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Households' carbon emissions (HCEs) plays an important role in the overall carbon emission (CE) reduction. This study conducts a comparative analysis of the trends and characteristics of the rural and urban HCEs in China in 1997–2020, by applying the environmental input-output (EIO) model. Then, a three-stage SDA model is applied to decompose the driving forces of the rural and urban HCEs evolution into population effect, per capita HCEs effect, expenditure effect, CE intensity effect, energy structure effect, and energy consumption intensity effect. It is obtained that the rural HCEs increases before 2015 and then decreases, but its per capita HCEs has always shown an upward trend. The urban HCEs has been increasing, but its per capita HCEs starts to decrease after 2015. Indirect HCEs accounts for over 80% of both the rural and urban HCEs, and Residence, Food and Tobacco, Transportation and Communication are the three biggest contributing sectors. To reveal the driving forces of the rural and urban HCEs evolution, this study conducts phase decomposition analysis with 2015 as a turning point. For rural HCEs, in 1997–2015, rural HCEs increased due to the dominant expenditure effect; from 2015 to 2020, the driving force reversed, leading to a decline in rural HCEs. For urban HCEs, the positive population and expenditure effects have always been larger than the negative energy structure and energy consumption intensity effects, resulting growth in urban HCEs. For urban per capita HCEs, in 1997–2015, the positive expenditure effect is greater than the negative CE intensity effect, leading to an increase in urban per capita HCEs, and a positive urban per capita HCEs effect. In 2015–2020, the driving force reversed, leading to a decline in urban per capita HCEs. Based on above results, countermeasures to promote rural and urban HCEs reduction are discussed. [ABSTRACT FROM AUTHOR]

Groundwater is essential for water resources, serving as a key drinking source in China. It supports daily needs for urban and rural residents, aids development, and maintains ecological balance. This study conducted a sampling survey of groundwater in the Wen River basin (WRB), assessing hydrochemical features, genesis mechanisms, water quality, and health risks. The findings reveal that groundwater in the WRB is weakly alkaline, with an average total dissolved solids (TDS) concentration of 755.24 mg/L. Freshwater constitutes approximately 81.48% of the groundwater, with the following order of concentration for cations: Ca2+ > Na+ > Mg2+ > K+, and for anions: HCO3− > SO42− > NO3− > Cl−. The predominant hydrochemical types are SO4·Cl-Ca·Mg and HCO3-Ca·Mg. The chemical composition of the groundwater is primarily influenced by silicate rock weathering, dissolution processes, cation exchange, and human activities. The average Environmental Water Quality Index (EWQI) value of 74.65 for the WRB signifies that the overall quality of the groundwater is quite good, indicating that the majority of the groundwater is suitable for drinking purposes. Notably, the inferior quality water is predominantly found downstream of the Wen River. Calculations of the sodium adsorption ratio (SAR), residual sodium carbonate (RSC), and percentage of sodium (%Na) indicate that groundwater at most sampling points is suitable for irrigation. Furthermore, the human health risk assessment (HRA) reveals that oral intake presents a greater health risk to individuals than dermal contact. The mean Hazard Index (HI) for children is 3.24, with a staggering 79.89% of non-carcinogenic health risk (NHR) values surpassing the acceptable standards. For adults, the mean HI is 1.39, with 53.44% of NHR values exceeding the standards. These data indicate that children are more susceptible to health risks than adults and that the midstream and downstream of the river exhibit higher health risks compared to the upper reaches. These findings can provide critical data for groundwater quality assessment and risk management in the WRB and offer guidance for future groundwater resource management and pollution control efforts. [ABSTRACT FROM AUTHOR]

The net primary productivity (NPP) of vegetation is the key indicator for assessing ecosystem productivity and carbon cycling. The Ulan Mulun River Basin (UMRB) in Northwest China is a typical coal mining area, including open-pit mining (OPM) and underground coal mining (UGM). There are limited studies utilizing long-term, high-resolution data to investigate the spatiotemporal and driving mechanisms of NPP in different types of mining and non-coal mining (NCM) areas. In this study, NPP was estimated using high-resolution Landsat data (30 m) and an improved CASA model for the period 1987–2022. The spatiotemporal variations in NPP across the basin were systematically investigated using Theil–Sen–MK trend analysis, partial derivatives, and multivariate regression residual to explore and quantify the impacts of climate variability (CV) and human activities (HAs) on the different coal mining and NCM areas. The research results show that the overall fluctuating upward trend of vegetation cover in the country is 64.84% during the period from 1987 to 2022. However, there is a decreasing trend of NPP in the coal mining areas. Precipitation was the major factor influencing the change in NPP (21.835 gC/m2/a), while HAs had a lesser effect (4.667 gC/m2/a). In addition, UGM and NCM were more positively affected by HAs than OPM, while OPM was more positively affected by CV than UGM and NCM. These findings can guide scientific ecological restoration strategies, assess carbon balance impacts, and optimize land management and planning in mining areas to achieve a balance between resource development and environmental protection. [ABSTRACT FROM AUTHOR]

Land Use and Land Cover (LULC) is the direct and concentrated reflection of the interaction between human activities and natural geographical environment. High-precision LULC information can also depend mainly on the global climate change, material cycle and dynamic balance of water and heat. Current environmental challenges were remained with the rapid increase in remote sensing observation platforms, the free disclosure of high-resolution satellite remote sensing data and the advancement of LULC mapping technology. Freely available medium and high-resolution land cover products are emerging for the open source. The medium and high resolution LULC datasets have also been constructed worldwide. However, there are different degrees of uncertainty in the multi-source data. It is a high demand for the suitable land cover products at the regional scale in various fields. Therefore, it is very important to evaluate the accuracy of the current commonly- used land cover data at the regional scale. Taking the Erhai Lake Basin as the study area, the consistency analysis was carried out to evaluate accuracy of commonly-used non-homologous LULC products. 2 947 validation samples were collected using Third National Land Survey data, the kilometer grid sampling, field surveys, and high-resolution image interpretation. Seven commonly-used heterogeneous LULC data products were evaluated, in terms of area, spatial consistency, confusion levels, and accuracy. The influence of LULC product mapping accuracy was quantitatively analyzed from four aspects: shrub forest proportion, landscape pattern index, elevation standard deviation and average patch area. The applicability of each dataset was also evaluated. The results reveal that the high, moderate, and low consistency areas were represented by 64.13%, 34.00%, and 1.87% of the total area, respectively, among the eight datasets of land cover. Notable confusion and misclassification occurred in shrub land and grassland, indicating the significant differences in the various products to represent different regions and land cover types. The overall accuracy of the LULC products was ranged from 69.5% to 81.1%. Notably, ESA_WC was offered the best data quality and spatial detail, especially for the cultivated land in fragmented landscapes. Additionally, the Shannon Diversity Index (SHDI) was found to share the most considerable impact on spatial consistency of land cover in the Erhai Basin, followed by the proportion of shrub land. In contrast, there was the less effect of some factors, such as elevation standard deviation, patch size, and cloud cover frequency. In all features of land cover, 10 m resolution data should prioritize the highest overall accuracy provided by ESA_WC. Among them, CRLC data was better performed, if the shrubland and grassland were not subdivided. While for 30 m resolution data, CLCD demonstrated the relatively high accuracy. The impermeable surface area was significantly underestimated, compared with the rest products unsuitable for urban expansion. This finding can serve as a valuable reference to assess the classification accuracy, strengths and weaknesses of the seven LULC products. Targeted data selection was facilitated for the specific applications. The accuracy of LULC products was directly evaluated for the applicability and adaptation of the data. The finding can provide the scientific basis for ecological environment protection, rational utilization of resources and sustainable development in plateau mountainous areas. [ABSTRACT FROM AUTHOR]

