Search Results (566)

Background: Increasing regulatory pressure for maritime decarbonization (e.g., IMO CII, FuelEU) drives adoption of low-carbon fuels and prompts reassessment of regional ports’ competitiveness. This study aims to evaluate the economic and environmental viability of rerouting deep-sea container services to regional ports in a decarbonizing world. Methods: A scenario-based analysis is used to evaluate total costs and CO₂ emissions across the entire container shipping supply chain, incorporating deep-sea shipping, port operations, feeder services, and inland rail/road transport. The Port of Liverpool serves as the primary case study for rerouting Asia–Europe services from major ports. Results: Analysis indicates Liverpool’s competitiveness improves with shipping lines’ slow steaming, growth in hinterland shipment volume, reductions in the emission factors of alternative low-carbon fuels, and an increased modal shift to rail matching that of competitor ports (e.g., Southampton). A dual-port strategy, rerouting services to call at both Liverpool and Southampton, shows potential for both economic and environmental benefits. Conclusions: The study concludes that rerouting deep-sea services to regional ports can offer cost and emission advantages under specific operational and market conditions. Findings on factors and conditions influencing competitiveness and the dual-port strategy provide insights for shippers, ports, shipping lines, logistics agents, and policymakers navigating maritime decarbonization. Full article

The introduction of mobile banking is essential in today’s financial sector, where technological innovation plays a critical role. To remain competitive in the current market, businesses must analyze client attitudes and perspectives, as these influence long-term demand and overall profitability. While previous studies have explored general adoption behaviors, limited research has examined how individual factors such as social influence, lifestyle compatibility, financial technology self-efficacy, and perceived usage cost affect mobile banking adoption among specific generational cohorts. This study addresses that gap by offering insights into these variables, contributing to the growing literature on mobile banking adoption, and presenting actionable recommendations for financial institutions targeting younger market segments. Using a structured questionnaire survey, data were collected from both users and non-users of mobile banking among the Gen Z population in the United States. The regression model significantly predicts mobile banking adoption, with an intercept of $-0.548$ ($p < 0.001$). Among the independent variables, perceived cost of usage has the strongest positive effect on adoption ($B=0.857$, $\beta =0.722$, $p < 0.001$), suggesting that adoption increases when mobile banking is perceived as more affordable. Social influence also has a significant positive impact ($B=0.642$, $\beta =0.643$, $p < 0.001$), indicating that peer influence is a central driver of adoption decisions. However, self-efficacy shows a significant negative relationship ($B=-0.343$, $\beta =-0.339$, $p < 0.001$), and lifestyle compatibility was found to be statistically insignificant ($p=0.615$). These findings suggest that reducing perceived costs, through lower fees, data bundling, or clearer communication about affordability, can directly enhance adoption among Gen Z consumers. Furthermore, leveraging peer influence via referral rewards, Partnerships with influencers, and in-app social features can increase user adoption. Since digital self-efficacy presents a barrier for some, banks should prioritize simplifying user interfaces and offering guided assistance, such as tutorials or chat-based support. Future research may employ longitudinal designs or analyze real-life transaction data for a more objective understanding of behavior. Additional variables like trust, perceived risk, and regulatory policies, not included in this study, should be integrated into future models to offer a more comprehensive analysis. Full article

This study intends to portray how varying degrees of environmental policy stringency and the growing pressure of global competition reflect on high-tech (HT) sectors’ cost rationalization strategies and lead to environmental consequences in 15 G20 countries (1992–2019). Moreover, we center the pattern of cost rationalization management regarding the opportunity cost of ecosystem service consumption and propose to test the fundamental hypothesis stating the possible transmission of competition-induced technological innovations to green economic transformation. Our new methodology estimates quantile-specific effects with MM-QR, while identifying the main interaction effects between regulatory pressure and trade competition uses an extended STIRPAT model. The results reveal a paradoxical finding: despite higher environmental policy stringency and opportunity costs of ecosystem services, HT sectors persistently adopt environmentally detrimental cost-reduction approaches. These findings carry important policy implications: (1) environmental regulations for HT sectors require complementary innovation subsidies, (2) trade agreements should incorporate clean technology transfer clauses, and (3) governments must monitor sectoral emission leakage risks. Our dual machine learning–econometric approach provides policymakers with targeted insights for different emission scenarios, highlighting the need for differentiated strategies across clean and polluting HT subsectors. Full article

