Search Results (684)

Tourism development potential and observed development performance do not necessarily evolve synchronously, particularly in old industrial and restructuring regions where attraction supply, market linkage, and visitor experience may be spatially uneven. This study develops a multi-source diagnostic framework for identifying tourism potential–performance mismatch across the 14 prefecture-level cities of Liaoning Province, China. Drawing on Ctrip review texts, rating scores, timestamps, platform-displayed reviewer-origin labels, A-level scenic-spot point data, and annual official city-level tourism statistics, the study constructs three dimension-specific sub-indices—the Scenic Experience Index (ESI), the Market Linkage Index (MLI), and the Attraction Foundation Index (AFI)—and synthesizes them into a Comprehensive Potential Index (CPI). The CPI is then compared with an Observed Performance Index (OPI) constructed from domestic tourist arrivals and domestic tourism revenue for 2016–2022. The results show that attraction foundation contributes most strongly to composite tourism potential, while market linkage and scenic experience condition how this structural basis is associated with observed outcomes. The CPI–OPI comparison identifies three relationship types: matched, potential-leading, and performance-leading cities. Dalian and Shenyang are high-level matched cities, Benxi and Jinzhou are high-potential but under-converted cities, and Anshan and Dandong are performance-leading cities. These findings demonstrate that favorable structural tourism conditions are not automatically transformed into realized market performance. The study contributes a multidimensional, gap-analysis-based diagnostic architecture that can support tourism-related spatial planning and territorial governance in transitional regions. Full article

Natural polysaccharides from Siegesbeckia orientalis L. have been reported to exhibit promising bioactivities. To enhance extraction efficiency, low-temperature plasma-assisted extraction was optimized for S. orientalis L. polysaccharides using single-factor experiments and response surface methodology. Column chromatography purified a homogeneous SIE-III fraction, followed by structural characterization. Optimal parameters were 80 kV discharge voltage, 153 Hz frequency, and 109 s treatment time, under which the polysaccharide yield reached 15.68%, significantly higher than that of the conventional hot water extraction method. Plasma treatment loosened the raw material’s surface, potentially facilitating polysaccharide release. SIE-III had a molecular weight of 20.831 kDa and comprised mainly galactose (51.7%), rhamnose (19.1%), arabinose (11.3%), and galacturonic acid (9.9%). It featured typical rhamnogalacturonan-I (RG-I) domains and a triple-helix conformation. Fourier transform infrared spectroscopy and nuclear magnetic resonance confirmed both α- and β- glycosidic linkages, and methylation analysis revealed a highly branched →3,4)-Galp-(1→ structure. This study provides an effective extraction method for plant polysaccharides and valuable insights into their potential applications in the food and other industries. Full article

This paper develops a theory-first framework explaining how tariff policy reshapes participation and position in global value chains (GVCs). Building on the input–output price model and value-added trade accounting, we formalize the tariff cascade and introduce three portable diagnostics: the Tariff Propagation Multiplier (TPM), the Backward/Forward Tariff Elasticity Decomposition (BFTED), and the Stage-Shift Metric (SSM). We derive sign-robust propositions and provide aggregate-level empirical evidence consistent with the cascade mechanism using a dyadic panel of 260,473 observations spanning 1999–2018. Results show that home input tariffs significantly compress both backward and forward GVC participation, and that apparent GVC upgrading frequently reflects measurement composition rather than genuine technological relocation. A policy simulation calibrated to 25% tariff escalation scenario projects significant participation losses, with Canada’s high-exposure manufacturing sectors facing amplified cascade effects due to their dense cross-border input linkages. The framework offers actionable diagnostics for trade and industrial policy in an era of renewed protectionism. Full article

Advanced thermal barrier coatings (TBCs) are essential for improving the efficiency and performance of gas turbine engines. Increasing engine operating temperatures and harsh service environments are pushing the current industry-standard 8 wt% yttria-stabilized zirconia (8YSZ) to its performance limits. High-rare-earth-oxide zirconates, such as Gd₂Zr₂O₇, have emerged as promising materials for next-generation engines due to their excellent high-temperature phase stability, lower thermal conductivity, and enhanced resistance to CMAS attack. In this work, dense vertically cracked (DVC) Gd₂Zr₂O₇/8YSZ bilayer coatings were developed using the air plasma spray (APS) process. Two approaches were employed for deposition of the NiCrAlYHfSi bond coat: (i) high-velocity oxygen fuel (HVOF), and (ii) APS flash-coated HVOF NiCrAlYHfSi bond coat. The durability of DVC TBC systems with the two bond coat types was evaluated by furnace cycling test (FCT) at 1125 °C. The TBC system with an APS flash-coated HVOF bond coat exhibited an FCT lifetime approximately twice that of the system with the HVOF bond coat alone. The improvement is primarily attributed to the higher surface roughness of the APS flash-coated bond coat, which enhances resistance to crack initiation, propagation, and linkage, thereby extending thermal cycling life. Full article

