Search Results (1,213)

Tourism digitalization is widely viewed as a tool for sustainable local development, yet whether policy-led digital transformation generates measurable improvements in tourism-economy quality remains insufficiently tested. Treating the staggered establishment of China’s National Big Data Comprehensive Pilot Zones as a quasi-natural experiment, a sustainability-oriented index of high-quality tourism-economy development was constructed using 2011–2019 provincial panel data, and the policy effect was estimated with difference-in-differences and propensity-score-matched difference-in-differences models. The results show that the pilot zones significantly improve the sustainability-oriented quality of the tourism economy, a finding supported by parallel-trends tests, placebo tests, and multiple robustness checks. Heterogeneity analyses indicate positive effects across regional contexts and relatively larger estimated responses in the innovation, green, and shared development dimensions. For pilot-zone type, a more precisely estimated positive effect is shown for regional pilot zones within the current sample. Mechanism-oriented analyses show empirical patterns consistent with improvements in digital infrastructure, digital industry development, and regional innovation capacity as plausible explanatory channels. Quasi-natural experimental evidence is thus provided on how digital policy supports sustainable tourism and local development, with implications for destination governance, tourism service quality, and responsible digital transformation. Full article

This study examines whether digital capabilities constitute a necessary condition for green innovation and firm competitiveness in the context of increasing sustainability and digital transformation pressures. Although prior research frequently links digitalization to improved environmental and business outcomes, limited evidence exists on whether firms must achieve a minimum level of digital capability to successfully generate green innovation and sustain competitive performance. To address this gap, the study investigates the relationships among digital capabilities, green innovation, and firm competitiveness using Partial Least Squares Structural Equation Modelling (PLS-SEM) and Necessary Condition Analysis (NCA). Using survey data from 740 firms across Hungary, Romania, Poland, Austria, and other Central and Eastern European (CEE) countries, the findings demonstrate that digital capabilities significantly enhance both green innovation and firm competitiveness. Green innovation further acts as a mediating mechanism through which digital capabilities translate into superior competitive outcomes. Importantly, the NCA results reveal that digital capabilities are not merely beneficial but represent a necessary condition for achieving high levels of green innovation and competitiveness within the studied sample of CEE firms, suggesting a threshold relationship that warrants further causal investigation. Firms with higher digital maturity consistently outperform less digitally developed firm. Firms with higher digital maturity consistently outperform less digitally developed firms in leveraging sustainability-oriented innovation strategies. The study contributes to the literature by advancing understanding of how digital transformation capabilities support sustainable competitiveness and by combining sufficiency and necessity analytical approaches to examine these relationships. The findings also provide practical implications for managers and policymakers by highlighting the strategic importance of investing in digital capabilities to simultaneously support environmental sustainability and long-term competitive performance. Full article

Google Earth Engine (GEE) has become a central platform for planetary-scale geospatial analysis, but its rapid evolution in the last few years is not yet reflected in the existing review literature. Earlier reviews mainly describe the platform’s architecture and its initial application domains, whereas a structured bibliometric and thematic overview of the post-2022 phase of GEE is still lacking. In this more recent phase, the platform has introduced foundation models, satellite embeddings, and native links to cloud databases. Drawing on a structured bibliometric analysis of 5591 Scopus and Web of Science indexed documents published between 2011 and 2025, the results reveal sustained long-term growth, with annual publications increasing from 3 records in 2011 to 1371 records in 2025, corresponding to a compound annual growth rate (CAGR) of 54.88%, indicating a shift from exploratory testing of the platform to more operational use. Logistic growth modelling ($R^2=0.991$) suggests that GEE research is transitioning from rapid expansion towards a scientific maturity phase, where the platform increasingly functions as a normalized analytical infrastructure embedded within broader cloud-native geospatial ecosystems. The full 2011–2025 corpus is used to establish long-term bibliometric trajectories, whereas the thematic synthesis focuses on the post-2022 transition towards Geospatial Artificial Intelligence(GeoAI), satellite embeddings, and cloud-database interoperability. The review examines how new satellite embedding datasets and BigQuery integrations help close the gap between raster-centric Earth observation (EO) workflows and tabular data science. We summarise methodological changes from traditional pixel-based classifiers to multimodal fusion approaches that combine Synthetic Aperture Radar (SAR), Global Ecosystem Dynamics Investigation (GEDI), and optical sensors, and we discuss how GEE’s highly integrated ecosystem influences reproducibility and the risk of vendor lock-in. Finally, we propose a roadmap for the ongoing transition of GEE towards GeoAI, offering researchers and policymakers a transparent and reproducible framework for deploying the platform in high-impact environmental governance. Full article

