Search Results (2,221)

Upstream textile small and medium-sized enterprises (SMEs) frequently exhibit constrained supply chain resilience owing to persistent information latency and structural dependence on downstream orders. To address these challenges, this study develops and validates a customer-to-manufacturer (C2M) intelligence framework that enables data-driven production planning using publicly available e-commerce data. The framework incorporates ethically compliant acquisition of consumer demand signals, semantic translation of unstructured market data into textile engineering attributes, machine-learning-based demand forecasting, and human-centric decision support. Utilizing 3.87 million consumer comments from 127,846 product listings, a Neural Boosted Tree model with entity embeddings for textile attributes was constructed. This model achieved a mean R² of 0.921 in cross-validation, surpassing benchmark methods. Consumer comment volume was validated as a proxy for sales activity, facilitating demand estimation. Forecasts were translated into production guidance using Monte Carlo simulation and a decision dashboard. In a 12-month field study at a Taiwanese dyeing SME, implementation resulted in a 28% reduction in inventory value, a 31% decrease in dye lot changeovers, and a 16% increase in capacity utilization. This research extends the C2M paradigm from downstream retail contexts to upstream textile SMEs, proposes an integrated and operationally feasible intelligence framework for resource-constrained manufacturers, and demonstrates how digital intelligence can enhance supply chain resilience while supporting, rather than replacing, human decision-making. The results indicate that upstream textile SMEs can leverage publicly visible e-commerce signals to enhance production planning responsiveness, minimize inventory exposure and dye-lot disruptions, and strengthen resilience to demand uncertainty through planner-centered digital decision support. Full article

Against the backdrop of low-carbon transportation and urban logistics transformation, metro-based freight is regarded as an important pathway for emission reduction. This paper constructs a tripartite evolutionary game model involving the government, logistics enterprises, and metro operators, and analyzes multi-agent strategy evolution and the influence of key parameters using replicator dynamics equations and numerical simulation. The results show that well-designed subsidies and penalties can effectively promote a stable state characterized by “active government intervention, active response from logistics enterprises, and low-carbon integrated passenger and freight transportation by metro operators”. Reducing the cost of transformation can improve evolutionary efficiency, while excessively high subsidies may weaken the government’s willingness to intervene. This study provides insights for optimizing low-carbon transportation policies and supporting the development of metro-based freight systems. Full article

The development of new energy vehicles (NEVs) is key to the green and high-quality upgrading of China’s automotive industry, with the penetration rate of domestic NEV passenger cars exceeding 50%. However, deepening industry–university–research (IUR) collaborative innovation to break core technological bottlenecks remains a critical challenge. To address the limitations of existing studies—mostly focusing on dyadic interactions or hypothetical numerical simulations—this study constructs a novel tripartite evolutionary game model of NEV enterprises, university–research institutions, and the government, fully incorporating the industry’s unique attributes of high technological complexity, industrial integration, and innovation risk. Innovatively, we calibrate and verify the model using actual operation data from the Yancheng Institute of Technology–Yueda New Energy Vehicle College, bridging the gap between traditional theoretical simulation and industrial practice. The quantitative findings show that: a 40–60% balanced benefit distribution and matching cost-sharing mechanism are the core conditions for the system to reach an evolutionarily stable state; when the achievement transformation coefficient exceeds 50%, the convergence rate of stable cooperation willingness between both parties increases by over 40%; a moderate government subsidy intensity of 55% effectively accelerates the system’s positive evolution, with the incentive effect of subsidies diminishing rapidly in the mature collaboration stage; and robust collaborative innovation technology can reduce government intervention demand by more than 60%. This study enriches the theory of NEV IUR collaborative innovation, breaks the limitations of traditional research frameworks, and provides actionable references for promoting the high-quality development of the NEV industry. Full article

