Qualification rate and associated factors regarding COVID-19 clinical skills training based on scenario simulation teaching to medical staffs in China: a hospital-based cross-sectional study.

BACKGROUND: The coronavirus disease (COVID-19) pandemic has accentuated the need for effective clinical skills training in infectious diseases. This study aimed to explore the influencing factors of infectious disease clinical skills training based on scenario simulation teaching for medical staff in China. METHODS: This hospital-based, cross-sectional study was conducted at the Third People's Hospital of Shenzhen between March and December 2022. Scenario simulation teaching was applied, and factors such as gender, educational level, professional background, and previous experience were examined to determine their impact on qualification outcomes. RESULTS: The study included participants primarily between the ages of 20-40 years, with a higher proportion of women holding university degrees. Nurses and physicians were more likely to qualify, indicating the significance of professional backgrounds. Women showed a higher likelihood of qualifying than men and higher educational attainment correlated with better qualification rates. Prior experience with protective clothing in isolation wards was a significant determinant of successful qualification. Multivariate analysis underscored the influence of sex, education, and previous experience on training effectiveness. CONCLUSION: Scenario simulation is an effective strategy for training clinical skills in treating infectious diseases. This study highlights the importance of considering sex, education, professional background, and prior experience when designing training programs to enhance the efficacy and relevance of infectious disease training.

The degree of population aging and carbon emissions: Analysis of mediation effect and multi-scenario simulation.

The continuous deepening of aging has posed new challenges for global sustainable development. Measuring the impact of population aging on carbon emissions is crucial for the next stage of climate governance. However, the structural changes in social production and consumption make it difficult to evaluate the impact effects. Therefore, this study constructed a bidirectional fixed Space Durbin Model to explore the mediating pathway of aging's impact on carbon emissions. Furthermore, we have established high-precision prediction models to simulate the evolution trajectory of carbon emissions under multi-factor driving scenarios. The main findings are as follows: (1) The process of carbon emission reduction due to population aging has significant energy hindrance effect and industrial structure effect, while the process of carbon growth is constrained by the consumption enhancement effect, technology progress effect and labor participation effect. (2) The moderating effects of energy consumption and technological innovation on carbon emissions under the aging process are 10.74% and 10.24%, respectively, while the moderating effects of industrial structure and labor force participation are relatively weak. (3) The goodness of fit of the MNGM-ARIMA and MNGM-BPNN models is over 97%. Carbon emissions in the high aging regions show a decreasing trend in all scenarios except the energy consumption-driven scenario, while in the medium and low aging regions decrease slowly only in the R&D- and labor supply-driven scenarios. This study advocates developing heterogeneous emission reduction measures based on the degree of aging, accelerating supply side upgrading, and increasing the proportion of green consumption.

Evaluating net primary productivity dynamics and their response to land-use change in the loess plateau after the 'Grain for Green' program.

Assessing net primary productivity (NPP) dynamics and the contribution of land-use change (LUC) to NPP can help guide scientific policy to better restore and control the ecological environment. Since 1999, the "Green for Grain" Program (GGP) has strongly affected the spatial and temporal pattern of NPP on the Loess Plateau (LP); however, the multifaceted impact of phased vegetation engineering measures on NPP dynamics remains unclear. In this study, the Carnegie-Ames-Stanford Approach (CASA) model was used to simulate NPP dynamics and quantify the relative contributions of LUC and climate change (CC) to NPP under two different scenarios. The results showed that the average NPP on the LP increased from 240.7 gC·m-2 to 422.5 gC·m-2 from 2001 to 2020, with 67.43% of the areas showing a significant increasing trend. LUC was the main contributor to NPP increases during the study period, and precipitation was the most important climatic factor affecting NPP dynamics. The cumulative amount of NPP change caused by LUC (ΔNPPLUC) showed a fluctuating growth trend (from 46.23 gC·m-2 to 127.25 gC·m-2), with a higher growth rate in period ΙΙ (2010-2020) than in period Ι (2001-2010), which may be related to the accumulation of vegetation biomass and the delayed effect of the GGP on NPP. The contribution rate of LUC to increased NPP in periods Ι and ΙΙ was 101.2% and 51.2%, respectively. Regarding the transformation mode, the transformation of grassland to forest had the greatest influence on ΔNPPLUC. Regarding land-use type, the increased efficiency of NPP was improved in cropland, grassland, and forest. This study provides a scientific basis for the scientific management and development of vegetation engineering measures and regional sustainable development.

