Assessment Indicator System Research Articles

Risk Evaluation for Human Factors of Flight Dispatcher Based on the Hesitant Fuzzy TOPSIS-DEMATEL-ISM Approach: A Case Study in Sichuan Airlines

To effectively mitigate unsafe events and accident symptoms stemming from flight dispatchers’ human factors, this paper proposes a novel risk evaluation model to accurately identify and evaluate potential human risks associated with flight dispatchers. First, the HFACS (Human Factors Analysis and Classification System, HFACS) model is employed to construct a human risk assessment indicator system for flight dispatchers. Second, the hesitant fuzzy set is introduced to represent the uncertainty during experts’ evaluation, and the improved TOPSIS (Technique for Order Preference by Similarity to Ideal Solution, TOPSIS) method is applied within a hesitant fuzzy environment to obtain rankings of human factors. Third, the hesitant fuzzy DEMATEL (Decision-Making Trial and Evaluation Laboratory, DEMATEL)-ISM (Interpretive Structural Modeling, ISM) approach is constructed to analyze the correlation among human factors, leading to the establishment of a multi-level hierarchical structure model. Finally, a case study of risk assessment for human factors of flight dispatchers in Sichuan Airlines is conducted to demonstrate the effectiveness of the proposed method. The results revealed the flight dispatchers’ human factors associated with higher risks and identified the key factors with a larger impact on other factors in Sichuan Airlines. Subsequently, a multi-level hierarchical structure model comprising five layers is developed to investigate the internal correlations among human factors, facilitating the formulation of targeted improvement suggestions for the higher risk indicators and key influencing factors.

Sustainability assessment of urban water public‐private partnership projects with environmental, social, and governance (ESG) criteria

AbstractWith the acceleration of urbanization, the sustainability of urban water infrastructure projects has become pivotal to social development. This research aims to construct a comprehensive sustainability assessment model for urban water public‐private partnership (PPP) projects based on the environmental, social, and governance criteria. First, a multi‐level assessment indicator system is established considering the economic, social, ecological, and governance sustainability dimensions. Then, the extended analytic hierarchy process is utilized to determine the weights of indicators based on expert scoring. To address the ambiguity of qualitative linguistic variables, the interval‐valued intuitionistic fuzzy set method is applied to aggregate expert assessments. An empirical case study of an urban water project verifies the feasibility of the proposed approach. Results demonstrate that experts emphasize economic sustainability, while social sustainability is an overlooked dimension that requires improvement. The research contributes to the field by developing an applied, industry‐specific sustainability evaluation model for urban water PPP projects using advanced decision‐making techniques. This model provides practical tools for stakeholders to enhance the sustainable performance of urban water infrastructure projects.

Building a system of indicators of stability and sustainability of industrial enterprise production system functioning

Subject. The article deals with the development of a system of indicators of stability and sustainability of production system functioning at industrial enterprises. Objectives. The aim is to develop a methodological approach to the formation of a system of indicators for operational assessment of production system stability, and sustainability of its functioning. Methods. We employ methods of analyzing the existing approaches to assessment of stability and sustainability of enterprise production system, their generalization, and synthesis of a system of indicators characterizing the impact of the most critical production risks on the production system. Results. The paper offers a methodological approach to the formation of the said system of indicators, underpins the need to assess stability and sustainability of production system functioning, introduces an indicator of stability reserve of production system functioning that enables to assess the level of stability of production system operation. Conclusions. The proposed methodological approach makes it possible to develop management solutions aimed at restoring the stability of the functioning of an industrial enterprise.