The shortage of interdisciplinary talent is a critical bottleneck constraining the development of smart agriculture. Taking Jiangsu Province as a case study, this research constructs and empirically validates an ecosystem model for cultivating interdisciplinary talent oriented toward smart agriculture. In the theoretical construction phase, an initial three-dimensional model covering “core actors,” “supportive environment,” and “resource elements” was proposed based on ecosystem theory and literature review. This model was subsequently refined through in-depth interviews (March–August 2024, 60–120 min each) and thematic analysis with 58 diverse stakeholders across 13 prefecture-level cities in Jiangsu Province, encompassing universities, agribusinesses, government agencies, research institutes, and frontline practitioners. In the empirical testing phase, structural equation modeling was employed to analyze 382 valid questionnaire responses covering six dimensions: policy environment, market environment, university–enterprise collaboration, curriculum resources, platform resources, and talent cultivation effectiveness (20 items in total). The findings indicate that: (1) the ecosystem model demonstrates good fit and strong explanatory power, with a pronounced “university–enterprise” dual-core driving effect; (2) government policy guidance and platform construction play pivotal supportive roles; (3) market demand and industrial policy constitute critical external driving forces; and (4) “industry–education integrated practice platforms” together with “modular interdisciplinary curricula” exert the most direct positive influence on cultivation outcomes. Based on these findings, this study offers systematic recommendations from three perspectives—mechanism coordination, policy optimization, and resource allocation—providing a theoretically grounded and practically referenced solution for cultivating interdisciplinary talent in smart agriculture. Full article

Against the backdrop of the comprehensive advancement of the law-based governance of China and the “dual carbon” strategic goals, existing research still lacks a systematic discussion on how corporate compliance management affects ESG performance, and few studies have constructed targeted compliance management indicators from a textual perspective. To fill this research gap, this paper aims to explore the influence of corporate compliance management on ESG performance. Using Chinese A-share listed firms on the Shanghai and Shenzhen Stock Exchanges from 2010 to 2023 as research samples, this study adopts text mining techniques, combined with a panel regression model and a mediating effect model, to construct an indicator of corporate compliance management and examine its impact on ESG performance. The empirical results show that compliance management significantly improves corporate ESG performance and functions mainly through three channels: promoting corporate green innovation, fostering corporate ethical culture, and reducing agency costs. Heterogeneity tests indicate that the positive relationship is more pronounced in state-owned enterprises and in firms with higher managerial power. Further analysis reveals that compliance management also helps reduce the divergence in ESG ratings among Chinese firms, and the construction of all dimensions of compliance management can contribute to the improvement of corporate ESG performance. These findings enrich the literature on the economic consequences of compliance management and the determinants of ESG performance and provide theoretical guidance for Chinese firms to enhance ESG performance via compliance management. Full article

As major sources of pollution and carbon emissions, energy enterprises have long faced challenges in their technological transformation due to the industry’s characteristics of high investment costs and strong lock-in effects. While formal mechanisms such as government subsidies can impose short-term constraints, they fail to stimulate the sector’s intrinsic motivation. Can the reputation incentive mechanism be the key to breaking the deadlock? This paper constructs a three-party evolutionary game model involving energy enterprises, the public, and the government from the perspective of informal institutions. For the first time, it incorporates the dual effects of reputation gains and losses into a unified framework. The results show that reputation incentives are not merely a “cherry on top,” but rather independently drive transformation by moderating enterprises’ cost–benefit structures. The evolution of the three-party strategies exhibits dynamic synergy, and the system equilibrium depends on the threshold matching of key parameters. Subsidy policies are effective in the short term, but may crowd out the role of reputation in the long term. This paper reveals the underlying logic by which the integration of informal institutions and formal regulations drives profound transformation, offering new theoretical perspectives and practical guidance for designing incentive-compatible multi-stakeholder governance frameworks. Full article

