Integrated assessment of resource synergy and emission reduction benefits in iron and steel and cement industries

How does inland Free Trade Zones in China trigger industrial green transformation? Evidence from policy text mining

Airline industry transformation: Does cost efficiency reflect business model convergence?

Subject. In the context of the digital transformation of the construction industry, the key barrier to increasing economic efficiency is not the lack of data, but the inability of existing management systems to integrate and analyze heterogeneous information flows to build reliable forecasts. The separation of static information models (BIM) and dynamic equipment telemetry (IoT) generates an asymmetry of management decisions, leading to systematic budget overruns and delays in the implementation of investment and construction projects. Objectives. Development of an economic and mathematical methodology for predictive management of construction processes, which minimizes operational risks and increases the investment attractiveness of projects by integrating deep machine learning methods into the production planning contour. Methods. The research is based on the synthesis of methods of systems analysis, theory of economic risks and technologies of intellectual data processing. Using Monte Carlo stochastic modeling algorithms, a unique dataset has been generated that combines the attribute characteristics of BIM models (IFC classes, material volumes), telemetry flows of construction equipment, and external factors (weather conditions, logistical delays). Recurrent neural networks of the LSTM architecture with mathematical formalization of the optimization process of the objective function of economic losses are used to identify hidden nonlinear dependencies. Results. A methodology for integrating heterogeneous data has been developed and verified, including strict rules for time synchronization (Δt = 1 hour) and semantic binding via globalId identifiers. A comparative analysis of four architectures (linear regression, Random Forest, XGBoost, LSTM) on a test sample demonstrated a statistically significant superiority of recurrent networks: the average absolute prediction error was reduced to 1.04 days (MAPE = 4.1%) with a coefficient of determination R2 = 0.94, which is 3 times more accurate than traditional methods and 38% more accurate than ensemble methods. algorithms of gradient boosting. Conclusions. The introduction of intelligent predictive management systems integrating BIM and IoT ensures the transition from reactive response to proactive risk management, creating an economic effect by reducing unproductive downtime, optimizing resource use and minimizing penalties for failure to comply with contractual obligations. The proposed methodology lays the foundation for the creation of self-learning digital counterparts of construction sites and can be scaled to enterprises of the investment and construction complex operating in a highly volatile environment.

With the exponential growth of sensing technology and computing power, artificial intelligence (AI) is increasingly deeply involved in the field of sports prediction, driving a paradigm shift in sports scientific research from "experience-driven" to "data-driven". This article systematically reviews the evolution logic of AI in sports prediction and analyzes the underlying mechanism of the migration from traditional statistical models to deep learning and reinforcement learning algorithms. The study finds that AI, through the deep mining of multi-source heterogeneous sports data, not only demonstrates remarkable accuracy in predicting competitive performance and assessing the risk of sports injuries, but also achieves functional reconfiguration in the dimensions of sports industry decision support and enhanced spectator experience. This article constructs a technical path model for AI sports prediction and conducts a deep examination of its value spillover and technical limitations in practical applications. The research aims to provide theoretical support for the digital transformation of China's sports industry and offer path references for the optimization and ethical governance of intelligent sports prediction algorithms.

Introduction Ecological resilience, which is the core capacity of ecosystems to withstand disturbances, maintain functions, and undergo adaptive transformation, is critical to ecological conservation and high-quality development in the Yellow River Basin. Methods This study examines 60 prefecture-level cities in the Yellow River Basin using 2006–2023 panel data. Adopting an evolutionary resilience perspective, we develop a three-dimensional ecological resilience framework—resistance, adaptation, and innovation—and combine boxplot analysis, GIS-based spatial analysis, and the Spatial Durbin Model (SDM) to reveal spatiotemporal patterns of ecological resilience and quantify local and spillover effects of natural and socio-economic drivers. Results (1) Ecological resilience in the Yellow River Basin followed a fluctuating upward trend between 2006 and 2023, with widening regional disparities and a distinct spatial pattern: high in the lower reaches, low in the middle, and volatile in the upper reaches. (2) SDM results reveal a U-shaped direct effect of economic development (PGDP) on ecological resilience; industrial structure (IS), government intervention (GOV), and population density (POP) significantly reduced local eco-logical resilience, while residents’ living standards (DPI) enhanced it. (3) Spatial spillovers highlight interregional complexity: PGDP’s indirect effect is also U-shaped; urbanization (UR), POP, and DPI positively influence neighboring areas. Conclusion The study concludes with policy recommendations to implement differentiated basin-wide collaborative governance strategies, promote green industrial transformation, and leverage the positive spatial spillover effects of urbanization and improvements in residents’ living standards.

