Areas Of High Value Research Articles

Analysis of Factors Influencing Tight Sandstone Gas Production and Identification of Favorable Gas Layers in the Shan 23 Sub-Member of the Daning-Jixian Block, Eastern Ordos Basin

The Daning-Jixian Block harbors abundant tight sandstone gas resources. However, significant variations in gas production exist among the different wells within the block. A comprehensive study was conducted on key factors such as sedimentary strata and petrophysical characteristics to elucidate their impact on gas reservoir productivity. Linear regression equations were employed to classify the favorable reservoirs within the study area. The analysis revealed that within the first 6 months of production from the Shan 23 gas layer, daily gas production ranged from 2576.19 to 156,078.17 m3/d, averaging 24,037.9 m3/d. Over the first year, average daily production varied from 2185.05 to 136,806.99 m3/d, averaging 23,469.23 m3/d, indicating relatively stable production from the Shan 23 layer alone. In the dominant central area of the underwater distributary channel delta front in Shan23, the sand body exhibits a superimposed cutting type, resulting in high production rates. Conversely, the sand bodies on the periphery gradually transition to superimposed and isolated types, leading to decreased production. Through a correlation analysis of gas layer thickness, porosity, permeability, and initial gas well production, it was determined that gas production from the wells within the same layer is primarily influenced by gas layer thickness, porosity, and permeability. Gas saturation demonstrates a minimal impact on production according to single-factor analysis. The evaluated factors such as the gas productivity coefficient, energy storage coefficient, and enrichment coefficient exhibited similar distribution patterns across the study area. The high-value areas for the gas productivity coefficient, energy storage coefficient, and enrichment coefficient are concentrated in distributary channel zones and delta lobes. In contrast, regions with underdeveloped skeletal sand bodies generally display lower values for these parameters. The linear relationships between these parameters and the average gas production were calculated to further classify the favorable reservoirs in the study area. This study aimed to establish a scientific basis for the efficient development of the tight sandstone gas reservoirs within the Daning-Jixian Block.

Spatial and temporal changes of ecosystem service value and its influencing mechanism in the Yangtze River Delta urban agglomeration

As the mainstream and trend of urban development in China, deeply exploring the spatiotemporal patterns and influencing mechanisms of ecosystem service value in the Yangtze River Delta urban agglomeration is of great significance for achieving sustainable development goals in urban agglomerations. This paper uses the normalized difference vegetation index and net primary productivity as dynamic adjustment factors to measure the ecosystem service value of the Yangtze River Delta urban agglomeration and analyze its spatiotemporal evolution characteristics. Furthermore, a panel quantile regression model is constructed to explore the response differences of ecosystem service value at different levels to various influencing factors. The results show that: (1) From 2006 to 2020, the ecosystem service value of the Yangtze River Delta urban agglomeration decreased by 37.086 billion yuan, with high-value areas mainly concentrated in the southern part of the urban agglomeration. (2) The value structure of various land type ecosystems and primary ecosystem sub-services in the Yangtze River Delta urban agglomeration is stable. (3) The number of grid units with reduced ecosystem service value is continuously increasing, mainly distributed in the eastern coastal areas. (4) The degree of interference of various types of land on ecosystem service value varies, and the response of ecosystem service value at different levels to the same influencing factor also shows heterogeneity. In summary, exploring the spatiotemporal patterns of ecosystem service value in the Yangtze River Delta urban agglomeration and analyzing its influencing mechanisms is conducive to adjusting the intensity of human utilization and protection methods of ecosystems, which is of great significance for enhancing the value of ecosystem products in urban agglomerations.

