A strong industrial foundation is crucial for developing new-quality productive forces and strengthening the manufacturing sector in China. It also serves as an industrial cornerstone for establishing self-reliant industrial and supply chains. Strategizing for industrial foundation enhancement will exert profound impact on the high-quality development of China’s manufacturing industry. This study assesses the significance of industrial foundation enhancement, identifies the progress and challenges in its advancement, and elaborates that its essence lies in achieving industrial infrastructure upgrading aligned with new-quality productive forces. Moreover, it proposes a fundamental principle of "structural optimization, innovation-driven growth, and quality-first development,” and clarifies the core mission of improving the innovation capacity of the industrial infrastructure. Additionally, the study specifies the strategic deployment of advancing the industrial foundation re-engineering project and winning the campaign for industrial foundation upgrading. Recommendations include positioning industrial foundation enhancement at a more prominent strategic level, leveraging both the decisive role of the market and the significant functions of the government, and promoting deep integration among industrial, innovation, financial, and talent chains.
This study introduces the concept of the digital-physics metaverse, aiming to clarify and expand the core connotations of industrial simulation software, address its limitations in generality, usability, computational power, and algorithmic capabilities, and provide both theoretical support and practical guidance for its future development. By tracing the evolution from computational physics to industrial simulation, and from simulation software to the digital-physics metaverse, the study offers a comparative analysis and elaborates on the fundamental attributes of this new paradigm. The study further distinguishes the digital-physics metaverse from traditional industrial simulation software, highlighting its key advantages, including timeliness, universality, insightfulness, non-invasiveness, and accessibility, as well as its potential to enhance education and fundamental research. To address the critical challenges in developing the digital-physics metaverse, such as establishing leadership in core computational physics technologies, bridging the gap between core technologies and real-world applications, breaking disciplinary silos to foster interdisciplinary knowledge integration, and transforming the inertia of traditional engineering research and development (R&D) to encourage broader participation, the study proposes a comprehensive development roadmap. This includes the establishment of interdisciplinary teaching and research centers, development of educational software based on the digital-physics metaverse framework, creation of industry-integrated R&D bases, and setup of independent verification bodies and academic exchange platforms. As an advanced form of industrial simulation software, the digital-physics metaverse is expected to reshape experimental paradigms in industry, provide novel tools and immersive experiences for education and fundamental research, and help position China at the forefront of the next wave of scientific and technological competition.
As the industrial Internet and artificial intelligence (AI) technologies further integrate, the research and development (R&D) of high-end equipment presents the characteristics of complex systems, multiple value entities, and high requirements for cross-lifecycle collaboration. Focusing on the co-creation ecology and the intelligent collaboration technology system within the R&D value chain of high-end equipment, this study analyzes the R&D scenarios of aero-engines, new energy vehicles and power batteries, as well as high-end surgical robots and their systems. It examines the value chain structure intrinsic to the high-end equipment R&D processes and delineates the evolutionary characteristics exhibited by this value chain across dimensions including value creation, operational mechanisms, regulatory approaches, and collaborative paradigms. Moreover, the study elaborates on three core mechanism design challenges regarding the co-creation along this value chain: self-organized management incentives based on value co-creation, adaptive operational regulation based on value distribution, and data‒model interoperability for collaborative R&D. Furthermore, it discusses four pivotal intelligent collaboration technologies: integration of deterministic and uncertain networks, intelligent sensing and scenario modeling, generative AI for intelligent decision-making, and intelligent Model-Based Systems Engineering (MBSE) platforms. Conclusively, four recommendations are proposed, such as strengthening the leading role of core enterprises (chain leaders), reinforcing the construction of platform-based collaborative infrastructures, and promoting the breakthroughs in core technologies alongside the establishment of standards for data‒model interoperability. This study aims to provide a theoretical basis and technical path for the collaborative innovation within the R&D value chain of high-end equipment, providing systematic support for promoting the intelligent, platform-based, and ecological transformation of the high-end manufacturing industry.
