Economics & Business Management ISSN Print: 3079-5214 ISSN Online: 3079-5222
Measurement and Influencing Factors of Green Logistics Efficiency in the Yangtze River Delta Region
Under the dual-carbon target and regional integration strategy, green logistics has become a core path for high-quality development of the logistics industry. This paper takes the Yangtze River Delta (Shanghai, Jiangsu, Zhejiang) as the research object, and constructs an input-output index system including capital, labor, energy, expected outputs and undesirable outputs (CO₂ emissions). The Super-SBM model with undesirable outputs and the Global Malmquist-Luenberger (GML) index are used to measure static and dynamic green logistics efficiency from 2006 to 2023. Furthermore, a fixed-effects model and fuzzy-set qualitative comparative analysis (fsQCA) are adopted to explore influencing factors. The results show that: 1) The overall green logistics efficiency of the region is on the rise with obvious inter-provincial differences, and Shanghai ranks first; 2) Technological progress is the main driving force for efficiency growth; 3) Economic development, logistics scale and environmental governance positively affect efficiency, while energy intensity has a negative impact. Finally, targeted strategies are proposed from the perspectives of resource allocation, energy structure and policy coordination.
Copyright (c) 2026 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by-sa/4.0
MMF-EDRM: A Multi-Modal Fusion and Dual-Risk Modeling Framework for Renewable Energy Project Financing and ECM/DCM Pricing
Renewable energy infrastructure investment faces substantial uncertainty arising from heterogeneous data sources, including environmental variability, policy interventions, and financial market fluctuations. Traditional valuation methods, such as discounted cash flow (DCF) and conventional multi-factor models, are limited in their ability to integrate unstructured information and to jointly capture risk–return dynamics in financing decisions. To address these challenges, this paper proposes MMF-EDRM (Multi-Modal Fusion and Dual-Risk Energy financing model), a unified framework for renewable energy project financing valuation and ECM/DCM pricing. The model integrates multi-modal deep learning with multi-factor statistical modeling to establish a risk–return–pricing linkage for infrastructure assets. Specifically, satellite imagery, meteorological time series, policy documents, and structured financial indicators are encoded through modality-specific networks and fused via a cross-modal attention mechanism. A heteroscedastic dual-head architecture is then employed to jointly estimate expected returns and conditional risk uncertainty. These outputs are further incorporated into an extended multi-factor pricing formulation to derive equity valuation (ECM) and credit spread estimation (DCM). The framework is optimized using a multi-task learning objective that aligns prediction and pricing consistency. Empirical results on the constructed multi-modal renewable energy dataset show that MMF-EDRM reduces RMSE for return prediction by 17.6% compared with CNN-LSTM baselines, while achieving 14.0% improvement in equity pricing error and 12.7% reduction in credit spread prediction error. In addition, the model attains a lower NLL of 0.742, indicating superior risk calibration under volatile policy conditions. These results confirm the effectiveness of the proposed framework.
Digital Marketing Research Report on Haba (Ningbo) International Trading Co., Ltd.
This study focuses on Haba (Ningbo) International Trading Co., Ltd., a cross-border e-commerce subsidiary of a Dutch group. It analyzes the company’s digital marketing macro and micro environment, STP strategy, and 4Ps marketing mix. The findings show the company has a sound marketing system and clear positioning but faces weak digital tech application, insufficient localization, over-reliance on parent brand, and imperfect after-sales service. Finally, targeted optimization strategies are proposed to boost its global competitiveness.
