The manufacturing of advanced， complex products， such as atomic bombs and artificial satellites， as well as highly integrated technological projects like lunar landings and rocket recovery， constitutes a highly complex， systemic process. In order to support the disruptive innovation and development of such system engineering as high-end complex product manufacturing， the basic concept of technology strategic supply chain is proposed.A thorough examination of the formation and development of this supply chain can address key scientific questions， such as how high-end complex products are created and evolve， the underlying drivers of this process， and the fundamental principles governing them， thereby facilitating the original creation and innovative development of these products. The complex systemic processes involved in the manufacturing of high-end complex products is first reviewed. Then， the concept of the technology strategic supply chain is defined based on the supply-demand relationship among science， technology， engineering， and industry. Based on an analysis of the typical characteristics of the evolution of technology strategic supply chains， the following four major scientific issues about technology strategic supply chains are explored： the formation mechanism and dynamic evolution， factor space and collaborative optimization， resilience design and risk management， and operational mechanisms and efficiency enhancement. Finally， strategic insights is offered for constructing technology strategic supply chains， focusing on innovation talent， innovation ecosystems， cutting-edge technologies， leading enterprises， and management mechanisms， with the goal of advancing China’s technology strategic supply chain and contributing to the high-quality development of China’s science， technology， and economy.

The new productive forces， characterized by their advanced technology， high efficiency， and superior quality， are propelling profound transformations in management science with unprecedented intensity. The developmental trends facing management science are discussed from the perspective of these new productive forces， both theoretically and practically， summarizing significant characteristics such as insightfulness， agility， universality， and integrative capacity. It further delves into the main challenges currently facing management science， including the asymmetry between technological innovation and theoretical development， the disconnect between theory and practice， and the adaptability of management strategies， along with their underlying causes. In response to these challenges， a developmental framework is proposed for modern management science structured around “one core， two bases， three principles， four pillars， and five directions.” It explores pathways for development through constructing new modern management system models， deepening fundamental concepts， refining core principles， and expanding application fields， thereby providing viable approaches and references for the advancement of modern management science.

In order to break through the complexity integrity of complex system management activities， based on the characteristic that management activities generate and evolve scenarios at any time point in the past， present， and future， using scenarios as a starting point， modeling complex system management scenarios to reproducing the past scenarios， reconstructing the current scenarios， and predicting the future scenarios of complex systems are used to provide practical guidance for complex system management activities. Based on the theoretical similarity between big data and scenarios， the concept and connotation of big data-driven complex system management scenario modeling are analyzed and it is clarified that big data-driven complex system management scenario modeling is the concept of big data-driven implementation of complex system management scenario modeling. Starting from the scenario dataset composed of big data collected or recorded through observation or experimental methods， reverse modeling is carried out. Six key technologies are proposed for this modeling process， including scenario structuring， big data transformation of scenario data， kernel scenario extraction， scenario cultivation， scenario verification， and federated modeling. A specific modeling process is designed， including exploratory analysis-conceptual scenario construction-data scenario construction-kernel scenario construction-computational scenario construction-scenario generation and verification， providing a basis for the implementation and application of scenario modeling in complex system management.

In the dual context of increasing urban risks and government policy promotion， the construction of resilient cities has gained remarkable momentum. Consequently， operational management issues have gradually emerged as an emerging research hotspot. Resilient city operational management seeks to fortify a city’s capacity to respond to risks， optimize the allocation of urban resources， and furnish decision-making support for the enhancement of urban security resilience. It serves as a catalyst for modernizing the urban governance system. Based on both domestic and international literature on resilient city operations management from 2013 to 2023， both quantitative and qualitative analyses are employed. Insights are distilled from these analyses to delineate six primary research domains within the purview of resilient city operational management. These domains encompass traffic management， logistics administration， water and energy management， environmental management， safety management， and smart urban development. Finally， progress in each domain is outlined， research focus areas within resilient city operations management is distilled， and， in conjunction with the latest research findings， the necessity and urgency of research in related fields are analyzed.

Management science is the twelfth discipline in the Chinese academic system and holds a unique significance in the context of accelerating the construction of an autonomous knowledge system. However，past perceptions of the discipline often failed to view the development of indigenous management science as a complete and continuous historical process. The construction of the discipline prior to 1978 was often regarded as a “pre-emergence stage”，leading to discontinuities and biases in the discipline’s self-perception，which in turn affects the realization of the discipline’s value. The method of disciplinary ethnography is employed to systematically analyze the more comprehensive development of the discipline，restoring the continuous efforts of numerous predecessors，including Qian Xuesen（Hsue-shen Tsien） and Wang Yingluo. It argues that the unique historical development of indigenous management studies in China has nurtured a distinct disciplinary tension，possessing positive significance that can be transformed into disciplinary impetus. This approach can sustain a coherent and complete developmental path from self-being and self-proving to leading towards self-conscious.

