Search Results (50)

Background/Objectives: CKD-associated pruritus (CKD-aP) is a serious systemic comorbidity occurring in patients with CKD. Despite the burden of CKD-aP, there are limited efficacious treatments available for its management; difelikefalin is the only approved treatment based on its efficacy and safety demonstrated in two clinical studies, namely KALM-1 and KALM-2. This study aimed to evaluate the cost-effectiveness of difelikefalin plus best supportive care (BSC) versus BSC alone when treating moderate-to-severe CKD-aP in patients receiving in-centre haemodialysis, from the perspective of the UK healthcare system. Methods: A de novo lifetime Markov health economic model was built to assess the cost-effectiveness of difelikefalin. The modelled efficacy of difelikefalin was based on data from KALM-1 and KALM-2 pooled at the patient level. The main efficacy driver was the total 5-D Itch scale score. Per-cycle probabilities of changing health states defined by CKD-aP severity were used to derive transition matrices; the model also estimated time-dependent annual probabilities of death and transplant for people on haemodialysis. An increased risk of mortality for modelled patients with very severe, severe, or moderate CKD-aP was applied. Health state utilities and management costs were based on published evidence. Results: Modelled patients treated with difelikefalin were estimated to have a reduced severity of CKD-aP. Consequently, difelikefalin plus BSC was associated with an increased life expectancy of 0.11 years per person and improved HRQoL compared with BSC alone. This translated to higher quality-adjusted life years, at 0.26 per person gained compared to BSC alone. Improved patient outcomes were achieved at an incremental cost of £7814 per person. Conclusions: Overall, at a price of £31.90/vial, difelikefalin was estimated to be a cost-effective treatment for moderate-to-severe CKD-aP at a willingness-to-pay threshold of £30,000/QALY, with conclusions robust to sensitivity analysis. Full article

Land use and land cover changes are critical drivers of terrestrial carbon stock dynamics, as they alter native vegetation and land-based production activities. Scenario-based simulation of land use and carbon stock evolution offer valuable insights into the carbon sink potential of different development strategies and support low-carbon land planning. We focus on the Dianchi Basin, integrating a Markov-PLUS land use simulation with the InVEST carbon assessment model to examine carbon stock changes from 2000 to 2030 under three scenarios: natural development and cropland and ecological protections. Results indicate that from 2000 to 2020, the region experienced significant urbanization, with cropland decreasing and forest land expanding. Forests contributed the most to the total carbon storage, followed by cropland. The total carbon stock initially increased but experienced a marked decline from 2010 to 2020, aa trend expected to continue, largely attributable to the transformation of cropland and grassland into construction land, as well as the conversion of forest into cropland. By 2030, carbon stock trajectories would vary across scenarios. Both the natural development and cropland protection scenarios resulted in carbon loss, whereas the ecological protection scenario increased carbon storage and reversed the declining trend. Spatially, carbon stock distribution in the basin exhibits strong heterogeneity, with higher values in the periphery and lower values in the urban center. We reveal the spatio-temporal characteristics of carbon stock change and the carbon consequences of land use policies, providing scientific evidence to support land use restructuring, carbon sink enhancement, and regional carbon emission reduction under the dual-carbon goals of China. Full article

As global urbanization accelerates and economic development progresses rapidly, a series of ecological and environmental challenges have emerged. In certain countries, particularly in developing nations such as China, India, and Bangladesh, flood detention areas (FDAs) have been increasingly encroached upon by urbanization, resulting in growing conflicts between flood control functions and economic development. Therefore, accurately predicting urban expansion trends in these regions is considered essential for providing scientific guidance for sustainable regional development. In this study, the PLUS model was selected as the baseline based on comparative experiments. On this foundation, a novel AIF-HOM-PLUS framework was developed. In this framework, a new method, Adjacent Image Fusion (AIF), was proposed to reduce local temporal noise by utilizing adjacent multi-temporal data. Subsequently, Higher-Order Markov chains (HOM) were incorporated to capture complex temporal dependencies and long-term transition patterns. The Middle-Reach Yangtze River urban agglomeration (MRYRUA), including FDAs in the Yangtze River Basin (YRB), was selected as the study area, and LULCCs in 2035 and 2050 were predicted. The results showed the following: (1) among the basic models, the PLUS model exhibited the best performance, while the AIF method significantly improved its overall accuracy (OA) by 2%; (2) the area of impervious surfaces within the FDAs of the YRB will increase at an average annual rate of 1.29%, which pertains to the conflict between the United Nations Sustainable Development Goals (SDGs) 9.1 and SDG 11.a, which has become a critical issue that needs urgent attention; (3) the area of impervious surfaces in the MRYRUA will increase at an average annual rate of 1.3%, primarily at the expense of cropland and water bodies. Full article

