Spatial Allocation Research Articles

The effects of digital economy on manufacturing resource allocation efficiency across Chinese cities: a spatial economic perspective

ABSTRACT The development of digital economy provides significant impacts on spatial distribution of industries. Using microdata from Chinese manufacturing enterprises and adopting a spatial economic perspective, this study reveals that the digital economy exerts non-linear impacts on resource allocation. As the level of urban digital economy improves, the efficiency of manufacturing resource allocation shows an inverted U-shaped trend, and high hierarchy cities exhibit higher critical values. The study finds a positive spatial correlation in manufacturing resource allocation efficiency across cities; however, the growth of digital economy in neighbouring cities may lead to backwash effects and impede local improvements in allocation efficiency. The mechanism analysis indicates that the digital economy primarily affects spatial resource allocation through urban agglomeration economies and diseconomies. Consequently, economic agents and policy makers in cities should consider the dual effects of ‘location optimization’ and ‘transforming the economy from substantial to fictitious’ during digital transformation processes to select optimal development paths and improve allocation efficiency.

A Novel Centralized Allocation Data Envelopment Analysis Model for Carbon Emission Allocation Under a Heterogeneous Abatement Cost: Application Within the Chinese Industrial Sector

This paper presents a mathematical approach to analyzing carbon abatement costs and the allocation of carbon emission allowances in China’s industrial sectors. We utilize input–output data from 30 Chinese provinces between 2009 and 2018 to estimate carbon abatement costs by applying the slack-based measure (SBM) efficiency model and its dual form. The SBM model captures inefficiencies and offers a rigorous framework for measuring abatement costs. Using these costs, we develop a centralized allocation data envelopment analysis (DEA) model, which maximizes sectoral benefits through optimal reallocation. This DEA model is formalized as a linear programming problem, with the aim of determining the efficient allocations of carbon allowances while maintaining the system’s economic productivity. Furthermore, we construct intertemporal, interregional, and spatiotemporal allocation DEA models to examine the dynamics of carbon emission allowance allocation over time, space, and combined spatiotemporal dimensions. These models offer insights into the efficiency of carbon markets under varying conditions. Our proposed new mathematical formulations reveal optimal allocation strategies that can balance emission reductions with industrial productivity. This study also provides novel mathematical frameworks for analyzing the carbon allowance distribution and contributions to both the theory and application of mathematical optimization in environmental policy design. Our findings reveal that China’s industrial carbon abatement costs exhibit significant interprovincial and regional differences. Developed provinces with higher levels of industrial development have higher carbon abatement costs, while provinces with less-developed industrial sectors have lower costs. Under the interregional allocation scenario of carbon emission allowances that consider abatement costs, developed provinces have smaller industrial carbon emission reductions, whereas less-developed provinces have larger reductions. In the intertemporal allocation scenario, provinces with larger industrial economies face greater emission reduction tasks. Under the combined interregional and intertemporal allocation scenario, industrial sectors in coastal developed provinces have lower carbon emission reductions, while those in inland less-developed provinces have higher reductions, mirroring the spatial allocation results of carbon emission allowances.

Pricing in spatial classification system in non-symmetric market demand based on the calculations of double interval grey numbers

PurposeThe contemporary landscape of supply chains necessitates a comprehensive integration of multiple components encompassing production, distribution and customer engagement. The pursuit of supply chain harmony underscores the significance of pricing strategies within the framework of dual-channel distribution, particularly when confronted with the dynamics of asymmetric demand performance.Design/methodology/approachThis paper delves into a nuanced decision-making challenge anchored in a dual-channel distribution context featuring a retailer and two distinct products. Notably, the retailer’s decision-making process employs the computational framework of dual grey numbers, a robust tool for handling uncertainty.FindingsThis study revolves around applying game theory to manufacturers. Each manufacturer presents its aggregated price proposition to the retailer. Subsequently, the retailer identifies the optimal pricing configuration among the manufacturers' aggregate prices while adhering to constraints such as spatial classification and inventory costs. This article’s contribution extends to delineating the retailer’s capacity to discern the influence of product market potential and the aggregate product cost on the overall demand.Originality/valueThe model’s innovation lies in its harmonious fusion of spatial classification, pricing strategies and inventory control. Notably, this novel integration provides a platform for unraveling the intricate interplay between non-symmetric market potential, production costs and cross-sensitivity. The investigation is underscored by the utilization of the double interval grey numbers, a powerful computational approach that accommodates the inherent uncertainty pervasive in the domain. This study fills a gap in the existing literature by offering an integrated framework unifying spatial allocation, pricing decisions and inventory optimization.