To achieve the carbon peaking and carbon neutrality goals， there is an urgent need to transform China’s electricity supply system to one that is dominated by renewable energy. The multi-energy complementary generation system， integrating traditional energy and new energy at the power supply side， is an important means to reduce power industry carbon emissions and consume new energy. However， existing research on the optimization of multi-energy complementary generation systems has either focused on small-scale distributed systems or on large-scale energy bases， ignoring the large amount of practice of the medium-scale centralized power plants level （e.g. thermal power plants）.Therefore， a conceptual framework is proposed for a multi-energy complementary generation system by considering the practice of the distributed power plant level， the centralized power plant level and the centralized power plant bases level. For the three types of co-located， co-combusted and co-operated multi-energy complementary generation systems in the proposed conceptual framework， the knowledge maps of existing researches， including journals， publication years， hot keywords and main contributors， etc. are visualized； By combing through the key literatures， three key research areas are refined for integrated optimization of the multi-energy complementary generation： capacity optimization of co-located system， emission optimization of co-combusted system and scheduling optimization of co-operated system； The current research status of ten key technologies in the three areas is summarized and future outlooks are troposed corresponding to each of them. The ten key technologies are： capacity configuration， energy management， and size optimization； coal-biomass co-combustion， coal-sludge co-combustion， coal-waste co-combustion， and coal-ammonia co-combustion； complementary evaluation， generation resource bundling， and scheduling optimization.It is an important reference for understanding multi-energy complementary generation systems from a system perspective in this paper.

Using 87 high-quality Chinese management journals and 1177 high-quality English management journals as the basis for literature retrieval， a bibliometric analysis is conducted on research related to Generative Artificial Intelligence in Economics and Management. The analysis covers journal distribution， author and institution collaboration networks， and keyword-based literature analysis， organized according to the four subfields under the Management Science Department of the National Natural Science Foundation of China： Management Science and Engineering， Business Administration， Economic Sciences， and Macro Management and Policy. The findings include： 1） There are differences between Chinese and English-language literature. Chinese literature focuses on information resource management and library and information science. Collaborative relationships are primarily influenced by disciplinary， institutional， and geographical similarities. In contrast， English literature spans a wider range of journals， and institutions. However， consistent research outputs from cross-institutional collaboration have yet to emerge. Strengthening cross-disciplinary， cross-regional， and cross-institutional collaboration remains a need for both Chinese and English research. 2） In both Chinese and English literature， studies are mainly concentrated in the subfields of Macro Management and Policy， as well as Management Science and Engineering， with a strong emphasis on empirical and applied research. Business Administration and Economics have relatively fewer studies， and literature focusing on generative artificial intelligence technologies and associated risks is also limited. Furthermore， English-language literature exhibits a broader range of research themes and application areas than Chinese literature， with higher research volumes and greater thematic focus. Future research should emphasize the integration of generative artificial intelligence with management tools， theoretical theories， and complex management scenarios， as well as on addressing specific management research paradigms.

As an important technical tool in today’s economy and society， risk analysis has shown its significant application value in many fields such as energy， finance， natural disasters and emergency response. However， risk analysis has not been widely regarded as an independent science， and its related theories and methods are still scattered in different subject areas， lacking systematic integration and systematic development. In addition， the traditional risk analysis method based on probability and loss modeling is relatively narrow， and it is difficult to fully and accurately reflect the diversity and complexity of risks in modern society and system. In view of this， based on the new ideas and theories emerging in the field of risk analysis in recent years， the construction of a new interdisciplinary science of “risk science” is advocated. Through in-depth analysis of the connotation and development process of risk science， a systematic framework system of risk science is put forward， and the research progress and future trends are summarized in six sub-fields of risk understanding， risk identification， risk assessment， risk perception， risk communication and risk control. It aims to integrate cutting-edge risk management concepts， integrate knowledge and methods in the field of risk analysis and management， and then promote the construction of a more complete and systematic risk management system to cope with the increasingly complex and changeable risk challenges in this paper.

The rapid development of artificial intelligence （AI） has catalyzed new corporate practices and marketing models， transforming the way companies interact with consumers and revolutionizing the theory and practice of marketing science. To reveal the full picture of this transformation， the gradual three-stage process of AI-driven marketing transformation is reviewed and mapped out， spanning from its emergence and development to its deepening， based on representative literature in the interdisciplinary field of AI and marketing science in recent years. A theoretical analysis framework of "AI Cognition—AI Empowerment—AI Interaction—AI Integration" is then proposed. Finally， combined with this framework， future research directions are outlined， including constructing more explanatory AI adoption models， developing fair AI pricing algorithms， exploring the psychological mechanisms of consumers in AI interactions， and designing effective human-machine collaborative management mechanisms， with the aim of promoting theoretical development and practical applications in the interdisciplinary field of artificial intelligence and marketing.

