At the milestone of the first decade of research on shrinking cities in China (2015-2024), this article reviewed the progress of the field and explored future directions based on journal articles retrieved from the CNKI and Web of Science Core Collection. The analysis was structured around three dimensions—development pattern, process of change, and research agenda—to reveal the internal logic and research trends of China's urban shrinkage studies, provide theoretical references for further exploration, and contribute Chinese experience to the global discourse on sustainable urban development. The review yielded three main findings. First, in terms of development pattern, scholarship on China's shrinking cities was characterized by multidisciplinary integration, spatial differentiation between research subjects and objects, and multi-actor engagement that links academia, policy, and practice. Second, regarding evolutionary stages, the literature followed a three-phase trajectory—initial (2015-2018), deepening (2019-2021), and transformative (2022-2024)—and exhibited a knowledge pathway that moved from international experience introduction to domestic empirical research and then to theoretical exploration. This trajectory aligned with a broader research logic of phenomenon identification → problem analysis → policy exploration. Third, with respect to topics, early studies concentrated on conceptual clarification, identification and measurement, and preliminary causal analysis; subsequent work foregrounded driving factors, spatial contradictions, and multi-dimensional impacts of shrinkage; the most recent and emerging research agendas extended to social-cultural effects, spatial governance innovations, alignment with national strategic priorities, and technology-enabled policy tools. Overall, the Chinese scholarship has progressed from descriptive recognition of the phenomenon to systematic theorization and policy-oriented inquiry, clarifying the interactions between international experience, local empirical evidence, and theoretical synthesis. Looking ahead, the field is poised to broaden its analytical horizons and deepen its substantive inquiry by tightly coupling international experience, theoretical development, and domestic empirical work to form a coherent research loop—from problem detection and mechanism diagnosis to evidence-based strategy design—that can support sustainable, context-sensitive governance of shrinking cities in China and beyond.
Urban shrinkage is an important challenge in the development of new urbanization. Housing vacancy is a prominent issue in the transformation and development of shrinking cities. Research on housing vacancy in shrinking cities has gradually become a critical topic in the geographical research. This study systematically reviewed the relevant literature on vacant housing in shrinking cities and summarized its changes and main issues, clarified the theoretical implications of housing vacancy in shrinking cities, and constructed a theoretical research framework for housing vacancy in shrinking cities. It also examined the research prospects for housing vacancy in shrinking cities in response to the shortcomings of existing research. The study found that existing research has achieved a transition in the identification methods for housing vacancy, moving from a single assessment approach to diversified methods based on multi-source research data. Based on spatial phenomenon representation, existing research reveal the spatiotemporal differentiation and patterns of change, mechanism of influence, linkage effect, and regulatory response strategies of housing vacancy from multiple dimensions. This provides an important basis for understanding the spatial pattern of shrinking cities. In the future, it is necessary to draw on multidisciplinary theories and methods to continue to promote optimized identification methods and the analysis of the influencing mechanisms of housing vacancy in shrinking cities, strengthen the study of the scale effects of housing vacancy in shrinking cities, enrich research perspectives and content, expand the theoretical research system, and promote the deepening and progress of research in the field of shrinking city studies.
The relationship between population shrinkage and carbon emission intensity (CEI) has recently become a prominent research topic. Exploring their intrinsic link is crucial for promoting regional sustainable development and achieving carbon reduction goals. This study identified 1218 shrinking counties and measured their CEI. Spatial analysis was first applied to reveal the spatial patterns and associations between population shrinkage and CEI. Subsequently, an interpretable Bayesian-optimized XGBoost model was employed to examine the effects of population shrinkage on CEI, as well as the interactive impacts between shrinkage and various socioeconomic variables. The results show that: 1) Population shrinkage at the county level in China was generally mild, shrinking counties exhibited relatively low CEI, and both of them displayed significant spatial clustering. 2) Population shrinkage was significantly and negatively associated with CEI, although the strength of this relationship varied across regions. 3) In terms of feature importance, shrinkage ranked at a medium level; at the local scale, high shrinkage was linked to high CEI, while low shrinkage corresponded to low CEI. 4) Mild shrinkage exerted a positive marginal effect on CEI, whereas moderate to severe shrinkage produced a negative marginal effect. 5) The interactions between shrinkage and socioeconomic variables had pronounced nonlinear impacts on CEI. This study systematically revealed the nonlinear relationship between population shrinkage and carbon emission intensity, thereby enriching the theoretical understanding of low-carbon development in shrinking regions and providing empirical evidence to support the formulation of differentiated emission reduction strategies across areas at different stages of shrinkage.