Visual Impact Assessment Method for Cultural Heritage: West Lake Cultural Landscape in Hangzhou, China

Visual Impact Assessment (VIA) is a critical tool in managing cultural heritage, evaluating the impacts of development and construction projects on the visual aspects of heritage values. However, VIA is often constrained by subjectivity, low public participation, and a lack of generalizability. This study aims to develop a methodological framework for a more objective and comprehensive assessment of visual impacts on cultural heritage. The study establishes criteria for indicators based on the value attributes of cultural heritage, develops an assessment indicator system, and integrates visual sensitivity assessment through multi-factor calculations with visual perception assessment using the AHP method and questionnaire surveys. This constructs an assessment framework that combines both objectivity and subjectivity. The Shangri-La Hotel East Building project at the World Heritage site of the West Lake Cultural Landscape of Hangzhou, China, is employed as a case study to empirically demonstrate the framework’s practicality and effectiveness. The results indicate that the Shangri-La East Building significantly impacts the attributes of the West Lake. The conclusion demonstrates that the indicator system, grounded in the attributes of cultural heritage, enhances the framework’s applicability across different contexts. The integrated assessment framework, which includes both a quantitative assessment of visual sensitivity and a public-participation-based assessment of visual perception, is shown to be effective in predicting the visual impacts of proposed projects on heritage values. The study also underscores the importance of Heritage Impact Assessment as a preliminary evaluation.

Risk Assessment of Community-Scale High-Temperature and Rainstorm Waterlogging Disasters: A Case Study of the Dongsi Community in Beijing

With the advancement of urbanization and acceleration of global warming, extreme weather and climate events are becoming increasingly frequent and severe, and climate risk continues to rise. Each community is irreplaceable and important in coping with extreme climate risk and improving urban resilience. In this study, the Dongsi Community in the functional core area of Beijing was explored, and the risk assessment of high temperatures and rainstorm waterlogging was implemented at the community scale. Local navigation observations were integrated into a theoretical framework for traditional disaster risk assessment. The risk assessment indicator system for community-scale high-temperature and rainstorm waterlogging disasters was established and improved from a microscopic perspective (a total of 22 indicators were selected from the three dimensions of hazard, exposure, and vulnerability). Geographic Information Systems (GIS) technology was used to integrate geographic information, meteorological, planning, municipal, socioeconomic and other multisource information layers, thus enabling more detailed spatial distribution characteristics of the hazard, exposure, vulnerability, and risk levels of community-scale high temperatures and rainstorm waterlogging to be obtained. The results revealed that the high-risk area and slightly high-risk area of high-temperature disasters accounted for 13.5% and 15.1%, respectively. The high-risk area and slightly high-risk area of rainstorm waterlogging disasters accounted for 9.8% and 31.6%, respectively. The high-risk areas common to high temperatures and waterlogging accounted for 3.9%. In general, the risk of high-temperature and rainstorm waterlogging disasters at the community scale showed obvious spatial imbalances; that is, the risk in the area around the middle section of Dongsi Santiao was the lowest, while a degree of high temperatures or rainstorm waterlogging was found in other areas. In particular, the risk of high-temperature and rainstorm waterlogging disasters along Dongsi North Street, the surrounding areas of Dongsi Liutiao, and some areas along the Dongsi Jiutiao route was relatively high. These spatial differences were affected to a greater extent by land cover (buildings, vegetation, etc.) and population density within the community. This study is a useful exploration of climate risk research for resilient community construction, and provides scientific support for the planning of climate-adaptive communities, as well as the proposal of overall adaptation goals, action frameworks, and specific planning strategies at the community level.

Factors influencing the coupled and coordinated development of cities in the Yangtze River Economic Belt: A focus on carbon reduction, pollution control, greening, and growth

Atmospheric pollutants PM2.5 and CO2 share similar sources and impact mechanisms. Green innovations and urban greening significantly reduce these pollutants while promoting economic growth. However, the synergies and trade-offs between carbon reduction, pollution control, green expansion, and economic growth remain understudied. This paper examines 110 cities in the Yangtze River Economic Belt (YREB), China's premier green development site, as a unified system. Using fractional-order synthesis analysis, this paper constructs an assessment indicator system and measures synergy with a coupled coordination degree model. The driving factors are explored using a system-generalized method of moments estimation. The findings indicate that most cities in the YREB are at an intermediate coordination stage. The coupling of greening with carbon reduction, pollution control, and growth has a low degree, highlighting an urgent need to strengthen greening efforts. Key drivers include the digital economy, advanced industrial structure, innovative talent aggregation, infrastructure construction, financial investment, and marketization. The digital economy significantly influences all regions of the Yangtze River. Notable heterogeneity exists in the impact of other drivers across different regions. These results offer valuable policy insights for managing carbon emissions and pollutants, contributing to sustainable urban development.