This paper addresses the challenge of fostering cooperation among virtual power plant (VPP) operators in competitive electricity markets, focusing on the application of evolutionary game theory (EGT) and static reward–punishment mechanisms. This investigation resolves four critical questions: the minimum reward–punishment thresholds triggering stable cooperation, the influence of initial market composition on equilibrium selection, the sufficiency of static versus dynamic mechanisms, and the quantitative mapping between regulatory parameters and market outcomes. The study establishes the mathematical conditions under which static reward–punishment mechanisms transform competitive VPP markets into stable cooperative systems, quantifying efficiency improvements of 15–23% and renewable integration gains of 18–31%. Through rigorous evolutionary game-theoretic analysis, we identify critical parameter thresholds that guarantee cooperation emergence, resolving longstanding market coordination failures documented across multiple jurisdictions. Numerical simulations and sensitivity analysis demonstrate that static reward–punishment systems enhance cooperation, optimize resources, and increase renewable energy utilization. Key findings include: (1) Reward–punishment mechanisms effectively promote cooperation and system performance; (2) A critical region exists where cooperation dominates, enhancing market outcomes; and (3) Parameter adjustments significantly impact VPP performance and market behavior. The theoretical contributions of this research address documented market failures observed across operational VPP implementations. Our findings provide quantitative foundations for regulatory frameworks currently under development in seven national energy markets, including the European Union’s proposed Digital Single Market for Energy and Japan’s emerging VPP aggregation standards. The model’s predictions align with successful cooperation rates achieved by established VPP operators, suggesting practical applicability for scaled implementations. Overall, through evolutionary game-theoretic analysis of 156 VPP implementations, we establish precise conditions under which static mechanisms achieve 85%+ cooperation rates. Based on this, future work could explore dynamic adjustments, uncertainty modeling, and technologies like blockchain to further improve VPP resilience. Full article

Energy-intensive industries (EIIs) face mounting pressure to reduce greenhouse gas emissions while maintaining international competitiveness—a balance that is central to achieving the EU’s 2030 and 2050 climate objectives. In this context, energy management (EM) emerges as a strategic instrument to decouple industrial growth from fossil energy consumption. This study proposes a redefinition of EM to support carbon-neutral supply chains within the European Union’s EIIs, addressing critical limitations of conventional EM frameworks under increasingly stringent carbon regulations. Using a modified systematic literature review based on PRISMA methodology, complemented by expert insights from EU Member States, this research identifies structural gaps in current EM practices and highlights opportunities for integrating sustainable innovations across the whole industrial value chain. The proposed EM concept is validated through an analysis of 24 EM definitions, over 170 scientific publications, and over 80 EU legal and strategic documents. The framework incorporates advanced digital technologies—including artificial intelligence (AI), the Internet of Things (IoT), and big data analytics—to enable real-time optimisation, predictive control, and greater system adaptability. Going beyond traditional energy efficiency, the redefined EM encompasses the entire energy lifecycle, including use, transformation, storage, and generation. It also incorporates social dimensions, such as corporate social responsibility (CSR) and stakeholder engagement, to cultivate a culture of environmental stewardship within EIIs. This holistic approach provides a strategic management tool for optimising energy use, reducing emissions, and strengthening resilience to regulatory, environmental, and market pressures, thereby promoting more sustainable, inclusive, and transparent supply chain operations. Full article

As Environmental, Social, and Governance (ESG) gradually become the common language for sustainable development of international society and international cooperation in China, it is worth discussing whether ESG practices can help Chinese enterprises shape a responsible international image, overcome the liability of foreignness (LOF) and improve the long-term performance of cross-border mergers and acquisitions (M&As). On the basis of theoretical discussion, combined with the panel data of cross-border M&As of China A-share listed companies from 2010 to 2021, this paper empirically examines that the ESG performance of acquirers has a significant positive impact on the long-term performance of cross-border mergers and acquisitions (M&As) of China A-share listed companies. Furthermore, the ESG performance of environment and governance dimensions and heavily polluting enterprises has stronger incentive effects on the long-term performance of cross-border M&As. The ESG performance of the acquirer positively affects the long-term performance of cross-border M&As of China A-share listed companies by acquiring capital market resources, product market competitiveness, regulatory legitimacy, and enhancing internal synergy. Full article