Accurate identification of shrinking cities based on physical urban areas represents a significant research trend in the field of urban shrinkage studies. This research focused on population shrinkage within the physical urban areas of Guangdong Province, incorporating often overlooked town-level entities. Utilizing urban physical units identified by artificial surface data from the GlobeLand30 dataset and spatially matching them with calibrated WorldPop population grid data, we analyzed the spatiotemporal change characteristics of population shrinkage in Guangdong's physical urban areas from 2000 to 2020. A machine learning framework combining random forest modeling and Shapley additive explanations (SHAP) was employed to explore key influencing factors, including their threshold effects and interaction effects. The findings reveal that: 1) Urban population shrinkage in Guangdong Province intensified after 2010, spatially spreading from the sub-districts of prefecture-level cities to the surrounding counties and county-level cities. 2) Population shrinkage exhibited distinct scale dependency; as the scale of urban areas decreases, the number of shrinking entities increases. 3) Demographic factors played the most significant role, economic factors exhibited nonlinear impacts, social factors exerted positive driving effects, locational factors demonstrated an "agglomeration shadow" effect, while environmental factors showed weaker influence. 4) Influencing factors exhibited nonlinear relationships with population shrinkage, alongside threshold interaction effects between factors. The research findings provide references for enriching methods of accurately identifying population shrinkage and deepening understanding of its driving factors.
Under the dual pressures of global climate change and the expansion of informal settlements, the complexity of health in informal settlements has gone beyond the traditional narratives of poverty and public health, becoming a central concern of several United Nations Sustainable Development Goals, including "Good Health and Well-being" (SDG3), "Sustainable Cities and Communities" (SDG11), and "Climate Action" (SDG13). To address the limitations of existing studies that rely primarily on single-disciplinary perspectives, this study developed a "structural-resilience" relational framework that integrates structuralist and post-structuralist perspectives to systematically explain the generative logic and mechanisms of response of health in informal settlements within the context of climate change. This framework elucidates how macro-structural factors consolidate informal settlements as "sacrifice zones" of climate change through processes of social-spatial deprivation, while also attending to how micro-level actors, through social network reconstruction, local knowledge innovation, and affective practices, carve out spaces of resilience for survival and health under overlapping structural and climatic constraints. The framework thus advances a paradigm shift from structural determinism to a co-construction of structure and resilience, offering an analytical pathway for identifying health vulnerabilities, stimulating community agency, and optimizing climate adaptation policies. The study also argues that future research should further explore the mechanisms and practices through which agency is generated, accumulated, and transformed into resilient forces capable of mitigating the impacts of the climate crisis within the structure-resilience framework.
In recent years, research on innovation networks and regional development has gained significant prominence within the field of economic geography. However, studies focusing on the mechanisms of innovation policies and their consequent regional development effects remain relatively underdeveloped. This study systematically reviewed the relevant literature in economic geography, focusing on three key aspects: knowledge mapping analysis, the mechanisms through which innovation policies influence innovation networks, and the resulting effects. The review revealed that previous research has predominantly concentrated on the effects of innovation policies within specific, bounded regions, often overlooking their impacts in cross-regional and global collaborative networks. Furthermore, a frequent misalignment exists between the application, objectives, and outcomes of policy instruments and the actual needs of regional innovation development. There is also a discernible lack of in-depth investigation into the mechanisms and effects of emerging policy paradigms. Finally, the measurement of innovation network effects under policy influence often relies on singular data sources, which inadequately captures the tangible outcomes of these policies. This study aimed to clarify the relationship between innovation policies and regional innovation networks, address existing research controversies, and provide directions for future scholarly inquiry.