A novel multi-source information fusion method for emergency spatial resilience assessment based on Dempster-Shafer theory

In the midst of today's intricate and ever-changing natural and social landscape, this study is committed to proposing a novel multi-source information fusion method for assessing the spatial resilience of urban emergency evacuation and rescue. Its overarching aim is to uncover latent challenges and contribute to the enhancement of a city's capacity to respond to emergencies. To achieve this, we have devised a comprehensive assessment indicator system, capable of not only quantifying a city's spatial resilience but also offering indispensable guidance for urban emergency evacuation and rescue spatial planning. Furthermore, we have introduced an innovative evaluation methodology framework. This framework includes the establishment of the basic probability assignment (BPA) model, a pioneering method for generating BPA for observed values, and the novel application of hierarchical weighting and fusion techniques. By extending the Dempster-Shafer theory, we have not only enhanced the method's ability to express and integrate uncertain information but also significantly improved the precision of the evaluation. Ultimately, we conducted a quantitative assessment using Shenzhen, China, as a case study, identifying existing issues and proposing highly-targeted improvement strategies. These research findings not only provide robust support for the augmentation of urban emergency capabilities in Shenzhen but also offer pioneering insights for the quantitative assessment and advancement of emergency spatial resilience in cities across the globe.

Resilient city construction efficiency and its influencing factors in China's Chengdu-Chongqing Economic Circle: Considering both construction input and resilience level of the city

The growing expansion of urban centers combined with a deteriorating climatic environment raises the likelihood and severity of urban disasters. Thus, there is an urgent need to accelerate the resilient city construction. Here, we use efficiency theory to structure an assessment indicator system of the resilient city construction efficiency to examine the actual correlation of urban resilience enhancement and urban resource consumption. Specifically, 16 key cities in the Chengdu-Chongqing Economic Circle (CCEC) are investigated using the super-efficient SBM and GTWR models. The findings reveal that the resilient city construction efficiency varies greatly among cities in the region, but most cities have relatively low resilient city construction efficiency, showing high resource consumption and low resilience levels. Also, the lower efficiency of regional resilient city construction is mainly due to the serious ineffectiveness of technical and resource inputs, insufficient outputs of ecological resilience and engineering resilience, and is also greatly influenced by factors such as the economic level and the technological innovation level. Upon this basis, we suggest the development strategies for resilient city construction in CCEC. This study broadens the perspective and scope of urban resilience research, and also provides a realistic reference for accelerating the construction of resilient cities.

Integrated Risk Assessment of Mountainous Long-Distance Oil and Gas Pipelines Based on Multisource Spatial Data.

Pipeline risk assessment is crucial for pipeline safety management and operation. The aim of this study is to develop a comprehensive assessment model that accurately evaluates pipeline risks and ensures the safe and reliable operation of the pipeline system. The model is based on multisource spatial data and is primarily applicable to long-distance oil and gas pipelines that traverse complex geological conditions in mountainous areas. The research is conducted using the example of the Jinliwen natural gas pipeline in Zhejiang Province, China. By analyzing the geological data of the study area and the potential risks that the pipeline may encounter, a comprehensive risk assessment indicator system for the pipeline was developed using slope units to divide pipeline sections. The pipeline risk levels are classified using the K-means clustering-entropy weighted-random forest algorithm. The model is evaluated using accuracy (Acc), precision (Pre), recall (R), F1-score, and the ROC curve. The results show that the model has an accuracy of 0.917, a precision of 0.92, a recall of 0.916, an F1-score of 0.914, and an AUC (Area Under Curve) of 0.93, indicating its strong predictive capability. The risk assessment results demonstrate a strong consistency when compared with actual incident events. This indicates that the constructed model effectively reflects the influencing factors of pipeline risk, providing a basis for pipeline risk assessment and disaster prevention and mitigation efforts in similar regions.

