Search Results (708)

The agri-food sector is a major contributor to global greenhouse gas emissions while facing increasing demand for food production driven by population growth. Transitioning towards sustainable and low-carbon agricultural systems is therefore critical. Green hydrogen, produced from renewable energy sources, holds significant promise as a clean energy carrier and chemical feedstock to decarbonize multiple stages of the agri-food supply chain. This systematic review is based on a structured analysis of peer-reviewed literature retrieved from Web of Science, Scopus, and Google Scholar, covering over 120 academic publications published between 2010 and 2025. This review provides a comprehensive overview of hydrogen’s current and prospective applications across agriculture and the food industry, highlighting opportunities to reduce fossil fuel dependence and greenhouse gas emissions. In agriculture, hydrogen-powered machinery, hydrogen-rich water treatments for crop enhancement, and the use of green hydrogen for sustainable fertilizer production are explored. Innovative waste-to-hydrogen strategies contribute to circular resource utilization within farming systems. In the food industry, hydrogen supports fat hydrogenation and modified atmosphere packaging to extend product shelf life and serves as a sustainable energy source for processing operations. The analysis indicates that near-term opportunities for green hydrogen deployment are concentrated in fertilizer production, food processing, and controlled-environment agriculture, while broader adoption in agricultural machinery remains constrained by cost, storage, and infrastructure limitations. Challenges such as scalability, economic viability, and infrastructure development are also discussed. Future research should prioritize field-scale demonstrations, technology-specific life-cycle and techno-economic assessments, and policy frameworks adapted to decentralized and rural agri-food contexts. The integration of hydrogen technologies offers a promising pathway to achieve carbon-neutral, resilient, and efficient agri-food systems that align with global sustainability goals and climate commitments. Full article

The accelerating climate emergency places the built environment under increasing pressure as both a major source of greenhouse gas emissions and a system highly vulnerable to climate impacts. Buildings contribute substantially to global operational energy use and embodied carbon, while much of the existing stock remains poorly adapted to changing climatic conditions. This paper examines the role of artificial intelligence (AI) in improving energy efficiency, enabling circular material flows, and enhancing resilience across the building lifecycle. Based on a structured synthesis of recent peer-reviewed literature, institutional reports, and documented case examples, the study maps AI applications in design, construction, operation, and end-of-life stages, including generative design, predictive maintenance, digital twins, and construction and demolition waste analytics. The analysis shows how AI can reduce operational energy demand, optimize material use, and support reuse and recycling strategies, while enabling new software-driven business models in the building sector. The paper argues that AI’s effectiveness depends on data availability, interoperability, regulatory alignment, and workforce capabilities, and that its benefits are maximized when integrated with circular economy strategies and supportive policy and financial frameworks. This integrated perspective highlights pathways for reducing emissions and improving the resilience of the built environment under climate stress. Full article

In this study, 7647 scrapped pieces of teaching and research equipment (T&R equipment) in an application-oriented university in China in 2024 were employed to assess their carbon emissions using lifecycle analysis. A lifecycle accounting framework was established based on expenditure–environmental expansion input–output (EEIO) models, and the greenhouse gas emissions across the producing, using, and scrapping stages of the T&R equipment at this type of university were estimated. Carbon emission reduction pathways for T&R equipment at this type of university were proposed. It is clear that the lifecycle emissions of the scrapped equipment at this type of university equal 8350.8 tCO₂, including producing, using, and scrapping stage emissions of 2277.9 tCO₂, 5848.9 tCO_2, and 223.9 tCO₂, respectively. It is noted that the producing stage accounts for the dominant contributor to carbon emissions, with 70.0% of the total amount. In view of subcategory emissions, information technology equipment (A0201) contributes the most emissions, with 18.0% during the producing and scrapping stages, whereas instruments (A0210) and electrical/electronic production equipment (A0233) contribute the most, with 21.4% and 15.7%, in the using stage. The results of scenario analysis show that, for most equipment, total carbon emissions can be reduced by about 233 tCO₂/a on average if scrapped one year in advance. However, for information technology equipment (A0201), emissions increase by 48 tCO₂/a. This method offers comparability and replicability in scenarios lacking physical measurements, providing quantitative evidence and carbon reduction pathways for green procurement, asset renewal, and end-of-life recycling in higher education institutions. Full article

