Search Results (25,852)

Coordinated regional development plays an increasingly important role in promoting low-carbon transitions across China’s urban agglomerations. This study examines the effect of the Beijing–Tianjin–Hebei Coordinated Development Strategy, elevated to major national strategic status in 2014, on urban carbon emissions. Using an unbalanced panel of 294 prefecture-level and higher-level cities from 2010 to 2023, we employ a two-way fixed-effects difference-in-differences model to evaluate the strategy’s overall carbon-mitigation effect, potential transmission pathways, and spatial heterogeneity. The baseline DID coefficient is −0.052, corresponding to a 5.0% decline in city-level total CO₂ emissions in the 13 treated cities relative to the national comparison group after policy implementation. Within the same comparison framework, further analysis documents significant positive post-policy differentials in both the industrial-structure-upgrading index and green invention-patent applications; the industrial-structure-upgrading results are consistent with a potential emissions-reduction pathway. The heterogeneity analysis indicates a statistically significant relative carbon-mitigation effect for the prefecture-level cities of Hebei compared with the national comparison group, with a larger relative effect in Beijing and Tianjin. This spatial pattern is consistent with differences in industrial structure, innovation capacity, and environmental governance capacity across cities. The findings provide city-level quasi-experimental evidence that coordinated regional development can promote urban carbon mitigation and support differentiated carbon governance, coordinated industrial upgrading, and cross-regional low-carbon policy design. Full article

Green innovation is an important means of mitigating environmental pollution, improving production efficiency, and promoting high-quality economic development. Using prefecture-level city data and China Family Panel Studies (CFPS) data from 2010 to 2022, this study applies the Super-SBM model to measure urban green innovation efficiency and examines its effects on residents’ consumption levels and household consumption structures. The results show that (1) green innovation efficiency significantly increases consumption among urban residents and urban households, whereas its effects on rural residents and rural households are not statistically significant; (2) green innovation efficiency significantly reduces the share of subsistence-oriented consumption while increasing the shares of housing, development-oriented, and tourism consumption, thereby promoting a shift in household expenditure from basic needs toward development- and enjoyment-oriented consumption; (3) wage income and housing debt constitute two distinct transmission channels. Higher wage income facilitates development- and enjoyment-oriented consumption, whereas housing debt increases the share of housing consumption and exerts a crowding-out effect on development-oriented and tourism consumption; and (4) green innovation efficiency has a more pronounced effect on development-oriented and tourism consumption among young and high-income households. For middle-aged and older households, as well as low-income households, the effects of green innovation are reflected more strongly in an increase in the share of housing consumption. The findings provide important empirical evidence that green innovation contributes to improving residents’ quality of life. Full article

Improving the flood adaptability of urban waterfront spaces is an essential entry point for enhancing regional stormwater regulation capacity, scientifically preventing flood disasters, and stabilizing urban water security. Existing flood adaptability assessments mostly rely on single weighting methods and individual evaluation models, inevitably causing systematic bias and low result robustness. Against this limitation, this study integrates remote sensing intelligent interpretation, spatiotemporal landscape pattern analysis, and multi-criteria decision theory to construct a comprehensive flood adaptability evaluation system under the pressure–state–response (PSR) framework. Innovatively, a game-theoretic combined weighting scheme integrating the entropy weight method, CRITIC method, and standard deviation method is proposed, and three complementary models including TOPSIS, VIKOR, and EDAS are coupled for cross-verification evaluation, which effectively improves the objectivity and robustness of spatial flood adaptability quantification. Taking Anqing City as a typical case, this study adopts Sentinel-2 time-series remote sensing images from 2016 to 2023 and applies an optimized random forest algorithm to automatically classify land cover. Five underlying surface types, including water bodies, vegetation, farmland, built-up areas, and bare land, are accurately extracted with an overall classification accuracy of around 90% for most years. Core landscape metrics such as Shannon’s diversity index and patch density are selected to systematically analyze the spatiotemporal differentiation characteristics of waterfront landscape patterns during the study period. The results indicate the obvious spatial heterogeneity of flood adaptability in Anqing City. Yingjiang District and Yuexi County present high comprehensive flood adaptability, while Wangjiang County and Huaining County show relatively low performance. Urban areas gain strong flood resistance from complete disaster prevention infrastructures and economic resilience; mountainous areas possess natural advantages in flood retention and drainage due to high vegetation coverage and topographic relief; by contrast, plain districts are severely restricted by low-lying terrain and insufficient drainage systems, resulting in prominent flood vulnerability. The proposed method is helpful for providing reliable scientific support for waterfront landscape optimization, zoned flood disaster management, and resilient water space planning in riverine cities. Full article