As a typical karst landform region, the Lijiang River Basin, located in Southwest China, is characterized by both soil erosion and ecological fragility. The transformation of land use, driven by long-term intensive human activities, has exacerbated the degradation of ecosystem services, threatening the region’s carbon sink function. To clarify the coupling mechanism between land use and land cover change (LUCC) and carbon storage, this paper integrates complex network theory with the PLUS-InVEST model framework. Based on land use data from five periods, i.e., 2001, 2006, 2011, 2016, and 2021, the key transformation types are identified, and the evolution of carbon storage from 2021 to 2041 is simulated under three scenarios, namely, inertial scenario, ecological protection scenario, and urban development scenario. The paper finds that (1) land use transformation in the basin exhibits spatial heterogeneity and network complexity, as evidenced by a significant negative correlation between the node clustering coefficient and the average path length, revealing that land type transitions possess small-world network characteristics. (2) The forested land experienced a net decrease of 196.73 km² from 2001 to 2021, driving a 3.03% decline in carbon storage. This highlights the inhibitory effect of unregulated urban expansion on carbon sink capacity. (3) Scenario simulations indicate that the carbon storage under the ecological protection scenario will be 1.0% higher than under the inertial scenario and 1.5% higher than under the urban development scenario. These suggest that restricting impervious land expansion and promoting forest and grassland restoration can enhance carbon sink capacity. Therefore, this paper provides a quantitative basis for optimizing territorial spatial planning and coordinating the “dual carbon” goals in karst regions. Full article

Climate change drives land use intensity changes in Xinjiang, a typical inland arid region. There are relatively few studies on the changes in land use intensity under future climate change. For this purpose, this study adopts the Patch-level Land Use Simulation (PLUS) model and the Markov chain model, combined with shared socioeconomic pathways (SSPs). This study uses the PLUS model to make projections of land use/land cover (LULC) in Xinjiang under different climate scenarios for 2025–2060, constructs a land use intensity atlas to visualize regional spatial patterns, and analyzes the driving factors. The results show that under the SSP126 scenario, the cropland area decreases sharply while the forest, grassland, and water area expand rapidly. However, under the SSP245 and SSP585 scenarios, this trend is obviously reversed; the cropland area expands quickly, and the area of grassland and water decreases. In addition, under the SSP126 scenario, the management and control of LULC are strict, and it may be significantly affected by the conversion of cropland to forest, and the change of forest is relatively active. Under the SSP585 scenario, productivity increases, which may exacerbate the use of constructed land, and the change of constructed land is relatively active. Land use intensity may not significantly promote changes in land type proportions in the region. Population density and GDP are key drivers of land use intensity, showing relatively significant spatial heterogeneity. This study conducts research on the trend of LULC changes under different future climate scenarios, providing data support for the sustainable development of LULC and helping the government formulate different policies to cope with future LULC changes. Full article

The scientific development and utilization of cultivated land reserve resource areas is an important basis for realizing national food security and regional ecological protection. This paper focuses on land use optimization simulations to explore the paths of sustainable land use in cultivated land reserve resources areas. Deep learning technology was introduced to calculate the growth probability of each land use type. A land use change simulation method coupling CNN-LSTM and PLUS was constructed to dynamically simulate the land use pattern, and the spatial accuracy of the simulation was improved. Markov chains and multi-objective planning (MOP) model were used to set historical development (HD) scenarios, ecological conservation (EP) scenarios, land consolidation (LC) scenarios, and sustainable development (SD) scenarios. The comprehensive impact of land use change on ecosystem service value (ESV), agricultural production benefits (APBs), and carbon balance (CB) was evaluated by systematically analyzing the quantitative and spatial distribution characteristics of land use change in different scenarios from 2020 to 2030. Da’an City, Jilin province, China was selected as the study area. The results of this study show the following: (1) The CNN-LSTM coupled with the PLUS model was designed to capture the dynamic change characteristics of land use, which achieves high accuracy (Kappa of 0.8119). (2) In the EP scenario, the increase in ESV was 4.36%, but the increase in APB was only 7.33%. In the LC scenario, APB increased by 22.11%, while ESV decreased by 3.44%. In the SD scenario, a dynamic balance was achieved between ESV and APB, and it was the optimal path for sustainable development. (3) The SD scenario performed best, with a CB of 5,532,100 tons, while the EP scenario was the lowest, at only 1,493,500 tons. The SD scenario shows the optimal potential of combining carbon reduction and agricultural development. In this paper, deep learning and spatial modeling for multi-scenario simulation were integrated, and a scientific basis for the planning and management of cultivated land reserve resource areas was provided. Full article