Evaluating wetland ecosystem services value and dominant functions: Insights from the Pearl River Delta

Weakening wetland ecosystem services (ES) significantly hinders the achievement of the Sustainable Development Goals (SDGs). It is essential to combine multiple methods for evaluating wetland ecosystem services value (ESV) and to clearly depict the spatial distribution of ESVs. Based on the local conditions of the Pearl River Delta (PRD), this study proposed a monetary evaluation system for wetland ESV, developed a method for spatially allocating individual ESVs, and identified the dominant wetland functions across different cities and types of wetlands. The results yielded the following findings: (1) The wetland ESV system effectively identifies differences in ESV across cities and wetland types. The PRD's ESV increased by 23.29% between 2000 and 2020. (2) The new spatial allocation method analyzed individual ESVs to provide spatial references for improving wetland ESV. Fishery products and flood control and water storage are the two largest wetland functions in the PRD. All ESVs increased except for temperature regulation and water purification. (3) The identification and classification of dominant wetland functions provide insights into site-specific management of urban wetlands informed by ESV. These results provide a reference for assessing wetland ESV in other delta regions facing high population density and wetland degradation pressures. Understanding the role of wetland ESs in supporting the SDGs and how they interconnect and contribute to their achievement will be a key future research topic.

Novel Application of Quantum Computing for Routing and Spectrum Assignment in Flexi-Grid Optical Networks

Flexi-grid technology has revolutionized optical networking by enabling Elastic Optical Networks (EONs) that offer greater flexibility and dynamism compared to traditional fixed-grid systems. As data traffic continues to grow exponentially, the need for efficient and scalable solutions to the routing and spectrum assignment (RSA) problem in EONs becomes increasingly critical. The RSA problem, being NP-Hard, requires solutions that can simultaneously address both spatial routing and spectrum allocation. This paper proposes a novel quantum-based approach to solving the RSA problem. By formulating the problem as a Quadratic Unconstrained Binary Optimization (QUBO) model, we employ the Quantum Approximate Optimization Algorithm (QAOA) to effectively solve it. Our approach is specifically designed to minimize end-to-end delay while satisfying the continuity and contiguity constraints of frequency slots. Simulations conducted using the Qiskit framework and IBM-QASM simulator validate the effectiveness of our method. We applied the QAOA-based RSA approach to small network topology, where the number of nodes and frequency slots was constrained by the limited qubit count on current quantum simulator. In this small network, the algorithm successfully converged to an optimal solution in less than 30 iterations, with a total runtime of approximately 10.7 s with an accuracy of 78.8%. Additionally, we conducted a comparative analysis between QAOA, integer linear programming, and deep reinforcement learning methods to evaluate the performance of the quantum-based approach relative to classical techniques. This work lays the foundation for future exploration of quantum computing in solving large-scale RSA problems in EONs, with the prospect of achieving quantum advantage as quantum technology continues to advance.

Full-space trifunctional metasurface with independent control of amplitude and phase for circularly polarized waves

Abstract Flexible and diverse manipulation of electromagnetic (EM) waves in half space (reflection or transmission) has facilitated strong aspiration toward full-space wave control. However, it remains challenging to achieve independent amplitude and phase control, which seriously hinder the real-world applications. Herein, an innovative strategy of trifunctional metasurface is proposed to independently and simultaneously manipulate the amplitude and phase of circular polarized waves in full space. The multifunctional design is composed of double-layer anisotropic metasurface sandwiched with a bandpass frequency selective surface, with a frequency-direction multiplexed paradigm for on-demand control of both amplitude and phase across three independent channels. To validate the concept, a multifunctional metadevice is designed and verified by simulations and experiments, showcasing arbitrary near-field and far-field power modulation in full space. Lateral and axial bifocal metalenses with desired intensity distribution are designed in two reflection channels at 9 GHz, while multibeam generator with desired spatial scatterings and power allocations is designed in transmissive channel at 13 GHz. The finding paves the way for attaining multifunctional metadevices with amplitude and phase modulation in full space, which have potential applications in high-quality imaging and high-capacity communication systems.