Koji production is a critical process that determines the flavor and quality of the final soy sauce product. However, the complex mechanisms underlying microbial metabolism and the evolution of the physicochemical environment still require further analysis. This study focuses on three parallel koji rooms in an industrialized koji fermentation process. This work tracked the dynamics of physicochemical indices, volatile flavor compounds, and microbial communities over a full 40 h cycle. Data integration and correlation analysis elucidated the close linkage between the microbial community, the fermentation environment, and flavor formation. Koji moisture declined gradually, with faster losses at later fermentation stages. This physiological dehydration arose from microbial metabolic heat, forced aeration and structural loosening of koji, not simple physical evaporation. System pH displayed a typical U-shaped trend across fermentation. Values dropped early, most likely driven by accumulating organic acids, before rising from mid to late fermentation. This pH rebound was tentatively attributed to ammonia release from proteolytic breakdown, which may neutralize acidic compounds. These observations cast doubt on the conventional assumption that organic acid levels may be reliably estimated solely from pH measurements. Physicochemical analysis showed continuous accumulation of amino acid nitrogen (0.6–0.9 g/100 g) and total acidity throughout fermentation. By contrast, reducing sugar concentrations differed across individual koji rooms, presumably owing to divergent microbial adaptation in early fermentation. A total of 77 common compounds were identified, among which 13 key odor-active compounds with OAV ≥ 1, such as 4-vinylguaiacol and 3-methylbutyraldehyde, constitute the characteristic flavor profile of soy sauce starter culture. High-throughput sequencing uncovered a distinct ecological pattern: eukaryotic communities, dominated by Aspergillus oryzae, converged under controlled regulation. While prokaryotic communities differentiated dynamically, driven by spatial heterogeneity in the semi-open fermentation environment. Spearman correlation analysis further indicated potential functional partitioning: high-abundance taxa (e.g., Aspergillus oryzae, Weissella) were predominantly associated with macromolecular substrate degradation, whereas rare low-abundance taxa (e.g., Alternaria) displayed significant correlations with the biosynthesis of key characteristic flavor compounds. This study clarifies the synergistic regulatory mechanisms linking physicochemical conditions, microbial metabolism, and flavor precursor formation during industrial koji production. The findings establish a scientific foundation for optimizing process parameters and achieving standardized quality control in soy sauce manufacturing. Full article

Urban sustainable development increasingly depends on interactions among multiple urban subsystems, yet existing studies often overlook cross-regional linkages and nonlinear development processes. This study investigates the coordinated development of urbanization, smart development, resilience, and low-carbon transition (USRL) from an efficiency perspective. Using panel data from 278 Chinese cities during 2010–2023, this work integrates the Super-SBM model, the Local–Tele Coupling Coordination Degree (LTCCD) framework, Dagum Gini decomposition, and machine learning techniques to examine the spatiotemporal evolution, spatial disparities, and driving mechanisms of coordinated development. The results show that coordinated development improved steadily over time, although subsystem evolution remained uneven, with resilience lagging behind other dimensions. Regional disparities gradually narrowed, but inter-regional differences remained the dominant source of spatial inequality. Innovation intensity, industrial upgrading, and high-quality foreign investment positively contributed to coordinated development, whereas fiscal and financial factors exhibited nonlinear effects. Interaction analysis further revealed that coordinated development is shaped by the combined influence of multiple drivers rather than by individual factors alone. Our findings suggest that urban sustainable development is jointly influenced by subsystem coordination, cross-regional interactions, and nonlinear development dynamics, highlighting the importance of integrating local and tele-coupling processes in urban sustainability research. Full article

