Long-term ecological restoration has promoted vegetation recovery across the Pisha sandstone region of the Yellow River Basin, yet why recovery remains spatially differentiated under a broadly shared restoration context is still poorly understood. This study examined how geomorphological setting and multidimensional topography organize vegetation recovery through nonlinear and interactive spatial responses. An integrated analytical framework was developed to resolve long-term recovery heterogeneity, geomorphological differentiation, nonlinear topographic responses, and multifactor spatial interactions. Vegetation cover increased overall but followed markedly heterogeneous recovery trajectories across the landscape. Distinct vegetation states among soil-covered, sand-covered, and exposed Pisha sandstone areas indicate that geomorphological setting provides an important environmental template for recovery. Vegetation responses to elevation, slope, and topographic position were nonlinear and range-dependent, whereas aspect showed comparatively weak marginal differentiation, revealing that topographic regulation varies substantially among terrain dimensions rather than following a uniform directional pattern. Multidimensional topographic combinations provided greater spatial explanatory power than individual factors, with particularly strong complementarity between broad-scale elevation and local topographic position. These findings indicate that regional ecological restoration does not produce spatially homogeneous recovery; instead, vegetation responses are progressively differentiated through geomorphological context, nonlinear topographic gradients, and complementary terrain structure. The study advances the interpretation of ecological recovery from regional greening patterns toward a nested spatial-filtering framework in which geomorphological background and multidimensional topography jointly organize recovery heterogeneity, providing a spatially explicit basis for context-dependent restoration assessment and management in erosion-prone fragile landscapes.

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This study addresses a fundamental question in smart city geography: how can multisource spatiotemporal data and artificial intelligence (AI) be transformed from tools for spatial representation and prediction into a coherent framework for dynamic spatial cognition and mechanism-oriented inference? Through a systematic synthesis of the field’s evolution from statistical analysis, spatial econometrics, and GIS-based decision-making to GeoAI-enabled spatial intelligence, the study organizes major advances in multisource sensing, deep learning, spatiotemporal knowledge graphs, causal inference, complex networks, reinforcement learning, and digital twins along a continuous “sensing–representation–inference–decision” pathway. A Cyber–Physical–Social Systems perspective is further used to conceptualize the coupling of physical, informational, and social spaces, from which a four-layer framework is developed encompassing multisource urban sensing, spatiotemporal relational cognition, intelligent scenario simulation, and collaborative governance. The synthesis identifies cross-scale effects and the Modifiable Areal Unit Problem, spatial confounding and causal identification, geographic heterogeneity and transferability, model interpretability, and spatial equity as major methodological constraints on the transition from predictive performance to decision credibility. Emerging priorities include mechanism-constrained GeoAI, cross-scale causal inference, digital-twin-based scenario analysis, human–AI collaborative planning, and trustworthy spatial intelligence. The study’s principal contribution is to recast smart city geography as an integrated spatial-intelligence framework that couples multisource observation with geographic knowledge, intelligent inference, and accountable governance, providing a theoretical and methodological basis for advancing urban analysis from static description and prediction toward dynamic, interpretable, and mechanism-oriented understanding.

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How shallow-marine redox conditions responded to changes in water-column dynamics during the Neoproterozoic remains poorly constrained, limiting process-based understanding of regional marine environmental heterogeneity. Here, we investigate the Neoproterozoic Hejiazhai Formation in western Henan, southern North China, by integrating stratigraphic and sedimentological observations, petrographic analysis, and elemental geochemistry to reconstruct depositional evolution and redox variability and to test their stratigraphic coupling. The Hejiazhai Formation comprises predominantly argillaceous limestone, limestone, and quartz-bearing detrital limestone, recording a shallow-marine carbonate system variably influenced by terrigenous input. Integrated lithofacies and microfacies analysis identifies at least three recurring deepening–shallowing cycles, whereas V/Cr and Ni/Co display stratigraphically coherent fluctuations that broadly track these depositional cycles, with relative deepening generally associated with less oxic conditions and shallowing with more oxic conditions. Although the geochemical proxies remain sensitive to provenance, mineralogical composition, and diagenetic modification, their recurrent correspondence with independently constrained facies changes supports a systematic sedimentary–redox relationship rather than isolated geochemical anomalies. We propose a process-based framework in which relative water-column changes may modify water-column structure and exchange, alter oxygen supply, and consequently influence redox-sensitive elemental responses. This framework reframes Neoproterozoic shallow-marine redox reconstruction from static proxy-based state identification toward sedimentary–redox coupling, providing a testable mechanism for evaluating how depositional and water-column dynamics contributed to the spatial and temporal heterogeneity of marine oxygenation along the southern North China Craton.

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The central scientific question in heritage geography is how heritage is constituted and transformed as a dynamic socio-spatial process under the joint influence of spatial heterogeneity, power relations, place identity, and governance. This study systematically synthesizes the theoretical evolution, spatial typologies, major research themes, methodological developments, and emerging frontiers of heritage geography, with particular attention to digital spatial representation, heritage tourism and place identity, vulnerability under global change, multicultural heritage, and spatial justice. By integrating GIS and remote sensing, sociocultural inquiry, spatial modeling, and interdisciplinary approaches, the study develops an analytical framework that links the power–identity–space nexus with an Observation → Heterogeneity → Infrastructure logic, connecting heritage value formation and spatial differentiation to institutional and governance responses. The synthesis indicates that heritage conservation is increasingly concerned not only with the preservation of material entities but also with the processes through which heritage values are selected, interpreted, spatially reproduced, and renegotiated across actors and scales. The framework further incorporates community participation, digital governance, sustainable development, and resilience as complementary dimensions for addressing heterogeneous heritage conditions under global change. By reconceptualizing heritage as a dynamic socio-spatial process rather than a fixed protected object, this study advances heritage geography toward a process-based and multiscale understanding of how heritage values, spatial heterogeneity, and governance infrastructure interact, providing a coherent theoretical foundation for comparative research and adaptive heritage governance.

