Agricultural land conversion is critical for reconciling food security with carbon neutrality, yet the carbon emission impacts of specific conversion pathways across diverse regions remain insufficiently understood. This study provides a pathway-specific and regionally stratified assessment of carbon-emission changes from agricultural land conversion across China's nine agricultural regions from 1980 to 2020. By distinguishing paddy fields, dryland, and orchard land, we integrated land-use transfer analysis, carbon coefficient accounting, and Geodetector modelling to elucidate the mechanisms linking agricultural restructuring to carbon dynamics. The results show that China's agricultural land experienced limited net area change but substantial internal restructuring, highlighted by an orchard land decline-then-growth trajectory after 2000. Agricultural land conversion generated a net carbon-emissions increase of 3.67 & times; 108 t. Dryland-related conversion remained dominant, accounting for 54.62% of the four-stage average emission change and 61.85% of the cumulative change. Spatially, emissions exhibited a significant 'east-high, west-low' gradient, reflecting stronger carbonsource effects in eastern plains and coastal regions and weaker emissions or sink-enhancing effects in western regions. Driving mechanisms shifted after 2000: while topographic constraints remained fundamental, socioeconomic factors-specifically nighttime light intensity, population density, and GDP-became increasingly dominant in shaping emission differentiation. Sensitivity analysis confirmed that the total estimates, dryland dominance, and spatial pattern were stable under +/- 10% agricultural land coefficient scenarios. These findings transition land-use carbon research from aggregate accounting toward mechanism-oriented agricultural land governance, offering a scientific basis for region-specific low-carbon agricultural strategies under carbon neutrality goals.

To overcome longstanding issues of error propagation and low computational efficiency in geographical information systems (GIS) and computer-aided design (CAD) systems, high-accuracy surface modeling (HASM) methods were developed through the systematic integration of systems theory, optimization cybernetics, and surface theory. Following nearly two decades of numerical experimentation and empirical investigation, a universal fundamental theorem of surface modeling (UFTSM) was formulated. This theorem provides a general theoretical framework applicable to spatial interpolation, upscaling, downscaling, data fusion, and model-data assimilation across a wide range of disciplines, including Earth surface system science, eco-environmental informatics, medical imaging, and computer-aided design. The UFTSM was first successfully applied to the simulation of eco-environmental surfaces. Within the conceptual framework of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), ecoenvironmental components were classified into three categories: nature (including species diversity, ecosystem structure, and geographical features), nature's contributions to people (such as food provision, freshwater supply, and environmental pollution remediation), and drivers of natural change (including climate change, land-use change, policies, and regulations). The term eco-environmental surface is used as a unified concept to represent surfaces describing nature, nature's contributions to people, or drivers of natural change. Numerous studies have demonstrated that intrinsic information (e.g., ground-based observations) and extrinsic information (e.g., satellite observations) provide complementary perspectives, and that neither alone can fully characterize an eco-environmental surface. Instead, such surfaces are governed by the joint influence of intrinsic and extrinsic information, and cannot be adequately understood without considering both. To address the challenge of simulating eco-environmental surfaces through the integration of these two information sources, an iterative differential method for HASM-based machine learning was developed, and a fundamental theorem for eco-environmental surface modeling (FTEEM) was proposed. In addition, a suite of high-efficiency algorithms suitable for classical computing platforms, including a modified conjugate gradient algorithm, a multigrid algorithm, an adaptation algorithm, and adjustment computation, was designed to accelerate eco-environmental surface modeling. Compared with existing surface modeling approaches, HASM-based applications exhibit substantially improved accuracy. However, computational cost remains a major bottleneck for global-scale eco-environmental surface modeling, particularly as spatial resolution becomes increasingly fine. To address the limitations of classical algorithms and hardware, HASM was reformulated as a large sparse linear system using the Lagrange multiplier method. This system was then implemented on both a real quantum computer and a virtual quantum computing platform by employing two widely used quantum linear solvers: the Harrow-Hassidim-Lloyd (HHL) algorithm and the iterative refinement of the variational quantum linear solver (iVQLS). Based on these approaches, two HASM-oriented quantum linear solvers, HASM-HHL and HASM-iVQLS, were developed, enabling the solution of simulation problems that are intractable for classical computers. The respective advantages and limitations of HASM-HHL and HASM-iVQLS, as quantum machine learning algorithms, were systematically evaluated through simulation experiments conducted on both real and virtual quantum computing platforms. The comparative results highlight the urgent need for a universal tool library that supports both classical and quantum intelligent computing. Moreover, the development of a full-stack quantum input-computing-output system is essential for overcoming the principal challenges currently faced by HASM-based quantum machine learning.