Spatiotemporal dynamics of wetlands and their future multi-scenario simulation in the Yellow River Delta, China.

Wetlands, known as the "kidney of the earth", are an important component of global ecosystems. However, they have been changed under multiple stresses in recent decades, which is especially true in the Yellow River Delta. This study examined the spatiotemporal change characteristics of wetlands in the Yellow River Delta from 1980 to 2020 and predicted detailed wetland changes from 2020 to 2030 with the patch-generating land use simulation (PLUS) model under four scenarios, namely, the natural development scenario (NDS), the farmland protection scenario (FPS), the wetland protection scenario (WPS) and the harmonious development scenario (HDS). The results showed that wetlands increased 709.29 km2 from 1980 to 2020 overall, and the wetland types in the Yellow River Delta changed divergently. Over the past four decades, the tidal flats have decreased, whereas the reservoirs and ponds have increased. The gravity center movement of wetlands differed among the wetland types, with artificial wetlands moving to the northwest and natural wetlands moving to the south. The movement distance of the gravity center demonstrated apparent phase characteristics, and an abrupt change occurred from 2005 to 2010. The PLUS model was satisfactory, with an overall accuracy (OA) value greater than 83.48 % and an figure of merit (FOM) value greater than 0.1164. From 2020 to 2030, paddy fields and tidal flats decreased, whereas natural water, marshes and reservoirs and ponds increased under the four scenarios. The WPS was a relatively ideal scenario for wetlands, and the HDS was an alternative scenario for wetland restoration and food production. In the future, more attention should be paid to restoring natural wetlands to prevent further degradation in the Yellow River Delta. This study provides insights into new understandings of historical and future changes in wetlands and may have implications for wetland ecosystem protection and sustainable development.

Integrated assessment and prediction of ecological security in typical ecologically fragile areas.

In order to safeguard and restore ecological security in ecologically fragile regions, a regionally appropriate land use structure and ecological security pattern should be constructed. Previous ecological security research models for ecologically fragile areas are relatively homogenous, and it is necessary to establish a multi-modeling framework to consider integrated ecological issues. This study proposes a coupled "PLUS-ESI-Circuit Theory" framework for multi-scenario ecological security assessment of the Ningxia Hui Autonomous Region (NHAR). Firstly, the PLUS model was used to complete the simulation of four future development scenarios. Secondly, a new ecological security index (ESI) is constructed by synthesizing ecological service function, ecological health, and ecological risk. Finally, the Circuit Theory is applied to construct the ecological security pattern under multiple scenarios, and the optimization strategy of ecological security zoning is proposed. The results show that (1) from 2000 to 2030, the NHAR has about 80% of grassland and farmland. The built-up area is consistently growing. (2) Between 2000 and 2030, high ecological security areas are primarily located in Helan Mountain, Liupan Mountain, and the central part of NHAR, while the low ecological security areas are dominated by Shapotou District and Yinchuan City. (3) After 2010, the aggregation of high-security areas decreases, and the fragmentation of patches is obvious. Landscape fragmentation would increase under the economic development (ED) scenario and would be somewhat ameliorated by the ecological protection (EP) and balanced development (BD) scenarios. (4) The number of sources increases but the area decreases from 2000 to 2020. The quantity of ecological elements is on the rise. Ecological restoration and protection of this part of the country will improve its ecological security.

Spatiotemporal evolution and driving factors of ecosystem service bundle based on multi-scenario simulation in Beibu Gulf urban agglomeration, China.

