skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: How has the COVID Crisis Impacted Local Governments’ Sustainability Efforts? An Examination of Initial Effects
Although many U.S. municipalities have adopted climate protection and sustainability as explicit objectives, they are not among their traditional responsibilities. As a result, compared to policies focused around core functions, those related to sustainability may be at greater risk of retrenchment or change in times of crisis. This research examines how the COVID-19 pandemic has impacted local governments’ sustainability efforts. Using data from a nation-wide survey, we examine the degree to which the pandemic has affected programmatic priorities, resources, and operations related to sustainability. Findings indicate that the pandemic hurt the implementation of sustainability initiatives in almost half of U.S. cities. At the same time, many cities increased the priority of economic and social sustainability initiatives in response to the pandemic. Cities which have formally included sustainability principles into a city plan appear more sensitive to COVID-induced challenges to their program operations.  more » « less
Award ID(s):
2021044
PAR ID:
10349898
Author(s) / Creator(s):
; ;
Publisher / Repository:
Sage
Date Published:
Journal Name:
State and Local Government Review
Volume:
55
Issue:
1
ISSN:
0160-323X
Page Range / eLocation ID:
27 to 40
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Shaw, Shih-Lung; Sui, Daniel (Ed.)
    When the World Health Organization (WHO) announced the pandemic of COVID-19, people around the globe scattered to stores for groceries, supplies, and other miscellaneous items in preparation for quarantine. The dynamics of retail visits changed dramatically due to the pandemic outbreak. The study intends to analyze how the store visit patterns have changed due to the lockdown policies during the COVID-19 pandemic. Using mobile phone location data, we build a time-aware Huff model to estimate and compare the visiting probability of different brands of stores over different time periods. We are able to identify certain retail and grocery stores that have more or fewer visits due to the pandemic outbreak, and we detect whether there are any trends in visiting certain retail establishments (e.g., department stores, grocery stores, fast-food restaurants, and cafes) and how the visiting patterns have adjusted with lockdowns. We also make comparisons among brands across three highly populated U.S. cities to identify potential regional variability. It has been found that people in large metropolitan areas with a well-developed transit system tend to show less sensitivity to long-distance visits. In addition, Target, which is a department store, is found to be more negatively affected by longer-distance trips than other grocery stores after the lockdown. The findings can be further applied to support policymaking related to public health, urban planning, transportation, and business in post-pandemic cities. 
    more » « less
  2. null (Ed.)
    We examine the uneven social and spatial distributions of COVID-19 and their relationships with indicators of social vulnerability in the U.S. epicenter, New York City (NYC). As of July 17th, 2020, NYC, despite having only 2.5% of the U.S. population, has [Formula: see text]6% of all confirmed cases, and [Formula: see text]16% of all deaths, making it a key learning ground for the social dynamics of the disease. Our analysis focuses on the multiple potential social, economic, and demographic drivers of disproportionate impacts in COVID-19 cases and deaths, as well as population rates of testing. Findings show that immediate impacts of COVID-19 largely fall along lines of race and class. Indicators of poverty, race, disability, language isolation, rent burden, unemployment, lack of health insurance, and housing crowding all significantly drive spatial patterns in prevalence of COVID-19 testing, confirmed cases, death rates, and severity. Income in particular has a consistent negative relationship with rates of death and disease severity. The largest differences in social vulnerability indicators are also driven by populations of people of color, poverty, housing crowding, and rates of disability. Results highlight the need for targeted responses to address injustice of COVID-19 cases and deaths, importance of recovery strategies that account for differential vulnerability, and provide an analytical approach for advancing research to examine potential similar injustice of COVID-19 in other U.S. cities. Significance Statement Communities around the world have variable success in mitigating the social impacts of COVID-19, with many urban areas being hit particularly hard. Analysis of social vulnerability to COVID-19 in the NYC, the U.S. national epicenter, shows strongly disproportionate impacts of the pandemic on low income populations and communities of color. Results highlight the class and racial inequities of the coronavirus pandemic in NYC, and the need to unpack the drivers of social vulnerability. To that aim, we provide a replicable framework for examining patterns of uneven social vulnerability to COVID-19- using publicly available data which can be readily applied in other study regions, especially within the U.S.A. This study is important to inform public and policy debate over strategies for short- and long-term responses that address the injustice of disproportionate impacts of COVID-19. Although similar studies examining social vulnerability and equity dimensions of the COVID-19 outbreak in cities across the U.S. have been conducted (Cordes and Castro 2020, Kim and Bostwick 2002, Gaynor and Wilson 2020; Wang et al. 2020; Choi and Unwin 2020), this study provides a more comprehensive analysis in NYC that extends previous contributions to use the highest resolution spatial units for data aggregation (ZCTAs). We also include mortality and severity rates as key indicators and provide a replicable framework that draws from the Centers for Disease Control and Prevention’s Social Vulnerability indicators for communities in NYC. 
