Search Results (387)

With the advancement of low-carbon and sustainable development initiatives, electric scooters, recognized as essential transportation tools and leisure products, have gained significant popularity, particularly among young people. However, the current electric scooter market is plagued by severe product similarity. Once the initial novelty fades for users, the usage frequency declines, resulting in considerable resource wastage. This research collected user needs via surveys and employed the KJ method (affinity diagram) to synthesize fragmented insights into cohesive thematic clusters. Subsequently, a hierarchical needs model for electric scooters was constructed using analytical hierarchy process (AHP) principles, enabling systematic prioritization of user requirements through multi-criteria evaluation. By establishing a house of quality (HoQ), user needs were transformed into technical characteristics of electric scooter products, and the corresponding weights were calculated. After analyzing the positive and negative correlation degrees of the technical characteristic indicators, it was found that there are technical contradictions between functional zoning and compact size, lightweight design and material structure, and smart interaction and usability. Then, based on the theory of inventive problem solving (TRIZ), the contradictions were classified, and corresponding problem-solving principles were identified to achieve a multi-functional innovative design for electric scooters. This research, leveraging a systematic industrial design analysis framework, identified critical pain points among electric scooter users, established hierarchical user needs through priority ranking, and improved product lifecycle sustainability. It offers novel methodologies and perspectives for advancing theoretical research and design practices in the electric scooter domain. Full article

Carbon emissions from freshwater aquaculture can exacerbate the greenhouse effect, thereby impacting human life and health. Consequently, it is of great significance to explore the carbon peak process and the role of emission reduction data in China’s freshwater aquaculture industry. This study innovatively employs the Logarithmic Mean Divisia Index model (LMDI) and the Tapio decoupling model to conduct an in-depth analysis of the relationship between carbon emissions and output values in the freshwater aquaculture industry, accurately identifying the main driving factors. Meanwhile, the global and local Moran’s I indices are introduced to analyze its spatial correlation from a new perspective. The results indicate that from 2013 to 2023, carbon emissions from China’s freshwater aquaculture industry exhibited a quasi-“N”-shaped trend, reaching a peak of 38 million tons in 2015. East China was the primary contributor to carbon emissions, accounting for 46%, while South China, Central China, and Northeast China each had an average annual share of around 14%, with Southwest, North China, and Northwest China contributing relatively small proportions. The global Moran’s I index showed a decreasing trend, with a p-value ≤ 0.0010 and a z-score > 3.3, indicating a 99% significant spatial correlation. High-high clusters were concentrated in some provinces of East China, while low-low clusters were found in Northwest, North, and Southwest China. The level of fishery economic development positively drove carbon emissions, whereas freshwater aquaculture production efficiency, industrial structure, and the scale of the aquaculture population had negative effects on carbon emissions. During the study period, carbon emissions exhibited three states: weak decoupling, strong decoupling, and expansive negative decoupling, with alternating strong and weak decoupling occurring after 2015. Full article

Innovation is a strategic driver of sustainable competitive advantage and long-term economic growth. This study proposes an empirical framework to support the sustained development of national innovation capacity by examining key enabling factors. Drawing on an extensive review of the literature, the research investigates the interrelationships among governmental support (GS), innovation agents (IA), university–industry R&D collaborations (UIRD), and innovation cluster development (ICD), and their influence on two critical innovation outcomes, knowledge creation (KC) and knowledge diffusion (KD). Using panel data from G7 countries spanning 2008 to 2018, sourced from international organizations such as the World Bank, the World Intellectual Property Organization, and the World Economic Forum, the study applies regression analysis to test the proposed conceptual model. Results highlight the foundational role of GS in providing a balanced framework to foster collaborative networks among IA and enhancing the effectiveness of UIRD. Furthermore, IA emerges as a pivotal actor in advancing innovation efforts, while the development of innovation clusters is shown to selectively enhance specific innovation outcomes. These findings offer theoretical and practical contributions for policymakers, researchers, and stakeholders aiming to design supportive ecosystems that strengthen sustainable national innovation capacity. Full article

