Search Results (7,310)

To systematically reveal the influence mechanisms and spatial effects of industrial intellectual property (IP) reform strategies and manufacturing craftsmanship spirit on regional energy intensity, this study aims to provide theoretical support and practical pathways for emerging market economies pursuing dual goals of energy efficiency governance and manufacturing transformation. Based on a “technology–culture synergistic innovation ecology” theoretical framework, the study deepens the understanding of energy intensity governance and introduces two spatial weight matrices—the economic distance matrix and the nested economic–geographic matrix—to uncover the spatial heterogeneity of policy and cultural effects. Using panel data from 30 Chinese provinces from 2010 to 2022 (excluding Tibet, Hong Kong, Macao, and Taiwan), we construct an index of manufacturing craftsmanship spirit (CSM) and its four dimensions—excellence in detail, persistent dedication, breakthrough orientation, and innovation inheritance—via the entropy method. Empirical analysis is conducted through Spatial Difference-in-Differences (SDID) and Double Machine Learning (DML) models. The results show that: (1) Industrial IP reform strategies significantly reduce local energy intensity through improved property rights definition and technology transaction mechanisms, but may increase energy intensity in economically proximate regions due to intensified technological competition. (2) All four dimensions of craftsmanship spirit indirectly mitigate regional energy intensity via distinct pathways, with particularly strong mediating effects from persistent dedication and innovation inheritance. In contrast, breakthrough orientation shows no significant impact, possibly due to limitations from the current stage of the technology lifecycle. (3) Spatial spillover effects are heterogeneous: under the nested economic–geographic matrix, IP reform strategies reduce neighboring regions’ energy intensity through synergistic effects, while under the economic distance matrix, competitive spillovers lead to an increase in adjacent energy intensity. Based on these findings, we propose the following: deepening IP reform strategies to build a technology–culture synergistic ecosystem; enhancing regional policy coordination to avoid technology lock-in; systematically cultivating the core of craftsmanship spirit; and establishing a dynamic incentive mechanism for breakthrough orientation. These measures can jointly drive systemic improvements in regional energy efficiency. Full article

This paper proposes a cryptocurrency portfolio trading system (CPTS) that optimizes trading performance in the cryptocurrency futures market by leveraging reinforcement learning and timeframe analysis. By employing the advantage actor–critic (A2C) algorithm and analysis of variance (ANOVA) portfolios are constructed over multiple timeframes. Data corresponding to the trade of 18 major cryptocurrencies on Binance Futures––between January 2022 and December 2023––are used to show that trading strategies can be effectively categorized into those with high-frequency (10, 30, and 60 min) and low-frequency (daily) timeframes. Empirical results demonstrate statistically significant differences in returns between these timeframe groups, with major cryptocurrencies (e.g., Bitcoin and Ethereum) exhibiting higher returns in high-frequency trading (16–17%) than in daily trading (6–7%) during training. Performance evaluation during the test period revealed that the low-frequency group achieved a 43.06% average return, significantly outperforming the high-frequency group (5.68%). The ANOVA results confirm that both the frequency type and portfolio selection significantly influence trading performance at the 5% significance level. This study offers a novel approach to cryptocurrency trading that considers the distinct characteristics of different timeframes. The effectiveness of combining reinforcement learning with statistical analysis for portfolio optimization in highly volatile cryptocurrency markets is demonstrated. Full article

E-commerce indicators are very complex and have a wide range of levels of complexity and applications. The digital economies that we are oriented towards also have complex features in terms of consumers and businesses. The research objectives are focused on determining the impact of e-commerce on the digital economy within countries with different stages of economic development, digitalization techniques, and e-commerce usage. This study evaluates how AI-based clustering reveals patterns in the e-commerce indicators influencing the digital economy. The research methods used are focused on AI techniques in order to evaluate and assess the usage of e-commerce in the digital economy. In this sense, the methods used in this research are clustering techniques in order to determine the stage of implementation of the digital economy. The research implications have a worldwide impact and soundness in establishing the evolution of the e-economy in different types of countries with different stages and levels of digitalization and different e-commerce development paths. The empirical results show there are significant differences between countries due to cultural, economic, social, and judicial differences. The conclusions of this study highlight that using AI techniques can be a solution for enhancing future digital economy development and labor market consolidation, especially by strengthening e-commerce indicator usage and application. Full article