In the mid-1970s, it was revealed that the 5′ end of the RNA genome of poliovirus (PV) was covalently linked to a peptide called VPg (viral protein, genome-linked). Subsequently, VPgs have been found attached to many other viruses and even phages. This review summarizes the patterns of physicochemical properties that are conserved within the VPgs of plus-strand RNA viruses where short-peptide VPgs have been identified. Mutagenesis and structural data indicate the importance of a 5 aa conserved motif at the N-termini of picornaviral VPgs (around the tyrosine 3 residue, which forms a covalent bond to UMP and the RNA). Hidden Markov models have been used to find motifs and VPgs in additional genera of picornaviruses, as well as dicistroviruses in insects and comoviruses in plants. These latter VPgs are bound to the RNA termina through linkages to serine or threonine. The role of free VPg and VPgpU needs clarification, especially in light of multiple genome copies in many of the viruses. Lysine and other positively charged side chains are hallmarks of VPgs. These may contribute to interactions with the viral RNA, polymerase, membranes and cellular proteins. The larger protein VPgs from potyviruses and noroviruses/caliciviruses may also show some areas of similar properties to these small peptides. Full article

As important energy suppliers in China, coal-resource-based cities are pivotal to achieving the nation’s 2060 carbon-neutrality goal. This study focused on Jincheng City, utilizing the LOW EMISSIONS ANALYSIS PLATFORM (LEAP) model to predict carbon emissions from energy consumption under various scenarios from 2020 to 2060. Then, combined with the Markov-PLUS model to map carbon emissions to land-use types, it evaluated spatial changes in carbon metabolism and analyzed carbon-transfer patterns across different land-use types. The results showed the following: (1) Across all scenarios, Jincheng’s carbon emissions exhibited an initial increase followed by a decline, with the industrial sector accounting for over 70% of total emissions. While the baseline scenario deviated from China’s carbon peaking target, the high-limit scenario achieved an early carbon peak by 2027. (2) High-negative-carbon-metabolism areas were concentrated in central urban zones and industrial parks. Notably, arable land shifted from a carbon-sink area to a carbon source area by 2060 in both the low- and high-limit scenarios. (3) In the baseline scenario, industrial and transportation land uses were the primary barriers to carbon metabolism balance. In the low-carbon scenario, the focus shifted from industrial and transportation emissions to urban construction land emissions. In the high-limit scenario, changes in urban–rural land-use relationships significantly influenced carbon metabolism balance. This study emphasizes the importance of industrial green transformation and land-use planning control to achieve carbon neutrality, and it further explores the significant impact of territorial spatial planning on the low-carbon transition of coal-resource-based cities. Full article

Analyzing the complex dynamics of land use, accurately assessing ecosystem service values (ESVs), and predicting future trends in land use and ESVs alterations within the spatial constraints of policies are essential for policymaking and advancing sustainable development objectives. This study analyzed land use/land cover (LULC) changes in Yunnan Province from 2005 to 2020. Policy constraints were incorporated into the scenario simulations, and an improved equivalent factor method, Markov-PLUS model, global spatial autocorrelation, and the Getis-Ord Gi* method were applied to predict and analyze LULC and ESVs under different scenarios for 2030. The findings revealed the following: (1) Forests and grasslands were the dominant land use categories in YNP, with notable alterations in land use patterns recorded between 2005 and 2020. (2) The total ESVs in the study area increased by CNY 8.152 billion during this period, exhibiting an initial decline followed by gradual recovery. (3) Simulations for 2030 indicated that the natural development scenario would lead to the most extensive urbanization, while the ecological conservation scenario would yield the greatest increase in total ESVs. In contrast, only the farmland conservation scenario led to an increase in food production-related ESVs, but resulted in the lowest total ESVs among the three scenarios. These results contribute to understanding the impacts of land use changes on ESVs, and provide insights for formulating scientifically sound and effective ecological protection and development policies. Full article

