The intensification of global climate change and human activities has exacerbated the supply-demand imbalance of urban water-soil ecosystem services (WSES). However, the compounded impacts of climate change and human-induced functional space transformation remain unclear. It is necessary to examine how dual pressures restructure the production, transfer and consumption of future WSES. Therefore, this study proposed an integrated supply-flow-demand framework to simulate the spatiotemporal WSES evolution under future composite scenarios. Through cross-scenario comparative analysis, the proposed framework examined the independent contributions and interactive effects of climate change (CC) and functional space change (FSC) on WSES supplydemand, and employed network analysis to elucidate the structural properties of WSES flow networks. Taking 69 cities in the Yellow River Basin as a representative case, CC contributed over 65.28% to the internal supplydemand variations, while FSC's influence strengthened, doubling for water yield and rising about 6.5-fold for soil retention, emerging as a key regulator. CC-FSC interactions exhibited antagonistic effects across 65.75% of the basin due to geographical heterogeneity. Moreover, extreme climate increases inter-urban WSES flows, raising water yield by 4.40-5.06 billion m3 and soil retention by 0.83-0.85 billion tons, partially alleviates internal supply-demand imbalances. The biophysical differences drive contrasting WSES network responses: water yield flow network become centralized and fragile, while soil retention flows gain redundancy. From the perspectives of internal supply-demand balance and external flow networks, this study highlights the critical role of ecosystem service flows in mitigating supply-demand imbalances across urban agglomerations, providing a scientific basis for precision spatial planning and climate-adaptive strategies.

Ecosystem-based disaster risk reduction (Eco-DRR) has garnered increasing attention due to its sustainability and cost-effectiveness. This study extends Eco-DRR to drought contexts by proposing the concept of Ecosystem-based Drought Disaster Risk Reduction (Eco-DDRR) and develops a framework to enhance Eco-DDRR capacity through zoning optimization and improvement factors. Focusing on urban agglomerations in the Yellow River Basin, this study develops separate indicator systems for Eco-DDRR capacity in terms of supply, demand, and influencing factors, which were quantified using the InVEST model, the Revised Wind Erosion Equation model, and the drought hazard-exposure-vulnerability model. Based on these indicators, cities with similar Eco-DDRR supply-demand structures were clustered using the Self-Organizing Map network, resulting in several improvement zones. Intervention priorities for each zone were assessed using the AHP-PCA-TOPSIS method, and Spearman’s rank correlation was used to identify synergistic improvement factors within each zone. The results showed that priority improvement zones of Eco-DDRR capacity were located in the eastern cities of the Central Plains Urban Agglomeration, the Shandong Peninsula Urban Agglomeration, and the Backward U-shaped Bend Metropolitan Area. To enhance Eco-DDRR capacity in these zones, strategies should focus on increasing forest coverage, reducing secondary industries, promoting the tertiary sector, integrating urban development with ecological protection, strengthening higher education resource allocation, and minimizing pollutant emissions. This study provides theoretical insights and practical guidance for building sustainable and disaster-resilient cities.

Integrating functional zoning based on ecosystem service (ES) supply-demand into urban planning has received little attention. Therefore, a novel framework for urban planning in rapidly urbanized cities from the functional zoning perspective is presented. Here, urban functional zones are first delineated based on the ES supply-demand. Then, key urban functional zones are designated as restricted development zones to simulate land use structures under different urban planning schemes. Next, the supply, demand, and supply-demand relationship of ESs under different schemes are re-evaluated to analyze the simulation effects. Finally, the applicability and operability of this framework is demonstrated through a case study of Suzhou, China. The results showed that Suzhou is divided into five functional zones, three of which are set as restricted development zones in the scenario simulation of corresponding schemes. The supply-demand imbalance of ESs except for soil retention and food production is significantly alleviated under the ecological protection scenario, but is aggravated under the economic development scenario. The food security scenario improves the supply-demand balance of carbon storage and food production, while detrimentally affecting other ESs. Overall, different planning schemes have distinct impacts on the ecological security of rapidly urbanized cities.