Identification of priority conservation areas in Beijing-Tianjin-Hebei using multi-scenario trade-offs based on different spatial scales and their drivers

Identifying priority conservation areas (PCAs) on the basis of ecosystem services (ESs) trade-offs is a crucial step in achieving optimal ecosystem management in the BTH, noting the complex spatio-temporal and scaling effects inherent in these ecosystems. A cross-scale quantification of spatial and temporal changes in ecosystem services and their correlations was conducted in the BTH. Furthermore, the Ordered Weighting Algorithm (OWA) was introduced to simulate a variety of scenarios in order to determine the PCAs and to explore the drivers of ES in the PCAs. The results indicated that Food supply (FS), Water yield (WY), and Soil retention (SR) in the BTH increased by 16.4 × 104t/hm2, 81.81 mm, and 37081.29 t/hm2, respectively. In contrast, Carbon storage (CS) and Habit quality (HQ) exhibited a decrease of 0.01 t/hm2 and 0.03. Spatially, the high-value areas of ESs at the county scale exhibited greater clustering. At the county scale, there were two trade-offs and eight synergies in 2000, and four trade-offs and six synergies by 2020. In contrast, at the raster scale, there were two trade-offs and eight synergies from 2000 to 2020. The mechanisms of change were similar and exhibited spatial heterogeneity. Scenario 6 represents the optimal priority conservation area, exhibiting the most balanced conservation efficiency of each ecosystem service. This scenario enables the simultaneous conservation of multiple ESs. The natural factors within the PCAs exert a stronger influence on ESs than socio-economic factors. The inclusion of interacting factor effects will enhance the explanatory power of the drivers. The findings of this study can serve as a theoretical foundation for cross-scale ecosystem service management decisions.

Combining ecosystem service value and landscape ecological risk to subdivide the riparian buffer zone of the Weihe River in Shaanxi

Riparian buffer zones are transitional and ecological intersections between land and water. These areas play a major role in advancing ecosystem protection and restoration. Here, we investigated the spatial and temporal evolution of land use, ecosystem service value (ESV), and landscape ecological risk (LER) in riparian buffer zones between 1990 and 2020 using the Shaanxi section of the Weihe River as a case study using a GIS grid, the unit-area-equivalent-factor method, the landscape ecological risk index, and the natural breakpoint method. We also established four ecological zones using standardized Z-scores. The predominant land categories in the study area comprised cultivated, construction, and grassland, which together accounted for more than 80 % of the total area. The construction land demonstrated the most significant increase of 4,062.33 hm2 area, and cultivated land showed most significant decrease of 13,048.56 hm2 area, between 1990 and 2020. Next, study-site ESV increased from 28.30 × 108 CNY in 1990 to 28.94 × 108 CNY in 2020, reflecting 2.26 % growth. High-value areas were principally bodies of water and primarily situated along the Weihe River, extending from west to east along the main channel. The LER of the study area was dominated by the lowest ecological risk, low ecological risk, and medium ecological risk classes, which collectively accounted for more than 70 % of the total area. The main land types in the areas with high ecological risk were forest land and cultivated land. The study area’s LER demonstrated a decreasing trend from 1990 to 2020, and the area with the lowest ecological risk and low ecological risk zones increased by 9.97 %. The riparian buffer zone was divided into ecological restoration, reconstruction, conservation, and protection zones based on two factors, namely, ESV and LER. The most dominant of these was the ecological reconstruction zone, which showed a 13.75 % decrease in area share between 1990 and 2020. The ecological restoration zone occupied the smallest area with the largest increase of 56.63 % during this period. In conclusion, this study contributes to ecological zoning and management of the riparian buffer zone along the main channel of the Weihe River. Appropriate zoning is integral to protecting this valuable ecosystem and achieving sustainable development of surrounding human communities.

