The spatial relationship between morphological and functional centers influences the efficiency of resource allocation and the coordination of spatial structure. However, existing studies predominantly focus on macro-level trend analysis, lacking not only typological examinations based on their spatial relationships but also investigations into the factors influencing these spatial relationships. Using ASEAN as a case study, this paper identifies its morphological and functional centers, conducts a comparative analysis across different economic development stages, and examines their spatial relationships, typologies, and underlying drivers. The results reveal three key findings: (1) Morphological centers in ASEAN cities generally display polycentric and dispersed patterns, while functional centers tend to be monocentric and concentrated. (2) The spatial linkage between morphological and functional centers is heterogeneous and closely tied to economic development stages. Among these factors, Fragmentation, Transportation, and History remain consistently influential across all stages. In contrast, the influence of Foreign Direct Investment (FDI) and industrial parks exhibits a growing trend during the middle and later stages of economic development. (3) While the number, scale, and spatial relationships of morphological and functional centers vary distinctly across different economic development stages, their spatial distribution characteristics exhibit notable commonalities.

In the context of increasing constraints on global fisheries resources and China's “total amount control and structural optimization” policy for distant-water fishery, exploring pathways for the highquality development of the coastal distant-water fishing (DWF) industry is of great practical significance. Based on the industrial value chain theory, this paper examines the current development status, key constraints and potential advantages of the DWF industry in Guangdong Province, and focuses on discussing its transformation pathways.The study indicates that the development level of Guangdong Province's DWF industry is inconsistent with its status as a major marine province. Restricted by national quota limits，shortcomings in technical equipment, and high operating costs, the existing industrial chain system presents a structural mismatch with the development of distant-water fishing.The key to overcoming the development constraints facing Guangdong's DWF industry lies in shifting from “scale competition” to “value enhancement.” By leveraging the resource endowments of the Guangdong-Hong Kong-Macao Greater Bay Area, Guangdong can strengthen regional collaboration, enhance the overall competitiveness of its fishing fleet, adopt high-end and differentiated product strategies, promote industrial restructuring and value chain upgrading, and accelerate the development of full industrial chain clusters, thereby facilitating the industry's transformation toward development driven by quality and efficiency.

The role of intermediaries in foreign direct investment (FDI) within global production networks remains significantly underresearched. While economic geographers have framed international cooperation zones as intermediate locations for investment, they have largely neglected the mechanisms through which these zones enable multinational enterprises (MNEs) to achieve territorial, network, and societal embeddedness. This article fills this gap by developing a framework that considers ethnicity as a critical intermediary in FDI of small and medium-sized enterprises (SMEs). Through a long-term case study of Chinese small and medium-sized MNEs in the Thai-Chinese Rayong Industrial Zone, this article reveals how extensive ethnic Chinese networks empower Chinese entrepreneurs to invest and embed in international cooperation zones as interplaces. In the preinvestment stage, these zones facilitate the territorial embeddedness of Chinese FDI in host countries by integrating both formal and informal relationships rooted in ethnicity-based social networks. In the postinvestment stage, the management committee of the industrial zone and ethnic Chinese associations function as key nodes of ethnicity-based social networks, promoting intermediation processes that enable collaboration between Chinese enterprises and local firms and societies. These intermediation processes contribute to the network and societal embeddedness of Chinese FDI into interplaces in host countries.

Typhoons are among the most destructive natural phenomena, posing significant threats to human society. Therefore, accurate damage assessment is crucial for effective disaster management and sustainable development. While social media texts have been widely used for disaster analysis, most current studies tend to neglect the geographic references and primarily focus on single-label classification, which limits the real-world utility. In this paper, we propose a multi-task learning method that synergizes the tasks of location extraction and damage identification. Using Bidirectional Encoder Representations from Transformers (BERT) with auxiliary classifiers as the backbone, the framework integrates a toponym entity recognition model and a multi-label classification model. Novel toponym-enhanced weights are designed as a bridge to generate augmented text representations for both tasks. Experimental results show high performance, with F1-scores of 0.891 for location extraction and 0.898 for damage identification, representing improvements of 4.3% and 2.5%, respectively, over single-task and deep learning baselines. A case study of three recent typhoons (In-fa, Chaba, and Doksuri) that hit China’s coastal regions reveals the spatial distribution and temporal pattern of typhoon damage, providing actionable insights for disaster management and resource allocation. This framework is also adaptable to other disaster scenarios, supporting urban resilience and sustainable development.