In this paper, we investigate a reaction–diffusion system governing the vector-borne and sexual transmission dynamics of the Zika virus between host (human) and vector (mosquito) populations. Adopting the compartmental methodology, the human population is divided into susceptible, exposed, infected, and recovered classes, while the mosquito population is partitioned into susceptible, exposed, and infected compartments. We consider several assumptions for disease transmission; in particular, we consider the transmission from infected to susceptible humans to occur through sexual interaction. The first contribution of this work lies in incorporating spatial displacement for both populations, modeled via Fick’s law with a spatially varying diffusion coefficient to capture environmental heterogeneity. To reflect an epidemiologically closed domain, homogeneous Neumann (zero-flux) boundary conditions are imposed, ensuring total population mass conservation principles. As a second advancement, we establish rigorous mathematical results regarding the well-posedness, boundedness, and positivity of solutions to the resulting reaction–diffusion system. The third innovation of the paper is the fact that an unconditional convergent and biologically consistent finite difference numerical scheme is developed to resolve the coupled non-linear governing equations. Finally, we provide a comprehensive set of numerical simulations focused on the parameter sensitivity. Full article

This study examines the role of digital transformation in strengthening organizational resilience in Moroccan tourism accommodation firms operating in crisis-prone environments. Successive economic, health, and environmental disruptions have highlighted the need to better understand how entrepreneurial orientation contributes to organizational resilience and whether digital transformation acts as a mediating mechanism. Data were collected from 137 owners and managers of Moroccan tourism accommodation firms and analyzed using partial least squares structural equation modeling (PLS-SEM). Entrepreneurial orientation was operationalized through its three dimensions (innovativeness, proactiveness, and risk-taking), while organizational resilience was assessed across the readiness, response, and recovery phases to provide a multidimensional perspective. The results show that digital transformation plays a significant mediating role in enhancing organizational resilience. Among the entrepreneurial orientation dimensions, proactiveness emerged as the strongest predictor, exerting significant direct and indirect effects on all resilience phases through digital transformation. Innovativeness contributed primarily through its indirect effect via digital transformation, whereas risk-taking showed no significant direct or indirect influence. These findings suggest that digital transformation constitutes a key organizational capability through which entrepreneurial orientation strengthens resilience. The study contributes to the literature by adopting a multidimensional approach to both entrepreneurial orientation and organizational resilience, and provides practical insights for managers seeking to enhance resilience through digital transformation. Full article

Many coastal regions endowed with rich marine biodiversity fail to translate these resources into high-value pharmaceutical products—a “valorization gap” that obstructs progress toward SDG 14 (Life Below Water), SDG 3 (Good Health and Well-Being), and SDG 9 (Industry, Innovation and Infrastructure). This study investigates the governance barriers underlying this gap using China’s Marine Traditional Chinese Medicine (MTCM) industry as an empirical case. Guided by an integrated analytical framework synthesizing innovation systems theory, Triple Helix dynamics, global value chain analysis, and organizational ambidexterity, the study draws on semi-structured interviews with 16 stakeholders in Dalian and NVivo-based thematic analysis. Four interconnected barriers are identified: value chain lock-in at the food-product level, a fragmented innovation ecosystem with misaligned incentives, a translational “valley of death” caused by absent pilot-scale platforms and quality standardization deficits, and an underdeveloped industrial ecosystem trapped in a low-value equilibrium. Through benchmarking against Qingdao’s marine pharmaceutical model and international marine bio-pharmaceutical policies (Japan, Republic of Korea, EU), this study proposes a “Dual-Wheel Driving” sustainable governance framework that simultaneously pursues “stock upgrading” (standardization, sustainable sourcing, branding) and “increment cultivation” (strategic pharmaceutical R&D with green innovation principles), coordinated through a “one core, multiple nodes” institutional architecture. The findings offer transferable policy insights for similarly positioned coastal regions seeking to develop marine bio-pharmaceutical capabilities within a sustainable blue bioeconomy framework. Full article

