The development goal of artificial intelligence is to enable machines to think and act like humans, solving complex problems, and more importantly, interacting effectively in a complex, dynamic, and uncertain physical world. Embodied intelligence emphasizes that intelligent agents continuously learn and evolve from perception, decision-making, and action processes. It is realized through dynamic interactions with their surroundings via physical embodiments. This approach overcomes the limitations of traditional static data-driven training models, demonstrating superior adaptability and generalization capabilities in the real world. It therefore has become a dominant way to achieve the goal of artificial intelligence. This study explores the conceptual connotations, computational frameworks, and system implementations of embodied intelligence, and, on this basis, further reviews its current development status, evolutionary trends, and challenges. In particular, the study highlights the pivotal role of generative artificial intelligence, especially large language models, multimodal large language models, and the advancing large “information ‒ physical ‒ cognitive” models, in accelerating the evolution of embodied intelligence. In the face of intensifying global competition in artificial intelligence, this study further summarizes the achievements and analyzes the risks in the development of embodied intelligence in China, and proposes key research directions and targeted policy recommendations to help China secure a leading position in the global race for embodied intelligence.
The next-generation network technology is an intelligent information technology with the characteristics of collaborative perception, ubiquitous communication, and computer assistance. It is highly related to the industrial upgrading of network infrastructure, services, operation, maintenance, and ecology, and has become the core factor to the development of network construction and digital economy. With the accelerated evolution of emerging information technologies, the underlying paradigm and security architecture of networks tend to be reshaped, and the focus of global competition has shifted from scale expansion to structural competition dominated by standards, algorithms, and engineering capabilities. This study reviews the development trends of the global information industry, and reveals that the network industry has entered a stage of multi-dimensional differentiation; the Internet of Things industry has entered the era of Intelligence 2.0; and the satellite communication industry has entered a stage of systematic competition among constellations. This study also identifies the development status and key challenges of China's information industry, and presents the main ideas for development and upgrading accordingly. To ensure the application and industrialized development of the next-generation network technology in China, the following policy recommendations are proposed: (1) coordinating the layout of technological research and development while supporting the implementation of major special projects; (2) developing large-scale industrial clusters by providing taxation policy support; (3) establishing public service platforms to strengthen the capacities for standards setting and security assurance; and (4) promoting international exchanges by pragmatically carrying out international cooperation.
Artificial intelligence (AI) technologies are being deeply integrated into critical infrastructures, making AI resilience essential to ensuring the secure and stable operation of such systems. This study defines AI resilience in terms of four core dimensions—robustness, defensibility, recoverability, and evolvability—and reviews the current state of research in this area. Focusing on these four dimensions, we survey key technical advances both in China and abroad, with particular attention to new challenges and emerging solutions brought about by technologies such as large language models (LLMs). On this basis, we identify several prominent issues hindering the development of AI resilience, including the lack of top-level planning for capability building, absence of evaluation frameworks grounded in realistic application scenarios, and insufficient emphasis on the resilience of LLMs. To address these challenges, we recommend strengthening strategic guidance to establish a systematic resilience framework; developing high-fidelity, multi-dimensional, and reproducible evaluation systems; and exploring the potentials of LLMs to enhance multi-level resilience across the entire lifecycle of training, deployment, operation, and update, thereby enabling the construction of more reliable, trustworthy, and sustainable intelligent systems.
Artificial intelligence (AI) is poised to usher in the fourth industrial revolution, highlighting the urgency and importance of China's independent development in information technology. Photonics-electronics convergence (PEC) offers critical hardware support for AI development and positions itself as a key technology for new quality productive forces. This study explores the significance, definition, and evolution path of PEC. It also presents the latest research advancements in related technologies and delves into the opportunities and challenges faced by PEC amidst the AI wave. As an inherent requirement for AI infrastructure development, PEC can be effectively promoted by AI. The study recommends leveraging AI to promote key technological breakthroughs in PEC research, implementing major innovation projects on PEC, fostering a vibrant innovation ecosystem, and nurturing globally competitive entities in this field. These measures aim to help China seize the opportunity of information hardware evolution amidst the AI wave and bolster the country's independent development capabilities in information technology.