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Trade geography is increasingly concerned not only with the spatial distribution of trade flows but also with how global production networks, regional development pathways, and cross-scale risks are reconfigured. Yet existing scholarship remains fragmented across theoretical traditions and spatial scales, with limited integration of relational networks, territorial conditions, and the infrastructures that sustain trade connectivity. This review traces the evolution of trade geography from comparative advantage, spatial friction, and the new economic geography to global value chains (GVCs), global production networks (GPNs), evolutionary economic geography, and relational approaches, while examining how geopolitical restructuring, supply chain resilience, digitalization, and the low-carbon transition are reshaping trade space. Three questions guide the review: how spatial heterogeneity generates trade linkages, how relational networks operate across scales, and how institutions and infrastructures mediate network restructuring and regional adaptation. Based on this synthesis, we develop a scale–relations–institutions–ecology framework linking regional capabilities, actor agency, network relations, institutional conditions, infrastructural configurations, and ecological constraints within a dynamic spatial perspective. The review shows that contemporary trade space is shaped less by a linear shift from globalization to regionalization than by the coexistence and reconfiguration of global connectivity, regional embeddedness, and reterritorialization. Digitalization and the green transition likewise transform rather than eliminate spatial frictions by introducing new digital, institutional, and ecological constraints. We further highlight methodological challenges in network de-geographization, causal identification, and balancing efficiency, resilience, and sustainability, and call for greater integration of complex networks, spatial analysis, multisource data, and agent-based modeling. The central contribution is a unified framework for explaining how scale, relational structure, institutional power, and ecological constraints jointly shape the restructuring and uneven development of global trade space, providing a theoretically grounded and empirically testable foundation for advancing contemporary trade geography.

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Soil erosion and land degradation are coupled, cross-scale processes arising from interactions among Earth-surface dynamics, environmental forcing, human activities, and ecological feedbacks. Despite extensive advances in erosion-process characterization, monitoring, modeling, and ecological management, a coherent framework for resolving multiprocess coupling, cross-scale transitions, driver attribution, and feedbacks within social–ecological systems remains underdeveloped. This review synthesizes recent progress in hydraulic, aeolian, gravitational, and compound erosion, together with advances and methodological limitations in field observations and tracer techniques, remote sensing and geographic information systems, empirical and process-based modeling, and artificial intelligence. Particular attention is given to how erosion processes propagate across spatial and temporal scales, interact with land degradation, and respond to global environmental change and human interventions. We further develop an integrated framework of “process observation–heterogeneity identification–mechanistic interpretation–risk prediction–management feedback,” which connects multisource observations, process-based models, data-driven approaches, and adaptive management through an “observation–interpretation–prediction–decision” chain. The framework identifies multiprocess interactions, cross-scale transitions, threshold responses, uncertainty propagation, and recovery capacity as key frontiers for advancing erosion–degradation research. The central contribution is to reposition soil erosion and land degradation from separately assessed processes toward an integrated, process-based social–ecological system, providing a conceptual and methodological basis for cross-scale prediction, resilience-oriented assessment, and adaptive land management.

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Soft-sediment deformation structures (SSDs) record sediment instability and transient dynamic disturbances, but their formation conditions and tectonic significance remain poorly constrained. We investigate the Upper Triassic Maichuqing–Sanhedong succession in the northeastern Lanping Basin using integrated stratigraphic, sedimentological, lithofacies, and SSD analyses. The succession records a progressive transition from terrigenous siliciclastic to transitional and shallow-marine depositional systems, accompanied by systematic changes in sedimentary conditions. SSDs are strongly stratigraphically selective and spatially heterogeneous, and their stratigraphic relationships and deformation characteristics indicate a predominantly syndepositional to early consolidational origin. Their occurrence reflects sedimentary preconditioning by water saturation, grain-size and density contrasts, rapid deposition, and local slopes, followed by transient disturbances that may have driven susceptible sediments beyond stability thresholds through liquefaction, loading instability, and gravitational deformation. We identify a dual-scale tectonic influence in which long-term Paleo-Tethyan tectonism regulated basin architecture and depositional-system evolution, whereas short-lived syn-depositional disturbances may have triggered SSD formation. We propose a “background regulation–event triggering” framework linking long-term tectonic control of depositional conditions with transient sedimentary responses, providing a process-based interpretation of SSDs as stratigraphically constrained records of multiscale tectonic–depositional coupling.

