Bivariate Spatial Autocorrelation Research Articles

Spatial Coupling and Resilience Differentiation Characteristics of Landscapes in Populated Karstic Areas in Response to Landslide Disaster Risk: An Empirical Study from a Typical Karst Province in China

Landslides pose a significant threat to the safety and stability of settlements in karst regions worldwide. The long-standing tight balance state of settlement funding and infrastructure makes it difficult to allocate disaster prevention resources effectively against landslide impacts. There is an urgent need to fully leverage the landscape resources of karst settlements and develop landslide risk prevention strategies that balance economic viability with local landscape adaptability. However, limited research has explored the differential resilience characteristics and patterns of landslide disaster risk and settlement landscapes from a spatial coupling perspective. This study, based on landslide disaster and disaster-adaptive landscape data from a typical karst province in China, employs the frequency ratio-random forest model and weighted variance method to construct landslide disaster risk (LDR) and disaster-adaptive landscape (DAL) base maps. The spatial characteristics of urban, urban–rural transition zones, and rural settlements were analyzed, and the resilience differentiation and driving factors of the LDR–DAL coupling relationship were assessed using bivariate spatial autocorrelation and geographical detector models. The key findings are as follows: (1) Urban and peri-urban settlements exhibit a high degree of spatial congruence in the differentiation of LDR and DAL, whereas rural settlements exhibit distinct divergence; (2) the Moran’s I index for LDR and DAL is 0.0818, indicating that urban and peri-urban settlements predominantly cluster in H-L and L-L types, whereas rural settlements primarily exhibit H-H and L-H patterns; (3) slope, soil organic matter, and profile curvature are key determinants of LDR–DAL coupling, with respective influence strengths of 0.568, 0.555, and 0.384; (4) in karst settlement development, augmenting local vegetation in residual mountain areas and parks can help maintain forest ecosystem stability, effectively mitigating landslide risks and enhancing disaster-adaptive capacity by 6.77%. This study helps alleviate the contradiction between high LDR and weak disaster-adaptive resources in the karst region of Southwest China, providing strategic references for global karst settlements to enhance localized landscape adaptation to landslide disasters.

Temporal and Spatial Relationship Between Ecological Resilience and Land Use Intensity in the Xi'an City

The study of the temporal and spatial relationship between ecological resilience and land use intensity is of great value for solving the ecological security problems caused by rapid urban expansion. Based on the three dimensions of resistance, adaptability, and resilience, the ecological resilience evaluation system was constructed, and the spatial and temporal change pattern of ecological resilience and land use intensity in Xi'an from 1990 to 2020 was analyzed by using the ecosystem service value equivalent factor, ecological resilience, landscape stability, and land use intensity models. Finally, we used bivariate spatial autocorrelation, geographical weighted regression, and other methods to explore the spatio-temporal relationship between the two. The results showed that： ① From 1990 to 2020, the ecological resilience of Xi'an showed a general decline in time and a low distribution pattern in the middle and northern parts of Xi'an and a high distribution pattern in the south. The ecological resilience of the northern urban center was the lowest, and the distribution area continued to increase. From the perspectives of resistance, adaptability, and resilience, except for adaptability, the distribution pattern was high in the south and low in the north, and the adaptability was low in the southern mountainous area because of the impact of landscape pattern fragmentation. ② From 1990 to 2020, the land use intensity in the northern urban center of Xi'an and the area with intensive human activities continued to increase. In other areas, land use intensity did not change significantly over time, showing a spatial pattern of high in the middle and north and low in the south. ③ The ecological toughness of Xi'an was negatively correlated with land use intensity, and the area where land use intensity had a greater impact on ecological toughness gradually moved southward over time. In 1990, the regression coefficient between ecological toughness and land use intensity was low, and the correlation was not obvious. The concentration of high land use intensity and low ecological toughness was dominant in the central and northern regions, while the concentration of low land use intensity and high ecological toughness was dominant in the southern region and showed a trend of initial decrease and subsequent increase.