Research on the Revitalization of Local Resources and the Integration of Agriculture, Culture, and Tourism: A Case Study Based on the Tea Industry - Tourism Integration Project in the Area of Source of Yaojiang River Region in Siming Mountain
Using the Source of Yaojiang River Tea-Tourism Integration Project in Siming Mountain as a case study, this paper explores effective approaches for revitalizing local resources and integrating agriculture, culture, and tourism. It analyzes challenges encountered during project implementation and proposes corresponding strategies. Through case analysis, the study provides a detailed examination of the region's practices in resource revitalization and integrated development. Findings indicate that resource revitalization can be achieved by enhancing tea industry quality, leveraging cultural assets, and utilizing natural landscapes; whereas integrated development manifests as a multifaceted convergence of the tea industry with tourism and cultural elements. The study aims to offer theoretical insights and practical guidance for the sustainable development of the Source of Yaojiang River region in Siming Mountain and similar rural areas.
Integration of agriculture, culture, and tourism
The Influence Mechanism and Heterogeneity Analysis of Internal Control Quality on Corporate Accounting Information Transparency
Addressing the practical issues of accounting information distortion and insufficient transparency in capital markets that constrain pricing efficiency, this study elucidates the mechanism and heterogeneous characteristics of internal control quality on corporate accounting information transparency. Using A-share listed companies from 2018 to 2023 as samples, we examined the causal relationship through a fixed-effects model, identified the transmission path using a mediation effect model, and conducted heterogeneity analysis via grouped regression. The results demonstrate that high-quality internal controls significantly enhance corporate accounting information transparency—a conclusion validated by endogeneity and robustness tests. The core transmission path involves curbing earnings management, reducing agency costs, and improving information disclosure quality, with the mediation effect of earnings management suppression being the strongest. This positive effect is particularly pronounced in non-state-owned enterprises, regions with high marketization levels, and small-to-medium-sized enterprises. The study provides empirical evidence for companies to optimize their internal control systems and for regulators to refine information disclosure regulations.
Copyright (c) 2026 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by/4.0/
The Impact of Supply Chain Concentration on Operational Efficiency: An Intermediary Analysis Based on Inventory Management
This study uses non-financial A-share listed companies in China from 2020 to 2024 as its research sample. Based on resource dependence theory and operations management theory, and utilizing micro-level firm data from the Guotai-An CSMAR database, the study empirically examines the impact of supply chain concentration on firm operational efficiency and its underlying mechanisms. Furthermore, it explores the heterogeneous effects of ownership structure, regional characteristics, and industry type on this relationship. The findings reveal that supply chain concentration and firm operational efficiency exhibit a U-shaped relationship: moderate supply chain concentration enhances operational efficiency, whereas excessive concentration inhibits such improvements due to increased supply chain risks. Inventory management (measured by inventory turnover rate) plays a partial mediating role between supply chain concentration and operational efficiency; specifically, supply chain concentration influences operational efficiency by affecting a firm’s inventory turnover efficiency. Heterogeneity analysis indicates that these effects are more pronounced in non-state-owned enterprises. From the perspective of optimizing supply chain layout and enhancing inventory management capabilities, this paper provides empirical evidence and practical recommendations for enterprises to cultivate new-quality productive forces and improve operational efficiency through supply chain management.
A Study on The Impact of Cross-Border Data Flow Restrictions on The Efficiency of China's Digital Services Trade Exports
This study first reviews the cross-border data flow restriction policies of various countries and the export situation of China's digital service trade. Using panel data from 2014 to 2023, a stochastic frontier digital gravity model is constructed to measure the export efficiency of China's digital service trade. The results show that the export efficiency of China's digital service trade remains at a relatively low level with slow growth. To explore the impact of cross-border data flow restrictions on China's digital service trade export efficiency, a trade inefficiency model is constructed for regression analysis, yielding the following conclusions: Increased levels of cross-border data flow restrictions significantly inhibit China's digital service trade export efficiency. The improvement in internet development levels of destination countries can mitigate the negative impact of their cross-border data flow restriction policies on China's digital service trade export efficiency; however, the expansion of institutional distance between the two sides exacerbates this negative effect. Specifically, policies in infrastructure connectivity, electronic transactions, payment systems, and intellectual property rights all exhibit significant inhibitory effects, with intellectual property royalties and the personal, cultural, and entertainment services sectors being more sensitive to cross-border data flow restriction policies.