Background: Sustainability in supply chain management (SCM) practices is becoming increasingly important as environmental responsibility and social concerns, as well as enterprises’ competitiveness in terms of innovation, risk, and economic performance, become increasingly urgent. This paper aims to identify and prioritize concepts for implementing sustainable supply chains, drawing on sustainable supply chain management (SSCM) and green supply chain management (GSCM) techniques. Corporate supply chain managers across various industries, markets, and supply chain segments brainstormed management practices to enhance the sustainability of their supply chains. Four industry sectors were surveyed across five different value chain segments. Methods: A group concept mapping (GCM) approach incorporating multi-dimensional scaling (MDS) and hierarchical cluster analysis (HCA) was used. A hierarchy of practices is proposed, and hypotheses are developed about achievability and impact. Results: A decision-making matrix prioritizes eight solution concepts based on two axes: impact (I) and ease of implementation (EoI). Conclusions: Eight concepts are prioritized based on the optimal effectiveness of implementing the solutions. Pattern matching reveals differences between emerging and developed markets, as well as supply chain segments, that decision-makers should be aware of. By analyzing supply chains from a multi-part perspective, this research goes beyond empirical studies based on a single industry, geographic region, or example case. Full article

The rapid development of the digital economy and the pursuit of green transformation are reshaping the innovation landscape of Chinese firms. However, limited attention has been paid to how digital industry agglomeration (DIA) influences corporate green innovation (CGI) at the firm level. Drawing on panel data from China’s A-share listed firms between 2017 and 2021, this study examines the differential effects of specialized agglomeration and diversified agglomeration of digital industry on CGI. The results indicate that DIA can promote CGI, with a 1% increase in DIA associated with a 1.503% increase in green innovation output. Further analysis reveals that specialized agglomeration exerts a significant positive effect, while diversified agglomeration has no evident impact. Our mechanism analysis indicates that knowledge spillovers serve as the key channel through which DIA fosters CGI. Moreover, heterogeneous effects analysis indicates that DIA exerts a stronger influence on non-high-tech enterprises and in regions where environmental regulation is less stringent. Drawing on these insights, fostering specialized digital clusters and strengthening knowledge-sharing mechanisms can help alleviate existing constraints on innovation diffusion, accelerating green innovation and supporting long-term sustainability. Full article

This study investigates the role of the digital economy (DE) in advancing the high-quality development of manufacturing in China, with a particular focus on the moderating effects of manufacturing agglomeration and digital literacy. Using provincial panel data from 2013 to 2023, we find that the digital economy significantly enhances manufacturing development across three key dimensions: green transformation, innovation, and high-end industrial upgrading. Manufacturing agglomeration strengthens this effect, especially in the Eastern and Western regions, by facilitating digital spillovers and leveraging digital infrastructure. However, in the Central region, the impact of agglomeration is weaker, hindered by fragmented industrial clusters and underdeveloped digital infrastructure. The study also highlights significant regional differences in the moderating effect of digital literacy. In the Eastern region, digital literacy negatively moderates the relationship between DE and manufacturing development due to skill mismatches, while in the Western region, localized concentrations of digital skills have a positive but geographically constrained impact. Temporal analysis reveals a shift in the moderating role of digital literacy, with its negative effect becoming more pronounced after 2018, suggesting a growing need for targeted skill development policies. These findings underscore the importance of regionally tailored strategies to promote digital manufacturing integration, with a focus on sustainable development through digital transformation and green manufacturing practices. Full article

Accurate forecasting of carbon emissions at the county level is critical to support China’s dual-carbon goals. However, most current studies are limited to national or provincial scales, employing traditional statistical methods inadequate for capturing complex nonlinear interactions and spatiotemporal dynamics at finer resolutions. To overcome these limitations, this study develops and validates a high-resolution predictive model using advanced gradient boosting algorithms—Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM)—based on socioeconomic, industrial, and environmental data from 2732 Chinese counties during 2008–2017. Key variables were selected through correlation analysis, missing values were interpolated using K-means clustering, and model parameters were systematically optimized via grid search and cross-validation. Among the algorithms tested, LightGBM achieved the best performance (R² = 0.992, RMSE = 0.297), demonstrating both robustness and efficiency. Spatial–temporal analyses revealed that while national emissions are slowing, the eastern region is approaching stabilization, whereas emissions in central and western regions are projected to continue rising through 2027. Furthermore, SHapley Additive exPlanations (SHAP) were applied to interpret the marginal and interaction effects of key variables. The results indicate that GDP, energy intensity, and nighttime lights exert the greatest influence on model predictions, while ecological indicators such as NDVI exhibit negative associations. SHAP dependence plots further reveal nonlinear relationships and regional heterogeneity among factors. The key innovation of this study lies in constructing a scalable and interpretable county-level carbon emissions model that integrates gradient boosting with SHAP-based variable attribution, overcoming limitations in spatial resolution and model transparency. Full article