Rapid urbanization is profoundly impacting the ecological environment and landscape patterns, leading to a decline in ecosystem services (ES) and posing threats to both ecological security and human well-being. This study aimed to identify the spatial and temporal patterns of ecosystem service bundles (ESB) in the Beibu Gulf urban agglomeration from 2000 to 2030, analyze the trajectory of ESB evolution, and elucidate the drivers behind ESB formation and evolution. We utilized the Patch-generating Land Use Simulation (PLUS) model to establish baseline (BLS), carbon sequestration priority (CPS), and urbanization priority (UPS) scenarios for simulating land use patterns in 2030. Following the assessment of ecosystem service values (ESV) through the equivalent factor method, we identified the spatiotemporal distribution patterns of ESB using the K-means clustering algorithm. By employing stability mapping and landscape indices, we identified and analyzed various types of ESB evolutionary trajectories. Redundancy analysis (RDA) was employed to pinpoint the drivers of ESB formation and evolution. The results revealed that from 2000 to 2030, land use changes were primarily observed in cropland, forestland, and construction land. Between 2000 and 2020, 92.88% of the region did not experience shifts in ESB types. In UPS, the ESB pattern in the study area underwent significant changes, with only 76.68% of the region exhibiting stabilized trajectories, while the other two scenarios recorded percentages higher than 80%. Key drivers of ESB-type shifts included initial food provision services, elevation, slope, changes in the proportion of construction land, and population change. This multi-scenario simulation of ESB evolution due to land use changes aids in comprehending potential future development directions from diverse perspectives and serves as a valuable reference for formulating and changing ecological management policies and strategies.

Relocating built-up land for biodiversity conservation in an uncertain future.

Land use changes associated with habitat loss, fragmentation, and degradation exert profoundly detrimental impacts on biodiversity conservation. Urban development is one of the prevailing anthropogenic disturbances to wildlife habitat, because these developments are often considered permanent and irreversible. As a result, the potential benefits of built-up land relocation for biodiversity conservation have remained largely unexplored in environmental management practices. Here, we analyze recent built-up land relocation in Shanghai and explore how such restoration programs can affect future land change trajectories with regards to biodiversity conservation. Results show that 187.78 km2 built-up land in Shanghai was restored to natural habitat between 2017 and 2020. Further simulation analysis highlights that relocating built-up land can substantially promote conserve biodiversity. In particular, there would be less habitat loss, better natural habitat quality and more species habitat-suitable range under the scenarios with built-up land relocation. Species extinction assessment suggest that amphibians, mammals, and reptiles will all have an increasingly high extinction risk without built-up land relocation. However, there will even be a marginal decrease in extinction risk over time for mammals and reptiles if the relocation of built-up land is permitted, but still a moderate increase in extinction risk for amphibians. This study highlights the importance of incorporating rigorous conservation planning prior to development activities, thereby underpinning a sustainable approach to environmental management.

A simulation-based approach for assessing regional and industrial flood vulnerability using mixed-MRIO model: A case study of Hubei Province, China.

Given the unstoppable forces behind regional economic integration trends, damages from a flood disaster in a specific area will influence correlative cities through industrial linkages and make economic systems more vulnerable. Assessing urban vulnerability is an essential part of flood prevention and mitigation, and also a hot topic of recent research. Therefore, this study (1) constructed a mixed multiregional input-output (mixed-MRIO) model to explore ripple effects on other regions and sectors when production in a flooded area is constrained, and (2) applied this model to characterize the economic vulnerability of cities and sectors in Hubei Province, China by simulation. First, various hypothetical flood disaster scenarios are simulated to reveal the ripple effects of different events. The composite vulnerability is assessed by analyzing economic-loss sensitivity rankings across scenarios. Then, the model is applied to the case of a 50-year return period flood that occurred in Enshi City, Hubei Province, on July 17, 2020 to empirically verify the usefulness of such a simulation-based approach in evaluating vulnerability. The results indicate vulnerability is higher in Wuhan City, Yichang City, and Xiangyang City and for three manufacturing sector types: livelihood-related manufacturing, raw materials manufacturing, and processing and assembly manufacturing. Such cities and industrial sectors with high vulnerability will significantly benefit from prioritization in flood management.