With the growing emphasis on holistic management throughout the entire product lifecycle, multi-stage production models that integrate distributed manufacturing, transportation, and assembly processes have gradually attracted research attention. However, studies in this area remain relatively scarce. This paper addresses the fuzzy distributed assembly hybrid flow shop scheduling problem (FDAHFSP), comprehensively considering the entire production flow from manufacturing and transportation to final assembly. A mathematical model is first established with the objectives of minimizing the fuzzy total weighted earliness/tardiness and the fuzzy total energy consumption. To effectively solve this problem, a Q-learning-based adaptive NSGA-II (Q-ANSGA) is proposed. The algorithm incorporates a hybrid strategy combining multiple rules to enhance the quality of the initial population. Additionally, a Q-learning-based adaptive parameter adjustment mechanism is designed to dynamically optimize genetic algorithm parameters, thereby improving the algorithm’s search efficiency and convergence performance. Furthermore, eight neighborhood search operators are developed, and an iterative greedy strategy is integrated to guide the local search process. Finally, comprehensive experiments on 45 test instances are conducted to evaluate the effectiveness of each improvement component and the overall performance of Q-ANSGA. Experimental results demonstrate that the proposed algorithm achieves superior performance in solving the FDAHFSP due to its systematic enhancements. Full article

The integration of a user-side energy storage system (ESS) faces notable economic challenges, including high upfront investment, uncertainty in quantifying battery degradation, and fragmented ancillary service revenue streams, which hinder large-scale deployment. Conventional configuration studies often handle capacity planning and operational scheduling at different stages, complicating consistent life-cycle valuation under degradation and multi-service participation. This paper proposes a life-cycle multi-service co-optimization model (LC-MSCOM) to jointly determine ESS power–energy ratings and operating strategies. A unified revenue framework quantifies stacked revenues from time-of-use arbitrage, demand charge management, demand response, and renewable energy accommodation, while depth of discharge (DoD)-related lifetime loss is converted into an equivalent degradation cost and embedded in the optimization. The model is validated on a modified IEEE benchmark system using real generation and load data. Results show that LC-MSCOM increases net present value (NPV) by 26.8% and reduces discounted payback period (DPP) by 12.7% relative to conventional benchmarks, and sensitivity analyses confirm robustness under discount-rate, inflation-rate, and tariff uncertainties. By coordinating ESS dispatch with distribution network operating limits (nodal power balance, voltage bounds, and branch ampacity constraints), the framework provides practical, investment-oriented decision support for user-side ESS deployment. Full article

Nanopesticides are pesticide formulations in which intentionally designed nanoscale carriers shape how an active ingredient (AIng) is deposited, transformed, and released. These systems can improve retention and efficacy, but carrier complexity introduces challenges: nanomaterials can transform in real soil–water matrices, reshaping exposure and risk. These processes are hard to quantify because test protocols and risk assessment frameworks for nanopesticides remain underdeveloped. In this review, we relate design choices across major carrier families—including polymer and lipid particles, nanoemulsions, porous inorganic carriers, and bio-based nanomaterials—to transformations in soil–water systems. We then connect these transformations to ecotoxicological evidence across key non-target taxa. We also address a central “measurement gap” in current risk assessment. Many standard tests were developed for dissolved chemicals. As a result, they do not capture (i) particle stability in realistic matrices, (ii) particle-bound versus dissolved (and ion-released) forms, or (iii) time-resolved exposure. Finally, we propose a Safe-and-Sustainable-by-Design roadmap that prioritizes low-hazard materials, predictable degradation, life-cycle thinking, and staged data generation to enable scalable, field-relevant adoption. Full article

