Search Results (514)

Foreign direct investment (FDI) plays a vital role in fostering sustainable economic development, particularly in emerging and post-conflict economies. Yet, the benefits of FDI inflows depend not only on the size of investment but also on how evenly it is distributed across regions. In the Kurdistan Region of Iraq (KRI), FDI inflows have grown considerably over the past two decades, remaining heavily concentrated, with 93% of total investment absorbed by the capital city, Erbil, and only 7% distributed across the remaining governorates. This study investigates the determinants of geographic imbalances in FDI inflows within the KRI. Drawing on a unique firm-level dataset from 2007 to 2021 and employing a negative binomial logit model, the results reveal that superior infrastructure, greater market accessibility, proximity to international borders, airport connectivity, and digital network penetration are significant drivers of FDI concentration. We suggest that such spatial inequality poses significant risks to inclusive and sustainable growth, threatening to entrench regional disparities and reduce resilience to economic and local political disruptions in the long term. To mitigate these issues, we recommend a regionally differentiated policy framework that includes targeted investment incentives tailored to local comparative advantages, strategic infrastructure upgrades in underdeveloped areas, strengthened investor protections, streamlined regulatory processes, and the establishment of investment promotion agencies (IPAs) to enhance investor engagement and aftercare. By diagnosing the causes of FDI concentration and offering actionable strategies, this study provides evidence-based insights for fostering balanced, inclusive, and sustainable economic development in the KRI and other post-conflict regions confronting similar challenges. Full article

Air pollution is steadily increasing due to industrialization, economic activities, and transportation. High levels pose a significant threat to human health and well-being worldwide. Saudi Arabia is a growing country with air quality indices ranging from moderate to unhealthy. Although there are many monitoring stations distributed throughout the country, mathematical modeling of air pollution is still crucial for health and environmental decision-making. From this perspective, in this study, a data-driven approach based on pollutant records and a Deep Learning (DL) Long Short-Term Memory (LSTM) algorithm is carried out to perform temporal modeling of selected pollutants (PM₁₀, PM_2.5, CO and O₃) based on time series combined with a spatial modeling focused on selected cities (Riyadh, Jeddah, Mecca, Rabigh, Abha, Dammam and Taif), covering ~48% of the total population of the country. The best forecasts were provided by LSTM in cases where the datasets used were of relatively large size. Numerically, the obtained performance metrics such as the coefficient of determination (R²) ranged from 0.2425 to 0.8073. The best LSTM results were compared to those provided by two ensemble methods, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost), where the merits of LSTM were confirmed mainly in terms of its ability to capture hidden relationships. We also found that overall, meteorological factors showed a weak association with pollutant concentrations, with ambient temperature exerting a moderate influence. However, incorporating ambient temperature into LSTM models did not lead to a significant improvement in predictive accuracy. The developed approach can be used to support decision-making in environmental and health domains, as well as to monitor pollutant concentrations based on historical time series records. Full article

Wireless sensor networks (WSNs) are widely used in environmental monitoring, industrial automation, and smart cities. The Hybrid Energy-Efficient Distributed (HEED) protocol is a popular clustering algorithm designed to prolong network lifetime by balancing energy consumption among sensor nodes. However, HEED relies on simple heuristics for cluster-head (CH) selection, which may not fully exploit the complex spatiotemporal patterns in node energy and topology. This paper introduces a novel protocol, Deep Learning–Hybrid Energy-Efficient Distributed (DL-HEED), which, for the first time, integrates a Graph Neural Network (GNN) into the clustering process. By leveraging the relational structure of WSNs and a comprehensive set of node and network features—including residual energy, node degree, spatial position, and signal strength—DL-HEED enables intelligent, context-aware, and adaptive CH selection. DL-HEED leverages the relational structure of WSNs through deep learning, enabling more adaptive and energy-efficient cluster head selection than traditional heuristic-based protocols. Extensive simulations demonstrate that DL-HEED significantly outperforms classic HEED achieving up to 60% improvement in the network lifetime and energy efficiency as the network size increases. This work establishes DL-HEED as a robust, scalable, and practical solution for next-generation WSN deployments, marking a substantial advancement in the application of deep learning to resource-constrained IoT environments. Full article

