Allocation Index Research Articles

Distinct Neural Mechanisms Underlying Dual-Task Priority During Gait Across Cognitive and Motor Networks.

Background: Prioritization strategy during gait significantly influences gait performance and successful gait relies on interactions between cognitive and motor functions. This study aimed to examine the within- and between-network connectivities of cognitive and motor networks associated with dual-task priority during gait. Methods: Twenty-nine healthy individuals (66.86 ± 8.53 years) underwent the timed-up-and-go (TUG) test alone, TUG with a cognitive task, and the cognitive task alone. The cognitive task involved sequentially subtracting three from a random number between 50 and 100. The resting-state functional magnetic resonance imaging was acquired on the same day. Using independent component analysis, the dorsal attention network (DAN), frontoparietal network (FPN), primary motor network (PM), and lateral motor network were assessed. The participants were divided into cognitive and motor priority groups based on the modified attention allocation index (mAAI). Group comparisons of within- and between-network connectivity were conducted using permutation tests. Additionally, correlation analysis was employed to investigate the association between-network connectivity and task priority. Results: The cognitive priority group showed cognitive dual-task facilitation. In comparison to the motor priority group, they also showed comparable motor dual-task costs and lower combined dual-task costs. They exhibited increased within-network connectivity in the left FPN and enhanced between-network connectivity between the right FPN and both the DAN and PM. These between-network connectivities were negatively correlated with mAAI scores. Conclusion: The results suggest distinct neural mechanisms across cognitive and motor networks based on individuals' dual-task strategies. This may have implications for understanding gait performance in complex contexts.

Regional Healthcare Resource Allocation and Decision-Making: Evaluating the Effectiveness of the Three-Stage Super-Efficiency DEA Model

This study addresses the challenge of achieving a more rational allocation of medical resources at the regional level, using Guangxi Province, China, as a case study. A three-stage super-efficiency Data Envelopment Analysis (DEA) model is employed to assess and analyze the effectiveness of resource allocation. The research methodology involves identifying input, output, and environmental variable indicators to construct a healthcare resource allocation index system. The indicator data are processed using Excel software. The three-stage super-efficiency DEA model is then applied to evaluate the healthcare system in Guangxi Province, focusing on Pure Technical Efficiency Change (PEC), Scale Efficiency Change (SEC), Efficiency Change (EC), Technological Change (TC), and Total Factor Productivity (TFP). Finally, the Malmquist index method is utilized to measure and dynamically analyze the efficiency of healthcare resource allocation. The study's results show that, from a static perspective, the average comprehensive efficiency is 1.067 before adjustment and 1.054 after adjustment, indicating relatively high overall efficiency in healthcare resource allocation in Guangxi Province. However, environmental factors and random errors have led to an overestimation of healthcare resource allocation efficiency, which the three-stage super-efficiency DEA model effectively corrects. Additionally, the average SEC and PEC values are 0.997 and 0.998, respectively, both below 1. This indicates that both scale efficiency and pure technical efficiency contribute to a decline in technical efficiency. Based on the results of the sensitivity analysis, the conclusions regarding the efficiency of healthcare resource allocation in Guangxi are deemed highly reliable. Despite the influence of uncertain factors, the model consistently provides stable and coherent assessment results in most scenarios. Therefore, special attention is needed to improve scale efficiency in healthcare resource allocation within the region, alongside enhancing management and technological capabilities in the healthcare sector. Overall, this study provides valuable reference and guidance for researchers and practitioners in related fields and offers scientific decision support for healthcare resource allocation.

Interpretation of Fairness Concept and Economic Impact Analysis of Carbon Quota Allocation between Industries in Indonesia

Abstract Quota allocation is critical in implementing supporting policies for carbon reduction efforts, such as the carbon cap and trade mechanism. Depending on the allocation rules, certain company types may benefit more economically. Inequality conditions can happen and reduce the willingness to participate in sustainability efforts. This study aims to propose fairness interpretations in the context of company-level carbon quota allocation. Four industries in Indonesia with inherently different emission and financial profiles were selected as case studies: energy, oil palm, basic materials, and finance. Fairness principles were analyzed to represent the interests of companies based on their financial and environmental performance. Indicators were selected to quantify the equality principles and aggregated into a Comprehensive Index (CI) for quota allocation, where the companies’ quota surplus/deficit is quantified into monetary value to estimate the economic impact with varying reduction targets. According to our analysis, the sectoral advantages/disadvantages would depend on the viewpoint of fairness principles, and the oil palm and financial sectors be less impacted than the basic materials and energy sectors with increased emission reduction targets. The sensitivity analysis suggests that the vertical, horizontal, polluter pays, and historical responsibility principles are more sensitive to weighting than the merit and basic needs principles.