A Data Analytics and Machine Learning Approach to Develop a Technology Roadmap for Next-Generation Logistics Utilizing Underground Systems

The increasing density of urban populations has spurred interest in utilizing underground space. Underground logistics systems (ULS) are gaining traction due to their effective utilization of this space to enhance urban spatial efficiency. However, research on technological advancements in related fields remains limited. To address this gap, we applied a data-driven approach using patent data related to the ULS to develop a technology roadmap for the field. We employed Latent Dirichlet Allocation (LDA), a machine learning-based topic modeling technique, to categorize and identify six specific technology areas within the ULS domain. Subsequently, we conducted portfolio analytics to pinpoint technology areas with high technological value and to identify the major patent applicants in these areas. Finally, we assessed the technology market potential by mapping the technology life cycle for the identified high-value areas. Among the six technology areas identified, Topic 1 (Underground Material Handling System) and Topic 4 (Underground Transportation System) showed significant patent activity from companies and research institutions in China, the United States, South Korea, and Germany compared to other countries. These areas have the top 10 patent applicants, accounting for 20.8% and 13.6% of all patent applications, respectively. Additionally, technology life cycle analytics revealed a growth trajectory for these identified areas, indicating their rapid expansion and high innovation potential. This study provides a data-driven methodology to develop a technology roadmap that offers valuable insights for researchers, engineers, and policymakers in the ULS industry and supports informed decision-making regarding the field’s future direction.

Spatial and temporal characteristic of PM2.5 and influence factors in the Yellow River Basin.

The Yellow River Basin has been instrumental in advancing ecological preservation and fostering national high-quality development. However, since the advent of China's reform and opening-up policies, the basin has faced severe environmental pollution issues. This study leverages remote sensing data from 1998 to 2019. As per the "Basin Scope and Its Historical Changes" published by the Yellow River Conservancy Commission of the Ministry of Water Resources, the Yellow River Basin is categorized into upstream, midstream, and downstream regions for analysis of their spatial and temporal distribution traits using spatial autocorrelation methods. Additionally, we employed probes to study the effects of 10 factors, including mean surface temperature and air pressure, on PM2.5. The study findings reveal that (1) the annual average concentration of PM2.5 in the Yellow River Basin exhibited a fluctuating trend from 1998 to 2019, initially increasing, then decreasing, followed by another increase before ultimately declining. (2) The air quality in the Yellow River Basin is relatively poor, making it challenging for large-scale areas with low PM2.5 levels to occur. (3) The PM2.5 concentration in the Yellow River Basin exhibits distinct high and low-value concentration areas indicative of air pollution. Low-value areas are predominantly found in the sparsely populated central and southwestern plateau regions of Inner Mongolia, characterized by a better ecological environment. In contrast, high-value areas are prevalent in the inland areas of Northwest China, with poorer natural conditions, as well as densely populated zones with high energy demand and a relatively developed economy. (4) The overall population density in the Yellow River Basin, as well as in the upstream, midstream, and downstream regions, serves as a primary driving factor. (5) The primary drivers in the middle reaches and the entire Yellow River Basin remain consistent, whereas those in the upper and lower reaches have shifted. In the upstream, air pressure emerges as a primary driver of PM2.5, while in the downstream, NDVI and precipitation become the main influencing factors.

Spatial Heterogeneity Analysis and Risk Assessment of Potentially Toxic Elements in Soils of Typical Green Tea Plantations

The spatial heterogeneity of potentially toxic elements (PTEs) in a typical green tea-producing area in Zhejiang was investigated with application of geostatistics. The positive matrix factorization (PMF) was conducted for analysis of pollution sources and risk assessment of the soil of the tea garden. The results revealed that 93.52% of the study area did not exceed the PTEs risk screening value in the soil pollution risk control standard of agricultural land. The results of the spatial heterogeneity analysis showed that Cd and Pb had moderate spatial auto-correlation, exhibiting similar spatial distribution patterns. The high-value locations were distributed in the southeast of the study area, while low-value locations were distributed in the southwest of the study area. The Cr, As, and Hg had strong spatial auto-correlation, while Cr and As had similar spatial distribution patterns whose high-value areas and low-value areas were concentrated in the west and center of the study area, respectively. The Cd, Pb, and As originated from the agricultural source, transportation source, and industrial source, respectively, while Cr and Hg were from the natural source on the basis of the results of the PMF model. The results of a potential ecological risk assessment revealed that five PTEs in the study area were of low potential risk. The single-factor ecological risk ranking was Cd > As > Hg > Cr > Pb. The overall ecological risk in the study area was slight. The human health risk model indicates that there was a non-carcinogenic risk for children in the study area, and the high-value area was concentrated in the northwest of the study area. It is concluded that emphasis shall be given to excessive Cd caused by agricultural sources in the southeast of the study area, and control and monitoring will be strengthened in the northwestern part of the study area. The relevant measures for prevention of soil pollution must be conducted.