Research on the Construction of a Risk Assessment Indicator System for Transportation Infrastructure Investment under Public–Private Partnership Model

This paper is dedicated to developing a risk assessment indicator system applicable to transportation infrastructure investment projects in the public–private partnership (PPP) model. Initially, through practical research, literature reviews, and expert interviews, key risk factors for transportation infrastructure investment projects in the PPP mode were identified. Based on these risk factors, a preliminary risk assessment indicator system was established. Subsequently, the principal component analysis (PCA) was used to reduce the dimensions of the indicators, simplify the structure of the system, and highlight key risk factors. Simultaneously, the CRITIC-EWM method was applied to calculate the weights of the indicators. Furthermore, to validate the effectiveness of the indicator system, a questionnaire survey was conducted, collecting 314 responses. Structural equation modeling was applied to validate the effectiveness of the indicator system and examine its application value in practical risk management processes. The primary contribution of this study lies in proposing a method for constructing a risk assessment indicator system that combines quantitative and qualitative analyses, providing new theoretical and practical guidance for risk management of transportation infrastructure investment projects under the PPP mode.

The spatiotemporal evolution and impact of green land use efficiency in resource-based cities in the yellow river basin under the background of sustainable development

Introduction: With the rapid advancement of industrialization and urbanization in the Yellow River Basin, the number of resource-based cities is constantly increasing, leading to an imbalance in land use structure and the gradual worsening of adverse effects such as damage to the ecological environment. Hence, improving the efficiency of green land use in resource-based cities has extremely crucial ecological and practical meaning for sustainable development.Methods: This study comprehensively considers interdisciplinary theories such as ecology and management, and combines the actual situation of resource-based cities in the Yellow River Basin to construct a new type of urban field green utilization efficiency assessment index system. To promote the coordinated development of different dimensions, the corresponding spatiotemporal evolution process and related influencing factors were also analyzed. Results and discussion: The research results indicate that the overall spatial evolution of the Yellow River Basin has active correlation. During the period from 2006 to 2022, the number of high value agglomeration cities in upstream resource-based cities increased by nearly 25%; Nearly 3% of resourcecities exhibit low-value heterogeneity, while the number of resource cities with low value agglomeration has decreased to 0. In addition, population density and scientific education expenditure level are key factors affecting the ratio of green land use in resource-based cities, while the impact of economic indicators is gradually decreasing. In summary, the assessment indicator system for urban land green utilization ratio proposed in the study is more comprehensive, and the spatial distribution results of influencing factors can provide a solid data foundation for decision-makers.

Capability Assessment of Cultivating Innovative Talents for Higher Schools Based on Machine Learning

The innovation capability largely determines the initiative for future development of a region. Higher school is the main position for training innovative talents. Accurate and comprehensive assessment of innovation cultivation capability is an important basis of higher schools for continuous improvement. Thus, this paper focuses on assessing innovative talent cultivation capability. First, by CIPP model (Context, Input, Process and Product Evaluation), an assessment indicator system is built, consisting of 89 indicators in 21 categories. Then, based on indicator characteristics, this paper uses public data statistics, database retrieving, student survey, teacher survey, support personnel and expert investigation, to collect indicator values. After this, by a powerful machine learning algorithm, gradient Boosting regression tree, a capability assessment model is established. And based on collected data, established model is compared with several regression models in innovative talent cultivation capability assessing. Results confirm the performance superiority of our solution.

Algorithm for community security risk assessment and influencing factors analysis by back propagation neural network