Green Supply Chain Management (GSCM) and sustainable economic development are two areas that have been studied extensively by scholars. However, there continues to exist a lack of cohesion or integration across academic fields regarding how GSCM can act as a catalyst for economic sustainability. This systematic literature review attempts to create a cohesive body of knowledge by exploring the drivers, barriers, and outcome measures associated with GSCM specifically within the context of creating sustainable economic growth in the long term. A structured literature review approach was used; this included conducting an extensive search of all relevant articles using multiple databases, followed by a thorough review and thematic analysis based upon the dimensions outlined above. The results indicate that GSCM is primarily influenced by the pressure of regulatory requirements and expectations of stakeholders. Financial constraints and technology gaps remain significant obstacles to the effective implementation of GSCM. Additionally, our analyses indicate that GSCM will enhance both environmental and economic performance when it is practiced with circular economy strategies and digital technologies such as AI and big data. The review shows that small- to medium-sized enterprises and firms in emerging economies face different practicalities than other types of organizations in terms of implementing GSCM strategically. However, SMEs and firms in emerging economies may benefit proportionally more than others from adopting GSCM strategically. Industry-specific case studies show that the success of GSCM practices varies widely depending on the sector; therefore, consideration of context is required. Additionally, the various theoretical frameworks discussed throughout the literature have developed from linear models towards more dynamic system-based models, indicating a developing discipline. In conclusion, we find that GSCM does not solely serve as an operational tool; rather, it acts as a strategic enabler of sustainable economic development, provided that it is implemented appropriately relative to organizational and regional context. Full article

The integrated usage of metro and bus services plays a key role in long-distance trips in big cities. Revealing the nonlinear relationship between the intermodal transfer demand and the built environment is significant for building a sustainable public transport system. This paper proposes a stacking ensemble explainable machine learning framework, which uses meta-learner to learn the prediction results of diverse base learners to improve performance, to detect how the impact factors impact the intermodal demand, including metro-to-bus and bus-to-metro directions. In this framework, the ensemble model is the stacking model; the ridge regression model is the second model. The base learners contain tree-based models (e.g., Random Forest, XGBoost and CatBoost) and non-tree-based models (e.g., SVR and KNN). The framework is applied to the case study of Beijing, China, based on one weekday (13 May 2019) and one weekend day (18 May 2019) of smart card data covering the main urban districts within the Sixth Ring Road. The results indicate that the stacking ensemble learning model outperforms the base learning models. For the metro-to-bus direction, transfer time, bus station count, and degree centrality are the top three influential factors; for the bus-to-metro direction, transfer time, bus station count, and shopping POI count are the top three, with lower predictive performance due to greater variability in this direction. However, the interaction effect of transfer time and bus station count is negative. This study could provide new insights into public transport planning and management. Full article

Digital technologies provide effective tools for formulating sustainable, evidence-based policies; however, this field has so far lacked a cohesive and practical framework to guide their application. Providing comprehensive answers to six primary research questions, this study aims to address this critical gap concerning the prerequisites, challenges, opportunities, key technologies, policy areas, and critical success factors (CSFs) for applying digital technologies in environmental sustainability policymaking. In this study, 39 articles were analyzed from 293 documents indexed in the Web of Science as of 19 August 2025, in accordance with the PRISMA 2020 guidelines. The prerequisites are categorized into the following themes: fiscal incentives, a culture of innovation and sustainability, effective regulations, robust digital infrastructures, participation, and reliable and accessible data. We identified significant challenges, including financial constraints, human resource deficits, infrastructural and regulatory gaps, and the adverse environmental impacts of digital technologies themselves. Opportunities emerged under two main domains: effective policymaking and enhanced environmental management. Our study indicates that pioneering technologies at the core of this transformation include artificial intelligence, big data, blockchain, the Internet of Things, machine learning, and robots. Their applications are predominant in key policy areas, including the environment, energy, climate change, urban sustainability, agriculture, industry, and food security. The analysis identifies four CSFs: the policy–digital–sustainability nexus, fundamental processes, soft capacities, and hard capacities. Full article