Driven by the national strategies of National Fitness, Healthy China, and Building China into a Leading Sporting Nation, the sports-oriented transformation of public space has become an important pathway for enhancing urban resilience, improving public service systems, and responding to the diversified demands for physical activities. Based on the "people-events-time-space" theoretical framework, this study provided a systematic review of Chinese and international research on the transformation and use of sports-oriented public space. Focusing on four major spatial carriers—ecological, transportation, cultural, and community public space—it synthesized the key transformation characteristics, including the diversification of participating subjects, the professionalization of physical activity behaviors, the fragmentation of activity rhythms, and the hybridization of spatial scenes. The study further constructed a multidimensional framework of influencing factors encompassing socioeconomic conditions, natural environments, built environments, and spatial attributes, and summarized the mechanism of spatiotemporal changes driven jointly by policy provision and behavioral demand. On this basis, future research directions were proposed, emphasizing all-element coupled representation, modeling of complex interaction mechanisms, and the development of dynamic regulatory strategies oriented toward spatiotemporal behavior. This study aimed to provide a systematic analytical framework and methodological insights for both theoretical advancement and planning practice related to the sports-oriented transformation of public space.
Enhancing energy efficiency is a critical engine for advancing the quality and effectiveness of ecological civilization construction. The interactions between manufacturing enterprise headquarters and their branches profoundly influence the transformation and upgrading of urban energy systems. Unveiling the energy efficiency driving effects of urban manufacturing enterprise network embedding has become a pivotal topic in contemporary economic geography. This study constructs manufacturing enterprise networks based on the investment data of geographically dispersed subsidiaries with equity control relationships from listed Chinese manufacturing companies from 2008 to 2023 to investigate the impact and mechanisms of network embedding on urban energy efficiency. The results show that: 1) The embedding of manufacturing enterprise networks significantly promotes urban energy efficiency. Larger network scale, higher link quality, and stronger brokerage capacity correspond to greater improvements in urban energy efficiency. Energy efficiency borrowing and green technology borrowing serve as key mediating channels between network embedding and energy efficiency enhancement. 2) The effect of manufacturing enterprise network embedding on urban energy efficiency exhibits spatial and industry-specific heterogeneity. Spatially, cities within the "optimization and upgrading" category of urban agglomerations demonstrate a more pronounced advantage. Industry-wise, the positive impact follows distinct patterns of "high energy consumption > low energy consumption > medium energy consumption" and "high technology > medium technology > low technology." The findings offer valuable insights for harnessing network effects to drive green transformation in the energy sector and for optimizing policies that support sustainable urban development.
Against the backdrop of rapid metropolitan regionalization and regional urbanization, urban integration as an advanced form of regional integrated development remains in the initial stage both theoretically and practically. Based on the Fitness Landscape Theory and the NK model, this study constructed an evaluation indicator system covering six dimensions and 18 comprehensive indicators, as well as a quantitative evaluation method. The six dimensions include economic synchronization, spatial centripetalism, transportation connectivity, facility linkage, public service reciprocity, and ecological co-conservation. An empirical analysis was carried out on 20 urban integration cases in China's metropolitan areas. The results show that the NK model can effectively measure the development level of urban integration and the differences of its key influencing factors. The current development level can be classified into three types: mature, growing, and emerging, with distinct formation mechanisms and optimization paths among different types. The evaluation method based on the NK model provides a new tool for measuring urban integration development level, and the findings can provide useful references for formulating urban integration development strategies and policies.
