With the rapid advancement of science and technology, artificial intelligence (AI) has become a significant force driving scientific development and social progress. In the field of geographical sciences, the application of AI technology is deepening, bringing revolutionary changes to the collection, analysis, and application of big data and spatio-temporal information, and demonstrating innovative and application potential in multiple aspects. This paper systematically reviews the development and application of AI in geographical sciences, providing a detailed introduction to the development trajectories of various AI fields such as machine learning, computer vision, natural language processing, planning systems, and large AI models, as well as their applications in geography. It discusses the problems and challenges of AI applications in geography and provides an outlook on the future development of interdisciplinary research between AI and geographical sciences.

Using the Mann-Kendall method, the growth rate of China’s urbanization since the reform and opening up was divided into 2 stages, 1978—1994 and 1995—2020, and the pattern of China’s provincial urbanization in different stages was analyzed. Using panel data, the dynamic mechanism of this pattern was discussed from the intra-regional and inter-regional levels. The results show that: 1) The spatial and temporal dynamic differences of China’s provincial urbanization are significant. In 1978, China’s regional urbanization pattern showed a pattern of high in the north and low in the south, and high in the east and low in the west. In 1994, the pattern of urbanization presented the urbanization rate of the provinces in the north and southeast coasts is relatively high, and southwestern provinces are relatively low. The urbanization level in 2020 has formed a pattern of gradual decline from east to west. 2) The estimation results of spatial Durbin model show that labor demand as a pulling force has a stronger effect on the urbanization rate between regions than within regions; The effect of the income gap between urban and rural areas on urbanization rate is firstly suppressed and then promoted, and the intensity of the effect between regions is always stronger than that within regions. The regional economic development disparities, acting as an inter-regional push factor, have a significant positive effect on urbanization only in the initial phase; the income gap between urban areas, serving as an inter-regional pull factor, overall shows an effect that initially suppresses and then promotes urbanization rates, with the impact shifting from being stronger inter-regionally to being stronger intra-regionally. 3) The results of Geographically Weighted Regression model show that, in economically developed regions, labor demand and urban-rural income gap, as regional push and pull forces, have a positive driving effect on the urbanization of each province. The positive effect of regional economic development differences and inter-regional urban income gaps on urbanization has obvious fluctuations in space. But the change has become stronger over time, indicating that the development gaps between regions and between urban and rural areas are constantly promoting the urbanization development of various provinces as a push and pull force between regions.

Under the background of carbon peak and carbon neutrality, data centers with high energy-consumption characteristics are facing a huge challenge of energy saving and emission reduction, which is related to the achievement of the goal of green and high-quality development of new infrastructure. To address this challenge, the national level has formulated the “East Data and West Calculation” Project to leverage the advantages of resource endowment in the west and alleviate the pressure on resources and the environment in the east. The carbon emission reduction effect and spatial transfer law resulting from this strategy are scientific issues worth studying. To investigate these issues, this study constructs a carbon accounting framework based on the fine-grained data of data centers in each region of China. It simulates and predicts the amount of carbon emissions and the scale of spatial transfer of data centers in 2 scenarios: with or without the implementation of the “East Data and West Calculation” Project in 2020—2030. The study also analyzes the potential of energy saving and emission reduction associated with the strategy. The results of the study demonstrate that the “East Data and West Calculation” Project can achieve energy saving and emission reduction in data centers by optimizing the spatial distribution of computational resources. In the context of the strategy, the total carbon emissions of national data centers in 2030 are expected to reach 2.11×10⁸ t, which is a reduction of 22.74×10⁶ t compared to the scenario without the strategy. Specifically, the strategy effectively relieves the pressure on carbon emissions in the regions of the Beijing-Tianjin-Hebei Hub and the Yangtze River Delta Hub, resulting in a reduction of 55.45×10⁶ t of CO₂ in the east. Additionally, the project facilitates the transfer of 17.89×10⁶ t of CO₂ to the central region and 13.33×10⁶ t of CO₂ to the western region, thereby slowing down the rate of increase of carbon emissions in high-carbon regions. The conclusions of this study provide data support for understanding the scale and spatial transfer pattern of carbon emissions from data centers, a new type of infrastructure, in the context of the east data and west calculation strategy.

