Location Decisions Research Articles

Environmental impacts of floating solar panels on freshwater systems and their techno-ecological synergies

Abstract Renewable energy sources, such as floating photovoltaic systems (FPVs), are crucial to mitigating the climate crisis. FPV deployments on freshwater bodies are rapidly growing, as they avert land-use change, operate with increased efficiency, and potentially improve water availability by reducing evaporation and the frequency of algal blooms. However, understanding of the ecological consequences for water bodies is very limited despite the variable and far-reaching range of potential impacts. Here, we bring novel insight by combining an established theoretical approach – techno-ecological synergies (TES) - with robust understanding of water body functioning, to direct sustainable FPV deployments. Specifically, we show the potential to integrate consideration of both energy and ecological systems for FPV deployment decisions based on current evidence and fundamental understanding of freshwater body states and processes. Based on 422 pieces of evidence from a systematic review of known physical (n=283), chemical (n=96) and biological (n=43) effects of FPV on freshwater systems, we outline eight potential TES that could be realised through FPV location, design, and operation decisions. There was most evidence (n=114) for the Water Use Efficiency TES, which all reported decreased evaporation rates, or increased water savings, due to panel shading. We highlight a lack of understanding of chemical and biological effects of FPV on hosting water bodies, as well as a need for comprehensive studies in which physical, chemical, and biological aspects of water body dynamics are integrated. Finally, we detail research priorities to ensure future FPV deployments bring benefit for both energy and water bodies. Ultimately, integrated energy and water body system knowledge, FPV deployments could mitigate both the climate and ecological emergencies, with notable benefits for society.

Practice Makes Perfect: Using the Total Cost of Ownership to Teach Global Locational Decision Making.

Practice Makes Perfect: Using the Total Cost of Ownership to Teach Global Locational Decision Making.

In-migration for transforming peripheral locations through a real-world experiment? Experiment insights on location decisions in the medium-sized city of Görlitz, Germany

A real-world experiment was implemented in the city of Görlitz to gain knowledge about the requirements for, and the opportunities of, in-migration to revitalize urban areas and increase local capacities for sustainability transformation.Many cities, in particular those in peripheral locations, are dependent on in-migration to compensate for the negative population development often experienced due to demographic and economic change. In addition, the necessary transformation towards urban sustainability requires appropriate know-how and innovation potential. A real-world experiment to stimulate the revitalization of existing urban areas and to enhance the local potential for sustainability transformation through targeted in-migration was implemented in the city of Görlitz, Germany. The aim of this article is to reflect upon the impact of such an experiment with special regard to learning effects for individuals, municipalities, and transdisciplinary collaborations. It explains that there is great value for the future development of a city in the long-term consideration and co-productive work of various stakeholders, but this is also associated with challenges.

Census Tract and Neighborhood Racial Change and the Locational Decisions of Financial Services in Metro Detroit

Scholarship on the geography of US retail financial services consistently finds racialized patterns in the locations of traditional (e.g. banks, credit unions) and higher-cost or alternative (e.g. payday lenders, check cashers) financial services. While scholars tend to offer explanations based on spatial void and market segmentation hypotheses, we theorize these racialized patterns as consistent with a social reproduction of investment and divestment. We use a novel, longitudinal dataset that follows census tract- and neighborhood-level changes in racial demographics and financial services in Detroit during the pre- and post-Recession periods. Changes in the locations of financial services follow changes in racial demographics, which occur in addition to the well-documented practice of targeting or avoiding tracts and neighborhoods based on their racial majority. These racialized patterns are heightened in the post-Great Recession period.

Data-Driven Shelf Stocking: Robustness and Vertical-Location Effect

In this article, we study a shelf-stock allocation problem in a data-driven setting where the demand distribution is unknown. The retailer needs to decide which commodities to place on each level of the shelf (location decision), and determine the stock level for each selected commodity at the shelf (stock decision) that can be adjusted based on updated demand prediction, under constraints on allocation and capacity, so as to maximize total expected profit. Key issues of the problem include demand endogeneity on vertical-location effect and the demand ambiguity that may arise from the ever shorter life cycles of commodities and other complicated determinants. We address the problem in a rolling-horizon fashion and develop a two-phase data-driven robust optimization model leveraging a decision-dependent Wasserstein metric that incorporates time-series demand forecasts and captures vertical-location effect under demand ambiguity. We derive tractable solution schemes for the model, in which the phase-I model determines the optimal location decision by solving one instance of mixed-integer linear program, while the phase-II model admits an analytical solution for updating the optimal stock. Furthermore, we discuss the sensitivity implications on how the ambiguity aversion impacts the optimal stock, and prove the consistency of the model solution under regularity condition on the underlying demand process that well justifies the asymptotic performance of the proposed data-driven approach. Finally, we extend our model to incorporate substitution effect, and conduct sufficient numerical experiments with real-life data that demonstrate the performance of our model.