Safeguarding water quality is fundamental to the sustainability of inter-basin water transfer projects. Because this goal requires coordinated efforts among multiple parties, an effective cost-sharing mechanism is essential. However, establishing such a mechanism remains challenging, as water quality safeguarding creates shared benefits while imposing uneven costs on different parties. To address this issue, this study develops a Stackelbergevolutionary game framework with payoffs constructed from operational equilibrium outcomes for a water transfer supply chain consisting of the supplier in the source area and the distributor in the beneficiary area, with government intervention and consumer behavior incorporated as external factors. The evolutionary game is used to examine the formation and stability of cost-sharing mechanisms. Instead of specifying payoff functions directly, the payoff matrix is constructed from equilibrium profits obtained by solving Stackelberg games under four cost-sharing strategy combinations. These profits, together with government incentives and coordination costs, are incorporated into the evolutionary analysis, thereby linking operational decisions with cost-sharing strategy selection. A case study of the Shandong section of China's South-to-North Water Transfer Project shows that bilateral cost-sharing yields the highest water quality level, level of water quality information disclosure, and social welfare, whereas mutual no cost-sharing yields the lowest outcomes. Under bilateral cost sharing, these three indicators are 27.6%, 14.9%, and 5.5% higher than those under mutual no cost-sharing, respectively. The results further show that cost-sharing ratios, government incentives, consumer preference, and consumer trust are key factors affecting the stability of bilateral cost-sharing. This study develops an analytical framework to examine the formation and stability of cost-sharing mechanisms for water quality safeguarding in inter-basin water transfer projects, thereby providing theoretical support for operational management and policy design in sustainable water governance.

Ecosystem-based disaster risk reduction (Eco-DRR) offers an effective, sustainable, and cost-efficient approach to mitigating drought risk. This study integrated Eco-DRR capacity into the components of drought risk, considering both the long-term characteristics of drought risk and the coupling coordination of its components, thereby establishing a robust drought risk assessment framework. Using county-level cities in China’s three northeastern provinces from 2000 to 2022 as a case study, time series data for drought risk and the coupling coordination of its components were constructed using the drought index, InVEST model, and AHP-CRITIC method. These time series data were then fitted with eight marginal distributions, and based on these distributions, the comprehensive drought risk for each city was classified into four levels: severe, dangerous, mitigated, and safe. The results show that cities in the three northeastern provinces generally experienced low or moderate drought hazards, high or very high ecological sensitivity, low or very low economic vulnerability, very low, low, or moderate drought risk, and basic or moderate coupling coordination. Spatially, cities in the western regions of these provinces and the eastern part of Heilongjiang Province exhibited higher drought risks. Under maximum probability, 151 cities were classified as severe, 40 as dangerous, 80 as mitigated, and 9 as safe. Severe cities should strengthen drought warning systems and emergency response, dangerous cities should coordinate economic development with ecological protection, mitigated cities should optimize industrial layout, and safe cities should enhance risk prevention. These findings contribute to promoting the development of sustainable, disaster-resilient cities.

This study examines the impact of drought on the agricultural productivity in Pakistan, focusing on wheat yield prediction. Using the Long Short-term Memory (LSTM) model, the research integrates the Standardized Precipitation Evapotranspiration Index (SPEI) and water availability data to assess the effects of seasonal and annual droughts. Drought trends from 1990 to 2020 reveal severe dry periods, particularly those in 2001, which led to crop failures despite a steady increase in wheat yields. Water availability has remained stagnant, highlighting the challenge of maintaining growth with limited resources. A correlation analysis shows a strong negative relationship between drought severity, water availability (–0.95), and wheat yield (–0.98). The LSTM model outperforms other ones, including ARIMA, Linear Regression, and Random Forest, with an R^2 score of 0.8, explaining 80