Climate change has significantly altered plant habitats within the Earth’s surface system, reshaping the global distribution and succession of vegetation. The spatiotemporal simulation of vegetation dynamics is essential for effective ecosystem management and conservation at regional scales. In this study, an improved method is developed to analyze the vegetation patterns and scenarios in the Poyang Lake basin, based on the High-Accuracy Surface Modeling (HASM) method and the improved Holdridge Life Zone (HLZ) ecosystem model. HASM is applied to generate high-resolution (250 m × 250 m) spatial grid data for key climate parameters, including mean annual biotemperature (MAB), total annual precipitation (TAP), and potential evapotranspiration ratio (PER), for each decade from 1961 to 2050. The distribution thresholds of vegetation types are calculated based on current vegetation data, MAB, TAP, PER, longitude, latitude, and elevation datasets. In the improved HLZ ecosystem model, the classification parameters of vegetation types have been expanded from three to six. The simulation results indicate that cultivated vegetation, subtropical coniferous forest, and subtropical grassland are the dominant vegetation types, accounting for 75.88% of the total area. Between 2020 and 2050, subtropical coniferous forest is projected to experience the greatest decrease in area, shrinking by an average of 2.65 × 103 km2 per decade. In contrast, subtropical evergreen–deciduous broadleaf mixed forest is expected to undergo the largest increase, expanding by an average of 1.96 × 103 km2 per decade. Vegetation types in high-altitude regions exhibit the most rapid changes, with an average decadal variation of 15.26%, whereas low-altitude regions show relatively slower changes, averaging 0.52% per decade. Overall, subtropical grassland, subtropical coniferous forest, and subtropical evergreen–deciduous broadleaf mixed forest in the Poyang Lake basin demonstrate high sensitivity to projected climate change scenarios.

Widespread soil acidification driven by nitrogen (N) fertilization and precipitation challenges the conventional notion of the long-term stability of soil inorganic carbon (SIC) in agroecosystems. However, the changes in SIC with precipitation and N fertilization remain ambiguous. Based on 4,000+ soil samples collected in the 1980s and 2010s and by developing machine learning models to fill the missing SIC of soil samples, this study generated 3,697 paired soil samples between the two periods and then investigated the cropland SIC change and explored its relationship with precipitation and N fertilization across the Sichuan Basin, China. The results showed an overall SIC loss, with a decline of the mean SIC by 15.73%. SIC change varied with initial soil pH and initial SIC and exhibited an exponential relationship with soil pH change, indicating the changing role of carbonates in providing acid-buffering capacity. There was a parabolical relationship between the magnitude of SIC decline and N fertilizer rates, and low N fertilizer rates contributed to a reduction in SIC loss, while SIC loss was promoted by N fertilization occurred when N fertilizing rates exceeded 250 kg ha(-1) yr(-1). The change in SIC showed a sinusoidal variation with precipitation, with 950 mm being the threshold controlling whether SIC increased or decreased. Meanwhile, N fertilization did not alter the sinusoidal relationship between SIC change and precipitation. In areas with rainfall<950 mm, the high N fertilizer rate did not cause SIC loss, while higher precipitation could also cause larger SIC loss in areas with lower N fertilizer rates. These results suggest that SIC dynamics are jointly driven by precipitation and N fertilization and are controlled by acid-buffering mechanisms associated with initial pH and SIC, with precipitation being the predominant driver. These findings emphasize the need for more regional soil observations and in-depth studies of SIC change and its mechanisms for accurately estimating SIC change.