Study on Parking Adaptability in Urban Complexes on Top of Subways Based on Shared Parking Spaces

Urban complexes on top of subways as a function of intensive building groups, including residential, office, business, and other types nature of land use where parking time differences are obvious, can implement shared parking spaces, thereby reducing the index of parking allotment. Currently, the parking space allocation index for complexes is only a simple superposition of different land uses, resulting in an over-allocation of parking allotments, leading to a waste of land resources and a low utilization rate of parking allotments. Considering the factor of shared parking spaces, this paper conducted an in-depth analysis of the parking adaptability of urban complexes on top of subways and selected five urban complexes on top of subway stations in Wuhan to conduct a parking survey to analyze the parking demand characteristics. This study also investigated the parking behavior of parkers and analyzed the characteristics of parking behavior in urban complexes on top of subways as well as the current parking demand prediction methods and models, establishing a parking demand prediction model based on shared parking spaces and conducting an adaptability analysis. Finally, using five urban complexes in Wuhan as examples, the number of parking spaces demanded by urban complexes on top of subways in 2025 was predicted, and Wuhan Golden Harvest Fashion Plaza was used as an example to verify the feasibility and implementation ability of the theoretical and applied research in this paper.

A multicriteria vulnerability index for equitable resource allocation in public health funding.

This paper describes an action research project with the Centre universitaire intégré de santé et de services sociaux - Capitale Nationale (CIUSSS-CN) who identified a need to assess vulnerability in their territories in order toensure equitable distribution of the Integrated Perinatal and Early Childhood Services (SIPPE) program funds. The objective was to design and validate a multicriteria model to provide a more accurate portrait of vulnerability based on recent social realities. Our multidisciplinary research team of 7 members included experts in analytics, decision aiding, and community and public health. In collaboration with 6 CIUSSS-CN professionals, we co-constructed, during 9 workshops, a multicriteria model to aggregate the multiple dimensions of vulnerability. We used a value-focused thinking approach and applied the method MACBETH assisted by a geographic information system. Criteria, scales, and weights were validated and led to a vulnerability score for each CIUSSS-CN territory. This score provides a more accurate portrait of territorial disparities based on data and the participants' experience. The model was implemented in a dynamic user-friendly tool and serves to support decision-makers in the resource allocation process. Knowledge transfer was conducted during and after the process. This multidisciplinary research has served to anchor public health funding in local realities, with an emphasis on equity and stakeholder engagement. Our mixed-method approach integrating qualitative and quantitative data is adaptable to other contexts. Our results can enhance intervention effectiveness and allow for a better response to the needs of the population targeted by the SIPPE program.

Network link prediction via deep learning method: A comparative analysis with traditional methods

In the domain of data-centric networks, Link Prediction (LP) is instrumental in discerning potential or absent connections among entities within complex networks. By employing graph data structures, LP techniques enable a detailed analysis of entity interactions across varied sectors, contributing significantly to overcoming challenges in data filtering and integrity restoration, primarily when the network does not provide embedded data. Although LP methods are widely applicable, especially in recommender systems, their efficacy in current social networks needs to be thoroughly investigated. This study introduces an innovative LP approach using Deep Neural Networks (DNNs). We compare our method against a comprehensive set of established techniques, including traditional score-based methods, classical baselines, and recent deep learning approaches like Graph Neural Networks (GNNs). Our DNN-based solution incorporates a robust feature extraction process and a binary classifier, optimized for accurate prediction of missing links within networks. We performed extensive experimental evaluations on diverse datasets, including co-authorship networks, e-commerce, and social media networks. The study encompasses a comparative analysis with traditional LP techniques, namely Common Neighbors, Resource Allocation Index, Jaccard’s Coefficient, and Adamic/Adar Index, as well as other selected baseline and deep-learning methods. Our findings demonstrate that the DNN-based approach significantly enhances predictive accuracy, outperforming the conventional baseline methods in link prediction.