As the manufacturing industry integrates deeply with the next-generation information technology and accelerates its transformation to intelligence, it is necessary to break the technical bottlenecks regarding industrial software development and high-end equipment manufacturing and establish an independent and controllable industrial Internet technology system to support the optimization of the entire process of intelligent manufacturing. This study analyzes the current status of intelligent manufacturing and industrial Internet, and presents an overall picture of the independent and controllable industrial Internet technology system for intelligent manufacturing from three aspects: industrial Internet technologies, intelligent manufacturing technologies based on industrial Internet, and independent and controllable software and hardware systems for industrial Internet. Moreover, this study summarizes the demonstrative applications of independent and controllable industrial Internet technologies for intelligent manufacturing, covering independent robotized intelligent manufacturing, industrial detection and perception based on independent and controllable industrial Internet, networked multi-robot collaboration for intelligent manufacturing, and multi-robot collaborative scheduling for intelligent manufacturing. The current challenges and technical directions of independent and controllable industrial Internet for intelligent manufacturing are also identified. Furthermore, it is proposed to actively apply technologies including the fifth-generation mobile communications, independent and controllable industrial software, cloud‒edge‒end collaboration of industrial Internet, robots equipped with domestic distributed operating systems, and independent and controllable multi-robot collaborative manufacturing. Meanwhile, it is necessary to accelerate the construction of an independent and controllable standards system to drive the integrated development of the industrial Internet and intelligent manufacturing, creating new paths for the upgrading and high-quality development of China's manufacturing industry.
Against the backdrop of the accelerated evolution of artificial intelligence (AI) technology, the integrated development of AI and industries has gradually become a trend and the core driving force for promoting industrial transformation and upgrading. As a result, the importance of intelligent assessment and structural analysis of industries has emerged and the demand is urgent. This study proposes an evaluation model (i.e., Patent-AIIIA) for the integrated development of AI and industry, which uses large language models to deeply explore the intrinsic connection between patent data and AI technology. Combined with the BGE (BAAI general embedding) model and the UMAP (unified manifold approximation and projection) algorithm, a problem space and a solution space of patents are formed. Based on the density peak clustering algorithm and the information entropy theory, an index quantification method for industrial span and technological width was constructed, and a comprehensive assessment of the entire industry and individual clusters was finally achieved. The Patent-AIIIA model was applied to conduct an empirical study on multiple types of industrial clusters in the advanced equipment manufacturing industry in a certain region. The indicators of industrial span and technological width, the distribution of AI technology industrial clusters, and enterprise technological benchmarking were analyzed, and the practicability of the relevant models was verified. Based on the assessment results, suggestions such as implementing targeted industrial incentive policies, optimizing the AI technology layout of industrial clusters, and building a collaborative innovation network with leading enterprises as the core were put forward. The Patent-AIIIA model can be regarded as a general analytical model with macro- and micro-characteristics. It has the application potential to expand into multiple fields and can provide data support and scientific basis for enterprise strategy planning and industrial policy formulation.
This study aims to explore the application of embodied intelligence in the field of intelligent manufacturing, providing both theoretical support and practical guidance for its implementation, thereby promoting the high-quality development and upgrading of the manufacturing industry. The evolution of intelligent manufacturing is reviewed across three stages: rule-based automated manufacturing, data-driven digital intelligent manufacturing, and embodied-intelligence-enabled intelligent manufacturing. From the perspectives of interaction models, key technical elements, and architectural frameworks, a technical system for embodied intelligence-driven intelligent manufacturing is constructed, emphasizing core technologies such as multimodal industrial data fusion and perception, embodied-intelligence-enabled intelligent manufacturing powered by foundation models, force control, and robotic motion planning. Embodied intelligence drives the development of intelligent manufacturing from the aspects of production, warehousing and logistics, inspection and maintenance, and human‒robot collaboration. However, it also faces practical challenges, including limitations due to a lack of multimodal data, difficulties in perception and understanding in complex manufacturing environments, application security risks caused by AI hallucinations, bottlenecks in software‒hardware integration, and compliance issues caused by the absence of ethical and legal standards. The study proposes the following recommendations: intensifying technical research to overcome key bottlenecks, improving the industrial ecosystem to promote real-world application, establishing standards to ensure production safety, and expanding application scenarios to unlock new market opportunities.