The agropastoral ecotone in central Inner Mongolia is a critical ecological transitional zone in northern China, where the vegetation carbon sink is vital for regional ecological security and achieving China's "dual carbon" goals. However, increasing drought events under global climate change pose a severe threat to the stability of carbon sink in this area. To investigate the resilience response process of the vegetation carbon sink to droughts, this study focused on 29 counties in central Inner Mongolia, using multi-source remote sensing and meteorological data from 2014 to 2023, we assessed the spatiotemporal dynamics of net ecosystem productivity (NEP) and identified years with extreme drought events. The XGBoost-SHAP machine learning model was employed to elucidate the impact mechanisms of key hydrothermal factors (vapor pressure deficit and soil moisture) on the spatial heterogeneity of NEP, revealing their nonlinear effects and threshold responses. A four-dimensional resilience indicator framework—comprising drought disturbance intensity, resistance, recovery, and drought debt—was constructed to systematically evaluate the dynamic response processes of the vegetation carbon sink to two typical extreme drought events in 2015 and 2018. The results show that: 1) The regional vegetation NEP exhibited a fluctuating but slightly increasing trend during the study period, yet demonstrated high sensitivity to drought, with significant declines observed across all drought severity levels in drought years. 2) Vapor pressure deficit was the dominant factor governing the spatial variation of NEP (SHAP contribution: 21.62%) and exhibited a significant "buffer-antagonistic" interaction with soil moisture, with all factors showing nonlinear influence characteristics and threshold effects. 3) The vegetation carbon sink exhibited an overall moderate level of resilience, demonstrating strong recovery and compensatory effects under moderate drought stress. Difference in resilience responses between the two drought events suggests the potential existence of ecological memory or adaptive adjustment strategies in vegetation. This study provides a scientific basis for adaptive ecosystem management and carbon sink risk early warning in the region.
Xinjiang is located in an arid to semiarid climate zone, where changes in precipitation exert a critical influence on regional agricultural production and ecosystem stability. As the material basis for precipitation formation, cloud water resources and their intrinsic linkage with precipitation require further clarification. Using the ERA5 monthly reanalysis dataset for 1991-2020, this study extracted key variables including precipitation, vertically integrated water vapor transport, vertical velocity, and air temperature, and systematically analyzed the spatiotemporal change of precipitation and its related variables over Xinjiang, with a particular focus on quantifying the contributions of cloud water resources and atmospheric moisture to precipitation. The main conclusions are as follows: 1) Both precipitation and cloud water resources exhibited a "more over mountains, less over basins" spatial pattern in Xinjiang. Since 2008, the regional climate has shifted from a warm-dry to a warm-wet regime, with precipitation and cloud water resources changing from a decreasing to an increasing tendency over most areas, while the warming trend is concentrated mainly in mountainous regions, revealing a spatial asymmetry between dry-wet changes and temperature responses. 2) Cloud water resources are a key driver of precipitation variability in Xinjiang. Over the 30 years of the study period, cloud water resources contributed 49% to the overall precipitation changes across the region, with pronounced spatial heterogeneity. The contribution exceeded 40% over the Altai Mountains, the Junggar Basin, and the northern and southern slopes of the Tianshan Mountains, and even surpassed 50% over the Altai Mountains and southern Tianshan, whereas it was only slightly above 30% over the Tarim Basin and Kunlun Mountains, indicating that moisture inflow and topography were crucial controls on cloud water formation. In addition, since the transition to a warm-wet climate, the relative contribution of cloud water resources to precipitation had decreased compared with the pre-transition period, while the relative contributions of water vapor transport and evapotranspiration had increased accordingly. The findings are great scientific significance for deepening the understanding of precipitation characteristics in arid and semi-arid regions.
The sustainable generation of positive local impacts through festival-centered tourism has long been a focal point of academic research. A central challenge in this field is the tension between the "carnivalesque" nature of festivals and the necessity for long-term development that supports the permanent social fabric of communities. Taking the Hantian Festival of the Dong ethnic group in Guizhou Province as a case, this study employed a longitudinal field investigation starting from 2013 to examine rural culture through the lens of everyday life, elucidating the spatiotemporal mechanism by which festival tourism fosters culture revitalization. The research methodology integrates participant observation with semistructured interviews involving 36 respondents, including local village residents, guesthouse owners, local elites, tourism entrepreneurs, and tourists. The findings are as follows: 1) From the perspective of everyday life, rural culture revitalization manifests as diversified livelihood strategies, expanded social interactions, and a reinforced ethnic identity within modernity, offering a viable pathway for sustainable development of culture. 2) By embedding itself within the spatiotemporal structures of everyday life, festival tourism steadily promotes rural revitalization. Spatially, it integrates regional tourism networks with the flexible transition between living and tourism spaces; temporally, it reconciles natural rhythms with ordered performances and institutionalized modern times; and discursively, the "rural idyll" narrative is adopted by local actors who not only reshape and reaffirm their rural identity but also further contribute to the construction of the idyllic discourse. 3) These transformations are shaped by multi-stakeholder interactions involving government-led regional development and spatial planning, stable temporal structures, and rural leisure discourse. This study provides theoretical and practical frameworks for understanding the interplay between festivals and rural culture revitalization, solving problems of preserving traditional festive culture through the development of tourism, and proposing models for "small-scale, aesthetically oriented" cultural tourism.
