Search Results (8,720)

This study examines how corporate participation in green technology standard-setting affects two dimensions of green innovation–substantive and symbolic green innovation–through the mediating role of knowledge integration and across different stages of the firm life cycle. Analyzing panel data from Chinese A-share listed firms (2010–2023), we find that standard-setting participation significantly enhances both types of innovation, with a stronger and more enduring effect on substantive innovation. The effects exhibit clear life cycle heterogeneity: substantive green innovation is consistently enhanced across all stages of the firm life cycle, whereas symbolic green innovation is predominantly reinforced during the maturity stage. Grounded in the knowledge-based view and institutional theory, our findings highlight how institutional engagement fosters sustainable innovation by strengthening firms’ capacity for knowledge acquisition and integration. This research advances understanding of the strategic value of standard-setting in sustainability efforts and provides actionable insights for aligning standardization practices with long-term innovation goals. Full article

Under the dual pressures of global climate change and accelerating urbanization, landscape design has been tasked with the critical mission of enhancing urban environmental resilience and ecological livability. However, conventional design practices often struggle to efficiently integrate complex sustainability norms with aesthetic creativity, leading to a disconnect between form and function. To address this issue, this study proposes and validates an AI-enabled sustainability decision-support framework. The framework is based on a “Generative-Critical” multi-agent workflow that enables “Self-Correcting” iterative optimization of design schemes through a built-in expert knowledge base and a quantitative scorecard. The framework’s effectiveness was validated through a cultural park case study and a blind evaluation by 10 experts. It guided a design from an initial concept with only aesthetic forms and lacking effective stormwater management, to an ecologically integrated scheme that strategically incorporated bioretention ponds at key nodes and converted hard plazas into permeable pavements. This transformation significantly elevated the scheme’s sustainability score from 59.3 to 88.0 (p < 0.001), while the framework itself achieved a high system usability scale (SUS) score of 85.5. These results confirm that the proposed “Generative-Critical” mechanism can effectively guide AIGC to adhere to ecological-technical norms and constraints while pursuing aesthetic innovation, thereby achieving a scientific integration of aesthetic form and ecological function at the early conceptual design stage. This study offers a scalable methodology for AI-assisted sustainable design and provides a novel intelligent tool for creating resilient urban landscapes that possess both environmental performance and aesthetic value. Full article

Mountain floods, one of the most common and destructive natural disasters worldwide, pose significant challenges to disaster prevention due to their sudden onset, high destructive power, and severe localized impacts. This study proposes an innovative flash flood early warning system based on a distributed hydrological model ensemble. The main objective is to improve the prediction and early warning accuracy of flash flood disasters by integrating multi-source data and regional modeling. The system simulates flood flow and risk levels under different rainfall scenarios to provide timely warnings in mountainous areas. A case study of a heavy rainfall event in Ma Jia Natural Village, Jiangxi Province was used to validate the system’s performance. Through regionalized parameter calibration within the ensemble, the system achieved Nash–Sutcliffe Efficiency (NSE) values exceeding 0.88, while the simulated peak discharges deviated from observed values by only 1.5%, 9.5%, and 4.8% under 3 h, 6 h, and 24 h rainfall scenarios, respectively, demonstrating the improved quantitative accuracy of flood prediction enabled by the ensemble-based framework. The system showed high consistency with observed data, accurately predicting flood responses at 3, 6, and 24 h time scales and providing reliable risk warnings. This approach not only enhances warning accuracy across multiple temporal scales but also supports risk-level early warnings at both river-section and village scales, offering significant practical value for the prevention of mountainous flood disasters. Full article