With the rapid evolution of virtual reality technologies such as digital twins, industrial Internet of Things, edge intelligence, and metaverse, virtual‒reality integration has become a core driving force for the construction of an intelligent society and the reshaping of industrial systems. Computing power, as the underlying supporting element of virtual‒reality integration, is rapidly evolving from a single centralized computing resource to a complex system characterized by multi-layered collaboration, intelligent scheduling, security, and trustworthiness. This study reviews the current development status and key characteristics of new computing power systems, pointing out that current computing power systems are showing a trend of cloud‒edge‒device integration, intelligent computing power is becoming the core engine for upgrading computing power structures, regional computing power layouts are gradually forming a pattern that emphasizes both differentiation and collaboration, and computing power application models driven by virtual‒reality integration are also showing diversified, ubiquitous, and autonomous characteristics. In the context of virtual‒reality integration, this study further analyzes the key technologies supporting the construction of new computing power systems, including the architecture design of computing power systems driven by virtual‒reality integration and the key technical elements for virtual‒reality integration scenarios, revealing the logical basis for matching computing power supply and demand from both system architecture and computing power orchestration perspectives. Through research on hybrid computing architectures, this study focuses on discussing their practical bottlenecks in areas such as heterogeneous collaboration, low-latency and high-bandwidth assurance, multi-source data security, and privacy protection. To address the aforementioned bottlenecks, we propose key development directions for new computing power systems, including constructing ubiquitous intelligent computing networks, developing trusted computing power systems, breaking heterogeneous collaboration barriers, improving security governance mechanisms, and cultivating a virtual‒reality computing power ecosystem. These will provide theoretical references and strategic support for the construction of future virtual‒reality computing power systems, optimization of industrial ecosystems, and allocation of computing power resources.
In recent years, generative artificial intelligence represented by large language models (LLMs) and multimodal large language models (MLLMs) has achieved remarkable progress. These ultra-large-scale and complex models impose extremely high demand on computational resources, driving an urgent need for the development of high-density computing power. From the perspective of the technological advancement of LLMs, this study explores the development stages of LLMs, computational optimization techniques, and their characteristics in terms of computing power requirements. Focusing on the demand of LLMs for computing power, this study analyzes the implications and characteristics of high-density computing power, its current development status, and key components. It further identifies five major challenges faced by the development of high-density computing power in China, including supply chain security risks, bottlenecks at the hardware layer, incomplete software stacks with high external dependency, the power wall and energy security, as well as cybersecurity risks. In response, this study proposes several strategies for high-density computing power development in China, including improving self-supporting of the supply chain, adhering to self-dependent innovation and standardization, building an open and collaborative software-hardware ecosystem, improving green computing innovation, and optimizing an integrated research and innovation system.
With the continuous expansion of cyberspace boundaries, trust-related security incidents are increasing annually, posing severe challenges to the cyberspace trust system. However, current research mostly focuses on macro-policy analysis and the application exploration of specific trust models, with insufficient systematic exploration of the developmental requirements and characteristic elements of trustworthiness. To address this gap, this study reviews the policy evolution trajectories of cyberspace trust frameworks across nations, analyzing the developmental demand for trust system models. Subsequently, we propose a model framework for the cyberspace trust system from a technical perspective, grounded in the cyberspace technological architecture comprising the communication, network, system, and application layers. Through a layer-by-layer analysis of trust challenges and corresponding mitigation techniques, the study further identifies five core functional characteristics essential for cyberspace trust: identity authentication, authorization management, accountability, data trustworthiness, and supply chain and service trustworthiness. To validate the model's applicability, we conduct an in-depth analysis of trust risks and key technical countermeasures across four representative scenarios: extreme-condition cyberspace, artificial intelligence, data flows, and the Internet of Vehicles. Furthermore, recommendations are proposed to support the development of China's trustworthy cyberspace ecosystem, providing theoretical foundations for policy formulation and technical architecture design.
To address the challenges of fragmented understanding of Large Language Model (LLM) security risks and the inadequacy of LLM risk classification and grading frameworks, this study aims to construct a comprehensive framework that integrates risk mechanism analysis, quantitative assessment, and governance practices. Theoretically, this study synthesizes and reconstructs multiple foundational theories, including socio-technical systems, social systems theory, and safety science, to reveal that risks originate from a dual trigger mechanism of the model's "internal complexity" and "external interaction." It consequently dissects risks into two primary dimensions—"internal safety" and "application security"—providing a unified theoretical foundation for a systematic governance framework. Methodologically, the study introduces "Risk Label Cards" as a standardized tool and employs an "Artificial Intelligence + Human Expert Collaboration" approach to structurally analyze real-world security incidents. Combined with an improved DREAD (damage, reproducibility, exploitability, affected users, discoverability) risk matrix model, it establishes a complete assessment methodology that spans from qualitative identification to quantitative grading. The research culminates in the construction of a systematic risk classification system and a three-tiered (high, medium, low) risk landscape covering major risk types. The "dual-dimensional driven" risk analysis and governance framework constructed in this study provides a systematic theoretical tool for the precise assessment and governance of LLM risks, effectively bridging the "theory-practice gap" in governance. Furthermore, with its theoretical compatibility and dynamic characteristics, the framework provides a reference for continuously tracking and understanding the evolution of LLM security risks and for security policy research.