A review of the measurement of digital economy development is presented from region，industry，and enterprise perspectives， offering insights for the development of a comprehensive new quality productivity （NQP） assessment system. Regional level measurement relies on traditional national economic accounting frameworks，digital economy satellite accounts，and comprehensive indices. Given the multifaceted nature of the subject matter，future research should concentrate on the development of satellite accounts for NQP， employing a combination of value-added and growth accounting techniques. Industry level studies typically calculate national industrial digital inputs but cannot reflect regional variations. The inclusion of inter-regional data is essential for accurately measuring industrial digital inputs. Enterprise-level measurement，which employs traditional methods such as IT expenditure or intangible asset investment ratios， is inadequate for capturing the intricacies of enterprise digital transformation. Newer methods，such as text mining from annual reports，offer promise but require further validation. The identified gaps are addressed by analyzing multi-regional input-output tables and constructing a regional digital economy factor “input-output ” matrix，combining industry dependence on the digital economy and regional digital economy scales. The findings suggest that industrial digitalization inputs across diverse regions exhibited an overall increase during the observation period. However，notable discrepancies are observed between the developed eastern regions and the less developed inland regions. Notwithstanding their comparatively lower levels of digital input，these regions exhibited rapid growth，thereby demonstrating substantial potential for digital transformation and the likelihood of narrowing the gap with eastern regions. The digitalization input index typically reflects a trend whereby the tertiary industry exhibits the highest level of digitalization input，follow ed by the secondary industry，and then the primary industry. Those industries that are highly dependent on information perform notably well，whereas traditional manufacturing，resource-based industries，and some service sectors show relatively lower levels of digitalization input. The eastern coastal regions，particularly Guangdong and Jiangsu，occupy a pivotal position within the digital economy network，demonstrating robust economic spillover and convergence capabilities. In contrast，the central and western regions exhibit pronounced convergence abilities. The theoretical references and methodological support are provided for measuring NQP，aiding policymakers in more precisely understanding the development trends of the digital economy and crafting targeted policies to reduce the regional digital divide.

A cooperative game describes real-world allocation problems by specifying the worth of all potential coalitions of players. A solution of cooperative games aims to distribute the worth of the grand coalition among all players fairly. The Shapley value is one of the most important solution concepts in cooperative game theory. The paper focuses on the Shapley values for cooperative games with a coalition structure or a communication network. A coalition structure is a partition of the player set， where each component acts as an intermediary node during the allocation process. The Aumann-Drèze value， the Owen value， and the two-step Shapley value are well-known extensions of the Shapley value to cooperative games with a coalition structure. A communication network consists of nodes and edges， where nodes and edges represent players and communication relationships between players， respectively. Only connected players in the network can communicate with each other. The Myerson value， the position value， and the average tree solution are well-known extensions of the Shapley value to cooperative games with a communication graph. The underlying paper aims to review these extensions. For coalition games， both the traditional coalition structure and the extended forms of coalition configurations and level structure are concerned. For network games， the extended form of hyper-network is also studied. Moreover， the extensions of the Shapley value to cooperative games with both a coalition structure and a communication network are also reviewed， where two cases are considered： 1） coalition network games in which the coalition structure and the communication network are independent； 2） two-layer network games in which the two are interrelated. According to these reviews， the relationship between different Shapley value extensions are more clear， which may support decision making in reality.

In the context of large language models， linguistic theories and technologies are increasingly integrated into management research. The interdisciplinary field of “Management+Language” within the emerging context of new business disciplines is systematically reviewed and synthesized. First， it traces the theoretical origins linking linguistics and management studies， establishing a foundational framework that integrates linguistic insights into management research. This framework identifies three core themes central to both fields： cognition and emotion， interpersonal dynamics， and group culture. Using bibliometric analysis of 1246 articles from 39 leading domestic and international management journals， the developmental trajectory of “Management+Language” research is ourlined， identifying three critical phases： the exploration phase （before 1999）， the integration phase （2000-2009）， and the diversification phase （2010 and after）. The interdisciplinary framework is applied to summarize the major contributions in four key management domains： accounting， marketing management， organizational behavior and human resource management， and strategic management. Finally， future research trends are examined and questions within the management linguistics field are unresolved. Insights and recommendations for deepening “Management+Language” research in the Chinese context are also provided.