Hazard Analysis and Critical Control Points (HACCP) is the globally recognized preventive framework for food safety management. Its application across supply chains has undergone significant evolution, driven by expanding business and regulatory demands. Despite extensive research, the synthesis on thematic trajectories, collaborative networks, and technological frontiers remains insufficient. This study employs a bibliometric analysis of 900 HACCP-related articles from the Web of Science (1990-2025) using CiteSpace to create a comprehensive knowledge graph. The main findings reveal that: (1) The publication volume exhibits distinct phasic trends, spanning diverse disciplines, including agriculture, medicine, environmental science, engineering, and biology. (2) Strongly integrated collaborations or stable innovative teams remain scarce, furthermore, manuscript outputs from developing regions require further enhancement. (3) The notable hotspots converge around four themes-standard system construction, precision hazard control, intelligent process optimization, and holistic risk governance. Building on these insights, future investigations will emphasize four critical directions: technology-driven inclusive innovation, sustainability-centric system integration, participatory trust building and transboundary regulatory synergy. This study provides a valuable reference for researchers seeking to advance safety governance and risk management.

Balancing urban development and ecological conservation through coordinated land-use structure remains a core challenge in land-use planning. This study aims to identify the optimal proportion ranges of land-use types under the dual objectives of ecological protection and urban development by developing an analytical framework integrating Extreme Gradient Boosting (XGBoost), Shapley Additive exPlanations (SHAP), and Accumulated Local Effects (ALE). Taking the Central Plains Urban Agglomeration as a case study, four ESs, including water retention, soil conservation, windbreak and sand fixation, and carbon storage, were quantified using the InVEST and RWEQ models. Meanwhile, four urban development indicators, including population density, economic density, the share of tertiary industry in GDP, and urbanization rate, were derived from statistical data. An XGBoost model was then used to establish relationships between land-use type proportions and these indicators, which were interpreted using SHAP and ALE to identify contribution patterns, local effects, and threshold ranges, thereby determining group-optimal and group-suboptimal ranges for land-use type proportions. The results showed that the group-optimal proportion ranges of land-use types in the Central Plains Urban Agglomeration were 32.17

Assessing urban ecosystem service supply (UESS) by considering the water-energy-food (WEF) system can facilitate the long-term effective management of urban natural resources and ecosystems. However, there exists a dearth of knowledge regarding the multi-scenario assessment of UESS from the WEF system perspective. To address this gap, a methodological framework was established for evaluating the UESS from the WEF system and multi-scenario perspective. First, the relationship between the WEF system and UESS was identified from theory to method, and the driving factors of UESS were evaluated. Next, land use patterns under alternative scenarios (current development continued (CD), economic development (ED), food security (FS) and ecological protection (EP) scenarios) were simulated. Lastly, the UESS under different scenarios was re-evaluated. Through the case studies of Shanghai, Suzhou and Hangzhou, China, the results demonstrated that there were significant correlations between the WEF system and the UESS under the influence of different indicators. Various factors had significantly different impacts on the UESS of different cities. The carbon storage supply in three cities under the CD, ED and FS scenarios reduced. The FS scenario improved the supply of water yield and food production, and the EP scenario enhanced carbon storage supply in Hangzhou.

Food and water security are critical challenges in Pakistan, exacerbated by rapid population growth, climate variability, and limited resources. This study explores the application of machine learning techniques to address these issues. We specifically examine the dimensions of food and water security in Pakistan, employing data-driven methods to enhance crop yield predictions, food production forecasting, and water resource management. Using secondary data, we refine machine learning models, such as random forest and linear regression, to analyze water availability, crop yield, and crop production. These models aim to optimize resource distribution, improve irrigation efficiency, and minimize water waste. We propose developing AI-based predictions to address food and water crises proactively. Our findings indicate that food insecurity persists in Pakistan, worsened by uneven distribution. Given the country’s high dependence on irrigation for crop production, we analyze the impact of population growth on food production and water demand. We recommend a comprehensive strategy that includes infrastructure development, improved water use efficiency in agriculture, and policy adjustments to balance food imports and exports.