Thermal Mitigation in Coastal Cities: Marine and Urban Morphology Effects on Land Surface Temperature in Xiamen

Amidst the rapid global urbanization and economic integration, coastal cities have undergone significant changes in urban spatial patterns. These changes have further worsened the complex urban thermal environment, making it crucial to study the interaction between human-driven development and natural climate systems. To address the insufficient quantification of marine elements in the urban planning of subtropical coastal zones, this study takes Xiamen, a typical deep-water port city, as an example to construct a spatial analysis framework integrating marine boundary layer parameters. This research employs interpolation simulation, atmospheric correction, and other techniques to simulate the inversion of land use and Landsat 8 data, deriving urban morphological elements and Land Surface Temperature (LST) data. These data were then assigned to 500 m grids for analysis. A bivariate spatial auto-correlation model was applied to examine the relationship between urban carbon emission and LST. The study area was categorized based on the influence of marine factors, and the spatial relationships between urban morphological elements and LST were analyzed using a multiscale geographically weighted regression model. Three Xiamen-specific discoveries emerged: (1) the marine exerts a significant thermal mitigation effect on the city, with an average influence range of 7.94 km; (2) the relationship between urban morphology and the thermal environment exhibits notable spatial heterogeneity across different regions; and (3) to mitigate urban thermal environments, connected green corridors should be established in the southern coastal areas of outer districts in regions significantly influenced by the ocean. In areas with less marine influence, spatial complexity should be introduced by disrupting relatively intact blue–green spaces, while regions unaffected by the ocean should focus on increasing green spaces and reducing impervious surfaces and water bodies. These findings directly inform Xiamen’s 2035 Master Plan for combating heat island effects in coastal special economic zones, providing transferable metrics for similar maritime cities.

The spatial coupling and its influencing mechanism between rural human-habitat heritage and key rural tourism villages in China

Exploring the influencing factors and its influencing mechanism of the spatial coupling between rural human-habitat heritage (RHH) and key rural tourism villages (RTV) at county scale from the perspective of space can expand the theoretical research on the spatial coupling mechanism between RHH and RTV, and further provide theoretical reference and data support for the coordinated development and high-quality development of RHH and RTV in China. At the same time, previous studies have failed to systematically analyze the influencing factors and influencing mechanisms of the spatial coupling between RHH and RTV at the county scale, which restricted decision makers from formulating coordinated development measures between RHH and RTV at the macro level. In this study, bivariate spatial autocorrelation model and spatial coupling coordination model were used to quantitatively analyze the spatial coupling level between RHH and RTV at the county scale in China. Then, the linear regression (OLS) model, geographically weighted regression (GWR) model, and optimal parameter GeoDetector (OPGD) model were integrated to systematically analyze the linear influencing, spatial heterogeneity effect and interactive effect of natural environment and socioeconomic factors on the spatial coupling level between RHH and RTV in China, and explore the interactive influencing mechanism. The results show that the spatial coupling level of RHH and RTV in China show a significant east-west differentiation. There were 2024, 473, 293, 55 and 6 areas of severe, moderate, mild, basic and moderate coordination between RHH and RTV, respectively. Among them, severe and moderate discoordination areas are mainly distributed in Northeast China, arid and semi-arid areas in Western China, plateau areas in Southwest China, densely populated urban agglomerations and plains agricultural areas in the Middle East China. Mild discoordination areas and basic and moderate coordination areas are mainly located in transition zones in mountainous and plain areas, economically developed mountainous and hilly counties along the southeastern coast, and coastal tourist cities. Economic and population factors are the fundamental factors that affect the spatial coupling between RHH and RTV. Rural tourism facilities and rural public service facilities are important external driving forces for the coupling development of RHH and RTV, and Sociocultural environment factors are the important internal driving forces. Different surface forms, different climate conditions and different ecological environment conditions can form different natural textures and spatial organizations. Suitable climate conditions, sufficient water sources and ecological environment conditions can form more suitable rural settlement construction conditions and production and living conditions, and ultimately affect the protection and activation of rural human settlement heritage and the development and layout of key tourist villages. The spatial coupling relationship between RHH and RTV is the result of the complex interaction between the natural directivity law caused by natural environmental factors and the humanistic directivity law caused by human social and economic activities.