Corporate resilience, a critical metric assessing firms’ capacity to withstand risks, recover rapidly, and maintain growth in dynamic environments, has garnered increasing attention from academia and industry. This study employs China’s Green Factory certification policy within its green manufacturing system as a quasi-natural experiment, utilizing a multi-period difference-in-differences (DID) model to evaluate the impact of green manufacturing implementation on corporate resilience. Results confirm that Green Factory certification significantly enhances firms’ resilience. Mechanism analyses identify three reinforcing pathways: alleviating financing constraints, optimizing resource allocation efficiency, and fostering green technological innovation. Heterogeneity analyses reveal more pronounced effects among heavily polluting industries, firms with low reputations, and those with higher levels of managerial myopia. Furthermore, the certification exhibits significant spillover effects, transmitting resilience improvements to industry peers and geographic clusters. This research expands the theoretical boundaries of corporate resilience literature while offering practical implications and empirical evidence for enterprises undergoing green manufacturing transitions. Full article

Amidst the dual challenges of escalating ecological environmental pressures and economic transformation globally, green innovation emerges as a pivotal pathway toward achieving high-quality sustainable development. To elucidate how digitalization and novel productive forces synergistically drive the green transition, the research utilizes panel data from 30 provincial-level administrative regions in China spanning 2009 to 2022, constructing a green innovation efficiency measurement frame-work grounded in the Super Slack-Based Measure (Super-SBM)model, alongside a novel productive forces evaluation system based on the triad of laborers, labor objects, and means of production. Employing spatial difference-in-differences and double machine learning methodologies within a quasi-natural experimental design, the research investigates the causal mechanisms through which digital empowerment and novel productive forces influence regional green innovation efficiency. The findings reveal that both digital empowerment and novel productive forces significantly enhance regional green innovation efficiency, exhibiting pronounced positive spatial spillover effects on neighboring regions. Heterogeneity analyses demonstrate that the promotive impacts are more pronounced in eastern provinces compared to central and western counterparts, in provinces participating in carbon trading relative to those that do not, and in innovation-driven provinces versus non-innovative ones. Mediation analysis indicates that digital empowerment operates by fostering the aggregation of innovative talent and elevating governmental ecological attentiveness, whereas new-type productivity exerts its influence primarily through intellectual property protection and the clustering of high-technology industries. The results offer empirical foundations for policymakers to devise coordinated regional green development strategies, refine digital transformation policies, and promote industrial structural optimization. Furthermore, this research provides valuable data-driven insights and theoretical guidance for local governments and enterprises in cultivating green innovation and new-type productivity. Full article

The continuous evolution of technology compels higher education leaders to adapt to VUCA (volatile, uncertain, complex, and ambiguous) and BANI (brittle, anxious, non-linear, and incomprehensible) environments through innovative strategies that ensure institutional relevance. While VUCA emphasizes the challenges posed by rapid change and uncertain decision-making, BANI underscores the fragility of systems, heightened anxiety, unpredictable causality, and the collapse of established patterns. Navigating these complexities requires agility, resilience, and visionary leadership to ensure that institutions remain adaptable and future ready. This study presents a bibliometric analysis of digital academic leadership in higher education transformation, examining empirical studies, reviews, book chapters, and proceeding papers published from 2014 to 2024 (11-year period) in the Web of Science—Science Citation Index Expanded (SCIE) and Social Science Citation Index (SSCI). Using CiteSpace software (version 6.3. R1-64 bit), we analyzed 5837 documents, identifying 24 key publications that formed a network of 90 nodes and 256 links. The reduction to 24 publications occurred as part of a structured bibliometric analysis using CiteSpace, which employs algorithmic thresholds to identify the most influential and structurally significant publications within a large corpus. These 24 documents form the core co-citation network, which serves as a conceptual backbone for further thematic interpretation. This was the result of a multi-step refinement process using CiteSpace’s default thresholds and clustering algorithms to detect the most influential nodes based on centrality, citation burst, and network clustering. Our findings reveal six primary research clusters: “Enhancing Academic Performance”, “Digital Leadership Scale Adaptation”, “Construction Industry”, “Innovative Work Behavior”, “Development Business Strategy”, and “Education.” The analysis demonstrates a significant increase in publications over the decade, with the highest concentration in 2024, reflecting growing scholarly interest in this field. Keywords analysis shows “digital leadership”, “digital transformation”, “performance”, and “innovation” as dominant terms, highlighting the field’s evolution from technology-focused approaches to holistic leadership frameworks. Geographical analysis reveals significant contributions from Pakistan, Ireland, and India, indicating valuable insights emerging from diverse global contexts. These findings suggest that effective digital academic leadership requires not only technical competencies but also transformational capabilities, communication skills, and innovation management to enhance student outcomes and institutional performance in an increasingly digitalized educational landscape. Full article