Strengthening the industrial manufacturing capacity of a country and enhancing the quality and service level of high-end manufacturing are crucial for the country’s economic and social development and the improvement of its comprehensive strength. An intelligent manufacturing technology system with industrial robots, artificial intelligence, and industrial Internet as the core elements has developed rapidly and become an important component of the new productive force of industrial manufacturing. This study presents the application background of industrial robots in intelligent manufacturing, and reviews the operation types of industrial robots, including intelligent visual inspection, efficient grinding and polishing, flexible precision assembly, and workpiece grasping and transfer. It also examines the representative manufacturing scenarios involving industrial robots, such as the manufacturing of aerospace equipment, marine vessels, rail transit equipment, new energy vehicles, and electronic information devices. Moreover, the study explores the research progress of related common technologies from four perspectives: (1) visual perception such as environmental understanding and state perception, as well as full-size three-dimensional detection; (2) decision-making planning such as robot multi-task scheduling and non-interference collaborative planning in complex scenes; (3) motion control such as multi-robot collaborative control and robot compliant control; and (4) agile mechanisms. Furthermore, it elaborates on the development trends of industrial robot technologies in intelligent manufacturing, including large-scale dynamic scene understanding, clustered operations, flexible operations, embodied intelligence, networked collaboration, and digital twins. This study is expected to provide a basic reference for deepening research on industrial robots, promoting the development of intelligent manufacturing, and cultivating new productive forces.
The large-scale generation and accumulation of bulk industrial solid waste in China have significant ecological and environmental impacts. Therefore, strengthening the multi-level recycling of bulk industrial solid waste to reduce stock and minimize increment becomes crucial for ecological civilization and the construction of a Beautiful China. This study analyzes the prominent challenges faced by the comprehensive utilization of bulk industrial solid waste in China. Drawing on international advanced concepts and the current progress in circular economy research, it proposes a new concept of "Earth macro-cycle" and elaborates on its background and core elements. The study focuses on creating new paradigms in "Earth macro-cycle" research, including the reconstruction of bulk industrial solid waste through simulated natural mineralization and mine backfilling, transformation of bulk industrial solid waste into soil-like materials for ecological reuse, ecological balance and risk control of historically accumulated bulk industrial solid waste stockpiles, and intelligent decision-making and management system platform for the "Earth macro-cycle." By precisely applying the concept of "Earth macro-cycle," the study aims to build a circular green production model, establish an "Earth macro-cycle" model for hard-to-dispose industrial solid waste, and drive the creation of a circular society through systematic design. This will gradually enable the large-scale disposal and ecological return of bulk industrial solid waste.
China's metallurgical, coal, and chemical industries annually produce 4 billion tons of bulk industrial solid wastes, posing significant challenges to the green transformation of these industries. China has been actively promoting the eco-friendly disposal of such wastes, with key initiatives including mine filling, ecological restoration, and soil improvement. However, current research on ecological utilization remains poorly defined in scope, and its overall progress is relatively slow. Therefore, based on a new paradigm of "Earth's macro-cycle" proposed by our research team, this study further proposes a new concept of soil-like utilization. Under the premise of controllable environmental risks, we propose a new development paradigm that expands the properties of solid wastes into soil-like properties at three levels: ecological backfilling of full solid wastes, ecological remediation through solid waste amendment, and ecological return of soil-like solid wastes. This development paradigm includes establishing a multi-dimensional perception and path decision-making system for solid waste disposal, developing coordinated mitigation technologies for obstacle factors as well as structure reconstruction ‒ critical biogeochemical element cycling technologies, and building an ecological safety evaluation technology and standardized system for the soil-like utilization of solid wastes. This study aims to address the challenges posed by resource and environmental constraints, facilitate the transformation from abandoned mines to lush mountains with lucid waters, and ultimately return solid industrial wastes to the nature. This will support the implementation of the sustainable development strategy, achievement of the carbon peaking and carbon neutrality goals, and development of zero-waste cities.