Unequal Impact of Road Expansion on Regional Ecological Quality

The expansion of road networks profoundly affects ecological systems by intensifying habitat fragmentation, altering hydrological processes, and exacerbating pollution. However, our understanding of the multi-scale spatiotemporal coupling between road networks and ecological quality remains limited. Thus, taking Fuzhou City in Southeastern China as a case study (~12,000 km2), we apply bivariate spatial autocorrelation, geographical detectors (GDs), and multi-scale geographically weighted regression (MGWR) to explore the multi-scale interactions between road networks and ecological quality. Results reveal the following: (1) From 2016 to 2021, kernel density estimation (KDE) analysis of the road network indicates coordinated growth in both urban and rural areas, with an increase of 0.759 km/km2. Analysis based on the remote sensing-based ecological index (RSEI) shows a decrease from 2000 to 2016, and then an increase from 2016 to 2021, with a trend of increasing gradually from urban center to rural area. (2) Predominant tradeoff relationships exist between KDE and RSEI in 2016 and 2021, while notable synergistic relationships emerge between ΔKDE and ΔRSEI. (3) Multi-scale GD analysis identifies ΔKDE as a principal factor influencing ΔRSEI, and the MGWR reveals their significant synergistic associations at an optimal scale of 3000 m. These findings highlight the unequal impact of road network expansion on ecological quality, underscoring the pivotal role of road density changes in its spatiotemporal dynamics. They offer essential insights for sustainable transport and ecological planning.

Bivariate Spatial Autocorrelation Analysis of Keshan Disease and Hair Selenium of Residents in Heilongjiang Province, China.

Bivariate spatial autocorrelation between the prevalence of Keshan disease (KD) and hair selenium levels in residents of KD-endemic areas has not previously been reported. In this study, we investigated the types of spatial clusters between hair selenium levels and the prevalence of KD among residents of KD-endemic areas in Heilongjiang Province, using a bivariate spatial autocorrelation analysis. Population demographic information and hair samples were obtained using questionnaires and sample collection, respectively. Hair selenium levels were determined using hydride generation atomic fluorescence spectroscopy. Bivariate spatial autocorrelation analysis was performed using Geoda 1.22.0.2. The results revealed that the prevalence of individuals with chronic Keshan disease (CKD) with hair selenium levels in the high-low (HL) cluster was greatest in Shangzhi. Furthermore, the prevalences of KD-hair selenium levels, CKD-hair selenium levels, and latent Keshan disease (LKD)-hair selenium levels were classified into low-high (LH) clusters in Lindian, Saertu, Mashan, Dongning, Ningan, and Lanxi. Four high-selenium areas, centered on Lindian, Saertu, Mashan, and Ningan, and one low-selenium area, centered on Shangzhi, were located in Heilongjiang Province. Thus, the residents of KD-endemic areas in Heilongjiang Province remain at risk of selenium deficiency. Additionally, the geographical scope of HL clusters in terms of the number of counties was much smaller than that of LH clusters. This study provides a basis for the identification of KD-endemic areas with low selenium levels and the development of precise prevention and control strategies for KD in Heilongjiang Province.

Spatial differentiation and coupling between village development intensity and landscape pattern of 100 villages in Anhui, China

Spatial development and landscape pattern are fundamental elements of the land system of village. Analysing the spatial differentiation and coupling relationship between spatial development intensity and landscape pattern is of great significance for the development and protection of village land resources. In order to address the current research lack on the coupling response between village spatial development intensity and landscape pattern, a technical method for analysing the spatial differentiation and coupling relationship between village spatial development intensity and landscape pattern is constructed based on the methods of village spatial development intensity model, landscape pattern index, bivariate spatial autocorrelation model, coupling degree and coupling coordination degree model. Taking 100 villages in Anhui Province, China as an example, the spatial distribution characteristics and coupling characteristics of village spatial development intensity and landscape pattern are analysed. The results show that there are obvious regional differences in the spatial distribution of village spatial development intensity and landscape pattern in Anhui Province. The village spatial development intensity shows a pattern of the Northern Anhui plain region (NAPR) > along the Yangtze River plain region (YRPR) > Jiang-huai Hilly region (JHHR) > Southern Anhui mountainous region (SAMR) > Western Anhui mountainous region (WAMR). The village landscape pattern in NAPR and YRPR are high fragmentation, while the village in JHHR has the lowest fragmentation, and the villages in SAMR and WAMR show relatively low fragmentation. The spatial coupling relationship between village spatial development intensity and landscape pattern is mainly characterised by high-high clustering and low-high clustering. The coupling coordinated development of villages in NAPR is the best, followed by YRPR, JHHR and SAMR, and WAMR is the worst. There is only a significant multi-linear relationship between village landscape pattern and multiple spatial development intensity indicators in WAMR and NAPR. The spatial differentiation and coupling relationship are influenced by both natural geographical factors and human activity factors. Finally, the study puts forward some targeted countermeasures and suggestions. The research results can provide theoretical method and practical application reference for village land space development and protection and village planning.