The coupling coordination degree (CCD) of the production-living-ecological space (PLES) functional index is an indicator of regional sustainable development potential. However, previous studies have failed to reveal the driving mechanisms of the CCD of PLES functional index on a global scale. Therefore, this study employed the CCD model to evaluate the CCD of the PLES functional index and spatial regression models to measure the heterogeneous drivers using multi-source data in 2000, 2010, and 2020. The results demonstrated that ecological spaces dominated (85%) globally, while living spaces comprised the smallest share (3%). The ecological functional index was higher than the production and living functional indices. Further analyses revealed that topographic factors were the main restricting factors for PLES; the proportion of production and living spaces decreased with increasing altitude and slope, whereas ecological spaces showed the opposite trend. The global CCD values of the PLES functional index in 2000, 2010, and 2020 were 0.186, 0.189, and 0.198, respectively, showing an increasing trend. High CCD was generally observed in areas with dense population and industry where human activity systems interact with natural ecosystems. The formation of the CCD of the global PLES functional pattern results from the joint action of natural and socioeconomic factors, with pronounced spatial heterogeneity. Our findings can help optimize global territorial space utilization, improve territorial space utilization efficiency, and realize global sustainable development goals. [ABSTRACT FROM AUTHOR]

The management of land use is a significant factor in maintaining the equilibrium of carbon stocks. However, the expansion of infrastructure, mining, industry, trade, economic activity and population has resulted in significant changes to land use, which have led to a reduction in carbon stock reserves. The objective of this study was to map the level of carbon stock reserves and to analyse the variable driving forces of carbon stock change. The variables used in this study encompass 21 distinct types, including: socio-economic, physical, locational, land and spatial planning aspects. The dependent variable comprises the level of carbon stock by land use type in 2014, 2018, and 2022. Spatial regression analysis was employed to ascertain the driving forces that exert a predominant influence on alterations in carbon stock reserves. The results of the spatial regression analysis between the dependent and independent variables yielded a highly significant correlation, as indicated by the R-square value exceeding 0.09. This condition is influenced by the use of complex and comprehensive variables, as well as the use of spatial regression which is able to analyse between variables by considering spatial aspects. The results of the analysis demonstrate that location variables (city centre, airport and road accessibility), physical variables (relative relief) and land variables (land title status) are the most dominant variables. The analysis of the driving force of carbon stock change represents a crucial aspect of land use management and the control of land use change from areas with high to low carbon stock. In order to maintain the balance of carbon stock reserves, it is essential to optimise mangrove areas and implement reforestation initiatives in forest areas. [ABSTRACT FROM AUTHOR]

Isolated marsh wetlands are crucial for maintaining regional hydrological connectivity and biological contiguity. The Sanjiang Plain is the most typical area of marsh wetland change in China. A large number of isolated marshy wetlands have been formed here due to natural and anthropogenic influences. However, there have been few quantitative studies of the dynamics of isolated marsh wetlands and their drivers at the regional scale. This study used Landsat series image data provided by the Google Earth Engine. Through field surveys, combined with visual interpretation and the Random Forest Algorithm, the distributional changes in isolated marsh wetlands, non-isolated marsh wetlands, and natural marsh wetlands in the Sanjiang Plain from 1975 to 2020 were identified and extracted. The dynamic change characteristics as well as the patch importance values (dIIC) of isolated and non-isolated marsh wetlands were analyzed using the dynamic degree, standard deviation ellipse model, and the integral index of connectivity (IIC). Finally, the driving factors and interactions affecting the distribution of isolated marsh wetlands were analyzed by the Geodetector model. The results show that (1) the temporal dynamics of the three types of marsh wetlands are less than 0 from 1975 to 2020, and the temporal dynamics of isolated marsh wetlands are the largest. The lost wetlands were concentrated in the northeastern and east–central regions of the Sanjiang Plain. The center of mass of the standard deviation ellipse moved from northeast to southwest, and the isolated marsh wetlands moved the most. (2) The IIC of non-isolated marsh wetlands and natural marsh wetlands decreased and then increased, and the non-isolated marsh wetlands with high-grade connectivity were mainly distributed in the northeastern and east–central regions. On the other hand, the IIC of isolated marsh wetlands increased and then decreased, and the isolated marsh wetlands with high-grade connectivity were mainly distributed in the northeastern region. (3) The elevation is the most important driving factor affecting the distribution of isolated marsh wetlands in the Sanjiang Plain. The interaction between the driving factors had a significantly higher effect on the distribution of isolated marsh wetlands than that of a single driving factor, with the strongest interaction between aspect and elevation in 1975, 1986, 2000, and 2010, and between aspect and slope in 2020. [ABSTRACT FROM AUTHOR]

[Objective] Tuojiang River Basin is facing serious ecological and environmental problems such as soil erosion, water environment pollution and soil erosion. It is of great significance to carry out the evaluation of the ecological and environmental quality of this basin in order to promote the ecological environmental protection of the upper reaches of the Yangtze River and the high-quality sustainable development of the basin's socio-economy. [Methods] Based on the Google Earth Engine platform, Landsat remote sensing images from 2000 to 2020 were selected to construct an ecological environment quality evaluation model with NDVI, WET, LST, NDBSI, and AI indices, and the coefficient of variation, one-way linear regression, and spatial autocorrelation analysis methods were used to analyze the spatial and temporal evolution pattern of ecological environment quality in the watershed, and to detect the driving force using the optimal parameter Geographic Detector Model(OPGD). [Results] (1) During the period of 2000—2020, the multi-year average value of ecological environmental quality in the Tuojiang River Basin is 0.66, and the grade is mainly good, accounting for 63.01%. The ecological environment quality in the surrounding districts and counties of Chengdu, Neijiang, Zigong, Luzhou and other towns and regions is poor, showing a spatial distribution pattern of low north-south and high middle. (2) The ecological environment quality of the Tuojiang River Basin is generally on a declining trend, and the trend of deterioration of ecological environment quality is significant, with the ratio of slightly degraded and significantly degraded areas being 19.75% and 5.88%. The urbanized areas with high population density and high level of socio-economic development are dominated by higher fluctuations in ecological environment quality, accounting for 18.15%. (3) The global autocorrelation Moran index of Tuojiang River Basin during 20 years ranged from 0.769 to 0.870, which was mainly distributed in the first and third quadrants, and the ranges of local autocorrelation high-high agglomerations and low-low agglomerations showed significant changes over time, with obvious fragmentation of the landscape patches. (4) The OPGD results show that land use type, slope, elevation, and nighttime lighting are the main drivers of ecological quality change in the watershed, population density, temperature, and GDP are secondary drivers, and precipitation and slope orientation factors are less influential. Land use type interacted with other factors to enhance the influence on the change of ecological environment quality in the watershed. [Conclusion] The improved remote sensing eco-index model provides a new method for regional ecological environment quality evaluation, and the results of the study provide a reference for ecological environment protection.

Abstract High-quality development paths in important cities are blurry and lacking. In order to explore the important engine for Chengdu high-quality development, driving forces and obstacles recognition has emerged as a pivotal technological solution. Using the Chengdu in Sichuan province of China as a research area and quantitative data from 2010 to 2019, this study has used content mining to recognize urban high-quality development (UHQD) variables, and calculated variables’ weights by entropy weight method, and explored driving forces and obstacles of UHQD by the technique for order preference by similarity to ideal solution (TOPSIS) method. The main findings are: (1) there are 36 UHQD variables; (2) Chengdu high-quality development overall level soars from 2017 to 2019, only with two negative growth rates in 2011, 2015; (3) There are 3 key driving force paths: (1) improving green development by volume of industrial wastewater discharged, comprehensively utilised ratio of industrial solid wastes, harmless treatment rate of domestic garbage; (2) stressing open development by total import and export/GDP, actual use of foreign capital, number of foreign tourists/total tourists; and (3) intensifying shared development by funds for urban residents under basic provision protection. (4) 3 clearing obstacles paths can also realize Chengdu high-quality development: (1) improving innovative development level by R&D internal outlay, patent authorisations, state high-level tech enterprises; (2) optimizing coordinated development level by the proportion of tertiary industry; (3) promoting shared development level by urban basic pension insurance. According to these findings, suggestions are put forward to promote Chengdu high-quality development from the perspective of policy implementation.