This paper examines stochastic cooperation in markets with two sellers who exhibit one-sided dependency. The independent seller’s pricing influences the dependent seller’s demand, but not vice versa. We study the one-dimensional hybrid game class whose parameter is the exogenously given probability of cooperation. In each game of this class, both sellers simultaneously choose prices that determine their endogenous threats, i.e., conflict profits. The sellers are aware of the cooperation probability but cannot condition prices on whether or not there is cooperation. We characterize the equilibrium prices and the sellers’ expected profits. Our main result shows that the independent seller earns higher expected profits when cooperation is more likely. In contrast, the dependent seller earns lower expected profits when the likelihood of cooperation is below a threshold that we characterize explicitly, and higher profits are earned thereafter. These findings suggest that, within our framework, antitrust concerns may be mitigated. Since dependent sellers can incur losses from cooperation, collusion attempts become less viable in markets with one-sided dependency. Full article

In the digital era, online search patterns provide a practical way to track changes in the public interest in sustainability. This study analyzes monthly Google Trends data in the United States (January 2019–December 2024) for five keywords: two institutional (“ESG”, “carbon neutral”), and three consumer-oriented (“eco friendly”, “zero waste”, and “plastic free”). Drawing on agenda-setting theory and the diffusion-of-innovations framework, we test the directional links between institutional and consumer attention. The methods include Granger causality tests, impulse response functions, and cross-correlation analysis. The findings reveal a consistent lead–lag structure in which institutional terms precede consumer-oriented searches, but the timing and persistence of influence vary across concepts. A broad discourse such as ESG produces slower, yet more sustained, effects, whereas action-oriented concepts like carbon neutrality generate quicker but shorter-lived responses. Seasonal analysis also shows recurring peaks in consumer interest around events such as Earth Day and Plastic-Free July, underscoring the cyclical nature of attention to sustainability. By integrating communication theory with multi-year digital trace data, this study provides evidence of how institutional messaging diffuses into consumer behavior, while highlighting the roles of timing and message framing. The results contribute to sustainability communication research and offer practical insights for policymakers, NGOs, and marketers relevant to aligning campaigns with evolving public attention. Full article

The bidirectional flow of population between urban and rural areas, not limited to rural-to-urban migration, influences the sustainable development of agricultural economic resilience in multiple ways. This study employs panel data from 31 provincial-level regions in China spanning 2017–2022 to comprehensively examine the impact of bidirectional urban–rural mobility on diverse dimensions of agricultural economic resilience, while further investigating its underlying mechanisms. Benchmark regression shows that the bidirectional urban–rural mobility exerts a suppressive effect on the agricultural economic resilience. Mechanism analyses indicate that such mobility contributes to strengthening agricultural economic resilience by catalyzing land-scale operational efficiency and amplifying labor productivity gains and that the advancement of smart agriculture technologies effectively mitigates the inhibitory impacts of bidirectional mobility on agricultural economic resilience. Furthermore, according to heterogeneity analysis, the mobility exerts a suppressive effect on the resistance (Res.) and reconstruction (Recons.) of agricultural economic resilience, while concurrently enhancing its restoration (Rest.). Meanwhile, the bidirectional mobility has significantly impeded the agricultural economic resilience of the eastern, central, and western regions, as well as the primary grain-producing areas, production and marketing balance areas, and the primary grain-selling areas. Further investigation reveals that the reverse mobility has a positive effect on the resistance but a negative effect on its restoration and reconstruction. Full article

This paper investigates the time-varying efficiency of Western European stock markets and examines how macroeconomic events defined as endogenous and exogenous shocks influence the degree of efficiency by either long-range dependence or mean reverting. We apply a rolling-window detrended fluctuation analysis (DFA) with two window sizes, complemented by the Efficiency Index to synthetize multiple measures of market efficiency. The results confirm that efficiency evolves dynamically in response to macroeconomic disruptions. Specifically, endogenous shocks tend to generate anti-persistent behavior, while exogenous shocks are associated with long-memory effect. These shifts in efficiency are also reflected in rolling Kurtosis estimates, suggesting that only the most severe shocks produce spikes in Kurtosis, fat-tailed returns distributions, and structural inefficiencies. This dual approach allows us to classify shocks as major or minor based on their joint impact on both market efficiency and tail behavior. Overall, our findings support the adaptive market hypothesis and extend its implications through the fractal market hypothesis by underlining the role of heterogenous investment horizons during periods of turmoil. The combined use of dynamic DFA and Kurtosis offer a framework to assess how financial markets adapt to different types of macroeconomic shocks. Full article