China’s innovative network exhibits high productivity combined with high volatility. While existing research has focused on the generative mechanisms of the network, the process of maintaining or dissolving innovative relationships and the underlying causes of high volatility remain unclear. Based on the “generation-maintenance” perspective, this study constructs a national innovative network using collaborative invention patents from 2001 to 2019, explores its evolutionary characteristics, and employs a separable temporal index random graph model to reveal the mechanisms of generating and maintaining innovative relationships. The results show that: 1) The scale of China’s innovative network is expanding, and the spatial pattern is evolving from a triangle to a “diamond + cross” shape. The innovation network maintenance process presents an evolutionary path of “upper triangle-full triangle-diamond-diamond+cross” in four stages. The formation process presents a spatial pattern similar to the maintenance process in the next stage, and it changes from the north-to-Shenzhen dominant to the urban agglomeration as the main body, reflecting the path-dependent effect. The unwinding process is dominated by the core nodes in the generating network, as well as cities in the northeast, southwest. 2) The evolutionary mechanisms of the innovative network show stage-dependent differentiation. The agglomeration effect has changed from inhibiting to promoting innovative relationships, while the intermediary effect has shifted from promoting to inhibiting innovative relationships. The effects of transmission, GDP, innovation level, geographic proximity, and organizational proximity on urban innovative relationships have continued to decline. 3) The evolutionary mechanisms of generating and maintaining innovative relationships have differences. Economic scale and innovation level have become ineffective in maintaining innovative relationships, while the effect of geographic proximity on generating innovative relationships has continued to decline but has increased in maintaining innovative relationships. This study addresses the lack of exploration into the mechanisms of maintaining innovative relationships in existing research and provides theoretical and methodological support for improving the growth and reducing the volatility of the innovative network, thereby optimizing the regional innovation system.

Wetlands play an important role in flood regulation, water purification, and biodiversity maintenance, etc., which are closely related to human well-being and survival. Wetland Science is an important part of geographical science and is of great significance in supporting scientific development and serving the construction of national ecological civilization. The National Natural Science Foundation of China (NSFC) is the main channel to fund basic research of Wetland Science, and the funding status can reflect the research hotspots and development directions in Wetland Science. In this study, 519 projects related to Wetland Science funded by the discipline of Geographic Sciences (application code D01) in 1986—2023 were covered by titles or keywords including “wetland”“marsh”“peatland”“mangrove” or “mudflat”. The systematic analysis was conducted from the perspectives of application code, research area, research content and keywords. The results show that the funded projects in Wetland Science have experienced two “decade” of rapid and steady growth from 2002 to 2012 and from 2013 to 2023; these projects are mainly concentrated in landscape geography and integrated physical geography (D0105), remote sensing science (D0113) and biogeography and soil geography (D0103). In terms of research objects, inland marsh wetlands and coastal wetlands are the main focus; in terms of research contents, “remote sensing monitoring”“process”“climate change”“vegetation” and “function” appeared more frequently. The keyword network relationship shows that “remote sensing and spectrum”“remote sensing and vegetation” and “landscape and pattern” co-occur more frequently, which characterizes the geographical features of the current development of wetland science and the changing research methods. Currently, the Geographical Sciences discipline of NSFC is further optimizing the branch discipline layout and keywords, strengthening the cross-field and cross-disciplinary interactions and fusions, guiding focus on the fundamental theories and frontier hotspots of Wetland Science, and promoting the high-quality development of wetland science research in China.

Under the “dual carbon” goals, conducting in-depth research on the role of the digital economy, in improving carbon emission performance and its impact mechanism on promoting the low-carbon transformation of the socio-economic structure, is beneficial for carbon reduction and helps to achieve the “dual carbon” targets on time. Based on the theoretical mechanism of how the digital economy affects carbon emission performance, this article calculates the development of the urban digital economy and carbon emission performance, visualizes the spatial-temporal evolution trends of digital economy development and carbon emission performance, and empirically tests the impact mechanism and spatial effects of the digital economy on the carbon emission performance of Chinese cities. The results show that: 1) The overall development level of the digital economy continues to improve, and the digital divide phenomenon has been alleviated to some extent. However, the overall level of carbon emission performance remains relatively low, with significant room for improvement. 2) The digital economy can significantly improve urban carbon emission performance, and the results remain robust after a series of sensitivity tests. The impact mechanism results reveal that the digital economy positively influences carbon emission performance mainly through advancements in green technologies, industrial agglomeration, and improvements in energy efficiency. 3) Heterogeneity analysis reveals that the impact of the digital economy on urban carbon emission performance varies by region and resource endowment. It can significantly improve the carbon emission performance in eastern regions and non-resource-based cities, but its impact on central and western regions and resource-based cities has yet to be seen. 4) Spatial spillover effect analysis shows that the development of the digital economy not only significantly improves local carbon emission performance but also promotes the improvement of carbon emission performance in neighboring regions through spatial spillover effects. This reflects its important role in regional green transformation. However, its spillover effect is highly sensitive to distance, and the spillover effect under the economic geography nested weight matrix and the inverse distance weight matrix is significantly lower than that under the geographical distance weight matrix.