Amid China’s “Manufacturing Power” push, full-chain digital restructuring in the Guangdong–Hong Kong–Macao Greater Bay Area remains hampered by mismatches among technology, organization, and environment. We therefore explored how shop floor actors perceive and shape this Technology–Organization–Environment (TOE) interplay. Semi-structured interviews with frontline operators, maintainers, and supply chain staff from GBA manufacturers were inductively coded, yielding 36 concepts, 10 categories, and 3 core TOE aggregates that were woven into a grounded model. The analysis shows that industrial internet platforms and smart equipment only create value when matched by flexible shop floor structures, cross-department data protocols, and skilled teams; otherwise, data silos, simulation–production deviations, and “buy-but-not-build” procurement stall adoption. Market pressure for customized, short-lead-time products and divergent municipal pilot policies further intensify the TOE balancing act, particularly for SMEs with weak absorptive capacity. By revealing a grassroots “technology-driven → organization-adapted → environment-adjusted” spiral that is moderated by frontline feedback, the study extends the TOE framework to micro-level, regional innovation theory and offers policy–practice levers for differentiated, cross-city manufacturing upgrading. Full article

The global environmental challenges of solid waste accumulation and aquatic eutrophication demand innovative and sustainable strategies. This study introduces a circular “waste-treats-waste” approach by converting dolomite-rich phosphate tailings (PT), a widespread industrial by-product, into a high-value adsorbent for phosphorus (P) removal. Thermal modification at 950 °C for 1 h dramatically enhanced the adsorption capacity by approximately 45 times, from 2.52 mg/g (raw PT) to 112.41 mg/g. This performance is highly competitive with, and often superior to, many engineered adsorbents. The calcination process was pivotal, decomposing carbonates into highly active CaO and MgO while developing a porous structure. Using a multi-technique characterization approach (X-ray diffraction (XRD), Fourier transform infrared spectra (FTIR), TESCAN VEGA3 tungsten filament scanning electron microscope (SEM), the Brunauer–Emmett–Teller method (BET)), the key immobilization mechanism was identified as hydroxyapatite formation, driven by Ca²⁺/Mg²⁺-phosphate precipitation and surface complexation. Nonlinear regression analysis revealed that the adsorption kinetics obeyed the pseudo-second-order model, and the equilibrium data were best described by the Freundlich isotherm. This indicates a chemisorption process occurring on a heterogeneous surface, consistent with the complex structure created by thermal modification. Notably, post-adsorption pore structure expansion suggested synergistic pore-filling and surface reorganization. This work not only demonstrates a circular economy paradigm for repurposing industrial solid waste on a global scale but also offers a cost-effective and high-performance pathway for controlling phosphorus pollution in aquatic systems, contributing directly to resource efficiency and sustainable environmental remediation. Full article

The transition to renewable energy technologies is one of the most important ways to achieve the sustainable development goals (SDGs) of affordable and clean energy (SDG7); industry, innovation and infrastructure (SDG9); responsible production and consumption (SDG12); and climate action (SDG13). The widespread use of renewable energy technologies in developing countries will reduce dependence on imported fossil resources, increase industrial competitiveness, and support low-carbon development. Despite all their advantages, the integration of renewable energy technologies into industrial and domestic systems in developing countries remains slow due to a number of barriers. Financial constraints, technical and technological deficiencies, political restrictions and uncertainties, and organizational and managerial inadequacies are some of the barriers to the widespread adoption of renewable energy technologies. This study aims to identify, classify, and prioritize the barriers to the implementation of renewable energy technologies by applying multi-criteria decision-making methods in a fuzzy environment, with Türkiye considered as a case study. The relative importance of the barriers identified using the Single-Valued Spherical Fuzzy SWARA method was assessed, and their interconnections and significance were systematically demonstrated. The findings will contribute to the development of policy and management strategies aligned with global sustainability goals, thereby facilitating a more effective and equitable transition to clean and resilient energy systems. Full article