In many developing countries, simultaneously advancing all aspects of rural human settlements is impractical due to limited resources. Understanding how housing, infrastructure, public services, and environmental systems interact to influence objective well-being is therefore essential for policymakers to set effective priorities. For this study, we surveyed 102 counties in China to develop a Human Settlements Well-being Index (HSWI), which evaluates objective well-being from the perspective of rural human settlements. We integrated the moving window method, generalized additive models, and social network analysis to break down the overall county network ordered by the HSWI into 63 progressively evolving local clusters, capturing stage-specific nonlinear interactions among human settlement elements. K-means clustering identified two thresholds in well-being scores (58 and 63), dividing counties into three development stages: low (<58), middle (58-63), and high (>63). At the low level, indicators are firmly connected, with housing improvements at the center of the network. At the middle level, isolated positive clusters form around village environmental improvements and elderly care services. The positive connections increase at the high level, and new clusters emerge, focused on wastewater treatment and waste sorting. Collectively, these findings provide actionable guidance for developing countries to optimize rural well-being through phased, priority-driven strategies.

Geopolitical risk (GPR) poses a significant obstacle to the achievement of sustainable development goals, yet its nuanced impact on the environmental, social, and governance (ESG) performance of multinational enterprises (MNEs) remains insufficiently examined. This study explores the influence of GPR on ESG performance by utilizing a comprehensive dataset of 12,699 subsidiaries of Chinese MNEs. The empirical results reveal an inverted U-shaped relationship between GPR and ESG performance: at moderate levels of geopolitical risk, firms tend to proactively improve their ESG practices as a risk management strategy. However, as GPR intensifies beyond a certain threshold, this approach loses its effectiveness, leading to deteriorating ESG outcomes. Further investigation uncovers the moderating roles of firm-specific advantages (FSAs) and country-specific advantages (CSAs). Robust FSAs equip firms with a greater capacity to uphold ESG standards under rising geopolitical uncertainty, while high CSAs strengthen subsidiaries' incentives to engage in ESG activities to buffer against external political threats. Subgroup analyses demonstrate that service-oriented MNEs, state-owned enterprises, and subsidiaries operating in high-income countries are particularly susceptible to the negative consequences of heightened GPR. By shedding light on the complex interplay between geopolitical risk and corporate sustainability, this study extends the ESG literature and provides practical implications for researchers, corporate strategists, and policymakers aiming to foster resilient and responsible global business operations.

International industrial transfer has driven rapid construction land expansion in emerging metropolitan areas, posing challenges for sustainable land management. However, existing research has largely overlooked the spatiotemporal patterns and driving mechanisms of this expansion, particularly in Southeast Asian metropolitan regions. To address this gap, we focused on the Ho Chi Minh City metropolitan area, utilizing construction land data from GLC_FCS30D to analyze the dynamics of construction land expansion during this period. Findings indicated that: (1) Continuous expansion of construction land, with the expansion rate during 2010–2020 being five times that of 2000–2010; (2) The spatial pattern evolved from initial infilling development in urban cores to subsequent leapfrogging and edge expansion toward peripheral counties and transportation corridors; (3) The expansion of construction land occurred alongside substantial losses of wetland and cultivated land. Between 2000 and 2020, the conversion of cultivated land to construction land increased significantly, particularly during 2010–2020 when cultivated land conversion accounted for 93.76% of newly developed construction land. Wetland conversion also showed notable growth during this period, comprising 3.86% of total newly added construction land; (4) Foreign direct investment (FDI) served as the primary catalyst, while industrial park development and transport infrastructure projects functioned as secondary accelerants. This study constructed a framework to systematically analyze the global and local driving mechanisms of metropolitan land expansion. The findings deepen the understanding of land-use transitions in emerging countries and provide both theoretical support and policy references for sustainable land management.

The recent rise in anti-globalization sentiment has renewed interest in how tariffs influence the location decisions of multinational enterprises (MNEs). However, these decisions have also been reshaped by ongoing geopolitical tensions-a factor that remains underexplored in the existing literature. In this study, we construct a panel dataset comprising 283,272 country-country-industry observations spanning the years 2009 to 2021. The data are drawn from the WITS, BvD, World Bank, and GDELT databases. Using fixed-effects regression, fixed-effects logit, and fixed-effects negative binomial models, we examine how MNEs respond to tariffs under varying levels of geopolitical risk. Our analysis yields three key insights. First, in contexts of low or no geopolitical risk, higher tariffs increase the likelihood of international investment by MNEs, consistent with the “tariff jumping” hypothesis. However, under high geopolitical risk, this effect disappears-regardless of tariff levels, MNEs are not more likely to invest abroad. Second, tariff increases can escalate low levels of geopolitical tension between home and host countries, further discouraging international investment. In contrast, high levels of geopolitical risk are not significantly correlated with tariff changes. Third, when low-level geopolitical tensions arise, MNEs may redirect investment to neighboring countries or major trading partners of the host country as a way to access its market indirectly.