Driven by the carbon peak and carbon neutrality targets, urbanization in China has gradually witnessed a significant shift from emphasizing speed and scale to focusing on green and high-quality development. The industry of building demolition and solid waste recycling has embraced new opportunities and challenges. This study centers on the bottleneck issues of extensive demolition, low product quality, and the disjunction between demolition and utilization during the process of building demolition and solid waste recycling. Considering the requirements for green and sustainable development of urban renewal and the construction industry, we propose three key issues from the aspects of demolition, utilization, and their interconnection: (1) The obsolete demolition technologies result in the difficulty in precise demolition; (2) the discrete performance of demolition materials gives rise to the difficulty in achieving high-value effectiveness of recycled products typified by recycled aggregate concrete; and (3) the disconnection between demolition and utilization causes the difficulty in systematic application. Furthermore, the research status and technical shortcomings are analyzed from multiple links of the industrial chain. In combination with the research review and actual situation of building demolition and solid waste recycling in China, this study proposes a solution and development plan for future research with the goals of precise demolition, products with high added values, and systematic application, expecting to provide a reference for the research on waste recycling based on building demolition, promote the upgrading of relevant industries, and facilitate the low-carbon, green, and high-quality development of the construction industry.
As global construction solid wastes surged and to achieve the carbon peak and carbon neutrality goals, the application of fully recycled coarse aggregate concrete (FRCAC, 100% replacement rate) in structural engineering has emerged as a breakthrough solution to addressing resource-environmental constraints and reshaping low-carbon structural systems. However, current codes and standards lack long-term empirical data under actual service conditions, constraining technological updates and large-scale application. This study aims to break technological bottlenecks, drive code upgrades, and promote low-carbon application. It explores the service performance of FRCAC structures using a 30-meter-span simply supported beam as the engineering prototype. Under self-weight loading, the maximum compressive stress at the edge of the compression zone of FRCAC is approximately 50% of its axial compressive strength, simulating the stress state under actual loading conditions, thus enabling quantitative analysis of performance evolution mechanisms and low-carbon benefits under long-term coupled effects of mechanical loading and environmental exposure. Through designing comparative beams with equivalent reinforcement and water-to-binder ratios between FRCAC and conventional concrete, we established a comprehensive framework covering material preparation, component behaviors, and carbon benefit quantification. We tracked deformations, crack patterns, and carbonation depth evolution over 4-year service periods, and quantified carbon absorption effects using life cycle assessment models. Results demonstrate that although FRCAC exhibits 7.8%~14% reduced elastic modulus under the same-condition curing, pre-cambering completely compensates deformation discrepancies, satisfying structural requirements. While bending cracks increased by 8% and the crack length increased by 15%, the average width remained comparable to conventional concrete. Considering service-period carbon absorption, FRCAC beams achieved 7.69% reduction in net carbon emissions. This study pioneers engineering prototype validation of FRCAC's feasibility under actual load-environment coupling conditions. The findings are expected to advance the transformation of construction wastes from extensive landfilling to high-value utilization, providing a forward-looking solution for low-carbon structural engineering.
Accelerating the cultivation and development of new-quality productivity and promoting the green and low-carbon transformation of development methods are inevitable paths to achieving high-quality socio-economic development. The cultivation and development of new-quality productivity are highly dependent on chemicals, especially given that many emerging industries extensively use chemicals, which has led to the emergence of new pollutants. These new pollutants originate from various sources and pose hidden environmental risks, posing serious threats to the ecological environment and human health. This study reviews the current situation and challenges of emerging contaminant control in the context of new-quality productivity, proposes a framework for emerging contaminant control, and offers governance suggestions from two aspects: priority issues and supporting measures. The research results indicate that multiple challenges exist in the control of emerging contaminants, including high dependence on chemicals, new pollutions, prominent waste-disposal issues, international trade barriers, and difficulties in substitution of high-risk chemicals. The priority issues for emerging contaminant control include strengthening investigation and supervision, promoting risk assessment, achieving breakthroughs in alternative technologies, and improving end-of-pipe treatment. Meanwhile, supporting measures should be deployed in strengthening scientific and technological innovation, formulating normative guidelines, and increasing regulatory efforts.