Traditional coal quality assessment methods rely exclusively on the laboratory testing of physical samples, which impedes detailed stratigraphic evaluation and limits the integration of intelligent precision mining technologies. To resolve this challenge, this study investigates geophysical logging as an innovative method for coal quality prediction. By integrating scanning electron microscopy (SEM), X-ray analysis, and optical microscopy with interdisciplinary methodologies spanning mathematics, mineralogy, and applied geophysics, this research analyzes the coal quality and mineral composition of the Shanxi Formation coal seams in northern Jiangsu, China. A predictive model linking geophysical logging responses to coal quality parameters was established to delineate relationships between subsurface geophysical data and material properties. The results demonstrate that the Shanxi Formation coals are gas coal (a medium-metamorphic bituminous subclass) characterized by low sulfur content, low ash yield, low fixed carbon, high volatile matter, and high calorific value. Mineralogical analysis identifies calcite, pyrite, and clay minerals as the dominant constituents. Pyrite occurs in diverse microscopic forms, including euhedral and semi-euhedral fine grains, fissure-filling aggregates, irregular blocky structures, framboidal clusters, and disseminated particles. Systematic relationships were observed between logging parameters and coal quality: moisture, ash content, and volatile matter exhibit an initial decrease, followed by an increase with rising apparent resistivity (LLD) and bulk density (DEN). Conversely, fixed carbon and calorific value display an inverse trend, peaking at intermediate LLD/DEN values before declining. Total sulfur increases with density up to a threshold before decreasing, while showing a concave upward relationship with resistivity. Negative correlations exist between moisture, fixed carbon, calorific value lateral resistivity (LLS), natural gamma (GR), short-spaced gamma-gamma (SSGG), and acoustic transit time (AC). In contrast, ash yield, volatile matter, and total sulfur correlate positively with these logging parameters. These trends are governed by coalification processes, lithotype composition, reservoir physical properties, and the types and mass fractions of minerals. Validation through independent two-sample t-tests confirms the feasibility of the neural network model for predicting coal quality parameters from geophysical logging data. The predictive model provides technical and theoretical support for advancing intelligent coal mining practices and optimizing efficiency in coal chemical industries, enabling real-time subsurface characterization to facilitate precision resource extraction. Full article

The growing demand for more sustainable food systems has driven the development of solutions based on food microbiology, capable of integrating safety, functionality, and environmental responsibility. This paper presents a critical and up-to-date review of the most relevant advances at the interface between microbiology, sustainability, and food innovation. The analysis is structured around three main axes: (i) microbial fermentation, with a focus on traditional practices and precision technologies aimed at valorizing agro-industrial waste and producing functional foods; (ii) microbial biocontrol, including the use of bacteriocins, protective cultures, bacteriophages, and CRISPR-Cas (Clustered Regularly Interspaced Short Palindromic Repeats–CRISPR-associated)-based tools as alternatives to synthetic preservatives; and (iii) the development of functional foods containing probiotics, prebiotics, synbiotics, and postbiotics, with the potential to modulate the gut microbiota and promote metabolic, immune, and cognitive health. In addition to reviewing the microbiological and technological mechanisms involved, the paper discusses international regulatory milestones, scalability challenges, and market trends related to consumer acceptance and clean labeling. Finally, emerging trends and research gaps are addressed, including the use of omics technologies, artificial intelligence, and unexplored microbial resources. Food microbiology, by incorporating sustainable practices and advanced technologies, is positioned as a strategic pillar for building a healthy, circular, science-based food model. Full article

