Assessment Index System Research Articles

Drought disaster risk assessment and yield insurance pricing of maize: a case study of Liaoning Province, China

Starting from the ‘four-factors’ theory of natural disaster risk formation, the study first selects indices that represent hazard, vulnerability, exposure, and emergency response and recovery capability to construct the drought risk assessment index system. Then the natural disaster risk index method is employed to evaluate the risk of maize drought disasters in Liaoning. Finally, the pure premium rate for maize yield insurance is determined based on the risk assessment results and the Copula function. The results show that: (1) The hazard, vulnerability, and exposure index in northwestern Liaoning are high, while emergency response and recovery capability is relatively low. Therefore, northwestern Liaoning is identified as a high-risk area for drought risk. (2) There exists a negative correlation between the drought risk index and maize yield in most areas of Liaoning; that is, higher drought risk is associated with lower maize yield. (3) Under the same coverage level, pure rates are higher in the northwestern and eastern Liaoning and relatively lower in the southwestern and southeastern Liaoning. Under different coverage levels, higher coverage corresponds to increased pure rates. The results can offer valuable guidance and benchmarks for relevant departments in Liaoning Province to assess the impact of drought risk on agriculture.

Profit-driven distributed trading mechanism for IoT data

Data trading is a crucial means of unlocking the value of Internet of Things (IoT) data. However, IoT data differs from traditional material goods due to its intangible and replicable nature. This difference leads to ambiguous data rights, confusing pricing, and challenges in matching. Additionally, centralized IoT data trading platforms pose risks such as privacy leakage. To address these issues, we propose a profit-driven distributed trading mechanism for IoT data. First, a blockchain-based trading architecture for IoT data, leveraging the transparent and tamper-proof features of blockchain technology, is proposed to establish trust between data owners and data requesters. Second, an IoT data registration method that encompasses both rights confirmation and pricing is designed. The data right confirmation method uses non-fungible token to record ownership and authenticate IoT data. For pricing, we develop an IoT data value assessment index system and introduce a pricing model based on a combination of the sparrow search algorithm and the back propagation neural network. Finally, an IoT data matching method is designed based on the Stackelberg game. This establishes a Stackelberg game model involving multiple data owners and requesters, employing a hierarchical optimization method to determine the optimal purchase strategy. The security of the mechanism is analyzed and the performance of both the pricing method and matching method is evaluated. Experiments demonstrate that both methods outperform traditional approaches in terms of error rates and profit maximization.

Research Framework of Regional Economic-Ecological System Vulnerability Evolution and Adaptive Leap under the Background of Climate Change

This study focuses on the vulnerability evolution mechanism of regional economic-ecological systems and its adaptive leapfrogging path under the background of climate change. Based on the theory of complex adaptive systems and the framework of social-ecological system coupling, a regional economic-ecological system vulnerability assessment index system was constructed. Using the system dynamics model and scenario simulation method, the impact transmission mechanism of climate change on regional economic-ecological systems was analyzed, and the spatiotemporal evolution law of system vulnerability was revealed. The study found that climate change has aggravated the vulnerability of regional economic-ecological systems by changing resource endowments, disturbing industrial structure, and affecting ecological service functions. In response to this problem, this study proposed a leapfrogging path based on adaptive management: including building a climate-resilient industrial system, optimizing resource allocation efficiency, enhancing ecosystem service functions, and improving regional adaptive governance mechanisms. The research results have important theoretical value and practical significance for promoting regional sustainable development and ecological civilization construction.

Evaluation of the sustainable development of logistics based on local characteristics: Matching logistics architecture with resources.

This paper sets out to investigate the alignment between the logistics architecture and resources across China's nine logistics regions, while also examining the individual advantages these regions possess. With the goal of establishing an evaluative framework for sustainable logistics development, the research introduces the principle of logistics equilibrium to augment traditional evaluation metrics, thus forming an assessment index system designed to quantify the congruence between logistics architecture and resources.Utilizing a democratic evaluation approach that emphasizes individual regional advantages, and leveraging empirical data from the decade spanning 2011 to 2020, this paper reveals a general upward trajectory in the matching level of logistics resources to architecture albeit with notable regional disparities. It is observed that while certain areas have made significant strides, others lag, underscoring the varied pace of development among different logistics regions.The research also brings to light the distinct advantage characteristics that emerge as each region evolves, suggesting that these attributes can be harnessed to foster a more environmentally conscious and forward-thinking approach to logistics. By implementing a democratic evaluation to rank these regions, this paper aims to identify exemplars that are not only recognized for their achievements but also serve as models for the formulation of green and sustainable logistics strategies tailored to each region's specific needs and potential.