Exploring land cover change is very important for the sustainable development of regional land resources. However, there are few studies on land cover change in the construction of Xinjiang electricity transmission energy channel. In this paper, the temporal variation characteristics of land cover in a 10 km buffer zone along Jiquan Line in Changji Hui Autonomous Prefecture were analyzed by using multi-scale geographically weighted regression model (MGWR) to explore the spatio-temporal changes and driving factors of land cover in this region during 2010-2022. The results showed that: ① Grassland, bare land and cultivated land were the main areas in the study area, and the proportion of grassland and bare land was more than 90%. During the construction of Xinjiang electricity transmission channel, the land cover changes were more drastic and complicated; ② There are differences in the driving factors in terms of time. In the preparation and formal construction stages of the channel construction, the distance to the base station and the distance to the power grid rank first and second respectively in importance. However, during the operation period after the construction is completed, the distance to the water source and the minimum temperature rank first and second respectively. ③ The spatial ranges of different factors are different. Annual minimum temperature and distance to water source mainly affect grassland, bare land and farmland. The research results can provide reference for the construction of similar projects in the future, and promote sustainable land use and ecological protection.

High-quality development paths in important cities are blurry and lacking. In order to explore the important engine for Chengdu high-quality development, driving forces and obstacles recognition has emerged as a pivotal technological solution. Using the Chengdu in Sichuan province of China as a research area and quantitative data from 2010 to 2019, this study has used content mining to recognize urban high-quality development (UHQD) variables, and calculated variables' weights by entropy weight method, and explored driving forces and obstacles of UHQD by the technique for order preference by similarity to ideal solution (TOPSIS) method. The main findings are: (1) there are 36 UHQD variables; (2) Chengdu high-quality development overall level soars from 2017 to 2019, only with two negative growth rates in 2011, 2015; (3) There are 3 key driving force paths: (1) improving green development by volume of industrial wastewater discharged, comprehensively utilised ratio of industrial solid wastes, harmless treatment rate of domestic garbage; (2) stressing open development by total import and export/GDP, actual use of foreign capital, number of foreign tourists/total tourists; and (3) intensifying shared development by funds for urban residents under basic provision protection. (4) 3 clearing obstacles paths can also realize Chengdu high-quality development: (1) improving innovative development level by R&D internal outlay, patent authorisations, state high-level tech enterprises; (2) optimizing coordinated development level by the proportion of tertiary industry; (3) promoting shared development level by urban basic pension insurance. According to these findings, suggestions are put forward to promote Chengdu high-quality development from the perspective of policy implementation. [ABSTRACT FROM AUTHOR]

The terrain in the loess hilly area of western Henan is fragmented, with steep slopes and weak soil erosion resistance. The substantial soil erosion in this region results in plenty of problems, including decreased soil productivity and ecological degradation. These problems significantly hinder the social and economic development in the region. Soil conservation planning and ecological development require accurate soil erosion surveys. However, the studies of spatio-temporal patterns, evolution, and the driving force of soil erosion in this region are insufficient. Therefore, based on a multi-stage, unequal probability, systematic area sampling method and field investigation, the soil erosion of the loess hilly area of western Henan was quantitatively evaluated by the Chinese Soil Loss Equation (CSLE) in 2022. The impact forces of soil erosion were analyzed by means of a geographic detector and multiple linear regression analysis, and the key driving factors of the spatio-temporal evolution of soil erosion in this region were revealed. The results were as follows. (1) The average soil erosion rate of the loess hilly area in western Henan in 2022 was 5.94 t⋅ha−1⋅a−1, with a percentage of soil erosion area of 29.10%. (2) High soil erosion rates mainly appeared in the west of Shangjie, Xingyang, and Jiyuan, which are related to the development of production and construction projects in these areas. The areas with a high percentage of soil erosion area were in the north (Xinan and Yima), west (Lushi), and southeast (Songxian and Ruyang) of the study area. Moreover, areas with the most erosion were found in forest land, cultivated land, and areas with a slope above 25°. (3) At the landscape level, the number and density of patches of all land types, except orchard land, increased significantly, and the boundary perimeter, landscape pattern segmentation, and degree of fragmentation increased. (4) The geographical detector and multiple linear regression analysis indicated that the driving forces of soil erosion are mainly topographic and climatic (slope length, elevation, precipitation, and temperature). Soil erosion was significantly influenced by the density of landscape patches. These maps and factors influencing soil erosion can serve as valuable sources of information for regional soil conservation plans and ecological environment improvements. [ABSTRACT FROM AUTHOR]

Human activities, such as reservoir construction and riverbed sand extraction, significantly influence the hydrological and sedimentary dynamics of natural rivers, thereby directly or indirectly affecting river landscape pattern distribution. This study primarily focused on the lower Beijiang River (LBR) in China, an area characterized by intensive human activity. River landscape patterns were studied using historical topographical data and time-series Landsat remote sensing images. Natural and anthropogenic factors were considered to explore the driving forces behind the evolution of landscape patterns. The results indicated that the topography of the LBR underwent significant downcutting from 1998 to 2020. The average elevation of the study area decreased by 3.6 m, and the minimum thalweg elevation decreased by 6.7 m. Over the past 30 years, the local vegetation showed a relatively stable spatial distribution, whereas the area of sand remained relatively stable before 2012, followed by a sudden decline, and tended to stabilize in the last decade. The water area exhibited a gradually increasing trend. The transition maps indicated that the spatial changes in sand were the most significant, with only 39.6% of the sand remaining unchanged from 1998 to 2009 and 32.3% from 2009 to 2020. The corresponding landscape patterns showed that the fragmentation degree of sand increased, with the mean patch size decreasing by 69.2%. The aggregation of water intensified, as its aggregation index increased from 93.31% to 95.41%, while the aggregation of vegetation remained relatively minor, ranging from 89.52% to 90.12%. The annual average temperature, annual average maximum temperature, and annual rainfall days had the strongest correlations with the vegetation landscape pattern indices. Additionally, human activities may have been the primary driver of the landscape pattern evolution of water and sand. The findings of this study have positive implications for the maintenance of the diversity and stability of river ecosystems. [ABSTRACT FROM AUTHOR]

Jining City of Shandong Province is located in the coal mining area of the lower reaches of the Yellow River Basin, facing serious problems such as the destruction of arable land resources. Based on the land use data and socio-economic data of Jining City from 1980 to 2020, this paper explores the spatial and temporal characteristics and the driving mechanism of arable land by land use transfer matrix and principal component analysis, and predicts the future arable land area by using the gray prediction model. The results show that: （1）arable land in Jining City exhibits a typical characteristic of "more in the west, less in the east" and a continual decrease, land areas for industrial, coal mining and transportation construction are increasing.（2）From 1980 to 2020, the predominant changes to arable land in Jining City involved its conversion into construction land, grassland water bodies. Notably, from 1980 to 1990, there were significantly higher dynamics in arable land changes across counties and districts, whereas from 2000 to 2010, there were relatively minor fluctuations in arable land area.（3） Economic factors emerge as the primary driving forces behind changes in arable land, coal mining's proportion of the total industry output value and the secondary industry's share of the total output value are the principal determinants influencing arable land area.（4）Through forecasting, it is projected that the arable land area will continue to exhibit a decreasing trend from 2020 to 2035. In response, crafting arable land conservation policies should comprehensively account for economic factors, adopt tailored strategies based on local conditions, and propel the development of resource-efficient urban construction. [ABSTRACT FROM AUTHOR]