Multi-scenario simulation of carbon budget balance in arid and semi-arid regions.

The carbon budget has emerged as a central focus in global carbon cycle research. The limited understanding of carbon budget balance dynamics has led to an increasing imbalance between ecological and socio-economic benefits. Building upon a comprehensive analysis of carbon storage and emission in Lanzhou from 2000 to 2020, this study develops a novel deep learning model (CNN-LSTM) to simulate carbon budget under various scenarios from 2030 to 2050. Additionally, scientifically grounded recommendations for carbon compensation are provided. The results demonstrate several key findings: (1) The deep learning model exhibits outstanding performance, with an average overall accuracy exceeding 0.93. The coupled model outperforms individual models, underscoring the significance and necessity of incorporating both temporal and spatial features in land use simulation. (2) Under the ecological protection redline scenario from 2030 to 2050, a noteworthy augmentation in carbon storage and a proficient constraint on carbon emissions are observed. This substantiates the effectiveness of ecological protection interventions. (3) Carbon compensation payment areas are predominantly concentrated in built-up land, with the extent of these areas expanding over time. (4) The disparities in carbon balance effects of forest were more conspicuous than that of built-up land across diverse temporal and scenarios.

Spatiotemporal pattern and multi-scenario simulation of ecological risk in mountainous cities: a case study in Chongqing, China.

Scientific descriptions and simulations of the ecological risks in mountainous areas can promote the sustainable use of land resources in these areas and improve the reliability of decision-making for ecological risk management. Taking Chongqing, China, as an example, we constructed a landscape ecological risk (LER) evaluation model based on land use data from 1995 to 2020 and analysed the spatiotemporal evolution characteristics of the LER pattern. Moreover, we coupled the patch-generating land use simulation (PLUS) model and multi-objective programming (MOP) method and input multiple scenarios (inertial development, ID; economic priority development, ED; ecological priority development, PD; and sustainable development, SD) to simulate the ecological risk pattern in 2030. The model coupling the "top-down" and "bottom-up" processes obtained optimal land use patterns in different contexts, and it was used to perform a spatially explicit examination of LER evolutionary trends in different contexts. The results showed that LER evolution in Chongqing has had obvious stage characteristics. The high-risk area decreased significantly under various constraints, including topographic, economic, and other constraints, and the distribution showed a trend of high in the west and low in the east. The LER spatial clustering characteristics were highly coupled with the risk level pattern. The ED scenario presented the most severe risk, the PD scenario presented a moderate risk, and the SD scenario balanced the land demand for economic and ecological development and had a better land use structure and LER compared with the other scenarios. The coupled model proposed in this study helps to obtain the optimal land use structure and mitigate ecological risks, thus providing a scientific basis for future urban development.

Simulation of land use landscape pattern evolution from a multi-scenario simulation: a case study of Nansi Lake Basin in China.

Reasonable regulation of the total amount and layout of land resources is the significant cornerstone for releasing the potential of land resources. This study explored the spatial layout and evolution characteristics of the Nansi Lake Basin from the perspective of land use and simulated the spatial distribution pattern under multiple scenarios in 2035 with the Future Land Use Simulation model which more effectively reflected the process of land use change in the actual situation, revealing the land use change of the Nansi Lake Basin under the influence of different human activities. Analysis indicated that the simulation results obtained using the Future Land Use Simulation model strongly agree with reality. By 2035, the magnitude and spatial distribution of land use landscapes will change significantly under three scenarios. The findings provide a reference for the adjustment of land use planning in the Nansi Lake Basin.

Multi-scenario analysis of land space based on PLUS and MSPA.