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Landscape Evolution Models (LEMs) are increasingly challenged to move beyond morphological reconstruction toward verifiable, multiscale representations of Earth-surface dynamics under strong spatiotemporal heterogeneity, nonlinear feedbacks, and multiprocess coupling. This review synthesizes the theoretical development, process formulations, and computational architectures of LEMs, with emphasis on representative models including SIBERIA, CHILD, Landlab, FastScape, and Badlands, and their treatment of fluvial incision, hillslope transport, gravitational processes, and source-to-sink sediment transfer. We focus on four unresolved issues that constrain predictive capability: the scale dependence of effective parameterizations, the transmission of transient signals across temporal and spatial scales, the storage and remobilization of sediment along source-to-sink pathways, and the identifiability of process mechanisms from incomplete observations. We further assess how multitemporal LiDAR/SfM observations, data assimilation, machine learning and physics-informed learning, GPU/HPC computing, and interoperable computational platforms are reshaping parameter inversion, process emulation, and dynamic prediction, while highlighting persistent limitations associated with model complexity, equifinality, observational incompleteness, and the representation of vegetation, anthropogenic forcing, and nonstationary environmental change. To address these challenges, we propose an integrated framework of “observation constraints–heterogeneity representation–cross-scale coupling–computational infrastructure”, in which multisource observations, process-based modeling, data-driven methods, and scalable computing are jointly organized around conservation principles and uncertainty-aware validation. This framework provides a coherent pathway for advancing LEMs from morphology-based reconstruction toward process-verifiable, cross-scale, and predictive Earth-surface system models, strengthening their capacity to resolve landscape responses to global change and Anthropocene disturbances.

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Resource geography is undergoing a fundamental transformation as resource systems become increasingly shaped by climate change, low-carbon transitions, cross-regional flows, and geopolitical restructuring. This review addresses a central scientific question: how can resource geography explain the spatially heterogeneous, networked, and adaptive evolution of resource systems under increasingly coupled human–environment interactions? By critically synthesizing advances in disciplinary theory, major research domains, methodological development, and emerging frontiers, the review identifies a progressive shift from a conventional “stock–space” perspective centered on resource endowments, territorial assessment, and supply–demand matching toward a “flow–space” perspective emphasizing cross-regional flows, telecoupling, network interdependence, and social–ecological feedbacks. Correspondingly, resource research is expanding from individual resource assessment toward integrated analysis of resource transitions, water–energy–food interactions, critical-mineral supply chains, systemic vulnerability, and resource equity. Methodologically, the convergence of multisource Earth observation, human-sensing data, artificial intelligence, process-based modeling, and complex-systems simulation is reshaping resource research from predominantly descriptive assessment toward dynamic, process-informed, and behaviorally explicit analysis, while highlighting persistent challenges in scale consistency, uncertainty, interpretability, and cross-regional transferability. Building on these developments, this review proposes an integrated conceptual framework linking “flow space–social–ecological systems–intelligent simulation–collaborative governance,” in which resource flows connect heterogeneous places, biophysical and social processes generate system dynamics, computational methods support multiscale inference, and institutions mediate adaptive and equitable governance. This framework reframes resource geography as a systems-oriented science of spatially connected and socially mediated resource dynamics, providing a coherent theoretical basis for understanding resource security, resilience, low-carbon transitions, and equitable governance under global change.

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Planetary surface evolution represents a fundamental scientific problem for understanding how internal dynamics, surface processes, volatile cycling, and environmental feedbacks jointly shape planetary trajectories and habitability. This review establishes an integrated framework linking comparative planetary geography with Earth system science to elucidate the universal mechanisms governing surface evolution across terrestrial planets, icy moons, and small Solar System bodies. By synthesizing advances in tectonics, impact processes, aeolian dynamics, cryogenic activity, volatile migration, and atmospheric escape, we propose a four-dimensional framework of endogenic dynamics–exogenic processes–volatile feedbacks–habitability evolution and develop a multi-scale classification scheme for comparative analysis of planetary surface processes. This framework reveals that variations in effective elastic thickness, thermal evolution, gravity fields, and fluid properties constitute fundamental controls on planetary morphology, geodynamic regimes, and evolutionary pathways. Furthermore, we highlight several unresolved questions, including the emergence of plate tectonics from stagnant-lid states, the governing principles of geomorphic processes under non-Earth-like environmental conditions, the role of water–rock interactions in icy worlds and planetary habitability, and the climatic consequences of atmospheric loss and runaway greenhouse transitions. Recent advances in artificial intelligence-assisted planetary remote sensing, multi-source data integration, deep-time environmental simulation, and planetary-scale modeling are critically assessed, alongside key challenges in cross-scale coupling, extreme-environment parameter inversion, and unified theoretical modeling. By placing Earth within a comparative Venus–Earth–Mars evolutionary framework, this review advances a transition from a single-planet perspective toward a generalized planetary system science paradigm, providing a new theoretical foundation for deciphering planetary evolution, assessing extraterrestrial habitability, and understanding Earth’s long-term environmental trajectory.

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Climate change is fundamentally reshaping hydrological processes, intensifying extreme events, and exacerbating water supply–demand imbalances, thereby challenging conventional paradigms of water resources management. This review synthesizes recent advances in understanding climate-driven hydrological responses, water-related risk assessment, adaptive governance, and management strategies, and critically evaluates emerging approaches, including integrated water resources management, multi-objective allocation, nature-based solutions, engineering interventions, policy instruments, and artificial intelligence- and digital twin-enabled decision support. The evidence indicates a paradigm shift from supply-oriented management toward resilience- and risk-based adaptive governance that integrates hydrological, ecological, socioeconomic, and technological dimensions. Despite substantial progress, major scientific challenges remain, including the propagation of uncertainties across climate–hydrology–socioeconomic systems, limited integration of multi-source observations and process-based models, insufficient cross-sectoral governance capacity, and the lack of robust frameworks for evaluating long-term adaptation effectiveness. Future research should prioritize multi-scale coupled modeling and uncertainty quantification, AI-enabled digital twins for real-time decision support, resilience-oriented adaptive assessment, and integrated governance frameworks for coupled human–water systems. By integrating advances across hydrological science, intelligent technologies, and adaptive governance, this review provides a comprehensive framework for advancing next-generation climate-resilient water resources management and identifies key priorities for improving long-term water security under accelerating climate change.