This paper aims to accurately assess and effectively manage various security risks in the community and overcome the challenges faced by traditional models in handling large amounts of features and high-dimensional data. Hence, this paper utilizes the back propagation neural network (BPNN) to optimize the security risk assessment model. A key challenge of researching community security risk assessment lies in accurately identifying and predicting a range of potential security threats. These threats may encompass natural disasters, public health crises, accidents, and social security issues. The intricate interplay of these risk factors, combined with the dynamic nature of community environments, presents difficulties for traditional risk assessment methodologies to address effectively. Initially, this paper delves into the factors influencing safety incidents within communities and establishes a comprehensive system of safety risk assessment indicators. Leveraging the adaptable and generalizable nature of the BPNN model, the paper proceeds to optimize the BPNN model, enhancing the security risk assessment model through this optimization. Subsequent comparison experiments with traditional models validate the rationality and effectiveness of the proposed model, with hidden layer nodes set at various levels like 10, 15, 20, 25, 30, and 35. These traditional models include Convolutional Neural Network (CNN), Long Short-Term Memory Network (LSTM), Bidirectional Encoder Representations from Transformers (BERT), Generative Pre-trained Transformer (GPT), and eXtreme Gradient Boosting (XGBOOST). Experimental findings demonstrate that with 20 hidden layer nodes, the optimized model achieves a remarkable final recognition accuracy of 99.1 %. Moreover, the optimized model exhibits significantly lower final function loss compared to models with different node numbers. Increasing the number of hidden layer nodes may diminish the optimized model's fit and accuracy. Comparison with traditional models reveals that the average accuracy of the optimized model in community risk identification reaches 98.5 %, with a maximum accuracy of 99.6 %. This marks an improvement of 9%–11 % in recognition accuracy across various risk factors compared to traditional models. Regarding system response time and resource utilization, the optimized model exhibits a response time ranging from 100 ms to 120 ms and consistently lower resource utilization rates across all scenarios, underscoring its efficiency in community security risk assessment. In conclusion, this experiment sheds light on the underlying mechanisms and patterns of community safety risk formation, offering novel perspectives and methodologies for researching community safety risk assessment. The paper concludes by presenting recommendations and strategies for addressing community safety risks based on experimental analysis.

SCIENTIFICALLY BASED RECOMMENDATIONS FOR INCREASING THE EFFICIENCY OF REAL ESTATE USE AT THE REGIONAL LEVEL

It is proved that real estate is an essential element affecting the functioning of regions, as its use forms the material basis of regional development. In recent years, there has been a decrease in the efficiency of real estate use at the regional level. In this context, we should note the negative impact of external and internal factors and the consequences of Russian aggression. Therefore, theoretical provisions regarding the definition of real estate need further improvement, considering international experience. In addition, the approach to applying the method and models for forming a quantitative basis for real estate use to develop relevant scientifically based recommendations needs reconsideration. The research has achieved the goal of developing scientifically based recommendations for increasing the efficiency of real estate use at the regional level. We have solved the following tasks in the article: substantiating theoretical provisions regarding the definition of regional real estate, forming directions for improving the efficiency of real estate use at the regional level, and proposing a mechanism for using real estate. Real estate is defined as a set of land plots and other objects located on it, forming spatial support from above and below, the use of which has a long-term nature and the movement of which leads to a violation of their functional characteristics. Spatial, functional, investment, and environmental factors influence the formation and use of real estate. Their interaction and definition allow building a multi-level system of assessment indicators to form a quantitative basis for making informed decisions. To increase the level of real estate use in the regions, we have further developed methodological approaches, as recommendations, to the assessment of real estate use based on methods and models that characterise the directions and features of the use of spatial, functional, investment, and environmental support, which provide the opportunity to form a mathematical basis and identify the appropriate toolkit for developing a method for assessing the level of real estate use in regions. Of particular importance are the results of mathematical modelling of real estate use processes at the regional level based on the establishment of relationships between systemic spatial, functional, investment, and environmental factors and an integral indicator by applying the method of correlation-regression analysis, model adequacy criteria, which allows determining directions for increasing the efficiency of use of the real estate. A mechanism for the use of real estate at the regional level is proposed based on the results of the assessment, mathematical modelling, and forecasting of the factors of real estate use, which, unlike the existing ones, made it possible to determine the directions of implementation of scientifically based recommendations and build geospatial monitoring maps. As a result of the study, we have developed a mechanism for real estate use at the regional level as a comprehensive tool for ensuring the efficiency of real estate use in the regions. Keywords: real estate, regional level, efficiency of use, recommendations, mechanism, geospatial monitoring maps, methods, models.