Vegetation greenness has been one of the most widely utilized indicators to assess the vegetation growth status for the better ecological environment. However, in typical tourist regions, the impact of the geographical environment, socioeconomic development, and tourism development on vegetation greenness changes is still a challenge. To address this challenge, we used the Google Earth Engine (GEE) cloud platform combined with a series of Landsat remote sensing images to calculate the fractional vegetation cover (FVC) which can be used as an indicator to characterize the spatiotemporal evolution of vegetation greenness in Hainan Island from 2000 to 2020. Furthermore, we employed geographic detector and structural equation models to quantify the relative importance and explanatory power of the geographical environment, socioeconomic development, and tourism development on vegetation greenness changes and to clarify the interaction of mechanisms of various factors in Haikou and Sanya. The results show that the annual growth rate of the FVC in Hainan Island was 0.0025/a. In terms of spatial distribution, the trend of the FVC changes was mainly characterized by non-significant and extremely significant improvement, accounting for 35.34% and 29.38% of the study area. Future vegetation greenness was dominated by weak counter-persistent increase and weak persistent increase. The geographical environmental factors were the main factors affecting vegetation greenness in Haikou, followed by the socioeconomic and the tourism development factors, while the geographical environmental factors also dominate in Sanya, followed by the tourism development factors and finally the socioeconomic factors. Specifically, the spatial distribution of vegetation greenness was primarily influenced by land use types, elevation, slope, and travel services. Geographical environmental factors could indirectly affect changes in socioeconomic and tourism development, thereby indirectly affecting the spatial distribution of vegetation greenness. These findings can provide some significant implications to guide the ecological environmental protection for sustainable development in Hainan Island in China. [ABSTRACT FROM AUTHOR]

Reservoir projects often have significant impacts on ecosystems. The resulting environmental problems hinder the ecologically sustainable development of project areas. Research focusing on landscape pattern vulnerability could shed light on ecological restoration in disturbed sites. However, few studies have specifically examined reservoir areas in this context. This study investigates the spatial distribution characteristics, change rules, spatial autocorrelation, and driving forces of landscape pattern vulnerability in the Qianping Reservoir area (3859.16 hm2) from 2000 to 2020 using land use data. The findings reveal several key points: (1) Over the study period, cultivated land, grassland, and forest land are the key landscape types, covering more than 90% of the area. Cultivated land decreased by 481.57 hm2 as other land use types expanded. (2) Vulnerability remained stable in the first decade but sharply increased from 2010 to 2020, showing a trend of spatial aggregation. Reservoir construction and simultaneous ecological restoration efforts led to shifts in vulnerability zones across the landscape. (3) Spatial distribution of landscape pattern vulnerability shows a positive correlation, which strengthened by 2020 compared to earlier years. (4) Man-made factors, particularly land use changes, significantly influence landscape pattern vulnerability, with their impact growing over time. These findings not only provide a scientific basis for ecological restoration and landscape reconstruction in the Qianping Reservoir area but also offer insights applicable to similar environments. Overall, this study enhances theoretical understanding of reservoir landscape pattern vulnerability and contributes valuable perspectives on ecological restoration strategies for reservoir areas. [ABSTRACT FROM AUTHOR]

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Studying the spatial distribution of soil organic matter (SOM) and exploring its driving factors in semi-arid grassland open-pit coal mining areas is crucial for sustaining ecological development and security. Currently, research on SOM in mining areas lacks large-scale investigation, sampling, spatial distribution, and driving force research for semi-arid grassland open-pit coal mining areas, and it is unable to comprehensively grasp the distribution characteristics and driving force of SOM in open-pit coal mines. In view of this, this study took the Shengli Coal Field in Xilinhot City, the hinterland of Xilingol Grassland, as an example to research the spatial distribution and driving forces of SOM in the semi-arid grassland open-pit coal mining area. The results show that: (1) Areas with high SOM content were mainly distributed in the north of openpit germanium mine, west No. 2 open-pit mine, and No. 1 open-pit mine. Areas with low SOM content were mainly distributed on the east and southeast sides of the city. From the spatial distribution perspective, mining has a certain impact on SOM in the study area. (2) Natural factors have a higher impact on SOM changes than human factors. The order of influence degree of each factor on the spatial distribution of SOM is NDVI > Water > Agriculture > Mine > Town > Industry. The sources of influence on SOM in the research area are relatively complex. (3) The interaction between two factors presents two relationships: nonlinear enhancement and dual-factor enhancement. A single factor is lower than the interaction between various factors. In the interaction between factors, the explanation rate of interaction between Town, Agriculture, Mine, NDVI, Water, and all other factors is above 0.85. This study has important practical significance for soil management in mining areas, ecological restoration, and planning of national land space, etc. [ABSTRACT FROM AUTHOR]

Urban green space (UGS) is a crucial physical area that supports the functioning of urban ecosystems, and its changes affect urban ecological balance. In order to accurately analyze the dynamic processes and transfer targets of UGS during urbanization, this study proposes a new method of UGS assessment based on multi-temporal Landsat remote sensing data. This method is integrated with intensity analysis and landscape pattern indices so as to explore the spatio-temporal dynamics of the evolution process, landscape pattern, and driving forces of UGS from 2000 to 2022 in the resource-based city of Taiyuan in central China. The results of the case study show that rapid urbanization brought about a continuous reduction in UGS in the study area, but the trend of decreasing gradually slowed down; UGS patches have become more dispersed and isolated, bare land has been targeted for both gains and losses of UGS, and ecological restoration of bare land mitigated the rapid reduction of UGS. The results of this study not only confirm the applicability of this methodology for monitoring and assessing the evolution of UGS, but also reveal the identification of the targeting or avoidance of other categories during the conversion of UGS. Thus, the potential factors influencing changes in UGS can be analyzed to guide and safeguard sustainable development. [ABSTRACT FROM AUTHOR]

In the northwest desert region, soil moisture plays a significant role in the water cycle of the soil vegetation atmosphere system by acting as a carrier of heat and moisture between the land and the atmosphere. The local industrial structure and underlying surface types have been significantly impacted by the Kekeya Greening Project's execution. The study of the Kekeya project's effects on soil moisture can offer pertinent theoretical underpinnings for the project's implementation in the areas of mountains, rivers, forests, farmlands, lakes, grasslands, and deserts. Using the soil moisture data from 2000 to 2020 as the data source, the author studied the temporal change and mutation year of soil moisture in Kekeya using trend analysis and mutation analysis first. The geographical dependency of soil moisture was then studied using the Moran index. Ultimately, factor detection, interaction detection, and risk area detection of geographical detectors provided information about the factors influencing soil moisture in Kekeya. According to the findings, there was a clear rising trend in the soil moisture in Kekeya, along with a seasonal variation in the usual distribution. When moderate rain occurs, the soil moisture reacts more strongly than when light rain occurs. The pace of urbanization has the least effect on soil moisture, while GDP (gross domestic product) has the largest. When two factors combine, the impact on soil moisture is greater than when one item acts alone. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has underscored the imperative for housing to adapt to various factors, such as spatial, functional, climatic, and aesthetic considerations. The pandemic has transformed the dynamics between occupants and their living environments, influencing how different age groups interact with dwelling spaces, as well as impacting work, learning, healthcare access, and services. Urban life dynamics, especially in metropolitan areas, have undergone significant shifts due to social, cultural, technological, environmental, and economic factors. These changes, propelled by urbanization, demographic changes, evolving social structures, challenges in housing accessibility, and the rapid advancement of communication technologies, demand innovative approaches to housing design and programming. This study aims to identify future housing design trends and tendencies within this evolving context. Using a systematic literature review methodology, the study analyzed 87 articles published between 2010 and 2023, selected from digital databases according to predetermined criteria. Through meta-synthesis, STEEP analysis, and Shannon entropy assessments, the study identified 21 megatrends and 72 driving forces across the social, technological, environmental, economic, and political dimensions of housing design. The findings lay the groundwork for future housing innovations and sustainability, contributing to a wider discourse and enabling the generation of foresight model scenarios for housing design. Identifying these trends is crucial for creating functional, flexible, and sustainable living spaces that are suitable for the changing lifestyles and needs of individuals. [ABSTRACT FROM AUTHOR]