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Rapid globalization, urbanization, and digital transformation are fundamentally reshaping rural systems, challenging conventional paradigms of rural geography and driving a transition toward integrated, process-oriented, and interdisciplinary research. This review synthesizes recent theoretical and methodological advances in rural geography, tracing the evolution from agricultural regional studies and political economy to social–ecological–technological system perspectives. It critically examines major research themes, including rural spatial restructuring, land-use transition, demographic change, rural governance, cultural landscape transformation, and urban–rural integration, while evaluating the growing role of geospatial technologies, big data, artificial intelligence, and digital twins in advancing multi-scale analysis and evidence-based decision-making. The review shows that contemporary rural geography is shifting from static spatial description toward dynamic analyses of coupled human–environment systems, emphasizing cross-scale interactions, place-based processes, and governance-oriented research. Despite substantial progress, major challenges remain, including limited theoretical integration across disciplines, insufficient understanding of micro-scale mechanisms, weak comparative analyses across regions, and inadequate translation of scientific knowledge into policy and practice. Future research should strengthen interdisciplinary theory development, multi-scale process modeling, intelligent spatial analytics, long-term monitoring, and adaptive governance to better address the complexity of rural transformation under global environmental and socioeconomic change. By integrating theoretical evolution, methodological innovation, and governance perspectives, this review provides a comprehensive framework for advancing next-generation rural geography research and identifies key priorities for supporting resilient and sustainable rural development.

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Population aging fundamentally reshapes the mechanisms and governance of disaster resilience in rural communities, yet the interactions among demographic transition, social networks, spatial processes, and multiscale governance remain insufficiently integrated. This review synthesizes recent advances in disaster resilience research and develops an integrated analytical framework linking four complementary dimensions: theoretical evolution, aging-induced social network reconfiguration, social–spatial system coupling, and multiscale governance. The review demonstrates that population aging influences rural disaster resili-ence not only by increasing exposure and vulnerability through labor loss, declining social capital, and reduced public service capacity, but also by restructuring resilience dynamics via changes in family support networks, spatial mobility, infrastructure accessibility, and community adaptive capacity. It further identifies multilevel governance coordination, embedded social networks, and the integration of digital and intelligent technologies as key determinants of spatial heterogeneity in rural disaster resilience. Critical research gaps include limited theoretical integration, inadequate understanding of social–spatial interaction mechanisms, insufficient dynamic process modeling, and a lack of cross-scale comparative analyses. Future research should prioritize interdisciplinary integration, multisource spatiotemporal data-driven resilience assessment, and digital-enabled adaptive governance to improve resilience in aging rural communities. By establishing a systematic framework that integrates demographic change, social–spatial interactions, and governance mechanisms, this review provides a new perspective for understanding and enhancing disaster resilience in aging rural systems.

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Rural transformation reflects the transition of rural areas from production-oriented spaces to multifunctional systems shaped by economic, social, ecological, and cultural interactions. This review synthesizes the theoretical evolution of rural transformation research and critically evaluates major analytical paradigms, including urban–rural restructuring, multifunctional development, sustainable livelihoods, spatial production, and social–ecological systems. Current research has advanced understanding of rural functional change, industrial restructuring, social reorganization, and spatial transformation, yet persistent divergences in conceptualization, mechanism explanation, and empirical approaches continue to hinder theoretical integration and comparative analysis across regions and scales. Particular attention is given to the interactions among globalization, institutional change, technological innovation, and urban–rural factor flows as key drivers of rural transformation. The review further highlights major challenges related to cross-scale integration, causal inference, social equity, and system resilience in the context of global change. By developing an integrated framework linking theoretical evolution, multidimensional dynamics, and driving mechanisms, this study identifies common patterns and explanatory pathways of rural transformation and advances a more coherent foundation for comparative and theory-building research.

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Energy geography examines the spatial distribution of energy resources, production–consumption systems, infrastructure networks, and their environmental and social impacts, providing important theoretical support for energy transition and sustainable development. With increasing energy security concerns, rapid renewable energy deployment, and the integration of digital technologies, the field has evolved from traditional resource-location studies toward broader issues involving energy networks, geopolitical dynamics, low-carbon transitions, and system resilience. This study systematically reviews the evolution, theoretical foundations, and methodological approaches of energy geography and proposes a four-dimensional analytical framework comprising resource endowment, spatial organization, system governance, and transformation innovation. The framework covers energy resource distribution and suitability, production–transportation–consumption networks, energy security and justice, geopolitical and ecological constraints, as well as emerging technologies such as hydrogen energy, energy storage, CCUS, multi-energy integration, and smart energy systems. The review identifies major research gaps, including limited understanding of multi-scale coupling mechanisms, insufficient integration of energy and carbon flow analyses, inadequate assessment of technological change, and weak attention to energy transitions in the Global South. Future research should strengthen AI- and geospatial-data-driven monitoring and forecasting, energy network simulation, urban energy resilience assessment, low-carbon scenario analysis, and policy experimentation. Energy geography is shifting from static resource allocation studies toward the governance of dynamic energy systems, providing new opportunities for theoretical innovation and evidence-based energy policymaking.