Raw lacquer, a natural polymer derived from the bast of lacquer trees (Toxicodendron vernicifluum), is renowned as the “King of Coatings” due to its exceptional film-forming properties, abrasion resistance, corrosion resistance, and biocompatibility. However, its inherent limitations—including stringent drying conditions, slow curing rates, deep coloration, and difficult application—have severely restricted its modernization and widespread adoption. This review systematically summarizes recent research advances in the modification and application of raw lacquer, focusing on four major modification strategies: (1) Nanocomposite modification—incorporating functional nanofillers such as Al₂O₃, cellulose nanofibrils (CNF), polydopamine (PDA) melanin-like nanoparticles, and SiO₂ to significantly enhance film hardness, compactness, UV-aging resistance, and drying kinetics. (2) Chemical structure modification—employing molecular design strategies including aminoanthraquinone grafting, tung oil blending, water-based emulsification, and terpene/allyl group functionalization to improve hydrophobicity, flexibility, fast-drying properties, and achieve dual photo/oxygen curing. (3) Biomass synergistic composites—utilizing natural polymers such as chitosan and lignin, along with bio-inspired adhesion mechanisms (e.g., PDA), to confer advanced functionalities including antibacterial and antifouling properties. (4) Curing behavior regulation—precisely controlling drying kinetics through inorganic salt ion microenvironment engineering, nonionic surfactants, and salicylaldehyde Schiff base-based driers. Building upon these foundations, this review further expands on the emerging high-value applications of modified lacquer in preventive conservation of cultural heritage, advanced functional coatings (anti-corrosion, super-hydrophobicity, flame retardancy), biomedical materials (hemostasis, antibacterial activity, drug-controlled release, water treatment adsorption), and intelligent responsive flexible electronics. Finally, addressing challenges including weak fundamental research, bottlenecks in green industrialization, and lack of standardization, future development directions are proposed encompassing interdisciplinary innovation, sustainable modification strategies, integration of multifunctional intelligent systems, and big data-driven research paradigms, aiming to provide theoretical guidance and technical references for the high-value utilization and modernization of lacquer resources. Full article

Ship exhaust emissions have become an increasingly prominent global atmospheric environmental issue, triggering a series of ecological disturbances and adverse public health consequences. However, comprehensive analyses of the research progress and evolution trends in this field remain scarce. This study systematically retrieved 1346 scholarly publications in the ship exhaust emissions field for the period 2011–2025 from the Web of Science Core Collection and carried out a bibliometric analysis encompassing publication outputs, contributing countries/regions, and keyword characteristics. The findings reveal a sustained and robust growth trajectory in global research output, with annual publications increasing nearly fivefold over the 15-year study period. Notably, academic interest in this field has increased significantly since 2020 due to the implementation of the global sulfur cap regulation. Core thematic clusters (mean silhouette S = 0.7205) in this field include source apportionment, numerical modeling analysis, atmospheric criteria pollutants, and technological emission reduction strategies. The geographical distribution of research output shows a significant positive correlation with the importance of regional maritime economies. China, the United States, and Germany are the leading contributors in terms of publication outputs, while frequent research collaborations have been observed among European countries. Since 2021, the emergence of Automatic Identification System data as a keyword with high burst strength (intensity = 3.60) marks a paradigm shift toward a “big data-enabled refined management” framework. Concurrently, the sustained burst activity of keywords including nitrogen oxides, volatile organic compounds, and traffic-related emissions from 2023 to 2025 indicates rapidly growing scholarly attention to secondary aerosol precursors from shipping, and the critical need for coordinated multi-pollutant control strategies. Future research directions for ship exhaust emissions are expected to transition from fundamental characterization research to big data-driven monitoring and estimation methods, as well as advanced emission reduction technologies. The bibliometric insights derived from this study provide a valuable reference framework for subsequent in-depth studies on ship exhaust emissions. Full article

Within the South-Western food industry of Nigeria, the overall impact, associated challenges, and implementation of Big Data Analytics (BDA) have remained underexplored. Thus, this study aimed to investigate the extent of BDA adoption, identify key barriers and enablers, assess the operational impacts of BDA adoption, and propose a structured framework to guide effective integration. The study adopted a deductive, mono-quantitative method. Data were collected from 151 participants through a stratified sampling technique using an online survey questionnaire and analysed using descriptive and inferential statistical methods, including Chi-Square, Likelihood Ratio, and Fisher-Freeman-Halton Exact tests, using SPSS version 26 and Excel as analytical tools. While awareness and appreciation of BDA’s strategic benefits are growing, significant challenges such as high implementation costs, a shortage of skilled personnel, regulatory uncertainties, and technological limitations persist. Nevertheless, organisations that have embraced BDA report notable improvements in operational efficiency, strategic decision-making, customer satisfaction, and competitive advantage. This study proposes a practical BDA adoption framework designed to address the identified barriers and enhance successful implementation and offers several recommendations. The research helps bridge the knowledge gap on BDA adoption in emerging economies and offers actionable insights for business leaders, policymakers, and practitioners seeking to drive innovation and sustainability in Nigeria’s food industry. Full article