Impact of educational financing on the social mobility of university graduates: An analysis using regression discontinuities

The aim of this research was to evaluate the impact of the financial aid programme at a private university in Venezuela on the outcome variables of post-graduation or social mobility, such as remuneration at the time of graduation and at present, and the probability of entering the labour market. We used the fuzzy regression design for multiple treatments, given that the programme consists of the application of percentage discounts on tuition, where the allocation index only shows the probability of being treated, thus finding beneficiary individuals to the right and to the left of the threshold at each of the cut-off points. The results show that there are differences in labour market insertion between treated and untreated individuals, in favour of the former, but higher wages for non-beneficiaries. As for the estimates of the fuzzy regression regressions for both remuneration at the different points in time and labour market insertion, it is not possible to identify a positive impact of the treatment.

Research on the supply and demand of elderly care service resources in China.

This study aims to assess the current status and spatial distribution differences of elderly care service resources supply and demand in China. Semi-structured qualitative interviews were utilized to gather participants' insights into the current demands for elderly care services, the status of resource allocation, and related indicators. The entropy weight method was employed to determine indicator weights, yielding objective demand and allocation indices for elderly care service resources. Kernel density estimation was used to illustrate the distribution characteristics of the demand and allocation indices. The coupling coordination degree model was applied to measure the coupling coordination level of China's elderly care service resource supply and demand system in 2020. The demand index ranks highest in Beijing (0.3291), Shanghai (0.2941), and Tianjin (0.2563), while the lowest are found in Tibet (0.1673), Guangxi (0.1727), and Guizhou (0.1737). Kernel density estimation shows that the demand index is concentrated in the range of 0.1800 to 0.2000. The top three regions for allocation index are Shanghai (184.0007), Qinghai (129.8177), and Beijing (109.5941), with the lowest in Liaoning (34.8558), Hainan (35.3168), and Yunnan (36.6366). Kernel density estimation indicates that the allocation index is concentrated in the range of 25-75. Calculations of coupling coordination degree show that Shanghai has high coordination quality (0.9368), Beijing has good coordination (0.8392), while Hainan and Yunnan experience severe imbalances (0.1990, 0.1831). There is a significant lack of coordination between the demand for elderly care services and the allocation of resources in Hainan and Yunnan provinces in China. Most provinces, with the exception of Beijing and Shanghai, exhibit some degree of misalignment. The Chinese government should address the varying needs of the elderly population in different regions, pay timely attention to regional disparities, enhance regional cooperation, and dynamically allocate elderly care resources in a rational manner.

A novel integration of regret-based methodology and bankruptcy theory for waste load allocation.

Developing a suitable index for Waste Load Allocation (WLA) is essential for both industrial polluters and environmental organizations. Identifying the index that best describes the quality conditions of the river is the main concern of this study. To achieve this purpose, a novel framework incorporating a regret-based index and a bankruptcy-based approach to address the impacts of low water quality and pollutant locations within the WLA are introduced. The framework includes a simulation-optimization model to minimize river quality regret for environmental organizations and total treatment cost for industrial polluters, employing Nash bargaining theory for conflict resolution. Additionally, a new bankruptcy approach, the Namin's rule, is proposed for redistributing the River Quality Regret Index among industrial polluters. Applying this methodology to data from the KhoramAbad River, a sensitivity analysis reveals that while there is no significant difference between the methodology and fuzzy risk when polluters are close, the methodology provides more accurate results as the distance between polluters increases. When the distance between two pollutants was 20km, the sum of WLA was evaluated to be 300kg per day higher than that in the compared method, potentially enhancing environmental justice.

Efficient and Robust Multirobot Navigation and Task Allocation Using Soft Actor Critic

In this research, we present a novel solution to the problem of multi-robot task allocation (MRTA) by utilizing a variant of the state-of-art Deep Reinforcement Learning algorithm, Soft Actor-Critic (SAC) within Multirobot Navigation Systems(MRS). Our approach addresses the existing research gaps in MRTA for MRS, which have received limited attention and have proven challenging to solve using conventional methods. Through extensive simulation evaluations, we demonstrate that our system outperforms current state-of-the-art methods in terms of performance metrics. These findings highlight the effectiveness and superiority of our proposed approach in overcoming the inherent challenges of large-scale MRS. Additionally, we contribute to the field by proposing improvements to the Task Allocation Index (TAI) metric, enabling a more comprehensive evaluation of MRTA performance.

Ensemble decision of local similarity indices on the biological network for disease related gene prediction.