Land space is an important link between human social-economic activities and the evolution of the natural environment. Its changes can directly reflect the transformation process of mankind's activities on the surface system, and it is a core element of the study of global environmental change. In the research, based on the "three districts and three lines" classification method of national land spatial, the urban space, agricultural space, and ecological space of Tianjin were divided. Natural trend, economic development, cultivated land protection, and ecological priority were set as four simulation scenarios, which were predicted by the Markov-Plus model for the spatial pattern of national land in 2030. Data statistics and the MSPA model were used to quantitatively analyze Tianjin's future land space from two aspects of structure and pattern. The main conclusions were as follows: (1) The overall accuracy of the simulation results of the Markov-Plus model was 0.971, and its kappa value was 0.948. The simulation accuracy was relatively high, which provides a reference for future spatial simulation prediction in this area. (2) In different simulation scenarios, the changing trend of Tianjin's land space scale from 2020 to 2030 was that urban space continues to increase, while agricultural space and ecological space decrease successively. (3) Each simulation scenario achieves good results for spatial prediction under the condition of setting limiting factors. In the natural trend scenario, the spatial variability of the types is more complex, the boundaries are more fragmented, and the spatial reference value of the territory is lower.

[Improving the Standby Process Completion Rate Among Nurses for High-Risk Neonates].

BACKGROUND & PROBLEMS: Readiness process effectiveness significantly impacts the safety of high-risk neonates and requires an immediately responsive and well-trained healthcare team. Analysis of our unit found the high-risk neonatal standby process completion rate among nursing staff to be very low. Reasons for this poor level of performance included absence of standardized procedures for high-risk neonatal standby, lack of an auditing system, inadequate education and training, multiple medical supplies in the standby kits, absence of a checklist for the kits, and failure to regularly inventory the contents of these kits. PURPOSE: This study was designed to improve the high-risk neonatal standby process completion rate among nursing staff. RESOLUTION: We developed standardized procedures and videos for high-risk neonatal standby situations, established an auditing system, conducted regular scenario-based training, organized medical supplies in the standby kits, designed a checklist, and defined procedures for stocking and using the supplies. RESULTS: The high-risk neonatal care completion rate among nursing staff increased to 100%, and the satisfaction rate with the standby procedure for high-risk neonates rose from 59.5% to 96.5%. CONCLUSIONS: Following proper standardized procedures and conducting education and training can ensure effective and high-quality care in critical healthcare situations.

Simulation and optimization of land use pattern to embed ecological suitability in an oasis region: A case study of Ganzhou district, Gansu province, China.

High-intensity land use has led to water resource imbalance and land degradation in oasis regions, which pose a great threat to ecological security. Optimization of land use patterns is crucial to ensuring the rational distribution of water and land resources and improving the stability of oasis ecosystems. The purpose of this study is to spatially allocate land use activities to more suitable regions. In this study, we first evaluated the land ecological suitability (LES) in Ganzhou District, a typical oasis region. Then, the LES evaluation results were embedded in an integrated CA-Markov model based on multiple criteria evaluation (MCE) and multi-objective land allocation (MOLA) to simulate and optimize land use patterns for the year 2025 under two scenarios, i.e., Business as Usual (BAU) and Land Ecological Optimization (LEO). The results revealed that the optimized land use pattern generated by LES was more reasonable. The growth rate of construction land was restricted, and a slightly increased area of construction land mainly occupied unused land. Farmland area had a decreasing trend, and was mainly converted to grassland. Moreover, the woodland and water areas had increasing trends. This study can serve as a scientific reference for planners and policy makers in formulating land use planning and land use resource management strategies in oasis regions.

How to reconcile land use conflicts in mega urban agglomeration? A scenario-based study in the Beijing-Tianjin-Hebei region, China.