Spatio-Temporal Dynamic Impacts of Land Use/Cover Change on Eco-Environment Quality in Li River Basin, China

Clarifying the spatio-temporal evolution characteristics of eco-environment quality (EEQ) under land use/cover change (LUCC) and its coordinated relationship is of great importance for formulating reliable environmental protection strategies and measures to promote regional sustainable development. Most studies have emphasized the importance of LUCC for regional ecological quality. However, deeply unraveling the complex interrelationships between them remains a significant challenge, particularly in ecologically fragile regions like the Li River Basin. Therefore, based on the historical land use data and the remote sensing ecological index (RSEI) of the Li River Basin from 1990 to 2020, we analyzed the spatio-temporal evolution characteristics of EEQ and LUCC, and explored the influences and non-linear effects between them by using the bivariate spatial autocorrelation and XGBoost model. The key findings are as follows: (1) Land use/cover (LUC) in the Li River Basin was predominantly characterized by forestland and cropland, which together accounted for approximately 97% of the region. The interconversion between forestland and cropland represented the primary form of regional LUCC, while built-up land demonstrated a growth trend by encroaching on cropland. (2) The EEQ exhibited a volatile upward trend within the research period, with an average RSEI value of 0.5891, indicating a generally favorable ecological condition. (3) A significant negative spatial correlation was observed between land use intensity (LUI) and the RSEI, characterized by H–L, L–H, and non-significant clusters. (4) There was a distinct non-linear relationship that existed between LUCC and the RSEI, underscoring that appropriately regulating regional land use scale can help maintain ecological balance. These findings provide a scientific basis for optimizing land spatial management models and formulating policies to improve ecological environment quality, while also offering a new framework and reference for further ecological research on EEQ influencing factors and driving mechanisms.

Spatial relationship between carbon emissions and ecosystem service value based on land use: A case study of the Yellow River Basin.

Land use changes significantly impact both carbon emissions and ecosystem service value (ESV). However, few studies have been conducted on the spatial relationship between land use carbon emissions (LUCE) and ESV. Thus, focused on the Yellow River Basin (YRB), this study independently calculates carbon emissions from land use change (LUCE) and ecosystem service values (ESV) in the region. Utilizing spatial autocorrelation methods, we analyze the spatiotemporal pattern of LUCE and ESV and subsequently apply the bivariate spatial autocorrelation method to explore their spatial relationship. The results prove that: (1) The YRB's LUCE has continuously increased, with construction land acting as the dominant carbon source and woodland acting as the main carbon sink. The LUCE in the YRB had a positive spatial autocorrelation. (2) The YRB's ESV increased. Spatially, the ESV in the YRB showed a positive autocorrelation. (3) Both LUCE and ESV exhibited negative spatial autocorrelation, with predominant patterns of bivariate localized spatial autocorrelation identified as High-Low agglomeration (H-L) and Low-High agglomeration (L-H). Cities with the L-H pattern were primarily located in Qinghai Province and Inner Mongolia. In contrast, cities with the H-L pattern were mainly observed in the western section of Shandong and the northeastern region of Henan. The study revealed the negative impact of increased carbon emissions from land use on the value of ecosystem services, providing assistance in the development of relevant environmental policies and promoting sustainable development in the YRB.