Groundwater quality and availability in coastal aquifers have become a serious concern in recent times due to increased abstraction for domestic, agricultural and industrial purposes. (1) Background: Zhuhai city is selected as a representative coastal aquifer in Southern China to comprehensively evaluate the hydrochemical characteristics, spatial distribution and controlling mechanisms of groundwater. (2) Methods: A detailed study utilizing statistical analyses, a Piper diagram, Gibbs plots, and ion ratios was conducted on 114 surface water samples and 211 groundwater samples. (3) Results: The findings indicate that the pH of most groundwater is from 6.06 to 6.52, indicating a weakly acidic environment. The pH of surface water ranges from 5.35 to 9.86, with most values being weakly alkaline. The acidity in the groundwater may be related to the acidic atmospheric precipitation, an acidic unsaturated zone, oxidation of sulphide minerals and tidal action. The groundwater chemical types are predominantly mixed, followed by Ca-Mg-HCO3 type. Surface water samples are predominantly Na-Cl-SO4 type. The NO3− concentration in groundwater is relatively high, with a mean value of 17.46 mg/L. The NO2− and NH4+ concentrations in groundwater are relatively low, with mean values of 0.46 mg/L and 7.58 mg/L. (4) Conclusions: The spatial distribution of the principal chemical constituents in the groundwater is related to the landform. The chemical characteristics of groundwater in the study area are mainly controlled by the weathering and dissolution of silicate and sulfate minerals, evaporation, seawater mixing and cation exchange. Nitrate in clastic fissure groundwater, granite fissure groundwater and unconfined pore groundwater primarily originates from atmospheric precipitation, agricultural activities of slope farmland and forest land. Nitrate in confined pore groundwater and karst groundwater primarily originates from domestic sewage and mariculture wastewater. Our findings elucidate the processes characterizing the hydrogeology and surface water interactions in Zhuhai City's coastal system, which are relevant to other catchments with similar geological characteristics. [ABSTRACT FROM AUTHOR]

Copyright of Arid Zone Research / Ganhanqu Yanjiu is the property of Arid Zone Research Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The fourth industrial revolution with its unique characteristics will transform the way we teach, learn, and think. Therefore, the aim of this research was to identify the driving forces influential on the primary school curriculum and analyze their implications. The study was conducted using a future studies approach and a combination of environmental scanning, qualitative content analysis, and Delphi method. Ultimately, six drivers were identified in this area: 1) the growth of digital and biological technologies, 2) childhood and learner-orienting discourse, 3) the structure of educational system, 4) changing the concept and approach to the knowledge, 5) globalization, and 6) the economic situation of the country. The results showed that most of the drivers are observing the global trends which are more inclusive. Analyzing them can play an important role in challenging the assumptions of planners and rethinking on the new models of education and learning in the future world. Finally, this research presents suggestions and strategies for institutionalizing the futures studies discourse in the educational system and taking advantage of the current research results. [ABSTRACT FROM AUTHOR]

[Objective] This research aims to explore the temporal and spatial status of the ecological quality evolution and the main driving factors of its evolution in the Loess Plateau, and to provide the theoretical basis for the improvement of regional ecological quality and sustainable development in the Yellow River Basin. Methods] Based on the 'Driving Force-Pressure-State-Impact-Response' (DPSIR) model, 14 indexes reflecting the social and economic situations, natural environment and ecological governance were selected to build the comprehensive evaluation index system. The weight of each index was defined by the analytical hierarchy process (AHP), and the comprehensive evaluation indexes were calculated by the weighted average. The Ridge Regression Model was used to quantitively assess the driving factors of the ecological quality evolution. [Results] The ecological quality of the Loess Plateau in the Yellow River Basin exhibited a distinctive spatiotemporal heterogeneity, with the high-quality areas concentrating in the midstream and low quality areas in the upstream, showing the unique spatial pattern of high-quality in the southeast and low- quality in the northwest'. The medium-quality areas and above increased from 40.56% in 2000 to 70.36% in 2020. The ecologically improved areas in the past 2 decades took up 94.44% of the total research area, and the ecologically improved counties accounted for 91.57% of the total counties. The Ridge Regression Model explained 99.4% of the ecological evolution, demonstrating the relatively higher contributions from the vegetative cover and the annual precipitation to such evolution. [Conclusion] The ecological quality in the Loess Plateau was improved, and the spatiotemporal evolution of ecological quality was mainly driven by vegetative cover and annual precipitation. It is recommended that the spatial pattern of ecosystems is further optimized on the basis of a scientific and rational increase in vegetation cover. [ABSTRACT FROM AUTHOR]

Food security is a major challenge for China at present and will be in the future. Revealing the spatiotemporal changes in cropland and identifying their driving forces would be helpful for decision-making to maintain grain supply and sustainable development. Hainan Island is endowed with rich agricultural resources due to its unique climatic conditions and is facing tremendous pressure in cropland protection due to the huge variation in natural conditions and human activities over the past few decades. The purpose of this study is to assess the spatiotemporal changes in and driving forces of cropland on Hainan Island in the past and predict future cropland changes under different scenarios. Key findings are as follows: (1) From 2000 to 2020, the cropland area on Hainan Island decreased by 956.22 km2, causing the center of cropland to shift southwestward by 8.20 km. This reduction mainly transformed into construction land and woodland, particularly evident in coastal areas. (2) Among anthropogenic factors, the increase in the human footprint is the primary reason for the decrease in cropland. Land use changes driven by population growth, especially in economically active and densely populated coastal areas, are key factors in this decrease. Natural factors such as topography and climate change also significantly impact cropland changes. (3) Future scenarios show significant differences in cropland area changes. In the natural development scenario, the cropland area is expected to continue decreasing to 597 km2, while in the ecological protection scenario, cropland conversion is restricted to 269.11 km2; however, in the cropland protection scenario, the trend of cropland reduction is reversed, increasing by 448.75 km2. Our findings provide a deep understanding of the driving forces behind cropland changes and, through future scenario analysis, demonstrate the potential changes in cropland area under different policy choices. These insights are crucial for formulating sound land management and agricultural policies to protect cropland resources, maintain food security, and promote ecological balance. [ABSTRACT FROM AUTHOR]