Farmers’ land transfer practices optimize the allocation of agricultural resources by transferring them to more efficient operators. This enhances agricultural productivity and advances rural revitalization. However, due to the lack of financial institution outlets in rural areas, the availability of financial services in rural areas is limited, which in turn hinders the transfer of rural land. This study examines the impact of digital financial inclusion, characterized by the deep integration of internet technology and financial services, on farmers’ land transfer behavior in China. The study uses data from the China Family Panel Studies (2012–2022) and provincial digital financial inclusion data. The results show that digital financial inclusion significantly promotes rural land transfer-out. The mechanisms reveal two pathways: (1) digital financial inclusion expands non-agricultural entrepreneurship by easing credit constraints and reducing reliance on land livelihoods; (2) it increases participation in commercial insurance, mitigating risks of land abandonment. Heterogeneity analysis reveals stronger effects in eastern China and among educated households. Theoretically, the study identifies the dual role of financial technology in reshaping rural land markets through credit access and risk management. Practically, it reveals how DFI influences land transfer behavior, providing a basis for the government to formulate policies that combine the two, ultimately enhancing the production capacity, operational efficiency, and market competitiveness of smallholder farmers. The findings offer global insights for developing countries that are leveraging digital finance to activate rural land markets and achieve digital financial inclusion. Full article

Driven by the urgent need to decarbonize the logistics sector—where conventional vehicles exhibit high energy consumption and emissions, posing significant environmental sustainability challenges—electrification represents a pivotal strategy for reducing emissions and achieving sustainable urban freight transport. Despite rising global electric vehicle sales, the penetration rate of electric logistics vehicles (ELVs) remains comparatively low, impeding progress toward sustainable logistics objectives. Battery-swapping mode (BSM) has emerged as a potential solution to enhance operational efficiency and economic viability, thereby accelerating sustainable adoption. This model improves ELV operational efficiency through rapid battery swaps at centralized stations. This study constructs a tripartite evolutionary game model involving government, consumers, and BSM-ELV manufacturers to analyze market dynamics under diverse strategies. Key considerations include market scale, government environmental benefits, battery leasing/purchasing costs, lifecycle cost analysis (via discount rates), and resource efficiency (reserve battery ratio $\lambda$). MATLAB-2021b-based simulations predict participant strategy evolution paths. Findings reveal that market size and manufacturer expectations significantly influence governmental and manufacturing strategies. Crucially, incorporating discount rates demonstrates that battery leasing reduces consumer enterprises’ initial investment, enhancing economic sustainability and cash flow while offering superior total cost of ownership. Furthermore, gradual reduction of government subsidies effectively stimulates market self-regulation, incentivizes leasing adoption, and bolsters long-term economic/operational sustainability. Market feedback can guide policy adjustments toward fiscally sustainable support mechanisms. This study proposes the following management implications for advancing sustainable logistics: 1. Governments should phase out subsidies systematically to foster market resilience; 2. Manufacturers must invest in BSM R&D to improve efficiency and resource circularity; 3. Consumer enterprises can achieve economic benefits and emission reductions by adopting BSM-ELVs. Full article

The role of science and technology in enhancing beer quality is crucial amid growing market demands. This pilot study assessed the clarity and physicochemical stability of laboratory beers treated post fermentation with three clarifying (fining) agents: two chitosan-based and one collagen-based (fish bladder/isinglass). The beers were brewed with Polish barley malt and hops (alpha acids 7.5% and 14.5%). The measured parameters included pH, colour, turbidity, viscosity, surface tension, and foam volume. Within this small-scale, low-power dataset, both the collagen- and chitosan-based agents improved clarity, with the collagen agent showing the lowest turbidity in this sample. The clarifying agents also influenced the colour and surface tension, while the pH was largely unchanged. The foam volume increased with fining. Shelf-life checks suggested improved stability in clarified beers, with no clear differences between agents under these conditions. These findings are preliminary. The results should be interpreted cautiously due to the limited number of replicates. Larger scale studies with adequate replication are required before translating these observations into brewing practice. Chitosan’s effectiveness as a clarifying agent aligns with its high charge density and ability to coagulate suspended particles. This study underscores the importance of selecting appropriate clarifying agents to optimize beer clarity and stability while maintaining essential physicochemical properties. These findings contribute to the brewing industry’s efforts to meet consumer expectations for high-quality, stable beer products. Full article

Efficient urban land use is a cornerstone of sustainable city development, yet the drivers of such efficiency are increasingly complex in an era of spatial transformation. As industrial specialization and collaboration deepen, cities are becoming interconnected through complex networks. These “invisible threads” are redefining the dynamics of land use and spatial efficiency. This study examines the influence of intercity industrial networks on urban land use efficiency by constructing urban networks from multi-regional input–output data and evaluating city performance using a super-SBM model. We employed Tobit regression and mediation analysis to identify the mechanisms. Results indicate that both the quantity and quality of urban network connections significantly enhance land use efficiency, with notable differences across city types. The positive effect of industrial network centrality is most pronounced in large cities. In growing cities, both the number and quality of industrial linkages promote efficiency, whereas in shrinking cities, connection quality is more critical than quantity. Mechanism analysis reveals that industrial networks improve land use efficiency primarily by expanding intermediate goods markets and fostering technological innovation. Full article