Urban flood resilience (UFR) and ecosystem services are crucial concepts and methods for mitigating flood risks and boosting the coordinated and sustainable development in urban agglomerations. While most studies assess UFR or ecosystem services from a single perspective, the interaction between the two remains insufficiently explored. Here, an integrated analytical framework was established to examine the relationship between the two. Taking the Yangtze River Delta Urban Agglomeration as a sample region, the research first constructed an assessment index system for evaluating UFR levels. Then four ecosystem services covering water conservation, soil conservation, water purification, and climate regulation, were quantified. Finally, Pearson correlation coefficient, coupling coordination degree (CCD) model, and geo-detector model were comprehensively utilized to investigate the relationship and underlying diving factors. The findings reveal significant positive correlations between UFR and ecosystem services, with particularly strong correlations observed between each ecosystem service and natural resilience. Spatial heterogeneities in CCDs between UFR and ecosystem services were prominent, with southern cities demonstrating higher coordination levels than northern cities. Moreover, the CCDs exhibited an improvement trend over the research period. Factor analysis identified both ecological environmental conditions and human socio-economic activities as significant determinants of these patterns, with technological investment emerging as the dominant driver in interactive detection. Accordingly, differentiated spatial governance strategies that leverage technological innovation to balance ecological conservation with economic growth, such as cultivating eco-economic industries, delineating ecologically vulnerable areas, and developing localized ecological engineering methods, were proposed to facilitate the coordinated and sustainable urban development. The methodological framework and management insights proposed can be also applicable to other regions.

This study investigates the impacts of climate extremes shifting precipitation patterns and rising temperatures on agricultural productivity and rangeland ecosystems in Pakistan. Using data from 23 Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate models (GCMs), the research projects future climate scenarios under two socioeconomic pathways, SSP2-4.5 and SSP5-8.5, from 2015 to 2100. Advanced machine learning (ML) techniques, including Long Short-Term Memory (LSTM) networks, Gradient Boosting, Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Linear Regression, were employed to analyze historical (1980–2014) and future climate trends. The results show that LSTM outperforms other models in predicting temperature and precipitation extremes, achieving higher R2 values and lower prediction errors (MSE/RMSE). Under the SSP5-8.5 scenario, severe vulnerabilities are projected for Pakistan, with intensified heatwaves, erratic rainfall, and prolonged droughts reducing crop yields and rangeland productivity. These climate extremes exacerbate food insecurity and disrupt pastoral livelihoods, making adaptive strategies, such as climate-resilient crops and sustainable water management, critical for mitigating these risks. The study highlights the urgent need for targeted policies, including early warning systems and farmer education, to strengthen Pakistan’s capacity to cope with climate impacts, ensuring food security and ecosystem resilience.

This study investigates the impacts of climate variability, particularly temperature extremes and drought conditions, on wheat and rice production in Pakistan, a South Asian country facing significant agricultural challenges due to its diverse geography and climate. The primary objective of this research is to assess the relationship between climate variables (Land Surface Temperature (LST), Precipitation, and Standardized Precipitation Evapotranspiration Index (SPEI)) and crop yields for wheat and rice. The methodology integrates high-resolution climate data, including ERA5 LST and water availability datasets, with agricultural statistics from the Pakistan Bureau of Statistics. The analysis includes a time series evolution of climate variables and their correlation with crop yields, followed by the application of multiple regression models, machine learning techniques (including Polynomial Regression, Random Forest, and Support Vector Regression), and ridge regression for dimensionality reduction and model optimization. Results indicate that both wheat and rice yields exhibit an upward trend over the study period, with notable fluctuations attributed to rising temperatures and increasing drought frequency. The Random Forest model outperformed other methods, demonstrating high predictive accuracy for yield forecasting. The findings highlight the significant role of temperature in influencing crop productivity, while drought conditions, as indicated by SPEI, exert a negative impact on yield variability. This research contributes valuable insights into the climate-agriculture relationship in Pakistan and underscores the need for adaptive agricultural practices and climate-resilient strategies to mitigate the adverse effects of climate change on food security.