Digital transformation (DT) has become a core strategic priority for major economies, with global investments exceeding $2 trillion worldwide and $0.55 trillion in China alone in 2025. As DT reshapes the norms of international competitiveness and sustainable development, experts frequently emphasize the need for innovative cross-domain frameworks to decode the mechanisms of DT success. Even though public economists view government attention to digital talent (GADT) as a key driver of DT, there is an acute shortage of empirical models that explain how it affects firm-level DT directly or indirectly through intermediary mechanisms, e.g., talent agglomeration, absorptive capacity, and subsidies. Thus, exploring this relationship empirically holds significant theoretical and practical value. Based on the latest keyword frequency data from government policies and annual reports from 2008 to 2022 for 3952 A-share listed companies across 243 cities in 31 provinces, this study constructs an interactive two-way fixed-effects panel regression model with 35,058 valid observations. The empirical results show that GADT significantly promotes the digital transformation of enterprises (EDT), supported by enterprise talent agglomeration, absorptive capacity, and government digital talent subsidies. Notably, the effects of GADT on EDT were heterogeneous, with a significant positive impact observed in labor-intensive enterprises, peripheral cities, and enterprises in non-digital-economy pilot areas. Moreover, the effects of GADT on EDT were less pronounced among technology-intensive enterprises (e.g., automotive, pharmaceutical, and manufacturing), central cities (e.g., Chengdu, Fuzhou), and those in digital economy pilot areas (e.g., Xinjiang, Ningxia). This study aims to examine the impact mechanism of GADT on EDT, thereby providing theoretical support and practical implications for more targeted and effective digital talent policies. Full article

The electronic and electrical architecture of vehicles has rapidly evolved to centralized. At present, there is no unified consensus on the R&D strategy of the platform in the industry, and there is also a lack of a quantitative decision-making framework that can be implemented. This study takes the centralized architecture platform as the research object, constructs a two-stage analysis framework of “advanced constraint screening-cost quantitative evaluation”, uses a fuzzy-set qualitative comparative analysis method to screen feasible R&D strategy combinations that meet the requirements of the architectural advancement, builds a total cost of ownership evaluation system around the software and hardware elements related to the architecture platform, and systematically analyzes the optimal cost R&D strategy combinations of car enterprises with different mass production scales under the two scenarios of Multi-Box and One-Board. The research results show that adaptive platform middleware and framework middleware are the core necessary elements to realize the advanced architecture; the amortization cost of architecture is negatively correlated with the scale of mass production, and the cost of in-house R&D is highly dependent on large-scale amortization; and there are differentiated optimal solutions in the framework selection and R&D strategy combination of automakers with different mass production scales. This study can provide quantitative reference and practical guidance for R&D decision making of centralized architecture platform for automakers. Full article

Against the backdrop of a technological revolution centered on green and low-carbon development, the deep integration of digitalization and greening has become a core engine for high-quality progress. Moving beyond linear perspectives of environmental governance, this study constructs tripartite evolutionary game models to dissect the strategic interactions among government, enterprises, and consumers. Focusing on the institutional context of e-commerce, we examine how platform-enabled transparency mechanisms (e.g., blockchain traceability and carbon labeling) shape these interactions through key parameters: greenwashing detection (θ), premium loss coefficient (η), and information screening cost (C_D). The analysis reveals that the long-term trajectory is fundamentally determined by the intrinsic economic viability of corporate transformation. Government intervention acts as an equilibrium selector, influencing the speed of convergence, while product value (consumer utility and premium) and platform transparency determine the sustainability of the equilibrium. Critically, the tripartite model shows that the optimal outcome—full enterprise transformation and consumer adoption—can be achieved without sustained government intervention when product fundamentals are sufficiently attractive. This demonstrates the potential for market self-regulation to sustain digital–green synergy. The study makes three contributions: it captures the full tripartite feedback loop, reveals the saturation effect of policy intensity, and embeds platform transparency mechanisms into an evolutionary framework. The findings reframe the government’s role as a temporary enabler and position e-commerce platforms as key governance intermediaries, offering a theoretical basis for adaptive governance strategies in digital commerce. Full article