Territorial sovereignty has long been a central issue in political geography. The Russia-Ukraine conflict challenges conventional understandings of territorial sovereignty and reveals deep tensions between the operational logic of the modern state system and practical geopolitical actions. Drawing on the theoretical framework of the "territorial trap" and "sovereignty regimes", this study examined the dislocation and tension between territory and sovereignty in the Russia-Ukraine conflict. The study showed that: 1) Russia's territorial imagination exhibits historical continuity and hybridity. The coexistence of multiple territorial conceptions means that the Russia-Ukraine relationship cannot be reduced to a single sovereignty dispute but instead represents a tension structure where historical continuity and geopolitical rupture coexist. 2) The conflict essentially reflects the dynamic combination of infrastructural power and despotic power of the state. Sovereignty practices are not simply based on legal claims or territorial possession but are generated through the coordination of multiple forms of power, producing what may be termed effective sovereignty. 3) The competition among different sovereignty regimes constitutes the internal driving force behind the evolution of the conflict. Russia advances an imperial sovereignty through civilizational narratives and re-territorialization; the United States and Europe counterbalance Russia within a framework of globalist sovereignty; while Ukraine enhances the effectiveness of its sovereignty through social integration and trans-scalar relational networks. Their interaction suggests that contemporary warfare has moved beyond traditional territorial competition, with sovereignty increasingly embedded in multi-scalar power relations and trans-territorial networks. This article highlights the dynamic coupling between sovereignty regimes and geopolitical practices in the Russia-Ukraine conflict, providing insights into the nature of contemporary international conflicts.
Multi-source remote sensing images can characterize the surface landscape from multiple dimensions including spectral, spatial, and topographic aspects, serving as a crucial data foundation for acquiring geospatial heterogeneity information, identifying land cover types, and supporting resource and environmental monitoring. Hyperspectral remote sensing image data and LiDAR data are frequently used jointly for image classification tasks. When jointly utilizing hyperspectral images and LiDAR data for classification, most existing studies employ multi-scale feature extraction methods. However, the lack of interactive connections between feature information at different scales hinders the effectiveness of feature extraction and limits classification accuracy. Furthermore, differences between sensors in terms of observation mechanisms, spatial scales, and feature expression also pose challenges for the effective fusion of multi-source remote sensing data in geographical applications. To address these issues, a Multi-scale Cross-Interaction Encoding Network (MCIENet) is proposed. This network effectively interacts feature information across scales and deeply fuses hyperspectral and LiDAR features to enhance the classification performance of remote sensing images. Specifically, a Cross-scale Interaction Feature Extraction Module is first designed. It utilizes convolution kernels of different sizes to extract multi-scale features and connects features through up-sampling and down-sampling operations, enabling complementarity between features at different scales. To effectively fuse features from heterogeneous data sources, an Information-Perceiving Fusion Encoding Module is designed. It employs a Gaussian-weighted guided Transformer encoder to learn inter-feature correlations, followed by a Cross-Branch Fusion Encoder to achieve deep fusion of multi-source remote sensing information. To validate the method's effectiveness, comparative experiments were conducted on four classic and widely used hyperspectral and LiDAR datasets. The results demonstrate that the proposed method achieves superior classification performance compared to existing state-of-the-art methods.