In the era of the digital economy, Artificial Intelligence (AI), as the core driving force of the new generation of information technology, has become a key support for promoting the transformation and upgrading of local industries and enhancing industrial core competitiveness. Currently, local industries in China are facing development bottlenecks such as homogeneous competition, low-end locking of industrial chains, insufficient innovation capability, and relatively low production efficiency. The deep integration of AI technology can effectively address these industrial development pain points and drive local industries toward intelligent, high-end, and green transformation. This article, based on the practical development of local industries, avoids experimental verification, dataset analysis, and review-style writing patterns, focusing instead on the practical logic, core pathways, and safeguarding strategies for AI to empower the capability enhancement of local industries. By combining typical local industry application scenarios, it clarifies AI's empowerment pathways in key fields such as agriculture, industry, and services. Through the use of charts to present key content, it provides actionable practical references for local governments and enterprises to promote deep integration of AI and industry and to enhance comprehensive industrial capabilities, thereby supporting high-quality local economic development. The AI empowerment practices of China's local industries summarized in this paper provide a reference model for the digital transformation of regional industries in emerging economies and offer a Chinese example for the global integration of digital technology and the real economy.

This study examines the transformation of the traditional batik industry in Lamongan through the integration of green economy principles and the ethical framework of Islamic philanthropy, analyzed from the perspective of maqāṣid al-sharīʿah. The research highlights structural inequalities in which small-scale artisans disproportionately bear ecological burdens while larger actors capture greater economic benefits, reflecting core concerns of political ecology. Employing a livelihood approach and a sustainable development framework, the study explores how zakat, infaq, ṣadaqah, and waqf (ZISWAF) can function as strategic instruments for social and economic transformation. Based on in-depth interviews and field observations, the study presents empirical findings on the extent to which batik artisans comply with green economy principles within the framework of maqāṣid al-sharīʿah, without extending claims beyond the observed indicators. The findings indicate that selected green economy practicesparticularly those aligned with ḥifẓ al-nafs (protection of life) and ḥifẓ al-māl (protection of wealth) contribute to improved resource efficiency, environmental awareness, and the preservation of local cultural practices. Furthermore, the utilization of Islamic philanthropy mechanisms supports resilience building, capacity enhancement, and more equitable livelihood outcomes within the batik ecosystem. The study concludes that an integrative model combining green economy principles and Islamic philanthropy constitutes a context-specific pathway for advancing sustainable and socially just development in Lamongan’s batik industry.

With the rapid development of information technology, social media has become a significant driving force for the transformation of the fashion industry. This paper uses quantitative research methods and takes Victoria's Secret as a case study to systematically analyze how social media has restructured the fashion management system from multiple dimensions. This paper proposes that fashion enterprises should build a data-driven agile management system and respond to the opportunities and challenges in the social media environment through content innovation, community operation, and collaborative governance. The research finds that social media not only changes the mechanism of fashion information dissemination but also reconstructs the relationship model between consumers and brands. The transformation case of the Victoria's Secret brand provides key data verification: from March 2024 to March 2025, the brand gained 2.6 million new followers on Instagram, significantly higher than the industry average, reaching 1 billion users. The October 2025 show created a media influence value of 213.6 million US dollars within 24 hours, driving an 8% year-on-year increase in fourth-quarter sales to 2.27 billion US dollars, with annual sales reaching 6.553 billion US dollars, a 5% increase. The transformation in the Chinese market was particularly significant, with a 217% increase in traffic on Tmall after Tian Xwei's endorsement, 70% of consumers under 25 years old, and a 40% year-on-year increase in sales in the first three quarters. However, the popularity of social media has also brought challenges such as information overload and data privacy concerns.

