widespread adoption. This study also addresses safety concerns and public perception, emphasizing the need for robust risk assessment methodologies and management approaches. Furthermore, this paper underscores the importance of technological innovations, such as high-temperature electrolysis and synergies with renewable energy sources, to enhance efficiency and sustainability. Policy recommendations include financial incentives, regulatory frameworks, and international cooperation to accelerate hydrogen adoption and balance its development with other low-carbon solutions. Full article

The accelerating global energy transition has substantially increased demand for critical minerals such as copper, nickel, and lithium, positioning mining firms as key actors in the decarbonization of energy systems. However, the expansion of mineral extraction raises important sustainability challenges because mining activities remain highly energy- and carbon-intensive. This study investigates whether green innovation can simultaneously improve environmental performance and financial performance in critical mineral mining firms and examines the moderating role of institutional governance. Using a balanced panel of 35 publicly listed mining companies from Australia, Canada, Chile, Brazil, and Indonesia over the period 2015–2024, the analysis applies fixed-effects panel regressions complemented by dynamic specifications and multiple robustness tests, including alternative variable definitions and System Generalized Method of Moments (GMM) estimation. The results show that green innovation significantly reduces carbon intensity, indicating that environmental investments in renewable energy integration, electrification, and process efficiency contribute to improving emissions performance in mining operations. Green innovation also enhances firm financial performance, although the benefits emerge gradually over time, suggesting delayed financial gains followed by long-term efficiency improvements. Furthermore, governance quality strengthens the positive relationship between green innovation and firm performance, highlighting the importance of institutional environments in shaping the economic returns of sustainability strategies. By providing firm-level evidence across major mineral-producing economies, this study contributes to the literature on critical minerals, environmental finance, and the institutional dimensions of the just energy transition. 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

Ecosystem disruption is a significant challenge of the contemporary age, arising from substantial CO₂/CO emissions resulting from dependence on fossil fuels as a primary energy source. Scholars across several fields are striving to mitigate these severe greenhouse gas emissions. The most promising method is to adsorb carbon and convert it into sustainable energy. We sought to diminish CO levels by electrocatalytic reduction using innovative catalytic surfaces, namely transition metal phosphides (TMPs). During this work, VP is recognized as a very effective surface for CO reduction and the synthesis of formaldehyde, methanol, and methane at −0.68 V. Further, hydrogen evolution reaction (HER) does not pose a challenge for any surface, despite all TMPs facilitating CO reduction. In summary, predictions derived from this density functional theory (DFT)-guided analysis provide experimentalists with insights to validate experiments and synthesize active catalysts for CO conversion and green energy generation. 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

Indonesia has identified clean hydrogen as one of the strategic initiatives for its energy transition, recognizing its potential as an energy carrier that can support the achievement of net zero emissions. To deepen the understanding of this emerging technology, this study assesses the readiness of green hydrogen development in Indonesia through a multi-stakeholder perspective combined with a technology readiness evaluation and insights from global developments. Based on stakeholder interviews across government, industry, academia, and energy institutions, this analysis identifies key enabling conditions and barriers for hydrogen deployment in the Indonesian context. This analysis indicates that the readiness level of green hydrogen technology in Indonesia has reached approximately technology readiness level (TRL) 5–TRL 6, suggesting that most initiatives remain at the pilot and demonstration stages. In addition, seven key factors influencing green hydrogen adoption were identified: infrastructure and technology, policy and regulation, finance, application sectors, public acceptance, standardization, and private sector participation. These results provide policy-relevant insights for accelerating hydrogen development and highlight priority areas for advancing Indonesia’s transition toward a low-carbon energy system. Full article

Energy hydrogen is emerging as a key driver for the deep decarbonization of energy systems in the Americas, particularly in sectors that are difficult to electrify, such as heavy industry, long-distance transportation, and seasonal energy storage. This article presents a comprehensive review of current prospects and long-term planning for hydrogen in North America, Central America, and South America, analyzing its role within energy transition strategies to long term. It examines technological advancements in green hydrogen production from renewable energy sources, projected costs, required infrastructure, and potential integration schemes with existing electricity systems. Furthermore, it assesses emerging regulatory frameworks, public policies, and national and regional initiatives that seek to position hydrogen as a pillar of energy security, economic competitiveness, and emissions reduction. The study identifies differentiated opportunities based on the availability of renewable resources, industrial capacities, and socioeconomic contexts, as well as common challenges related to investment, standardization, and social acceptance. Finally, implications for long-term energy planning are discussed, highlighting the potential of hydrogen to strengthen the resilience and sustainability of the energy system in the Americas. Full article