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Against the backdrop of accelerating climate change and human activity, identifying ecological thresholds—critical tipping points for state shifts and functional degradation—has become a frontier in physical geography and ecology. This study systematically reviews the theoretical frameworks, methodologies, and controlling mechanisms of dryland ecosystem thresholds. It first standardizes threshold identification logic, emphasizing the spatial heterogeneity inherent in coupled climate-geomorphology-vegetation-soil systems. Next, it synthesizes advancements in remote sensing, GIS analysis, long-term observation, and numerical modeling, highlighting the necessity of multi-source, multi-scale data integration. By analyzing nonlinear feedbacks driven by climate variability, extreme events, and human disturbance, this study reveals both regional and global patterns in threshold evolution. Finally, it addresses key challenges, including the cumulative effects of compound stresses, cross-scale process coupling, and uncertainty quantification. As crossing these thresholds often leads to irreversible land degradation, this physical geography-based framework provides essential insights for improving predictability and risk assessment, ultimately supporting climate adaptation and the UN Sustainable Development Goals in drylands.

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Land use/land cover change (LUCC) is a major anthropogenic driver of global change and exerts profound influences on the formation of terrestrial carbon sinks and regional to global carbon budgets by altering ecosystem structure and processes, thereby playing a critical role in climate change mitigation and the pursuit of carbon neutrality. This study aims to systematically synthesize current scientific understanding, regulatory mechanisms, and recent advances regarding LUCC impacts on the terrestrial carbon cycle. Based on a comprehensive literature review, we summarize the effects of major LUCC types, including cropland expansion, deforestation and forest restoration, and urbanization, on carbon stocks and fluxes, and identify the key processes governing carbon source–sink transitions. We further evaluate the roles and trade-offs of land management practices in croplands, forests, grasslands, wetlands, and urban systems in enhancing carbon sequestration and reducing emissions. Particular emphasis is placed on the influences of coupled carbon–nitrogen–water cycles, soil microbial regulation, and land–atmosphere energy feedbacks under LUCC on regional and global carbon dynamics. In addition, we review recent applications and limitations of remote sensing, ecosystem modeling, isotope tracing, and artificial intelligence in assessing LUCC-related carbon effects. The results indicate that LUCC impacts on the carbon cycle are characterized by strong nonlinearity, pronounced scale dependence, and tight process coupling, while current assessments remain constrained by insufficient process representation and weakened feedbacks from human decision-making. Future research should prioritize multi-scale process integration, mechanistic understanding of microbial functions, and deep coupling between human–land systems and Earth system models to provide robust scientific support for land-use and climate policies oriented toward carbon neutrality.

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Astronomical geography, as an emerging interdisciplinary frontier linking Earth system science and space science, aims to elucidate the evolution of Earth’s geographic characteristics within the Solar System and the broader cosmic environment, as well as the mechanisms driven by astronomical processes. This field encompasses Earth’s rotation–revolution dynamics, orbital and tidal interactions within the Sun–Earth–Moon system, multiscale disturbances of the atmosphere and geomagnetic environment induced by solar activity, and the potential impacts of Near-Earth Objects (NEOs) on the Earth system. This paper systematically reviews the theoretical foundations of astronomical geography and provides a comprehensive assessment of recent advances in remote sensing observations, high-precision satellite measurements, Global Navigation Satellite Systems (GNSS), deep-space exploration, big data analytics, and artificial intelligence, particularly in applications such as Earth deformation monitoring, tidal force modeling, space weather forecasting, and planetary geography research. Special emphasis is placed on the coupled effects of solar radiation and geomagnetic storm events on climate and the ionosphere, the regulatory mechanisms of Earth–Moon tidal forces on hydrological cycles and surface processes, and emerging approaches for inferring extraterrestrial geographic information. Based on a synthesis of the existing literature, this study further identifies key future directions, including the integration of multisource observational data for advanced modeling, multiscale mechanistic analysis of complex space–Earth systems, enhancement of AI-driven space weather prediction models, and the development of an application framework for astronomical geography oriented toward sustainable development and deep-space exploration. This review aims to provide a theoretical foundation and methodological reference for studies of Earth system evolution, space environment risk mitigation, and space resource utilization, thereby promoting the advancement of astronomical geography toward mechanistic understanding, intelligent modeling, and deeper interdisciplinary integration.