Исследование пространственно-временных связей между старением населения и экономическим развитием в регионах Китая

This paper investigates the relationship between population ageing and economic factors in different regions of China. By collecting data on the proportion of the elderly population, the old-age dependency ratio, regional gross domestic product (GDP) and regional average labour productivity, the paper analyses the spatial and temporal distribution, the shift of the centre of gravity, and the spatial interactions between them in different regions of China from 2002 to 2022. The findings indicate that both population ageing indicators and economic factors demonstrate positive spatial autocorrelation, indicating that the distribution of these indicators across China’s regions is uneven, with inter-regional disparities continuing to widen.The centres of gravity of these two groups of indicators exhibit opposite movements, suggesting that the economic burden of old-age security in the northern regions is likely to increase as population ageing deepens. In addition, a positive bivariate spatial autocorrelation is evident between the two sets of indicators, although this correlation is unstable and gradually weakening.The study reveals differences in the spatial and temporal distribution and dynamics of population ageing indicators and economic indicators.To prevent developmental imbalances, future policies should focus on socio-economic growth and the rational allocation of resources for the elderly, especially in the northern regions

Evaluation of Ecological Carrying Capacity in Western Jilin Province from the Perspective of “Production–Living–Ecological Spaces” Coupling Coordination

Under the combined influences of climate change and human activities, the western Jilin (WJ) Province, as a typical ecologically fragile area, has experienced ecological degradation and resource depletion. Therefore, it is urgently needed to assess its ecological carrying capacity (ECC) to provide scientific support for regional ecological protection and resource management. This study integrated the “Pressure-State-Response” (P-S-R) model with the “production, living, and ecological spaces” (PLES) conceptual model to construct a comprehensive evaluation indicator system for ECC. The indicator weights were calculated using a Bayesian BWM-CRITIC-CWDF linear combination method, and the spatial–temporal distribution of ECC was then assessed using an improved TOPSIS and gray relational analysis (GRA). This evaluation model overcomes the limitations of traditional methods in weight allocation, indicator correlation, and non-linear effects, providing a more accurate, reliable, and objective assessment of ECC. Furthermore, a bivariate spatial autocorrelation model was applied to reveal the interaction between the “coupling coordination degree (CCD) of PLES” and ECC. The results indicate that the ECC value was divided into a period of decline (2000–2010) and a period of growth (2010–2020); spatially, the ECC level transitioned from a high-west, low-east to a high-east, low-west pattern. This change was primarily driven by factors such as fertilizer usage, per capita GDP, and per capita output. The “CCD of PLES” and ECC indicated positive spatial correlation, primarily forming “high-high” and “high-low” clusters. This study provides a reliable evaluation index system and an evaluation model for evaluating ECC in WJ. The findings provide a theoretical foundation for the region’s sustainable development and offer valuable insights for ecological carrying capacity research.

Inequity in accessibility to urban parks in environmental gentrification areas based on Multi-G3SFCA: A case study of Wuhan’s main urban districts

Inequity in accessibility to urban parks in environmental gentrification areas based on Multi-G3SFCA: A case study of Wuhan’s main urban districts

Ecosystem Services’ Response to Land Use Intensity: A Case Study of the Hilly and Gully Region in China’s Loess Plateau

The hilly and gully region of the Loess Plateau represents one of China’s most ecologically vulnerable landscapes, characterized by severe soil erosion, intensive land use, and pronounced disturbances to the structure and functionality of ecosystem services. Taking Zichang City as a case study, this research integrates grid-scale analysis with the InVEST-PLUS model and bivariate spatial autocorrelation techniques to examine the spatiotemporal dynamics and inter-relations of four critical ecosystem services—carbon storage, water yield, biodiversity, and soil retention—under varying land use intensity scenarios from 1990 to 2035. The findings indicate that (1) between 1990 and 2020, land use intensity in Zichang City steadily declined, exhibiting a spatial distribution pattern typified by central-area clustering and gradual peripheral transitions. (2) Across three development scenarios, the spatial distribution of the four ecosystem services aligned with the patterns observed in 2020, with central areas showing pronounced fluctuations, whereas peripheral regions experienced relatively minor changes. Specifically, from 1990 to 2020, the proportion of low-carbon storage areas increased by 2.89%, and high water yield areas expanded by 9.45%, while the shares of low habitat quality and low soil retention areas decreased by 5.59% and 6.25%, respectively. (3) A significant spatial autocorrelation was observed between land use intensity and the four ecosystem services, with widespread cold and hot spots reflecting dynamic spatial clustering patterns. These results offer valuable insights for optimizing land use strategies, improving ecosystem service performance, and advancing ecological conservation and sustainable development initiatives.