The construction of a physical literacy assessment index system for junior high school students: a Chinese study

In recent years, the internalisation of education in China has led to an increase in the prevalence of a highly competitive, test-focused approach to learning. This has had a detrimental impact on the physical health and wellbeing of children and adolescents in China, resulting in a concerning lack of physical literacy among junior high school students. Accordingly, the objective of this study is to construct an evaluation index system for physical literacy in junior high school students with Chinese characteristics, quantify the relative importance of each domain, and provide a framework for students to assess and self-evaluate the sustainable development of their physical literacy. The indicator system was modelled on CAPL-2, and valid indicators were extracted from the literature. A pre-selected indicator system for physical literacy in junior high school students was constructed through three rounds of Delphi expert questionnaire surveys (48 questionnaires were distributed in total). A total of 16 experts participated in all three rounds of the surveys until a consensus was reached on the indicators, resulting in the establishment of a hierarchical analysis-based theoretical model. This was developed using the experts’ opinions and determined the items to be subdomains and weights. The AHP was employed to ascertain the relative importance of the subdomains and items within the assessment system. The index weight scores of the Chinese junior high school students’ physical literacy assessment index system are aligned with the core tenet of the physical literacy concept, comprising four primary indicators, eight secondary indicators, and 26 tertiary indicators. The weights of the physical literacy evaluation indexes for junior high school students in China were calculated using the AHP to establish a progressive hierarchical model and the consistency test. The primary indicators were found to be in the following order of importance: Physical Competence (29.82%) > Motivation and Confidence (29.64%) > Knowledge and Understanding (20.27%) = Daily Behavior (20.27%). The system is an effective means of evaluating the impact of physical activity and health promotion among junior high school students, and it effectively addresses the needs of this age group in these areas. It provides a comprehensive assessment of the effects of physical activity and health promotion among junior high school students, and it offers a scientific foundation for the development of precise and individualized physical activity and health promotion programs.

Spatial distribution and risk assessment of heavy metal pollution from enterprises in China

Heavy metal pollution (HMP) directly affects the safety of agricultural products, thereby impacting human health. Industrial emissions, as the main source of soil HMP in China, require in-depth research on their pollution risks. Based on national heavy metal (HM) enterprise data, this paper analyzes the spatial distribution characteristics of key enterprises involved in the HMP across the country. It constructs the risk assessment index system of enterprise HMP based on the “source-pathway-receptor” (SPR) process of the HMP, evaluates and partitions the risk of the HMP from enterprises nationwide. The results show that: (1) Enterprises and pollutant discharge outlets are mainly distributed in the eastern and southeastern coastal regions. Jiangxi, Yunnan, Guangdong, and Hunan Province are the main distribution regions of smelting enterprises, with the most types of HM pollutants. The hazard of pollution sources shows a spatial distribution pattern of higher risk in the southwest and north, and lower risk in the central region. Counties with high-risk pollution sources are mainly distributed in Yunnan, Hunan, Guangdong, Inner Mongolia, and Jiangxi Province. (2) The hazard of pollutant transmission pathways shows a spatial distribution pattern of higher risk in the southeast and lower risk in the central region. About 31.5 % of counties are at extremely high risk, mainly distributed in the southeastern coastal regions of Guangdong, Jiangsu, Zhejiang, Jiangxi, Shandong, and Fujian Province. (3) The vulnerability of the receptors shows significant clustering characteristics in the northeast and central regions. About 3.3 % of counties have a receptor vulnerability level of “extremely high,” mainly distributed in Inner Mongolia, Jilin, Heilongjiang, Liaoning Province in the northeast, as well as Hubei and Jiangsu Province. (4) About 1.55 % of counties nationwide have a comprehensive risk level of the HMP classified as “extremely high,” mainly distributed in Guangdong Province and Inner Mongolia. Additionally, some counties in Yunnan, Hunan, Jiangsu, Jiangxi, and Zhejiang Province have a risk of exceeding pollution standards, requiring further preventive measures to reduce pollution risks in the future. This paper can provide a scientific basis for the prevention and control (P&C) of the HMP in China.

