Objective:Although extreme heat can impact the health of anyone, certain groups are disproportionately affected. In urban settings, cooling centers are intended to reduce heat exposure by providing air-conditioned spaces to the public. We examined the characteristics of populations living near cooling centers and how well they serve areas with high social vulnerability. Methods:We identified 1402 cooling centers in 81 US cities from publicly available sources and analyzed markers of urban heat and social vulnerability in relation to their locations. Within each city, we developed cooling center access areas, defined as the geographic area within a 0.5-mile walk from a center, and compared sociodemographic characteristics of populations living within versus outside the access areas. We analyzed results by city and geographic region to evaluate climate-relevant regional differences. Results:Access to cooling centers differed among cities, ranging from 0.01% (Atlanta, Georgia) to 63.2% (Washington, DC) of the population living within an access area. On average, cooling centers were in areas that had higher levels of social vulnerability, as measured by the number of people living in urban heat islands, annual household income below poverty, racial and ethnic minority status, low educational attainment, and high unemployment rate. However, access areas were less inclusive of adult populations aged ≥65 years than among populations aged <65 years. Conclusion:Given the large percentage of individuals without access to cooling centers and the anticipated increase in frequency and severity of extreme heat events, the current distribution of centers in the urban areas that we examined may be insufficient to protect individuals from the adverse health effects of extreme heat, particularly in the absence of additional measures to reduce risk. 
                        more » 
                        « less   
                    This content will become publicly available on July 30, 2026
                            
                            Can Cash Incentives Reduce Syringe Litter? Evidence From Boston's 311 Service Requests
                        
                    
    
            ABSTRACT Syringe littering in public places is a public health problem in many big cities nationwide amid the ongoing opioid crisis. Besides needle exchanges and/or safe disposal efforts, cash incentives have come into play as a policy tool to address the issue. In December 2020, the City of Boston launched the Community Syringe Redemption Program (CSRP), which offers a nominal cash “buy back” incentive for used syringes at designated centers. This study examines the impact of Boston's CSRP on reducing syringe litter. It employs a distance band‐based, near‐far identification strategy and uses the difference‐in‐differences (DID) to analyze Boston's 311 service requests related to needle pickup and the number of discarded needles discovered in response to the pickup requests before and after CSRP's implementation. Results show that both 311 requests and publicly discarded syringes have reduced significantly in neighborhoods located within a 0.5‐mile radius of the CSRP redemption center. There are no statistically significant reductions observed in 0.5–1‐mile or 1–1.5‐mile donut bands around the center. In effect, although CSRP could be effective in reducing syringe littering, its impact is geographically bounded and sensitive to the redemption centers' locations. Results from alternative models, robustness tests, and placebo tests are consistent with main findings. 
        more » 
        « less   
        
    
                            - Award ID(s):
- 1924154
- PAR ID:
- 10633432
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Policy Studies Journal
- ISSN:
- 0190-292X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
- 
            
- 
            null (Ed.)The main purpose of this paper is to illustrate the application of causal inference method to administrative data and the challenges of such application. We illustrate by applying Bayesian networks method to 311 data from Miami-Dade County, Florida (USA). The 311 centers provide non-emergency services to residents. The 311 data are large and granular. We aim to explore the equity issues and biases that might exist in this particular type of service requests. As a case study, the relationship between population characteristics (independent variables) and request volume and completion time (dependent variables) is examined to identify the disparities, if any, from the observational data. The empirical analysis shows that there are no biases in services provided to any specific demographic, socioeconomic, or geographical groups. However, the administrative data do have various challenges for inferring causality due to missing or impure data, inadequacy, and latent confounders. The precautions of applying causal techniques to analyzing administrative data like 311 are discussed.more » « less
- 
            Penkert, B; Hellingrath, B; Rode, M; Widera, A; Middelhoff, M; Boersma, K; Kalthoner, M (Ed.)This paper introduces a machine learning tool for service systems, focusing on accurate classification of service requests and swift anomaly detection, particularly crucial during emergencies. Employing a Support Vector Machine model, this tool automatically classifies service calls into predefined categories with high accuracy, while effectively detecting irregular requests that require specific attention from operators. This approach streamlines resource management by reducing the manuaI categorization workload and enables early detection of emerging service needs. Examining Orange County, Florida 311 System data, with a specific focus on the COVID-19 period, we illustrate the tool's success in automatic request categorization and anomaly detection. Overall, this tool presents an effective automation approach to help with efficient resource management of service systems and proactive assessment of public service needs, promising to revolutionize service request management during crises. Future work will explore additional classification models for enhanced accuracy and integrate automated alerts for proactive disaster management.more » « less
- 
            On-demand transit is attracting the attention of transportation researchers and transit agencies for its potential to solve the first-mile/last-mile problem. Although on-demand transit has been proved to increase transit accessibility significantly, its impact on transit equity and equality has not been addressed. In this study we examined the potential impact of the On-Demand Multimodal Transit System (ODMTS) in Atlanta (GA), on both transit equity and equality compared with the existing transit system. The results showed that ODMTS could have a positive impact on transit equality by reducing the disparity in transit service between neighborhoods close to and far from the existing transit network; however, it may not improve transit equity.more » « less
- 
            Abstract The energy demands from data centers contribute greatly to water scarcity footprint and carbon emissions. Understanding the use of on-site renewable power generation is an important step to gain insight into making data centers more sustainable. This novel study examines the impact of on-site solar or wind energy on data center water scarcity usage effectiveness (WSUE) and carbon usage effectiveness (CUE) at a U.S. county scale for a given data center size, water consumption level, and energy efficiency. The analysis uncovers combinations of specific metrics associated with grid-based carbon emissions and water scarcity footprint that enable predictions of the improvements anticipated when implementing on-site solar or wind energy. The implementation of on-site renewables has the most benefit in reducing carbon footprint in areas with high existing grid-based emissions such as the western side of the Appalachian Mountains (e.g., central and eastern Kentucky). The largest benefit in reducing water scarcity footprint is generally seen in counties with low water scarcity compared to adjacent areas (e.g., northern California).more » « less
 An official website of the United States government
An official website of the United States government 
				
			 
					 
					