    more » « less
  3. Cities are increasingly making decisions related to sustainability, and information from the field of urban ecology may be useful in informing these decisions. However, the potential utility of this information may not translate into it actually being used. We surveyed municipal sustainability staff through the Minnesota GreenStep Cities program documenting their information needs and information sources, and used these results to identify the frequency with which urban ecologists are publishing studies of potential relevance to practitioners. We also quantified funded awards from the U.S. National Science Foundation in urban ecology that explicitly describe active partnerships with city policy makers. Our results show that urban ecologists are increasingly generating information of potential relevance to city sustainability efforts, with rapid increases in the number of articles published and grants funded on areas identified as key information needs. Our results also suggest that the transmission of information from academic urban ecologists to practitioners occurs mostly through indirect pathways, as municipal sustainability staff reported relying heavily on general web searches and government agency websites to find information. We found evidence of an increasing frequency of active collaborations between urban ecologists and policy makers from NSF grant abstracts. Our findings are consistent with previous findings that traditional models of passive communication to practitioners through academic journals results in a low efficiency of use of this knowledge, but that the potential for urban ecologists to help inform municipal sustainability initiatives through active collaborations with practitioners is great. 
    more » « less
  4. Kacprzyk, Janusz; Pal, Nikhil R; Perez, Rafael B; Corchado, Emilio S; Hagras, Hani; Kóczy, László T; Kreinovich, Vladik; Lin, Chin-Teng; Lu, Jie; Melin, Patricia (Ed.)
    The COVID-19 pandemic was lived in real-time on social media. In the current project, we use machine learning to explore the relationship between COVID-19 cases and social media activity on Twitter. We were particularly interested in determining if Twitter activity can be used to predict COVID-19 surges. We also were interested in exploring features of social media, such as replies, to determine their promise for understanding the views of individual users. With the prevalence of mis/disinformation on social media, it is critical to develop a deeper and richer understanding of the relationship between social media and real-world events in order to detect and prevent future influence operations. In the current work, we explore the relationship between COVID-19 cases and social media activity (on Twitter) in three major United States cities with different geographical and political landscapes. We find that Twitter activity resulted in statistically significant correlations using the Granger causality test, with a lag of one week in all three cities. Similarly, the use of replies, which appear more likely to be generated by individual users, not bots or public relations operations, was also strongly correlated with the number of COVID-19 cases using the Granger causality test. Furthermore, we were able to build promising predictive models for the number of future COVID-19 cases using correlation data to select features for input to our models. In contrast, significant correlations were not identified when comparing the number of COVID-19 cases with mainstream media sources or with a sample of all US COVID-related tweets. We conclude that, even for an international event such as COVID-19, social media tracks closely with local conditions. We also suggest that replies can be a valuable feature within a machine learning task that is attempting to gauge the reactions of individual users. 
    more » « less
  5. Appearing at the end of 2019, a novel virus (later identified as SARS-CoV-2) was characterized in the city of Wuhan in Hubei Province, China. As of the time of writing, the disease caused by this virus (known as COVID-19) has already resulted in over three million deaths worldwide. SARS-CoV-2 infections and deaths, however, have been highly unevenly distributed among age groups, sexes, countries, and jurisdictions over the course of the pandemic. Herein, I present a tool (the covid19.Explorer R package and web application) that has been designed to explore and analyze publicly available United States COVID-19 infection and death data from the 2020/21 U.S. SARS-CoV-2 pandemic. The analyses and visualizations that this R package and web application facilitate can help users better comprehend the geographic progress of the pandemic, the effectiveness of non-pharmaceutical interventions (such as lockdowns and other measures, which have varied widely among U.S. states), and the relative risks posed by COVID-19 to different age groups within the U.S. population. The end result is an interactive tool that will help its users develop an improved understanding of the temporal and geographic dynamics of the SARS-CoV-2 pandemic, accessible to lay people and scientists alike. 
    more » « less