Spatiotemporal Correlation Analysis of Landscape Pattern and Habitat Quality in and Around China’s Tropical Rainforest National Park

With the establishment of the national park system in China, balancing ecological conservation within national parks and development in surrounding areas has become a critical issue. Despite its importance, studies on the scale effects of landscape fragmentation and its relationship with habitat quality (HQ) in these regions remain scarce. Therefore, this study focuses on the Hainan Tropical Rainforest National Park (HTRNP) in China and the surrounding nine cities and counties. The optimal scale for analyzing the spatiotemporal characteristics of landscape patterns, we assessed the best grain size and extent of landscape pattern indices using the regional information loss assessment model, area loss index, and semivariance function analysis. Based on this, the Fragstats and InVEST models analyzed the spatiotemporal evolution of landscape patterns and HQ from 1980 to 2020. Additionally, bivariate spatial autocorrelation theory was applied to examine the spatial correlation between the two. The results show that (1) the landscape indices in the study area vary with grain size and extent. The optimal grain size is 300 m, and the optimal extent is 2 km × 2 km. (2) Over the past 40 years, the landscape pattern of HTRNP has remained stable. In contrast, the surrounding areas exhibit notable spatial differentiation, with landscapes in coastal town centers becoming increasingly fragmented. (3) Over the past 40 years, the overall HQ in the study area has been relatively high, showing a spatial distribution pattern that decreases gradually from the center of HTRNP outward to rural areas, towns, and coastal regions. HTRNP has maintained a high and stable HQ, while the surrounding areas exhibited a decreasing trend in HQ, although the decline was relatively modest. (4) The spatial distribution of HQ aligns closely with landscape patterns, indicating a significant spatial correlation. The landscape connectivity of HTRNP remains high, with consistently superior HQ. In contrast, coastal town centers are characterized by highly fragmented landscapes and poor HQ. This study reveals the spatiotemporal dynamics of landscape patterns and HQ, along with their spatial associations. These findings provide a scientific basis for ecological conservation in national parks and the planning optimization of surrounding areas.

Identifying the Interactive Coercive Relationships Between Urbanization and Eco-Environmental Quality in the Yangtze and Yellow River Basins, China

Urbanization, as an important engine of modernization, plays an important role in promoting regional economy and improving living standards. Nevertheless, unchecked urban expansion over recent decades has strained natural resources and the environment, leading to crises, especially in densely populated urban areas that act as ecological barriers within river basins. The investigation of the interactive coercive relationship between the urbanization level (UL) and eco-environmental quality (EEQ) can facilitate the identification of sustainable pathways towards regional sustainability. Therefore, this study employed a set of multidisciplinary approaches, integrating simple linear regression, bivariate spatial autocorrelation, and coupling coordination degree (CCD) models, alongside multi-source remote sensing data to analyze the interactive coercive relationship between UL and EEQ in the Yangtze and Yellow River basins (YYRBs) in China. Key findings included a 6.97% improvement in EEQ in the Yellow River basin (YLRB) from 2001 to 2020, with higher values in the southeastern and southwestern regions and lower values in the central region, while the Yangtze River basin (YTRB) saw only a 1.28% increase, characterized by a lower EEQ in the west and higher levels in the middle and east, although the Yangtze River Delta showed a decline and significant variation among tributaries. UL rose steadily in both basins, especially in the middle reaches of the YLRB. Spatial autocorrelation analysis revealed a positive correlation between UL and EEQ in the YLRB, whereas a negative correlation was found in the YTRB. The CCD between UL and EEQ in the YYRBs improved, particularly in the middle and lower reaches, indicating the need for integrated urban development strategies that consider regional ecological capacities. These findings provided a scientific basis for ecological protection and sustainable urban development at a large river basin scale.