This study, guided by the concept hat “lucid waters and lush mountains are invaluable assets”, focuses on explicating the ecological vulnerability characteristics of the Nanpan and Beipan River Basins, a typical karst river basin in Guizhou Province. In this article, a value equivalent table was built to calculate the ecosystem service value (ESV) within the basin from 2000 to 2020. The patch landscape and urban simulation model (PLUS) was improved to forecast ecosystem changes under four scenarios in the future. The Getis-Ord Gi*statistic, a spatial analysis tool, was introduced to identify and interpret the spatial patterns of ESVs in the study area. The research indicates that: (1) from 2000 to 2020, the spatial pattern of ecosystem has significantly improved, and with a notable ESV increase in the Nanpan and Beipan River Basins, especially the fastest growth from 2005 to 2010. Forest and grassland ecosystems are the main contributors to ESV within the basin, and the spatial distribution of ESV shows a decreasing trend from southeast to northwest. (2) Under different scenarios, forest ecosystem still would have the highest contribution rate to update the ESV between 2010 and 2035. The ESV is the lowest under the cropland protection scenario, amounting to CNY 104.972 billion. Compared to other scenarios, the ESV is higher under the sustainable development scenario, reaching CNY 106.786 billion, and this scenario provides a more comprehensive and balanced perspective, relatively achieving a harmonious coexistence between humans and nature. (3) The hot spots of ESV are mainly concentrated in the southeast and along the riverbanks of the study area. Urban ecosystems are the cold spots of ESV, indicating that protecting the ecosystems along the riverbanks is crucial for ensuring the ecological security and sustainable development of karst mountainous river basins. In the future development of karst mountainous river basins, it is necessary to strengthen ecological restoration and governance, monitor soil erosion through remote sensing technology, optimize the layout of territorial space to implement the policy of green development, and promote the harmonious coexistence of humans and nature, ensuring the ecological security and sustainable development of the basins.

Analyzing the spatial dynamics of China's forest carbon storage under future climate scenarios is crucial for understanding terrestrial carbon sequestration potential, addressing climate change impacts, and formulating optimized carbon management strategies. This study examines its spatiotemporal changes under five Shared Socioeconomic Pathways (SSP) scenarios from the 2020s-2090s, focusing on land cover change impacts and realistic carbon storage pathways constrained by natural and basic human activities. Findings reveal an overall increasing trend in China's forest carbon storage. Growth rates are relatively stable under SSP1-2.6 (24.84 %), SSP2-4.5 (25.09 %), and SSP4-3.4 (24.23 %), with the most significant increase (26.46 %) under SSP3-7.0. Even the high-emission, high-growth SSP5-8.5 scenario sees substantial growth (25.82 %). Spatially, carbon storage varies significantly across longitudes and latitudes. Longitudinal variations (influenced by forest coverage and topography): low/stable in the west, sharp fluctuations with multiple peaks in the center, then fluctuating decline in the east; latitudinally, variations (driven by climate suitability and forest distribution) follow a north-south parabolic pattern-lower at low/high latitudes, peaking at mid-latitudes with larger fluctuations. Land cover affects forest carbon storage in two ways: conversion balance (non-forest to forest net gains boost storage, while forest conversion to other uses may hinder it); and conversion quality (the quality gap between lost high-carbon mature forests and added low-carbon young forests impacts storage). These results suggest future forest management and climate mitigation strategies must adapt to diverse climate scenarios and regional socioeconomic conditions to ensure optimal, effective implementation for forest protection and carbon storage conservation.