Artificial Intelligence and Supply Chain Disruption Risks of Chinese Outward-Oriented Enterprises
Against the backdrop of increasing uncertainty in the global economic and trade environment, exploring the role of artificial intelligence in reshaping the supply chain resilience of overseas enterprises and mitigating disruption risks is of great significance. Drawing on information asymmetry theory, supply chain dynamic capabilities theory, and co-evolutionary theory, this study systematically investigates the impact of AI on supply chain disruption risks of Chinese overseas enterprises using a sample of Chinese listed overseas enterprises from 2012 to 2023, employing text mining and ordinary least squares methods. The results show that AI significantly reduces supply chain disruption risks for Chinese overseas enterprises. This finding remains robust after a series of robustness checks, including instrumental variable method and Heckman two-stage estimation. The conclusions of this study help Chinese overseas enterprises develop more precise risk warning and response strategies, offering managerial insights for leveraging AI to prevent supply chain risks and ensure stable international operations.
Research on Book Topic Planning Based on Online Review Mining
In the context of digital reading and the growing importance of online reviews for publishing decisions, traditional topic planning faces challenges such as information overload and delayed feedback. This study analyzes over 59,000 valid reviews from Dangdang.com’s TOP15 best-selling literary books (April 2025). Using a “text mining + multi-dimensional comparison” approach and an optimized DTC framework with a topic mapping layer, we identify 17 core features of literary books via TF-IDF and K-means. Findings show that keywords like “classic,” “moving,” and “healing” significantly influence topic decisions and can be directly translated into content, author, and format strategies. The study validates the DTC model’s applicability in publishing and provides data-driven support for topic planning.
Research on the Impact of Green Finance on Ecological Environment Governance
To investigate the impact of green finance on ecological environment governance, this study measures the development level of green finance in 30 Chinese provinces from 2008 to 2020. Based on existing research and theoretical analysis, an empirical test was conducted to explore the effect of green finance development on the emission of "three wastes" pollutants. The results indicate that the adoption of green finance can effectively reduce regional "three wastes" pollutant emissions, holding significant positive implications for environmental pollution control. Through transmission mechanism analysis, it is found that industrial structure upgrading effectively promotes regional environment governance, with green finance playing a pivotal role in this process. Furthermore, regional heterogeneity analysis reveals that compared to central provinces, the eastern and western provinces exhibit a more prominent performance in environmental governance through green finance. Based on these conclusions, policy recommendations are proposed, including improving the level of environmental information disclosure by financial institutions, perfecting the green finance policy system, strengthening green finance incentive mechanisms, and enhancing government supervision and management to promote pollution reduction and ecological quality improvement.
Research on Operational Efficiency Improvement of Auto Parts Manufacturing Enterprises from the Perspective of Lean Management
Aiming at the common problems of chaotic production processes, excessive work-in-process inventory, and low equipment utilization in small and medium-sized auto parts manufacturing enterprises in China, this paper adopts the core lean management theory of industrial engineering, takes an auto chassis parts manufacturing enterprise in Sichuan Province as the research object, and applies lean tools including value stream mapping, 5S management, standardized operation, and quick die change to conduct systematic process optimization. The results show that after 6 months of lean management implementation, the production cycle of the enterprise's core product is shortened by 32.7%, work-in-process inventory is reduced by 41.2%, overall equipment effectiveness is increased by 28.5%, and the first pass yield of products reaches 98.6%. This paper verifies the significant positive effect of lean management on the operational efficiency of manufacturing enterprises, and provides a replicable lean implementation path for similar small and medium-sized manufacturing enterprises.