MMC has been globally recognised as a promising solution for the current global social housing crisis, although persistent challenges remain in relation to limited early-stage design coordination and chronic design inconsistencies, which often cause costly post-design modifications. In response, digital twinning enabled through BIM has emerged as a compelling approach to tackle these challenges. BIM serves a transformative role in advancing sustainable social housing supply by integrating BIM with advanced smart technologies such as AR/VR, IoT, AI, and robotics. Nevertheless, significant constraints continue to impede a wide adoption of BIM due to technical capacity, organisational readiness, knowledge dissemination, and legal frameworks that support embracing BIM and associated smart technologies. Moreover, a notable knowledge gap persists in the application of BIM-enabled digital twinning across the entire project lifecycle of MMC projects, which may be addressed through the integration of Industry 5.0 principles with BIM, emphasising human-centricity, resilience, and sustainability as foundational pillars for future innovation. Full article

As China has increased implementation of its opening-up strategy and the “Belt and Road” initiative, Chinese enterprises have encountered significant historical opportunities to expand their outward foreign direct investment (OFDI). However, international organizations and major nations are increasingly focusing on nonfinancial indicators for multinational corporations; as a result, enterprises frequently encounter social responsibility crises in cross-border investments. Consequently, Chinese firms must enhance their environmental, social, and governance (ESG) practices to bolster their comprehensive competitiveness, which is crucial for promoting successful international engagement and sustainability. This research explores the U-shaped relationship between ESG performance and OFDI, examining how different stages of the corporate lifecycle affect OFDI. The findings indicate that ESG investments compete with OFDI for internal resources during the introduction, growth, and decline phases, thereby inhibiting OFDI activities. In contrast, strong ESG performance in the maturity phase provides a competitive advantage in international markets, facilitating OFDI. The empirical analysis uses a fixed-effects model on a sample of Chinese A-share-listed companies from 2009 to 2022 and employs the PSM, 2SLS, and System GMM methods to test for endogeneity. The results reveal that (1) a positive U-shape relationship between ESG performance and corporate OFDI, and the inflection point occurs when the ESG score equals 69.04. Moreover, (2) the corporate lifecycle intensifies this nonlinear relationship, with growth-phase firms showing a significant inhibitory effect and mature-phase firms showing a pronounced promotional effect. Finally, (3) the U-shaped relationship between ESG performance and corporate OFDI is more pronounced in nonstate-owned enterprises. Based on these findings, this paper provides targeted policy recommendations for enterprises and governments. Full article

With the widespread adoption of renewable energy, distribution grids face increasing challenges in efficiency, safety, and economic performance due to stochastic generation and fluctuating load demand. Traditional operational models often exhibit limited adaptability, weak coordination, and insufficient holistic optimization, particularly in early-/mid-stage distribution planning where feeder-level network information may be incomplete. Accordingly, this study adopts a planning-oriented formulation and proposes a distributed energy storage system (DESS) planning strategy to enhance distribution network resilience under high uncertainty. First, representative wind and photovoltaic (PV) scenarios are generated using an improved Gaussian Mixture Model (GMM) to characterize source-side uncertainty. Based on a grid-based network partition, a priority index model is developed to quantify regional storage demand using quality- and efficiency-oriented indicators, enabling the screening and ranking of candidate DESS locations. A mixed-integer linear multi-objective optimization model is then formulated to coordinate lifecycle economics, operational benefits, and technical constraints, and a sequential connection strategy is employed to align storage deployment with load-balancing requirements. Furthermore, a node–block–grid multi-dimensional evaluation framework is introduced to assess resilience enhancement from node-, block-, and grid-level perspectives. A case study on a Zhejiang Province distribution grid—selected for its diversified load characteristics and the availability of detailed historical wind/PV and load-category data—validates the proposed method. The planning and optimization process is implemented in Python and solved using the Gurobi optimizer. Results demonstrate that, with only a 4% increase in investment cost, the proposed strategy improves critical-node stability by 27%, enhances block-level matching by 88%, increases quality-demand satisfaction by 68%, and improves grid-wide coordination uniformity by 324%. The proposed framework provides a practical and systematic approach to strengthening resilient operation in distribution networks. Full article