Using panel data from Chinese cities spanning 2010–2023 and leveraging the natural experiment provided by the establishment of the National Big Data Comprehensive Pilot Zone (NBDPZ), we employed the difference-in-differences (DID) method alongside double machine learning (DML) to systematically examine how these policies influence urban economic resilience efficiency. The empirical results demonstrate that the NBDPZ significantly enhances urban economic resilience efficiency. This finding is robust under parallel trend and placebo tests, confirming that the improvement is a policy-driven causal effect. Mechanism analysis reveals that the policy enhances urban economic resilience efficiency primarily by promoting the upgrading and rationalization of industrial structure to consolidate the micro-foundation of sustainable economic transformation; increasing innovation output to facilitate the sustainable accumulation of knowledge capital; and enhancing urban entrepreneurial activity to inject sustainable endogenous vitality into the economic system. Heterogeneity analysis indicates that the positive effects are more pronounced in eastern and western regions, second-tier cities, and cities with lower industrial agglomeration, better digital infrastructure, and stronger legal and regulatory environments. The study’s findings offer both theoretical support and practical guidance for refining the policy framework of the NBDPZ policy and promoting sustainable urban economic development. Full article

Texture shapes how we perceive and like food, yet clear links between mechanical measurements and sensory perception of texture remain elusive. Here we combine sensory data from a blind tasting involving 101 participants with mechanical texture profile analysis across six burgers to identify the textural features that drive consumer perception and liking. We compare five burgers—generated via artificial intelligence—with animal-based, plant-based, mushroom-based, and hybrid animal-mushroom patties, and the classical Big Mac^®. Three main findings emerge: First, animal-based burgers occupy a distinctive and coherent sensory–mechanical region associated with attributes such as firm, fatty, and holds together. Second, mushroom- and plant-based burgers deviate from this region in protein-dependent ways: mushroom-based burgers are associated with springy and gummy textures, while plant-based burgers are associated with dry, brittle, and crumbly textures. Hybrid animal–mushroom burgers, however, maintain sensory profiles comparable to fully animal-based burgers. Third, resilience emerges as the strongest mechanical correlate of perceived meatiness and sensory texture, while stiffness and hardness show no statistically significant association with consumer perception. Texture independently predicts overall liking alongside flavor: increasing texture liking by one point increases overall liking by 0.28. Among all sensory attributes, meatiness is the dominant predictor of texture liking. These findings suggest that resilience may be a promising target for texture engineering and establish texture as a critical design objective for sustainable alternative proteins. Full article

Sustainable Supply Chain Management (SSCM) integrates economic, environmental, and social considerations across global supply networks. Despite extensive research, the field remains fragmented across strategic, tactical, and operational levels, with limited theoretical integration and inconsistent alignment of sustainability dimensions. To address this gap, this study develops a data-driven perspective on SSCM research using a hybrid analytical framework. A PRISMA-guided systematic review is combined with bibliometric science mapping with VOSviewer 1.6.16 and transformer-based topic modeling (BERTopic) with Python 3.10 to analyze literature published between 2011 and 2025. The analysis integrates two complementary datasets: highly cited studies to capture established research structures and recent publications to identify emerging trends. The findings reveal the growing prominence of digital technologies, including artificial intelligence, blockchain, and big data analytics, alongside the central role of collaboration and governance mechanisms in enabling sustainability. The topic modeling identifies eleven coherent research themes, highlighting both well-established areas, such as circular economy, and emerging directions, such as risk-oriented decision-making and digital traceability. By introducing a cross-method semantic correspondence approach that integrates citation-based and embedding-based analyses, this study advances a more coherent and multi-layered understanding of SSCM research. This integrated perspective reveals the field’s evolution, core thematic structures, and emerging gaps, while providing a robust foundation for future theoretical and practice-oriented developments. Full article