Research on Strategies for Enhancing Manufacturers' Resilience Under Competition from E-Commerce Private Brands in the Smart Home Industry
Against the backdrop of platform-based transformation in the smart home industry, growing competition from e-commerce private brands and frequent supply chain disruptions have put manufacturers in a dilemma between channel dependence, market competition, quality improvement, and resilience building. To fill the research gap that separates quality competition and supply chain resilience, this paper establishes a two-echelon supply chain game model with a budget constraint, integrating quality investment and resilience investment. Using backward induction, we derive the subgame perfect Nash equilibrium and conduct comparative static analysis. The results reveal a stable proportional relationship between resilience and quality investments, which shrinks under resource constraints, thus restraining quality input. Platform private brand pricing positively motivates manufacturers’ quality investment. Supply chain disruption risks affect platform pricing depending on resilience efficiency, while tight budgets push platforms to lower prices. Resource constraints reduce manufacturers’ profits. This study constructs a quality-resilience coordinated decision framework, reflects realistic resource limits, and deepens the understanding of platforms’ dual identity. It provides managerial insights for manufacturers’ resource allocation and strategic adjustment, and supports platforms in maintaining ecosystem health.
Research on the Driving Effect of Corporate Digital Transformation on Green Technological Innovation
Under the "dual carbon" goals and digital economy integration, green innovation drives manufacturing's high-quality development and environmental protection, while digital transformation helps firms break green innovation bottlenecks. Using text analysis to measure digital transformation, this paper takes A-share manufacturing listed companies (Shanghai & Shenzhen, 2019–2024) as samples. With a year fixed-effects model, it tests digital transformation's impact, mechanisms, and heterogeneity on green innovation.Results show digital transformation significantly raises corporate green innovation. This holds after robustness tests including PSM, instrumental variable method, core variable replacement, observation period extension, multi-fixed-effects control, and sample scope adjustment.Mechanism tests reveal three paths: resource effect (easing financing constraints), governance effect (weakening agency conflicts), and multiplier effect (enhancing growth capacity), providing financial guarantee, governance support, and endogenous motivation.Heterogeneity analysis shows the positive effect is stronger in non-high-tech enterprises and heavily polluting industries. Further, digital transformation significantly drives both green invention patents and green utility model patents.This study expands research on digital transformation's economic consequences and green innovation's drivers, offering empirical evidence and practical references for manufacturing's digital-green synergy and for differentiated industrial policies.
The level of data element utilization in the integration of the digital and real economies drives AI technological innovation
Using a sample of Chinese A-share listed companies in core digital economy industries from 2015 to 2024, this study examines how data element utilization drives AI technological innovation. Employing a panel fixed‑effects regression model, we find that the level of data factor utilization has a significant positive impact on AI patent output. This effect is more pronounced in firms with low total factor productivity (TFP), exhibiting a "contrarian" catch‑up characteristic. The conclusions remain robust after substituting different TFP measurement methods. This study reveals the unique mechanism through which data elements enable late‑entrant firms to catch up technologically, providing empirical evidence for deepening data element market reforms.
Integration of the digital and real economies
An Analysis of the Current State and Future Trends in Climate Finance Research
Climate finance constitutes a pivotal driver of global climate governance. Drawing upon bibliometric methodology, this study presents a comprehensive review of climate finance research spanning the period from 2015 to 2024. The analysis reveals a notable asymmetry in existing research perspectives, characterized by a "mitigation-dominant" imbalance, with insufficient scholarly attention devoted to adaptation financing and issues of distributive justice in fund allocation. Core research themes predominantly concentrate on the impact of climate policies on climate finance efficacy, as well as the catalytic role of climate finance in fostering energy economy transitions and low-carbon technological advancement. However, extant scholarship primarily emphasizes aggregate assessment, while lacking dynamic analyses of capital flow tracking and the allocation of transnational responsibilities. Future research endeavors should transcend prevailing climate policy frameworks, expand the scope of climate financing subjects and objects, strengthen information disclosure mechanisms among multiple financing entities, and enhance investigations into the economic consequences of climate finance.