In past decades, the macroeconomic stability of India has been tested repeatedly by major global disruptions, including oil price shocks, the 2008 global financial crisis and the COVID-19 pandemic. Analysing how macroeconomic variables respond to these shocks is essential for evaluating external vulnerability and policy resilience in emerging economies. Our study provides a comprehensive empirical investigation of the dynamic responses of wholesale price inflation, industrial output, oil prices and exchange rates in India by employing monthly data from January 1993 to December 2024. To examine long-run equilibrium relationships along with short-run adjustment dynamics, the present study employs co-integration analysis within a Vector Error Correction Model (VECM) framework. Further, we applied impulse response functions and forecast error variance decomposition to track volatility spillover mechanisms. Quantile regression and ARCH–GARCH models were further estimated to account for distributional heterogeneity and time-varying volatility. The findings of our study suggested stable long-run linkages among the selected variables, where oil price shocks emerged as a key external source of macroeconomic fluctuations. Short-run dynamics suggested that shocks in oil prices are transmitted primarily through inflation and exchange rate channels and then affect industrial output. Distributional estimates revealed the effects were stronger during stress periods, indicating tail risks that were not captured by the mean-based models. Lastly, volatility analysis confirmed persistent clustering, especially during phases of crisis. Overall, the findings suggest that India’s macroeconomic system remains externally sensitive, with adjustment mechanisms that operate gradually but come under strain during global disruptions. These results underscore the importance of energy risk management and crisis-responsive macroeconomic stabilisation policies. Full article

Urban carbon neutrality is increasingly shaped by cross-regional interactions rather than a closed balance between local emissions and sequestration. From an open-system perspective, this study conceptualizes urban carbon neutrality as the outcome of interactions between embodied carbon transfer (ECT) and carbon sequestration service flows (CSSFs). Using panel data for 297 Chinese cities in 2012, 2017, and 2022, an integrated measurement framework is developed to examine spatiotemporal patterns, typological heterogeneity, and driving mechanisms. The results reveal significant disparities in emission responsibility and ecological support across city types. Ecological conservation-oriented cities act as major carbon sequestration providers, while industrial- and service-oriented cities face higher emission pressures and weaker local sequestration capacity. The joint effects of ECT and CSSF reshape urban carbon neutrality through responsibility reallocation and ecological support transfer, enhancing overall performance while intensifying inter-city differentiation. Spatial Durbin model results indicate that carbon neutrality is jointly influenced by socioeconomic development, energy structure, factor mobility, ecological conditions, and institutional regulation, with both local and spillover effects. These findings suggest that urban carbon neutrality is a relational process embedded in production–consumption linkages and ecosystem service networks, highlighting the need for differentiated governance pathways to support coordinated mitigation and ecological compensation. Full article

Existing accounts of platform-mediated complementor growth rest on two limiting assumptions: that platform enablement constitutes a homogeneous environmental input and that firm growth is a unitary outcome. This double simplification obscures how distinct platform provisions generate qualitatively different forms of firm transformation. This study asks which combinations of mechanistically distinct platform enablement types and internal strategic response capabilities activate which forms of leapfrog growth among complementor firms operating under dual institutional governance. We employ fuzzy-set Qualitative Comparative Analysis (fsQCA) on survey data from 374 complementor firms in China’s autonomous driving platform ecosystem. Five antecedent conditions are examined across two dimensions: platform enablement, comprising rule-based enablement (RE) and business platform enablement (BPE); and strategic response capabilities, comprising network linkage capability (NLC), organizational ambidexterity (OA), and policy responsiveness (PR). Three outcome variables capture three non-reducible leapfrog dimensions: technology-chain (TL), value-chain (VL), and institutional-chain (IL) transitions. A reverse-causality robustness check and a common-method-bias assessment corroborate the validity of findings. The analysis identifies equifinal configurational pathways with distinct dominant logics across the three chains. Technology-chain transitions are predominantly network-linkage-driven; value-chain transitions are policy-responsiveness-anchored; institutional-chain transitions exhibit genuine equifinality between network-linkage and policy-responsiveness pathways, both requiring dual-platform enablement as a universal structural precondition. No single enabling condition or capability suffices; leapfrog growth is irreducibly configurational and causally asymmetric. The study offers a dual-enablement, three-chain configurational framework for understanding platform-mediated firm growth under dual institutional governance. For complementor firms, findings support dimension-selective capability investment over uniform accumulation strategies. For platform orchestrators, differentiated governance design calibrated to specific complementor upgrading trajectories outperforms homogeneous resource provisioning. For policymakers, institutionalized consultative channels linking private platform governance with public regulatory processes are recommended to facilitate coordinated digital industrial transformation. Full article