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In the context of achieving global carbon neutrality, forests play a pivotal role in sequestering atmospheric CO2, particularly in China, where forest management is central to national climate strategies. This study evaluates the forest carbon sink capacity in Zixi County, a subtropical region, under varying climate scenarios (SSP2-4.5 and SSP5-8.5). Using the Forest-DNDC (Denitrification–Decomposition) model, combined with high-precision climate data and a random forest model, we simulate forest carbon density and forest carbon sink under different management strategies. The results indicate that under the baseline scenario, forest carbon density in Zixi County increases by 31% over 42 years under the SSP2-4.5 climate scenario and by 28.6% under SSP5-8.5. In the enhancing economic scenario, carbon density increases by 8.5% under SSP2-4.5 and by 7.2% under SSP5-8.5. For the natural development scenario, a significant increase of 130% is observed under SSP2-4.5, while SSP5-8.5 shows an increase of 120%. Spatially, forest carbon sinks in Zixi County total 843,152 T C in 2020, 542,852 T C in 2030, and 877,802 T C in 2060 under the baseline SSP2-4.5 scenario; under SSP5-8.5, these values are 841,321 T C in 2020, 531,301 T C in 2030, and 1,016,402 T C in 2060. In the enhancing economic scenario, the total carbon sink is 34,650 T C in both 2020 and 2030, increasing to 427,351 T C in 2060 under SSP2-4.5, while under SSP5-8.5, it is 46,200 T C in 2020, 34,650 T C in 2030, and 415,801 T C in 2060. The natural development scenario shows the total carbon sink under SSP2-4.5 as 11,157,332 T C in 2020, 3,441,910 T C in 2030, and 1,409,104 T C in 2060, and under SSP5-8.5, it is 10,903,231 T C in 2020, 3,337,960 T C in 2030, and 1,131,903 T C in 2060. Spatial analysis reveals that elevation and forest type significantly affect carbon density, with high-altitude areas and forests dominated by Chinese fir and broadleaf species showing higher carbon accumulation. The findings highlight the importance of targeted forest management, prioritizing species with higher carbon sequestration potential and considering spatial heterogeneity. These strategies, applied locally, can contribute to broader national and global carbon neutrality efforts.

Based on TM/Landsat8 OLI image data and land use data， the ecological environment quality index and distribution index were used to analyze the production-living-ecological space land use and eco-environment effect in the central Guizhou water conservancy project area from 2000 to 2020. The response characteristics of land use change and ecological environment effect to topography and landform was also analyzed. The results show that from 2000 to 2020， the land use in the central Guizhou water conservancy project area has the overall characteristics that the ecological land is the most， followed by production land and living land. The area of ecological land first decreases and then increases. The area of production land has a decrease-increase-decrease trend while the area of living land shows a trend of continuous increase. The conversion among grassland ecological land， forest ecological land and agricultural production land is higher than other types of conversion， and the grassland ecological land area has the highest conversion. The ecological environment quality is fluctuated in the project area. The ecological environment quality in the eastern and western parts of the project area is generally better than that in the central part of the project area. The ecological environment quality in the areas with elevation greater than 1700m and slope greater than 15° and the eastern and southern slopes is better， while the ecological environment quality in the low altitude areas is gradually improving. The eco-environmental quality of non-karst and pure karst areas is better than that of sub-karst areas， but the eco-environmental quality of non-karst and pure karst areas shows a declining trend.

[Objective] The factors affecting landscape ecological risk in karst watersheds were determined in order to formulate ecological risk prevention and control measures and landscape management planning in mountain watersheds. [Methods] This study was conducted in the Guizhou section of the Hongshui River basin (a typical karst basin). The study utilized the perspective of “productive-living-ecological” space, and used GIS spatial analysis, landscape ecological risk index, and other methods to explore the temporal and spatial change mechanism of landscape ecological risk in the study area. The relationship between the temporal and spatial distributions of landscape ecological risk and topography was analyzed by means of the distribution index method. [Results] ① Since 2000, land use transformation based on the productive-living-ecological space in the Hongshui River basin was characterized by a reduction in the production and ecological space and a rapid increase in the living space. The contribution rates of regional landscape ecological risk changes caused by the transfer of different land use types were different. ② During the period from 2000 to 2020, the overall landscape ecological risk showed a moderate trend, and the landscape ecological risk in the southern part of the Hongshui River basin was generally better than in the northern part. ③ From the distribution of landscape ecological risk on the terrain gradient, the degree of landscape ecological risk was inversely proportional to the terrain gradient. In addition, the landscape ecological risk in the non-karst area and pure karst area was lower than that in the subkarst area. [Conclusion] Although the landscape ecological risk in the Hongshui River basin has improved, some landscape ecological problems are still prominent, and it will be necessary to strengthen the management and protection of the ecological environment.