Innovative graph neural network approach for predicting soil heavy metal pollution in the Pearl River Basin, China

Predicting soil heavy metal (HM) content is crucial for monitoring soil quality and ensuring ecological health. However, existing methods often neglect the spatial dependency of data. To address this gap, our study introduces a novel graph neural network (GNN) model, Multi-Scale Attention-based Graph Neural Network for Heavy Metal Prediction (MSA-GNN-HMP). The model integrates multi-scale graph convolutional network (MS-GCN) and attention-based GNN (AGNN) to capture spatial relationships. Using surface soil samples from the Pearl River Basin, we evaluate the MSA-GNN-HMP model against four other models. The experimental results show that the MSA-GNN-HMP model has the best predictive performance for Cd and Pb, with a coefficient of determination (R2) of 0.841 for Cd and 0.886 for Pb, and the lowest mean absolute error (MAE) of 0.403 mg kg−1 for Cd and 0.670 mg kg−1 for Pb, as well as the lowest root mean square error (RMSE) of 0.563 mg kg−1for Cd and 0.898 mg kg−1 for Pb. In feature importance analysis, latitude and longitude emerged as key factors influencing the heavy metal content. The spatial distribution prediction trend of heavy metal elements by different prediction methods is basically consistent, with the high-value areas of Cd and Pb respectively distributed in the northwest and northeast of the basin center. However, the MSA-GNN-HMP model demonstrates superior detail representation in spatial prediction. MSA-GNN-HMP model has excellent spatial information representation capabilities and can more accurately predict heavy metal content and spatial distribution, providing a new theoretical basis for monitoring, assessing, and managing soil pollution.

Contamination Characteristics and Source Apportionment of Heavy Metal in the Topsoil of a Small Watershed in South Taihang

With the rapid development of industrialization and urbanization, the issue of soil environmental pollution is becoming more and more prominent, especially concerning heavy metal contamination, which has garnered significant scholarly attention. The surface watershed formed by waterline is influenced by various factors such as topography, industrial emissions, and agricultural runoff, resulting in a complex process of migration and accumulation of heavy metal elements from multiple sources. In this study, the pollution characteristics and sources of heavy metal elements Hg, As, Pb, Ni, Cd, Cr, Cu and Zn in 165 surface soil samples from the Manghe River watershed in Jiyuan City were comprehensively analyzed using a variety of methods, including statistics, geostatistics, enriched factor analysis and the Positive Matrix Factorization Model (PMF). The results showed that the concentrations of Hg, Cd, As, Cu, Pb and Zn exceeded their corresponding background values with varying degrees of enrichment. Notably, the average contents of Cd, Hg and Pb were 26.70 times, 3.69 times and 4.49 times higher than those in Chinese soils on average, respectively, showing obvious enrichment characteristics. Moreover, there were distinct spatial distribution patterns for each heavy metal element; Ni and Cr exhibited similar trends mainly controlled by the parent material, while human activities significantly affect the other six elements forming high-value areas around mining and related industries. It is noteworthy that Cu, Hg and Zn were influenced by dominant wind direction in autumn and winter, forming sub-high-value zones in southern forested areas; meanwhile, Cu and Zn were also influenced by agricultural fertilizer application as well as surface runoff, leading to secondary high-value areas in the dryland areas. Further analysis revealed a significant positive correlation among these heavy metal elements, suggesting that they may share common sources. Through the PMF Model, four main factors were identified, with factor 2 (36.25%), factor 1 (23.00%), factor 3 (21.20%) and factor 4 (19.55%) ranked in descending order of contribution rate. The heavy metal pollution in the study area was attributed to anthropogenic activities and natural factors, accounting for 63.75% and 36.25%, respectively. Coal mining, chemical industry smelting, vehicle emissions and excessive use of agrochemicals were identified as the main sources of heavy metal pollution. These pollutants entered the soil through direct emissions, atmospheric deposition, transportation and agricultural activities, exerting a significant impact on the soil environment. Therefore, delving into the spatial distribution pattern of soil heavy metal pollution and precise analysis of its sources are of great importance for effective treatment and remediation of soil heavy metal pollution in small watersheds, maintaining healthy soil ecology and safeguarding human health.