The utilization of sustainable feedstocks offers significant opportunities for innovation in sustainable and efficient processing technologies, targeting a vacuum residue upgrade industry projected to be valued at around USD 26 billion in 2024. This review examines advances in catalytic strategies for upgrading waste-derived products (plastics, tires) and biomass, in addition to heavy oil feedstocks. Particular emphasis is placed on hydrogen addition pathways, specifically, residue hydroconversion facilitated by dispersed nanocatalysts and waste co-processing methodologies. Beyond nanoscale catalyst design and reaction performance, this work also addresses refinery-level sustainability impacts. The advanced catalytic conversion of heavy oil residue demonstrates superior conversion efficiency, significant coke suppression, and improved carbon utilization, while life cycle and illustrative techno-economic comparisons indicate greenhouse gas reductions and a net economic gain of approximately USD 2–3 per barrel relative to conventional refining under scenarios assuming decarbonized hydrogen production. Co-processing of plastics, tires, and biomass with heavy oil feedstocks is highlighted as a practical and effective approach. Together, these findings outline a rational catalytic pathway toward optimized refining systems. Within the framework of the circular carbon economy, these catalytic processes enable enhanced feedstock utilization, integration of low-carbon hydrogen, and coupling with carbon-capture technologies. Full article

Interventions on Heritage Buildings (HBs) involve significant challenges due to their tangible (embodied in the material, architectural, physical and technical integrity of the cultural asset), and intangible values (linked to socio-historical–cultural and collective identity, memory, customs and symbols meanings), which must be preserved while also adapting to current sustainability and circular economy goals. However, current conservation and management practices often lack systematic tools to trace, assess, and organise material and component information, hindering the implementation of circular strategies. In line with the European Union’s objectives for climate neutrality and resource efficiency and sufficiency, Material and Product Passports (MPPs) have emerged as digital tools that enhance data traceability, interoperability and transparency throughout a building’s lifecycle. This paper examines the potential of MPPs to support circular management of HBs by analysing the structure of MPPs and outlining the information flows generated by rehabilitation, maintenance and adaptive reuse strategies. A mixed methods approach, combining literature review and data structure analysis, is adopted to identify how the different categories of data produced during maintenance, rehabilitation and adaptive reuse processes can be integrated into MPP modules. The research highlights the conceptual opportunities of MPPs to document and interlink historical, cultural, and technical data, thereby improving decision-making and transparency across intervention stages. The analysis suggests that adapting MPPs to the specificities of historic contexts, such as authenticity preservation, reversibility, and contextual sensitivity, can foster innovative, sustainable, and circular practices in the conservation and management of HBs. Full article

Langasite (LGS)-based surface acoustic wave (SAW) sensors are promising for high-temperature pressure detection. However, their performance is limited by the low pressure sensitivity of conventional sealed-cavity packaging and temperature-induced measurement drift. To address these issues, this study introduces a novel LGS SAW pressure sensor featuring two key innovations: a Kovar alloy point-force packaging structure to amplify pressure-induced LGS substrate deformation, enhancing sensitivity compared to traditional designs, and SAW resonators fabricated on an LGS (0°, 138.5°, 26.7°) cut, selected based on electromechanical simulations for its superior intrinsic pressure sensitivity and monotonic frequency–temperature response, effectively mitigating temperature interference on pressure measurements. Experimental characterizations show the resonator achieves a high Q-value of ~3000 at ~357 MHz. Tested under conditions of 250 °C and 0–0.4 MPa, the sensor exhibits a pressure sensitivity of 0.1866 MHz/MPa with a relative error of only 4.8% versus the finite element method (FEM)-simulated 0.196 MHz/MPa, demonstrating the proposed design’s effectiveness for accurate, stable pressure monitoring in harsh high-temperature environments such as turbine engines and high-temperature manufacturing lines. Full article