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Amid rising geopolitical uncertainty, the fragmentation of international rule-based systems, and the growing systemic vulnerability of global supply chains, the world economy is transitioning from a phase of “hyper-globalization” centered on efficiency maximization toward a new stage of “slow globalization” characterized by fragmentation, regionalization, and security prioritization, posing fundamental challenges to traditional economic geography frameworks that explain spatial division of labor and factor allocation. From a review perspective, this article systematically synthesizes the major theoretical advances and core debates in economic geography under conditions of globalization retreat, highlighting a shift from an emphasis on local–global “strategic coupling” within global production networks (GPNs) toward increasing attention to mechanisms of spatial reorganization driven by strategic decoupling, institutional frictions, geopolitical risks, and the “weaponization” of global value chains, thereby revealing the structural tensions among efficiency, resilience, and security and their profound implications for firm location choices and regional development. Building on this synthesis, the paper compares the economic-geographical logics of supply chain reconfiguration pathways such as nearshoring, friend-shoring, and reshoring, elucidating the multiscalar co-evolutionary processes among the blockization of global value chains, the resurgence of national strategic autonomy, and the adaptive restructuring of local industrial systems. It further assesses the amplifying and compounding effects of digital and green transitions on reshaping spatial divisions of labor, as well as their potential to intensify the “re-marginalization” of certain regions. This review aims to develop an integrated analytical framework that bridges geopolitical considerations and economic rationality, deepening systematic understanding of the logics of economic geographical restructuring, pathways of spatial governance, and future research agendas in the era of globalization retreat, while providing theoretical foundations and policy insights for fostering regional resilience and interpreting the emerging global economic geographical order under conditions of heightened uncertainty.

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Under the context of global climate change, the cryosphere, as a critical component of the Earth system, is undergoing rapid transformation that triggers complex multi-sphere feedback mechanisms and profoundly influences atmospheric circulation, hydrological processes, ecosystems, and human societies. In recent years, the integration of multi-source remote sensing technologies with in situ monitoring networks has substantially enhanced the observational capacity for key cryospheric variables, while Earth system models with increasingly optimized physical parameterizations have progressively improved the simulation accuracy of coupled ocean–atmosphere–ice–land processes. Multidisciplinary impact assessment approaches have facilitated a transition from single-causal impact analyses toward evaluations centered on social–ecological system resilience and adaptive capacity, revealing regional vulnerabilities and transboundary risks associated with cryospheric change. Nevertheless, spatial discontinuities in observational data, the complexity of nonlinear feedback pathways, and difficulties in identifying critical thresholds continue to constrain further advances in cryosphere research. Future efforts should establish an integrated research framework that combines coordinated monitoring, high-resolution modeling, and dynamic resilience assessment to promote the translation of scientific findings into regional sustainable development strategies. This review systematically synthesizes the multi-sphere feedback mechanisms, impact assessment approaches, and adaptation strategies related to global cryosphere change, to provide a theoretical foundation and practical reference for scientific research and policy making in this field.

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Artificial intelligence geography, as a frontier interdisciplinary field integrating geography and artificial intelligence, is driving a systematic transformation in spatial cognition paradigms, analytical methodologies, and decision-making models. Based on a comprehensive review of current knowledge, this study constructs a theoretical framework of artificial intelligence geography, highlighting advances in core technologies such as intelligent processing of geospatial data, spatial prediction and simulation, and spatial optimization and intelligent decision-making, while summarizing representative applications in urban governance, environmental sensing, social spatial behavior analysis, and disaster management. Addressing key bottlenecks in current research—including insufficient capability in spatial causal inference, limited model interpretability and trustworthiness, challenges in capturing cross-scale consistency, and concerns over data privacy and algorithmic fairness—this study provides a critical analysis from the perspectives of integrating data-driven paradigms with geographic process mechanisms, and advancing geographic knowledge representation and reasoning. On this basis, future research directions are proposed, including developing foundation models with stronger geographic constraints, promoting interpretable and trustworthy geographic artificial intelligence technologies, constructing planetary-scale geographic intelligence systems, and fostering AI-empowered participatory geography and sustainable development decision-making. This work aims to provide a systematic reference and research implications for theoretical enhancement, technological innovation, and strategic applications of artificial intelligence geography.

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With the accelerated advancement of digitalization and platformization, the spatial representation of human emotions is shifting from predominantly experiential and qualitative paradigms toward data-driven, computable, and visual analytical frameworks centered on social media digital footprints, thereby driving profound transformations in both the theoretical construction and methodological systems of emotional geography. From a review perspective, this article systematically synthesizes recent progress in social media–driven emotional geography, focusing on the evolution of theoretical foundations and key concepts, types of social media data and emotion recognition approaches, multiscale emotion–space coupling modeling, as well as the representation and application of emotional maps and emotional landscapes. Existing studies have achieved important breakthroughs in revealing the spatiotemporal heterogeneity, dynamic evolution, and social embeddedness of individual and collective emotions; however, significant limitations remain in terms of data representativeness, cross-platform comparability, methodological robustness, theoretical interpretability, and ethical and privacy governance. The applicability boundaries, complementary relationships, and synergistic potential among different technical pathways and research paradigms have yet to be systematically compared and integrated within a unified framework. Accordingly, this review further summarizes the common challenges and key controversies in current research and outlines future directions from the perspectives of multisource data integration, methodological synergy and optimization, theoretical deepening, and responsible research practices. This article aims to provide a systematic, critical, and forward-looking review framework for emotional geography in the digital era, offering a reference for theoretical innovation, methodological integration, and practical application in related fields.

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With the rapid advancement of digital technologies, geographical research is undergoing a profound transformation from traditional physical space toward digital space, giving rise to a new paradigm of “digital spatial geography” characterized by virtual–real integration. This study systematically reviews the applications and evolution of key technologies such as the metaverse and digital twins in spatial production, behavioral reconstruction, and governance mechanisms, and critically compares their differences and commonalities in spatial attributes, ontological foundations, and human–environment relations, revealing a dynamically coupled and complex system formed through the processes of mapping, augmentation, integration, and feedback between virtual and real spaces. The analysis further highlights major challenges confronting current scholarship and practice, including the restructuring of spatial power dominated by digital platforms, algorithm-driven social stratification, and the widening digital divide. Looking ahead, digital geography should integrate multidimensional mixed methods, advance theoretical framework innovation, strengthen ethical and governance-oriented research, and promote the development of “synthetic geography” and “algorithmic justice.” By constructing a theoretical framework and practical pathways for virtual–real integration, this article aims to provide a systematic perspective and guidance for the geographical community, thereby supporting theoretical breakthroughs in spatial science and evidence-based policy making in the digital era.