Sustainable development is a significant scientific issue of global concern. Geography, as a comprehensive discipline focusing on the coupled relationship between human activities and the natural environment, provides systematic research and solutions for achieving the United Nations Sustainable Development Goals (SDGs). However, there is currently a lack of comprehensive reviews. This paper summarizes the theoretical framework and research progress of Geography supporting the SDGs and explores its future key research areas. This article indicates that: (1) Geography, in conducting integrated research on human-nature systems and serving regional and global sustainable development processes, has innovatively proposed and developed theoretical frameworks such as social-ecological systems, pattern-process-service-sustainability, metacoupling, and Classification-Coordination-Collaboration. These research frameworks include elements of human-environment system interconnections, process coupling, spatial coupling, and systematic regulation oriented towards SDGs, forming a comprehensive theoretical framework supporting sustainable development research in Geography, also referred to as "sustainable geography theoretical framework". (2) Geography has made positive progress in supporting the United Nations SDGs research in areas such as multi-source data acquisition, localization of indicator systems and multi-scale progress assessment, analysis of inter-target linkage mechanisms, and SDG achievement pathways. Geography provides important theoretical and methodological support for SDG research. (3) Geography and sustainable development-related research mainly focus on climate-ecological crisis response, sustainable utilization of food-energy-water resources, regional development and planning, human well-being and social governance, and the construction of SDG assessment indicators and databases. (4) In future research, there is a need to innovate and develop sub-disciplines of Sustainable Geography, optimize the construction of SDGs indicator systems, develop SDGs assessment and decision-making models, strengthen artificial intelligence geography, deepen research on human-nature system coupling, and promote regional and global sustainable development in the process of advancing innovation in the discipline of Geography.

New quality productive forces are key factors for the national development strategy, important powers to promote the deep integration of the digital economy and real economy, and the new dynamic energies for the development of the cultural tourism industry. New quality productive forces of the cultural tourism industry are the key forces to crack the dilemma of traditional cultural tourism development and enhance the resilience of the cultural tourism industry. There is a great need to build a research framework for digital cultural tourism based on the new round of technological revolution and industrial change. This study took connotation analysis-research progress-hot topics as the logical framework, and systematically examined and summarized the research results of digital cultural tourism from the aspects of conceptualization, development, hot topics, review, and prospect. The results show that: 1) Studies have defined the basic concept of digital cultural tourism from the perspective of digital technology, cultural tourism consumption, and cultural tourism industrialization, and digital cultural tourism has positively contributed to the high-quality development of cultural tourism and cultural tourism consumption experience. 2) The development of digital cultural tourism has gone through three phases of budding exploration, initial development, and explosive growth, and academic research and industrial practice have maintained a close connection. 3) The new generation of digital technologies has profoundly influenced the research content of digital cultural tourism, and the research results of digital cultural tourism are summarized based on subject-object-medium. 4) Digital cultural tourism research presents the development trend of combining qualitative and quantitative research methods, and mathematical and statistical methods, questionnaire survey, experiment, and case study are the main methods of digital cultural tourism research. 5) The study areas are often meso and micro scales, involving specific regions, cultural heritage sites, and tourist attractions. Finally, this study provides a prospect for the future of digital cultural tourism research from strengthening theoretical research, enriching research content, expanding research areas, and innovating research methods. The results contribute to enriching the theoretical system of digital cultural tourism research, promoting the high-quality development of digital cultural tourism, and implementing the digital China strategy.