The continuous emergence of new-generation information technology has driven the deep integration of supply chain and platform economy， and supply chain management has stepped into a new stage of platform supply chain. The development of platform supply chain， while driving the evolution and transformation of business model， is also reshaping the boundaries between different market participants， which raises numerous challenges for academics and practitioners. Despite extensive research on platform supply chain has been conducted in recent years， a systematic literature review on this field is still lacking， especially regarding how to address the complex challenges in platform supply chain management and how to seize new opportunities in future development.Driven by reality and theoretical needs， a combination of descriptive statistics and content analysis is adopted to conduct a comprehensive and systematic review of the relevant research in the field of platform supply chain management in the core set of Web of Science and CNKI database from 2013 to 2023. Through quantitative analysis of literature from the past decade， the research hotspots and development trends in this field are identified， and further content analysis is conducted from both problem-oriented and method-driven perspectives. Based on the logic of “why-what-how”， the issues of platform supply chain management are summarized that have been addressed in the past decade from three angles： “why promote the construction of platform supply chain-what are the obstacles to promoting the construction of platform supply chain-how to promote the construction of platform supply chain”. The research challenges are then discussed， and opportunities for future research are identified.It is found that the research challenges in platform supply chain management include the complexity of collaboration and integration， the dual dilemmas of technology and data， the pressure of ecological design and innovation， and the governance dilemma and regulatory issue. Based on these findings， new opportunities of platform supply chain management in the future are proposed from four aspects： new environment， new technology， new ecology and new governance， which are platform supply chain collaborative operation under complex environment， platform supply chain operation decision-making considering technology empowerment， platform supply chain operation mode innovation under ecological background， and platform supply chain governance with multi-subject participation. It is hoped that new perspectives for theoretical innovation and deeper research in academia are provided and reference and practical guidance are offered for enterprises and organizations in coping with real-world challenges.

With the accelerated pace of urbanization， issues such as traffic congestion as “traffic diseases”， have become increasingly prominent， rendering the urgent need for effective health management of urban transportation systems. To address current challenges in urban transportation health management， including insufficient health capacity assessment， inadequate understanding of systemic pathological mechanisms， and fragmented diagnostic methods， “complexity” perspective is proposed to better comprehend the health of complex systems and to preliminarily develop health management theory for urban transportation systems. Accordingly， a system-level framework for urban transportation health management is developed， integrating health assessment， pathological knowledge discovery， and intelligent diagnosis. Methods for evaluating health capacity， uncovering systemic disease patterns， and intelligent diagnostics are systematically reviewed， supporting systematic decision-making in urban transportation systems and promoting the scientific management of urban transportation system health.

In the context of unprecedented global changes， the goal of fully building a modern socialist country has been recognized as requiring the guidance of an China’s independent knowledge system. Scholars in China’s management field have actively sought to develop a uniquely China’s management knowledge framework. Time-space theory is grounded in the fundamental concepts of “time” and “space”， establishing a systematic framework to enhance the effectiveness and efficiency of individual and organizational thinking and actions. This theory offers a novel perspective for addressing complex management challenges and is widely applied across various fields. The purpose of this study is to summarize and analyze the research development and application status， conceptual framework， and future research directions of time-space theory. A literature review and theoretical research methods are employed to examine the research and application status， conceptual framework， and future research directions of time-space theory. It is structured as follows in this paper： first， the research development and application status of time-space theory since its introduction in 2016 is reviewed. Next， the content of time-space theory is systematically outlined， covering its core concepts and applications in fields such as leadership and management， personal growth， individual learning， organizational learning and organizational development. Subsequently， the discussion focuses on how time-space theory challenges traditional Western management paradigms by reinterpreting management issues through a time-space perspective， enabling managers to optimize decision-making and effectively guide organizational growth， thereby providing a novel theoretical framework to unlock individual and organizational potential. It is suggested that future research on time-space theory should prioritize six areas： first， deepening the study and application of existing concepts and perspectives within time-space theory； second， broadening its research and applications across diverse fields； third， conducting qualitative studies to further explore time-space interpretations and predictions of real-world events； fourth， undertaking quantitative research to further enrich and develop time-space theory； fifth， promoting practical applications of time-space theory in key areas relevant to national development and public welfare； and sixth， promoting the spread and application of time-space theory in the world. Time-space theory represents a transformative paradigm shift in management research， advancing the field and supporting the development of an China’s independent knowledge system. It aims to contribute to the establishment of impactful management theories originating from China， enabling Chinese management theories to gain recognition and influence in the global academic community.

Online reviews reflect customers’preferences for various product features. Mining this preference information can help potential consumers better understand the products， leading to more informed purchasing decisions， while also providing valuable insights for product improvement， market positioning and promotional strategies. In recent years， scholars have conducted extensive research on the mining of customer preferences from online reviews， but there is a lack of systematic literature review in this field. To systematically understand the current status， limitations， and future research trends， a literature review is conducted using bibliometric analysis and content analysis. Initially， the publication of relevant literature and keyword clustering are quantitatively analyzed. Based on the process of mining consumer preferences from online reviews， this literature is scrutinized and categorized into three research themes： identification， analysis， and application of customer preferences derived from online reviews， thereby constructing a systematic research framework. Subsequently， a comprehensive analysis of each theme is conducted from both current status and limitations. Finally， future research trends are proposed， focusing on enhancing the accuracy of customer preference identification， exploring personalized and dynamic preferences， expanding the application domains of preferences and promoting the integration of multimodal information.