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Driven by the dual forces of the “spatial turn” and the digital revolution, urban geography is undergoing profound epistemological reconfiguration and methodological transformation. This study systematically examines the theoretical trajectory of the discipline from classical urban morphology and the spatial analysis school toward New Marxist, post-structuralist, and complexity-oriented perspectives, and reveals the intrinsic mechanisms underlying the paradigm shift in urban studies from static “spatial geometry” to dynamic “relational networks” and “spaces of flows.” It critically synthesizes recent advances in key research themes, including urban socio-spatial differentiation, factor mobility, functional resilience, and urban metabolism, with particular emphasis on how multi-source spatiotemporal big data, artificial intelligence (AI), and geo-simulation systems are reshaping both qualitative and quantitative research traditions and fostering a deep integration of computational and humanistic approaches. Building on a triadic framework of theory–method–practice interactions, the paper further explores how localized empirical studies conducted under diverse institutional contexts—especially in rapidly urbanizing regions such as China—challenge and recalibrate dominant Western urban theories. Finally, it outlines future research agendas for urban geography in the domains of data-driven and intelligent urban governance, coupled social–ecological systems, and spatial justice, aiming to provide forward-looking guidance for the development of an urban science knowledge system that combines a global perspective with local adaptability.

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With the rapid advancement of artificial intelligence (AI), research in physical geography is undergoing a profound paradigm shift from traditional empirical statistics toward intelligent prediction and process-based modeling. Leveraging cutting-edge techniques such as deep learning, physics-informed neural networks (PINNs), and multimodal data fusion, substantial progress has been achieved in characterizing complex geographical processes, enhancing the physical consistency of models, and enabling cross-scale system coupling. Meanwhile, the synergistic integration of AI and physical models has promoted a transition from a purely data-driven paradigm to a mechanism-driven framework, ushering geoscience intelligence into a new stage. Nevertheless, challenges, including data scarcity, limited model interpretability, interdisciplinary integration barriers, and ethical and sustainability concerns, continue to constrain further development. This paper systematically reviews the key applications and theoretical evolution of AI in physical geography, critically examines current technical bottlenecks and future directions, and aims to provide theoretical guidance and practical insights for advancing intelligent geographical research and for building more robust and scientifically sound Earth system modeling frameworks.

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Amid the accelerating processes of globalization, informatization, and networkization, the social sciences are undergoing a profound transformation from “sedentarism” to the “mobility turn.” This paper systematically reviews the evolution of mobility and flow theories, tracing their intellectual genealogy and logical development from classical spatiotemporal theories to the “new mobilities paradigm.” It highlights the foundational contributions of Harvey’s “time–space compression,” Lefebvre’s “production of space,” Bauman’s “liquid modernity,” and Castells’ “space of flows” to understanding the dynamic structures of modern society. Furthermore, the paper explores the multidimensional mechanisms through which mobility reconstructs social space, including urban and regional reorganization driven by physical mobility, spatial disembedding and re-embedding triggered by virtual mobility, and the reshaping of global production networks and social relations through capital and information flows. It also critically examines issues of power differentials and social inequalities embedded in mobility, advancing the theoretical proposition of “mobility justice.” Finally, the paper outlines future research directions, such as the social impacts of intelligent mobility and virtual presence, post-human mobility theories, integrative approaches combining qualitative and big-data methods, and mobility-oriented strategies for urban planning and governance. Overall, this review aims to elucidate the central role of mobility–flow theory in interpreting contemporary social–spatial transformation and to provide a theoretical foundation for developing an interdisciplinary and systematic framework for mobility studies.

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Behavioral geography is an interdisciplinary domain at the intersection of geography and behavioral science, centered on understanding the mechanisms of spatial cognition, perception, and decision-making among individuals and groups, and offering a critical lens for examining the complexity of human–environment interactions. This review synthesizes and evaluates major advances and persistent challenges across theoretical paradigms, methodological innovations, and emerging applications in the field. At the theoretical level, it traces the evolution from classical time geography to cognitive–behavioral theory, as well as the rise of spatiotemporal behavior modeling grounded in complex systems. Methodologically, it highlights how mobile sensing, geographic information systems, spatiotemporal big-data analytics, and artificial intelligence (AI) jointly enable more precise micro-behavioral analysis and multi-scale behavior simulation. The review then examines representative research themes, including spatial cognition (sense of place), individual travel decision-making, urban activity patterns, and environmental behavioral responses. Building on these insights, it identifies frontier research directions for the next stage of behavioral geography. Overall, this review provides scholars in geography, urban science, and computer science with a systematic framework for understanding human spatiotemporal behavior, while illuminating the opportunities and challenges faced by behavioral geography in advancing theoretical innovation and practical applications in data-intensive environments.