As urbanization accelerates, megacities are facing challenges such as inefficient land use and traffic congestion, particularly in the context of rail transit-oriented development, where land use optimization remains a significant research gap. Current urban planning still relies heavily on the experience and intuition of government planning departments, without achieving quantitative, intelligent, and scientific decision making. This study takes Panda Avenue Subway Station as a case study to analyze the evolution of land use patterns around subway stations and explore optimization strategies to enhance land development efficiency and spatial utilizationTo fill this research gap, this paper proposes a CNN-AIMatch model based on machine learning algorithm and an enhanced PLUS-Markov prediction model using the increase and decrease of floor area ratio as a control measure, which adopts an increase in plot ratio as a control measure to improve the accuracy of the Kappa coefficient in different plot ratio scenarios and the prediction of 3D urban spatial growth trends. The model effectively overcomes the limitations of the conventional 2D perspective in predicting urban expansion. By simulating urban renewal and ecological preservation scenarios, it provides an innovative solution for land use pattern optimization and plot ratio control at the block level in subway station areas. The goal of this study is to optimize land use and floor area ratio control strategies through the application of this model, intelligently respond to the challenges of high-density development and quality of life assurance, achieve the best use of land, and promote sustainable urban development and the construction of smart cities. Full article

Analyzing the current trends and causes of carbon storage changes and accurately predicting future land use and carbon storage changes under different climate scenarios is crucial for regional land use decision-making and carbon management. This study focuses on Beijing as its study area and introduces a framework that combines the Markov model, the Patch-based Land Use Simulation (PLUS) model, and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model to assess carbon storage at the sub-district level. This framework allows for a systematic analysis of land use and carbon storage spatiotemporal evolution in Beijing from 2000 to 2020, including the influence of driving factors on carbon storage. Moreover, it enables the simulation and prediction of land use and carbon storage changes in Beijing from 2025 to 2040 under various scenarios. The results show the following: (1) From 2000 to 2020, the overall land use change in Beijing showed a trend of “Significant decrease in cropland area; Forest increase gradually; Shrub and grassland area increase first and then decrease; Decrease and then increase in water; Impervious expands in a large scale”. (2) From 2000 to 2020, the carbon storage in Beijing showed a “decrease-increase” fluctuation, with an overall decrease of 1.3 Tg. In future carbon storage prediction, the ecological protection scenario will contribute to achieving the goals of carbon peak and carbon neutrality. (3) Among the various driving factors, slope has the strongest impact on the overall carbon storage in Beijing, followed by Human Activity Intensity (HAI) and Nighttime Light Data (NTL). In the analysis of carbon storage in the built-up areas, it was found that HAI and DEM (Digital Elevation Model) have the strongest effect, followed by NTL and Fractional Vegetation Cover (FVC). The findings from this study offer valuable insights for the sustainable advancement of ecological conservation and urban development in Beijing. Full article

Rapid urbanization and changing land use dynamics require robust tools for projecting and analyzing future land use scenarios to support sustainable urban development. This study introduces an integrated modeling framework that combines the Patch-generating Land Use Simulation (PLUS) model with Markov Chain (MC) analysis to simulate land use and land cover (LULC) changes for Montreal Island, Canada. This framework leverages historical data, scenario-based adjustments, and spatial drivers, providing urban planners and policymakers with a tool to evaluate the potential impacts of land use policies. Three scenarios—sustainable, industrial, and baseline—are developed to illustrate distinct pathways for Montreal’s urban development, each reflecting different policy priorities and economic emphases. The integrated MC-PLUS model achieved a high accuracy level, with an overall accuracy of 0.970 and a Kappa coefficient of 0.963 when validated against actual land use data from 2020. The findings indicate that sustainable policies foster more contiguous green spaces, enhancing ecological connectivity, while industrial-focused policies promote the clustering of commercial and industrial zones, often at the expense of green spaces. This study underscores the model’s potential as a valuable decision-support tool in urban planning, allowing for the scenario-driven exploration of LULC dynamics with high spatial precision. Future applications and enhancements could expand its relevance across diverse urban contexts globally. Full article

Exploring the dynamic response of land use and ecological vulnerability (EV) to future climate change and human ecological restoration policies is crucial for optimizing regional ecosystem services and formulating sustainable socioeconomic development strategies. This study comprehensively assesses future land use changes and EV in the Yellow River Basin (YRB), a climate-sensitive and ecologically fragile area, by integrating climate change, land management, and ecological protection policies under various scenarios. To achieve this, we developed an EV assessment framework combining a scenario weight matrix, Markov chain, Patch-generating Land Use Simulation model, and exposure–sensitivity–adaptation. We further explored the spatiotemporal variations of EV and their potential socioeconomic impacts at the watershed scale. Our results show significant geospatial variations in future EV under the three scenarios, with the northern region of the upstream area being the most severely affected. Under the ecological conservation management scenario and historical trend scenario, the ecological environment of the basin improves, with a decrease in very high vulnerability areas by 4.45% and 3.08%, respectively, due to the protection and restoration of ecological land. Conversely, under the urban development and construction scenario, intensified climate change and increased land use artificialization exacerbate EV, with medium and high vulnerability areas increasing by 1.86% and 7.78%, respectively. The population in high and very high vulnerability areas is projected to constitute 32.75–33.68% and 34.59–39.21% of the YRB’s total population in 2040 and 2060, respectively, and may continue to grow. Overall, our scenario analysis effectively demonstrates the positive impact of ecological protection on reducing EV and the negative impact of urban expansion and economic development on increasing EV. Our work offers new insights into land resource allocation and the development of ecological restoration policies. Full article