[目的]探索长三角城市群2010—2020年的城市韧性和城市土地利用效益的时空演变特征、耦合协调程度及相对发展状况,为推动长三角城市群的协调可持续发展提供参考借鉴.[方法]以长三角城市群26个城市为研究对象,基于熵值法、耦合协调度模型及相对发展度模型,通过构建综合评价指标体系,对长三角城市群城市韧性和城市土地利用效益的耦合协调关系展开了相关研究.[结果](1)研究时间段内,长三角城市群城市韧性总体呈上升趋势,其中超过80％的城市其韧性水平出现上升;城市土地利用效益呈波动变化趋势.二者皆呈现出"东中部高、南北边缘低"的空间分布格局,且城市韧性由相对零散分布向集聚分布转变,城市土地利用效益由"7"字形分布向"大"字形分布转变;(2)二者的耦合度总体较高,以高水平耦合为主;协调程度总体较低,但呈现出上升的发展趋势和"东高西低、中心高外围低"的空间分布格局;(3)近1/2城市的城市韧性和城市土地利用效益实现了同步发展,其余城市在2010—2015年多以超前型(城市韧性超前于城市土地利用效益)为主,2015—2020年多以滞后型(城市韧性滞后于城市土地利用效益)为主.[结论]长三角城市群城市韧性和城市土地利用效益的耦合协调度水平较低、发展不平衡,二者没有实现同步发展,未来应加强城市产业结构及土地利用布局的调控和引导,促进城市经济及土地利用的区域协调,统筹城市韧性与城市土地利用协调发展.

[目的]针对厘清喀斯特耕地非农化时空演变过程的科学问题,以贵州省为例,定量揭示喀斯特耕地非农化的空间格局变化及其平均重心时空偏移趋势和迁移路径,为喀斯特区域耕地资源持续发展利用提供数据和方法支撑.[方法]文章基于1980-2020年的贵州省土地利用长时序空间数据,利用ArcGIS空间分析技术,定量获取近40年来耕地非农化的空间分布数据信息,综合引入耕地非农化速度模型、重心模型、耕地非农化标准差异椭圆方法、空间自相关模型,定量揭示贵州省近40年来耕地非农化的空间格局分布特征、时空迁移路径与集聚特征.[结果](1)1980-2020年贵州省的耕地非农化虽然非均衡化发展态势,但其非均衡程度呈降低趋势,耕地非农化严重区主要分布在黔中和黔西北地区.(2)自1980年来,黔中地区及市州所辖区的耕地非农化速度总体快于其他地区,尤其是云岩区非农化速度最快,耕地非农化重心空间迁移路径呈"V"型形态,表现为先向东南方向后持续向西北方向偏移的趋势.(3)贵州省耕地非农化空间集聚性总体上呈增强趋势,其中高—高聚集区和低—低聚集区分别集中在西北部和东南部,并且高—高和低—低聚类的变化主导了耕地非农化空间自相关关系的格局演变.[结论]揭示1980-2020年贵州省耕地非农化的空间格局的时空变化趋势、偏移方向与迁移路径,可为喀斯特区域耕地资源保护与持续利用提供科学依据和辅助决策.

Explicating the relationship between vegetation and precipitation changes with different spatial dimensions is critical for clearly identifying the interaction mechanism between climate change and ecosystem health in the semi-arid and semi-humid (SASH) area, China. Due to investigating the dynamic pattern-effect relationships between precipitation and vegetation in the directions of longitude and latitude in the SASH area of China, the division method of SASH area, the identifying method of spatial distribution change of NDVI and precipitation, and the sensitivity index of NDVI to precipitation were developed in this study, based on the NDVI data and monthly precipitation data from 1991 to 2020. The research results showed that the NDVI was significantly related with precipitation in the whole SASH area. The relationship between NDVI and precipitation in the semi-arid area was higher than that in the semi-humid area between 1991 and 2020. The response of NDVI to pre-cipitation (R-(NDVI,R-Pre) = 0.90, P < 0.01) in the longitude direction was more than that (R-(NDVI,R-Pre) = 0.55, P < 0.01)in the latitude direction. The sensitivity of 79% NDVI to precipitation was increased with increasing latitude within the same longitude range, and the sensitivity of 97% NDVI to precipitation was decreased with increasing longitude within the same latitude range. The NDVI was more sensitive to precipitation in the semi-arid area than that in the semi-humid area. In summary, the responses of NDVI to precipitation showed significant heterogeneity between 1991and 2020 in the whole SASH area. Thus, the planning of ecological protection should give more consideration to the precipitation condition in the SASH area in the future.