As global markets navigate the dual transition of digitalization and sustainability, the risk of “digital greenwashing” has emerged as a critical corporate governance challenge. Utilizing a comprehensive dataset of Chinese A-share listed firms from 2018 to 2024—an ideal laboratory characterized by rapid regulatory shifts and unique state-market dynamics that provide highly generalizable insights for other emerging economies—this study empirically investigates whether corporate digital transformation acts as a genuine driver for Environmental, Social, and Governance (ESG) enhancement or merely serves as a symbolic disclosure tool. Fortified by rigorous identification strategies, including Propensity Score Matching and Lewbel heteroskedasticity-based instrumental variable estimations, the results confirm that digitalization serves as an incremental yet statistically significant driver for corporate sustainability. Crucially, mechanism analyses reveal a “full moderation” effect: the positive impact of digitalization on ESG performance is completely activated only in the presence of premium external assurance (e.g., Big 4 audits). Without high-quality IT auditing to act as a credibility enforcer and verify the substance of digital signals, technological adoption alone fails to yield significant ESG improvements. Furthermore, a nuanced structural asymmetry is identified: foundational data infrastructures (Cloud Computing and Big Data) directly enhance quantifiable Environmental and Governance metrics, whereas premium audits are strictly required to activate the “soft,” qualitative Social dimension. Finally, the synergy exhibits distinct boundary conditions. It is heavily concentrated within high-pollution industries where digital transition acts as a regulatory survival imperative rather than mere market expansion, and its reliance on external assurance is fundamentally driven by the market-signaling needs of non-State-Owned Enterprises (non-SOEs) rather than the policy-distorted mandates of SOEs. These findings offer critical theoretical extensions and policy implications for standardizing digital-audit infrastructures globally. Full article

Background: The 2025 EPI Managers’ Meeting for West African countries in Guinea was a critical platform for EPI managers to make an in-depth analysis of immunization programmes. We present a structured analysis of immunization status in West Africa using a WHO Health System model to move beyond descriptive reporting toward systemic analysis for actionable solutions. Methods: The meeting convened EPI managers from 14 of the 17 West African countries and partners supporting the immunization program. Country and regional presentations, immunization and surveillance data and meeting discussions were analysed through a framework identifying (1) core problems, (2) systemic barriers using WHO health systems building blocks and (3) actionable recommendations or call for action. Results: Analysis revealed stagnating immunization coverage. Recovery from COVID-19 pandemic disruptions remained limited, with persistent outbreaks of vaccine-preventable diseases (VPD). Among the five Immunization Agenda 2030 objectives assessed, only Maternal and Neonatal Tetanus (MNT) elimination was on track. Four critical challenges emerged: (1) Routine immunization stagnation with DTP3 median coverage of 76%. This was associated with challenges related to poor data quality, weak implementation of innovative vaccination strategies and donor dependency, as 88.2% of countries financed less than 50% of routine vaccine costs domestically. (2) Sub-optimal progress in Big Catch-Up (BCU) implementation in some countries, revealing poor health system resilience. (3) Inability to sustain high coverage for new vaccine introductions despite significant progress, highlighting demand and service delivery gaps. (4) Persistent VPD outbreaks with geographical expansion and the resurgence of diphtheria epidemics since 2023. Conclusions: Persistent immunization challenges in West Africa appear to reflect interconnected systemic challenges, suggesting the need for a fundamental shift toward subnational strategies, integration of immunization services within primary health care (PHC) and improved data quality. Sustainable financing of the national EPI and acceleration of local vaccine manufacturing is essential to achieve immunization sovereignty in West Africa. Country Call for Action provides strategic guidance to reverse the trend toward the Immunization Agenda 2030 targets. Full article

Traffic simulation models are widely used in transportation analysis, often oriented toward keeping urban systems sustainable from various points of view, ranging from energy consumption to air quality. However, their accuracy depends on the quality of the data used to represent both the road network and travel demand. Although open-source datasets can be used to develop simulation networks and observed traffic information is available from big-data platforms, integrating these heterogeneous datasets remains challenging. Indeed, different road segmentation schemes may be used across different platforms, and common identifiers are often not adopted. This study proposes a GIS-based framework for spatially joining traffic data from big-data platforms with road networks used in traffic simulation models. The methodology integrates a microscopic simulation network derived from OpenStreetMap and implemented in SUMO with traffic data obtained from the TomTom Traffic Stats service. The workflow is implemented in QGIS (3.34 prizren) and combines spatial buffering, directional filtering, overlap analysis, and hierarchical match cleaning to associate traffic segments with the corresponding simulation network edges. The framework is applied to an urban case study in the city of Biella, Italy. Results show that more than 80% of the simulation network edges can be successfully linked with traffic segments, enabling the integration of hourly traffic indicators such as travel time and speed. The resulting dataset supports several applications, including network calibration, simulation validation, detector placement, and traffic demand estimation, contributing to the development of more reliable traffic simulation models for comparing and selecting sustainable urban mobility actions within the transportation planning process. Full article