With deepening global higher education reforms, outcome-based education has emerged as the core paradigm for teaching model innovation. This study investigates the structural dependencies and teaching effectiveness of the Environmental Design curriculum at Hubei Engineering University in medium-sized cities, China, addressing challenges of enrollment decline and market contraction critical for urban sustainability. Using network analysis, we construct curriculum support and contribution networks and course temporal networks to assess structural dependencies and teaching effectiveness, revealing structural patterns and optimizing the OBE-based Environmental Design curriculum to enhance educational quality and student competencies. Analysis reveals computer basic courses as knowledge transmission hubs, creating a course network with a distinct core–periphery structure. Technical course reforms significantly outperform theoretical course reforms in improving student performance metrics, such as higher average scores, better grade distributions, and reduced performance gaps, while innovative practice courses show peripheral isolation patterns, indicating limited connectivity with core curriculum modules, which reduces their educational impact. These findings provide empirical insights for curriculum optimization, supporting urban sustainable development through enhanced professional talent cultivation equipped to address environmental challenges like sustainable design practices and resource-efficient urban planning. Network analysis applications introduce innovative frameworks for curriculum reform strategies. Future research expansion through larger sample validation will support urban sustainable development goals and enhance professional talent cultivation outcomes. Full article

Accelerated urbanization and intensified urban development globally lead to increased sewage discharge, challenging environmental protection. Therefore, exploring the correlation mechanism between the economic development level (EDL) and sewage discharge intensity (SDI) is crucial for sustainable development. This study uses panel data from 288 Chinese cities between 2003 and 2021, employs spatial analysis techniques to uncover the spatiotemporal evolution characteristics of SDI, and investigates the influence of economic development on this intensity using spatial panel models. The results reveal that (1) while the spatial distribution of SDI in China generally exhibits a downward trend, changes in the Northeast region are relatively modest, with SDI remaining higher than in other regions. Global autocorrelation analysis further indicates significant spatial agglomeration and positive correlation effects in urban SDI. (2) Economic development exerts a notable inhibitory effect on SDI, with a 0.570% decrease for every 1% rise in GDP per capita, thus demonstrating a significant spatial spillover effect. (3) For megacities, large cities, and small and medium-sized cities, EDLs have significant negative spatial spillover effects on SDI, with a more pronounced impact on large cities. This study provides a theoretical foundation for sewage management and empirical support for environmental policies, crucial for sustainable urban development. Full article

China’s rapid urbanization has driven tremendous socioeconomic development while posing new forms of social–spatial inequalities that challenge environmental sustainability and spatial justice. This study investigates urban park-accessibility patterns across 10 s-tier provincial capital cities in China, examining how these patterns relate to housing-price dynamics to reveal diverse manifestations of social–spatial (in)justice. Using comprehensive spatial analysis grounded in distributive justice principles, we measure park accessibility through multiple metrics: distance to the nearest park, park size, and the number of parks within a 15 min walk from residential communities. Our findings reveal significant variation in park accessibility across these cities, with distinctive patterns emerging in the relationship between housing prices and park access that reflect different forms of social–spatial exclusion and inclusion. While most cities demonstrate an unbalanced spatial distribution of parks, they exhibit different forms of this disparity. Some cities show consistent park access across housing-price categories, while others display correlations between high housing prices and superior park accessibility. We argue that these divergent patterns reflect each city’s unique combination of economic development trajectory, politically strategic positioning within national urban hierarchies, and geographical constraints. Through this comparative analysis of second-tier cities, this study contributes to broader understandings of social–spatial (in)justice and urban environmental inequalities within China’s urbanization process, highlighting the need for place-specific approaches to achieving equitable access to urban amenities. Full article

To promote high-quality agricultural development and implement the “storing grain in the land” strategy, the construction of Well-Facilitated Farmland (WFF) plays a critical role in enhancing grain production capacity and optimizing the spatial distribution of food supply, thereby contributing to national food security. However, accurately assessing the potential impact of WFF construction on China’s grain production and regional self-sufficiency by 2030 remains a significant challenge. Existing studies predominantly focus on the provincial level, while fine-grained analyses at the city level are still lacking. This study quantifies the potential increase in grain production in China under the 2030 WFF construction target by employing effect size analysis, multi-weight prediction, and Monte Carlo simulation across multiple spatial scales (national, provincial, and city levels), thereby addressing the research gap at finer spatial resolutions. By integrating 2030 population projections and applying a grain self-sufficiency calculation formula, it further evaluates the contribution of WFF to regional grain self-sufficiency: (1) WFF could generate an additional 31–48 million tons of grain, representing a 5.26–8.25% increase; (2) grain supply in major crop-producing regions would expand, while the supply–demand gap in balanced regions would narrow; and (3) the number of cities with grain self-sufficiency ratios below 50% would decrease by 11.1%, while those exceeding 200% would increase by 25.5%. These findings indicate that WFF construction not only enhances overall grain production potential but also facilitates a transition from “overall supply-demand balance” to “structural security” within China’s food system. This study provides critical data support and policy insights for building a more resilient and regionally adaptive agricultural system. Full article