Leveraging Digital Technologies for Public Health Education in High-Density Community Spaces: A Geospatial Analysis

Public health education (PHE) plays a crucial role in mitigating the impact of public health crises, particularly in the context of high-density and aging populations. This study aims to address the challenges posed by these demographic trends in community public service spaces (CPSSs) by integrating geospatial and population data. Using bivariate spatial autocorrelation analysis, this research investigates the relationship between PHE and social determinants of health across 40 CPSSs in Macao. Additionally, it highlights the underutilization of digital technologies (DTs) in PHE. A non-participatory, short-term field survey and observational study were conducted to analyze PHE data quantitatively and descriptively in Macao’s CPSSs. Moran’s I and LISA index were used to test spatial autocorrelation at 90% and 99% confidence levels. The results revealed significant positive spatial correlations between the distribution of community public service institutions (CPSIs) and the population in southern Macao, as well as between the elderly population and PHE themes and formats. PHE topics predominantly focus on health/fitness, geriatrics, chronic diseases, and mental health. Despite this, PHE remains heavily reliant on offline formats, with limited integration of DTs. Challenges such as low digital literacy and limited acceptance of DTs among community workers and the public hinder their broader adoption. This study provides valuable insights for optimizing the allocation of health education resources in densely populated and aging urban areas, offering both practical recommendations and theoretical support for health policy making and implementation.

Global Urbanization and Habitat Quality: Interactive Coercive Relationships

Urbanization is inevitably accompanied by drastic changes in regional land use and therefore presents an evident influence on ecosystems. Habitat quality (HQ) reflects the ability of a habitat to provide suitable conditions for the survival of an individual or population, and clarifying the interrelationships between urbanization level (UL) and HQ provides insights into sustainable urbanzation and ecosystems conservation. Much attention has focused on how urbanization related to HQ at multi-scales, but few studies have analyzed the interactive coercive relationships between UL and HQ on the global county level. To address this gap, we adopted the bivariate spatial autocorrelation and coupling coordination degree (CCD) model to determine their interactive coercive relationships at the county level globally. Results showed that the global average UL was 0.0807, 0.0838, and 0.0857 in 2000, 2010, and 2020, respectively, with a continuously increasing trend. The global average HQ was 0.6186, 0.6133, and 0.6111 for 2000, 2010, and 2020, reflecting opposite declining trends. The Moran’s I of population urbanization and HQ in 2000, 2010, and 2020 globally remained negative but showed an increasing trend, with values of −0.189, −0.228, and −0.254, respectively, while those of economic urbanization and HQ and land urbanization and HQ also remained negative and exhibited a similar increasing trend. The spatial autocorrelations for UL and HQ in different dimensions indicated that deserts and plateaus functioned as catchment areas with low UL and HQ. The CCD between UL and HQ in each county globally ranged from 0 to 0.70, while CCD between land urbanization and HQ was the most optimal at 0–0.90. The CCD between population urbanization and HQ was similar to that of economic urbanization in terms of spatial distribution. Nevertheless, the CCD of land urbanization in HQ exhibited the greatest degree of coordination among the three urbanization dimensions during the study period. These findings provided important support for sustainable urbanization and ecosystem protection globally.

Multi-Scenario land cover changes and carbon emissions prediction for peak carbon emissions in the Yellow River Basin, China

Research on future land cover changes and carbon emissions is essential for effective land resource management and developing feasible carbon mitigation strategies. This study focused on the Yellow River Basin and employed the Future Land Use Simulation (FLUS) and Autoregressive Integrated Moving Average (ARIMA) models to project future land cover and carbon emissions. Additionally, bivariate spatial autocorrelation was utilized to analyze the relationship between them. Key findings are as follows: 1) Historically, the Yellow River Basin has experienced an expansion in construction land, forests, grasslands, and water, while cropland and unused land have diminished. Notably, construction land displayed the most significant changes, whereas grasslands showed minimal modification. Looking ahead, both the ecological protection and inertial development scenarios exhibit consistent trends with historical patterns across the land type categories. In contrast, the economic priority development scenario forecasts an increase in construction land, cropland, and grasslands, indicating a distinct shift compared to the other scenarios. However, the ecological protection scenario proves to be more sustainable. 2) In the absence of intervention, the simulated carbon emissions from construction land throughout the basin display a linear increase across various scenarios, with provincial-level variations showing an increase from southwest to northeast. However, Henan and Sichuan are expected to experience slower reductions in carbon emissions, compared to other projections. There is a notable positive correlation between carbon emissions and the comprehensive index, indicating that regions with high emissions typically experience substantial land and economic development. 3) Energy consumption projections for 2030 and 2060 indicate that to align with China’s carbon goals, it is essential to reduce energy consumption and adjust the fossil to non-fossil fuel ratio to reduce carbon emissions. Substituting coal with clean energy and enhancing energy efficiency will be more effective for achieving low-carbon emission targets. In summary, this study provides significant guidance for China’s ecological conservation, low-carbon emission strategies, and global carbon emission control efforts.