Evaluation of Water System Connectivity Based on Node Centrality in the Tarim River Basin, Xinjiang, China

Water system connectivity is an important measure to optimize the balanced spatial allocation of water resources and water security patterns. Inland river basins in arid zones are generally insufficiently connected, so the scientific evaluation of the current status of water system connectivity and the centrality of its nodes in the water system network has practical significance for the scientific construction of regional water networks. Taking the Tarim River Basin in Xinjiang, China, as an object, this study conducted a comprehensive evaluation of basin water system connectivity by constructing a water system connectivity evaluation system with a total of 12 indicators for the three aspects of pattern connectivity, structural connectivity and functional connectivity. Subsequently, the TOPSIS model, with combined weights of the analytic hierarchy process and the entropy weight method, was used to comprehensively evaluate the connectivity of the watershed’s water system. The research evaluated the node centrality of the water system network that was closely related to the basin water system connectivity by using complex network analyses. The study results indicated the following: (1) among the source streams in the Tarim River Basin, the connectivity of the Aksu Basin was the best and that of the Keriya Basin was the worst and (2) the distribution patterns of the eigenvector centrality and betweenness centrality of the basin hydrological network nodes were similar, with nodes of highest centrality concentrated in the vicinity of the mainstems and the source–mainstem intersections. This work provides a basis and reference for the construction of water system connectivity and the selection of key control sections for ecological flow in the Tarim River Basin.

Estimation of Urban Tree Chlorophyll Content and Leaf Area Index Using Sentinel-2 Images and 3D Radiative Transfer Model Inversion

Urban trees play an important role in mitigating effects of climate change and provide essential ecosystem services. However, the urban environment can stress trees, requiring the use of effective monitoring methods to assess their health and functionality. The objective of this study, which focused on four deciduous tree species in Rennes, France, was to evaluate the ability of hybrid inversion models to estimate leaf chlorophyll content (LCC), leaf area index (LAI), and canopy chlorophyll content (CCC) of urban trees using eight Sentinel-2 (S2) images acquired in 2021. Simulations were performed using the 3D radiative transfer model DART, and the hybrid inversion models were developed using machine-learning regression algorithms (random forest (RF) and gaussian process regression). Model performance was assessed using in situ measurements, and relations between satellite data and in situ measurements were investigated using spatial allocation (SA) methods at the pixel and tree scales. The influence of including environment features (EFs) as model inputs was also assessed. The results indicated that random forest models that included EFs and used the pixel-scale SA method were the most accurate with R2 values of 0.33, 0.29, and 0.46 for LCC, LAI, and CCC, respectively, with notable variability among species.

Promoting coordinated spatial governance of mega-cities in China via spatial organization of metropolitan areas

Super large-sized cities and very large-sized cities (“mega-cities” for short) have become the backbone of China's socio-economic development, but they also face outstanding challenges of "urban diseases" due to their high concentration. In comparison to their foreign counterparts, China's mega-cities possess unique characteristics such as rapid growth, high population density, and large numbers of residents. They naturally have the attributes of metropolitan areas due to an administrative management system of an urban system within city territory. Promoting coordinated spatial governance of mega-cities via spatial organization of metropolitan areas not only helps mega-cities better allocate resources at a larger scope and helps expedite the transformation of development patterns, but is also crucial for better implementing new-type urbanization strategies. In response to the issues in China’s previous research and practices related to mega-cities and metropolitan areas, leveraging the “Critical Technology for Spatial Optimization of Metropolitan Areas of City Clusters” of the national key R&D plan in the “14th Five-year Plan” period, we have employed a scientific approach that combines data and models to propose spatial governance strategies. These strategies encompass both the overall metropolitan area level and the cross-border regional level. Our primary focus lies in optimizing the spatial layout of the “three domains” – ecological space, production space, and living space, facilitating functional dispersal, enhancing spatial patterns, adjusting land use structures and spatial resource allocation, coordinating rail transit and the organization of functions along transit lines, and innovating service management for improvement of administrative efficiency. Through the implementation of these diverse measures, we aim to establish a comprehensive composite collaborative system.