As an important aspect of the notion that "spatial is special", spatial heterogeneity has been a central topic of geospatial analytics. It is also closely related to the methodological tradition of geography and replicability of geographic research. The emergence of fine-grained big geospatial data and the development of Geospatial Artificial Intelligence (GeoAI) have brought new opportunities and challenges to spatial heterogeneity modeling. Spatial heterogeneity may refer to (1) values of geographic variables; and (2) associations, or the generation process of geographic variables, which correspond to data and process heterogeneity, respectively. Moreover, the specification of spatial heterogeneity may be categorized as continuous and discrete. Based on the dichotomies above, we summarize the main scenarios of spatial heterogeneity modeling and review corresponding methods: (1) homogeneity-based regionalization; (2) local spatial regression; and (3) spatial regime regression. In particular, we discuss approaches to delineate spatial regimes in an endogenous manner. We also review related methodological advances in GeoAI, where the principle of spatial heterogeneity is reflected in the design of neural network models. Finally, we point out several potential directions for future research. As emerging directions in spatial heterogeneity modeling, spatial regime regression and GeoAI methods need more attention from researchers.

To enhance the scientific rigor and practical relevance of disaster resilience evaluation in mountainous rural communities, this study developed a multilevel assessment framework based on the Pressure-State-Response (PSR) model by integrating the entropy method and Analytic Hierarchy Process (AHP). The framework comprised three dimensions (pressure, state, and response), nine elements, and 32 indicators tailored to the unique environmental and socioeconomic contexts of mountainous regions. Focusing on four representative communities (Taoyuan, Caogu, Niulang, and Qunying) in the Anning River Basin of Liangshan Prefecture, Sichuan Province, China, a combination of field surveys, GIS spatial analysis, and multi-source datasets were used to empirically evaluate community resilience. The key findings revealed the following: (1) The comprehensive resilience scores ranked Taoyuan > Niulang > Qunying > Caogu. Taoyuan's top performance stemmed from its designation as a national disaster prevention demonstration community featuring robust infrastructure and frequent emergency drills, whereas Caogu's lowest resilience resulted from its high-altitude topography, aging population, and inadequate infrastructure. (2) State resilience contributed most significantly to overall resilience (51.43%), with the building quality (C9) being the pivotal driver. Pressure resilience was predominantly influenced by the proximity to active faults (C2) and population exposure to geological hazards (C6), whereas response resilience relied on disaster-monitoring equipment (C26) and early warning efficiency (C27). (3) A synergistic optimization strategy was proposed, emphasizing risk zoning and engineering controls (pressure layer), housing retrofitting and social capital cultivation (state layer), and intelligent early warning systems integrated with indigenous knowledge (response layer). The study validates the applicability of the PSR model in mountainous rural contexts, highlighting a "state resilience dominance with response capacity gaps" pattern. Notably, communities with higher state resilience demonstrate stronger recovery capabilities despite elevated hazard pressures, underscoring the importance of robust infrastructure and social cohesion. Conversely, insufficient investment in monitoring technologies and external rescue coordination hinders response effectiveness in remote villages such as Caogu. The framework provides methodological support for tailored disaster-prevention planning, particularly in ethnic regions where traditional ecological knowledge complements modern governance. However, limitations include a focus on earthquakes and geological hazards, excluding concurrent multi-hazard scenarios (e.g., wildfires and pandemics), and a static assessment that overlooks temporal resilience dynamics. Future research should incorporate longitudinal monitoring and cross-scale interactions to refine the generalizability of the model. This study advances the theoretical integration of socioecological systems into resilience assessments and offers actionable insights for sustainable rural development in hazard-prone mountainous areas.

The new wave of technological revolution driven by artificial intelligence (AI) has become a competitive advantage pursued by countries worldwide, and it is also expected to bring about significant innovations in urban geography. This paper reviews the development trajectory of AI and proposes a conceptual model of the urban human-environment system oriented towards AI. It systematically outlines and summarizes the involvement of AI in urban geography research from four perspectives: data representation, scenario applications, spatial transformations, and urban development. Additionally, it identifies the challenges, contexts, exploration paths, and future prospects faced in the intelligent transformation of academic disciplines. The study finds that: (1) The interactions between elements within the urban human-environment system oriented towards AI are becoming increasingly complex. (2) The trend of AI becoming a primary tool in urban geography research is increasingly evident, offering high efficiency, low cost, and strong learning capabilities in data processing. This has significant implications for spatial perception and intelligent decision-making. AI has sparked spatial transformations, not only creating complex virtual spaces but also reconstructing social spaces. Additionally, AI supports the development of smart cities and the establishment of cutting-edge urban application platforms. (3) Urban geography research in the context of intelligent transformation faces challenges related to data and technology, as well as the broader contexts of global and local changes, technological ethics, and the development of humanistic values. Future development paths could explore overcoming technical barriers, focusing on urban spatial construction and governance, and emphasizing the research on the effects of multiple intelligence shifts. The discipline urgently needs comprehensive transformation and upgrading.