Digital village construction (DVC) is an important tool for promoting rural revitalization and increasing farmers’ income. This paper selects panel data at the county level and employs the difference-in-differences (DID) method, combined with mediation effect models, heterogeneity tests, and multi-dimensional robustness tests, to systematically explore the impact of DVC on farmers’ income and its internal transmission path. According to the research, the DVC has a positive impact on farmers’ income at the 1% significance level, a conclusion that remains valid after robustness tests such as PSM-DID and substitution of the explained variable. Industrial restructuring, agricultural mechanization, and enterprise agglomeration are positively significant at the 5%, 1%, and 1% levels, respectively, indicating that these three are the core intermediary mechanisms for increasing farmers’ income, promoting farmers’ income growth by releasing structural dividends, efficiency dividends, and agglomeration dividends, respectively. The income-increasing effect of DVC exhibits significant heterogeneity, being positively significant at the 5% and 1% levels in areas with a deep digital divide and non-grain-producing areas, but not significant in areas with a shallow digital divide and major grain-producing areas. Therefore, policy recommendations are to optimize resource allocation, broaden income-increasing pathways, and implement differentiated policies. Full article

Using 29,278 firm-year observations of Chinese A-share listed firms from 2012 to 2023, this study examines whether data assetisation improves corporate investment efficiency and whether bank fintech conditions shape this relationship. Data assetisation refers to the process through which firms transform data resources into economically valuable, governable, and deployable assets. We construct a text-based proxy from annual reports using a Word2Vec-expanded lexicon and further distinguish between own-use and transactional data assets. The study finds: (1) Data assetisation significantly enhances corporate investment efficiency, with self-use data assets demonstrating a stronger driving effect. (2) Mechanism analysis reveals that data assetisation alleviates underinvestment by easing financing constraints and leveraging the “talent effect”. Concurrently, it mitigates overinvestment by reducing agency problems and accelerating digital transformation, thereby enhancing investment efficiency. (3) Heterogeneity tests indicate that the positive impact of data assetisation on investment efficiency is more pronounced among growth-stage enterprises, technology-intensive firms, and companies operating in regions with high bank liquidity. (4) Banking fintech positively moderates the enhancement of corporate investment efficiency through data assetisation, with a more pronounced effect on alleviating underinvestment. However, it may also exacerbate overinvestment. This study contributes to sustainable economic development by improving resource allocation efficiency, reducing capital misallocation, and supporting high-quality, low-waste, and sustainable growth of the real economy. Consequently, enterprises should vigorously develop data assetisation, applying different types of data assets to specific use cases to unlock data dividends. This approach supports the scientific development of corporate investment decisions and enhances investment efficiency, laying a micro-level foundation for sustainable socio-economic development. Full article

The production and leasing of electric construction machinery play a critical role in the low-carbon transition. However, from a multi-cycle dynamic perspective, there is a lack of targeted research on how to enhance electric goodwill and AI-enabled maintenance service levels while maximizing enterprise profits. To fill this gap, this study incorporates AI-enabled O&M effort, R&D technology, AI-enabled maintenance effort, and advertising effort into a long-term dynamic framework to examine optimal decisions for the manufacturer and the lessor. We assume that the information in the leasing supply chain is symmetric, that the marginal profits of the manufacturer and the lessor are fixed parameters, and that the AI-enabled maintenance service effort level and the electric goodwill are taken as state variables. We develop differential game models across four decision cases: centralized (Case C), decentralized (Case D), unilateral cost-sharing contract (Case U), and bilateral cost-sharing contract (Case B). Results demonstrate monotonic state variable trajectories. Both Case U and Case B can achieve supply chain coordination, with the profit-sharing mechanism in Case B proving superior. In addition, the optimal cost-sharing proportion depends on the relative sizes of the manufacturer’s and the lessor’s marginal profits in both Case U and Case B. The AI-enabled maintenance service plays a significant role in enhancing equipment reliability and supply chain resilience. In addition, the impacts of key parameters on optimal decision variables, state variables, profits, and coordination of the leasing supply chain are comprehensively discussed. Full article