Automatic Planning Method of Construction Schedule under Multi-Dimensional Spatial Resource Constraints

To enhance the application of building information modeling (BIM) in schedule management for engineering projects, improve spatial planning capabilities, and minimize subjective factors in schedule preparation, this paper presents a framework and method for automatic construction schedule planning based on BIM and data-driven approaches under multi-dimensional spatial resource constraints. The method includes data acquisition, construction process reasoning, time parameter calculation, and schedule generation and scheduling. Initially, the construction data from the BIM model and manual input are imported into the reasoning system to establish the logical relationships of the construction process. Construction time parameters and spatial resource requirements are then automatically calculated using MySQL. Subsequently, the spatial layout and resource allocation data exported from the BIM model are compared to assess whether the space meets the project requirements. Finally, a construction schedule that satisfies the space constraints is generated. The method was validated through a case study, demonstrating its effectiveness in automatic schedule planning under human-computer interaction. This study introduces an innovative approach that considers spatial conflicts in the automatic scheduling process, improving planning efficiency and reducing the risk of spatial conflicts on construction sites.

Detecting and sourcing GHGs and atmospheric trace gases in a municipal waste treatment plant using coupled chemistry and isotope compositions

Landfill operations and waste processing facilities are important and highly heterogeneous sources of both greenhouse gases (GHGs) and non-GHG air pollutants in the atmosphere. This arises the need for detailed apportionment of waste sources in order to locate and subsequently reduce emissions from landfills. Here, a time series of in situ measurements of atmospheric trace gases and spatial allocation of specific emission source types under different processing phases and environmental conditions were conducted in and in the surroundings of a Municipal Solid Waste Treatment Plant (MSWTP) in south-western Poland. Results revealed that several individual GHG sources dominated across the waste processing facility and that GHGs concentrations displayed spatial seasonality. An increase in the ground-level CH4 concentrations, from ∼ 30.3 to 56.3 ppmv, was observed close (∼5 – 10 m) to the major emission sources within the MSWTP. While hotspot areas generally yielded elevated CH4 concentrations near the soil surface, these were relatively low (2.4 to 8.9 ppmv) along the facility’s fence line. The study of the corresponding δ13C delineated the extent of dispersion plumes downwind emission hotspots, characterized by a 13C depletion (around 4.0 ‰) in the atmospheric CH4 and CO2. For CH4, emissions were isotopically discriminated between the extraction wells at active quarters/cells (δ13C = –58.3 ± 1.1 ‰) and biogas produced in the biological waste treatment installation (δ13C = –62.7 ± 0.7 ‰). Most of the trace compounds (non-methane hydrocarbons, halocarbons, oxygen-bearing organic gases, ketones, nitrogenous and sulphurous gases, and other admixture compounds) detected at the ground surface were linked to the CH4- and CO2-rich spots. Despite the relatively high variability in the concentrations of organic and inorganic compounds observed at the MSWTP active zones, our results suggest that they do not have a meaningful impact on the surrounding air quality.

Examining global biodiversity accounts: Implications of aggregating characterization factors from elementary flows in multi‐regional input–output analysis

AbstractExtending multi‐regional input–output (MRIO) models with spatially explicit life cycle impact assessment (LCIA) models allows practitioners to quantify biodiversity impacts at every step of global supply chains. Inconsistencies may be introduced, however, when high‐resolution characterization factors (CFs) are aggregated so as to match the low spatial granularity of MRIO models. These aggregation errors are greater when CFs are aggregated via proxies, such as ecoregion land shares, instead of based on spatially explicit elementary stressor flows. Here, we describe our approach to tailoring application‐specific CFs for use in MRIO studies. We apply a global agricultural production model, Spatial Production Allocation Model (MapSPAM), with the LCIA database, LC‐IMPACT, to create crop‐specific national CFs. We investigated i) if the differing aggregation approaches and the increased spatial explicitness of the constructed CFs deviate substantially from those in LC‐IMPACT, and ii) what the resulting consequences for national production and consumption‐based biodiversity footprints are when combining the tailor‐made CFs with the EXIOBASE MRIO model. For the year 2020, we observe an increase in global production‐based biodiversity impacts of 23.5% for land use when employing crop‐specific CFs.

Analysis of the Spatio-Temporal Evolution of Urban Sports Service Facilities in the Yangtze River Delta

The spatial allocation of urban public sports facilities is critical for ensuring equitable access to basic public services and maintaining urban spatial cohesion. This study examines central cities in the Yangtze River Delta, utilizing Point of Interest (POI) data to characterize urban sports service facilities. Employing methods such as kernel density estimation, the nearest neighbor index, spatial autocorrelation, and coefficient of variation, this study analyzes the spatial aggregation, synergy, and equalization of sports service facilities at the community scale. The findings indicate that: (1) the spatial distribution of sports service facilities within community life circles demonstrates a clustered pattern, forming a concentric core-to-periphery structure, with notable variations in clustering degrees across different cities; (2) synergy among sports service facilities has significantly improved, with the emergence of multiple high-value clusters and low-value dispersions across various cities; and (3) the level of equalization of sports service facilities in community life circles follows the general order of Shanghai > Nanjing > Hangzhou > Hefei. These insights offer valuable guidance for the planning and optimization of urban public sports facilities.