Review on road congestion pricing: a long-term land use effect perspective

Road congestion pricing (RCP) is an important way of controlling traffic congestion from the perspective of traffic demand management. This paper introduces the concept and four main types of applications of RCP, summarises the development of RCP theory, reveals the first- and second-order effects of RCP on transportation systems and land use, and analyses the long-term impact of RCP on the spatial location structure of land use. Relevant research progress considering land use effects is systematically and comprehensively reviewed from three research perspectives: demand modelling (pre-RCP), monitoring and analysis (post-RCP) and modelling methods (integration models and other models). The results showed that RCP will affect residents’ travel behaviour in the short term and will lead to changes in spatial location decisions in the long run, thus creating new travel demands and travel modes, affecting the transportation system and urban spatial patterns again. Implementing cordon charges has an agglomeration effect on the population in city centres. Cordon charges also have a significant negative impact on businesses in the toll area and a dispersive effect on jobs. Combined with appropriate strategies for shared autonomous vehicles, RCP can simultaneously improve transportation efficiency and regional accessibility.

An Examination of the Decision of Microentrepreneurs to Remain in Urban Flood-prone Areas in Marikina City, Metro Manila (NCR), Philippines

Despite the impact of urban flooding on businesses, the choice of microentrepreneurs continuing to stay in flood hazard areas runs counterintuitive to the principles of profitability and sustainability albeit both are the most vulnerable aspects of the business sectors. Existing literature has provided substantial knowledge on the factors affecting an entrepreneur’s choice of operating location but does not go deeper into why entrepreneurs decide to remain and operate in frequently flooded urban areas. A multi-stage cluster sampling technique was used in this study to establish an adequate representation of microentrepreneurs in flood-prone areas of Marikina City in Metro Manila, Philippines, using various statistical tools to evaluate decision factors. The research revealed that component factors such as economic considerations, the microentrepreneur’s socio-spatial relations, and experiences and perception of resilience influence the decision to remain in urban flood-prone areas. Moreover, specific sub-factors under each component factor were identified, which can be grouped into ‘pre-existing’ and ‘learned’ categories, introducing a new location decision model attuned to the dynamics that exists between microentrepreneurs and a natural disaster such as urban flooding. The findings of the study may guide policymakers and urban planners in designing targeted intervention measures that translate to more micro enterprise-responsive disaster risk reduction and management plans. Moreover, by treating microenterprises as separate from the traditional occupations, communities might appreciate the crafting of local land use plans which would successfully improve the overall resilience of this sector in urban flood-prone areas.

Firms’ location decisions, supply chain disruptions and government subsidies: A dynamic game

This paper develops a dynamic game to study the interaction between firms’ production location decisions and a government subsidy policy. On the one hand, firms consider the cost of shielding their production capacity from (potential) supply chain disruptions. On the other hand, the government is interested in curbing production offshoring to increase domestic tax revenues. The location decision of each firm is affected by the government subsidy policy and the location choices of other firms, while the share of firms producing domestically influences the government’s decision on whether and to what extent to provide subsidies to firms. We show that government subsidy policy must target a sufficiently large number of firms to encourage production reshoring and achieve the Pareto-efficient equilibrium. However, a subsidy policy providing a high number of low subsidies is likely to be ineffective in increasing domestic production, highlighting the importance of careful fine-tuning of the policy. Finally, in some scenarios, industrial policy becomes crucial to increase the resilience of domestic supply chains and foster reshoring.

Does prior success influence risk‐taking in foreign location decisions? A prospect theory perspective

AbstractResearch SummaryResearch suggests that foreign direct investment decisions can be biased and deviate from economic theory. This study deploys prospect theory to analyze the impact of the success of previous investments on risk‐taking in subsequent investment decisions. Our theorizing suggests that prior success influences foreign investors' decisions to take host‐country risk, and that this moderating influence on the risk‐investment relation is specific to the type of risk. The results of an event‐history study of 1259 location decisions support the notion that the success of previous investments encourages investors to enter host countries with high negative or positive risk, whereas it discourages them from entering locations with high mixed risk. The effects are stronger for investment locations that are similar than for those that are dissimilar.Managerial SummaryRegarding the varying risks that investors face in foreign countries, investment decisions may sometimes appear rather confident and sometimes rather cautious. Our study relates these decisions to the success that investors had with similar investments in the past, since research suggests that experiencing gains or losses can influence decision behavior. The results suggest that prior success affects risk‐taking in foreign direct investment decisions and comes in contrary forms, depending on the type of risk: prior success leads to overconfident investment decisions in the case of negative and positive risk, whereas it induces overcautious investment decisions in the case of mixed risk. Being aware of these behavioral tendencies can prevent managers from making biased investment decisions.