In the context of the rapid evolution of digital finance, artificial intelligence (AI) models are deeply integrated into critical business processes such as risk assessment, asset pricing, and anti-fraud. The resultant lack of model interpretability has progressively become a significant source of risk, constraining financial stability and public trust. This study aims to comprehensively explore the causes, harms, identification, and governance of AI model interpretability risks. It finds that the interpretability risks of AI models primarily stem from the high complexity of algorithmic structures, implicit biases within data samples, inconsistency between modeling objectives and interpretability regulatory goals, and failure of explanations due to continuous model iteration. Building upon this, the study systematically reveals the multi-layered harms of AI model interpretability risks across four key dimensions: financial stability, social inclusion, legal compliance, and technical security. Concurrently, an identification framework for AI model interpretability risks is constructed, centered on the core methodology of transparency quantification, bias identification, compliance validation, and security detection. Finally, we propose a comprehensive governance system encompassing model engineering optimization, data governance and feature management, multi-party auditing and regulatory coordination, and construction of standards systems and responsibility delineation. This framework seeks to achieve a dynamic balance among technological efficiency, regulatory controllability, and social trust in the collaborative development of digital finance and AI.
As the Internet of Things (IoT) in critical information infrastructure rapidly scales up, its widespread deployment in sectors such as energy, transportation, and industry is introducing unprecedented security challenges. While driving intelligent transformation across industries, the IoT technology also brings systemic security risks that impact national security, economic operations, and social stability. This study examines security issues in traditional IoT architectures—cloud, pipe, edge, and device—from both general and scenario-specific risk perspectives, and explores emerging threats in trends such as embodied intelligence and four other key application areas. Based on current conditions and development trends, the study clarifies the security implications and core risks of IoT in critical infrastructure. Aligned with its architectural features and practical needs, a "universal guarantee + customized enhancement" protection approach is proposed, advocating a new-generation security system that offers full coverage, dynamic collaboration, and adaptive intelligence. Systematic efforts in strategy, regulation, technology, talent, and international cooperation are recommended to achieve sustainable and evolving security governance.
As an important strategic resource for safeguarding national information security and the healthy development of digital economy, cryptography is the cornerstone of protecting the security of critical infrastructure, government affairs data, financial transactions, and other fields. Currently, the rapid development of quantum computing poses a disruptive challenge to the existing public-key cryptosystems and a fatal threat to the global information security landscape, making research on post-quantum public-key cryptography (PQC) both necessary and urgent. This study expounds on the current development status of five mainstream technical routes in PQC: lattice-based, code-based, hash-based signature, multivariate, and isogeny-based cryptography, and finds that each of these five routes has its own focus yet is in a stage of continuous iteration and verification. The study identifies the existing issues of the development of PQC including performance bottlenecks, insufficient ecological compatibility, and difficulties in security evaluation. Meanwhile, the development of PQC in China is confronted with challenges such as a weak foundation in theoretical research, lagging standards formulation, an imperfect industrial ecosystem, and a shortage of professional talents. To safeguard China's information security in the quantum computing era, we propose the following suggestions: strengthening basic research to enhance the independent innovation capability of the industry; accelerating standards formulation to boost international discourse power; improving the industrial ecosystem to promote the phased migration to PQC; intensifying talent cultivation to build a professional talent team. These measures are expected to help China construct a multi-dimensional and highly resilient digital security ecosystem, and drive the leap-forward development of China's cyberspace security capability from classical to quantum security.