Seasonal-Spatial Distribution Variations and Predictions of Loliolus beka and Loliolus uyii in the East China Sea Region: Implications from Climate Change Scenarios.

Global climate change profoundly impacts the East China Sea ecosystem and poses a major challenge to fishery management in this region. In addition, closely related species with low catches are often not distinguished in fishery production and relevant data are commonly merged in statistics and fishing logbooks, making it challenging to accurately predict their habitat distribution range. Here, merged fisheries-independent data of the closely related squid Loliolus beka (Sasaki, 1929) and Loliolus uyii (Wakiya and Ishikawa, 1921) were used to explore the construction and prediction performance of species distribution models. Data in 2018 to 2019 from the southern Yellow and East China Seas were used to identify the seasonal-spatial distribution characteristics of both species, revealing a boundary line at 29.00° N for L. uyii during the autumn, with the highest average individual weight occurring during the summer, with both larvae and juveniles occurring during the autumn. Thus, the life history of L. uyii can be divided into winter-spring nursery and summer-autumn spawning periods. L. beka showed a preference for inshore areas (15-60 m) during the summer and offshore areas (32.00-78.00 m) during the winter. High-value areas of both species included inshore areas of the southern Yellow and mid-East China Seas during the autumn, enlarging during the spring to include central areas of the survey region, before significantly decreasing during the summer. Therefore, this study provides both a novel perspective for modeling biological habitat distribution with limited data and a scientific basis for the adjustment of fishery resource management and conservation measures in the context of climate change.

The promotion of sustainable land use planning for the enhancement of ecosystem service capacity: Based on the FLUS-INVEST-RUSLE-CASA model.

Land Use/Land Cover (LULC) is one of the most significant human variables influencing the efficiency of Ecosystem Services (ESs) in terrestrial ecosystems. Theoretical and technical assistance for regional sustainable land use planning and management, as well as ecosystem conservation and restoration, is provided by investigating the influence of changes in the LULC pattern on the efficiency of ESs. This research focuses on the interactions between socioeconomic activities and natural ecological processes in the Three Gorges Reservoir Area (TGRA). We use LULC data from the TGRA for the years 1990, 2000, 2010, and 2020. The study includes the analysis and calculation of the spatiotemporal evolution features of the current LULC pattern and the efficiency of ESs, including their spatiotemporal distribution. Considering the TGRA's national development orientation and guidance, three potential LULC patterns are constructed under various develop-ment scenarios. To calculate the efficiency of ESs, the GeoSOS-FLUS future LULC simulation model is linked, and several methodologies such as INVEST, RUSLE, and CASA are used. The goal is to investigate the influence of future changes in LULC patterns on ESs efficiency. The findings show the following: (1) From 1990 to 2020, the values of water conservation services in the TGRA decreased and subsequently increased. High-value areas are primarily located in the reservoir's centre and eastern sections, whereas low-value areas are mostly found in the western section. Soil conservation service values initially declined and later climbed. The TGRA's carbon storage services have in-creased yearly, from 552.64 g/m2 in 2000 to 615.92 g/m2 in 2020. (2) In the ecological protection scenario, carbon storage and soil erosion increased compared to the ecosystem services in 2020. The ecological system service benefits are greater when compared to the natural development scenario. (3) The four ESs show positive spatial correlations across all three scenarios, and local spatial au-tocorrelation analysis findings demonstrate that carbon storage, water yield, and habitat quality have comparable spatial distributions across all three scenarios. To some extent, high-value areas for water conservation, soil retention, carbon storage, and habitat quality overlap.