As China enters a critical transition period towards a knowledge-based economy, the optimization of the educational attainment structure and spatial distribution of the civil service, a crucial component of modernizing the national governance system and capacity, exerts a profound influence on enhancing government efficiency and fostering social progress. Based on recruitment data from the 2023 Guangdong Provincial Civil Service Examination, this study employs spatial statistical methods and Multivariate Logistic Regression analysis to examine the urban hierarchy migration model of newly graduated students admitted to civil service positions and its influencing factors. The research findings indicate: (1)The migration model of admitted newly graduated students are predominantly migrate down the urban hierarchy. For non-equivalent-level migrations, destinations are mainly fourth-, third-, and first-tier cities, whereas for equivalent-level migrations, destinations are primarily fourth-, third-, and second-tier cities. (2)Among personal attributes (including gender, university ranking, and the tier of the city where the graduation school is located), except for graduates from universities in the fourth- and fifth-tier cities, all other groups predominantly migrated down the urban hierarchy. (3)In terms of work-unit level and job requirements (including educational qualifications and work experience), the admitted candidates across all groups predominantly migrated down the urban hierarchy. (4) The results of the multiple logistic regression showed that personal attributes, work-unit level, and job requirements jointly affected the choice of migration mode for admitted candidates. Personal attributes and job requirements have a relatively significant impact; the better the personal attributes and the higher the job requirements, the more likely they are to migrate up the urban hierarchy. The effect of job conditions was not significant. (5)The results of the mechanism analysis revealed that the choice of migration model among newly graduated students is a complex and dynamic decision-making process underpinned by the interplay of multiple factors, such as government policy guidance, job characteristics, personal factors, and urban conditions. The decision-making and selection process in the selection mechanism for the migration mode of newly recruited graduates in the civil service examination is complex and dynamic, involving the interaction of multiple factors, such as government policy guidance, job characteristics, personal factors, and urban conditions, with objective limitations, personal abilities and resources, and subjective willingness. Compared to other non-establishment employment models, this employment model has a stronger possibility of migrating down the urban hierarchy, which may be caused by significant differences in the sources of job settings, competition intensity, and subjective willingness. To achieve the strategic goal of building a high-quality young civil service, the government should consider the multifaceted and complex interactions within the recruitment process, leverage its administrative functions, flexibly adjust job settings, and attract and retain outstanding newly graduated students through measures such as optimizing the urban environment and enhancing public service levels. This study has significant implications for local governments in formulating scientific and reasonable civil service recruitment policies tailored to local conditions, and guiding newly graduated university students to make informed and rational decisions when applying for civil service positions.

In the era of the knowledge economy, talent is important for the development of any country, region, or city, and attracting foreign talent to stay and settle down has gradually become an important issue. In this study, we analyzed the spatial distribution of highly educated migrants, their willingness to settle in the place they migrated to, and the social influencing factors from the perspective of migration networks using the 2017 China Migrants Dynamic Survey. We explored the differences in the influence of inter-provincial and intra-provincial migration patterns on the willingness of migrants to settle. The main conclusions are as follows: First, the willingness of the highly educated migrants to settle down shows a distribution pattern of "high in the north and low in the south." Migrants, especially highly educated people who are willing to migrate across provinces, showed highest preference for settling down in Beijing, Shanghai, and Tianjin, whereas those willing to migrate within provinces showed preference for settling down in Tibet, Shandong, and Hubei.. Second, the friendship and kinship networks of highly educated migrants show a clear pattern of "high in the north and low in the south," and the business network shows a clear pattern of "high in the west and low in the east," and the network of township ties has a clear pattern of "high in the east and low in the west." Third, migrant networks provide material and emotional social support for the migration of highly educated individuals and significantly influence their willingness to settle. Among these networks, kinship and friendship offer the greatest support and exert the strongest pull on their migration decisions. In contrast, the influence of township ties on the willingness of highly educated migrants to settle is smaller, highlighting a clear difference from the stronger reliance of migrant workers on township ties. Fourth, highly educated intraprovincial migrants, owing to their shorter migration distances and lower migration costs, are more influenced by kinship in their willingness to settle. By contrast, highly educated inter-provincial migrants who lack kinship ties in their destination areas showed a lower willingness to settle and are more influenced by friendships and hometown connections. Future research on talent migration and policies should place greater emphasis on social factors, thereby enriching the study of talent mobility from a societal perspective. This strategy is also beneficial in practice for attracting and retaining talent, not only through economic incentives but also by leveraging social policies. This fosters the integration of talent into destination cities and contributes to the socioeconomic development of these areas, thereby propelling the implementation of a talent-driven national strategy.