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The Critical Zone (CZ), as an integrated interface connecting the lithosphere, hydrosphere, biosphere, and atmosphere, represents a core scientific unit for understanding the coupled structure and function of Earth’s surface systems. In recent years, Critical Zone science has developed rapidly worldwide, emerging as a frontier field for exploring multi-sphere interactions and environmental sustainability within the Earth system. This study systematically reviews the theoretical evolution and research progress of CZ science, outlining its disciplinary trajectory and future development trends. Through bibliometric analysis and theoretical synthesis, it first reveals the conceptual evolution of the CZ from the “Earth’s living skin” to the “Earth’s reactor,” highlighting its theoretical significance in surface system coupling studies. It then comprehensively summarizes recent advances in CZ research from the perspectives of hydrological processes, biogeochemical cycling, and multi-sphere matter–energy exchanges, comparing different research paradigms regarding deep weathering, interface reactions, and feedback mechanisms. Furthermore, it examines the applications and limitations of Critical Zone Observatories (CZOs), multi-scale monitoring technologies, and numerical simulations in promoting the integration and prediction of surface processes. The findings indicate that model complexity, data uncertainty, and cross-scale integration remain key bottlenecks constraining the systematization of CZ research. Finally, under the context of global change, this paper proposes future directions for CZ science: strengthening process modeling driven by artificial intelligence and big data, deepening the study of deep biosphere–rhizosphere interactions, and constructing a digital twin of the Critical Zone to achieve dynamic perception and predictive simulation of Earth surface systems. Through theoretical integration and forward-looking analysis, this work aims to promote the development of Critical Zone science toward greater predictability, sustainability, and systematization.

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This paper provides a comprehensive review of the deep integration and recent advances in economic geography and global value chain (GVC) research, aiming to construct a theoretical framework for understanding the dynamic spatial evolution of global production. It first traces the development from production networks to GVC theory, emphasizing key economic geography concepts such as “geographical embeddedness,” “governance patterns,” and “industrial upgrading,” and revealing the mechanisms underlying uneven value capture and spatial differentiation. The paper then analyzes new industrial spatial characteristics under the triple impacts of globalization backlash, digital transformation, and geopolitical conflicts, including the agglomeration of advanced functions driven by the digital economy, the role of tacit knowledge flows in shaping innovation clusters, and the generation of regional economic resilience. A critical assessment of traditional governance modes, such as modular and relational governance, highlights the central role of lead firm power structures in shaping global spatial inequalities and value distribution, while also projecting the profound influence of the “dual-carbon” strategy within the green transition on value chain governance logic. Finally, focusing on cutting-edge topics including digital platform algorithm governance, fragmentation, and re-territorialization of global production systems, the paper identifies gaps in current research concerning cross-scale dynamic analysis, operationalization of new governance models, and responses to non-traditional risks. It calls for constructing an integrated framework that combines geographical space, technological change, and governance dynamics to support emerging economies in restructuring participation in global value chains and achieving sustainable and inclusive development.

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Biogeography, an integrative discipline investigating the distribution patterns of organisms in geographical space and their ecological and evolutionary driving mechanisms, encompasses important branches such as ecological biogeography, historical biogeography, and molecular biogeography. In recent years, with the rapid development of remote sensing, big data analytics, and artificial intelligence technologies, biogeography has achieved significant breakthroughs in research paradigms, methodological systems, and application domains. This article systematically reviews the core theories and research content of biogeography, focusing on research frontiers such as global biodiversity patterns, species distribution models, species response mechanisms under global change, and ecosystem adaptive dynamics. Building on this, the article deeply analyzes the key challenges currently facing biogeography, including the comprehensive impacts of factors like climate change, land-use transitions, and human disturbances on ecological processes. Concurrently, it explores the potential application value of big data fusion and intelligent algorithms in species distribution prediction, biogeographical regionalization optimization, and conservation priority area identification. Finally, the article summarizes the future development trends of biogeography, emphasizing that interdisciplinary collaboration, data-driven research, and integrated model construction will be crucial pathways for promoting the continuous evolution of this field. This study aims to provide a systematic review and forward-looking reference for the theoretical deepening and practical expansion of biogeography.

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Amid the accelerating pace of global urbanization, the structure and operation of urban systems have become increasingly complex, rendering traditional governance models inadequate to meet the demands for real-time responsiveness, scientific decision-making, and collaborative management. The emergence of smart cities has advanced the informatization and intelligence of urban governance; however, their technology framework—centered on sensing, data acquisition, and platform integration—has gradually revealed limitations such as data silos, semantic fragmentation, and delayed decision responses. In recent years, digital twin cities, as an evolution and deepening of smart cities, have redefined the logic and paradigm of urban governance through their capabilities of virtual–real mapping, dynamic interaction, and intelligent simulation. This paper systematically reviews the conceptual evolution from smart cities to digital twin cities, delineates the key transition of Geographic Information Systems (GIS) from static spatial representation to dynamic intelligent computation, and highlights the mechanisms of core technologies such as C(BIM)+GIS integration, GeoAI, and game engine–based immersive visualization in enabling digital twin construction and refined urban governance. Furthermore, from the perspective of governance transformation, the study elucidates the innovative features of digital twin cities in proactive prediction, computation-driven decision-making, and holistic collaboration, while also analyzing challenges related to computational bottlenecks, data ethics, and cross-disciplinary theory. The paper aims to construct a systematic cognitive framework for the evolution from smart to digital twin cities, revealing the GIS-driven technological logic and paradigm shift of refined urban governance, and providing practical insights for the intelligent, sustainable, and human-centered development of future cities.

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