The current efficiency of residential land utilization is witnessing a decline, attributable to accelerated urbanization and inefficient resource allocation, thereby presenting unprecedented threats and challenges to the quality of urban living and the pursuit of high-quality urban development. To enhance residents' satisfaction and well-being, and to effectively activate existing land resources, it is imperative to accurately identify inefficient residential landuse and its driving factors. While the literature on identifying inefficient urban landuse is expanding, research specifically focusing on residential land, which is closely linked to residents' lives, remains limited. Furthermore, the factors contributing to inefficient land use are relatively inadequate. Therefore, this study employs a "two-step identification method" to comprehensively identify inefficient residential landuse and utilizes standard deviation ellipses and kernel density assessment methods to analyze the spatial distribution characteristics of such land. Subsequently, the study employs the Random Forest (RF) model to quantitatively analyze factors such as building quality, economic, social, and ecological factors, aiming to provide a scientific basis for subsequent redevelopment initiatives. The findings reveal that inefficient residential landuse is primarily concentrated in city centers, particularly in districts such as Nangang and Xiangfang. In relative inefficient residential areas, aside from Nangang District and Xiangfang District, Songbei District also holds a significant proportion. The intensity of these associations with inefficient residential landuse formation varies depending on urban development history and regional development intensity. In areas other than Songbei District, factors such as aging residential neighborhoods and inadequate green spaces are major contributors to inefficient land use efficiency, whereas in Songbei District, insufficient medical and educational facilities are the primary factors. The RF algorithm, distinguished by its flexibility and accuracy, offers novel perspectives and methods for analyzing issues related to inefficient residential landuse. Moreover, it effectively manages nonlinear relationships between the data, avoiding overfitting and generating precise regression and classification results. Thus, the RF algorithm demonstrates significant promise for widespread application in urban land studies. [ABSTRACT FROM AUTHOR]

The urban heat island (UHI) is an environmental problem of wide concern because it poses a threat to both the human living environment and the sustainable development of cities. Knowledge of the spatiotemporal characteristics and the driving factors of UHI is essential for mitigating their impact. However, current understanding of the UHI in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is inadequate. Combined with data (e.g., land surface temperature and land use.) acquired from the Google Earth Engine and other sources for the period 2001–2020, this study examined the diurnal and seasonal variabilities, spatial heterogeneities, temporal trends, and drivers of surface UHI intensity (SUHII) in the GBA. The SUHII was calculated based on the urban-rural dichotomy, which has been proven an effective method. The average SUHII was generally 0–2°C, and the SUHII in daytime was generally greater than that at night. The maximum (minimum) SUHII was found in summer (winter); similarly, the largest (smallest) diurnal difference in SUHII was during summer (winter). Generally, the Mann-Kendall trend test and the Sen's slope estimator revealed a statistically insignificant upward trend in SUHII on all time scales. The influence of driving factors on SUHII was examined using the Geo-Detector model. It was found that the number of continuous impervious pixels had the greatest impact, and that the urban-rural difference in the enhanced vegetation index had the smallest impact, suggesting that anthropogenic heat emissions and urban size are the main influencing factors. Thus, controlling urban expansion and reducing anthropogenic heat generation are effective approaches for alleviating surface UHI. [ABSTRACT FROM AUTHOR]

Imphal city and its surrounding regions have been experiencing a wide range of changes in land use and land cover (LULC) over recent years. However, there is a shortage of comprehensive studies that specifically examine the long-term dynamics in general and driving factors behind these changes in particular. This study provides a detailed analysis of LULC changes in Imphal city and its surrounding regions from 1991 to 2021, using advanced geospatial techniques to investigate underlying dynamics and driving factors behind urbanization. Multi-spectral datasets from Landsat 7 and 8 were utilized for four distinct time periods (1991, 2001, 2011, and 2021). The supervised classification method, employing the maximum likelihood classifier method, was used to generate the classified LULC maps in the ERDAS 2014. The classified images produced by this technique were evaluated for accuracy through matrix union using the statistical kappa coefficient and overall accuracy measures. Change detection for the periods 1991–2001, 2001–2011, and 2011–2021 was conducted using matrix union (intersection) to identify apparent changes in various LULC classes. The findings reveal a significant increase in built-up areas, expanding from 2440.35 hectares in 1991 to 8205.98 hectares in 2021, at an annual rate of 192.19 hectares/year. In contrast, agricultural lands decreased from 7484.13 hectares in 1991 to 3585.42 hectares in 2021, declining annually by 129.96 hectares/year. The study utilizes classified supervised images from various years, alongside seven independent spatial variables, to analyze urban growth and expansion using the method of random forests regression technique in Python, validated through receiver operating characteristic curve. Analysis of these seven variables, including distance to roads, proximity to the city centre, and infrastructure development, identifies them as primary drivers of urban expansion. The study concludes that rapid urbanization is significantly reshaping LULC dynamics, underscoring the urgent need for sustainable urban planning and management strategies. [ABSTRACT FROM AUTHOR]

Ancient trees play an important ecosystem service role in high-density cities, revealing the zonal distribution characteristics of vegetation under climate influence. The ancient trees in Guangzhou and Foshan in 2018 and 2023 were taken as study objects to explore the evolution of their spatio-temporal patterns and to analyze the spatial differentiation characteristics of their driving factors using the geographical weighted regression (GWR) model. The results showed the following: (1) The ancient trees in Guangzhou and Foshan were composed of typical subtropical evergreen broad-leaved forest communities, mainly represented by broad-leaved species of evergreen dicotyledonous plants. The dominant species mainly included Litchi chinensis, Ficus microcarpa, Canarium pimela, Ficus virens, and Dimocarpus longan. However, there was a significant difference between Guangzhou and Foshan. (2) The number of ancient trees in Guangzhou showed negative growth, while Foshan saw a significant increase. However, species diversity in both areas increased, with the highest diversity in the northeast, higher diversity in the south-central part, and lower diversity in the western and northwestern parts. (3) The maximum kernel density of ancient trees in Guangzhou and Foshan differed 22-fold, indicating a spatial distribution pattern of multiple clusters. (4) The GWR model effectively explained the driving factors of the heterogeneity of the spatial distribution of ancient trees. The results showed that artificial disturbance was the most important factor affecting the spatial distribution of ancient trees in high-density urban agglomerations in the same vegetation zone. The study clarified the characteristics of the spatial distribution and species diversity of ancient trees in the region, revealed the driving factors for the evolution of the spatial pattern of ancient trees in highly urbanized areas, and provided guidelines for policies and measures for enhancing biodiversity and conserving germplasm resources in the region. [ABSTRACT FROM AUTHOR]

The rational allocation of land resources is crucial to ensuring human well-being, livelihood, and survival. The study of Production-Living-Ecological Space (PLES) provides new perspectives on land resource allocation. However, few studies have assessed the feasibility of PLES optimization in ecological transition zones. For this study, using the composite functional space classification method, a classification and functional utility scoring system were constructed. Various methods, including dynamic attitude, transfer matrix, and spatial autocorrelation, were employed to characterize the evolution of the quantity and quality of PLES in the Hexi Corridor. Moreover, the mechanisms driving these changes were explored using a geodetector. Our findings revealed that: (1) The distribution of Production-Ecological Space (PES) is higher in the west and south and lower in the east and north. Production-Living Space (PLS) is scattered. Ecological-Production Space (EPS) is mostly distributed in the south or west, whereas Ecological Space (ES) is mainly located in the north and west of the Hexi Corridor. (2) From 1980 to 2020, the area of PES and PLS increased by 2037.84 km2 and 673 km2, respectively; the area of EPS was relatively stable, and the area of ES decreased by 2523.06 km2. (3) The evolution of PLES quality indicated that the high functional utility area of PES and PLS was roughly the same as the expanded functional utility area, whereas the expanded functional utility area of EPS and ES is similar to the median functional utility area. (4) The spatiotemporal evolution of PLES is closely linked to natural, economic, and social factors. [ABSTRACT FROM AUTHOR]