As digital technologies increasingly permeate urban governance and economic systems, the digital economy (DE) is widely regarded as a key driver of green urban transformation. However, its environmental effects remain complex under the dual constraints of resource dependence (RD) and spatial structure. Drawing on panel data from 277 Chinese prefecture-level cities from 2011 to 2019, this study systematically evaluates the green impacts of the DE across varying resource conditions and urban lifecycle stages. The results reveal a dual-effect pattern: while digitalization significantly promotes local green sustainable development (GSD), it simultaneously suppresses the green performance of neighboring cities through siphoning effects, creating spatial divergence. Cities with lower levels of RD are more likely to benefit from digital dividends, whereas in high-dependence settings, the green effects of digitalization reverse beyond a critical threshold. Grouped regressions for resource-based (RBCs) and non-resource-based cities (NRBCs) further confirm this moderating mechanism. Moreover, lifecycle heterogeneity among RBCs leads to differentiated green outcomes. By introducing the dual mechanisms of “resource lock-in” and “digital siphoning” into the framework of GSD, this study expands the theoretical understanding of the interaction between digitalization and RD. The findings provide empirical support for interpreting the structural divergence in DE–GSD linkages and offer a quantitative basis for differentiated policy strategies in resource-intensive urban contexts. Full article

Despite the immense potential of digital intelligence technologies to enhance corporate profitability, manufacturing enterprises often face the “digital–green paradox”, which indicates that while companies invest in digital and intelligent transformation, their energy consumption increases rather than promoting green transition. To provide reasonable transformation solutions for manufacturers still caught in this paradox, this paper adopts a single-case study approach from a product lifecycle perspective. Focusing on FAW-Volkswagen—a manufacturing enterprise demonstrating outstanding performance in digital-intelligent green transformation—this study conducts an in-depth investigation into the stage characteristics and underlying mechanisms. The results show that the following: (1) The digital-intelligent green transformation of manufacturing enterprises is an iterative process evolving from “green design, low-carbon production, intelligent service to enterprise spiral value-added”, with distinct digital-intelligent empowerment models at each stage. (2) By leveraging digital-intelligent technologies, manufacturing enterprises can build a multi-tiered “internal-external dual circulation” green development system encompassing the “enterprise—industrial chain—full ecosystem,” driving comprehensive green upgrades across the entire industry and ecosystem. This paper reveals the intrinsic mechanisms through which digital-intelligent technologies facilitate manufacturing enterprises’ green transformation. It expands and enriches the research context and theoretical implications of product lifecycle management, offering management insights and strategic references for other enterprises pursuing green transformation and upgrading pathways in the digital-intelligent economy era. Full article

In today’s volatile digital environments, conventional data governance practices fail to adequately address the dynamic, context-sensitive, and risk-hazardous nature of data use. This paper introduces DRADG (Dynamic Risk-Adaptive Data Governance), a new paradigm that unites risk-aware decision-making with adaptive data governance mechanisms to enhance resilience, compliance, and trust in complex data environments. Drawing on the convergence of existing data governance models, best practice risk management (DAMA-DMBOK, NIST, and ISO 31000), and real-world enterprise experience, this framework provides a modular, expandable approach to dynamically aligning governance strategy with evolving contextual factors and threats in data management. The contribution is in the form of a multi-layered paradigm combining static policy with dynamic risk indicator through application of data sensitivity categorization, contextual risk scoring, and use of feedback loops to continuously adapt. The technical contribution is in the governance-risk matrix formulated, mapping data lifecycle stages (acquisition, storage, use, sharing, and archival) to corresponding risk mitigation mechanisms. This is embedded through a semi-automated rules-based engine capable of modifying governance controls based on predetermined thresholds and evolving data contexts. Validation was obtained through simulation-based training in cross-border data sharing, regulatory adherence, and cloud-based data management. Findings indicate that DRADG enhances governance responsiveness, reduces exposure to compliance risks, and provides a basis for sustainable data accountability. The research concludes by providing guidelines for implementation and avenues for future research in AI-driven governance automation and policy learning. DRADG sets a precedent for imbuing intelligence and responsiveness at the heart of data governance operations of modern-day digital enterprises. Full article