Revisiting 30 years of advances in firefighter protective clothing: a quantitative assessment and bibliometric analysis

Firefighter protective clothing research has undergone rapid development and garnered extensive attention over the past three decades. However, its trajectory lacks quantitative evaluation due to the complex relationship among humans, clothing, and the environment. Here, we aimed to (1) develop an objective and reproducible evaluation index system for the quantitative assessment of firefighter protective clothing research, (2) delineate the developmental stages and their analytical characteristics over the past three decades through bibliometric analysis, and (3) scrutinize the current status, research hotspots, gaps, and future concerns. Our review of 314 publications from the Web of Science Core database (1994–2023) led to the creation of an evaluation index system that integrates the catastrophe progression method with bibliometric analysis, incorporating 10 indicators across 4 dimensions: productivity, influence, development, and innovation. The bibliometric analysis was conducted using the Bibliometrix R package. The results reveal a rapid increase in firefighter protective clothing studies from 1994 to 2023, comprising two stages: the start-up phase (1994–2009) and the rapid development phase (2010–2023). Thematic evolution over three decades indicates a shift from single specific to composite domains, with emerging themes such as achieving a balance in the interaction between the human body, clothing, and environment signaling future research directions. In addition, we visualized the most relevant countries, institutions, journals, authors, articles, and potential challenges during the last 30 years. This review provides an overview of current evidence and minimizes subjectivity bias compared to traditional manual reviews, thus presenting specific future research questions advancing firefighter protective clothing research.

Projecting urban flood risk through hydrodynamic modeling under shared socioeconomic pathways

Under future climate change, accurate risk assessment of urban flooding disasters is paramount for effective adaptation and mitigation strategies. However, conventional indicator-based assessment methods often fall short of accurately capturing the complexity of flooding dynamics. Current research predominantly focuses on predicting future hazard shifts while overlooking changes in other critical indicators. In this study, we establish a comprehensive index system for risk assessment, and quantified future changes in most indicators, utilizing the InfoWorks ICM model for hazard simulation and the CLUMondo model for land use predictions. Based on risk assessment results and regional characteristics, we further analyze the key factors driving future risk and discuss corresponding measures. The results indicate an exacerbation of future urban flood risk, with an 18% increase in high risk areas, primarily concentrated in the center of the study area. The dominant indicators are inundation depth and land use over the whole study area. However microtopography significantly affects risk in low-lying areas. Overall, under higher emission scenarios, the influence of GDP and population rises. These findings offer methodological insights for future urban flood risk assessment research and provide policymakers with valuable guidance to develop targeted adaptation measures in response to climate change.

Construction of an index system of core competence assessment for sleep medicine nurse specialists in China: a Delphi study

AimTo construct an evaluation index system for the core competence of nurses in sleep medicine specialties.BackgroundSpecialized nurses in sleep medicine must handle treatment, nursing, and management well, which requires nurses to have excellent competence in knowledge, skills, and ability. However, a competency evaluation system for sleep medicine nurses has not been established in China.MethodsWe used a literature review and an expert meeting to establish a draft indicator system. Subsequently, two rounds of correspondence were conducted with 27 experts from 5 provinces using the Delphi method to solicit their opinions on the core competency evaluation indicators for sleep medicine nurse specialists and to qualitatively evaluate the experts’ scores. The consensus was defined in advance as a mean score of 4.0 or above, with at least 75% agreement among participants. In this way, the final indicator framework was determined.ResultsThe final evaluation indexes of the core competencies for sleep medicine Nurse Specialists included 6 first-level indexes (Practical ability, Theoretical Knowledge, Critical thinking, Communication and coordination, Nursing Management, and Professional Development), 16 s-level indexes, and 64 third-level indexes. The effective response rates of the two expert consultation rounds were 100%. The expert authority coefficients were 0.878 and 0.865 in the first and second rounds of consultation, respectively. In the second round of consultation, the first, second, and third indexes of Kendall’s coefficient of concordance were 0.373, 0.351, and 0.286, respectively (P < 0.05).ConclusionThe core competence evaluation index system for sleep medicine nurses established in this study is scientific and reliable and can provide a theoretical reference for the training, assessment, and evaluation of sleep medicine nurses in the future.Implications for nursing managementThe evaluation index system of sleep medicine nurses’ core competence will provide an effective practical framework for nursing management to evaluate, train, and examine the core competence of sleep medicine nurses.