Poverty threatens human development especially for developing countries, so ending poverty has become one of the most important United Nations Sustainable Development Goals (SDGs). This study aims to explore China's progress in poverty reduction from 2016 to 2019 through time-series multi-source geospatial data and a deep learning model. The poverty reduction efficiency (PRE) is measured by the difference in the out-of-poverty rates (which measures the probability of being not poor) of 2016 and 2019. The study shows that the probability of poverty in all regions of China has shown an overall decreasing trend (PRE = 0.264), which indicates that the progress in poverty reduction during this period is significant. The Hu Huanyong Line (Hu Line) shows an uneven geographical pattern of out-of-poverty rate between Southeast and Northwest China. From 2016 to 2019, the centroid of China's out-of-poverty rate moved 105.786 km to the northeast while the standard deviation ellipse of the out-of-poverty rate moved 3 degrees away from the Hu Line, indicating that the regions with high out-of-poverty rates are more concentrated on the east side of the Hu Line from 2016 to 2019. The results imply that the government's future poverty reduction policies should pay attention to the infrastructure construction in poor areas and appropriately increase the population density in poor areas. This study fills the gap in the research on poverty reduction under multiple scales and provides useful implications for the government's poverty reduction policy. [ABSTRACT FROM AUTHOR]

Microplastic pollution in surface waters is a global concern, but the driving factors remain unclear. The characteristics of microplastics were assessed at 2,430 sampling sites in Chinese aquatic environments using published data. Geographical detectors, correlation analysis, analysis of variance, correspondence analysis, and structural equation modeling were used to examine both the effects of human activities and natural processes on microplastic characteristics and the potential influence of experimental methods on these characteristics. The abundance of microplastics was found to be significantly influenced by both human activities and natural processes, including farmland areas, landscape connectivity and annual mean precipitation. Furthermore, significant variations in the characteristics of microplastics were observed between experimental treatments, with the sampling method identified as a key factor influencing their observed abundance. Microplastic abundance showed a significant negative correlation with sample size, but no clear linear relationship with sieve aperture. The influence of the experimental methods reduced the association between the characteristics of microplastics and both human activities and natural processes. In water, the experimental methods reduced the correlation between natural processes and microplastic characteristics by 70 %, and in sediment by 39.6 %. However, the impact of human activities proved to be more complex, with the correlation increasing in water but decreasing by 47.9% in sediment. These findings suggest that variations in experimental methods can distort the accurate depiction of microplastic characteristics, obscuring their true relationship with environmental factors. This experimental inconsistency creates confusion about how microplastics spread and behave in the environment. This highlights the necessity for the development of standardized experimental methods and protocols for microplastics.

Human interference and climate change have led to the loss of ecosystem services in typical ecologically fragile areas, thus triggering a variety of ecological risks (ER). Assessing ER can provide decision support to managers in addressing ecological problems. However, previous studies have focused more on the improvement and practical application of assessment methods, while the interactions between different ER and their driving mechanisms remain unclear. Thus, this paper examined the Sichuan-Yunnan ecological barrier area and constructed an ER characterization method based on the relative loss of ecosystem services, analyzed the dynamic changes of single risk (i.e., soil conservation risk (SC_R), grain production risk (GP_R), carbon storage risk (CS_R), water yield risk (WY_R), habitat quality risk (HQ_R)) and comprehensive risk from 2000 to 2020, and explored the interactions between ER and their driving factors by using correlation analysis, spatial autocorrelation, self-organization method and geo-detector. The results show that (1) the SC_R and WY_R increased significantly during 2000–2020, exacerbating regional ecological degradation, while the CS_R, GP_R and HQ_R showed a decreasing trend. (2) The comprehensive risk was rising, with significant increases in the middle-high rolling hills in the southwest and the Chengdu Plain in the east, which are mainly attributed to farmland reclamation and urban expansion. (3) Competition between different land uses exacerbated the trade-offs between GP_R and CS_R, SC_R, HQ_R, while showing spatial heterogeneity under the constraints of natural factors and topography. (4) A total of four ER clusters were identified, with the SC_R-GP_R-WY_R cluster dominating, and gradually transforming into the SC_R cluster as the landscape pattern changes. (5) Compared with socio-economic and natural factors, the proportion of forest land and cropland was the dominant factor influencing most ER changes. The results can provide scientific support for ecological conservation policy formulation and ER avoidance in the Sichuan-Yunnan ecological barrier area.

Over past 20 years analysis of driving forces of landscape change have become a popular research topic, but there is still insufficient amount of research on this issue in the countries of Eastern Europe. This is all the more important as a number of factors have accelerated changes in the landscape in the countries that have joined the European Union in recent years. The results of the research presented in the article concern the identification of those forces that significantly influenced the shape of changes from the perspective of residents living in different types of municipalities from the Lower Silesia region. The research approach used was to identify the landscape transformations that took place in the 3 time intervals 2005–2010, 2010–2015, 2015–2020, and then to present them in the form of a questionnaire to the inhabitants of the 6 municipalities that lived in the study area during the study period, asking them to identify which of the indicated phenomena or processes contributed to the indicated landscape transformations. The driving forces most often indicated by residents of the analyzed municipalities can be categorised as political forces (40.66 %), socio-economic forces (20.74 %) and cultural forces (16.61 %). However, the results showed differences in the reported drivers of landscape change depending on the type of landscape in which the changes occurred. In urban landscapes, the proportion of cultural drivers increased significantly, in contrast to agricultural or forest landscapes where natural and political forces were more important.

Studying land use and land cover (LULC) patterns, identifying driving forces, and simulating future scenarios are vital for grasping the complex connection between human actions and the environment. This helps in shaping sustainable land management strategies and preparing for the impacts of climate change. However, there is a necessity for a comprehensive system modeling framework that can accurately capture spatial and temporal changes in LULC, analyze driving mechanisms, and provide an integrated analysis of future simulations. In this paper, a comprehensive IM-RF-Markov-PLUS analysis framework is developed, focusing on the Fuxian Lake Basin (FLB) as a study area. The study aims to achieve accurate prediction of LULC by combining the microscopic LULC change trend and the contribution of macroscopic driving forces. The results show that: (1) The IM-RF-Markov-PLUS framework can explore the change patterns and driving mechanisms of LULC in the FLB, and accurately predict the LULC in the FLB. Compared with the PLUS model, its accuracy is improved by 2 %. (2) IM analysis reveals that LULC transformation in the FLB is both general and specific. Although the area of grassland, buildings, roads and structures converted to desert and bare land is minimal, it shows relatively tendentious and specific change characteristics. (3) Different land types are significantly affected by driving factors, with the expansion of LULCs is constrained by major factors. The distance to the lake has the most significant impact on the distribution of garden land, while the primary road has the greatest impact on the distribution of forestland. (4) Under different scenarios, the spatial heterogeneity of LULC patterns is obvious. In 2035, under baseline and economic development scenarios, cultivated land will decrease, while other LULC types will increase. Under the cultivated land protection scenario, cultivated land is protected, with an increase of 5.85 %. Under the ecological protection scenario, there is an increase in ecological land. The largest increase is in forestland, which increases by a total of 3.46 %. The ecological protection scenario presents a viable approach for ensuring the sustainable development of the FLB. The results of this paper may serve as a reliable foundation for implementing LULC strategies in the FLB and offer guidance for crafting sustainable development regulations at the regional level.