Food fermentation is driven by microbial interactions. This article reviews the types of microbial interactions during food fermentation, the research strategies employed, and their impacts on the quality of fermented foods. Microbial interactions primarily include mutualism, commensalism, amensalism, and competition. Based on these interaction patterns, the safety, nutritional composition, and flavor quality of food can be effectively improved. Achieving precise control of fermented foods’ qualities via microbial interaction remains a critical challenge. Emerging technologies such as high-throughput sequencing, cell sorting, and metabolomics enable the systematic analysis of core microbial interaction mechanisms in complex systems. Using synthetic microbial communities and genome-scale metabolic network models, complicated microbial communities can be effectively simplified. In addition, regulatory targets of food quality can be precisely identified. These strategies lay a solid foundation for the precise improvement of fermented food quality and functionality. Full article

What motivates industrial companies to decarbonise? While climate policy has intensified, the specific factors driving corporate decisions remain underexplored. This article addresses that gap through a mixed-methods study combining qualitative insights from a leading automotive supplier with quantitative data from over 800 manufacturing companies in Germany. The study distinguishes between internal motivators—such as risk reduction, future-proofing, and competitive positioning—and external drivers like regulation, supply chain pressure, and investor expectations. Results show that internal economic logic is the strongest trigger: companies act more ambitiously when decarbonisation aligns with their strategic interests. Positive motivators outperform external drivers in both influence and impact on ambition levels. For instance, long-term cost risks were rated more relevant than reputational gains or regulatory compliance. The analysis also reveals how company size, energy intensity, and supply chain position shape motivation patterns. The findings suggest a new framing for climate policy: rather than relying solely on mandates, policies should strengthen intrinsic motivators. Aligning business interests with societal goals is not only possible—it is a pathway to more ambitious, resilient, and timely decarbonisation. By turning external pressure into internal logic, companies can move from compliance to leadership in the climate transition. Full article

Mental health disorders, particularly depression and anxiety, have become increasingly prevalent among elite athletes, exacerbated by factors such as competitive pressure and the Coronavirus Disease 19 (COVID-19) pandemic. This study analyzes trends in the use of antidepressants, anxiolytics, and cannabinoids (delta-9-tetrahydrocannabinol (THC)/cannabidiol (CBD)) among Italian athletes from 2011 to the first half of 2023 (FH2023), referring to anti-doping reports published by the Italian Ministry of Health. Data from 13,079 athletes were examined, with a focus on non-prohibited medications, banned substances, and regulatory impacts, including threshold adjustments for THC since 2013 and the legalization of CBD. The results show fluctuating use of antidepressants/anxiolytics, with peaks in 2021 and the FH2023, coinciding with post-pandemic awareness. Positive THC cases rose following regulatory changes, reflecting socio-cultural trends. Gender disparities emerged, with THC use predominantly among males (e.g., nine males vs. one female in 2013), though female athletes were underrepresented in testing. This study highlights the need for personalized, evidence-based strategies that balance therapeutic efficacy and anti-doping compliance. Clinicians should carefully consider prescribing selective serotonin reuptake inhibitors (SSRIs) and benzodiazepines to address depression and anxiety and should monitor the risks of CBD contamination. Future research should adopt longitudinal, gender-sensitive approaches to refining guidelines and combating stigma in professional sports. Full article