A multi-objective optimization framework for regional land-use allocation: Fully utilizing terrestrial vegetation to mitigate carbon emissions

Human-induced changes in land use and land cover (LULC) considerably impact the global carbon cycle. Rational LULC optimization with full utilization of terrestrial vegetation facilitates reconciling carbon reduction and socio-economic development. To this end, this study develops a multi-objective framework for low-carbon LULC spatial optimization that incorporates carbon suitability, historical suitability, and socio-economic needs. The framework is applied to Hubei Province, China. The key findings include: (1) Considering spatial variations in vegetation carbon sinks could lead to remarkable carbon reductions of 2.75 × 106 t C by 2030 and 7.68 × 106 t C by 2060. It would also lower carbon emissions per unit of gross domestic product (GDP) and enhance carbon sequestration per unit of vegetation by 2030, signifying enhanced carbon reduction efficiency. (2) Integrating carbon sequestration potential and historical suitability can considerably minimize emissions while sustaining socio-economic growth. Our method, compared with strategies based solely on historical development laws, can reduce carbon emissions by approximately 1.3–1.6 × 106 t C·a−1. (3) We also propose targeted low-carbon development suggestions for different LULCs. This study provides optimal spatial LULC allocations and strategic advice for regional LULC planning aimed at low-carbon development, demonstrating the feasibility of reconciling the tension between carbon reduction and socio-economic growth at the regional scale.

Optimization of industrial layout in airport economic zone through government-enterprise interaction

To avoid the issue of industrial layout plans in Airport Economic Zones (AEZs) being hard to implement due to mismatched the land supply and demand, it is necessary to clarify industrial layout factors that the government (land supplier) and locating enterprises (land demanders) focus on. This study constructs a AEZ's industrial layout optimization model from the government's perspective and enterprises’ locating utility. Based on the Gale-Shapley algorithm, aiming at eliminating blocking pairs, we establish government-enterprise interacting rules and coordinating mechanisms, corresponding to two types of government-enterprise interactions, namely public consultation and investment promotion joint meeting. According to government transparency and enterprise participation, the optimization model and Agent-Based Model are adopted in the combination to simulate the government-enterprise interactions in the decision-making of industrial layout in AEZ, under two interaction rules of “enterprises provide feedback in one go - the government makes revisions in one go” and “enterprises provide feedback one by one - the government makes revisions one by one”, respectively. By analyzing the changing curves of their objective functions, the equilibrium point where both parties can reach an agreement is identified, and the industrial layout scheme in AEZ is determined that will be mutually accepted. It is found that positive interaction with high government transparency and high enterprise participation will help improve the rationality of the industrial layout in AEZ, maximizing the efficiency of spatial allocation of elements and resources.

Fine-tuning the spatial allocation of phytohormones for smart canopy, smart breeding, and smart agriculture.

Fine-tuning the spatial allocation of phytohormones for smart canopy, smart breeding, and smart agriculture.

Mapping World: A Comparative Analysis of Functionality and Usability between QGIS and ArcGIS