Optimal vegetation coverage from the perspective of ecosystem services in the Qilian Mountains

Abstract The Qilian Mountains (QLMs) serve as a vital ecological security barrier in the northwest China, determining the optimal fractional vegetation cover (FVC) of different land-use types is crucial for ecological restoration and protection. Based on the normalized vegetation index, meteorological data, soil data and land-use data, the InVEST model, bivariate spatial autocorrelation and elastic analysis, we calculated the water conservation, soil conservation and carbon storage, analyzed the spatio-temporal distribution characteristics of FVC and ecosystem services (ES) and the correlation between them. This study aims to evaluate the optimal FVC of the different land-use types and vegetation restoration’s effect on it in the QLMs. The results showed that the FVC and ES showed an increasing trend from 2000 to 2020, with a spatial distribution pattern of high values in the southeast and low values in the northwest. There was a significant and positive correlation between FVC and ecosystem service functions (p < 0.05), FVC had the most obvious effect on soil conservation, followed by water conservation. The optimal FVC varied by land-use type, with its value between 0.4 and 0.48 for forest land, 0.24 and 0.30 for grassland and 0.19 to 0.20 for unused land. In the eastern of the QLMs, nearly 70% of the forests and 50% of the grasslands exceeded the optimal vegetation coverage, while 36% of the grasslands and 62% of the unused lands were under-covered in the western of the QLMs. The findings suggest that differentiated ecological restoration and management measures should be formulated according to the status of vegetation restoration, to enhance the comprehensive capacity of ecosystem services in the QLMs.

Impact of Forest Landscape Patterns on Ecological Quality in Coastal Cities of Fujian, China, from 2000 to 2020

The Fujian coastal zone, a key region in China’s coastal belt, has experienced significant landscape and ecological changes due to intense human activities. Understanding the relationship between landscape patterns and ecological quality is critical for sustainable development and ecological protection. Taking the coastal cities, including Fuzhou, Xiamen, and Ningde in Fujian Province of China, as a case, the spatio–temporal changes in landscape patterns and the remote sensing-based ecological index (RSEI) during 2000 and 2020 were explored by the Google Earth Engine (GEE) cloud platform, and then their spatial relationships were identified through Pearson correlation analysis and bivariate spatial autocorrelation analysis. The findings reveal that (1) forest land was the dominant landscape in Fuzhou and Ningde, while cropland prevailed in Xiamen. Significant changes occurred in the land use landscape patterns of the three cities, mainly due to a substantial increase in the built-up land and varying degrees of reduction in arable and forest land. At the landscape level, both Fuzhou and Xiamen exhibited increased landscape fragmentation, while Ningde showed a trend of landscape aggregation; at the class level, forest land in Fuzhou and Xiamen exhibited increased fragmentation, whereas in Ningde, it showed an aggregation trend. (2) Between 2000 and 2020, the ecological–environmental quality of Fuzhou and Ningde continuously improved, while the improvement in Xiamen was less significant. Poor and fair ecological environments in the three cities were mainly concentrated in city centers and coastal zones, and areas of ecological quality degradation were primarily concentrated in coastal zones. (3) Correlation analysis indicates that, whether at the landscape level or the class level, the ecological quality of the three cities is significantly negatively correlated with the fragmentation index and significantly positively correlated with the aggregation index. Moreover, the positive correlation between ecological quality and the forest landscape aggregation index, as well as the negative correlation with the forest landscape fragmentation index, are both significantly stronger than those at the landscape level. As urbanization progresses, forest landscape fragmentation intensifies, especially in city centers and coastal areas, having a significant negative impact on ecological quality. These results highlight the importance of landscape pattern management in maintaining ecological quality. This paper provides insights for coastal cities on balancing urban development with ecological preservation in the context of rapid urbanization.