Automated parcel lockers (APLs) are transforming urban last-mile delivery by reducing failed distributions, decoupling delivery from recipient availability, optimizing carrier routes, reducing carbon foot-print and mitigating traffic congestion. The paper investigates the optimal design of APLs systems under stochastic demand and operational constraints, formulating the problem as a resource allocation optimization with service-level guarantees. We proposed a data-driven discrete-event simulation (DES) model implemented in ARENA to (i) determine optimal locker configurations that ensure customer satisfaction under stochastic parcel arrivals and dwell times, (ii) examine utilization patterns and spatial allocation to enhance system operational efficiency, and (iii) characterize inventory dynamics of undelivered parcels and evaluate system resilience. The results show that the configuration of locker types significantly influences the system’s ability to maintain high customers service levels. While flexibility in locker allocation helps manage excess demand in some configurations, it may also create resource competition among parcel types. The heterogeneity of locker utilization gradients underscores that optimal APLs configurations must balance locker units with their size-dependent functional interdependencies. The Dickey–Fuller GLS test further validates that postponed parcels exhibit stationary inventory dynamics, ensuring scalability for logistics operators. As a theoretical contribution, the paper demonstrates how DES combined with time-series econometrics can address APLs capacity planning in city logistics. For practitioners, the study provides a decision-support framework for locker sizing, emphasizing cost–service trade-offs. Full article

In advanced countries such as the USA and China, some cities are characterized by “knowledge spillover industries”, which play crucial roles in driving innovation, entrepreneurship, and economic growth. However, the excessive expansion of megacities in China has led to the overabsorption of labour from other cities. The unchecked growth of individual megacities causes metropolitan malaise and regional imbalance, further limiting the emergence of new “knowledge spillover” cities, which is detrimental to overall economic development. This study analyses China’s employment population structure to identify the critical population size required for the formation of “knowledge spillover” cities. The results show that 10 million is the unique threshold for which cities with populations above this size see a significant improvement in the prominence of “knowledge spillover” industries. Therefore, a population base of approximately 10 million is essential for these cities to thrive. This result suggests that China should pay more attention to the construction of urban agglomerations as geographic or administrative units to better distribute resources and promote balanced regional development. This approach can help foster the emergence of more “knowledge spillover” cities, thereby enhancing national innovation capacity and economic growth. Full article

Traffic congestion poses a persistent challenge for modern cities, yet the complex behavior of urban road networks—particularly multistability in traffic flow—remains poorly understood. To address this gap, we analyzed a high-resolution traffic dataset from four Chinese cities over 20 working days (5-min intervals), applying percolation theory to characterize system performance via congestion rate (f) and the size of the largest functional cluster (G). Our analysis revealed clear bimodal and multimodal distributions of G versus f across different periods, ruling out random failure models and confirming the presence of multistability. Leveraging data-driven clustering and classification techniques, we demonstrated that road segments with high betweenness centrality are disproportionately likely to become congested, and that the top 1% most topologically important roads accurately predict both stable state types and the joint behavior of G and f. These findings offer the first large-scale empirical evidence of multistability in urban traffic, laying a quantitative foundation for forecasting phase transitions in congestion and informing more effective traffic management strategies. Full article

Prevailing theories and empirical studies have suggested that the internal spatial structures of large cities have transformed from monocentric to polycentric. However, the existing literature primarily focuses on the definition, measurement, and quantity of the urban centers, with a lack of in-depth comparison of urban polycentricity in terms of dynamic centralization or dispersion. By analyzing the spatial distribution of firms in 35 large Chinese cities, this study examines the quantity, centralization degree, and primacy ratio of urban centers to compare spatial structure of the cities and explores the different types of urban polycentricity by employing nonparametric regression methods. The findings indicate that the spatial structures of most cities are polycentric forms, which display three types: emerging polycentricity, centralized polycentricity, and dispersed polycentricity. Further analyses suggest that social and economic factors such as GDP and population size are associated with the typology. Through the comparison of the 35 cities’ spatial structures, this study identifies three types of urban polycentricity and sheds light on the underlying forces of urban spatial restructuring process. Full article