Data Factor Sharing and Digital Inclusive Finance: From the Perspective of Open Government Data
Against the backdrop of building a high-standard data factor market in China, digital inclusive finance has become a core engine for advancing inclusive economic growth, yet it faces a critical bottleneck in the transformation from "breadth coverage" to "in-depth services" rooted in long-standing information asymmetry and insufficient demand-side participation. This paper takes the opening of urban public data platforms in China as the analytical entry point, constructs a systematic theoretical framework to examine the impact of open government data (OGD) on the development of digital inclusive finance, and deconstructs its underlying transmission mechanisms. The theoretical analysis demonstrates that OGD, as a fundamental institutional innovation in the data factor market, can significantly promote the high-quality development of digital inclusive finance through two parallel core paths: reducing information error in the financial market and enabling financial technology innovation. Further theoretical exploration reveals the heterogeneous characteristics of OGD’s inclusive effect: its promoting role is more prominent in regions with sound market ecology, lower level of bank digital transformation, and higher degree of financial agglomeration. This paper breaks through the dominant supply-side research paradigm in digital inclusive finance, expands the theoretical research on the economic value of public data in the financial sector, and provides targeted policy references for building a data-driven inclusive financial system in China.
Internationalization and Competitive Dynamics of Electric Vehicles in Southeast Asia
Against the backdrop of global automotive electrification, Southeast Asia (ASEAN) has emerged as a core growth market for electric vehicles (EVs). This paper conducts a management-oriented systematic review to explore the internationalization strategies and competitive dynamics of EV firms in ASEAN. Grounded in the OLI paradigm, institutional theory and the Uppsala model, it synthesizes key internationalization approaches including regional integration, localization and strategic partnerships, and analyzes competitive interactions between global and local players driven by cost advantages, technological disruption, policy instruments and value chain restructuring. The review identifies critical research gaps including insufficient cross-national comparative research, limited long-term performance analysis, and inadequate attention to digitalization and institutional entrepreneurship. It further proposes a future research agenda to advance international business theories and managerial practices.
Measuring Collaborative Governance Performance in Supply Chains: A Multi-Dimensional Evaluation Approach
This study proposes a multi-dimensional evaluation approach to measure collaborative governance performance in supply chains. By integrating Analytic Hierarchy Process (AHP) and fuzzy comprehensive evaluation, the research constructs a structured model capturing coordination, capability, and operational efficiency. The findings indicate that collaborative structures significantly influence system performance, while institutional conditions shape governance outcomes through constraint and incentive mechanisms. The study contributes to both methodological development and governance optimization.
How Green Public Funds Empower Enterprises' New Quality Productivity — An Empirical Study Based on Path Analysis and Threshold Characteristics
Against the backdrop of China's current efforts to deeply integrate green finance with high-quality development, green public funds, as an important market-oriented investment force, require in-depth research on their impact mechanism on corporate new-quality productivity. Based on data from A-share listed companies that received investments from green public funds between 2015 and 2024, this paper empirically investigates the influence of green public funds on corporate new-quality productivity. The study finds that introducing green public fund investments can significantly enhance corporate new-quality productivity, a conclusion that remains valid after robustness testing and endogeneity treatment. Heterogeneity analysis results indicate that green public funds have a more pronounced effect on promoting new-quality productivity in state-owned enterprises and non-heavy-pollution enterprises. Pathway analysis reveals that green public funds can improve corporate new-quality productivity by reducing carbon emission intensity, incentivizing green innovation, and alleviating financing constraints. Further research also demonstrates a dual threshold effect in the impact of green public funds on corporate new-quality productivity, with the promoting effect significantly strengthening when the fund's shareholding ratio exceeds a specific threshold value. This study provides evidence for green finance to boost new-quality productivity development and offers reference value for China to accelerate green transformation and build a modern industrial system.