This study investigates the performance of B-series marine propellers enhanced through geometric modifications, namely face camber ratio (FCR) and cupping percentage modifications, using a machine learning (ML)-driven optimization framework. A large dataset of over 7000 open-water propeller configurations is curated, incorporating variations in blade number, expanded area ratio (EAR), pitch-to-diameter ratio (P/D), FCR, and cupping percentage. A multi-layer artificial neural network (ANN) is trained to predict thrust, torque, and open-water efficiency (η_o) with a high coefficient of determination (R²), greater than 0.9999. The ANN is integrated into an optimization algorithm to identify optimal propeller designs for the KRISO Container Ship (KCS) using empirical constraints for cavitation and tip speed. Unlike prior studies that rely on boundary element method (BEM)-ML hybrids or multi-fidelity simulations, this study introduces a geometry-coupled analysis of FCR and cupping—parameters often treated independently—and applies empirical cavitation and acoustic (tip speed) limits to guide the design process. The results indicate that incorporating 1.0–1.5% cupping leads to a significant improvement in efficiency, up to 9.3% above the reference propeller, while maintaining cavitation safety margins and acoustic limits. Conversely, designs with non-zero FCR values (0.5–1.5%) show a modest efficiency penalty (up to 4.3%), although some configurations remain competitive when compensated by higher EAR, P/D, or blade count. The study confirms that the combination of cupping with optimized geometric parameters yields high-efficiency, cavitation-safe propellers. Furthermore, the ML-based framework demonstrates excellent potential for rapid, accurate, and scalable propeller design optimization that meets both performance and regulatory constraints. Full article

The spotted wing drosophila, Drosophila suzukii is an injurious polyphagous pest threatening worldwide soft fruit production. Its high adaptability to new colonized environments, short life cycle, and wide host range are supporting its rapid spread. The most common techniques to reduce its significant economic damage are based on multiple insecticides applications per season, even prior to the harvest, which reduces agroecosystem biodiversity and affects human and animal health. Environmental concerns and regulatory restrictions on insecticide use are driving the need for studies on alternative biological control strategies. This study aimed to assess the effect of T. drosphilae in controlling D. suzukii infestations and its interaction with P. vindemiae, a secondary parasitoid naturally present in Apulia (South Italy). Field experiments were carried out in organic cherry orchards in Gioia del Colle (Bari, Italy) to test the efficacy and adaptability of T. drosphilae following weekly releases of artificially reared individuals. Additionally, the interaction between P. vindemiae and T. drosphilae was studied under laboratory conditions. Results from field experiments showed that D. suzukii populations were significantly lower when both parasitoids were present. However, T. drosophilae was less prone to adaptation, so its presence and parasitism were limited to the post-release period. Laboratory experiments, instead, confirmed the high reduction of D. suzukii populations when both parasitoids are present. However, the co-existence of the two parasitoids resulted in a reduced parasitism rate and offspring production, notably for T. drosophilae. This competitive disadvantage may explain its poor establishment in field conditions. These findings suggest that the field release of the two natural enemies should be carried out with reference to their natural population abundance to not generate competition effects. Full article

Sustainable Supply Chain Management (SSCM) emerges as a vital catalyst for inclusive growth and sustainable development, particularly in emerging economies where the manufacturing sector is central to economic progress. This study offers an in-depth analysis of the current research landscape on SSCM in the context of developing nations, outlining key theoretical frameworks and advocating for a solid conceptual foundation alongside a structured agenda for future research initiatives. This study employs a structured literature review technique to analyze 92 published articles indexed by Scopus from 2013 to 2024, revealing a burgeoning trend in the subject of global supply chains in developing nations. The analysis identifies key keywords such as “sustainable supply chain management,” “manufacturing industries,” “inclusive growth,” and “supply chain and sustainability,” and develops a conceptual model that elucidates how SSCM practices can be effectively integrated into manufacturing sectors to facilitate equitable growth and enhance business competitiveness. This work’s novelty lies in employing a systematic literature review to develop a holistic SSCM conceptual framework constructed upon six primary drivers: business model innovation, inclusive SSCM, corporate governance and leadership, technological and innovation capabilities, policy and regulatory environment, and circular feedback. This model addresses the ambiguity surrounding SSCM and inclusive growth, providing a robust foundation for future research and performance measurement. This study contributes to the field by providing a practical and theoretically grounded framework for researchers, policymakers, and practitioners seeking to implement impactful and effective SSCM initiatives in developing nations’ manufacturing sectors to promote inclusive growth and sustainable development. Full article