Transformers are key devices in the new electricity system, and the entire life cycle is associated with a considerable resource consumption and carbon footprint (CF). Understanding CF is essential for accelerating the low-carbon transition of the industry. Therefore, a systematic CF model for transformers is constructed in this study based on life cycle assessment (LCA). The results indicate that the operation stage is the overwhelmingly dominant phase for CF of transformer, with electricity acting as the main carbon source. The CF at the raw-material stage mainly originates from steel and copper. Through analysis, eight key impact factors were identified, leading to the formulation of three-dimensional carbon reduction pathways. It was observed that a 10% reduction in total losses of a transformer results in an approximate 10% decline in CF. At the same time, the transition of the electricity grid to clean energy helps reduce CF during operation. In addition, the effectiveness of a multi-factor carbon reduction pathway was examined. The results showed that, under this integrated pathway, the CF across all transformer rated voltages could be reduced by 9.75%. Based on this, a system pathway centered on enhancing operational energy efficiency is proposed, supported by green materials and processes, and coordinated through smart operation and maintenance, and circular recycling. This provides quantitative evidence and decision support for the green transition of transformers, contributing to the broader goals of sustainability development in electricity system. Full article

Background: Healthcare systems contribute significantly to environmental pollution, energy consumption, and resource depletion, making sustainability an increasingly important environmental and public health priority. Nurses, as frontline healthcare professionals, play a critical role in promoting environmentally responsible practices and advocating for sustainable healthcare within clinical settings. Objective: The study aimed to examine the associations between nurses’ sustainability consciousness, green advocacy, and work grit in hospital settings. Methods: A descriptive cross-sectional correlational study was conducted among 377 nurses working in two university-affiliated hospitals in Egypt. Data were collected using validated instruments assessing sustainability consciousness, green advocacy, and work grit. Descriptive statistics were calculated to summarize participant characteristics and study variables. Associations among sustainability consciousness, green advocacy, and work grit were examined using Pearson correlation analysis. Multiple linear regression analysis was conducted to identify significant predictors of green advocacy, while noting that the study design allows for identification of associations rather than causal relationships. Results: The findings indicated generally high levels of sustainability consciousness among nurses. Significant positive associations were observed between sustainability consciousness, green advocacy, and work grit (p < 0.01). Multiple linear regression analysis identified sustainability consciousness and work grit as significant predictors of green advocacy, explaining 34.2% of its variance. Conclusions: These findings highlight the interconnected roles of sustainability awareness, advocacy behaviors, and psychological resilience in promoting environmentally sustainable healthcare practices. Strengthening nurses’ sustainability consciousness and work grit may enhance green advocacy and contribute to the development of sustainable healthcare systems, supporting global environmental and public health goals aligned with the United Nations Sustainable Development Goals. Full article

This study explores interpretation methods for determining the preconsolidation stress from one-dimensional consolidation test results. Twelve reconstituted clay specimens with targeted preconsolidation stresses of 60 and 120 kPa were prepared using commercial kaolinite and marine clays collected from coastal regions of South Korea. Five representative interpretation methods were applied, and the influence of maximum applied stress levels of 320, 640, and 1280 kPa was evaluated. The results indicate that the estimated preconsolidation stress varies considerably depending on both the interpretation method and the maximum applied stress, particularly for rounded compression curves with gradually changing virgin compression slopes. To address these limitations, a new interpretation approach is proposed. The method utilizes the rebound slope obtained from an unload–reload cycle and defines the virgin compression line through linear regression without identifying the recompression–virgin compression boundary. The proposed method demonstrated reduced sensitivity to the maximum applied stress and provided more reproducible estimates for rounded compression curves, although further validation using natural clays considering sample disturbance effects is required. Full article

Across the globe, companies are facing significant pressure to reduce waste, improve resource efficiency, and report their sustainability efforts transparently. ESG frameworks have become essential tools for sustainability transformation. However, traditional business models, based on a linear “take–make–dispose” approach, continue to dominate industries, limiting the impact of ESG efforts. The circular economy offers a compelling alternative: it encourages designing products for reuse, recycling, and regeneration, thus aligning closely with ESG principles. When businesses transition to circular models, they reduce their environmental footprint, create new green jobs and social inclusion opportunities, and strengthen accountability across business value chains. This study explores how selected firms in the mining, energy, consumer cyclical, technology, and healthcare sectors are aligning circular principles with ESG practices. Using a longitudinal, multi-sector comparative analysis of ESG indicators spanning 2014–2024, the research examines sector-level ESG evolution, firm-level ESG leadership, and the alignment of ESG performance with circular business model pathways. Rather than directly measuring circular transformation, ESG indicators are interpreted as signals of emerging circular business model pathways. This study identifies ESG-based ways and enabling conditions through which circularity may be increasingly embedded across different sectors. Full article

Deep learning models represent one of the most advanced and effective approaches in predictive modeling. Their hierarchical architectures enable the extraction of complex, non-linear feature relationships and the identification of latent patterns within data, making them highly suitable for tasks involving high-dimensional or unstructured inputs. However, these models are computationally demanding, requiring significant processing resources and time. Furthermore, their predictive performance is largely contingent upon the availability of large-scale datasets. In this study, a Deep Green Framework is employed for the prediction of two computer vision tasks. CIFAR-10 and CIFAR-00 have been taken for image classification. Fifteen convolutional neural network (CNN) variants categorized into light-weight and heavy-weight are trained for the prediction of these two datasets. Based on energy footprint, time, memory usage, Top-1 accuracy, Top-3 accuracy, model size, and model parameters. The study highlights that MobileNetV3-Small produces the best outcomes when compared to other trained models having low task latency and higher efficiency, making it highly suitable for edge environments where resources are scarce. Full article