Analysis of Legal Issues Regarding the Purge of Registration of Limited Liability Companies
In recent years, with the efforts of the people's courts to strengthen enforcement and limit the high consumption of debtors, the number of cases filed by company legal representatives or directors, supervisors, and senior executives to eliminate or change the company's business registration has increased significantly. However, there are slight differences in the handling approach. This paper takes limited liability companies as the research object and sorts out the main disputes in the disputes over the elimination of registration. Combining case studies, the paper argues and proposes the judicial path for the commercial disputes of eliminating registration, and builds a "three-in-one" trial model integrating internal company relief, communication coordination with the registration authority, and case trial, hoping to provide some useful reference for unifying the judicial thinking.
Enterprise Supply Chain Risk Management Under Digital Transformation
Against the backdrop of a complex and volatile global economic environment, enterprise supply chains are exposed to multiple forms of disruption, including pandemics, geopolitical tensions, market instability, operational breakdowns, and digital vulnerabilities. Recent studies have shown that supply chain risk management has evolved from a narrow operational concern into a broader governance issue closely associated with resilience, adaptability, and long-term viability. At the same time, digital transformation has created new opportunities for improving visibility, coordination, forecasting, and decision-making, while also introducing risks related to cybersecurity, information fragmentation, and technological dependence. In this background, this paper proposes a conceptual framework for enterprise supply chain risk management by integrating recent literature on risk typology, resilience, and digital innovation. The study identi-fies four major categories of supply chain risk: supply risk, demand risk, opera-tional risk, and information risk. It further argues that effective risk manage-ment should follow a dynamic process of risk identification, assessment, re-sponse, and monitoring. On this basis, the paper proposes a three-dimensional strategic framework composed of preventive strategies, responsive strategies, and innovation-oriented strategies under digital transformation. The findings suggest that enterprises can strengthen supply chain resilience not only through traditional measures such as supplier diversification, safety stock, and emer-gency planning, but also through digital technologies including big data analyt-ics, the Internet of Things, artificial intelligence, blockchain, and digital twins. This study contributes to the understanding of supply chain risk governance in the digital era and provides practical implications for firms seeking to balance efficiency, resilience, and sustainability.
The Impact of Digital Transformation on Green Technological Innovation of Multinational Manufacturing Enterprises
Fueled by accelerating digital advances and intensified national carbon neutrality commitments, the strategic convergence of digital transformation and environmental sustainability has become a pivotal lever for upgrading China’s manufacturing toward high-quality, resilient growth. As the cornerstone of the Fourth Industrial Revolution, digital technologies reconfigure innovation architectures, compress green R&D timelines, and catalyze the synthesis, dissemination, and market deployment of green knowledge. Using a comprehensive panel of A-share manufacturing listed firms from 2014 to 2024, this study constructs a holistic digitalization index across four dimensions. Employing dynamic panel models and machine learning-enhanced robustness checks, we assess digitalization’s causal effects on green innovation quantity, quality, and translational efficacy, further unpacking mechanisms, boundary conditions, and theoretical foundations for theory and policy.
Government Guidance Funds and Corporate Organizational Resilience
As an important instrument for supplementing fiscal funds and correcting market failures, government-guided funds play a significant role in cultivating and enhancing corporate organizational resilience. Based on data from China's Shanghai and Shenzhen A-share listed companies from 2008 to 2023, this paper analyzes the impact mechanism of government-guided funds on corporate organizational resilience. The findings reveal that government-guided funds significantly strengthen corporate organizational resilience. Mechanism tests indicate that these funds enhance organizational resilience by alleviating financing constraints, boosting innovation capability, and raising risk-taking levels. Heterogeneity analysis shows that the promoting effect of government-guided funds is more pronounced in high-tech enterprises, firms with higher managerial ownership, and enterprises in the growth stage. The research conclusions provide important insights for enhancing corporate organizational resilience to achieve high-quality development, and also offer policy references for government-guided funds to support the sustainable development of the real economy.