Advanced materials, serving as the core material foundation underpinning economic transformation, national defense security, and technological revolution, directly determine national competitiveness through their research and development (R&D) efficiency and engineering capabilities. Materials genome engineering (MGE) has revolutionized the R&D paradigm of advanced materials and profoundly reshaped the global industrial landscape through integrating cutting-edge multidisciplinary technologies. This study analyzes MGE's transformative impacts on the advanced materials industry from the aspects of strengthening engineering application, breaking bottlenecks in industry‒university‒research‒application collaboration, and restructuring industrial chains and market ecosystems. It further explores critical technologies regarding advance materials, such as integrated computational engineering, big data, artificial intelligence agents, digital thread, and digital twin, as well as their development tendencies. Moreover, it presents the functionalities and future directions of core MGE platforms, including high-efficiency computational design platforms, autonomous experimentation platforms, materials databases and data centers, new materials pilot-scale test platforms, and intelligent technology innovation platforms. Facing the future competition landscape of the global new materials industry, the study proposes the development strategies of industrial MGE toward 2030 and 2045. It also proposes development suggestions from multiple dimensions, including emphasizing top-level design, advancing the entire industrial chain of advanced materials to accelerate innovation projects, developing critical technologies and supporting platforms, promoting the standardization of materials data, and strengthening talent cultivation and international cooperation. These efforts aim to enhance the core competitiveness of the advanced materials industry.
Synthetic biology is emerging as a frontier interdisciplinary discipline that is increasingly regarded as a key driver of sustainable innovation in advanced materials. By leveraging the design-build-test-learn (DBTL) cycle and through genetic circuit reconstruction, metabolic pathway optimization, and enzyme engineering, predictable and programmable microbial cell factories are being created, enabling bio-based material production. This study summarizes the technological foundations that underpin synthetic biology in material innovation, with a focus on elucidating the mechanisms of novel material synthesis in microbial cell factories, the advantages of biosynthetic approaches, and the functional roles of key biological elements in material production. On this basis, it highlights representative applications in bio-based, smart, functional, and biodegradable materials, and discusses how synthetic biomanufacturing is reshaping the green and intelligent transformation of the materials sector. Finally, it outlines future development trends and proposes development strategies. Future progress will rely on closer interdisciplinary integration, more rapid translation into industrial practice, and the development of platform-based and modular strategies, which together will support the carbon neutrality goals and the high-quality growth of the materials industry.
Carbon capture, utilization, and storage (CCUS) technology is an essential component of China's strategy to achieve carbon neutrality. It represents the necessary pathway for near-zero emissions from fossil fuels and provides viable solutions for deep decarbonization in challenging sectors like steel and cement industries. Developing highly efficient and low-cost CCUS technologies is a critical pathway toward carbon neutrality, where the performance of key materials directly determines the economics, efficiency, and applicability of carbon capture and conversion technologies. Thus, creating novel materials with high-efficiency separation and catalytic conversion functions is central to current technological advancements and industrialization efforts. This study investigates carbon capture and conversion from four dimensions: technological principles, performance characteristics of key materials, application scenarios, and development pathways. By reviewing industrial demonstration projects in China and abroad, this study highlights research progress and technical bottlenecks of key materials, including absorbents, adsorbents, and separation membranes for carbon capture, as well as thermal catalytic, electrocatalytic, and photocatalytic materials for carbon conversion. It identifies that simultaneously improving material performance and controlling costs remains the primary challenge for industrial-scale deployment. Additionally, common issues such as simplistic material structure design, complex process integration, insufficient industrial chain coordination, and limited policy support are analyzed. Consequently, this study proposes targeted development strategies, including sector-specific innovations in material systems, large-scale demonstration of disruptive technologies, enhanced upstream‒downstream industry collaboration, and strengthened policy frameworks. These recommendations aim to accelerate the scaled application of key materials for carbon capture and conversion, foster low-carbon process restructuring, and establish a negative-carbon system, thereby laying a solid foundation for achieving China's carbon neutrality goals.