With the rapid advancement of industrialization and urbanization, emerging pollutants has brought unprecedented challenges to environmental protection and posed significant threats to human health. In this context, artificial intelligence (AI), leveraging its efficiency and precision, is gradually becoming a critical tool for emerging pollutant governance. This study reviews the current status and major challenges regarding emerging pollutant governance, and proposes an AI-based framework for managing emerging pollutants. In the screening phase, deep learning and natural language processing technologies are utilized to identify potential emerging pollutants from vast amounts of data, enhancing screening speed and accuracy. In risk assessment, machine learning models integrate multidimensional data to construct a dynamic evaluation system that can quantitatively assess environmental behaviors and health risks of pollutants in real time. In the control phase, AI technology enables intelligent monitoring, optimal technology selection, and dynamic regulation, promoting continuous optimization of governance strategies. Furthermore, the study proposes a large model framework for emerging pollutants, aiming to integrate multimodal environmental data to assist in the identification, risk assessment, and optimization of governance strategies for emerging pollutants. Research recommendations include establishing an intelligent identification and monitoring system for emerging pollutants, developing a data-driven risk assessment and prediction platform, optimizing pollution control technology and management platforms, and building a knowledge-driven large-model-assisted decision-making system. These efforts aim to precisely improve AI-based governance of emerging pollutants, providing references for scientific research, industry applications, and policy-making in related fields.
In recent years, China has demonstrated significant green development, with remarkable achievements in air pollution control evidenced by the sustained decline in annual average fine particulate matter (PM2.5) concentration and continuous reduction of heavy pollution days. However, the structural and essential stress on air quality improvement remains prominent, as manifested by the increasing proportion of secondary components in PM2.5 and the high-level fluctuations of ozone concentration, indicating that severe challenges remain in atmospheric environment governance in China and highlighting the urgent need to address multiple pressures, including multi-objective synergy, multi-pollutant collaborative control, and compliance with international environmental conventions. This study analyzes the current status of atmospheric environment governance in China, identifying prominent challenges including insufficient theoretical innovation in systematic atmospheric environment governance, urgent needs to harness the potential of synergistic effects from carbon-pollution co-governance, and the requirement for formulating multi-scale-integrated air-quality management strategies. Furthermore, it elucidates the intrinsic relationships among atmospheric environmental issues, particularly the interactions between regional and global atmospheric problems as well as the cross-sphere mechanisms of multi-pollutant, multi-media processes. A critical framework of systematic atmospheric environment governance is proposed, comprising fundamental theories and applications of atmospheric oxidation capacity, along with innovative technological chains of systematic environmental management. Strategic recommendations are outlined, including implementing top-level design for systematic governance, initiating scientific innovation programs for holistic pollution control, establishing coordinated management mechanisms, and deploying action plans. These measures aim to advance systematic air-quality management in China and enhance health risk prevention and ecological risk control capabilities in atmospheric environment governance.
Microbial technologies in environmental engineering are vital for the construction of ecological civilization. This study provides a comparative analysis of research progresses and core technological pathways in four major fields: water treatment, soil remediation, waste gas treatment, and solid waste disposal. It highlights the profound transformation of the field, driven by key technological breakthroughs such as microbial metabolism regulation, functional carrier innovation, and synthetic biology applications, evolving from empirical processes to rational design paradigms, from single-strain inoculants to synergistic microbial consortia, and from localized remediation to system-level optimization. Meanwhile, it analyzes the policy drivers, market landscape, and technical bottlenecks constraining industrial development in China. To foster high-quality development and accelerate industrial applications, the study proposes future directions including the design of functional microbial communities using multi-omics, precise delivery of microbial agents via smart materials, and dynamic process optimization through digital twin systems. It also discusses strategies for building a collaborative innovation system across the technologies, industry, and market, aiming to forge a full-chain path from technological breakthroughs to industrial applications.