Digital Transformation and Carbon Emission Reduction in Manufacturing Firms: The Dual Roles of Resource Allocation Efficiency and Green Technology Innovation
Against the backdrop of China’s “Digital China” initiative and “dual carbon” goals, this study examines the impact of digital transformation on carbon emission reduction in Chinese A-share listed manufacturing firms from 2011 to 2022. Using a three-way fixed effects model, we find that digital transformation significantly reduces corporate carbon emission intensity, and this result remains robust after endogeneity treatment and a series of robustness checks. The carbon reduction effect is realized through two core mediating paths: improving resource allocation efficiency and promoting green technology innovation. Further analysis shows that carbon disclosure quality positively moderates the above relationship, and the carbon reduction effect of digital transformation is heterogeneous across firm ownership, regional distribution, and pollution attributes. This study provides empirical evidence for the coordinated digital and green transformation of China’s manufacturing sector.
Hypernetwork Symbiosis and Industrial Chain Resilience: A Multilayer Network Perspective
This study aims to explore how hypernetwork symbiosis influences the resilience of industrial chains within the context of increasingly complex global production networks. Drawing on symbiosis theory and a multilayer network perspective, the paper constructs a theoretical framework that conceptualizes cross-national cooperation as an integrated system composed of industrial cooperation, technological collaboration, and trade networks. By examining the structural interactions within and across network layers, the study identifies four key mechanisms through which hypernetwork symbiosis affects industrial chain resilience: intra-layer self-interaction, intra-layer cross-node interaction, inter-layer same-node interaction, and inter-layer cross-node interaction. The analysis shows that intra-layer interactions enhance redundancy, substitutability, and stability within individual networks, while inter-layer interactions promote resource integration, functional complementarity, and coordinated adjustment across networks. Together, these mechanisms strengthen the capacity of industrial systems to absorb shocks, adapt to disruptions, and recover from crises. The findings suggest that hypernetwork symbiosis provides an important structural foundation for improving industrial chain resilience in an increasingly uncertain global economic environment.
Mediating Role of Training Perception And Commitment To The Relationship Between Management Practices and Hygiene Practices In Haidilao Company In China
This study aimed to evaluate the hygiene practices of employees in selected Haidilao branches in Guangzhou, China, examine management practices concerning communication and employee involvement, and understand employees' perceptions of training and organizational commitment. It further explored whether training perception and commitment served as mediating variables between management practices and employees' hygiene behaviors, with the intention of developing a comprehensive hygiene management plan based on the findings. Additionally, the study profiled respondents in terms of age, gender, educational attainment, and years of service to grasp their demographic characteristics and their connections to workplace hygiene performance. A quantitative-descriptive research design was adopted, employing a validated Likert-scale questionnaire administered to 161 frontline employees selected through random sampling via the roulette method. Data analysis utilized frequency and percentage for respondent profiling; mean and standard deviation for assessing hygiene practices, communication, involvement, training, and commitment; mediation regression analysis to test the mediating effects of training and commitment; and structural equation modeling (SEM) to validate direct and indirect relationships among variables. All statistical treatments were facilitated by the Statistical Package for Social Sciences (SPSS) to ensure the accuracy and reliability of the results. The findings revealed that respondents demonstrated moderate hygiene practices, with strong compliance in critical food safety tasks but inconsistent adherence in some areas. Management practices in communication and employee involvement were rated as fair, exhibiting strengths in openness and feedback but weaknesses in information accessibility and structured participation. Training and organizational commitment were also evaluated at fair levels, indicating general adequacy yet highlighting the need for improvement. Mediation analysis confirmed that commitment significantly mediated the relationship between management practices and hygiene behaviors, whereas training showed no significant mediating effect. Based on these results, a comprehensive hygiene management plan was proposed to enhance communication, involvement, training effectiveness, and employee commitment across Haidilao branches.