Estimation of Near-surface Ozone Concentration in the Beijing-Tianjin-Hebei Region Based on XGBoost-LME Model

High spatiotemporal resolution data on near-surface ozone concentration distribution is of great significance for monitoring and controlling atmospheric ozone pollution and improving the living environment. Using TROPOMI-L3 NO2, HCHO products, and ERA5-land high-resolution data as estimation variables, an XGBoost-LME model was constructed to estimate the near-surface ozone concentration in the Beijing-Tianjin-Hebei Region. The results showed that： ① Through correlation analysis, surface 2 m temperature （T2M）, 2 m dewpoint temperature （D2M）, surface solar radiation downwards （SSRD）, tropospheric formaldehyde （HCHO）, and tropospheric nitrogen dioxide （NO2） were important factors affecting the near-surface ozone concentration in the Beijing-Tianjin-Hebei Region. Among them, T2M, SSRD, and D2M had strong correlations, with correlation coefficients of 0.82, 0.75, and 0.71, respectively. ② Compared with that of other models, the XGBoost-LME model had the best performance in terms of various indicators. The ten-fold cross-validation evaluation indicators R2, MAE, and RMSE were 0.951, 9.27 μg·m-3, and 13.49 μg·m-3, respectively. At the same time, the model performed well at different time scales. ③ In terms of time, there was a significant seasonal difference in near-surface ozone concentration in the Beijing-Tianjin-Hebei Region in 2019, with the concentration changing in the order of summer > spring > autumn > winter. The monthly average ozone concentration in the region showed an inverted "V" trend, with a slight increase in September. The highest value occurred in July, whereas the lowest value occurred in December. In terms of spatial distribution, the near-surface ozone concentrations in the Beijing-Tianjin-Hebei Region during the months of February and March were generally at the same levels. In January, November, and December, there was a relatively insignificant trend of higher concentrations in the north and lower concentrations in the south. For the remaining months, the spatial distribution of near-surface ozone concentrations in this area predominantly exhibited a pattern of higher concentrations in the south and lower concentrations in the north. High-value areas were predominantly found in the plain regions of the southern part with lower altitudes, dense population, and higher industrial emissions； low-value areas, on the other hand, were primarily located in mountainous areas of the northern part with higher altitudes, sparse population, higher vegetation coverage, and lower industrial emissions.

Construction of ecological network and its temporal and spatial evolution characteristics: A case study of Ulanqab

Constructing an ecological network is crucial to maintaining ecosystem stability, optimizing ecological space, and ensuring regional ecological security. Using circuit theory, the study integrated habitat condition, ecosystem function, and landscape structure to construct ecological network in Ulanqab. The ecological networks consist of four basic components, including sources, resistance surfaces, corridors and nodes. Ecological sources refer to patches with high habitat quality, while the resistance to species movement between patches is named as ecological resistance surfaces, the important corridors for movement between patches are referred to as ecological corridors, and the vital spots in ecological network are referred to ecological nodes. In addition, the drivers affecting these changes were also analyzed using geographical detector. The results indicated that the area of ecological sources dropped by 19.1 % and the area of high value of ecological resistance surfaces increased from 2000 and 2020. The length of ecological corridors increased by 793.80 km. Mean annual precipitation is the main natural factor influencing the ecological network, and land use intensity and human footprint are the main anthropogenic factors. Our findings suggest that in this region, urban developing should minimize the encroachment on grasslands, forests and waters. Additionally, the availability of water resources should be considered in the planning and implementation of ecological restoration and protection. The findings of the study offer reasonable suggestions for the protection and restoration of priority regions, as well as a scientific foundation optimization and sustainable development of the regional ecological network.