As key materials for medical devices and consumables, biomedical elastomer materials play a significant role in improving people's health and quality of life. The biomedical elastomer material industry has broad application prospects, high added values, and rapid growth, and currently faces new situations such as accelerated technological iteration and clinical demand upgrading. Therefore, it is urgent to conduct systematic research on the development of this industry. This study reviews the basic situations, industrial statuses, and development directions of related materials, categorized as follows: biomedical polyurethane elastomers and their medical products, biomedical silicone rubber and their medical products, and other biomedical elastomers (i.e., medical natural rubber, medical thermoplastic vulcanizates, medical thermoplastic elastomers, medical eucommia ulmoides gum elastomers, biodegradable biomedical elastomers, and hydrogel biomedical elastomers) and their medical products. The research indicates that the application range of biomedical elastomer materials in China is rapidly expanding, the market size is continuously growing, and the level of industrial development is steadily improving. However, there remain obvious shortcomings regarding material performance optimization, breakthroughs in key technologies, and construction of industrial standards systems. To this end, it is recommended to develop complex medical products that are widely and urgently needed clinically, promote the innovation and engineering application of raw materials and products, enhance the safety and market competitiveness of raw materials and products, and accelerate technological breakthroughs and industrialization through collaborative innovation, so as to promote the high-quality and sustainable development of the biomedical elastomer material industry in China.
Silicon energy is a new form of energy based on silicon materials and driven by semiconductor technology at its core. Silicon energy materials are strategic basic materials that support the green and low-carbon transformation of industries and the leap of high-end manufacturing. The related industries have become an important force in reshaping the global energy and materials landscape. Based on a comprehensive review of the current development status of China's silicon energy materials industry, this study identifies the technological development trend of the industry from the aspects of resource, smelting, material, and recycling ends, and analyzes the new situation and problems faced by the industry. Research suggests that China's silicon energy materials industry has considerable development potentials, reflected in its abundant resource endowment, solid industrial foundation support, optimized industrial layout, emerging synergy and agglomeration effects, expanded application scenarios, and good potential for demand growth. The industrial upgrading paths include (1) strengthening raw material guarantee to build a multi-pole support pattern; (2) coordinating industry, education, research, and application to achieve breakthroughs in key technologies; (3) further promoting green and low-carbon development to build a green power circulation system; and (4) accelerating the pace of intelligent transformation to consolidate the core advantages of manufacturing. This study is expected to provide a theoretical support for building a globally leading industrial system of silicon energy materials, and promoting the reconstruction of the energy system, upgrading of the manufacturing system, and leap in core competitiveness of China.
High-end magnetron sputtering targets serve as the core foundational materials for fabricating critical functional films used in integrated circuits and flat panel displays. China currently faces a high external dependency in this field, along with persistent structural challenges such as weak fundamental research, lagging high-purity material preparation technologies, and reliance on imported core equipment. This study reviews the application status of high-end magnetron sputtering targets in two major fields—semiconductor chips and flat panel displays, and identifies common issues including micro-composition and structural homogeneity of raw materials and targets, preparation and testing equipment for sputtering targets, and the transition to smart manufacturing. It proposes key development directions for the sputtering target industry, such as research on micro-homogenization of composition and structure of ultra-high purity targets, collaborative efforts across the industrial chain to tackle key equipment for target processing and testing, driving research paradigm transformation through digital‒intelligent integration, and focusing on forward-looking layouts of critical fields. Practical actions can be taken in areas including developing high-purity material purification and processing technologies, deepening industry‒university‒research‒application collaborative innovation mechanisms, improving the industrial chain and standards systems, balancing independent talent cultivation with targeted recruitment, and reshaping the innovation ecosystem of the sputtering target industry. These measures will help transition China's sputtering target industry from a follower model featuring "equipment introduction‒process imitation" to an innovative "data-driven‒standard-led" paradigm, thereby supporting the development needs of strategic emerging industries such as integrated circuits and flat panel displays.
Redox flow batteries, as a key long-duration energy storage technology underpinning new power systems, have become a strategic focus in global energy transition. Their industrialization process is highly dependent on performance breakthroughs and cost optimization of key materials. This study reviews global technological advancements and industrial development status of key materials for redox flow batteries. It examines the impact of these materials on economic viability and market demand of redox flow batteries, identifying multiple constraints facing China in this field: difficulty in simultaneously achieving a long lifespan and low cost; supply chain vulnerabilities due to high dependence on imported high-end materials; challenges in market acceptance and business model development; and insufficient policy and standards frameworks to support material innovation. The study proposes a phased development pathway for key materials, advocating for the evolution of the material system from the current mainstream "all-vanadium‒all‒fluorinated" route toward a "novel‒nonfluorinated" direction. It further recommends accelerating the large-scale application of flow batteries within zero-carbon energy systems by strengthening industrial chain collaboration, innovating business models aligned with material characteristics, and improving material standards and evaluation systems. These efforts will help China secure a proactive position in global energy competition.