The sponge city pilot policy (SCP) is a green infrastructure initiative that integrates ecological stormwater management, land-use planning, and urban sustainability goals. This study employs the super-efficiency slack-based measure (SBM) model to evaluate the green total factor productivity (GFP) of 278 prefecture-level and above cities in China from 2010 to 2022. It then applies a difference-in-differences (DID) model to identify the causal effect of the SCP on urban GFP while further examining transmission mechanisms and heterogeneous policy effects. The empirical findings show that: (1) the SCP significantly enhances urban GFP, with pilot cities exhibiting an average increase of approximately 6.08% relative to non-pilot cities, indicating broader medium- to long-term ecological–economic co-benefits beyond the policy’s immediate hydrological objectives; (2) the policy effect is more pronounced in cities with stronger economic foundations, larger urban scales, greater environmental governance pressure, weaker resource dependence, and more favorable locational conditions; and (3) the SCP promotes industrial structure transformation (IST) and green technological innovation (GTI), which jointly mediate the relationship between ecological infrastructure and green productivity. Drawing on ecological modernization theory and structural change theory, this study explains how ecological infrastructure, as a techno-structural reform mechanism, can internalize environmental externalities, stimulate innovation, and facilitate sustainable urban transformation. These findings provide evidence that green infrastructure policies can generate both ecological and economic co-benefits, offering useful insights for climate-resilient and sustainable urban planning.

In the context of the evolving social media communication landscape, social media native IPs—characterized by lightweight content and strong emotional engagement—have emerged as a key driver of growth in the cultural industry. This study takes Chiikawa as a case to examine the growth mechanism and commercialization path of native IPs from content dissemination to industrial transformation. Grounded in theories of symbolic consumption, art therapy, fan economy, and the IP value chain, this research adopts a mixed-method approach combining literature review, case analysis, and questionnaire survey, with quantitative analysis based on 133 valid responses. The findings indicate that Chiikawa fosters emotional projection and builds stable user attachment through highly symbolic character design and narratives reflecting real-life pressures. Furthermore, the development of social media native IPs follows an evolutionary path of "symbol construction–emotional resonance–fan participation–industry extension," in which emotional value serves as the core driver of both content dissemination and commercial conversion. This study provides theoretical insights and practical implications for the sustainable development and industrialization of social media native IPs.

High-valent first-row transition metal-oxo species are active intermediates in oxidation reactions crucial to many biological processes, oxidative chemical transformations in industry, and water splitting for renewable energy generation. The direct activation and hydroxylation of unactivated aliphatic C-H bonds remain a challenging yet important transformation in efficiently synthesizing complex organic molecules and pharmaceuticals with oxygenated functionalities. Despite documenting over 100 synthetic Fe-O species, terminal metal-oxo complexes involving the late transition metals Co, Ni, and Cu are much rarer, primarily due to the "oxo wall" phenomenon. The additional electrons in the metal-oxo unit weaken the M-O bond and destabilize these species. At the same time, as one traverses past the oxo wall, the M-O unit gains increasing amounts of metal-oxyl character, which holds promise for the generation of highly active catalysts for C-H bond activation, as exemplified by copper monooxygenase enzymes in nature. While forming late transition metal-oxo complexes is challenging, a few have been successfully prepared by targeting specific symmetries or unusual spin states. This review explores nonheme iron oxo complexes, the "oxo wall" concept, and recent advances in overcoming this limitation to prepare metal-oxo complexes of cobalt, nickel, and copper beyond the oxo wall.

As sustainable industrial transformation accelerates, understanding how green policies shape enterprise innovation becomes essential, yet conventional econometric methods cannot capture how policy effects spread across industries, regions, and supply chains. This study introduces the Graph-Based Framework for Green Innovation Network, which models multi-dimensional enterprise relations and applies relation-specific attention to reveal dominant diffusion channels. Using manufacturing data from China from 2019 to 2023, the framework surpasses three baseline methods in determination coefficient, innovation classification, and Policy Diffusion Index. Ablation studies confirm the roles of contrastive alignment and reconstruction, and results show that policy incentives and peer learning drive green innovation diffusion while offering interpretable indicators for policy and enterprise decisions.

The penetration of digital technology over the past two decades has transformed patterns of public consumption, including in Biak Numfor Regency. This study aims to critically examine how exposure to fast fashion advertising on digital platforms reproduces the mechanisms of the culture industry and instrumental rationality as conceptualized by Max Horkheimer, and how these processes shape public consciousness, preferences, and consumption practices. This research employs a descriptive qualitative approach, with data collected through in-depth interviews with purposively selected informants and observations of social media usage patterns. The findings indicate that Facebook, Instagram, TikTok, and TikTok Shop serve as the primary channels for the dissemination of fast fashion advertisements. The high frequency of advertisement exposure, particularly following search or purchase activities, demonstrates the operation of algorithmic personalization as a manifestation of instrumental rationality within digital capitalism. From a Critical Theory perspective, this condition represents the transformation of the culture industry into the digital sphere, where platforms not only market products but also construct false needs and normalize rapid consumption as part of a modern lifestyle. The study further reveals that exposure to fast fashion advertising contributes to the commodification of identity and the reproduction of consumerist values, gradually shifting the community’s collective orientation toward individualistic and symbolic logic. Clothing is no longer understood merely as a functional necessity but as a means of social legitimation mediated by platform capitalism. These findings affirm the subtle operation of ideological domination through digital media and underscore the importance of strengthening critical literacy in responding to the expansion of global consumer culture.