Analysis of Short-Term Heavy Rainfall-Based Urban Flood Disaster Risk Assessment Using Integrated Learning Approach

Accurate and timely risk assessment of short-term rainstorm-type flood disasters is very important for ecological environment protection and sustainable socio-economic development. Given the complexity and variability of different geographical environments and climate conditions, a single machine learning model may lead to overfitting issues in flood disaster assessment, limiting the generalization ability of such models. In order to overcome this challenge, this study proposed a short-term rainstorm flood disaster risk assessment framework under the integrated learning model, which is divided into two stages: The first stage uses microwave remote sensing images to extract flood coverage and establish disaster samples, and integrates multi-source heterogeneous data to build a flood disaster risk assessment index system. The second stage, under the constraints of Whale Optimization Algorithm (WOA), optimizes the integration of random forest (RF), support vector machine (SVM), and logistic regression (LR) base models, and then the WRSL-Short-Term Flood Risk Assessment Model is established. The experimental results show that the Area Under Curve (AUC) accuracy of the WRSL-Short-Term Flood Risk Assessment Model is 89.27%, which is 0.95%, 1.77%, 2.07%, 1.86%, and 0.47% higher than RF, SVM, LR, XGBoost, and average weight RF-SVM-LR, respectively. The accuracy evaluation metrics for accuracy, Recall, and F1 Score have improved by 5.84%, 21.50%, and 11.06%, respectively. In this paper, WRSL-Short-Term Flood Risk Assessment Model is used to carry out the risk assessment of flood and waterlogging disasters in Henan Province, and ArcGIS is used to complete the short-term rainstorm city flood and waterlogging risk map. The research results will provide a scientific assessment basis for short-term rainstorm city flood disaster risk assessment and provide technical support for regional flood control and risk management.

Uncertainty analysis of impact factors for a comprehensive assessment of dam failure consequence under earthquake effects

ABSTRACT Utilizing cloud models to analyze the uncertainty and consequences of dam failure factors. Enhanced Analytic Hierarchy Process (AHP) with a scale criterion based on index scale and an expert score constraint mechanism. Focus on the downstream area of the Zipingpu Dam for assessing the uncertainty of dam comprehensive evaluation factors. The final outcome is represented by three numerical eigenvalues of the cloud model, determining the weights of each factor in the evaluation index system. This approach offers a novel method for the comprehensive evaluation of dam failure consequences.

Ecological and geological security risk assessment of underground space development in cold and arid canyon cities-Taking Ping'an District, Haidong City, Qinghai Province, as an example.

The ecological and geological problems caused by the rise of groundwater level due to the development of underground space in cold and arid canyon cities are particularly typical. Reasonably assessing the ecological and geological security risks of utilizing underground space is conducive to reducing the occurrence of ecological and geological problems during the construction and operation of underground engineering projects. Taking Ping'an District of Haidong City as an example, the topography and geomorphology of the research area were investigated in the field, and the distribution of topography and geomorphology in the research area was understood; through geological drilling and geotechnical engineering testing, the distribution of different strata in the research area was obtained; through pumping and seepage experiments, the recharge, runoff, and discharge relationship between surface water and groundwater in the research area and the water abundance of different strata are obtained, and the causes and mechanisms of geological safety risks in forest and grassland ecosystems, farmland ecosystems, and human settlements ecosystems were analyzed based on literature. Corresponding ecological geological safety risk assessment index systems and methods were established, and the ecological geological safety risks before and after the development of underground rail transit projects along both banks of the Huangshui River in the study area were evaluated. PRACTITIONER POINTS: The development of underground space in canyon type cities can easily lead to ecological and geological problems. Taking Haidong City, Qinghai Province, as an example, this study investigates the causes of ecological and geological problems caused by the development of underground spaces in canyon type cities. An ecological geological security risk assessment index system and method for canyon-type cities were established. An evaluation was conducted on the ecological and geological safety risks before and after the development of the underground rail transit projects on both sides of the research area.