Plastic pollution has become a pressing global environmental challenge. Polyethylene terephthalate (PET), one of the most widely used synthetic polymers, represents a major contributor to this problem. The development of efficient PET degradation strategies is therefore critical for advancing waste management and resource recovery. Recent studies have demonstrated that biocatalytic approaches, centered on PET-degrading enzymes, can enable closed-loop recycling of PET. As a result, the optimization of PET-degrading enzymes has become a central focus of research in this field. The interaction mechanisms between PET-degrading enzymes and their substrates have been elucidated, providing the foundation for diverse strategies in molecular engineering. Advances have been achieved through directed evolution, semi-rational design, rational design, and more recently, machine-learning-driven approaches. Notably, machine learning has emerged as a transformative tool that accelerates the design of enzymes with enhanced catalytic performance. Despite these advances, major challenges remain. Current PET-degrading enzymes display insufficient activity at low temperatures, limiting their utility in settings such as composting. Moreover, the depolymerization efficiency against highly crystalline PET remains low, hindering industrial-scale application. The convergence of machine learning and enzyme engineering is expected to be a key direction for overcoming these barriers, enabling the development of robust and efficient biocatalysts. Such progress would help break through the bottlenecks in the industrialization of PET biodegradation and promote the transition toward a sustainable circular economy.
In the context of global climate change and rapid urbanization, floods caused by extreme rainstorms in Chinese cities have been on the rise. Consequently, urban flood risks evolve into a systematic threat to urban safety. Based on literature, disaster data, and statistical yearbooks, this study summarizes the evolution characteristics of urban extreme rainstorms and disaster losses in China. Four new characteristics are revealed: intensifying extremity with historical records broken; localized and nocturnal occurrences becoming more pronounced; disasters shifting to highly vulnerable areas with a surge in urban multi-dimensional exposure; and flood risks evolving into compound hazards like fourfold convergence of typhoons, rainstorms, high tides, and floods, with escalating multi-hazard effects. This study analyzes the core challenges in urban flood prevention based on a theoretical framework of "risk source-risk exposure-mitigation force" in urban safety. By integrating the concept of resilience into the entire cycle of planning, construction, and management, we propose a scientific response path to extreme rainstorm and flood disasters in cities, aiming to provide theoretical support for constructing climate-adaptive cities.
This study aims to systematically analyze the new characteristics, evolution trends, and causes of major extreme weather in China in the context of global climate change, and evaluate the current forecasting, early-warning, and defense capabilities of the country. Based on this, a systematic improvement strategy is proposed. By integrating existing observational data, research results, and operation practices, this study sorts out the changing patterns of extreme weather such as typhoons, heavy rain, severe convection, high temperature and drought, as well as low temperature, freezing rain, and blizzard. The results indicate that climate change is leading to significant alterations in the intensity, frequency, duration, and geographical distribution of extreme weather in China. The core driving mechanisms are the increase in atmospheric water-holding capacity caused by global warming and the large-scale circulation adjustment resulting from the Arctic amplification effect triggered by Arctic warming. The specific manifestations are as follows: the intensity of typhoons increases, their movement slows down, and their impact on the inland deepens. Although the total frequency of heavy rain has slightly decreased, its extremity, affected areas, and northward movement trend have intensified. Severe convective weather is more sudden and shows a more obvious trend of fragmentation. The number of compound events of high temperature and drought has increased. The harm caused by low temperature, freezing rain, and blizzard has increased each time. China's meteorological modernization level has significantly improved, and the defense work for extreme weather has made positive progress, including the establishment of a ground-air-space three-dimensional monitoring network, significant improvement in the level of independently developed numerical models and artificial intelligence models, and gradual improvement in the early warning system and information dissemination channels. However, there are still severe challenges in aspects such as the uniformity of observational data distribution, precise forecasting of extreme weather, and chain disaster defense. It is recommended to take practical actions in strengthening core technology research and development to enhance the country's disaster prevention and mitigation capabilities, implementing cross-departmental data sharing to build an intelligent collaborative early-warning platform, improving the progressive service and full-chain response mechanism to enhance the technological support capacity for resilient infrastructure, improving public participation, and strengthening the construction of the social security system. Meanwhile, it is necessary to carry out a systematic transformation from technological breakthrough to social empowerment to enhance China's capabilities to defense against extreme weather conditions.