The national strategy for ecological protection and high-quality development is raising the ecological security protection to an unprecedented level in the Yellow River Basin (YRB) of China. Due to the explicitly analyzed land cover changes under climate change and rapid urbanization in the YRB area since 1990, land cover dynamic degree index, transfer matrix, and geo-detector method were used to explicate land cover changes and their key driving factors, based on the spatial data of land cover from 1990 to 2020. The results show that grasslands, croplands, and forests are the main land cover types, accounting for 48.37%, 25.05%, and 13.50%, respectively, of the total area in the YRB area. Grassland, cropland, and cropland are the major land cover type, accounting for 61.49%, 37.13%, and 66.33%, respectively, in the upstream, midstream, and downstream of the YRB area. Built-up land has showed a continual increasing trend, and its dynamic degree was up to 3.38% between 2010 and 2020. Population density was a key factor for land cover change, with an average contribution rate of 0.264; then, elevation and temperature also expressed an important role to drive the land cover change in the YRB area during the period from 1990 to 2020.

Cropland soils are considered to have the potential to sequester carbon (C). Warming can increase soil organic C (SOC) by enhancing primary production, but it can also cause carbon release from soils. However, the role of warming in governing cropland SOC dynamics over broad geographic scales remains poorly understood. Using over 4000 soil samples collected in the 1980s and 2010s across the Sichuan Basin of China, this study assessed the warming-induced cropland SOC change and the correlations with precipitation, cropland type and soil type. Results showed mean SOC content increased from 11.10 to 13.85 g C kg(-1). Larger SOC increments were observed under drier conditions (precipitation < 1050 mm, dryland and paddy-dryland rotation cropland), which were 1.67-2.23 times higher than under wetter conditions (precipitation > 1050 mm and paddy fields). Despite the significant associations of SOC increment with crop productivity, precipitation, fertilization, cropland type and soil type, warming also acted as one of major contributors to cropland SOC change. The SOC increment changed parabolically with the rise in temperature increase rate under relatively drier conditions, while temperature increase had no impact on cropland SOC increment under wetter conditions. Meanwhile, the patterns of the parabolical relationship varied with soil types in drylands, where the threshold of temperature increase rate, the point at which the SOC increment switched from increasing to decreasing with warming, was lower for clayey soils (Ali-Perudic Argosols) than for sandy soils (Purpli-Udic Cambosols). These results illustrate divergent responses of cropland SOC to warming under different environments, which were contingent on water conditions and soil types. Our findings emphasize the importance of formulating appropriate field water management for sustainable C sequestration and the necessity of incorporating environment-specific mechanisms in Earth system models for better understanding of the soil C-climate feedback in complex environments.

如何实现自然与人文双重驱动下的特大城市群地区土地覆被变化的情景模拟,不仅是当前土地覆被变化研究领域的热点问题,也是城镇化可持续发展研究的核心主题之一.本文在对现有土地覆被变化情景模型缺陷进行分析和修正的基础上,构建了自然要素与人文要素耦合驱动的土地覆被情景曲面建模(SSMLC)方法.结合IPCC 2020年发布的共享社会经济路径(SSPs)与典型浓度路径(RCPs)组合的CMIP6 SSP1-2.6、SSP2-4.5和SSP5-8.5的气候情景数据,以及人口、GDP、交通、政策等人文参数,分别实现了SSP1-2.6、SSP2-4.5和SSP5-8.5情景下的京津冀土地覆被变化的情景模拟.模拟结果表明:SSMLC对京津冀地区土地覆被变化模拟的总体精度为93.52％;京津冀地区的土地覆被在2020-2040时段内的变化强度最高(3.12％/10a),2040年以后的变化强度将逐渐减缓;在2020-2100年间,建设用地增加速度最快,增加率为5.07％/10a.湿地的减少速度最快,减少率为3.10％/10a.2020-2100时段内的京津冀土地覆被在SSP5-8.5情景下的变化强度整体高于在SSPl-2.6和SSP2-4.5情景下的变化强度;GDP、人口、交通和政策等人文因子对京津冀地区耕地、建设用地、湿地和水体的影响强度高于对其他土地覆被类型的影响强度.研究结果证实了SSMLC模型能够有效模拟和定量刻画京津冀地区土地覆被空间分布格局在未来不同情景的时空变化趋势和强度,模拟结果可为京津冀协同一体化的国土空间优化配置与规划、以及生态环境建设提供辅助依据和数据支撑.