Integrated risk assessment of landslide in karst terrains: Advancing landslides management in Beiliu City, China

Beiliu City, Guangxi Zhuang Autonomous Region, China, like other karst landscape areas, is also suffering from the threat of landslides. This research applied the circular variance method to delineate 31,465 slope units and then combined it with a geographic information system and a well-established risk assessment system to synthesize three perspectives-susceptibility, hazard, and vulnerability to realize the risk assessment of karst landslides in Beiliu City. Using perennial field surveys, aerial images-derived landslide inventories, slope unit investigations, and a literature review combined with a Fully Convolutional Neural Networks approach, we present a culminating in the derivation of landslide susceptibility assessment outcomes within the study area. Because Beiliu City is located in a karst area, this research employs the quantification of soil erosion intensity as a metric to assess landslide hazard. In this study, based on a comprehensive analysis of the basic characteristics of landslides and their associated disaster-bearing bodies, a robust landslide vulnerability assessment index system was constructed for risk assessment of karst landslides in Beiliu City. The landslide risk assessment model in karst area is constructed by combining the landslide susceptibility and the vulnerability in the danger range. Detailed field surveys verified the scientific validity and accuracy of the assessment results. This research extends beyond merely furnishing scientific data support for local governmental efforts in disaster prevention and mitigation. It impressively enhances the methodological framework for conducting landslide risk assessments within karst landscapes, thereby offering a robust foundation for both immediate and strategic responses to the inherent geological vulnerabilities of these areas.

A Bayesian and Analytic Hierarchy Process-Based Multilevel Community Resilience Evaluation Method and Application Study

Cities are complex systems influenced by a multitude of factors, encompassing society, economy, culture, and environment. These factors make urban development highly vulnerable to various disturbances. Communities work as the fundamental building blocks of a city and directly impact both its social structure and spatial layout. Moreover, urban planning and policies play a crucial role in shaping the development trajectory of communities and the living environment for residents. This study aims to develop a Bayesian and analytic hierarchy process (BAHP)-based multilevel community resilience evaluation method to assess the ability of the community system to withstand disturbances and recover from them. First, the proposed method establishes a comprehensive assessment index system that can evaluate social and environmental resilience as well as institutional and managerial resilience at multiple levels. This system serves as a quantitative decision-making tool to elucidate the impact of various factors on community resilience. Furthermore, the “relative demand coefficient” (RDC) is proposed to compare different communities’ resilience by using Bayesian inference to determine its most probable value (MPV). To validate the applicability of the proposed method, an empirical study was conducted in the Dafapu community located in the Longgang District of Shenzhen. Meanwhile, a simulated virtual community is employed for comparison with the Dafapu community as an illustrative example showcasing the proposed method’s superior performance after integrating the RDC. The empirical study demonstrates that the proposed BAHP-based method can effectively and quantitatively highlight the recovery capabilities and limitations for different communities in various dimensions while providing a clear direction for enhancing urban community resilience. This research contributes new insights to the theory, provides a practical tool to quantify community resilience, and offers a viable path for the actual enhancement of community resilience.

Comprehensive Risk Assessment of Farmer Households in Flood and Earthquake Prone Areas Based on Multidimensional Information Diffusion Model