Identifying an optimal cancer risk threshold for resection of pancreatic intraductal papillary mucinous neoplasms

BackgroundIPMN consensus guidelines make implicit judgments on what cancer risk level should prompt surgery. We used decision modeling to estimate this cancer risk threshold (CRT) for BD-IPMN patients. MethodsWe created a decision model to compare quality-adjusted life years (QALYs) following surgery or surveillance for BD-IPMNs. We simulated treatment decisions for hypothetical patients, varying age, comorbidities and lesion location (pancreatic head/tail). The base case was a 60-year-old patient with mild comorbidities and pancreatic head IPMN. Probabilities, life expectancies, and utilities were incorporated from literature/public datasets. CRT was defined as the level of cancer risk at which the expected value of QALYs for surgery first exceeded that of surveillance. ResultsIn the base case, surgery was preferred over surveillance, yielding 21.90 vs. 21.88 QALYs. The optimal CRT for a BD-IPMN patient depended on age, comorbidities, and location. CRT in the base case was 20 % and 3 % for an IPMN in the head and tail of the pancreas, respectively. Other drivers of preferred treatment were age and likelihood of postoperative mortality. ConclusionFor BD-IPMNs, the optimal CRT varies depending on patient age and risk of surgical complications. Personalized risk threshold values could guide treatment decisions and inform future treatment consensus guidelines.

Perceived impact of abortion access on quality of cancer care and on future practice location decisions among oncology fellow physicians: An ASCO State of Cancer Care in America study.

43 Background: The Supreme Court decision in Dobbs v. Jackson Women’s Health Organization (2022) allows states to ban or limit abortion without health exceptions for pregnant persons. Notably, about 1 out of every 1000 pregnant persons has cancer and abortion is utilized in up to 28% of pregnant patients with cancer. To investigate the impact of this court decision on oncology practice, we studied perceptions among oncology fellows of the effect of abortion restrictions on quality care and whether they anticipate care implications to influence their decisions on future practice location. Methods: Fellows who took the 2024 ASCO Medical Oncology In-Training Exam (ITE) were invited to take an optional post-ITE survey assessing perceived (1) effect of abortion policies on ability to provide 9 key aspects of quality cancer care, including care access and delays, trust, and professional well-being and (2) impact of abortion restrictions on quality care in decisions of future practice location. Agreement (Agree, Strongly Agree) and impact on future practice location (Somewhat likely, Very likely) were compared by respondent gender and abortion policies (banned/under legal contention vs legal) in their own or neighboring state using chi-squared analysis. Results: Of 1884 US medical oncology fellows who took the ITE, 1254 (67%, inc. 49% male, 46% female) participated in this survey; 34% trained in states with bans, 44% in states adjacent to states with bans, and 22% in and adjacent to states where abortion is legal. Overall, 33% of fellows report that abortion restrictions affected their ability to deliver at least one aspect of quality care, including 22% reporting limited availability/delays of drugs and 28% reporting poorer job satisfaction. For all 9 aspect of quality care, agreement was strongly associated with local abortion status (p<0.001). For example, 37% of fellows in abortion-restricted states report reduced patient trust and relationship quality vs. 20% among those in states adjacent to bans vs. 18% among those in and adjacent to legal states (p<0.001). Over half (51%) of fellows report that they are likely to consider the impact of local abortion policies on quality care when deciding future practice location (28% were neutral, 21% unlikely to consider). More fellows training in states where abortion is legal report being likely to consider the impact of local abortion policies on care in future practice location decisions (55%) than those training in abortion-restricted states (45%) (p<0.001). More women (58%) than men (45%) report being likely to consider the impact of local abortion policies on quality care in future practice location (p<0.001). Conclusions: Findings provide early insights into the impact of abortion restrictions both on key aspects of quality care as well as the geographic distribution of future oncologists.