Multi-attribute decision-making research on investment suitability assessment of hydropower-wind-photovoltaic-storage complementary system based on dynamic social network

The hydropower-wind-photovoltaic-storage (HWPS) complementary system is one of the essential ways for China to build a new type of power system and to realize the dual-carbon goal. However, China's HWPS complementary system is still in the exploratory stage, and it is important to propose a rational multi-attribute decision-making (MADM) framework for investment suitability assessment based on completed hydropower plants. Therefore, this study first established a limitation indicator system for preliminary screening hydropower plants and an HWPS investment suitability assessment indicator system containing six dimensions: nature, market, infrastructure, land, society, and environment. Second, the weights among experts were determined based on the dynamic social network, and a mechanism for updating expert weights was proposed. Again, triangular intuitionistic fuzzy number (TIFN) was used to quantify the qualitative indicators and combined the weights according to the best-worst method (BWM) and criteria importance through intercriteria correlation (CRITIC) methods. Finally, the TODIM (an acronym in Portuguese for interactive and MADM) method was used for scheme suitability assessment with sensitivity analysis and method comparison. The results show that 62 eligible hydropower plants were preliminarily screened out of 647 completed medium- and large-scale hydropower plants in China by combining GIS technology. Natural factors are the most important of the six dimensions, followed by social factors. According to the suitability assessment results of TODIM, #23 hydropower station has the highest suitability for HWPSCS investment, which provides a reference for the layout of China's HWPS complementary system. With different decision makers' risk preferences and different MADM methods, #23 hydropower plant is still the best investment option, which proves the effectiveness of the MADM framework proposed in this study.

Multi-uncertainty river health assessment in the Loess Plateau based on a novel comprehensive similarity cloud model

With the development of related research on river health assessment, the traditional assessment methods cannot solve the uncertainty problems caused by diversified indicators and ambiguous weights. In order to comprehensively understand the river health status in the Loess Plateau and solve the uncertainty of assessment results, this study takes Wuding River as the research object, and constructs an assessment indicator system incorporating the ecological environment and social functions of the river. The genetic algorithm-BP neural network was used to establish a weight learning mechanism for assessment indicators, and the system weight was calculated by combining various weighting preferences. Based on the cloud model theory, a sample data cloud and a health level cloud were constructed, and a comprehensive similarity model considering the shape and distance of the cloud diagram was developed to identify the health level of Wuding River. The spatial and temporal variability of the river health status and the sources of pressure causing unhealthy problems were analyzed. It is shown that Wuding River is in basic health status, which gradually decreases from the upstream reaches to the downstream reaches. The health status of social service functions is better than that of the ecological environment attributes such as water resources and water quality. The sources of health pressure in the upstream and downstream reaches are different. In the upstream reaches, the pressure mainly comes from natural attributes such as successive declines in water production, slow governance of water and soil erosion, and the poor stability of the river. The health pressure in the midstream and dowmstream reaches comes from the interference of human economic activities, which include deterioration of water quality caused by the discharge of fossil energy industry, destruction of aquatic habitats, and flood safety hazards caused by insufficient standards for flood control project design. In addition, SDSCM provides river managers with accurate and comprehensive health risk information to help them manage rivers more effectively.

Rated load state performance assessment and analysis of ultra-supercritical coal-fired power plant

ABSTRACT To address the problems of unclear assessment standards and incomplete assessment systems in the performance assessment of ultra-supercritical coal-fired power plants. This study utilized the running dataset from a 660 MW ultra-supercritical coal-fired power plant in Xinjiang, China. By combining the analytic hierarchy process and the entropy weight method, a system of assessment indicators was established that used three layers of criteria. A running line graph representing power plant performance and containing 149 moments was obtained, which provided a theoretical basis and optimization direction for improving the power plant. The results show that the 149 running states of the power plant performance are all at level I. The boiler running level had 143 moments (95.97%), the steam-turbine level had 135 moments (90.60%), the power consumption rate level had 148 moments (99.33%), and the environmental performance level had 143 moments (89.94%) of running states at level I.