In recent years, the global practice of regional natural disaster reduction and comprehensive risk prevention has promoted the integration of multiple disciplines, and the theory and technology of comprehensive risk assessment for multiple natural disaster chains urgently need innovation. This article is based on multiple natural disaster chain scenarios and sets secondary indicators for multiple influencing factors based on the first level indicators composed of basic risk factors such as hazard factors, vulnerability of disaster bearing bodies, and disaster prevention and reduction capabilities. By using multidimensional information diffusion technology, multiple hazard factor hazard combination factors, vulnerability combination factors of disaster bearing bodies, and comprehensive disaster prevention and reduction capability combination factors reflecting various natural disaster scenarios are constructed, A household comprehensive risk assessment index system for multi-dimensional information diffusion assessment of natural disaster comprehensive risk was designed by quantifying risk factors such as the intensity of comprehensive disaster causing factors, vulnerability of disaster bearing bodies, and comprehensive disaster prevention and reduction capabilities in disaster scenarios. This paper expands the traditional information diffusion comprehensive risk assessment model, makes up for the lack of information on geographical units, establishes a multi impact factor multi dimension natural disaster risk comprehensive assessment model for multi risk factors of complex disaster scenarios with small samples, obtains the theoretical value of multi-dimensional information Diffusion model comprehensive risk assessment for complex disaster scenarios, breaks through the sample size limit of natural disaster risks, and makes up for the lack of information on small samples of small probability events, Improved the reliability and accuracy of comprehensive risk assessment for multiple natural disaster chains. Then, take the disaster scenario formed by the comprehensive risk of earthquake and flood in a place in the upper reaches of the Fujiang River basin as the analysis sample, calculate the comprehensive risk assessment value of flood and earthquake damage in the study area's micro samples (rural households in villages and towns). By comparison, it is found that the comprehensive risk value assessed by the multi-dimensional information Diffusion model has good reliability and high accuracy, which is significantly better than the traditional Kriging interpolation method and geographical weighted regression method. This article provides a comprehensive risk assessment of natural disasters at the micro household level, which can better reflect the diversity and regionality of risks, help clarify the hidden risks of rural natural disasters, and provide scientific basis for government emergency management decision-making.

Turning peril into opportunity: Construction and validation of a resilience assessment system for China's coastal megacities

Enhancing urban resilience and strengthening risk resistance capacities have emerged as pivotal concerns in the construction and governance of contemporary urban landscapes globally. Given the uniqueness of coastal megacities in terms of geography and population, a more targeted and effective resilience assessment is urgently needed. This investigation develops a comprehensive, multidimensional index system to assess the resilience life cycle of China's coastal megacities. First, we creatively introduce the process perspective of the 4R model of crisis management into urban resilience assessment, revealing the preventive, defensive, recovery, and transformative resilience of China's coastal megacities. Second, we propose a spatio-temporal assessment matrix that integrates four processes of urban resilience and six representative subsystems in coastal megacities: social, economic, ecological, infrastructural, technological, and institutional. Third, we select 59 indicators to construct a three-level assessment index system for China's coastal megacities. This index system is strictly screened and optimized through qualitative and quantitative methods. Fourth, we validate the assessment system using Qingdao's data by calculating an urban resilience index and analyzing trends. We find the most important improvement goals and resilience enhancement suggestions for Qingdao by analyzing the indicator sensitivity. This study contributes to the development of urban resilience theories and identifies weaknesses in different dimensions to strengthen and improve the resilience of coastal megacities.

Research on User Default Prediction Algorithm Based on Adjusted Homogenous and Heterogeneous Ensemble Learning

In the field of risk assessment, the traditional econometric models are generally used to assess credit risk. And with the introduction of the “dual-carbon” goals to promote the development of a low-carbon economy, the scale of green credit in China has rapidly expanded. But with the advent of the big data era, due to the poor interpretability of a traditional single machine learning model, it is difficult to capture nonlinear relationships, and there are shortcomings in prediction accuracy and robustness. This paper selects the adjusted ensemble learning model based on the homogeneous and heterogeneous factors for user default prediction, which can efficiently process large quantities of high-dimensional data. This article adjusts each model to adapt to the task and innovatively compares various models. In this paper, the missing value filling method, feature selection, and ensemble model are studied and discussed, and the optimal ensemble model is obtained. When comparing the predictions of single models and ensemble models, the accuracy, sensitivity, specificity, F1-Score, Kappa, and MCC of Categorical Features Gradient Boosting (CatBoost) and Random undersampling Boosting (RUSBoost) all reach 100%. The experimental results prove that the algorithm based on adjusted homogeneous and heterogeneous ensemble learning can predict the user default efficiently and accurately. This paper also provides some references for establishing a risk assessment index system.