Robust cooperative hub location optimization considering demand uncertainty and hub disruptions

Amidst the rise of economic globalization and increased commodity trading, the logistics industry is experiencing rapid growth, encountering intricate transportation demands and fierce market competition. Simultaneously, it faces challenges related to infrastructure development and resource allocation efficiency. To enhance the adaptability and robustness of transportation networks when facing uncertain demands and potential hub disruptions, this paper proposes a two-stage robust optimization model for cooperative hub location problem. The model utilizes a hybrid algorithm that effectively combines the global search capability of genetic algorithms with the step-by-step optimization efficiency of Benders decomposition. Case study analyses demonstrate that, irrespective of the uncertainty environment, the cooperative model maintains lower total costs. Particularly in the case of large demand fluctuations, its cost advantages over non-cooperative models become notably prominent, showcasing remarkable performance in meeting service demands and enhancing resource utilization. Additionally, in cooperative networks, hub disruptions have a more significant impact on hub location decisions, with penalty and supplementary costs further exacerbating this influence. These research findings offer crucial insights for management practices: in uncertain market environments, adopting cooperative and robust planning strategies is pivotal for mitigating operational risks. When making decisions on cooperative hub location selection, carriers should comprehensively consider the interaction between uncertainty and economic benefits to achieve an optimal balance between risk and cost, ensuring the sustained economic feasibility of cooperative ventures.

Correlations between an Urban Three-Dimensional Pedestrian Network and Service Industry Layouts Based on Graph Convolutional Neural Networks: A Case Study of Xinjiekou, Nanjing

Urban high-density development has led to the emergence of complex three-dimensional pedestrian networks. As a crucial component of city centers, these networks significantly influence the spatial distribution of service industries. Understanding the correlation between pedestrian networks and service industry layouts is vital for effective planning and development. This study proposes a technical framework for analyzing the relationship between three-dimensional pedestrian networks and service industry layouts. Using the Xinjiekou central area in Nanjing as a case study, we constructed a three-dimensional pedestrian network model using the sDNA method. Focusing on catering formats, we introduced a method to study the spatial distribution characteristics of service industries in three-dimensional spaces and employed a graph convolutional network model to systematically analyze the correlation between pedestrian network closeness and betweenness with catering formats. The results indicate that pedestrian network closeness is significantly positively correlated with the number and average spending of catering formats, while betweenness shows almost no correlation. High-closeness areas, due to their traffic convenience and walkability, are more conducive to the concentration of catering formats and higher spending levels. Our findings provide valuable insights for catering format location decisions and the optimization of three-dimensional pedestrian networks, contributing to sustainable urban development.

The Fiscal Capitalization of Public Spending: Does Municipal Operating and Capital Spending Increase Property Values?

This study investigates the impact of local public spending on property values. The theory of fiscal capitalization, first proposed by Charles Tiebout and Wallace Oates, holds that a positive relationship exists between local public spending and property values. The theory posits that individuals and businesses make locational decisions based on their preferences for a community’s mix of taxes and spending on services. Although an extensive literature exists supporting fiscal capitalization, studies rarely investigate the separate effects of operating versus capital expenditures. Furthermore, few studies have investigated whether a distinct capitalization effect exists between the signal created by ongoing capital expenditures and a longer term amenity effect realized once projects are completed. This article extends the fiscal capitalization theory by assessing these distinctive effects on property values. The study uses a border discontinuity research design, limiting the sample to residential properties near municipal boundaries. The results show capital and operating expenditures have different effects on property values. In addition, the spending on capital improvements has short-term and long-term effects in the property market. The findings provide local policymakers with guidance on the relative benefits of operating versus capital spending when balancing the two choices.

Design of a reverse logistics network for medical waste considering location and routing decisions

Medical waste generated in healthcare facilities is categorized as hazardous due to its infectious, toxic, or radioactive properties, posing substantial risks to human health and the environment. This research proposes a bi-objective Mixed-Integer Linear Programming model for designing reverse logistics networks that enable both economical and safe medical waste management. Said model determines optimal quantity, depot locations, and routes for transporting waste from its generating points (hospitals) to the treatment and disposal sites (depots).This research addresses a bi-objective location routing problem, with multiple depots, time windows, capacity constraints, and variated waste types, across a multi-period horizon. It is innovative because no previous works have successfully dealt with such a combination of pragmatic features. The Location Routing Problem is an already well-known NP-Hard problem, with the additional aforementioned features further adding to its complexity. Thus, a genetic algorithm to solve large-sized realistic instances within reasonable computing times had been developed.Numerical experiments considered 20 random instances contrasting exact and approximate solution approaches. Also, a medical waste collection case study of a public hospital network in Atlántico Department, Colombia, delivering a Pareto frontier solution set, was introduced. The results demonstrated the efficacy of the proposed genetic algorithm in successfully addressing small instances of the problem, delivering outcomes comparable to the Mixed-Integer Linear Programming model. Furthermore, it yields good-quality solutions in a reduced computational time for larger instances deemed unfeasible for the Mixed-Integer Linear Programming model.This article contributes to both scientific literature and practitioners by presenting a decision-making tool designed to address the medical waste reverse logistics challenge under realistic scenarios. It approaches the issue from a bi-objective standpoint, jointly considering economic factors and biomedical safety. Additionally, the article introduces realistic and pertinent assumptions that have not been explored in previous literature.