Optimizing the regional spatial pattern of land use and high-quality economic development requires an accurate understanding of the multifunctional evolution of land use. Based on remote sensing data and socio-economic data from 2000 to 2023, this study utilizes a land transfer matrix, an evaluation index system, an obstacle degree model, and regression analysis to deeply explore the spatial distribution characteristics and influencing factors of the production–living–ecological functions (PLEF) in the Yangtze River Economic Belt (YREB) over the 23-year period. The results show the following: ① the living function area of the YREB has increased by 22,400 km², while the production function area has decreased by 20,600 km², and the ecological function area has decreased by 1800 km². ② The production and living function spaces are characterized by high values in the eastern region and low values in the western region, and the ecological function space is characterized by high values in the western region and low values in the eastern region. ③ In the YREB, production function was the main obstacle to the PLEF between 2000 and 2023. ④ Population growth, economic development, agricultural technology, and agricultural efficiency are the main factors that influence the spatial and temporal evolution of the PLEF. This study suggests exploring an interactive compensation mechanism of the PLEF that combines the government and the market to form a differentiated development strategy. Full article

Enhancing green innovation performance is crucial for manufacturing enterprises to achieve sustainable development. This paper employs the strategic tripod framework (organization, industry, institution) using the K-means clustering algorithm to identify types of manufacturing performed by listed companies in China’s Shanghai and Shenzhen markets and adopts the CART decision tree algorithm to analyze influencing factors of green innovation performance across different enterprise types. The study finds that manufacturing enterprises can be divided into three types, with significant differences in influencing factors of green innovation performance. From the perspective of internal drivers, the improvement in green innovation performance mainly relies on organizational resource endowments, among which R&D ability is particularly key. From the perspective of the external institutional environment, the driving logic of mimetic pressure shows differentiated characteristics between different enterprise groups and differentiated response strategies need to be formulated accordingly. In addition, when the overall impact of external factors is weak, the level of industrial structure still has a prominent promoting effect on green innovation performance. Based on the data-driven perspective, this paper identifies the influencing factors of green innovation performance of different types of manufacturing enterprises, which is helpful to improve the green innovation performance of manufacturing enterprises. Full article

A comprehensive overview is provided on factors and processes influencing the final quality of a cup of coffee, with an emphasis on the brewing method’s central role. Coffee quality assessment, both at the bean and cup level, combines objective parameters (color, moisture, bean defects, density) with a notable degree of subjectivity, as consumer sensory perception is ultimately decisive. The brewing technique is described as a critical determinant of the final chemical, physical, and sensory attributes. Key parameters such as aroma profile, pH, titratable acidity, total and filtered solids, lipid and fatty acid content, viscosity, foam (crema), and colorimetric indices are detailed as essential metrics in coffee quality evaluation. Roasting creates most of coffee’s key aroma compounds. The brewing method further shapes the extraction of both volatile and other bioactive compounds like caffeine, chlorogenic acids, and lipids. Brewing methods significantly affect acidity, “body,” and crema stability, while water quality, temperature, and pressure are shown to impact extraction results and sensory properties. Attention is paid to how methods such as Espresso, filter, French press, and cold brew yield distinct physicochemical and sensory profiles in the cup. Overall, the review highlights the multifaceted nature of coffee cup quality and the interplay between raw material, processing, and preparation, ultimately shaping the coffee sensory experience and market value. Full article

Hedeoma multiflora Benth. is an endangered aromatic and medicinal wild species native to Argentina, widely used in traditional medicine, whose cultivation represents a small and untapped sector within the agricultural industry. Current market supply relies on wild harvesting practices by rural communities, leading to population decline and genetic diversity loss through inappropriate extraction methods, including uprooting during suboptimal phenological stages. This study evaluated phenotypic and phytochemical variability in four populations of H. multiflora in the province of Córdoba to develop conservation strategies and cultivation protocols. Forty individuals were sampled from each population to analyze morphological, chemical, phenological, edaphological, and climatic variables. Significant differences were observed between populations: Tulumba had the tallest plants and largest leaves, while Bialet Massé had the highest number of internodes. Essential oil yield ranged from 1.01% to 2.10%, with Ongamira having the highest content. Phytochemical analysis revealed two chemotypes: pulegone-dominant (Bialet Massé and Río Cuarto) and menthone-dominant (Ongamira and Tulumba). Phenological patterns differed significantly, with Ongamira showing the greatest reproductive synchronization. Soil organic matter content significantly influenced morphological and chemical traits. The high variability among populations underscores the importance of preserving genetic diversity for domestication and conservation strategies. Full article