In order to study the historical change and development law of the river-lake-reservoir water system in Guangdong Province, the characteristics of spatiotemporal changes, landscape patterns, and land use transfer from 1980 to 2021 were analyzed, and the driving factors of the water change were analyzed by correlation coefficient. The results show that: ① From 1980 to 2021, the river area continued to shrink while the reservoir area continued to grow, and the trend of both changes changed abruptly in 1995. ② The water landscape pattern tended to be fragmented, complicated, and discrete. ③ In the past 40 years, the land use change of 50 m around the water area was mainly the transformation of rivers into reservoirs, and a large area of the river-lake-reservoir water system was transformed into urban and rural, industrial, mining, and residential lands from 1995 to 2021. ④ Annual mean temperature, permanent population density, and economic output values were the main driving factors of water area and landscape pattern change. The research results clearly define the change characteristics of the river-lake-reservoir water system in Guangdong Province and can provide a certain basis for the formulation of targeted water protection policies.

This study examined how socio-economic driving forces influence households' choice of water, ranging from a piped water supply provided by Veolia to untreated sources contaminated with high levels of arsenic and pathogens. Households fall into three cluster groups based on variations in socio-economic status and physical, infrastructure, and institutional elements. About 64% of the variations are related to differences in awareness, willingness, and ability to pay for safe water sources. Families with higher monthly income showed interest in accepting Veolia's house connection options, resulting in the shutdown of six community tap points and ultimately affecting the low-income households' accessibility to Veolia water. A causal loop diagram showed five feedback loops influencing the choice of drinking contaminated water. Bayesian Network models were sensitive to the ability, accessibility, and willingness to pay for safe water, deep tube well distributions, installation and maintenance costs, ownership of tube wells, household income level, and the level of awareness. Results suggest that the risks of drinking contaminated water can be minimized by raising awareness; accepting arsenic removal techniques; sharing expenses; training for deep tube well installations and maintenance; increasing Veolia pipeline coverage; and redesigning the tap point distributions. These results help identify the relative importance of such interventions to improve water security in safe water-poor areas. [ABSTRACT FROM AUTHOR]

The confrontation and enmity of America against the Islamic Republic of Iran is one of the vital and important issues in international relations. This confrontation has wide-ranging effects on the West Asian region and even the international arena. After the Islamic Revolution of Iran in 1979, this confrontation appeared more widely. It has led to tensions, sanctions and even military conflicts. In this research, the researchers have investigated the drivers of the possible military confrontation of the United States against the Islamic Republic of Iran in the West Asian region in the next 10 years. The research method is based on the Qualitative-Quantitative approach and using the analysis of existing documents and Cross-impact analysis (CIA) with the help of MicMac software. In the first part of the research, with the help of library studies and using the opinions of experts, 12 main drivers affecting the possible military confrontation of the United States against the Islamic Republic of Iran were identified. In the second part, with the help of Cross-Impact Analysis Method (CIAM), the main key drivers were identified. The results show that based on the analysis of the results of the Matrix of Direct Influence (MDI) and the analysis of the Matrix of Indirect Influence (MII) of these drivers are: "Military Alliance", "Naval Conflict", "War Scope", "Military Power" and "Air Attacks". They have the greatest impact on the possible military confrontation of America against the Islamic Republic of Iran. They are among the most important drivers. [ABSTRACT FROM AUTHOR]

Rice is a globally important food crop, and it is crucial to accurately and conveniently obtain information on rice fields, understand their spatial patterns, and grasp their dynamic changes to address food security challenges. In this study, Chongqing's Yongchuan District was selected as the research area. By utilizing UAVs (Unmanned Aerial Vehicles) to collect multi-spectral remote sensing data during three seasons, the phenological characteristics of rice fields were analyzed using the NDVI (Normalized Difference Vegetation Index). Based on Sentinel data with a resolution of 10 m, the NDVI difference method was used to extract rice fields between 2019 and 2023. Furthermore, the reasons for changes in rice fields over the five years were also analyzed. First, a simulation model of the rice harvesting period was constructed using data from 32 sampling points through multiple regression analysis. Based on the model, the study area was classified into six categories, and the necessary data for each region were identified. Next, the NDVI values for the pre-harvest and post-harvest periods of rice fields, as well as the differences between them, were calculated for various regions. Additionally, every year, 35 samples of rice fields were chosen from high-resolution images provided by Google. The thresholds for extracting rice fields were determined by statistically analyzing the difference in NDVI values within the sample area. By utilizing these thresholds, rice fields corresponding to six harvesting regions were extracted separately. The rice fields extracted from different regions were merged to obtain the rice fields for the study area from 2019 to 2023, and the accuracy of the extraction results was verified. Then, based on five years of rice fields in the study area, we analyzed them from both temporal and spatial perspectives. In the temporal analysis, a transition matrix of rice field changes and the calculation of the rice fields' dynamic degree were utilized to examine the temporal changes. The spatial changes were analyzed by incorporating DEM (Digital Elevation Model) data. Finally, a logistic regression model was employed to investigate the causes of both temporal and spatial changes in the rice fields. The study results indicated the following: (1) The simulation model of the rice harvesting period can quickly and accurately determine the best period of remote sensing images needed to extract rice fields. (2) The confusion matrix shows the effectiveness of the NDVI difference method in extracting rice fields. (3) The total area of rice fields in the study area did not change much each year, but there were still significant spatial adjustments. Over the five years, the spatial distribution of gained rice fields was relatively uniform, while the lost rice fields showed obvious regional differences. In combination with the analysis of altitude, it tended to grow in lower areas. (4) The logistic regression analysis revealed that gained rice fields tended to be found in regions with convenient irrigation, flat terrain, lower altitude, and proximity to residential areas. Conversely, lost rice fields were typically located in areas with inconvenient irrigation, long distance from residential areas, low population, and negative topography. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Guangxi Normal University - Natural Science Edition is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Context and Purpose: The globalization of health care has led to the emergence of a new type of tourism in cities, commonly known as medical and health tourism. In Iran, the metropolises of Tabriz and Ardabil are known as two of the most important destinations for domestic and foreign tourists due to their superior geographical location and suitable medical facilities and equipment. Therefore, this research investigates and identifies the most important driving forces affecting the development of medical tourism in the cities of Tabriz and Ardabil from 2022 (AH 2023) to 2032 (AH 2033). Design/methodology/approach: This research is applied in terms of purpose, follows the normativeanalytical futures study science, and was carried out with a combination of documentary and survey methods. In order to collect the required data, a questionnaire was used; it included 65 factors that were identified and extracted using the opinion of experts and completed using the opinion of 25 experts using the Delphi method. In the next step, the strategic variables in the development of medical tourism in Ardabil and Tabriz cities were used to analyze the cross-effects of factors in Mic-Mac software. Findings: The findings show that 13 out of the 65 factors investigated are the key effective factors in the development of medical tourism in the cities of Tabriz and Ardabil. Conclusion: The results of the research show that these 13 factors for the city of Tabriz are expertisebased management, favorable judgment, international cooperation, macro and integrated management, use of experiences from other countries, public security, the existence of a supervisory body, solving legal problems, issuing visas, medical tourism, support of the private sector, economic system, health town, and creation of facilities; these factors will have the greatest impact among the factors affecting the future development pattern of medical tourism in Tabriz city. Examining the displacement of factors in the two groups of direct and indirect effects analysis for Ardabil city also shows that expertise-based management, favorable narrative judgment, international cooperation, health town, support of the private sector, transparent pricing, all-round support, macro and integrated management, use of the experiences of other countries, access to medical centers' information, the existence of a supervisory body, a fixed exchange rate, and marketing strategy are proposed as key factors and will have the greatest impact on the future development of medical tourism in Ardabil. Originality/value: Compared to past studies in the field of medical tourism, adopting a futures study approach while forming new ideas for optimal planning in the direction of developing medical tourism with a macro view and considering multiple indicators and their analysis can be a guide for managers, and those involved in the development of medical tourism should be located in the right country and have a good vision for the market of the medical tourism industry. [ABSTRACT FROM AUTHOR]