It can support decision-making for formulating rational management policies to advance regional sustainable development by evaluating ecosystem services (ESs) and analyzing their relationship on the metropolitan area scale under setting various development goals in future. Hence, with a view to achieve this research purpose, a general conceptual framework was built in this paper. Selecting the Nanjing metropolitan area (NMA) for the case, this paper first quantified the spatio-temporal evolution of ESs. Next, three development scenarios (business-as-usual (BAU), economy-centered development (ECD), and ecological protection priority (EPP) scenarios) were defined. Then the spatial distribution patterns of the NMA under these scenarios were simulated. Finally, the relationship between pairs of ESs was evaluated to evaluate their trade-offs/synergy. The analysis revealed that the issue of developed land encroaching cultivated land was far more serious in the BAU and ECD scenarios, and the occupied area were 2730.00 km2 and 3019.90 km2, respectively. Meanwhile, the ecological land had expanded under the EPP scenario, among which the area of water body has increased by 859.62 km2. Water yield had synergy relationship with other three ESs. However, nitrogen export had trade-off relationship with carbon storage/soil retention. ESs were significantly improved, and the synergy relationship between ESs became stronger in the EPP scenario, while the trade-off relationship between ESs under the BAU scenario were weaker. Therefore, plausible and effective land use management policies were presented to strengthen the sustainable development of metropolitan areas.

Green development, as a development strategy in China, imposes higher demands on the water-energy-foodecosystem (WEFE) nexus. To alleviate resource and ecological pressures, this study constructs a complex mega-system that focuses on water, energy, food, and ecology as well as economic, social, and environmental aspects based on complex systems science. Taking the Yangtze River Economic Belt (YREB) as an example, system dynamics is used to establish the causal feedback mechanisms of the WEFE system. The study simulates the supply-demand and ecological changes of water, energy, and food in the YREB from 2020 to 2030, and conducted a simulation of regional green development policy under the WEFE nexus. The results showed that under the ecological priority policy, the green space area increased by 3.4%, and food production rose by 1.7%, significantly enhancing the ecological environment and increasing food yields. It is estimated that by 2030, the energy self-sufficiency rate will reach 60.2% with the implementation of strengthened hydroelectric energy policy, reducing industrial SO2 emissions by 36,000 tons and reversing the declining trend of energy selfsufficiency while controlling industrial SO2 emissions. However, the challenge of insufficient energy supply during the "transition period" remains significant. Under the policy of technological innovation, water usage decreased by 26.6%, energy consumption decreased by 11.2%, and food production increased by 20.827 million tons, resulting in varying degrees of improvement in water, energy, food, and ecology subsystems. Nevertheless, technological innovation policies cannot reverse the continuing decline in energy self-sufficiency. The policy of comprehensive coordinated development significantly improves ecological quality, the development and utilization rate of water resources, energy self-sufficiency, and food surplus.

Water quality-improving and quantity-saving are crucial for the water environmental protection and sustainable water resources utilization in water diversion projects. Water diversion projects, which possess both public benefit and profit-driven characteristics, are not only influenced by the market transaction mechanisms but also influenced by the government interventions. This study develops a water diversion supply chain framework involving the government, supplier, distributor, and consumers to enhance social welfare by integrating water quality-improving and quantity-saving. Considering consumers' preferences for water quality, a Stackelberg game model is used to capture decision-making interactions among stakeholders under a benchmark and three government intervention scenarios. The effectiveness of three interventions is evaluated via theoretical derivation and real-case analysis. We further examine the impacts of consumer preferences, precipitation, and government criteria on social welfare. The results indicate that all three government interventions can enhance both supply chain's profit and consumer surplus when the water quality-improving cost coefficient is below the threshold of 0.556. Further, under dynamic environmental conditions (i.e., precipitation variation) or changes in government concern criteria, hybrid intervention shows greater robustness compared to single quality subsidy or quantity regulation, consistently yielding the highest social welfare. Note that misjudging consumer preferences for water quality may lead to substantial social welfare losses, with the maximum deviation in social welfare under different preference scenarios reaching 59.63%. Our findings provide practical insights for government to improve quality and save quantity in the water diversion projects for social welfare improvement.