Skilled migration is a pivotal phenomenon underpinning globalization that has attracted widespread scholarly interest; a nuanced understanding of the patterns and mechanisms of skilled migration is considered essential for fostering coordinated regional development and enhancing labor market allocation. To compare the domestic and international talent mobility research in the 21^st century and advance China's talent mobility theory, In this paper, we reviewed Chinese and English literature from fields such as geography, urban planning, demography, economics, and management since 2000 via quantitative and qualitative analysis. Initially, we conducted bibliometric and keyword co-occurrence analyses using CiteSpace to identify research hotspots and trends; subsequently, we performed Qualitative Data Analysis Miner qualitative analysis as a supplementary approach to derive in-depth insights and determine connections between literature sources. Herein, we summarize the research hotspots regarding skilled migration, domestically and internationally. We found that concerning research hotpots, domestic research emphasizes the spatial distribution of different types of talent, the impact of urban factors on skilled migration, and the interplay between talent and urban innovation. In contrast, international research emphasizes more on the mobility decisions of highly skilled migrants within a globalization context, focusing on the impact of international high-skilled migration on the destination and origin countries, the micro-level mechanisms of migration decision-making (such as family dynamics, gender roles, and early career stages), and social adaptation in destination countries (including identity, sense of belonging, and related factors). Theoretically, both domestic and international studies are based on labor migration theory, examining skilled migration mechanisms from macro-regional and micro-individual perspectives, and expanding theoretical frameworks to include amenities and the creative class. However, owing to differences in social, economic, institutional, and developmental contexts among countries, these theories are not entirely applicable in practice, particularly regarding their explanatory power in the Chinese context, which requires further examination. Furthermore, in terms of research progress, we found that international studies are increasingly highlighting the life course of migrants and the socio-cultural micro-mechanisms influencing skilled migration, emphasizing the adoption of a combination of quantitative and qualitative analytical methods. Conversely, domestic research predominantly focuses on the spatial patterns of skilled migration and the effects of macro-urban factors, relying on quantitative analyses. Future research in China should aim to examine micro-mechanisms, develop indigenous theoretical frameworks to foster theoretical innovation, and enhance the integration of qualitative and quantitative methods. Moreover, exploiting the potential of big data and emerging technologies could contribute towards overcoming limitations associated with data acquisition. On the basis of our survey of current theories and methods, we propose the following four directions for future research: (1) strengthening investigations into the micro-mechanisms of skilled migration; (2) developing indigenous theoretical frameworks to support theoretical innovation in China; (3) promoting the integration of qualitative and quantitative research methods; and (4) enhancing data acquisition and exploiting big data and advanced technologies to overcome the current limitations associated with acquiring data.

Since the beginning of the 21st century, the phenomenon of global regional inequality has been continuously exacerbated, posing severe challenges to regional sustainable development. Regional inequality is a classic proposition in economic geography, and its concepts and research frameworks are constantly being updated. Solving the problem of regional development inequality is an important prerequisite for promoting high-quality regional development. However, existing research lacks systematic understanding of regional inequality. Based on this situation, this article reviewed the development process and perspective shift of international research on regional inequality, and outlined the prospects for the direction of regional inequality research through comparative analysis of international trends and situations in China. The research findings are as follows: 1) Regional inequality research has gone through four stages: the early emerging stage (before the end of the 19th century), the single perspective stage (from the early 20th century to the mid-20th century), the dual perspective stage (from the mid-20th century to the end of the 20th century), and the dynamic perspective stage (since the 21st century). The research perspective has shifted from convergence and divergence to dynamic openness. 2) With the increasing integration of regions into the global system, regional inequality research has shifted from economic inequality to multidimensional inequality, from static equilibrium to exogenous shocks, and from localization to a "global-local" interactive perspective, with continuously expanding breadth and depth of research. 3) The current trend of paradigm shift in regional inequality research is reflected in the improvement of measurement methods, integration with the modern economic geography schools, and attention to scale effects. 4) Future research on regional inequality urgently needs to integrate and draw on theories and methods from multiple disciplines, explore issues such as the synergistic effects of internal and external factors on regional inequality, dynamic evolutionary trends and cyclical effects, as well as interactions of multiple scales of power and mechanisms of feedback among diverse entities.