To address the challenges of global resource scarcity and environmental pollution， circular supply chain management has become an important practice promoted by governments and implemented by enterprises. Based on the existing literature and enterprises’practices， an analytical framework is proposed for circular supply chain management research. Using this framework， the bibliometric analysis is applied using data collected from 898 English sample articles in 12 prestigious journals in the field of operations management， to conduct frequency analysis， co-word analysis and clustering of keywords. Based on analysis results， the evolution of circular supply chain management research is summarized， namely： ‘from internal operation to supply chain-wide coordination’，‘from reverse logistics and closed-loop supply chain to circular supply chain’ and ‘the emergence of new research themes （applications of digital technologies， supply chain resilience， Chinese practices）’. Finally， four possible future research directions are presented， namely： ‘open-loop recycling of end-of-life products’， ‘circular supply chain network collaborations and ecosystems’，‘application of digital technology in circular supply chain management’ and ‘circular supply chain resilience’.Some implications are provided for other researchers and it brings insights to the development of circular supply chain management practices in this study.

Megaprojects serve as experimental platforms for forging megaproject complex equipment， where challenging construction environments and stringent technical demands drive the need for customized， integrated， and adaptive innovation. This fosters the emergence and evolution of the innovation network for megaproject complex equipment. Simultaneously， the boundaries between corporate innovation management and project management are increasingly blurred and intertwined， rendering traditional innovation network theories inadequate in addressing the novel challenges and issues arising during the manufacturing of megaproject complex equipment. By focusing on the unique attributes of megaprojects and their associated equipment innovations， it aims to construct a theoretical framework for the innovation network for megaproject complex equipment in this study， facilitating a precise understanding of the innovation patterns within complex equipment. Through a comprehensive review of related research on equipment innovation and innovation networks， the concept of the “innovation network for megaproject complex equipment”is delineated， deconstructing its basic structure from three dimensions： production and manufacturing， projects， and cross-sectoral collaboration. It particularly examines three primary operational mechanisms： project-driven construction， complex equipment integration， and multi-dimensional and cross-sectoral linkage. Additionally， it anticipates future research directions from three perspectives： network emergence， network evolution， and network governance， providing a theoretical foundation and analytical framework for subsequent studies.

With the rapidly increase in installed capacity of wind and solar power in recent years， the high proportion of wind and solar power consumption is considered as the key to achieving the goal of low-carbon energy transition， and the scholars have conducted extensive researches on the topics of consumption mechanisms and strategies. The method combining the bibliometric analysis and thematic modelling is adopted to analyze the evolution path and developing trend of this field， and then the shortcomings and the frontier direction are discussed. The hotspots in different stages were first identified. Based on the emergence and co-occurrence time series analysis， the dynamic evolution of the research hotspots is explored. Secondly， the thematic modelling is conducted in five time slices， and the main evolutionary paths are then identified and analyzed. Based on the research results， a specialized analysis is conducted on the main modules of the current consumption system. The topics including the new environment based on the coordination of electricity-carbon-green certification market， the new methods based on deep learning and artificial intelligence and the new mode based on the coupling with industrial sectors are expected to become the breakthrough points in future research on wind and solar power consumption. The research results can provide guidance for the subsequent studies， as well as experience reference and theoretical support for the realistic consumption problem.

Digital humans are virtual entities that replicate human appearance， behavior， and cognition processes， capable of interacting in both virtual and real worlds. The emergence of digital humans has created new opportunities for brand image building and consumer engagements， accelerating the shift from traditional digital marketing to a hybrid model that integrates both virtual and real elements. Despite growing interest from scholars and practitioners， significant gaps remain between theoretical research and practical applications in digital human marketing， which need to be addressed. 213 English-language articles from the Web of Science core collection and 216 Chinese-language articles from the CNKI database are analyzed. The external and internal characteristics of digital humans are first explored， identifying three theoretical foundations of digital human marketing： individual cognition， information processing， and emotional interaction， and summarizing four key dimensions of its effectiveness. Based on these analyses， two mediating mechanisms are identified： cognitive responses and emotional responses， and outline three boundary conditions that affect the effectiveness of digital human marketing from the perspectives of consumers， corporations， and environment. Finally， unresolved issues in digital human marketing research are discussed and insights into potential future directions for advancing this field are provided.