Mangrove ecosystems are pivotal to the global carbon budget. However, there is still a dearth of research addressing the impact of regional mangrove land use and land cover change (LUCC) on carbon sequestration and its associated spatial distribution patterns. To investigate the impact of different development scenarios on the carbon storage capacity of mangrove ecosystems, we focused on Hainan Island. We used LUCC data from 2010 to 2020 from mangrove-inhabited regions. The Markov-PLUS model was applied to predict the spatiotemporal dynamics of mangrove coverage under the natural increase scenario (NIS) and the mangrove protection scenario (MPS) over the next 40 years. Carbon storage was estimated using the InVEST model based on field-measured carbon density data. The outcomes show the following: (1) The Markov-PLUS model, with an overall accuracy of 0.88 and a Kappa coefficient of 0.82, is suitable for predicting mangrove distribution patterns on Hainan Island. (2) Environmental factors were the main drivers of historical mangrove changes on Hainan Island, explaining 54% of the variance, with elevation, temperature, and precipitation each contributing over 13%. (3) From 2025 to 2065, the mangrove area on Hainan Island is projected to increase by approximately 12,505.68 ha, mainly through conversions from forest land (12.73% under NIS and 12.37% under MPS) and agricultural land (39.72% under NIS and 34.53% under MPS). (4) The carbon storage increment within Hainan Island’s mangroves is projected at 2.71 TgC over the whole island, with notable increases expected in the eastern, northern, and northwestern regions, and modest gains in other areas. In this study, we comprehensively investigated the spatiotemporal dynamics and future trends of carbon storage in the mangroves of Hainan Island, offering invaluable guidance for the long-term management of mangrove ecosystems and the realization of carbon neutrality goals by 2060. Full article

The serious problem of soil erosion not only has a profound impact on people’s lives but also results in a series of ecological and environmental challenges. To determine the impact of changes in land use type on soil erosion in the urban agglomeration on the northern slopes of the Tianshan Mountains, this study commences by employing the InVEST-SDR (integrated valuation of ecosystem services and tradeoffs–sediment delivery ratio) model to calculate soil erosion levels spanning from 2000 to 2020. Subsequently, it forecasts land use and land cover (LULC) conditions for the year 2030 under three scenarios: Q1 (natural development), Q2 (ecological protection), and Q3 (economic priority). This projection is accomplished through the integration of a coupled Markov chain and multi-objective planning model (MOP) alongside patch-generating land use simulation (PLUS) models. Ultimately, based on these outcomes, the study predicts soil erosion levels for the year 2030. There has been a consistent decline in soil erosion from 2000 to 2020 with high-intensity erosion concentrated in the Tianshan Mountain region. Grasslands, glaciers, and permafrost are identified as the most erosion-prone land types in the study area, with forests exhibiting the highest capacity for soil retention. Converting from grassland and barren land to forest within the same area results in a substantial reduction in soil erosion, specifically by 27.3% and 46.3%, respectively. Furthermore, the transformation from barren land to grassland also leads to a noteworthy 19% decrease in soil erosion. Over the past two decades, the study area has witnessed a significant decline in the area of grasslands, with a notable shift towards barren and impervious surfaces due to economic development and mining activities. The three predicted scenarios depict significant expansion towards barren land, grassland, and impervious area, respectively. Soil erosion decreases under different shared socio-economic pathway (SSP) scenarios relative to 2020. There is an increase in soil erosion in the Q1 scenario and in the Q3 scenario, whereas the amount of soil erosion in the Q2 scenario exhibits a continued decrease when only the effect of land change on soil erosion is considered. Persistently rapid economic development can exacerbate soil erosion problems, underscoring the need to find a balance between economic growth and ecological conservation. As economic expansion slows down, greater emphasis should be placed on environmental protection to maintain ecological stability. Full article