The current review conducts a comprehensive comparison of Quantum GIS (QGIS) and ArcGIS in the context of healthcare applications, aiming to assist users in selecting the most appropriate GIS platform for their specific needs. Geographic Information Systems (GIS) are essential tools in healthcare, providing capabilities for spatial analysis, disease mapping, and resource allocation. Driven by the need to improve healthcare delivery and public health strategies, this study compares the functionalities, costs, and support systems of QGIS and ArcGIS. The major objective is to determine which GIS platform offers the best balance of cost-efficiency and performance across various healthcare scenarios. The methodology encompasses a detailed comparative analysis, incorporating quantitative data from user satisfaction surveys, performance evaluations, and real-world case studies. Findings reveal that QGIS, as an open-source GIS software, is highly cost-effective and customizable, making it particularly advantageous for budget-limited projects and smaller organizations requiring flexibility. Its extensibility via plugins and robust user community fosters continuous innovation and improvement. QGIS proves exceptionally effective in healthcare applications such as disease mapping and accessibility analysis, where customization and cost-efficiency are critical. Conversely, ArcGIS is distinguished by its advanced analytical capabilities and strong performance in handling complex, large-scale studies. Its sophisticated tools and comprehensive spatial analysis functionalities are essential for thorough healthcare research and planning. Although ArcGIS incurs higher costs, its extensive feature set, professional support, and exhaustive documentation justify the investment for large organizations and research institutions engaged in detailed GIS projects. In conclusion, both QGIS and ArcGIS exhibit significant strengths in key healthcare applications. QGIS excels in affordability and flexibility, while ArcGIS provides superior analytical power and support for extensive projects. Future research should focus on integrating real-time data and enhancing user experience to further optimize GIS applications in healthcare.

A local-to-global emissions inventory of macroplastic pollution

Negotiations for a global treaty on plastic pollution1 will shape future policies on plastics production, use and waste management. Its parties will benefit from a high-resolution baseline of waste flows and plastic emission sources to enable identification of pollution hotspots and their causes2. Nationally aggregated waste management data can be distributed to smaller scales to identify generalized points of plastic accumulation and source phenomena3–11. However, it is challenging to use this type of spatial allocation to assess the conditions under which emissions take place12,13. Here we develop a global macroplastic pollution emissions inventory by combining conceptual modelling of emission mechanisms with measurable activity data. We define emissions as materials that have moved from the managed or mismanaged system (controlled or contained state) to the unmanaged system (uncontrolled or uncontained state—the environment). Using machine learning and probabilistic material flow analysis, we identify emission hotspots across 50,702 municipalities worldwide from five land-based plastic waste emission sources. We estimate global plastic waste emissions at 52.1 [48.3–56.3] million metric tonnes (Mt) per year, with approximately 57% wt. and 43% wt. open burned and unburned debris, respectively. Littering is the largest emission source in the Global North, whereas uncollected waste is the dominant emissions source across the Global South. We suggest that our findings can help inform treaty negotiations and develop national and sub-national waste management action plans and source inventories.

Benefits and costs of measures to tackle the outbreak of African swine fever in Sweden

A common rule in many countries for mitigating the damage caused by African swine fever (ASF) is to eradicate the virus at the outbreak in order to prevent its dispersal and the associated social costs of depopulating infected domestic pigs. The economic performance of this practice, as measured by five different evaluation criteria (net present value, benefit-cost ratio, rate of return, internal rate of return, and payback time), depends on the type of control cost and the spatial and dynamic allocation of benefits, i.e. avoided losses from infected domestic pig farms. The present paper calculates the direct and indirect costs of immediate control measures during an ASF outbreak in wild boars in Mid Sweden. The direct costs include expenses incurred for surveillance, laboratory tests, depopulation of wild boar etc., while the indirect costs are borne by firms and people in the area in relation to movement restrictions. The calculations showed that the total cost of control measures amounted to 28 million euros, with indirect costs making up 40% of this figure. The benefits were greatly dependent on the speed of ASF dispersal and assumptions about pig farmers’ investment responses, which implied large variations in each of the five evaluation criteria.

Attention to food stimuli in binge eating disorder: Electrophysiological evidence

Attentional biases towards food play an important role in the pathology of binge eating disorder (BED). Later stage electrophysiological potentials (P300, late positive potential) present promising markers of motivated attention with high temporal, albeit low spatial resolution. Complementing this, the N2pc is an earlier-latency component providing the possibility of more directly analyzing visuospatial attention. Therefore, we tested a group with BED (N = 60), as well as an overweight (OW; N = 28) and normal weight (NW; N = 30) group without BED in a Go/No-Go paradigm using food and nonfood distractor images. Only the OW group in exclusively the Go trials displayed a stronger spatial attention allocation towards nonfood distractors as evidenced by an increased N2pc amplitude. In the P300's time window, the OW group displayed no attentional bias towards food and the NW group only did so in the absence of a target. Solely the BED group allocated more motivated attention towards food distractors both in Go and No-Go trials. In the following late positive potential (LPP), the OW group exhibited a general attentional bias towards food distractors, while the BED group only did so in the absence of a target. These results are discussed in light of the incentive sensitization theory and a potential early attentional suppression of potent distractors.