Dealing with uncertainty in climate-economic modeling is a challenging task and a major point of disagreement in many climate policy studies. Uncertainty exists in both the natural climate system and the socioeconomic system，as well as in their interactions，such as the socio-economic impacts of climate change and human adaptation. In addition，the limited nature of human knowledge（uncertainty）regarding the complex climate economic systems also contributes to uncertainty in modeling. The study of uncertainty in climate-economy complex systems has gained increasing attention since the development of the first fully meaningful climate-economy complex systems model by the Nobel Prize winner in economic sciences， William D Nordhaus，in the early 1990s. With the development of uncertainty decision theory and methodology，climate science， and computational technology， there have been many advances in the study of uncertainty in climate-economy complex systems. At the same time，there are still many challenges in the understanding of the mechanism of the system，model coupling，parameter calibration，solution algorithms and other aspects that need to be overcome. Climate-economy complex systems models generally consist of the climate system module，climate impact module，socio-economic system module，and mitigation and adaptation（climate policy）module，which are coupled into a closed-loop system. The key uncertainties are categorized and classified in these four key modules in the climate economy complex system， the research progress in handling uncertainties is summaried in system modeling，and the future research directions are explored， with a view to better support climate economy modeling and decision-making. In terms of methodologies to address uncertainty，the stochastic dynamic programming approach is gradually replacing the classical sensitivity analysis and Monte Carlo simulation methods for dealing with risk-based uncertainty in climate-economy models；and the decision analysis approach is emerging as a new trend for dealing with deep uncertainties （ambiguities and misspecification） in climate-economy models. In terms of specific modeling of uncertainty： （1） the simple “carbon-climate response” is often incorporated into cost-benefit assessments under uncertainty. （2） stochastic process modeling is commonly used to model uncertainty in economic growth and technological progress. （3） catastrophic risks such as climate tipping points，and the non-economic losses from climate change are gradually incorporated into models. （4） criteria for evaluating utility or social welfare are more focused on inter- and intra-generational equity，as well as the uncertainty averse preferences. （5） endogenous technological progress in emission reduction is more reflected in the model. （6） negative-emission technologies and solar geo-engineering may become hot spots in the modeling of climate-economy complex systems. Finally，responding to climate change is a cross-cutting scientific issue that requires the full cooperation of multidisciplinary experts in earth sciences，economic and management sciences，computational sciences，and other disciplines. The intrinsic characteristics of uncertainty in the climate economic system and the subjective conditions that are not yet fully recognized scientifically make the modeling of the climate-economy under uncertainty face many challenges. Model-solving remains one of the bottlenecks in the development of climate economic modeling under uncertainty. In addition to balancing model complexity，data availability and computational feasibility，future modeling of climate economic uncertainty needs to incorporate the heterogeneity and gaming mechanisms of different levels of decision makers and groups；the analytical framework for climate decision making should not be limited to cost-benefit analysis；and climate economic modeling should also incorporate the political-economic factors as well as the complex impacts of the different players.

Resource depletion and environmental degradation have rendered traditional high-consumption， high-emission production models unsustainable. The manufacturing industry is urgently seeking low-carbon models to address current crises and secure future development. As a crucial component of the circular economy， remanufacturing extends product lifecycles， reduces resource consumption， and minimizes waste emissions， making it an effective approach to addressing resource scarcity and environmental deterioration.Since Lund introduced the concept of remanufacturing in 1983， it has garnered attention from governments， industries， and academia alike. A plethora of literature on the subject exists. Based on this， relevant literatures are reviewed and an analytical framework is proposed for remanufacturing research.From the perspectives of waste product recovery， remanufacturing efficiency and benefits， and market strategies for remanufacturing， 14 thematic terms are identified. It involves searching for these thematic terms in the titles， abstracts， and keywords of articles. During the search， a total of 12 journals are selected as sources of literature， all of which are mainstream journals in the field of operations management， with six from domestic and six from international sources. The time frame is set from 2014 to 2024. After the initial retrieval， the articles are manually screened and duplicates are removed using bibliometric analysis tool Cite space. Ultimately， 561 articles are identified， including 275 Chinese articles and 286 English articles. The visualization in this study focused on keyword analysis， conducting both co-occurrence and cluster analyses of the keywords.Furthermore， content analysis on the development of remanufacturing is conducted from three perspectives： research scope， research subjects， and research focus. Three research trends are identified： from resource efficiency-driven topolicy-driven for waste product recycling， from traditional supply chain management to intelligent supplychain management for remanufacturing closed-loop supply chains， and from market competition and cooperationstrategies to supply chain coordination optimization for remanufacturing market.At last， in terms of the shortcomings of existing research， future directions for remanufacturing are proposed. These include： shifting from a supply chain perspective to a consumer-oriented approach， balancing remanufacturing intelligence with supply chain resilience， andachieving multi-objective coordination of economic， environmental， social， andcultural goals. It aims to help researchers understand the current state and challenges of remanufacturing research， providing a reference for future studies.