Combined Forecast of Grain Production in Anhui Province: Based on an Induced Ordered Set Submodel
To enhance the accuracy of grain yield forecasting in Anhui Province, a combined forecasting model based on the Inductive Ordered Weights Grouping by Average (IOWGA) operator was established. This model utilises the GM(1,1) model and the quadratic exponential forecasting model as individual forecasting components. Application of this model to forecast grain production in Anhui Province from 2013 to 2022 reveals that the combined prediction results significantly outperform the aforementioned individual forecasting models. This approach thus offers valuable insights and methodologies for projecting future grain yields in Anhui Province.
The Current Status, Problems, and Countermeasures of the Publishing Industry’s Transition Toward Data‑Driven Development
In the context of the emerging era of digital intelligence, the high-quality development and transformation of the publishing industry depend not only on digital technological innovation but also on the central role of data. As a critical factor of production, data plays an indispensable role in promoting the transformation of the publishing industry toward a data-driven paradigm. Within this context, publishing forms are gradually evolving from traditional print-based publishing to data-centered data publishing. This transformation enables the publishing industry to achieve multidimensional improvements in the process of digital development. Based on the contemporary technological and social context, this paper examines the current status of the publishing industry’s transition toward data-driven development, analyzes the problems encountered in the transformation of publishing business models, and proposes policy recommendations and strategic pathways for the future development of data publishing. Based on literature analysis and an examination of industry practices, this paper analyzes the current status, identifies key obstacles including technological bottlenecks, copyright dilemmas, and international expansion challenges, and proposes development pathways such as building a new data circulation ecosystem, establishing a compliance governance system, and reshaping narrative systems to promote international markets.
Carbon Emission Prediction for Sichuan Province Using a SHAP-Explained Machine Learning Framework
This study develops a provincial carbon accounting and forecasting framework for Sichuan Province covering 2005–2022 under a consumption-based boundary. Direct fossil fuel emissions and indirect electricity-related emissions are clearly distinguished to ensure accounting consistency and additive closure. On this basis, a Transformer-based machine learning model integrated with SHAP is constructed to predict carbon emissions and identify peak characteristics. The results show high historical fitting accuracy and project that Sichuan’s carbon emissions will reach a plateau-type peak around 2031, followed by a gradual decline. SHAP-based interpretation indicates that total energy consumption and the carbon emission factor are the dominant drivers, while economic scale and energy intensity exert secondary but significant effects. Robustness tests confirm the stability of the explanatory structure. The proposed framework integrates accounting consistency, predictive modeling, and interpretability analysis, providing empirical support for regional peak management and low-carbon transition planning.
The Impact of Technological Revolution on the Natural Gas Industry
China's natural gas industry has entered a new stage of continuous and coordinated development. With the ongoing promotion of green development and the concept of harmony between humans and nature, the three major state-owned natural gas enterprises will play an increasingly critical role in driving progress. At the same time, the construction of natural gas pipelines and storage facilities is accelerating, and the supply of natural gas continues to grow steadily. Previously, China’s natural gas industry faced several challenges, including mismatched supply and demand, excessive reliance on imports, delayed pipeline construction, an imperfect pricing mechanism, and inadequate regulatory frameworks. As the country enters a new era, with the deepening of energy structure reforms and continuous improvement in industrial infrastructure, natural gas consumption demand is expected to experience explosive growth. In the future, technological advancements will drive increased natural gas supply through multiple channels and optimize pipeline networks through digital technologies, improving energy distribution efficiency. At the same time, the application of artificial intelligence and big data analytics will help refine the natural gas market pricing mechanism, fostering a more flexible and transparent pricing system. To better adapt to industrial changes, it is also necessary to accelerate the development of regulations and improve the regulatory framework for the natural gas industry, ensuring its healthy and sustainable development. This development trend, supported by advanced technologies and innovative practices, will address current industry bottlenecks and propel the natural gas industry into a more efficient and intelligent new era.