With the development of global urbanization, land use conflicts have become one of the major issues hindering sustainable land use and human-environment coordination in urbanized areas. In this context, reconciliation of land use conflicts requires urgent attention. By taking the Beijing-Tianjin-Hebei (BTH) urban agglomeration as a case study area, the spatial comprehensive conflict index (SCCI) was constructed to identify and evaluate land use conflicts. Besides, the impacts of rapid urbanization and terrain restriction on land use conflicts were also explored using the coupling coordination degree (CCD) model and terrain index, respectively. Then, the Dyna-CLUE model was adopted to simulate land use conflicts under three adaptive scenarios in 2030. Results show that: (1) During 2000-2015, land use conflicts in the BTH region demonstrated an overall mitigating trend, and their spatial patterns remained relatively stable, characterized by significant cluster and belt agglomeration. (2) Land use conflicts were significantly intensified in areas experiencing rapid urban-rural transformation and terrain transition, and two typical conflict zones were identified, i.e. the urban-rural interface of the Beijing-Tianjin region and the terrain transition area located in the Taihang Mountains, Yan Mountains and Bashang Plateau. (3) In 2030, land use conflicts in the BTH region manifest overall mitigation under the ecological security (ES) scenario, while demonstrating an intensifying trend under the business as usual (BAU) scenario and cropland protection (CP) scenario. Based on simulation results, land use spatial optimization modes at county level for the BTH region were formulated. In face of increasingly prominent land use conflicts globally, this study will provide a scientific reference for policymaking in pursuit of sustainable land use management for the BTH region and urban agglomerations in other parts of the world.

Projecting LUCC dynamics and ecosystem services in an emerging urban agglomeration under SSP-RCP scenarios and their management implications.

Improving our knowledge of future dynamics of ecosystem services (ESs) in the face of climate change and human activities provides a crucial foundation to navigate complex environmental challenges, which are essential to attaining sustainable development particularly in urban regions. However, an existing dearth persists in thoroughly forecasting the intricate interplay of trade-offs and synergies, as well as ecosystem services bundling under distinct future scenarios. This study adopts an integrated research framework to understand the spatiotemporal dynamics of ESs in the Changsha-Zhuzhou-Xiangtan Urban Agglomeration (CZTUA) under three Shared Socioeconomic Pathway and Representative Concentration Pathway (SSP-RCP) scenarios (i.e., SSP126, SSP245 and SSP585). Our future scenarios suggest that the core urban area of CZTUA is projected to expand at the cost of forests and croplands by 2050. Furthermore, human-induced urbanization, particularly the high-intensity LUCC along the Xiangjiang river, significantly impacts ESs, resulting in lower ESs values. The trade-off effects between ESs are primarily observed between WY (water yield) and other ESs. Ecosystem service bundles (ESB) previously dominated by WY have significantly transitioned to CS (carbon storage)-HQ (habitat quality) bundle, especially in the urban core of CZTUA, which serves as an early warning of potential challenges related to water resources. Our study utilizes the latest climate and land use change predictions to evaluate ecosystems in urban agglomerations, and adopts a layered zoning strategy based on ESs, which provides decision-makers with reproducible tools to explore ecosystem changes.

Multiple scenario land use simulation based on a coupled MOGA-PLUS model: a case of the Yellow River Basin.

Land use changes have profoundly influenced global environmental dynamics. The Yellow River (YR), as the world's fifth-longest river, significantly contributes to regional social and economic growth due to its extensive drainage area, making it a key global player. To ensure ecological stability and coordinate land use demand, modeling the future land allocation patterns of the Yellow River Basin (YRB) will assist in striking a balance between land use functions and the optimization of its spatial design, particularly in water and sand management. In this research, we used a multi-objective genetic algorithm (MOGA) with the PLUS model to simulate several different futures for the YRB's land use between 1990 and 2020 and predict its spatial pattern in 2030. An analysis of the spatiotemporal evolution of land use changes in the YRB indicated that construction land expansion is the primary driver of landscape pattern and structure changes and ecological degradation, with climate change also contributing to the expansion of the watershed area. On the other hand, the multi-scenario simulation, constrained by specific targets, revealed that economic development was mainly reflected in land expansion for construction. At the same time, grassland and woodland were essential pillars to support the region's ecological health, and increasing the development of unused land emerged as a potential pathway towards sustainable development in the region. This study could be used as a template for the long-term growth of other large river basins by elucidating the impacts of human activities on land use and rationalizing land resource allocation under various policy constraints.

Trajectories of socio-ecological systems: A case study in the tropical Andes.