The coordinated development of ecological sustainability and high-quality economic growth has long been a central concern for both academia and governments worldwide. With the deepening of economic globalization, economic and trade interactions as well as policy interdependence among countries have become increasingly close. Therefore, it is necessary to incorporate cross-country policy interactions into the analytical framework examining the impact of environmental regulation on export technological complexity. Using panel data for 67 countries from 1995 to 2020, this study employs a Spatial Durbin Model to investigate the spatial spillover effects of environmental regulation on export technological complexity from the perspective of policy externalities. The results indicate that (1) regardless of whether countries are developed or developing, environmental regulation exhibits a significant “U-shaped” direct effect on domestic export technological complexity; (2) from the perspective of cross-border spillover effects, the impact of environmental regulation on other countries’ export technological complexity differs significantly across spatial weight matrices: it displays a “U-shaped” pattern under the geographical distance weight matrix, but an inverted “U-shaped” pattern under both the economic distance weight matrix and the nested spatial weight matrix. (3) Further analysis reveals that, compared with geographical factors, economic linkages possess stronger influence and explanatory power in the spatial spillover effects of environmental regulation. In other words, the process of global sustainable development relies more heavily on economic networks and industrial interconnections than on geographical proximity alone. (4) The heterogeneity analysis shows that the turning points of both the direct and indirect effects are generally higher for developing countries than for developed countries. This suggests that developing countries may have greater room for environmental policy adjustment during the process of export technology upgrading, while simultaneously facing a longer path toward green technological transformation. In light of these foregoing results, this study develops a series of targeted policy recommendations. Full article

PM2.5 and near-surface O₃ compound pollution is a major challenge for further air quality improvement in the Yangtze River Delta (YRD). Despite research on the chemical coupling mechanisms and concentration co-variation between PM2.5 and O₃, the directional linkages of compound pollution events among cities and the network mechanisms underlying their formation remain unclear. Here, we identified PM2.5–O₃ compound pollution events for 41 YRD cities from 2015 to 2024 using city-year-specific P80 dual-threshold criteria. We then constructed annual directed synchronization networks based on event-leading relationships and used temporal exponential random graph models to identify the formation mechanisms of significant leading ties. PM2.5–O₃ compound pollution events in the YRD generally decreased during 2015–2024, with characteristics shifting from high frequency, persistence, and strong intercity linkage in the early stage to lower frequency, weaker intensity, and continued episodic fluctuations. Directed event networks exhibited a clear stage-dependent evolution: network density, total edge weight, reciprocity, and local closure were relatively high during 2015–2018, networks became markedly sparse during 2020–2022, and a partial rebound occurred after 2023. Spatial backbone analysis indicated reorganization of the dominant linkage structure, shifting from the Shanghai–southern Jiangsu–northern Zhejiang coastal core toward the northern Jiangsu, Anhui, and interprovincial corridors. Key node analysis further revealed a clear functional differentiation among cities, with some cities acting as potential leading sources, some as receiving nodes, and several non-traditional core cities serving as cross-regional bridges. Significant leading ties were jointly shaped by reciprocity, local closures, temporal memory, economic development, industrial structure, and digital governance. Therefore, as well as a problem of co-occurrence, PM2.5–O₃ compound pollution in the YRD is a cross-city event-network process characterized by directionality, stage-dependent evolution, and differentiated urban roles. This study provides empirical evidence for dynamic joint prevention and control based on event linkages, urban roles, and cross-city coordination. Full article

Greenwashing in food marketing undermines consumer trust and impedes Sustainable Development Goal 12 (SDG 12). While prior research has established linkages between greenwashing perception, green skepticism, and purchase intention, no publicly deployed decision support tool has been developed for practitioner use. This study applies Design Science Research (DSR) methodology to translate validated behavioral models into a deployable decision support system rather than re-testing established relationships. We present the development, deployment, and evaluation of the Greenwashing Advertising Message Assessment (GWAMA), a web-based DSR artifact grounded in a validated Stimulus–Organism–Response (S-O-R) structural equation model. GWAMA integrates factor-loading-weighted composite scoring with SEM-derived parameters to generate real-time greenwashing risk diagnostics for food advertising messages. Usability was evaluated with 150 Thai food industry professionals using a Technology Acceptance Model (TAM) instrument applied to the live system. Results provide indicative evidence of stakeholder acceptance, with high perceived usefulness, ease of use, and intention to use. This study contributes by demonstrating how validated behavioral models can be translated into a publicly deployable decision support artifact, with practical implications for sustainable marketing governance and SDG 12 implementation in emerging economies. Full article