Against the backdrop of industrial digital transformation, artificial intelligence (AI) technology is profoundly reshaping the employment structure in the manufacturing sector. Based on survey data from 423 manufacturing enterprises in Guangdong, this study employs analysis of variance (ANOVA) and benchmark regression models to systematically analyze the impact of AI applications on changes in employment scale, job demand, and skill requirements. The results indicate that AI generally exerts a significant positive effect on the employment scale in the manufacturing sector; factors such as the proportion of R&D investment in AI, the growth rate of AI investment, and the proportion of AI-related personnel all significantly drive employment growth. there is marked heterogeneity in employment changes across industry types, enterprise sizes, and enterprise nature, with more pronounced adjustments observed in the equipment manufacturing sector, high-tech manufacturing, as well as in medium-to-large enterprises, foreign-invested, and joint-venture enterprises; AI is driving a shift in job demand toward intelligent roles such as AI R&D engineers and industrial data analysts, while simultaneously prompting an upgrade in skill requirements toward composite competencies in data analysis, human-machine collaboration, and innovative problem-solving. The study’s conclusions can serve as a reference for human resource structural adjustments and employment policy formulation in Guangdong’s manufacturing sector.

Introduction Multi-dimensional fishery resource allocation presents a complex and highly nonlinear optimization challenge under the converging pressures of ecological redlines and industrial transformation. Existing allocation approaches often struggle to coordinate production, processing, and marketing under rigid ecological and capacity constraints, thereby limiting the sustainable transition of China’s fishery sector. Methods Using decade-long longitudinal data from China (2014–2023), this study develops a production–processing–marketing (PPM) synergy framework for multi-dimensional fishery resource allocation. The framework integrates rigid constraints, including strict catch limits, processing capacities, and spatial thresholds, to simultaneously optimize economic returns, production structures, and infrastructure efficiency. To solve the resulting ultra-high-dimensional and non-convex optimization problem, an Improved Adaptive NSGA-III (IA-NSGA-III) is proposed. The algorithm incorporates two key strategies: an adaptive reference point relocation mechanism to improve Pareto front coverage under non-uniform objectives, and a constraint-violation-feedback-based heuristic evolutionary operator with hierarchical selection logic to accelerate convergence in high-feasibility regions. Results The empirical results show that IA-NSGA-III outperforms standard NSGA-III and MOEA/D in both convergence and solution quality. Specifically, the proposed algorithm achieves a Hypervolume (HV) of 0.96 and a minimum Inverted Generational Distance (IGD) of 0.012. In addition, the proposed model improves synergistic resource efficiency by 15.2%–20.4% while maintaining near-perfect ecological security satisfaction ( η eco ≈99.981%, η eco ≈99.981%). The ablation analysis further reveals that neglecting midstream processing results in a 23.1% decline in social reliability, whereas digitalization significantly enhances systemic resilience. Discussion These findings indicate that the proposed PPM synergy framework and IA-NSGA-III provide an effective decision-support tool for balancing ecological protection, industrial coordination, and resource efficiency in multi-dimensional fishery systems. The study offers a robust analytical basis for promoting the sustainable transformation and resilience of China’s fishery sector under rigid ecological and industrial constraints.

Digital transformation in the banking industry prompted BNI to launch Wondr by BNI in July 2024 as a replacement for its previous mobile banking application. Despite its advanced features, the application faces significant challenges in user adoption and user complaints regarding stability and functionality. This study aims to analyze the factors influencing continuance intention and continuance behavior of Wondr by BNI users, utilizing the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework. A quantitative approach was employed through an online survey of 379 Wondr users. The data were analyzed using multiple linear regression and the PROCESS macro. The results show that Performance Expectancy, Social Influence, Price Value, and Habit have a positive and significant influence on Continuance Usage Intention (CUI). In turn, Continuance Usage Behavior (CUB) is significantly influenced by Continuance Usage Intention (CUI), Facilitating Conditions (FC), and Habit (HT). Age was also found to moderate the relationship between Hedonic Motivation and CUI. These findings provide managerial implications for BNI to encourage users to download and continue using the app, which prioritizes application stability, implements age-segmented strategies, increases rewards and promotions, adds Quick Actions and Do Again features on the home screen, introduces Group features, incorporates gamification and marketing segmentation features for younger users, adds clear micro-guidance, and provides live customer service chat.