When selecting investment locations,evaluating a region's or country's investment environment is crucial for enterprises.However,existing research often falls short of meeting the current practical needs of businesses.To address this gap,we conducted a field research and developed a comprehensive indicator system to assess Vietnam's investment environment across six key dimensions:factor endowment,infrastructure,industrial development,market conditions,financial environment,and institutional context.Based on this framework,we categorized Vietnam's investment environment,analyzed the key regions for investment and priority investment areas,and proposed four specific investment strategies.The study yielded vital findings:1)Vietnam's investment environment across multiple dimensions—factor endowment,infrastructure,industrial development,market conditions,financial environment,and institutional context—generally falls within average to medium levels.There is a notable spatial distribution pattern where some areas show high local values,but the overall investment environment remains relatively poor.2)The investment environment in Vietnam exhibits a clear spatial imbalance.Provinces such as Ho Chi Minh City,Hanoi,Hai Phong,Binh Duong,Dong Nai,and Ba Ria-Vung Tau stand out,ranking above the national average.3)Vietnam's investment environment can be categorized into five types:priority investment zones,key investment zones,general investment zones,potential investment zones,and cautious investment zones.The spatial distribution of these zones aligns with Vietnam's key economic regions,which are in the north and the south.4)Investment preferences vary by region.The northern critical economic zone develops industries like electronics,machinery manufacturing,and new energy.In contrast,the southern critical economic zone focuses on industries such as textiles and garments,food processing,and high-tech sectors.This research provides a scientific basis for enterprise investment decisions in Vietnam,fosters China-Vietnam economic and trade cooperation,and supports the long-term development of a resilient China-Vietnam community with a shared future.

Exploring the mechanisms that drive land use and cover change (LUCC) is essential for informing the formulation and implementation of effective policies aimed at optimizing land use patterns. In this study, we examined the spatial and temporal patterns of LUCC within the Lancang–Mekong River Basin (LMRB) using Globeland30 data for the years 2000, 2010, and 2020. Firstly, we analyzed the quantitative characteristics of LUCC within the LMRB in terms of the value of change and rate of change. Additionally, we investigated the converting characteristics of LUCC within the LMRB by employing land use transition matrices and land use transition probability matrices. Furthermore, we depicted the spatial distribution of LUCC within the LMRB through land use mapping and statistical analysis. The results indicate a substantial decline in forests, coupled with a notable expansion in cultivated land. Given the vital role of forests as carbon sinks, reforestation can enhance ecological services and address challenges related to climate change. Converting cultivated land to forests is an effective human intervention promoting forest transition. This study applies binary logistic models to explore the mechanisms that influence the conversion from cultivated land to forests. The results reveal that slopes ranging from 5° to 15° have the lowest probability of conversion, whereas distances between the cultivated land and the nearest tourist attraction ranging from 9 km to 18 km have the highest probability. Moreover, the conversion process is positively associated with traffic conditions and significantly influenced by human interventions. Within the study area, China, Laos, and Myanmar show a tendency to convert cultivated land into natural LULC types, while Cambodia, Thailand, and Vietnam tend to encroach on cultivated land and expand artificial surfaces. Promoting ecological restoration in the LMRB requires cooperation among these countries.

The Gross Domestic Product (GDP) per capita is one of the most widely used socioeconomic indicators, serving as an integral component for climate change impact analysis. However, a national scale assessment may induce considerable bias because it conceals any internal variations within a country. The lack of a long-term sub-national scale GDP data is a substantive hinderance. Leveraging the close relationship between nighttime lights and GDP, we address this gap by developing a novel methodological framework in two steps. First, under the modeling philosophy of spatial statistics, we developed a novel approach based on deep and machine learning techniques to establish a complex mapping between two inconsistent nighttime lights (NTL) datasets: the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP) and the National Polar-Orbiting Partnership’s Visible Infrared Imaging Radiometer Suite (VIIRS). The models achieve accuracies ranging from 0.945 to 0.980 (correlation coefficients). By taking the estimations ensemble of the two techniques, the time series of DMSP data was extended to 2021. Next, a novel modeling strategy based on multi-layer perceptron was developed to derive the non-linear relationship between NTL and GDP per capita at sub-national scale to alleviate scale effects at this granularity, while explicitly capturing regional heterogeneity effect. The trained models achieve average accuracies of 0.967, 0.959, and 0.959 on the training, validation, and test sets, respectively. We evaluate the developed dataset at the global, national, and sub-national scales from various perspective, and the results offer solid evidence on the reliability of the estimated economic data. By linking to historical global climate change data, we quantify global economic losses attributed to extreme heat to demonstrate how the estimated GDP data can be useful in the climate change impact analysis.