Link prediction (LP) is a task for the identification of potential, missing and spurious links in complex networks. Protein-protein interaction (PPI) networks are important for understanding the underlying biological mechanisms of diseases. Many complex networks have been constructed using LP methods; however, there are a limited number of studies that focus on disease-related gene predictions and evaluate these genes using various evaluation criteria. The main objective of the study is to investigate the effect of a simple ensemble method in disease related gene predictions. Local similarity indices (LSIs) based disease related gene predictions were integrated by a simple ensemble decision method, simple majority voting (SMV), on the PPI network to detect accurate disease related genes. Human PPI network was utilized to discover potential disease related genes using four LSIs for the gene prediction. LSIs discovered potential links between disease related genes, which were obtained from OMIM database for gastric, colorectal, breast, prostate and lung cancers. LSIs based disease related genes were ranked due to their LSI scores in descending order for retrieving the top 10, 50 and 100 disease related genes. SMV integrated four LSIs based predictions to obtain SMV based the top 10, 50 and 100 disease related genes. The performance of LSIs based and SMV based genes were evaluated separately by employing overlap analyses, which were performed with GeneCard disease-gene relation dataset and Gene Ontology (GO) terms. The GO-terms were used for biological assessment for the inferred gene lists by LSIs and SMV on all cancer types. Adamic-Adar (AA), Resource Allocation Index (RAI), and SMV based gene lists are generally achieved good performance results on all cancers in both overlap analyses. SMV also outperformed on breast cancer data. The increment in the selection of the number of the top ranked disease related genes also enhanced the performance results of SMV.

Study on regional carbon quota allocation at provincial level in China from the perspective of carbon peak

This paper constructs a carbon quota allocation index that takes into account equity, efficiency and ecological construction, and calculates carbon emissions and energy consumption data in important periods based on the expected carbon emission targets and economic and social development indicators of the Chinese government. Based on the calculated carbon emissions, the zero-sum game data Envelopment model (ZSG-DEA) is used to discuss the initial allocation of regional quotas and the optimal carbon quota scheme. The results show that:(1) there is a large gap between the optimal carbon quota and the initial carbon quota allocation in Shandong, Guangdong, Jiangsu and other provinces in 2025, and the implementation of emission reduction measures should be accelerated. (2) By 2030, the final allocation of Beijing, Tianjin, Inner Mongolia, Qinghai, Shanghai, Ningxia, Liaoning and Xinjiang will be positive. The provinces with negative final allocation should carry out the work of carbon peak as soon as possible to avoid increasing the pressure of emission reduction in the future. (3) The central region faces greater pressure of emission reduction, while the western region can accept the transfer of carbon emissions from other regions over time. The research conclusions have important policy implications for establishing a fair and effective carbon quota allocation mechanism, achieving the national total carbon emission control target, stimulating the vitality of the unified carbon market, and promoting regional coordinated emission reduction.

The free-viewing matrix task: A reliable measure of attention allocation in psychopathology

Aberrant attention allocation has been implicated in the etiology and maintenance of a range of psychopathologies. However, three decades of research, relying primarily on manual response-time tasks, have been challenged on the grounds of poor reliability of its attention bias indices. Here, in a large, multisite, international study we provide reliability information for a new eye-tracking-based measure of attention allocation and its relation to psychopathology and age. Data from 1567 participants, across a wide range of psychiatric diagnoses and ages, were aggregated from nine sites around the world. Of these, 213 participants also provided retest data. Acceptable overall internal consistency and test-retest reliability were observed among adult participants (Cronbach’s alpha = 0.86 and r(213) = 0.89, respectively), as well as across all examined psychopathologies. Youth demonstrated lower internal consistency scores (Cronbach’s alpha = 0.65). Finally, the percent dwell time index derived from the task statistically differentiated between healthy participants and participants diagnosed with social anxiety disorder, major depression, and post-traumatic stress disorder. These results potentially address a long-standing reliability crisis in this research field. Aberrant attention allocation patterns in a variety of psychiatric disorders may be targeted with the hope of affecting symptoms. The attention allocation index derived from the matrix task offers reliable means to measure such cognitive target engagement in clinical contexts.