Artificial Intelligence of Things (AIoT), which refers to the combination of Artificial Intelligence (AI) and the Internet of Things (IoT), has become an important research area in smart farming. Although the literature on AIoT has expanded rapidly, little is known about its overall development, intellectual structure, and emerging research directions. To bridge this research gap, this paper examines the AIoT literature in smart farming using a bibliometric approach based on publications indexed in the Web of Science Core Collection. Bibliometric techniques, including performance analysis, keyword co-occurrence analysis, and bibliographic coupling, were employed to examine publication trends, most relevant journals in the field, top scholars, countries, institutions, and research themes. The findings show a rapid growth of AIoT research, driven mainly by engineering and computer science disciplines. Five major research themes are identified, covering AI applications in agriculture, IoT-enabled smart farming, intelligent sensing systems, agricultural data management, and prediction models. Based on these findings, the study discusses the evolution of AIoT research, identifies current research gaps, and proposes a future research agenda focusing on artificial intelligence, interoperability, cybersecurity, data quality, technology adoption, sustainability, and responsible innovation. This study provides an extensive overview of AIoT research in smart farming and provides useful directions for researchers and practitioners interested in the future development of digital agriculture. Full article

As climate governance and low-carbon transition continue to advance, environmental information disclosure (EID) has become an important tool for strengthening corporate environmental accountability. Based on a sample of Chinese A-share listed firms from 2008 to 2023, this study constructs a firm-level FinTech index by employing a BERT large language model and examines its relationship with EID via a two-way fixed effects model. The results show that FinTech development is positively associated with EID levels, with stronger effects observed in non-high-tech and non-manufacturing firms. In addition, mechanism tests suggest that FinTech may enhance EID by promoting internal green innovation and reducing external information asymmetry. Further analysis indicates that stronger environmental regulation and higher internal control quality may reinforce the positive relationship between FinTech and EID. These findings provide empirical evidence for understanding how FinTech supports the modernization of corporate environmental governance. Full article

Sustainable business models (SBMs) describe how organizations create, deliver, and capture value while simultaneously generating economic, environmental, and social benefits for a broad set of stakeholders. Although the field has expanded rapidly since the late 2000s, the literature remains conceptually dispersed: definitions proliferate, design tools and archetypes coexist without integration, and findings on the link between sustainability practices and firm performance remain inconsistent. To address this fragmentation, this study conducts a PRISMA 2020-guided systematic literature review of foundational and recent contributions published between 2008 and 2025. Of 300 records identified through database searching, 30 studies met the eligibility criteria and were synthesized qualitatively; these are interpreted together with complementary theoretical and methodological literature. Rather than reporting bibliometric indicators, the review synthesizes the field across three dimensions: the evolution of the SBM concept, its dominant design logics and archetypes, and its contemporary thematic landscape. The findings trace a progression from foundational triple-bottom-line conceptualization, through archetype- and tool-based design approaches such as the Triple-Layered Business Model Canvas and pattern taxonomies, toward digitally enabled, circular, and resilience-oriented models. These patterns are interpreted through complementary theoretical lenses—the resource-based and natural-resource-based views, dynamic capabilities, information-processing theory, and stakeholder theory. The review consolidates these insights into an integrative conceptual framework that links theoretical foundations, value-design dimensions, enabling capabilities, and triple-bottom-line outcomes. It further discusses implications for managers and outlines a research agenda emphasizing digital and circular enablers, longitudinal designs, and the under-examined context of emerging markets, with an applied emphasis on Central Asia and Kazakhstan. Full article