With the implementation of the ‘Go Global’ strategy and the Belt and Road Initiative, Chinese enterprises have gradually grown into the main force of outward foreign direct investment (OFDI) around the world. Overseas and domestic Chinese voluntary associations (CVAs) have actively promoted inward foreign direct investment in China, while their role in helping Chinese enterprises invest abroad has not been fully revealed. To address this lacuna, we explore the role of CVAs in the foreign direct investment of Chinese enterprises in Southeast Asia and the heterogeneity of their roles based on different types of associations. Our main argument is that CVAs play an intermediary role in shaping Chinese OFDI by facilitating bilateral information exchange and resource matching to enable enterprises' global–local interactions. This research contributes to verifying the impact of CVAs on Chinese enterprises' OFDI and providing implications for both host countries to attract investment and multinational enterprises from China and other developing countries to achieve internationalisation.

The research on the distribution of rural buildings is one of the fundamental works of urban–rural development in Vietnam. Adopting a Mask R-CNN deep learning framework and collecting sub-meter remote sensing images, this research used a remote sensing interpretation model of rural buildings trained based on East Asian characteristics of rural buildings and successfully recognized about 2.87 million rural buildings in 34 Vietnamese provincial administrative districts with a total area of rural buildings of 2492 million square meters. The reliability of the identification results was verified by manual detection and quantitative statistics, and a multi-scale database of rural buildings in Vietnam based on individual rural buildings was created. Based on the database, this paper analyzes the distribution characteristics of rural buildings and summarizes characteristics of rural building distribution at the country, regional, and provincial scales. The identification results lay the foundation for the next study of urban–rural relations in Southeast Asia and the construction of a basic database on villages.

The countryside is the principal area of population agglomeration with a high incidence of global poverty problems. As a shelter for the daily life of rural inhabitants, the rural buildings constitute the element of rural settlements. Moreover, they can directly characterize the level of rural development. Therefore, in the new stage of the Sustainable Development Goals (SDGs), this study selected Laos as the main study area and investigated the effect of different factors on the spatial heterogeneity of rural development based on the rural building spatial database. With the geodetector, the results are summarized as follows: (1) The spatial pattern of rural buildings in different regions of Laos varies significantly, with hot spots areas of rural buildings mainly located in the central and southern regions, while cold spots areas are mainly concentrated in the northern region. (2) Slope, transport infrastructure, and public service are the dominant elements influencing the spatial differentiation of rural buildings in Laos, but spatial heterogeneity existed in different regions of factors. (3) The interaction detector shows that slope ∩ road is the dominant interaction factor influencing the spatial distribution pattern of rural buildings nationwide, and there are marked divergences in the interaction factors. Finally, this study combines the findings to propose corresponding countermeasures for promoting the development and construction of rural areas in Laos.

Accessibility is a crucial way to overcome geographical restrictions and provides an essential path for alleviating regional poverty. Existing studies have focused on the impact of single accessibility factors on poverty reduction, while less attention has been paid to the effect of multi-accessibility on poverty patterns. Therefore, the contribution of this study is to analyze the driving mechanism of multidimensional accessibility factors on the spatial differentiation of poverty and explore the impact of different accessibility interactions on poverty patterns. The results are as follows: (1) The effects of multidimensional accessibility on spatial stratified heterogeneity of poverty were significant, among which economic accessibility, market accessibility, and traffic accessibility are the main driving factors. (2) Poverty was lower in districts closer to special economic zones, provincial capitals, and primary roads, while educational facilities and natural factors have limited influence on poverty patterns. (3) From the results of the interaction detector, the interaction of two accessibility factors is more obvious than that of a single factor. Market accessibility and traffic accessibility are the main interaction factors. This study is of great significance for poverty reduction in Laos and can be a good case for high-quality development in Southeast Asia.