Hydrochemical characterization and health risk assessment of different types of water bodies in Fenghuang Mountain Area, Northeast China.

Groundwater, as an essential resource, holds significant importance for human production and livelihoods. With the deterioration of the water environment, the issue of groundwater quality has become an urgent international concern. This study focused on the Fenghuang Mountain Area (FMA) and collected a total of 41 sets of samples including pore groundwater (PGW), fissure groundwater (FGW), karst groundwater(KGW), and river water(RW). Hydrochemical analysis methods were employed to identify the hydrochemical characteristics and controlling factors. The entropy-weighted water quality index (EWQI) and health risk assessment model were utilized to assess the groundwater quality and nitrate health risk, respectively. The results indicated that the dominant anion and cation in both groundwater and surface water in the FMA were HCO3- and Ca2+, respectively, with the main hydrochemical type being HCO3-Ca. Groundwater and surface water in the FMA were primarily controlled by rock weathering process, with ion concentrations influenced mainly by the dissolution of halite, sylvite, carbonates (calcite and dolomite), silicates, and gypsum, as well as by reverse anion exchange process. PGW was significantly affected by agricultural activities, with NO3- concentration closely related to human activities. The water quality of FGW was relatively good, with Class I and Class II water accounting for the highest proportion, reaching 84.62%. The high-value area of EWQI in PGW was influenced by human activities. The impact of nitrate health risk on children was significantly greater than on adults, with FGW having the lowest health risk and PGW having the highest health risk. The research results can provide important guarantees for the rational development and utilization of water resources in the FMA and the sustainable development of the economy in Northeast China.

Good team players? The impact of team member knowledge, skills and abilities on sourcing teamwork and sourcing task-work effectiveness

PurposeThe aim of this paper is to enhance understanding of the use of sourcing teams (STs) by organisations in their procurement and supply chain management. The paper achieves this by exploring, within the context of the supply chain directorate of a global aerospace manufacturing company (GAMC), both the relationship between sourcing teamwork effectiveness (TE) and sourcing task-work effectiveness (TA) and the relationship between individual team member knowledge, skills and abilities (KSAs) and TE.Design/methodology/approachThe authors develop a theoretical model positing positive links between both KSAs and TE and TE and TA. The model is empirically validated using partial least squares structural equation modelling in a survey of 108 ST members from a GAMC.FindingsThe authors identify that, within GAMC, four of five KSAs drive TE and further discover the direct effects of TE on improved TA. Additionally, the authors observe within GAMC the indirect effects of KSAs on TA cascading through TE.Research limitations/implicationsLimitations include the use of a single firm and self-report measures for data collection. In spite of this, the paper has numerous research implications. Previous research on STs has combined factors related to TE and TA. In this paper, TE and TA were disaggregated, and the relationships between them were explored. The relationships were found to be positive within GAMC, a finding that strengthens the evidence base supporting the use of STs by organisations in their procurement and supply chain management. In addition, the paper also strengthened the evidence base regarding the importance of KSAs to TE, which complements existing research highlighting the importance of team-level factors and individual technical attributes.Practical implicationsThe findings from GAMC suggest that executives/managers should take an individual as well as a team-level perspective when developing STs and should consider KSAs as well as technical knowledge when judging individuals’ suitability for inclusion within an ST. There are established KSA tests in the literature that could be used by managers for this task. The findings also inform executives/managers that TE matters for TA and needs attention and investment, especially where sourcing tasks concern high-value areas and/or critical incidents within supply chains.Originality/valueTo the best of the authors’ knowledge, this is the first paper to explore the relationship between TE and TA. Establishing that this relationship is a positive one provides critically important evidence regarding the efficacy of STs, which are widely used within procurement and supply chain management. It is also a rare study looking at TE from the perspective of individual team member KSAs, with further positive relationships revealed. Both findings enhance what is a very limited literature on a widely used practice within procurement and supply chain management.