Assessment of the losses of ecosystem services of non-flowing waters as a consequence of military intervention

This study explores the challenges in developing a contemporary system of assessment indicators for determining damage to biodiversity. It specifically focuses on the eco-economic aspects of assessing losses in ecosystem services of non-flowing water bodies due to military intervention. To achieve the set objectives, the article utilizes a combination of general scientific and specialized methods from modern ecosystem theory and general ecological assessment, including analysis, synthesis, generalization, systematization, abstract-logical methods, and more. The methodological and theoretical foundation of the research is based on key principles of modern ecosystem theory (following the CICES methodology) regarding the assessment of ecosystems based on selected categories (types) of ecosystem services. In this context, a comprehensive evaluation of lost ecosystem services in non-flowing water bodies is proposed, focusing on major types of potential losses (functional features: provisioning, regulation and support, cultural and recreational services). Using the case study of the destroyed Kakhovka Reservoir, the article applies scoring methods (according to Ya. Didukh) and the «transfer of value» method (according to R. Costanza) to calculate losses in ecosystem services. The article suggests a comprehensive structure for the eco-economic assessment of losses in ecosystem services of non-flowing water bodies, combining two distinct components: ecological and economic. The ecological component may include scoring, expert, and experimental assessments, while the economic component may involve the assessment of losses in ecosystem services of freshwater ecosystems in non-flowing water bodies: a) by their major types (functional features); b) in general. The practical application of the proposed methodological approaches allowed for the monetary assessment of the complex of lost ecosystem services of the Kakhovka Reservoir due to military actions, amounting to approximately $27,452,889,000, which should be fully compensated by the aggressor state. Future research prospects lie in the theoretical-methodological justification and development of a domestic mechanism for assessing losses in ecosystem services of surface waters, particularly non-flowing water bodies, resulting from military intervention. These prospects are rooted in a comprehensive examination of scoring, expert, and experimental assessment indicators within the framework of general ecological assessment

Assessment of land degradation risks in the Loess Plateau

AbstractHuman activity and climate change are degrading the environmentally fragile Loess Plateau in dry and semiarid regions. Land deterioration threatens human and ecological existence. To prevent additional land degradation and ensure the ecological development and quality of arable land in the region, China launched “Grain for Green” in the late 1990s. This effort greatly boosted Loess Plateau vegetation. However, land degradation is complex, and so we must also examine natural and social variables to prevent additional degradation. Thus, this study presents a comprehensive index system to quantify land degradation on the Loess Plateau and uses machine learning to anticipate high‐risk locations. The project improved land degradation, and the spatial distribution of degradation risk is high in the northern and low in the eastern and southern regions of the Plateau. Gross Domestic Product and population density are the main drivers of land degradation. Industrialization and urbanization have raised the risk of land degradation, which now accounts for 1%–2% of the area. This study emphasizes sustainable land management in the Loess Plateau, a critical area for sustainable development in China. The integrated assessment indicator system and random forest modeling machine learning help grasp the current status and future preventive measures. The outcome of this study advances the Loess Plateau land degradation research and sustainable land management. The research findings possess significant scientific reference value in terms of mitigating and managing land degradation in environmentally vulnerable regions worldwide.

Vulnerability assessment and analysis of cryosphere changes in the Western Sichuan Plateau

ABSTRACT A vulnerability assessment model and indicator system for the changes in the cryosphere in the Western Sichuan Plateau are constructed based on the “exposure–sensitivity–adaptive capacity” framework. The model aims to capture the multiple characteristics of vulnerability to cryosphere changes in the region by selecting influencing factors from three dimensions: exposure, sensitivity, and adaptive capacity. Furthermore, the study explores the interrelationships among vulnerability dimensions and employs geographical detectors to analyze the driving mechanisms of vulnerability. The ultimate goal is to provide a scientific and rational basis for developing effective adaptation strategies to prevent and mitigate cryosphere-related disasters in the area. The main findings are as follows: (1) The vulnerability to cryosphere changes in the Western Sichuan Plateau exhibits a distribution pattern characterized by higher vulnerability in the west and lower vulnerability in the east. (2) There is a significant correlation between vulnerability and geographical location, characterized by two types of distribution patterns: high–high aggregation and low–low aggregation. The aggregation patterns are relatively stable but exhibit spatial heterogeneity. (3) The vulnerability to cryosphere changes in the Western Sichuan Plateau is the combined result of natural geographical conditions, socioeconomic development levels, and other factors.