With the rapid development of digital and intelligent technologies， the digitalization and smart transformation of the manufacturing industry are accelerating， and product innovation empowered by these technologies has become a core driver of real economic growth. By leveraging technologies such as AI， big data， and IoT， companies can quickly respond to market demands， optimize product designs， and improve production efficiency， thereby enhancing innovation capabilities and competitiveness. However， modern product innovation is no longer solely driven by technological advancements； companies are increasingly integrating Environmental， Social， and Governance （ESG） standards into their product innovation and supply chain management strategies. Supported by digital technologies， businesses can not only improve production efficiency but also implement green supply chain practices， reduce carbon emissions， and ensure the environmental sustainability of products throughout their lifecycle. Meanwhile， intelligent technologies enable companies to enhance supply chain transparency， focus on social responsibility and fair trade， and further strengthen brand value and societal trust. This approach， which combines technological innovation with ESG standards， achieves both economic benefits and sustainable development， driving long-term growth for companies.In today’s highly competitive and uncertain market environment， the focus of product innovation competition in manufacturing enterprises is gradually shifting towards supply chain-based competition. Under traditional business models， manufacturers often conduct product R&D and production independently， resulting in product innovations that may not fully align with consumer demands， leading to greater market risks. The development of digital and intelligent technologies has enabled manufacturing companies to create innovation models and management strategies based on the supply chain. For example， in product crowdfunding， consumers not only participate in pre-ordering but also in product design and decision-making， fostering greater interaction. This process enhances product quality and innovation， while strengthening brand loyalty and word-of-mouth promotion， ultimately driving long-term profits. At the same time， the shift in digital technologies and business philosophies has given rise to the new supply chain model of the ecological chain. The ecological chain integrates upstream suppliers， online and offline platforms， and downstream sales networks through core enterprises， forming a comprehensive system. Compared with traditional supply chains， the competitive and collaborative relationships in an ecological chain are more complex， bringing about new business models and supply chain structures， while also presenting new challenges in product innovation management， particularly in resource sharing， risk control， and innovation collaboration. In this context， companies must not only drive innovation but also ensure sustainable development based on ESG standards， enhancing brand value and social responsibility to stand out in the global competition. This transformation has not only created new business models and supply chain structures but has also brought about numerous new management challenges， including the mining of information and knowledge for product innovation， supply chain management based on product innovation， collaborative product innovation among supply chain members， and the design of supply chain mechanisms that are rooted in product innovation.These challenges provide scholars in fields such as information systems， information management， operations， and supply chain management with vast research opportunities. They encompass product innovation within horizontal supply chain enterprises， vertical product innovation between upstream and downstream supply chain companies， product innovation among external supply chain members， and the coordination mechanisms of supply chains based on product innovation. Based on a thorough analysis of the core challenges in managing product innovation in supply chains during the digital and intelligent era， relevant research findings and looks ahead to future research directions are systematically reviewed. In particular， it explores how to deeply integrate ESG factors with product innovation to promote the synergistic development of sustainable practices and product innovation. Specifically， five key research directions are identified that urgently need exploration in the field： information mining and knowledge discovery for product innovation empowered by digital and intelligent technologies， enterprise operations management based on product innovation， horizontal competition in product innovation among supply chain enterprises， vertical competition in product innovation between upstream and downstream supply chain companies， and the design of supply chain mechanisms based on product innovation. These five dimensions encompass various aspects， ranging from information and technology-driven innovation discovery to the design of collaborative mechanisms both within and outside the supply chain， involving not only technological applications but also in-depth discussions at the management and strategic levels. Addressing these challenges requires the application of methods such as data mining， machine learning， empirical analysis， modeling optimization， and mechanism design in specific supply chain product innovation practices. Integrating these five dimensions with data analysis， ESG standards， and the ecological chain concept will help drive innovation within supply chains， promote sustainable development， enhance coordination among supply chain partners， and provide theoretical support for more resilient and socially responsible business models.

Accurately predicting asset returns is essential for informed investment decision-making and maintaining financial market stability. With the rapid advancements in artificial intelligence and computing technologies， machine learning （ML） has demonstrated notable advantages in handling high-dimensional data and modeling complex， nonlinear relationships. A comprehensive review of ML applications in asset return prediction， encompassing stocks， funds， cryptocurrencies， and bonds is presented. The existing research on algorithm selection， model construction， and performance evaluation is systematically sumarized. This review begins by examining the origins and significance of asset return prediction， challenging the efficient market hypothesis and contributing to behavioral finance by analyzing irrational investor behaviors and sentiments. A spectrum of ML methods is then explored， ranging from traditional linear approaches to advanced deep learning and large language models （LLMs）， highlighting their ability to address the complexities of financial markets. Techniques such as LASSO and Ridge regularization effectively manage high-dimensional datasets， while neural networks and recurrent neural networks （RNNs） capture long-term dependencies in time series data. Moreover， LLMs like BERT and GPT have shown promise in sentiment analysis and processing textual data， further improving predictive accuracy. The findings reveal that ML methods， particularly ensemble learning and deep learning models， consistently outperform conventional statistical models. For instance， Random Forests and Gradient Boosting Machines achieve superior out-of-sample accuracy， and integrating LLMs with financial text data opens new avenues for sentiment-based return prediction. The data sources employed， including historical prices， macroeconomic indicators， financial news， and social media sentiment， enable comprehensive model evaluations under diverse market conditions. By identifying current research gaps and future directions， this review underscores the importance of balancing predictive accuracy with model interpretability， as well as expanding the scope of asset classes examined. In summary， the analysis provides a holistic perspective on ML applications in asset return prediction， emphasizing their potential and challenges. This work informs investors， policymakers， and researchers， facilitating more effective strategies and decisions in the ever-evolving financial landscape.