Microorganism-based ecological environment monitoring technologies utilize the sensitive response of microorganisms to environmental changes, assessing environmental quality by detecting the physiological and biochemical characteristics, community structure, or functional variations of microorganisms. This approach demonstrates unique advantages in biodiversity conservation, environmental pollution prevention, and ecosystem health diagnostics. This study reviews the fundamental principles and research progress of microbial environment monitoring technologies through literature analysis. Based on patent studies, it uncovers the technological development paths and key features of advancements in this field. Combined with industry research, it evaluates the conditions for industrial development, including policy support, market demand, and technological innovation, while outlining the current state and challenges of the industry. Research indicates that microbial monitoring technologies have become important approaches to overcome limitations of traditional physicochemical monitoring, and innovations in microbial indicator methods, microbial sensors, and microbial ecology assessment are driving environment monitoring into a new phase. The development of synthetic biology technologies, environmental DNA analysis, and intelligent detection equipment has enriched monitoring methodologies, while market demand in specialized sectors continues to grow. The study recommends strengthening technological innovation and improving the standardization system for microbial genomics, constructing an industrial innovation ecosystem for microbial environment monitoring, and enhancing policy support and biosafety management, thereby promoting the high-quality development of China’s microbial environment monitoring industry.
Odor pollution is one of the environmental problems which have caused the strongest public complaints and drawn great attention of the government. Strengthening odor control is important for comprehensively accelerating the ecological civilization construction of China. This study overviews the development status of odor control in China from the aspects of research progress on odor characteristics, development status of odor control technologies, establishment status of the odor-related standards system, and odor control performance. It also analyzes the problem of odor annoyance caused by the emissions which meet the standards, and the drawbacks in terms of odorant measurement and monitoring, major odor contributor identification, standards system construction, secondary pollution control, and coordinated control of multiple pollutants. Furthermore, it suggests that highly sensitive and economically feasible measurement and online monitoring technologies should be further developed for the odorants to complement the odor profile of various odor sources. It is worthy developing the identification methods for major odor contributors, particularly applying big data and artificial intelligence, to support targeted pollution control. It is also necessary to develop the odor-related standards system based on the odor characteristics of different industries. Moreover, effective, environmentally friendly, and collaborative control technologies should be developed for odorants, greenhouse gases, and other pollutants considering their coexisting characteristics.
In recent years, global marine plastic waste pollution has become increasingly severe, posing a serious threat to marine ecosystems and human health, and it has become one of the urgent environmental issues of great concern. In China, a large coastal country, the prevention and management of marine plastic waste pollution is crucial for ensuring national marine ecological security. This study sorts out the main sources of marine plastic wastes in China, the migration characteristics of these wastes into the sea, and the current status of pollution prevention and control. It analyzes the major problems regarding environmental leakage, plastic outflow accounting, and key driving factor identification in the production, discharge, disposal, and recycling of plastics. Moreover, the study identifies the deficiencies in the monitoring, identification, interception, and disposal modes of marine plastic wastes in China, as well as the major challenges faced in the land‒sea integrated management, and further explores new types of marine plastic waste management modes, including plastic waste reduction at source, biodegradable product substitution, synergistic participation of multiple subjects of liability, and empowerment by artificial intelligence. Furthermore, the study suggests clarifying the key nodes for marine plastic waste governance, developing core technologies of intelligent regulation and resource utilization, and establishing a cross-sectoral collaborative management mechanism, thus to strengthen China's land‒sea integrated governance capacity in marine plastic waste pollution control, with a view to promoting the sustainable development of the marine environment and assisting in the construction of China's marine ecological civilization.
Space exploration activities have progressed into a stage of value-sustainable development. Leading space-faring nations prioritize full lifecycle cost reduction to drive sustainability, enhanced by value-oriented objectives in uncharted frontiers. This progression necessitates addressing the fundamental theoretical challenge of achieving cognitive adaptive sustainability for exploration-driven missions, which involves unknown states across macro/micro mission scopes and internal/external system boundaries. Building on Qian Xuesen's foundational theories of engineering cybernetics, systems engineering, and systems science, this study integrates empirical insights into space science and applications from over 30 years of engineering practice in China's Manned Space Program. Guided by system theory and Metasynthetic Wisdom, we establish a value-sustainable space exploration systems engineering (TSE) framework derived from first principles through theoretical synthesis and deductive development. The TSE framework comprises an AI-driven methodology of "probing universal truths via macro‒micro reciprocation"; a data-knowledge-logic cognitive triad structure with its general basic principles and architecture; a triple-nested adaptive control mechanism grounded in mathematical‒physical underpinnings; and principles for constructing system resilience under unknown states. The comprehensive implementation of China's manned space science—advancing cost reduction and scientific exploration—together with foward-looking applications in lunar projects, demonstrates that TSE framework significantly enhances value sustainability in space exploration. This work advances Qian Xuesen's "Creating Systematology" vision while providing systematic methodologies and engineering support for strengthening China's space sector in the new era.