Sustainable risk and disaster management in the built environment has become a critical research focus amid escalating environmental challenges. Building Information Modeling (BIM) is recognized as a key digital tool for enhancing disaster resilience through simulation, data integration, and collaborative management. This study systematically reviews BIM applications in sustainable risk and disaster management from 2014 to 2024, employing the PRISMA framework, literature coding, and network analysis. Five primary research clusters are identified: (a) sustainable construction and life cycle assessment, (b) performance evaluation and implementation, (c) technology integration and digital innovation, (d) Historic Building Modeling (HBIM) and post-disaster reconstruction, and (e) project management and technology adoption. Despite increasing scholarly attention, the field remains dominated by conceptual studies, with limited empirical exploration of emerging technologies such as artificial intelligence (AI). Four key challenges are highlighted: weak foundational integration with structural risk research, technological bottlenecks in AI and digital applications, limited practical implementation, and insufficient linkage between sustainability and risk management. Future trends are expected to focus on achieving Industry 4.0 interoperability, advancing AI-driven intelligent disaster response, and adopting multi-objective optimization strategies balancing resilience, sustainability, and cost-effectiveness. This study provides a comprehensive overview of the field’s evolution and offers insights into strategic directions for future research and practical innovation. Full article

Generative artificial intelligence (GenAI) is a tool that can be applied to virtually all aspects of business and life, including the construction industry. However, the adoption of GenAI in the construction industry, as with other innovations, is slow, and many of its applications thus far have been rather simplistic or failed to deliver a useful, credible output. There is a limited understanding of how GenAI is adopted in current practice and its potential to improve future practice in architecture, engineering, construction, and operations (AECO). Using a systematic literature review approach, this study aims to map the current issues in applying GenAI. The literature review initially identified 1013 peer-reviewed articles from ProQuest, Scopus, and Web of Science. The articles were further filtered based on specific criteria, resulting in 28 articles being retained for thematic analysis. The findings show a cluster of patterns in which GenAI is being adopted and shows promise. The core themes identified are as follows: (1) project brief, (2) architectural design, (3) building information modelling, (4) structural design, (5) construction and demolition, (6) operations, and (7) urban governance. A typical trend noted in the AECO industry has been training AI models that achieve quicker results, improve quality, and use fewer resources. Full article

Innovation has become a pivotal factor in driving economic growth for cities and regions. Urban innovation spaces are urban spaces where innovative economic and industrial activities, such as research, teaching, and high-tech manufacturing, are clustered. They have become hot research topics in recent years. Evaluating the performance of urban innovation spaces to promote rational resource allocation and enhance land development potential has become a critical task in urban planning. However, existing studies suffer from insufficient depth of research scales and a lack of quantitative indicators and data analysis. In response to the above gaps, this study constructed a framework for evaluating the performance of urban innovation spaces from 25 indicators of five major types, including core elements of innovation, entrepreneurship support institutions, service facilities, external environments, and diversities, aiming to quantify the performance heterogeneity of innovation spaces at the micro scale. This study took Harbin as an example and employed the entropy, kernel density estimation, and entropy-weighted TOPSIS methods, identifying four high-scoring areas of innovation spaces—the Science and Technology Innovation City area, the High-tech Industrial Development area, the core area of the old city, and the Harbin Veterinary Research Institute area—which were divided into three types: the Entrepreneurial leading area, Environmental Support area, and Balanced Development area. Finally, this study analyzed the interaction between each indicator. It was found that the correlation between the core elements of innovation and the indicators of entrepreneurship support institutions was strong and had a high degree of importance. The correlation of different types of service facility indicators is quite different, and the external environment indicators and diversity indicators are mainly affected by other indicators, especially the core elements of innovation and entrepreneurship support institutions. This paper provides a valuable tool for the performance evaluation of urban innovation spaces for researchers and urban planning decision makers. Full article