Potassium (K), a critical macronutrient for the growth and development of Korla fragrant pear (Pyrus sinkiangensis Yu), plays a pivotal regulatory role in sugar-acid metabolism. Furthermore, K exhibits a highly specific response in near-infrared (NIR) spectroscopy compared to elements such as nitrogen (N) and phosphorus (P). Given its fundamental impact on fruit quality parameters, the development of rapid and non-destructive techniques for K determination is of significant importance for precision fertilization management. By measuring leaf potassium content at the fruit setting, expansion, and maturity stages (decreasing from 1.60% at fruit setting to 1.14% at maturity), this study reveals its dynamic change pattern and establishes a high-precision prediction model by combining near-infrared spectroscopy (NIRS) with machine learning algorithms. “Near-infrared spectroscopy coupled with machine learning can enable accurate, non-destructive monitoring of potassium dynamics in Korla pear leaves, with prediction accuracy (R²) exceeding 0.86 under field conditions.” We systematically collected a total of 9000 leaf samples from Korla fragrant pear orchards and acquired spectral data using a benchtop near-infrared spectrometer. After preprocessing and feature extraction, we determined the optimal modeling method for prediction accuracy through comparative analysis of multiple models. Multiplicative scatter correction (MSC) and first derivative (FD) are synergistically employed for preprocessing to eliminate scattering interference and enhance the resolution of characteristic peaks. Competitive adaptive reweighted sampling (CARS) is then utilized to screen five potassium-sensitive bands, specifically in the regions of 4003.5–4034.35 nm, 4458.62–4562.75 nm, and 5145.15–5249.29 nm, among others, which are associated with O-H stretching vibration and changes in water status. A comparison between random forest (RF) and BP neural network indicates that the MSC + FD–CARS–BP model exhibits the optimal performance, achieving coefficients of determination (R²) of 0.96% and 0.86% for the training and validation sets, respectively, root mean square errors (RMSE) of 0.098% and 0.103%, a residual predictive deviation (RPD) greater than 3, and a ratio of performance to interquartile range (RPIQ) of 4.22. Parameter optimization revealed that the BPNN model achieved optimal stability with 10 neurons in the hidden layer. The model facilitates rapid and non-destructive detection of leaf potassium content throughout the entire growth period of Korla fragrant pears, supporting precision fertilization in orchards. Moreover, it elucidates the physiological mechanism by which potassium influences spectral response through the regulation of water metabolism. Full article

Platform markets are concentrating, even as their content suppliers fragment, yet this asymmetric evolution is poorly understood. Using panel data from 11–12 Korean home shopping firms (2015–2023), we employ Hansen threshold regression, instrumental variables, and panel fixed-effects models to examine its competitive impact. Our analysis of 104 firm-year observations reveals four key findings. First, platform concentration alone explains 94.4% of transmission fee variation, with fees rising from 41.15% to 68.72% as platform HHI increased from 1390 to 2154 while content HHI declined from 1797 to 1118. Second, we identify critical fee thresholds at 62.2% (p = 0.012) and 73% (p = 0.002) that divide markets into three distinct operating regimes. Third, the fee–profitability relationship reversed from negative (r = −0.145) to positive (r = 0.554), indicating fees’ evolution from cost burdens to selection mechanisms. Fourth, instrumental variable estimates (0.473) exceed OLS estimates (0.184) by 2.6 times, revealing severe selection bias. Simulations indicate a 60% fee cap would affect 25 firms (24%) while increasing total surplus by 15.1% and improving SME profitability by 2.9 percentage points. We propose the Asymmetry Ratio (Platform HHI/Content HHI) as a regulatory tool, with ratios exceeding 1.0 triggering enhanced scrutiny. Our findings demonstrate that asymmetric market evolution creates new sources of platform power requiring novel regulatory approaches. Full article

This article proposes recognizing innovation as the European Union’s “fifth freedom”, alongside the free movement of goods, services, capital, and people, with the aim of embedding it into renewable energy legislation. Focusing on renewable energy storage—a critical but overlooked component of decarbonization—it identifies structural barriers in EU cleantech innovation, including regulatory fragmentation, slow financing, and weak industrial coordination. To address these, this article introduces the Risk–Resilience–Reward (RRR) framework, a strategic tool for more anticipatory policymaking. It outlines how the proposed Clean Energy Delivery Agency and Clean Energy Deployment Fund could operationalize the RRR framework to accelerate storage deployment and strengthen EU competitiveness. Embedding purpose-led principles in energy policy, this article positions storage as the “unsung hero of decarbonization” and offers takeaways for advancing a just, sustainable EU economy. Full article