Sustainability, Local Environmental Behaviour and Firm Location Decisions

AbstractThis paper uses Mercantile Register data to analyse the location decisions of firms in Catalan municipalities (between 2010 and 2019). Using count data panel estimations, we focus on local sustainability characteristics. The identification of the location patterns and the effects of local environmental policies on firms belonging to different sustainability industries constitute a contribution to the empirical location literature. Our results help in understanding entry processes at the local level and how both citizens' environmental values and local environmental policies shape these. We show that (i) firms locate differently depending on their sustainability profile and (ii) local environmental policies have stronger effects on the location decisions of sustainability‐oriented firms.

Too Cold to Venture There? January Temperature and Immigrant Self-Employment Across the United States

Immigrants in the United States have higher self-employment rates than native-born Americans. However, immigrant self-employment rates vary considerably across areas of the country. The authors examine the percentage of immigrant workers in local areas who are self-employed (i.e., the self-employment rate for the foreign born). Areas with colder winter temperatures have especially low self-employment rates among their immigrant populations compared to warmer areas. The relationship between winter temperature and immigrant self-employment persists after controlling for numerous individual and local area characteristics. The relationship holds for numerous subsamples of immigrants but is strongest for immigrants arriving to the United States as adults. Child immigrants and native-born Americans exhibit a weaker relationship, possibly because of previous exposure and attachment to particular locations chosen by their parents that constrain the migration responses of potential entrepreneurs. Entrepreneurial immigrants arriving to the country as adults appear especially footloose and particularly responsive to January temperatures in their location decisions.

Designing a multi-level reverse logistics network for waste batteries of electric vehicles under uncertainty—A case study in the Yangtze River Delta Urban Agglomerations of China

As a breakthrough for alleviating the impact of the energy crisis and improving the environment, the electric vehicle (EV) industry is entering a period of rapid development. The growth of this industry is expected to lead to a dramatic increase in the quantity of end-of-life EV traction batteries in the future, posing significant challenges to environmental pollution and the sustainable development of the EV industry. This study addresses the cascade utilization and uncertainties of waste batteries, by incorporating the development weight of the match degree between urban commercial environments and facility development, proposes a fuzzy chance constrained programming model for the design of multi-level reverse logistics network (RLN) for retired EV batteries. The objective is to minimize network costs and carbon emissions, determining optimal facility locations and transportation distribution schemes. The mathematical model is validated through a real case study in the Yangtze River Delta Urban Agglomerations in China, and sensitivity analysis is conducted to explore the impact of various influencing factors on network design. The results indicate that the development weight significantly influence facility location decisions, while the quantity and quality of battery recycling have a substantial impact on the operational quantities of network facilities at different levels.

Promoting the sustainable development of CCUS projects: A multi-source data-driven location decision optimization framework

Carbon Capture, Utilization, and Storage (CCUS) technology is vital for achieving global carbon reduction targets. However, the uncertainties in technology and economic viability are influenced by location. To promote the sustainable development of CCUS technology, the study proposes a data-driven framework for optimizing location decisions. Firstly, the framework considers multiple factors, including geospatial data on resources, risks, power production, transportation, and environment. It also evaluates qualitative and quantitative data across economic, social, environmental, and technological dimensions. Secondly, the two-stage model is conducted as follows: Using Geographic Information System (GIS) technology, the first stage identifies suitable regions for CCUS projects, while the second stage prioritizes these regions using the TODIM method. Further, validated in China, the Junggar Basin, Tarim Basin, Ordos Basin, Sichuan Basin, and Bohai Rim Basin are identified as suitable for CCUS deployment. The Huaneng Luohuang Power Plant is the most conducive location for CCUS projects as pilot demonstrations. Final sensitivity analysis, scenario analysis, and comparative analysis have respectively affirmed the stability, dynamism, and reliability of the model. These analyses have also been instrumental in elucidating the final preferred outcomes under various decision-making preferences and strategic orientations. The framework for decision-making and data-driven priority model for CCUS projects layout proposed in the study can provide technical support and practical evidence for decision-makers in planning CCUS projects and formulating supportive policies.

A Decision–Support Tool to Inform Coconut Log Procurement and Veneer Manufacturing Location Decisions in Fiji

A Decision–Support Tool to Inform Coconut Log Procurement and Veneer Manufacturing Location Decisions in Fiji