Ecosystem health assessment is a prerequisite for understanding the dynamics of urban ecosystems. However, there is a lack of response analysis of urban ecosystem health before and after considering the material flows of key ecosystem services. Therefore, this study developed an urban ecosystem health assessment framework (VORMES) that integrates ecosystem services related to material flows and proposed the support vector regression redefined by northern goshawk optimization (NGO-SVR) to improve the evaluation accuracy. Then, taking the Nanjing metropolitan area as an example, the spatiotemporal evolution of the ecosystem health was assessed. The comparative framework variation index (CFVI) quantifies the differences between the VORMES and traditional frameworks, with spatial agglomeration and hotspot distribution used to explore their underlying causes. The results showed that the ecosystem health exhibited a multilayered composite core-edge bipolar diffusion pattern spatially. Compared to traditional frameworks, the assessment result of the VORMES framework was lower in general, and the CFVI had a significant spatial aggregation pattern. The hotspot analysis found that cold spot areas were concentrated in north-central areas of low total ecosystem service provision, while hotspot areas were concentrated on east-central areas of balanced ecosystem service. The external supply and internal equilibrium of ecosystem services are key factors driving this difference. Moreover, the SVR and the support vector regression with Bayesian optimization (BO-SVR) were chosen for the algorithm comparison test. The performance of NGO-SVR has an obvious advancement relative to SVR. The fitting accuracy was ranked as NGO-SVR > BO-SVR > SVR. Finally, based on the assessment results, differentiated management strategies for the one belt and three zones are proposed to safeguard the sustainable development of metropolitan ecosystems. This study provides a powerful tool for guiding metropolitan ecosystem restoration, offering valuable insights for metropolitan areas that face similar ecological pressures within and beyond China. (c) 2025 American Society of Civil Engineers.

To mitigate the greenhouse effect and promote green, sustainable development, this study evaluated the carbon emission efficiency (CEE) of 27 cities in the Yangtze River Delta urban agglomeration (YRDUA), China. An interpretable machine learning model, based on the SHapley Additive exPlanations (SHAP) method and optimized with ant colony optimization (ACO), was employed to identify the effects of relevant factors on CEE. Furthermore, the SHAP-based results were innovatively applied to Moran’s I test to examine the spatial clustering of these effects. The findings indicate that (1) the spatial clustering pattern of CEE within the YRDUA became increasingly evident during the study period, accompanied by pronounced interprovincial disparities and increasingly differentiated provincial and municipal distribution patterns. (2) Human activities partially offset the positive effects of some factors on CEE, while technological progress and energy efficiency improvements significantly enhanced the positive effects of others. (3) The impact of population density on CEE exhibited significant spatial clustering effects, forming three distinct clusters due to the interaction between urban development on different stages and other influencing factors. By developing an integrated analytical framework that combines efficiency evaluation, interpretable machine learning, and spatial analysis, this study advances CEE research and provides targeted insights for urban decarbonization.

Ensuring the supply-demand security of water-energy-food systems (WEF) is paramount in sustainable cities. Leveraging ecosystem services (ESs) as a bridge between WEF supply and demand, this study proposes a conceptual framework for assessing the supply-demand security of WEF from the perspective of intra-city coupling and inter-city linkages of WEF-related ESs. Considering 14 Liaoning cities, we developed supply-demand indices for three ESs: water yield, carbon storage, and food production. The supply-demand security pattern of WEF was evaluated using Spearman's rank correlation, the Copula model, the coupling coordination degree model, the gravity model, and social network analysis, using these indices. The results show that the supply-demand security of WEF was higher in eastern Liaoning cities and weakened westward. Dandong had the highest supply-demand security, with a 98% probability of achieving moderate surplus in WEF resources. The regions of DandongLiaoyang-Anshan-Yingkou-Panjin-Jinzhou and Tieling-Fushun-Benxi formed two extreme WEF coupling coordination gravity networks. Liaoyang, Panjin, and Fuxin emerged as hubs in the WEF coupling coordination gravity networks and exhibited the highest degree and betweenness centrality values. Additionally, Fushun, Liaoyang, Dandong, and Tieling were identified as WEF high coupling coordination nodes. This supply-demand security assessment framework for WEF offers a scientific basis for developing sustainable city strategies.