Against the dual backdrop of worsening air pollution and industrial intelligent transformation, industrial robot technology has become an important means to promote air pollution synergistic governance. This study innovatively incorporates public environmental satisfaction and industrial robot application as dynamic mechanism variables, constructing an evolutionary game model involving the government, industrial enterprises, and the public. Through theoretical analysis and numerical simulation, the study reveals the influence mechanism of key cost–benefit parameters on stakeholders’ strategic interaction and the system’s evolution path. The conclusions are as follows: (1) The government’s environmental supervision directly affects enterprises’ green transformation willingness, and enterprises’ behavior reversely impacts public satisfaction and supervision effectiveness, forming a “supervision–response–feedback” closed-loop. (2) The cost and benefit parameters related to industrial robots are crucial for the evolution of the game system, and there is significant heterogeneity in their impact on the strategic choices of the three parties. The robot adaptation transformation of enterprise industrial depends on the comprehensive consideration of the transformation cost and the green benefits. Public supervision is regulated by both the supervision cost and the incentive benefit. The government regulation takes into account both the regulatory cost and the loss of social reputation. Various parameters dynamically regulate the system’s equilibrium by altering the party’s cost–benefit structure. (3) The application of industrial robots and the feedback of public environmental satisfaction form a coupling effect, jointly determining the long-term evolution direction of the game system. When the cost benefit and supervision incentives are well-matched, enterprises will actively promote the green transformation of industrial robots in order to achieve intelligent pollution control. The effectiveness of public supervision has also been fully realized. The dynamic adaptation of the two components can lead the system towards an efficient and stable equilibrium in air pollution governance. Full article

As a significant source of global carbon emissions, the construction industry (CI) urgently needs to promote green transformation with the help of digital twin (DT) against the backdrop of human–machine collaboration and sustainable development advocated by CI 5.0. However, there is still a lack of systematic research on its specific driving mechanism and carbon reduction path. This study uses a systematic literature review (SLR) to explore how five key DT-enabled capabilities, namely, resource management (RM), process optimization (PO), real-time monitoring (R-Tm), sustainable design (SD), and predictive maintenance (PM), influence three performance indicators: efficiency improvement (EI), energy optimization (EO), and cost control (CC). Data from 490 companies were analyzed using partial least squares structural equation modeling (PLS-SEM) and a multilayer perceptron (MLP) with Shapley additive explanation (SHAP). The results show that the PLS-SEM and MLP models showed consistent patterns, with EO exhibiting the strongest predictive performance (Q² = 0.372; R² = 0.3666), followed by EI (Q² = 0.307; R² = 0.3109) and CC (Q² = 0.305; R² = 0.2609); the SHAP results further indicated that RM contributed most to EI (0.242), while PO was the most important driver for both EO (0.304) and CC (0.259). Academically, it introduces a quantitative approach combining PLS-SEM and machine learning. Practically, it highlights the priority of key technologies with cross-dimensional effects and offers guidance for governments to optimize digital resource allocation and carbon performance evaluation, as well as for enterprises to apply DT more effectively. Full article

Existing studies on greenwashing have primarily focused on post-incident supervision, with limited attention given to proactive mechanisms. This study aims to develop an early warning evaluation model for greenwashing behavior in building materials enterprises exposed to negative public opinion. The main findings are as follows: (1) Drawing on actor network theory, gray system theory, the analytic network process, and gray fuzzy comprehensive evaluation, this study constructs an early warning evaluation model for greenwashing behavior in building materials enterprises. This model comprises 5 first-level dimensions and 20 s-level indicators, integrating key stakeholders (i.e., government, negative public opinion, media, the public, and enterprise) and is validated through case analysis. (2) Government dimension: Environmental regulation intensity emerges as the most critical indicator. (3) Negative public opinion dimension: Attention is the most critical indicator. (4) Media dimension: Media visibility ranks as the most critical indicator. (5) Public dimension: Public sentiment is the most influential indicator. (6) Enterprise dimension: The environmental performance level is the most critical indicator. This study offers both theoretical and practical foundations for the early warning, monitoring, and governance of enterprise greenwashing, contributing to the advancement of sustainable development and transparent environmental communication in the building materials industry. Full article