This paper aims to simulate and predict global permafrost distribution, and analyse its change from 2010 to 2100 under different climate scenarios. Based on different factors (topography, land cover, climate and location) and global permafrost distribution status, logistic regression model (LRM) is chosen and constructed to simulate and predict the global permafrost distributions. Thus, the global permafrost distributions at T1 (2010-2040), T2 (2040-2070) and T3 (2070-2100) are predicted under different climate scenarios (RCP26, RCP45 and RCP85). From T1 to T3, the area of global permafrost has the largest degradation under RCP85 scenarios. From RCP26 to RCP85 at T3, the area of the degraded permafrost reached 0.671 x 10(8) km(2). The degraded permafrost mainly distributes in east Asia, west Asia, north Europe and north America. The west Asia has the highest degrading distance, about 600 km under the situations of both RCP85 from T1 to T3 and from RCP26 to RCP85 at T3.

通过晶体管小型化增加芯片上的晶体管数量一直是计算进步的最基本部分.然而,晶体管小型化已达到极限水平.与此同时,现有计算机已无法满足许多实际问题对计算资源的巨大需求.幸运的是,量子计算机初见端倪.有关研究表明, 54量子比特的量子计算机在数分钟内就可完成传统计算机需要用1万年才能完成的计算任务;机器学习可提高量子算法的精度.机器学习方法可区分为监督学习、非监督学习、强化学习和多法混合学习.本文引入的高精度曲面建模（HASM）方法是一种强化学习方法,它可转换为大型线性稀疏系统.这个大型线性系统可运用HHL量子算法进行求解.我们将HASM机器学习与HHL量子算法的合成称为HASM-HHL量子机器学习.训练实验表明,精度设置对HASM-HHL性能和量子电路参数有很大影响;量子计算对量子比特总数的需求依赖于计算域的栅格总数.运用HASM-HHL模拟整个地球表面时,在1 km×1 km空间分辨率,需要40量子比特;在1 m×1 m空间分辨率,需要45量子比特. HASM-HHL可实现相对传统计算机算法的指数级加速.由于HASM已成功应用于各种空间尺度的数字高程模型构建以及生态多样性变化、人口动态、土壤属性动态、食物供给动态、碳储量动态、二氧化碳浓度变化、气候变化和新冠病毒传播动态等的模拟分析, HASM-HHL有望成为模拟分析地球表层系统及其生态环境要素的通用量子机器学习平台.

The computer advances of the past century can be traced to the increase in their numbers on chips that has accompanied the miniaturization of transistors.However,computers are nearing the fundamental limits of such miniaturization[1].Many practical problems require huge amounts of computational resources that exceed the capabilities of today's computers.A 54-qubit quantum computer on the other hand can solve in minutes a problem that would take a classical machine 10,000 years[2].

The economy in the poverty-stricken areas of China has grown rapidly in response to poverty alleviation policies in the 21st century. To explicate the response of the eco-environment to rapid economic growth in the 14 contiguous areas of dire poverty in China, we developed a method of evaluating the impact of poverty alleviation policies on ecological health. Based on the yearly data of gross domestic product (GDP) per capita and normalized difference vegetation index (NDVI) from 2000 to 2019, the dynamic changes in NDVI and GDP were calculated, and the development patterns in the 14 contiguous areas of dire poverty were evaluated and classified. The results show that both annual GDP per capita and average annual NDVI exhibited an increasing trend, increasing by 43.81% and 0.84% per year, respectively. The development of the 14 contiguous areas of dire poverty all presented a coordinated and sustainable (A) development pattern during the period from 2000 to 2019. The consistency of economic and ecological health development between 2000 and 2013 was less than that between 2014 and 2019. Moreover, the result indicates that it is necessary to make timely adjustments to poverty alleviation strategies based on the positive consistency between economic growth and ecological health.