Structural Health Monitoring (SHM) of large-scale civil infrastructure is essential to ensure safety, minimise maintenance costs, and support informed decision-making. Unsupervised anomaly detection has emerged as a powerful tool for identifying deviations in structural behaviour without requiring labelled damage data. The study initially reproduces and implements a state-of-the-art methodology that combines local density estimation through the Cumulative Distance Participation Factor (CDPF) with Semi-parametric Extreme Value Theory (SEVT) for thresholding, which serves as an essential baseline reference for establishing normal structural behaviour and for benchmarking the performance of the proposed anomaly detection framework. Using modal frequencies extracted via Stochastic Subspace Identification from the Z24 bridge dataset, the baseline method effectively identifies structural anomalies caused by progressive damage scenarios. However, its performance is constrained when dealing with subtle or non-linear deviations. To address this limitation, we introduce an innovative ensemble anomaly detection framework that integrates two complementary unsupervised methods: Principal Component Analysis (PCA) and Autoencoder (AE) are dimensionality reduction methods used for anomaly detection. PCA captures linear patterns using variance, while AE learns non-linear representations through data reconstruction. By leveraging the strengths of these techniques, the ensemble achieves improved sensitivity, reliability, and interpretability in anomaly detection. A comprehensive comparison with the baseline approach demonstrates that the proposed ensemble not only captures anomalies more reliably but also provides improved stability to environmental and operational variability. These findings highlight the potential of ensemble-based unsupervised methods for advancing SHM practices. Full article

Antibiotics have become an integral part of human life and production. The presence of sulfachloropyridazine (SCP), one of the most ubiquitous antibiotics, in water has been a growing concern owing to its long persistence and the difficulty in removing it by conventional water treatment processes. This study introduced ozone (O₃)-activated sodium percarbonate (SPC) as an innovative technique of advanced oxidation processes (AOPs), and the degradation of SCP from water by this method was thoroughly investigated. The impact of a variety of parameters, such as the dosage of SPC, the dosage of O₃, the pH value, and water matrix constituents, on the removal of SCP was evaluated with regard to the pseudo-first-order kinetic model. It was found that the removal effectiveness of SCP improved initially and then decreased with the rising dosage of SPC, with an optimal SPC dose achieved at 20 mg/L. Moreover, •OH, ${\mathrm{O}}_2^{•-}$ and ¹O₂ played important roles during SCP degradation based on radical quenching tests and electron paramagnetic resonance (EPR) tests. The SCP degradation pathways were predicted using density functional theory (DFT), which primarily involves the cleavage of S-C or S-N bonds and Smiles-type rearrangements, accompanied by hydroxylation. Furthermore, the toxicity of degradation intermediates was evaluated by the ECOSAR 1.1 software in terms of acute toxicity and chronic toxicity, and most of them exhibited lower levels of toxicity. The results can expand the research scope of SPC and reveal significant insights for SPC’s application in controlling antibiotic contamination. Full article

Although green housing is widely regarded as an effective solution to energy and environmental challenges, its actual rate of adoption remains lower than expected. In the context of increasingly prominent sustainable development goals, promoting residents’ adoption of green housing has become a key issue in advancing sustainable transformation within the housing sector. Consequently, enhancing residents’ willingness to adopt green housing is critical to its broader diffusion. Drawing on diffusion of innovation theory, attitude theory, and perceived value theory, this study develops a multidimensional integrated model to identify factors influencing the adoption of green housing. The model examines how the innovation attributes of green housing and residents’ psychological evaluations jointly shape adoption intention. A questionnaire survey was conducted among 387 residents in Nanjing, China, and the data were analysed using partial least squares modelling. The results indicate that the five attributes derived from diffusion of innovation theory are significant antecedents of residents’ attitudes. Relative advantage, compatibility, trialability, and observability exert significant positive effects on residents’ attitudes toward adopting green housing, with relative advantage emerging as the most influential factor. Complexity has a negative, though comparatively weaker, effect on residents’ attitudes toward green housing adoption. Residents’ attitudes and perceived value are identified as significant predictors of green housing adoption intention. These findings contribute to a clearer understanding of residents’ green housing adoption intentions for both researchers and practitioners. More importantly, the study offers general policy and managerial implications for governments and developers seeking to enhance the uptake of green housing. Full article