Analysis of Regional Differences in Teacher Resources in Secondary Vocational Schools in the Context of Beijing-Tianjin-Hebei Synergy

Under the background of beijing-Tianjin-Hebei coordinated development, the allocation of secondary vocational education resources has been deeply affected.Firstly, based on the relevant data of teachers' educational resources in Beijing, Tianjin and Hebei secondary vocational Firstly, based on the relevant data of teachers' educational resources in Beijing, Tianjin and Hebei secondary vocational schools in 2020, this paper defines each index of resource allocation in Beijing, Tianjin and Hebei secondary vocational schools. Secondly, the coefficient of variation method is used to calculate and analyze the related factors that affect the educational resources of secondary vocational schools. Finally, optimization strategies are proposed for the unbalanced resource allocation of teachers in Beijing-Tianjin-Hebei secondary In addition, the Joint Committee also recommends that the Government of the Republic of China (RDC), in cooperation with the Ministry of Education of the Republic of China and the Ministry of Education of the People's Republic of China (MEC), should consider the possibility of establishing a new system of education for teachers. reference for the establishment of modern vocational education system and optimization of resource allocation in first-class vocational education in The aim of this study is to provide a comprehensive overview of the development of vocational education in the Beijing-Tianjin-Hebei region.

Formation Transformation Based on Improved Genetic Algorithm and Distributed Model Predictive Control

In order to solve the problem of multiple aircraft formation transformation to a designated formation, a distributed formation transformation algorithm that decomposes the formation transformation problem into target-matching problems and trajectory-planning problems was studied. According to the actual formation transformation requirements, the target allocation index was proposed, and the improved genetic algorithm which is 23% better than other algorithms was used to achieve target matching. The adaptive cross-mutation probability was designed, and the population was propagated without duplicates by the hash algorithm. The multi-objective algorithm of distributed model predictive control was used to design smooth and conflict-free trajectories for the UAVs in formation transformation, and the trajectory-planning problem was transformed into a quadratic programming problem under inequality constraints. Finally, point-to-point collision-free offline trajectory planning was realized by simulation.

Index policy for multiarmed bandit problem with dynamic risk measures

The multiarmed bandit problem (MAB) is a classic problem in which a finite amount of resources must be allocated among competing choices with the aim of identifying a policy that maximizes the expected total reward. MAB has a wide range of applications including clinical trials, portfolio design, tuning parameters, internet advertisement, auction mechanisms, adaptive routing in networks, and project management. The classical MAB makes the strong assumption that the decision maker is risk-neutral and indifferent to the variability of the outcome. However, in many real life applications, these assumptions are not met and decision makers are risk-averse. Motivated to resolve this, we study risk-averse control of the multiarmed bandit problem in regard to the concept of dynamic coherent risk measures to determine a policy with the best risk-adjusted total discounted return. In respect of this specific setting, we present a theoretical analysis based on Whittle’s retirement problem and propose a priority-index policy that reduces to the Gittins index when the level of risk-aversion converges to zero. We generalize the restart formulation of the Gittins index to effectively compute these risk-averse allocation indices. Numerical results exhibit the excellent performance of this heuristic approach for two well-known coherent risk measures of first-order mean-semideviation and mean-AVaR. Our experimental studies suggest that there is no guarantee that an index-based optimal policy exists for the risk-averse problem. Nonetheless, our risk-averse allocation indices can achieve optimal or near-optimal policies which in some instances are easier to interpret compared to the exact optimal policy.

Research on the Construction Index of Parking Lots of Large Commercial Complexes around Transportation Hubs

<p>Based on the construction regulations and plans of large -scale commercialcomplexes near rail hubs in various cities in Jiangsu Province and the understanding of parking construction standardsin Wuxi City, this paper analyzes the provisions of parking construction indicators and integrates other large-scale commercial complexes adjacent to the hub. The parking demand and the distribution of berths during the operation period were investigated and compared horizontally. Taking the Baolong 2BC plot in Wuxi City as an example, a forecasting method of berth demand for large-scale commercial complexes near the rail hub was proposed, and it was concluded that it is suitable for the surrounding of the rail hub. General method for determining the allocation index of commercial complexes.</p>

Cortical thickness is related to cognitive-motor automaticity and attention allocation in individuals with Alzheimer’s disease: a regions of interest study