Currently, the cognitive synergy between artificial intelligence and human editors will become an important direction to promote the development of scientific and technical journals. The purpose of this paper is to illustrate the evolutionary game dynamics between multiple stakeholders in the intelligent transformation of the publishing industry, reveal the behavioural rationality and strategic decisions of each participant, and explore the optimal coping strategies under the interaction between AI technology and human resources. In order to deeply study the dynamic interaction between technology and human resources under the framework of co-construction of AI and science and technology journals, we divided the relationship between each stakeholder. We established a game relationship model, taking the government and ethical regulators, the editorial boards of scientific and technical journals, and research groups as game participants. Then, we addressed the stabilisation strategy problem and examined the strategic choice dilemmas faced by these three parties. We identified four stabilisation points and studied the evolutionary game through four stages of technology, early stage, development phase, surge phase and maturity phase respectively. Based on the results of the game analysis, the coping strategies of gradient adaptation of technology embedding and business process, capacity cultivation of human capital and organisational development, value reconstruction of academic ecology and scientific research culture, precise insight of user needs and cognitive behaviours, and globalisation and regional differences are proposed from the perspectives of governmental and ethical regulators, editorial boards of science and technology journals, and scientific research groups, respectively. The human-machine collaborative editing model, by integrating human creativity and the efficient processing capability of AI, will achieve a double rise in quality and efficiency in the fields of content review, intelligent proofreading, editing and processing, precise pushing, and knowledge dissemination.

Introduction Reconciling the conflict between economic growth and ecological conservation is a central challenge for global sustainable development. Innovative financial systems offer a transformative pathway to overcome this dilemma by translating ecosystem services into tangible economic outcomes. This study investigates the impacts of China’s Green Finance Reform and Innovation Pilot Zones (GFPZ) policy and digital finance on ecological product value realization (EPVR). Methods By constructing a multidimensional indicator based on the gross ecosystem product (GEP) framework, we develop an original EPVR dataset for China from 2011 to 2021. Employing a difference-in-differences (DID) approach, we evaluate the policy effects of the GFPZ and the role of digital finance. Results The results demonstrate that the GFPZ policy significantly improves EPVR by 6.4% compared to non-pilot regions. Mechanism analyses reveal that this policy effect is primarily driven by facilitating industrial green transformation and boosting the development of ecological industries. Furthermore, the direct impact of digital finance on EPVR is contingent upon regional structural thresholds, exerting a significant positive effect in areas with abundant natural resources, deep digital-real economy integration, and strict environmental regulations. Nevertheless, digital finance can significantly amplify the positive impact of the GFPZ policy. Discussion These findings underscore the critical synergy between financial policies and natural resources management, providing replicable insights for advancing financial systems to achieve harmonized human-nature coexistence and sustainable development.

This article discusses how worker-led decarbonisation strategies can move beyond the “jobs vs environment” tension in the context of European just green transition plans. Focusing on the Green Steel Plan, a decarbonisation plan developed by the trade union at the IJmuiden steel factory in the Netherlands in response to the threat of job losses, we show how workers in alliance with environmental communities articulated a vision for industrial transformation that links production with broader questions of reproduction. This case challenges two dominant narratives. First, that environmental protection inevitably threatens jobs, and second, that climate action is primarily driven by environmental movements. Whilst existing research on labour environmentalism and feminist political ecology has examined alliances between labour and environmental movements, labour-environment dynamics remain under-researched and under-theorised. Drawing on interviews, field observations, document analysis, and media coverage, we trace how the trade union and workers at the factory attempted to give rise to an ecological consciousness, driven not only by conditions of production but also by questions around reproduction. While the Green Steel Plan initially generated strong support from environmental groups, tensions and rifts emerged over time. This case reveals that, despite efforts to reconcile the tensions between production and reproduction, labour-environment dynamics are being continuously reconfigured and renegotiated. However, understanding these entanglements offers an avenue to move beyond the “jobs vs environment” tension.