In the digital economy era, computing power, as a novel factor of production, serves as a vital engine for driving high-quality economic development. Building upon China’s traditional 42-sector input–output table, this paper incorporates computing power networks as a new sector to construct a 43-sector dynamic input–output (IO) model. Based on this framework, a Dynamic Stochastic General Equilibrium (DSGE) analysis framework is constructed to systematically reveal the dynamic transmission mechanism of computing power within industrial linkages and capital accumulation. From an energy perspective, energy consumption is implicitly captured through carbon emissions and energy structure, which together reflect the scale, efficiency, and composition of energy use in computing power networks. The findings show that the optimal computing power allocation follows a temporal evolution pattern from the service sector to the manufacturing sector, with ICT manufacturing’s computing power quota reaching 31% by 2030. An investment inflection point occurs in 2026, aligning with the digital infrastructure cycle of China’s 14th Five-Year Plan. The “Eastern Data, Western Computing” strategy reduces unit carbon emissions from computing power by 41%. Policy simulations demonstrate that R&D tax credits generate a 2.9-fold multiplier effect through industrial linkages, boosting GDP by 2.3%. The integrated IO-DSGE framework developed in this study provides a quantitative tool for the full-cycle management of “construction–application–regulation” in computing power networks. It holds significant theoretical value and practical implications for enhancing resource allocation efficiency and promoting green, climate-friendly development. Full article

To address the scattered protection efforts and uneven effectiveness of traditional villages in southwestern Hubei, this study focuses on 92 nationally recognized traditional villages in Enshi Prefecture. By integrating literature research, field investigation, and multi-source data fusion, we developed an innovative model that combines the Analytic Network Process (ANP), entropy weight, and fuzzy comprehensive evaluation, thereby integrating subjective and objective weighting to improve evaluation accuracy. A quantitative evaluation was conducted across 13 criteria and 32 indicators, including traffic conditions, intangible cultural heritage resources, and industrial foundation. The results reveal that traditional villages in Enshi Prefecture exhibit a significant spatial pattern of “overall dispersion with local concentration,” accompanied by a high concentration index. Traffic conditions, intangible cultural heritage, and infrastructure emerge as the core factors affecting protection effectiveness, and a spatial differentiation pattern of “two cores and one corridor” is identified within the region. Based on the quantitative evaluation, we propose targeted cluster protection strategies, including a “dual-core multi-node” transportation network, “three-industry linkage” industrial collaboration, and a living heritage approach that integrates cultural relics with intangible cultural heritage. These strategies were validated in pilot villages such as Yejiaoyuan Village, resulting in significant increases in village satisfaction and tourist volume. The findings provide methodological support and practical paradigms for the systematic protection and sustainable development of traditional villages in southwestern ethnic minority areas. Full article

Amid the dual pressures of the global energy transition and green technology upgrading, corporate environmental responsibility increasingly depends on interactions among firms rather than on isolated firm-level resources. From a systems perspective, this study focuses on the inter-firm green innovation linkages within the new energy sector, where knowledge diffusion, technological learning, and governance signals are jointly shaped by network structure. Using quarterly panel data from 52 listed Chinese new energy firms from 2018Q1 to 2023Q2, we employ the Adaptive Elastic Net Generalized Method of Moments approach to reconstruct a green innovation network from the observed dynamics of the panel data, and examine how firms’ positions within the network affect their environmental responsibility. The results show that the network exhibits a clear core–periphery spillover structure. Inter-firm ties are more likely to form when firms are located in the same province and when target firms have higher green patent citation impact and more executives with environmental backgrounds. Higher network centrality is associated with better corporate environmental responsibility, especially among firms facing intense market competition, state-owned firms, and non-key environmental regulatory units. These findings suggest that green innovation networks can alleviate innovation imbalances and strengthen informal inter-firm governance mechanisms in emerging green industries. Full article