Alzheimer's disease (AD) is characterized by a distinct pattern of cortical thinning and resultant changes in cognition and function. These result in prominent deficits in cognitive-motor automaticity. The relationship between AD-related cortical thinning and decreased automaticity is not well-understood. We aimed to investigate the relationship between cortical thickness regions-of-interest (ROI) and automaticity and attention allocation in AD using hypothesis-driven and exploratory approaches. We performed an ROI analysis of 46 patients with AD. Data regarding MR images, demographic characteristics, cognitive-motor dual task performance, and cognition were extracted from medical records. Cortical thickness was calculated from MR T1 images using FreeSurfer. Data from the dual task assessment was used to calculate the combined dual task effect (cDTE), a measure of cognitive-motor automaticity, and the modified attention allocation index (mAAI). Four hierarchical multiple linear regression models were conducted regressing cDTE and mAAI separately on (1) hypothesis-generated ROIs and (2) exploratory ROIs. For cDTE, cortical thicknesses explained 20.5% (p = 0.014) and 25.9% (p = 0.002) variability in automaticity in the hypothesized ROI and exploratory models, respectively. The dorsal lateral prefrontal cortex (DLPFC) (β = - 0.479, p = 0.018) and superior parietal cortex (SPC) (β = 0.467, p = 0.003), and were predictors of automaticity. For mAAI, cortical thicknesses explained 20.7% (p = 0.025) and 28.3% (p = 0.003) variability in attention allocation in the hypothesized ROI and exploratory models, respectively. Thinning of SPC and fusiform gyrus were associated with motor prioritization (β = - 0.405, p = 0.013 and β = - 0.632, p = 0.004, respectively), whereas thinning of the DLPFC was associated with cognitive prioritization (β = 0.523, p = 0.022). Cortical thinning in AD was related to cognitive-motor automaticity and task prioritization, particularly in the DLPFC and SPC. This suggests that these regions may play a primary role in automaticity and attentional strategy during dual-tasking.

Buffer allocation in construction projects: a disruption mitigation approach

PurposeA variety of buffer allocation methods exist to distribute an aggregated time buffer among project activities. However, these methods do not pay simultaneous attention to two key attributes of disruptive events that may occur during the construction phase: probability and impact. This paper fills this research gap by developing a buffer allocation method that takes into account the synergistic impact of these two attributes on project activities.Design/methodology/approachThis paper develops a three-step method, calculating the probability that project activities are disrupted in the first step, followed by measuring the potential impact of disruption on project activities, and then proposing a risk-informed buffer allocation index by simultaneously integrating probability and impact outputs from the first two steps.FindingsThe proposed method provides more accurate results by sidestepping the shortcomings of conventional fuzzy-based and simulation-based methods that are purely based on expert judgments or historical precedence. Further, the paper provides decision-makers with a buffer allocation method that helps in developing cost-effective buffering and backup strategies by prioritizing project activities and their required resources.Originality/valueThis paper develops a risk-informed buffer allocation method that differs from those already available. The simultaneous pursuit of the probability and impact of disruptions distinguishes our method from conventional buffer allocation methods. Further, this paper intertwines the research domains of complexity science and construction management by performing centrality analysis and incorporating a key attribute of project complexity (i.e. the interconnectedness between project activities) into the process for buffer allocation.

Cost-Effectiveness of Nature-Based Solutions under Different Implementation Scenarios: A National Perspective for Italian Urban Areas

Worldwide, national governments and private organizations are increasingly investing in Nature-Based Solutions (NBS) to foster both human well-being and biodiversity while achieving climate and environmental targets. Yet, investments in NBS remain uncoordinated among planning levels, their co-benefits underestimated, and their effectiveness undermined. This study aims to provide a spatially explicit approach to optimize the budget allocation for NBS implementation across Italian urban areas while maximizing their effectiveness in terms of environmental health. We explored three different NBS implementation scenarios oriented to (i) maximize the Ecosystem Services supply of NBS (Scenario BP), (ii) minimize costs of NBS (Scenario LC), and (iii) maximize Ecosystem Services supply of NBS at the lowest cost (Scenario CP). Once selected, we prioritized their allocation through the territory following an environmental risk index for population, and we explored the relationship between costs and effectiveness for the three scenarios. The implementation of Scenario BP costs EUR 777 billion while showing 31 billion of effectiveness. Scenario LC costs 70% less than scenario BP (EUR 206 billion) while losing 70% of its effectiveness. Scenario CP costs 60% less than Scenario BP (EUR 301 billion), offering just 20% less effectiveness. Our results show that employing the risk index for NBS allocation would allow for reducing the surface of interventions by saving 67% of the budget in the three scenarios with a negligible loss in terms of return for human health. The here-proposed approach can guide the national funds’ allocation system, improving its cost-effectiveness and equitableness.