Studies on the quantification and time-sequence development of integration of industry and education level in higher vocational education-based on the panel data of China’s provincial from 2016 to 2022

An extensive assessment index system was developed to evaluate the integration of industry and education in higher vocational education. The system was designed using panel data collected from 31 provinces in China between 2016 and 2022. The study utilized the entropy approach and coupled coordination degree model to examine the temporal and spatial changes in the level of growth of the integration of industry and education in higher vocational education, as well as the factors that impact it. In order to examine how the integration of industry and education in higher vocational education develops over time and space, as well as the factors that affect it, we utilized spatial phasic analysis, Tobit regression model, and Dagum’s Gini coefficient. The study’s findings suggest that between 2016 and 2022, the integration of industry and education in higher vocational education showed a consistent improvement in overall development. Nevertheless, there are still significant regional differences, with certain areas showing limited levels of integration, while the bulk of regions are either in a state of low integration with high clustering or low integration with low clustering. Most locations showed either a “low-high” or “low-low” level of agglomeration, indicating a significant degree of spatial concentration, with a clear trend of higher concentration in the east and lower concentration in the west. The progress of industrial structure and the degree of regional economic development have a substantial impact on the amount of integration of industry and education in higher vocational education. There is a notable increase in the amount of integration between industry and education in higher vocational education, which has a favorable effect. Conversely, the local employment rate has a substantial negative effect on this integration. Moreover, the direct influence of industrial structure optimization is restricted. The Gini coefficient of the development level of integration of industry and education in higher vocational education exhibits a slight rising trend. Simultaneously, there is a varying increase in the Gini coefficient inside the group and a decrease in the Gini coefficient between the groups. The disparities in the level of integration between Industry and Education in the provincial area primarily stem from inter-group variations across the locations. To promote the integration of industry and education in higher vocational education, it is recommended to strengthen policy support and resource allocation, address regional disparities, improve professional configuration, and increase investment in scientific and technological innovation and talent development.

Assessing China's rare earth supply security

As strategic minerals become increasingly critical in the competition among great powers, combined with high-quality development requests, their security issues gain more prominence. Based on an in-depth understanding of the connotation of China's strategic mineral security, this article developed a rare earth security assessment index system with 3 sub-objects: global supply stability, domestically economic security and coexistence, then conducted sensitivity analysis using Monte Carlo simulation. The results show that: (1) Global supply stability increased, then fluctuated and declined due to deficiencies in sustainable development and politics, affecting China's long-term rare earth security index; (2) Domestically economic security was weakening due to reduced mine supply, impacting China's short-term rare earth security index; (3) The coexistence index was rising, influenced by industry chain security changes, with increases benefiting China's rare earth security index; (4) China's rare earth security level rose from 2010 to 2022, tracking the coexistence index, indicating that international market integration boosts rare earth security. Based on the above findings, the article puts forward corresponding policy implications.

Spatial Analysis and Risk Assessment of Meteorological Disasters Affecting Cotton Cultivation in Xinjiang: A Comprehensive Model Approach

A systematic understanding of the spatial distribution of meteorological disasters that affect cotton growth, such as rainstorms, gales, and hail, is important for reducing plant losses and promoting sustainable development. Our study aimed to evaluate the risk of meteorological disasters during cotton growth and analyze their spatial distribution and driving factors. A risk assessment model for major meteorological disasters during cotton cultivation in Xinjiang was established by integrating entropy weight methods and an analytic hierarchy process. A cotton meteorological disaster risk assessment index system, including the vulnerability of disaster-bearing bodies, hazards of disaster-causing factors, and exposure of disaster-bearing bodies, was constructed using Google Earth Engine. We determined the comprehensive risk levels of major meteorological disasters for cotton in various regions of Xinjiang. Research shows that the selection of indicators is very important, and crop risk assessment with a clear disaster-bearing body can make the results more accurate. It is necessary to consider the risk assessment of multiple disaster species for meteorological disaster risk assessment. The results revealed spatial differences in the meteorological disaster risk for cotton in 2020. The very high and high risks for cotton accounted for 42% of the cotton planting area, mainly distributed in Karamay, Tacheng, Kashgar, Changjizhou, Kezhou, and Ilizhou. Consequently, this study provides a scientific basis for cotton cultivation in Xinjiang, China.