In this article, we propose a first-order integer-valued autoregressive (INAR(1)) model based on binomial thinning, in which the innovation sequence follows a zero–one–two-modified Poisson (ZOTMPo) distribution. The proposed model accommodates overdispersion and allows for inflation or deflation at low-count values, particularly at zero, one, and two, which are commonly observed in public health count time series. We derive the main probabilistic properties of the model and estimate the unknown parameters using the conditional maximum likelihood (CML) method. A Monte Carlo simulation study is conducted to evaluate the finite-sample performance of the estimators. Furthermore, we establish the information-theoretic properties of the model, specifically deriving the Shannon entropy and conditional entropy bounds to quantify the dynamical complexity and predictability of the stochastic process. The practical utility of the model is illustrated using two real-world datasets on dengue fever incidence and Escherichia coli (E. coli) enteritis. Model performance is assessed using standard information criteria and forecast accuracy measures, as well as the Euclidean distance between observed and fitted probabilities for zero, one, and two. Diagnostic checks, including analysis of residual autocorrelation, cumulative periodograms, and jump process behavior, provide further confirmation of the fitted model’s adequacy. The results indicate that the proposed ZOTMPo–INAR(1) model provides an effective framework for modeling overdispersed count time series with a modified low-count structure. Full article

To improve the bending resistance and support the domestic development of deepwater subsea wellhead equipment, a three-dimensional finite element model (FEM) of the SXW-15 subsea wellhead system was established in ANSYS Workbench considering contact nonlinearity and rigid-locking mechanisms. Based on representative deepwater drilling and completion conditions, the effects of the friction coefficient, locking preload, blowout preventer (BOP)/lower marine riser package (LMRP) top load, casing hanger internal pressure, and tubing load on the ultimate bending capacity of the system were systematically investigated. The results show that the bending capacity increases with increasing friction coefficient and locking preload, whereas it decreases with increasing top load and tubing weight. The internal pressure of the casing hanger exhibits a pronounced nonlinear influence on the bending resistance, and the maximum bending capacity occurs at an internal pressure of approximately 4000 psi. Based on the mechanical analysis, several innovative designs were proposed, including a layered load-bearing structure, a gravity-set metal sealing assembly, a self-supporting rigid-locking mechanism, and an integrated multifunctional tool system, aiming to improve load-transfer efficiency, connection stiffness, and high-pressure sealing stability. Land-based testing and offshore field applications were subsequently conducted to verify the engineering applicability of the system. The subsea wellhead system with innovative designs maintained satisfactory structural integrity and sealing reliability under conditions of 15,000 psi internal pressure and 12.7 million lb axial load. Good agreement was obtained between finite element predictions and field test results. The present study provides theoretical support and guidance for the structural designs and engineering applications of deepwater subsea wellhead systems. Full article

The consistent increase in atmospheric CO₂ concentration, mostly driven by the global combustion of fossil fuels, is considered one of the primary contributors to the increasing severity of environmental problems, like climate change and global warming. Attending to this issue requires innovative strategies that transform CO₂ into a valuable resource. In this work, we report a sustainable and fully metal-free approach for the synthesis of cyclic carbonates via the direct coupling of CO₂ with epoxides, using natural sugars as readily available, non-toxic organocatalysts in combination with tetrabutylammonium iodide (TBAI) as a cocatalyst. Seven representative mono- and disaccharides were screened, employing styrene oxide as a model substrate under mild reaction conditions (80 °C, 20 bar CO₂, 2 h). Among them, D-xylose exhibited the best catalytic performance. The robustness of this catalytic system was further demonstrated through the efficient transformation of a wide range of terminal, internal, and biomass-derived epoxides into their corresponding cyclic carbonates with high yields and selectivity (up to 99%). Additionally, a set of computational simulations based on density functional theory (DFT) calculations was carried out to gain insight into the atomistic mechanisms involved in this chemical transformation. We identified that the hydroxyl groups of the sugar catalyst play a pivotal role in activating the epoxy ring-opening process, leading to cyclic carbonate formation. This bio-inspired strategy provides a green, cost-effective, and scalable pathway to produce key precursors for organic chemistry, contributing to the development of a circular carbon economy and the advancement of sustainable chemistry. Full article