Applying landscape ecological principles in comprehensive landscape protection: Šumava National Park as a case study

In the face of ongoing anthropogenic pressure and biodiversity loss, there is a need to protect nature more effectively. Therefore, we propose a comprehensive and consecutive approach utilising landscape ecological principles and methods for effective landscape protection and spatial nature conservation. Methods applicable in various conditions are exemplified through case studies from the Šumava National Park, the largest NP in Czechia. Using a set of spatial environmental, landscape ecological and geographical data we can: Characterise the area of interest from the physical-geographical, socioeconomic, and management point of view to create the concept´s framework and review important background information for analysis of the area. Therefore, the key factors for landscape protection and biodiversity conservation are defined. Analyse trends and processes of landscape dynamics in terms of land cover, landscape structure and habitat fragmentation and connectivity, which helps us to set main objectives of landscape protection and nature conservation. Use data about environment conditions and key species and habitat occurrence to model habitat suitability, identify their suitable areas, and thus improve their protection. As a result, areas of high conservation value are distinguished. Synthetize outputs of the above-mentioned steps and prioritise the target goals of landscape protection and biodiversity conservation in the area of interest. This leads to the effective zonation, which is a necessary condition for the application of appropriate management measures.

Prospect of the high conservation value areas as tourisms destination in the oil palm plantation, Pasangkayu Regency, West Sulawesi, Indonesia

Prospect of the high conservation value areas as tourisms destination in the oil palm plantation, Pasangkayu Regency, West Sulawesi, Indonesia

High Conservation Value area in oil palm landscape as a potential ecotourism destination for birdwatching activity

High Conservation Value area in oil palm landscape as a potential ecotourism destination for birdwatching activity

Evolution and zoning of the "production-living- ecological" spatial pattern in the coastal urban agglomeration of Guangdong, Fujian and Zhejiang

Clarifying the spatial differentiation patterns and evolutionary characteristics of urban territories is the basis and prerequisite for enhancing regional spatial governance capacity and realizing sustainable urban development patterns. In order to make up for the inadequacy of the existing coastal urban agglomerations of Guangdong, Fujian and Zhejiang in the exploration of regional evolutionary characteristics and zoning. Based on the theory of production-life-ecology space, the land use data from 2010 to 2020 are used to analyze the land space pattern and evolution characteristics of urban agglomeration by applying the methods of transfer matrix, kernel density analysis, and comparative advantage index. The results show that: (1) the ecological space continued to decrease and the area of production space and ecological space continued to increase. (2) The high-value areas of production space show multinuclear diffusion, the high-value areas of ecological space are mainly distributed in a faceted manner, and the living space are distributed in a band along the coastline. (3) The entire urban agglomeration was categorized into six types of land function zones based on the comparative advantage indices of the three spaces at the county scale.

Enhancing Seasonal PM2.5 Estimations in China through Terrain–Wind–Rained Index (TWRI): A Geographically Weighted Regression Approach

PM2.5 concentrations, closely linked to human health, are significantly influenced by meteorological and topographical factors. This study introduces the Terrain–Wind–Rain Index (TWRI), a novel index that integrates the Terrain–Wind Closed Index (TWCI) with relative humidity to quantitatively examine the coupling effect of natural elements on PM2.5 concentration and its application to PM2.5 inversion. By employing Geographically Weighted Regression (GWR) models, this study evaluates the inversion results of PM2.5 concentrations using TWRI as a factor. Results reveal that the annual average correlation between TWRI and site-measured PM2.5 concentrations increased from 0.65 to 0.71 compared to TWCI. Correlations improved across all seasons, with the most significant enhancement occurring in summer, from 0.51 to 0.66. On the inversion results of PM2.5, integrating TWRI into traditional models boosted accuracy by 1.3%, 5.4%, 4%, and 7.9% across four seasons, primarily due to the varying correlation between TWRI and PM2.5. Furthermore, the inversion results of coupled TWRI more effectively highlight the high value areas in closed areas and the low value areas in humid areas.