The bacterium Xanthomonas oryzae pv. oryzae is an important pathogen that causes wilt leaf blight disease in rice (Oryza sativa L.), leading to a reduction in rice yield. Therefore, this study aimed to investigate the potential of a surfactant complex composed of CO₂ nanobubbles (CO₂-NBs) coated with sorbitan monostearate (Sp60) and a crude extract of Trichoderma polyalthiae as active ingredient delivery agents for controlling leaf blight under both laboratory and greenhouse conditions. The addition of Sp60 and crude extract as surfactants significantly influenced the size uniformity and stability of CO₂-NBs at the nano level, with the nanobubbles remaining intact in water for up to 14 days. In addition, CO₂-NBs with crude extract and Sp60 reduced the severity of wilt, with an minimum inhibitory concentration (MIC) value of 64 µg/mL and an minimum bactericidal concentration (MBC) value of 128 µg/mL, and inhibited the disease by more than 50% in greenhouse conditions. Therefore, this study presents a creative and eco-friendly approach to managing bacterial leaf blight in rice that is innovative and relevant to sustainable plant protection. Full article

This article presents a conceptual framework linking responsible leadership competencies to organizational processes relevant for sustainability transformation. A leader’s personal impact is conceptualized as indirect through organizational governance, prioritization, collaboration, and innovation. The article provides a conceptual mapping approach that systematically connects individual leadership competencies with strategic organizational impact areas. The framework builds on the Competency Assessment for Responsible Leadership (CARL) and the Positive Impact Organization (PIO) concept to relate leadership capabilities to organizational transformation requirements. Five responsible leadership competencies—ethics and values, self-awareness, stakeholder relations, change and innovation, and systems thinking—are mapped to five organizational impact areas: governance alignment, sustainability culture, external stakeholder validation, purpose-driven product and service innovation, and transformative sustainability. The article identifies how specific leadership competencies align with distinct organizational leverage points, clarifying the mechanisms through which leaders shape structures, decision processes, cultural norms, and innovation pathways associated with sustainability transformation. By specifying these relationships, the framework distinguishes leadership impact at the organizational level and provides a structured basis for analyzing how leadership competencies contribute to an organization’s capacity for societal and environmental contribution. The framework is applicable to research on responsible leadership and business sustainability and informs leadership development and management education concerned with sustainability-oriented organizational change. Full article

Background: The COVID-19 pandemic forced an unprecedented global transition to emergency remote teaching, fundamentally disrupting traditional higher education delivery methods. This study investigated how Greek higher education students adapted to mandatory distance learning during the acute phase of the COVID-19 pandemic (March 2020 to May 2021), providing baseline evidence of forced technology adoption patterns that can inform understanding of subsequent hybrid learning developments and future educational design. Methods: A quantitative cross-sectional design surveyed n = 477 students from Greek higher education institutions using a structured questionnaire measuring technology access, platform usage, learning modality preferences, challenges encountered, and future educational perspectives. Data analysis employed descriptive statistics, Pearson correlations, and multiple linear regression to identify predictors of distance learning satisfaction and preferences. Results: Most students expressed positive attitudes toward distance learning (67.9%) and reported comfort with online courses (71.6%), with 69.8% strongly preferring hybrid approaches combining synchronous and asynchronous modalities. Internet connectivity emerged as the primary predictor of satisfaction (β = 0.393, p = 0.052), while demographic factors showed minimal influence (R² = 0.048). Most students achieved platform proficiency within ten days (73.6%), though 67.9% recognized that distance learning poses differential accessibility challenges. Conclusions: Despite successful adaptation to emergency remote teaching, students envision a hybrid educational future that strategically integrates online and face-to-face modalities rather than wholesale replacement of traditional methods, emphasizing the need for infrastructure investment and pedagogical innovation while preserving valued social learning dimensions. Full article