A new desmid microalga species, Cosmarium yassinii A.A. Saber, El-Sheekh, Kouwets et Cantonati sp. nov., was isolated from two hyper-arid mountain valleys, so-called “wadis”, in the Eastern Desert of Egypt. The distinctive morphological features of this new species were established using light and scanning electron microscopy observations, and also by documenting its life-cycle stages. Taxonomically, C. yassinii is characterized by a cell wall sculpture consisting of isolated granules or small warts arranged circularly in the swollen mid-region of each semicell, never forming parallel vertical ridges or costae as in morphologically similar species, and the interesting shape of the marginal granules appears as small emarginate “combs” or crenae, including its knobby zygospores. Similarities and differences with the morphologically most closely related species are discussed in detail. Ecologically, C. yassinii seems to prefer alkaline freshwater environments with lower nutrient concentrations and a NaCl/HCO₃ water type. The detailed assessment and documentation of the biodiversity of these peculiar freshwater ecosystems are a fundamental prerequisite to adequately inform their protection strategies. Full article

Life-cycle assessment is crucial for evaluating materials’ environmental impact and guiding the development of low-carbon and sustainable buildings. However, conventional LCA methods often overlook critical impacts during the operation and maintenance stage. To address this gap, this study proposes an improved framework using four composite indicators to enable systematic evaluation of six wall materials across China’s five climate zones. Using a university teaching building in the Hot Summer and Cold Winter Zone as a case study, this study quantitatively analyzed the economic viability and carbon reduction potential of each material. Results indicate that lower thermal conductivity does not necessarily imply superior economic and carbon reduction performance. Factors including the material carbon emission factor, cost, and thermal properties, must be comprehensively considered. Buffering materials also exhibit climate dependency—WPM and BWPM (moisture-buffering plastering mortars) perform better in hot–humid zones than temperate zones. All five buffer materials reduce operational energy consumption; WPM and BWPM stand out with 15.7% and 16.7% life-cycle cost savings and 17.3% and 18.0% carbon emission reductions, respectively. This study addresses the limitations of traditional LCC/LCA and provides theoretical and practical support for scientific material selection and low-carbon building design. Full article

After nearly two decades of developments in Historic/Heritage Building Information Modeling (HBIM), the field has reached a stage of maturity that calls for a critical reassessment of its evolution, achievements, and remaining challenges. Digital representation has become a central component of contemporary heritage conservation, enabling advanced methods for analysis, management, and communication. This review examines the maturation of HBIM as a comprehensive framework that integrates extended reality (XR), artificial intelligence (AI), machine learning (ML), semantic segmentation and Digital Twin (DT). Six major research domains that have shaped recent progress are outlined: (1) the application of HBIM to restoration and conservation workflows; (2) the expansion of public engagement through XR, virtual museums, and serious games; (3) the stratigraphic documentation of building archaeology, historical phases, and material decay; (4) data-exchange mechanisms and interoperability with open formats and Common Data Environments (CDEs); (5) strategies for modeling geometric and semantic complexity using traditional, applied, and AI-driven approaches; and (6) the emergence of heritage DT as dynamic, semantically enriched systems integrating real-time and lifecycle data. A comparative assessment of international case studies and bibliometric trends (2015–2025) illustrates how HBIM is transforming proactive and data-informed conservation practice. The review concludes by identifying persistent gaps and outlining strategic directions for the next phase of research and implementation. Full article