The traditional literature primarily examines the interplay between water， energy， and food in the context of sustainable development， drawing from disciplines like environment， geography， and ecology， as well as the long-term evolution of the system. In light of the escalating frequency and severity of natural and social emergencies， there is a pressing need to investigate strategies for enhancing the resilience and resistance of the water-energy-food linkage system to low probability emergencies. However， a comprehensive theoretical framework in this area is currently lacking， as well as research paradigms and a global understanding of the issue. Consequently， it seeks to comprehensively summarize quantitative research literature on the water-energy-food linkage system in this article， focusing on mechanism modeling， risk measurement， and resilience enhancement. Additionally， it explores the future trend of digital technology empowering resilience research in this field through cross-scale risk measurement and intelligent decision-making， leveraging the advancements in big data and artificial intelligence technology.

The equipment manufacturing industry is a crucial indicator of a nation’s industrial strength， and the competitiveness of this sector hinges on the product competitiveness of manufacturing enterprises. The strategic approach to manufacturing product competition forms the core foundation for the development of equipment manufacturing businesses. In this study， after analyzing the existing theories of product strategy management and their deficiencies， a novel strategic analytical tool-the Six-Base-State Product Competitive Strategy-is proposed based on the intrinsic logic behind the successful evolution and development of Weichai’s product competitive strategies. Central to this strategy is the concept of starting with customer value preferences and comparing products with similar competitors in the market from two dimensions： technical performance and market performance. It introduces the Six Base States for technical performance and market performance， along with an analysis of the factors influencing these base states. Through the analysis of these two base states， it determines the competitive condition of the product. In line with the company’s product development goals， it provides optimized strategies and pathways for product strategy. Finally， a case study of Weichai Group’s WP12 product is conducted to validate the model. This product competitive strategy model holds significant reference value and importance for the product strategy management of equipment manufacturing enterprises.

Developing new production operations management models that align with the evolution of new productive forces is becoming a prevailing trend. Facing multiple challenges such as system complexity， sustainability， and technological updates， mathematical programming algorithms play an especially important role in production and operations management optimization. It focuses on the latest research trends in the field of mathematical programming algorithms in production and operations management optimization in the past five years in this study， summarizing and analyzing relevant literature from five aspects： job scheduling and production planning optimization， production system and capacity planning optimization， production facility layout and location optimization， inventory management optimization in production systems， and supply chain optimization for production systems. Future research directions are proposed， including data-driven production and operations management research， production optimization research enabled by digital intelligence technology， end-to-end optimization algorithm research for production operations， and new methods of production management based on Chinese practices. It aims to provide researchers and practitioners in the field with valuable references and insights in this study， thereby contributing to the advancement of production and operations management optimization.

The taxation in the securities market is an important source of fiscal revenue in China. In recent years， the market has continued to be sluggish， and the stamp duty has been reduced repeatedly. Although this can boost market confidence in the short term， it has a significant impact on the total tax revenue. Therefore， whether to reform the transaction tax， how to tax capital gains on the basis of the current reduction in stamp duty， and what market impact the tax reform will have are important issues that the regulatory authorities are concerned about.In this context， an experimental study is conducted on the optimization of transaction taxes by constructing an artificial stock market with the microstructure and trading behavior characteristics of the A-share market. The specific experimental scenarios are as follows：（1）Uniform stamp duty rate experiment. This experiment replicates the current real-market situation of a fixed stamp duty rate and serves as the baseline experiment for the study. Only when this baseline model accurately represents the main features of the stock market， can the subsequent experiments have some persuasiveness.（2）Differential stamp duty rate experiment. In this experiment， the stamp duty rate is adjusted from a uniform mode to a differentiated mode based on the holding period to explore the impact of stamp duty rate adjustments on investor trading and market tax revenue. The stamp duty rate is set as a progressive differential mode based on the holding period， making the trading cost lower for investors with longer holding periods， encouraging long-term investment， and discouraging short-term speculative trading.（3）Capital gains tax and reduced stamp duty experiment. Building on the differentiated stamp duty， this experiment further reduces the stamp duty rate and introduces capital gains tax to explore the impact of a progressive stamp duty reduction and the introduction of capital gains tax on investor trading and market tax revenue. Lowering the stamp duty rate will further reduce trading costs， promoting market activity. The progressive capital gains tax is designed to stabilize the financial market and encourage long-term value investments while maintaining overall market tax balance.（4）Increased capital gains tax and exemption from stamp duty experiment. Building on the previous experiment of capital gains tax and reduced stamp duty， this experiment further exempts stamp duty and raises the capital gains tax rate to explore the impact of exempting stamp duty and increasing capital gains tax on investor trading and market tax revenue. Exempting stamp duty will further reduce trading costs and increase market activity. The progressive increase in capital gains tax ensures overall market tax balance.The research findings show that compared to the previous uniform 0.1% fixed stamp duty rate， implementing the current halved stamp duty rate along with a progressive and differentiated capital gains tax based on holding periods will effectively increase market trading volume without affecting the total tax revenue. From the perspective of balancing market activity and tax revenue， this article suggests considering a pilot program in the A-share market to levy capital gains tax while further reducing or exempting stamp duty.