The rapid digitalisation of the construction and built-environment sectors has increased demand for innovative teaching methods in higher education. Among emerging digital tools, metaverse applications offer immersive and interactive learning environments with the potential to transform traditional pedagogical practices. This study evaluates the adoption of the metaverse application (EON-XR) in built-environment education by examining educator training outcomes using the Kirkpatrick model. The evaluation focuses on a structured training workshop designed to equip built-environment educators with the skills to integrate metaverse-based applications into first-year teaching modules. A post-training evaluation employed a Google Forms survey instrument explicitly aligned with Kirkpatrick’s four evaluation levels: reaction, learning, behaviour, and results, with Levels 3 (Behaviour) and 4 (Results) operationalised as behavioural-intention and perceived-results proxies collected immediately post-training, ahead of any classroom implementation. The instrument captured educators’ perceptions of usability, relevance, pedagogical value, behavioural intention to apply acquired skills, and the broader institutional and disciplinary implications of metaverse adoption. Both qualitative and quantitative data were analyzed to assess the training programme’s effectiveness across these four dimensions. Findings indicate a generally positive reaction to metaverse training, with educators recognizing the relevance and user-friendliness of the EON-XR application. Evidence of learning was demonstrated by increased confidence and perceived competence in applying metaverse tools for teaching. At the behavioural level, participants expressed strong intentions to integrate metaverse applications into their teaching practice. At the results level, the training was perceived as enhancing teaching engagement, stimulating interest in construction digitalisation, and contributing to the institutional standing of higher education providers. The study concludes that metaverse-based educator training can play a critical role in advancing digital pedagogy within built-environment education. By applying the Kirkpatrick model, this research provides a structured, replicable framework for evaluating the adoption of immersive technology in higher education contexts. Full article

Spherical/aspherical mirrors are widely used in satellite communication and imaging systems, but their reflectivity is affected by the surface roughness value. The vibration of the machine tool system is one of the most important factors affecting the surface roughness. This paper systematically suppresses vibration through different ways, reduces the peak and valley value of workpiece surface roughness, and improves the specular reflectivity (i.e., zero-order diffraction efficiency) of the machined surface. This paper establishes a reflectance model for machined surfaces considering surface aberration using rigorous coupled wave theory. Based on this model, the relationship between reflectivity and processed surface morphology was calculated. According to the influence of different vibration modes on surface morphology, the influence of different vibration morphology on reflectivity is studied. In order to reduce cutting vibration, a new adaptive interpolation point planning algorithm has been innovatively proposed for planning the position of each interpolation point on the meridian of the workpiece. The specular reflectivity of uncoated bare spherical/aspherical mirror surfaces processed by adaptive interpolation point planning algorithm can reach over 90%, providing a high-quality substrate for subsequent high-reflection optical coatings. Full article

Introduction: Transparency and public reporting have become central components of quality governance in cardiothoracic surgery, promoting professional accountability, supporting informed patient decision-making, and strengthening public trust. Their importance is particularly evident in a specialty characterized by technically demanding procedures and high clinical risk. Aims and Objectives: This narrative review examines the impact of public reporting of cardiothoracic surgical outcomes on clinical practice, ethical decision-making, surgeon well-being, and patient choice. It explores both the intended benefits of transparency and its potential unintended consequences. Unlike previous reviews focusing primarily on quality metrics or reporting systems, this review integrates clinical evidence with ethical analysis to propose a balanced framework for responsible transparency in cardiothoracic surgery. Materials and Methods: A narrative literature review was conducted using the PubMed and ScienceDirect databases. Relevant articles published between 2000 and 2026 were identified using the search terms “cardiothoracic surgery,” “public reporting,” “transparency,” and “risk adjustment.” Results: Public reporting has been associated with improved benchmarking, enhanced quality improvement initiatives, greater institutional accountability, and more informed patient choice. Evidence also suggests modest improvements in selected clinical outcomes. However, these benefits may be accompanied by unintended effects, including risk-averse decision-making, avoidance of high-risk cases, distortion of clinical judgment, reduced willingness to adopt innovative techniques, and potential inequities for institutions caring for more complex patient populations. Conclusions: Public reporting should evolve beyond the simple disclosure of performance metrics toward an ethically grounded model of transparency that is context-sensitive, appropriately risk-adjusted, and patient-centered. Such an approach should promote shared accountability, emphasize continuous quality improvement rather than punitive performance assessment, and incorporate expert clinical interpretation to ensure fairness, preserve innovation, and support equitable patient care. Full article