After the global economic crisis of 2008, China's electronic information manufacturing industry faced the dual challenge of expanding the scale of agglomeration and improving growth quality. The spatiotemporal pattern of the development of the electronic information industry is constantly changing because of globalization, national policies, and local production networks. To understand the spatiotemporal changes and mechanisms of the Chinese electronic information manufacturing industry in the post-economic crisis period, this study uses electronic information manufacturing enterprises (EIMEs) at the city scale from 2009 to 2018 as the research object, and uses the Gini coefficient, spatial correlation analysis, and negative binomial regression model to analyze the evolution of the spatiotemporal pattern of the Chinese electronic information manufacturing industry and its influencing factors from 2009 to 2018. The main conclusions are: (1) The characteristics of the spatiotemporal evolution of the electronic information manufacturing industry are as follows. On a national scale, the Chinese electronic information manufacturing industry is developing rapidly, but its development is unstable due to the influence of the international situation. At the scale of the city cluster, the electronic information manufacturing industry mainly concentrates on the Pearl River Delta and Yangtze River Delta, and the middle reaches of the Yangtze River are ready for development. At the city level, the ranking of the top ten cities in China is stable. The cities in the central region and Sichuan-Chongqing regions are developing rapidly, while cities in northeast China are in a developmental dilemma. Spatial correlation analysis revealed a significant positive spatial correlation between the distribution of EIMEs in China. (2) The empirical analysis of location choice shows that there is heterogeneity among different city clusters owing to different factors. At the national level, EIMEs are more likely to be located in cities with high labor costs, a high degree of industrial collaborative agglomeration, strong local innovation ability, and low economic development. Enterprises in the Yangtze River Delta region prefer cities with high innovation abilities. In contrast, those in the Chengdu-Chongqing city cluster prefer cities with low innovation abilities. Labor costs have a positive effect in the Beijing-Tianjin-Hebei city cluster and the middle reaches of the Yangtze River. Industrial collaborative agglomeration has a positive impact on the Chengdu-Chongqing region. The level of industrial marketization has a positive effect on the Yangtze River Delta and the Chengdu-Chongqing city clusters. The economic development level negatively affects the middle reaches of the Yangtze River and the Chengdu-Chongqing city cluster. The level of transportation infrastructure has a positive effect on the Yangtze River Delta and Beijing-Tianjin-Hebei. The minor contributions of this study include two aspects: In terms of theoretical contribution, this study enriches the theoretical results of research on the electronic information industry in economic geography and provides a specific reference for research on other sectors. In a practical sense, this study is conducive to an in-depth understanding of the changes and development trends of China's electronic information manufacturing industry in the post-economic crisis period and provides a reference for local governments to formulate corresponding industrial development strategies and policies and promote the upgrading of China's industrial structure and the coordinated development of the regional economy.

Controlling land use change in coastal areas is one of the world's sustainable development goals and a great challenge. Existing research includes in-depth studies of land use change in relatively developed regions, but research on economically less developed but fast-growing regions is lacking. Since the reform and opening up in Vietnam, the influences of globalization have prompted the economy of the coastal area to develop rapidly, making it one of the less developed but rapidly developing regions where human activities and global changes vigorously interact. Therefore, taking the coastal area of Vietnam as the study area, we used the land use change index and random forest model to analyze the spatial variations of land use change and its impact factors. The research shows that: (1) land use shows a trend of continuous and rapid increase in construction land, with the proportion of construction land increasing from 2.72% in 2000 to 4.40% in 2020. However, natural landscapes, such as forests and grasslands, are decreasing. (2) Land use also shows obvious spatial variation characteristics, which are mainly manifested in the differences in change rate, development intensity, and distribution characteristics. Among them, the region with the largest rate of change was the Central Coastal Area. The region with the highest development intensity is the Mekong River Delta. (3) The main factors affecting land use change are foreign direct investment (FDI), the industrialization index, and population. Based on that, we analyzed the mechanism influencing the above factors from the perspectives of urbanization and population growth, and industrialization and park construction, as well as globalization and FDI, which can explain well the relationship between the impact factors and the spatial variation. This study can provide a valuable decision-making reference for formulating reasonable regional land development policies and is a good example of land use research for other rapidly developing areas.

To contribute to the debate on the importance of state vis-avis inter-firm competition in regional development, this paper examines a representative dataset of the relocation of manufacturing firms in Guangdong province of China with multinomial logistic regression models. To improve the competitiveness of manufacturing in the Pearl River Delta, the Guangdong government implemented pro-active policies to encourage the relocation of existing manufacturing firms to their designated industrial parks during the 2000s. Although the initial results appear to support the usefulness of relocation policy, further examination reveals its effectiveness depends on the industrial sector and profiles of the relocated firms. In fact, the relocation of large-scale labor-intensive firms is not driven by local government initiatives. The physical proximity of high-technology parks to airports/ports has a bigger impact on the relocation of small-scale locally-funded high-technology firms into designated parks. In the case of locally-funded firms in polluting sectors, they are expanding rather than relocating to designated industrial parks. The empirical evidence indicates the non-binary nature of the industrial relocation policy. The nuances of relocation policy and its multi-scalar effects on relocated firms rejects any simplistic generalization.