Scenario and policy assessments in socioeconomic and environmental studies face significant challenges in socio-ecological systems (SES). There are a limited number of studies that have looked at the impact of different scenarios within integrated approaches, and many have used a static approach with a single driver of change. The present work analyzes the SES dynamics for a strategic basin in the Colombian Andes when implementing and analyzing scenarios and policies related to land cover and land use change using a system dynamics simulation model. The model includes natural, ecosystem services, sociocultural, and economic components. Scenarios and policy options are analyzed both individually and jointly to identify synergies or trade-off effects between the different SES components. The results showed the different trajectories of the socio-ecological system according to the cases studied, and its impact on different variables in the analyzed components. Some counterintuitive effects were also identified, such as the importance of intrinsic motivations in decision-making processes, and determinants in land management and policy design.

Research on the evolution characteristics, driving mechanisms and multi-scenario simulation of habitat quality in the Guangdong-Hong Kong-Macao Greater Bay based on multi-model coupling.

Since the 20th century, the global urbanization has led to a series of pollution issues, posing a severe threat to the habitat quality of human habitat. The quality of habitat determines whether ecosystems can provide suitable living conditions for humans and other species. Therefore, systematic study of the habitat quality is essential for the maintenance of sustainable development. In this study, we coupled models such as SD, InVEST and PLUS with a series of indicators to analyze the characteristics of land cover and habitat quality evolution in the Guangdong-HongKong-Macao Greater Bay Area (GBA) from 2000 to 2020 and deconstruct the driving mechanisms of habitat quality. Then simulate the evolution of land cover and habitat quality under different scenarios in 2030. The results show that: 1) Over the historical research period, the GBA exhibited "rapid expansion of artificial surfaces and rapid shrinkage of ecological land". Artificial surfaces increased by approximately 4878.95km2,while ecological land, such as agricultural land, decreased by about 3095.93km2.2) The degradation of habitat quality gradually accelerated and the habitat quality was characterized by "stepwise decline from the periphery to the interior", which was directly related to the land cover changes brought about by the topographic gradient effect in the Bay Area.3) Pollution control driven by environmental investments has had a moderating effect on habitat degradation, but it has not been able to change the overall degradation trend. 4) Scenario analysis suggests that future habitat quality in the GBA will degrade to a certain extent due to the impact of artificial surface expansion. We deduce that this will affect the structure of the city's ecological network as well as the conservation function of the ecological zones. This study provides a scientific basis for understanding the historical and future trends of habitat quality in the GBA, offering new insights into the intrinsic driving mechanisms of habitat quality. It also provides a theoretical support for relevant authorities to undertake sustainable development initiatives.

Assessment of the Restoration Potential of Forest Vegetation Coverage in the Alxa Desert Region of China.

To scientifically evaluate the sustainability of tree planting and afforestation in the Alxa Desert region, this study, grounded in the principles of water balance within the natural water cycle, employed multi-source remote sensing products and ground-based measurements to construct a quantitative response relationship model. This model links evapotranspiration (ET) with meteorological variables and the Enhanced Vegetation Index (EVI). Furthermore, the study estimated the recovery thresholds and potential of forest and grassland vegetation coverage in the Alxa Desert region under various precipitation scenarios. The findings reveal that ET exhibited an increasing trend in 84.17% of the Alxa Desert region, with a significant increase observed in 61.53% of the area, indicating positive outcomes from the implementation of the Three-North Shelterbelt Forest Program. Notably, however, ET in the southeastern plain region demonstrated a decreasing trend, which is strongly associated with human activities. The response relationship model demonstrated that linear relationship areas constituted 47.52%, while nonlinear relationship areas accounted for 45.51% of the total. The overall model exhibited an R2 value of 0.69, indicating a high level of predictive accuracy. Analysis of forest and grassland coverage revealed that, under wet year scenarios, the vegetation coverage showed a significant trend of recovery, with an average recovery threshold of (75.4 ± 12.5)% and an average recovery potential of (8.5 ± 3.6)%. It is noteworthy that the vegetation coverage in 31.25% of the area had already surpassed the recovery threshold. The outcomes of this study provide a theoretical foundation for the formulation of more scientifically rigorous ecological restoration strategies in the future.