As the new-generation information technologies such as generative artificial intelligence develop rapidly in the era of digital economy, the innovation and risk management models of financial technologies (hereinafter referred to as fintech) are undergoing profound changes. This study sorts out the theoretical basis and innovation needs of fintech, explores emerging challenges such as algorithm bias and data privacy leakage, and analyzes the evolution of the intelligent service mode and ecosystem of fintech. On this basis, a fintech service system is constructed and a "1-2-6-N" fintech innovation path is proposed, clarifying the direction for fintech development and providing a solid theoretical support for subsequent empirical analyses and system simulation verification. The wide application of fintech in key areas such as risk control, credit assessment, and customer management has not only improved service efficiency, but also brought diversified and complex risks. Moreover, targeted measures are proposed to optimize fintech risk management, including establishing a data-driven dynamic risk-management model, improving the policy guarantee for fintech risk prevention and control, and strengthening the coordinated improvement in risk management capabilities of financial institutions and regulatory technologies. The research shows that the steady development of fintech is inseparable from the synergistic drive of technological innovation and institutional guarantee, and the effective prevention and control of financial risks depends on the in-depth application of regulatory technologies, which can help achieve precise risk control by improving the compliance efficiency of financial institutions. To promote the high-quality development of the financial industry, it is necessary to deepen technological innovation and further expand the global layout of fintech.
With the progressive depletion of fossil fuel resources and intensifying global environmental challenges, microbial energy has emerged as a sustainable and clean energy substitute, playing a pivotal role in safeguarding national energy security and advancing the green transformation of the socio-economic landscape. This study provides a comprehensive analysis of the current status, technological breakthroughs, and challenges of China's microbial energy industry, highlighting its potential to optimize energy structures and promote sustainable development. While China has achieved milestones in microbial fuel cell, lipid production, and ethanol technologies, critical challenges remain, including feedstock diversification, core technological bottlenecks, and energy conversion efficiency. To achieve high-quality development of the industry, China must prioritize policy-guided initiatives, accelerate research and development of key technologies, and establish efficient industrial eco-clusters. Specifically, high-quality development should focus on: (1) technological innovation, such as metabolic pathway optimization and electrochemical coupling; (2) industrial scalability, including cost reduction and supply chain optimization; and (3) policy support, encompassing legal frameworks and financial incentives. Through these efforts, microbial energy is expected to become a vital component of China's new energy system, supporting the national goals of carbon peaking and carbon neutrality while contributing to global energy sustainability.
Coastal areas are rich in resources, economically developed, and densely populated. Affected by climate changes and human activities, regional geological disasters are prone to occur and are widely distributed, especially chain disasters triggered by the continuous evolution of individual disasters, posing a great threat to coastal stability and safety of coastal cities. This study analyzes the current status of typical geological hazards such as coastal erosion, shallow gas activities, and offshore submarine landslides in coastal areas worldwide, and sorts out their concepts, distribution, failure mechanisms, and prevention measures. Additionally, it summarizes the major monitoring techniques, theoretical methods, physical models, and numerical simulation methods for geological hazard research, and explores their applicable conditions and development bottlenecks. It is found that coastal erosion, shallow gas activities, and offshore submarine landslides mostly occur in the same area, and have correlations in terms of source and causal chains; that is, continuous coastal erosion can cause shallow gas leakage or directly lead to submarine landslides, and large amount of shallow gas leakage in geological layers can also trigger submarine landslides. To prevent and control the geological hazard chain in coastal areas, this study proposes a development strategy for future geological hazard monitoring and early warning, suggesting to build a space-air-Earth-sea multi-dimension integrated monitoring system, carry out mechanism analysis of big data and numerical simulation, and improve the early warning and forecasting of disaster chains with artificial intelligence, so as to provide a reference for urban safety and geological hazard source control in coastal areas.