Groundwater is a crucial source of water supply and an important ecological element globally. Research on the dynamic characteristics of groundwater and their causative mechanisms is fundamental to objectively evaluating groundwater resources and their sustainable utilization. Based on the large amount of hydrogeological data collected and analyzed in typical hilly plain areas, a multi-factor weighted comprehensive evaluation system (MFWCES) based on GIS was used to evaluate the response of groundwater dynamics to combined stress elements in Tangshan City. The study area is located in the plains and hilly regions of Tangshan City. The evaluation system was based on seven influencing factors, including hydraulic conductivity, soil media, aquifer thickness, depth of groundwater, land use type, extraction intensity of groundwater, and groundwater evaporation. The results of groundwater dynamics in the study area were obtained by weighted comprehensive evaluation, with their score size ranging from 2.4 to 12.7. The spatial distribution of groundwater dynamics was classified into four categories: rapid response (10.3–12.7), dual response to precipitation and anthropogenic extraction (9.6–10.3), delayed response (7.6–9.6), and strong superimposed response to human activities (2.4–7.6). The related conclusions will provide key references for regional water resource planning, ecological protection, and the development of differentiated groundwater management strategies under compound stress. Full article

Excess mortality due to heat is a major public health concern globally. In this study, we investigated the association between extreme heat and mortality in three distinct locations in São Paulo state, Brazil—São Paulo city (the capital), Campinas (a large countryside city), and Marília (a typical medium-sized rural city)—from 2004 to 2018. We applied a generalized linear model (GLM) with a Poisson distribution and a logarithmic link function for each city, using the excess heat factor (EHF) as the exposure metric. The results showed that increases in the EHF were associated with relative risks of 1.0018 (95% CI: 1.0015–1.0022) in São Paulo, 1.0029 (95% CI: 1.0023–1.0036) in Campinas, and 1.0033 (95% CI: 1.0025–1.0041) in Marília. Altogether, 2319 heat-attributable deaths were estimated, representing an economic burden of USD 6.03 billion based on the value of a statistical life. By integrating economic valuation with mortality risk estimates, our study offers a broader perspective on the consequences of extreme heat, reinforcing the need for public health and policy interventions. Full article

The Urban Green Space Index (UGSI) is an indicator that measures the quantity, quality, accessibility, and distribution of green spaces in urban environments. This study focused on analyzing the UGSI in Ecuadorian cities through a multiple linear regression model, analyzing the UGSI from both territorial and public management perspectives. Ecuador was selected as a case study due to the limited availability of research on urban green spaces in the country, despite its high ecological diversity and increasing urbanization. The model was used to explore relationships among various factors influencing urban green spaces. Government variables and key factors, such as budget allocations, were analyzed. The model revealed an inverse relationship between urban population size and per capita green space availability. In cities with 50,000 inhabitants, the average is 60 m² per person, which decreases significantly to just 5 m² per person in cities with 300,000 residents. This trend highlights the pressure of urbanization on green spaces and emphasizes the need for evidence-based urban planning to ensure equitable access and to improve quality of life. However, challenges such as the lack of updated data and opportunities for improvement in territorial planning were also identified. Full article

The increasingly observable financial vulnerability of households in emerging market countries makes it imperative to investigate the factors influencing it. Considering that China stands as a representative of emerging market economies, analyzing the factors influencing household financial vulnerability in China presents great reference significance for the sustainable development of households in emerging market countries. Using data from the China Household Finance Survey (CHFS) household samples, this paper presents the regional distribution of households with financial vulnerability in China. Utilizing machine learning (ML), this research examines the factors that influence household financial vulnerability in China and determines the most significant ones. The results reveal that households with financial vulnerability in China takes up a proportion of more than 63%, and household financial vulnerability is lower in economically developed coastal regions than in medium and small-sized cities in the central and western parts of China. The analysis results of the SHAP method show that the debt leverage ratio of a household is the most significant feature variable in predicting financial vulnerability. The ALE plots demonstrate that, in a household, the debt leverage ratio, the age of household head, health condition, economic development and literacy level are significantly nonlinearly related to financial vulnerability. Heterogeneity analysis reveals that, except for household debt leverage and insurance participation, the key characteristic variables exerting the most pronounced effect on financial fragility differ between urban and rural households: household head age for urban families and physical health status for rural families. Furthermore, digital financial inclusion and social security exert distinct impacts on financial vulnerability, showing significantly stronger effects in high per capita GDP regions and low per capita GDP regions, respectively. These findings offer valuable insights for policymakers in emerging economies to formulate targeted financial risk mitigation strategies—such as developing household debt relief and prevention mechanisms and strengthening rural health security systems—and optimize policies for household financial health. Full article