Coastal zone is transitional area between land and sea. Under the impacts of climate change, coastal zone is threatened by stressors such as sea-level rise, intensified storm surges. It is a common trend in global coastal governance and ecological security to protect the coastal zone by ecological barriers. This paper illustrates the evolution of coastal ecological barriers from pristine natural state, agricultural civilization, industrial civilization to ecological civilization. It reviews the evolution of understanding of resilience, damage process, and restoration domestically and internationally, elucidates the trend of future coastal protection engineering which is moving toward resilience enhancement. Then the concept of Resilient Coastal Ecological Barrier Engineering (RCEBs) is proposed, it covers conceptual and theoretical foundations, structures, functions and classification, as well as the framework of engineering technical standards and implementation guarantees. The paper discusses the challenges in RCEBs, and gives the suggestions, such as optimizing the spatial allocation of coastal zones, building a perception network with intelligent terminal devices, and forming a full closed loop centered on improving coastal resilience and realizing the value of ecological products. Finally, this paper prospects the future development directions of coastal protection projects: shifting from single protective elements to integrated land-sea systematic protection, from static assessment to dynamic response, and from resilience built by engineering to the resilience built by natural-social systems. These efforts will further facilitate disaster prevention and mitigation in coastal zones, and coordinate ecological protection with economic development.
Forest ecosystems play a fundamental role in sustaining global water cycles and ecological security, and accurate modeling of their eco-hydrological processes is essential for forest management, watershed regulation, and eco-engineering design. To address the limitations of conventional methods regarding high-precision quantification of the spatial heterogeneity of forest structures and its nonlinear coupling with hydrological processes, this study reviews the advances and engineering application potentials of light detection and ranging (LiDAR) and artificial intelligence (AI) fusion technologies in forest eco-hydrological modeling. The study first analyzes the limitation of homogeneity assumption in conventional models and the challenge of insufficient spatiotemporal data coverage. It highlights the technological breakthroughs of terrestrial and spaceborne LiDAR in constructing high-fidelity forest three-dimensional (3D) structures, as well as the critical role of AI in leaf-wood separation from point clouds, complex hydrological process simulation, and physics-informed machine learning (PIML). Using the National Park of Hainan Tropical Rainforest as a representative case, the study evaluates the application performance of LiDAR + AI fusion in three key areas: estimation of rainfall interception by forest canopies based on fine-scale 3D structures, quantitative analysis of the effects of forest age and structure on evapotranspiration and runoff, and refined accounting of forest water-related ecosystem services. The results demonstrate that LiDAR + AI fusion significantly improves the spatial resolution and predictive accuracy of models, effectively revealing the micro-scale regulation mechanisms of forest vertical structures on rainfall redistribution and evapotranspiration processes. Finally, a “data‒model‒platform‒feedback” fusion modeling framework for forest eco-hydrological processes is proposed. This framework explores the implementation of deep integration between multi-source data and mechanistic models, and identifies current challenges in data standardization, fusion complexity, and computational cost. This study further provides a technical pathway toward high-precision, interpretable forest eco-hydrological modeling with potentials for engineering application, offering scientific support for forest water-related ecosystem service assessment and ecological engineering management.
Non-carbon dioxide (CO2) greenhouse gases exhibit significant differences in physicochemical properties from CO2, with large total emissions, dispersed sources, and obvious cross-industry characteristics. Against the background of China's commitment to the nationally determined contribution target of reducing greenhouse gas emissions across the entire economy by 2035, controlling non-CO2 greenhouse gas emissions becomes both challenging and urgent. This study reviews the situation of non-CO2 greenhouse gas emission control in China, and analyzes the key control elements, including emission processes and formation mechanisms, emission monitoring and accounting methods, emission reduction and resource utilization technologies and measures, as well as emission reduction pathways and governance strategies. Moreover, the study proposes a management approach guided by systematic governance, incorporating non-CO2 greenhouse gases into a unified governance framework. Additionally, an overall governance structure consisting of "one body, two supporting wings, a three-tier architecture, and four types of support" is constructed. An integrated and coordinative governance strategy covering various gases, major industries, emission reduction and socioeconomic development, as well as domestic and international dimensions are implemented, with a focus on path innovation in governance enabled by artificial intelligence. Active actions can be taken in strengthening top-level design and institutional development, enhancing technological innovation and talent support, stimulating market vitality and industrial innovation, and planning global agendas and international cooperation. These efforts aim to accelerate the improvement in China's non-CO2 greenhouse gas governance system, promote comprehensive greenhouse gas reduction, and deeply participate in global climate governance.