Anthropogenic climate change increasingly disrupts livelihoods, floods coastal urban cities and island nations, and exacerbates extreme weather events. There is near-universal consensus among scientists that in order to reverse or at least mitigate climate disruptions, limits must be imposed on anthropogenic sources of climate-forcing emissions and adaptation to changing global conditions will be necessary. Yet adaptation to current and future climate change at the individual, community, and national levels vary widely from merely coping, to engaging in adaptive change, to transformative shifts. Some of those affected simply cope with lower crop yields, flooded streets, and higher cooling bills. Others incrementally adapt to new environmental conditions, for example, by raising seawalls or shifting from one crop to another better suited for a hotter environment. The highest—and perhaps least likely—type of change involves transformation, radically altering practices with an eye toward the future. Transformative adaptation may involve a livelihood change or permanent migration; it might require shuttering whole industries and rethinking industrial policy at the national level. Entire island nations such as Fiji, for example, are considering relocating from vulnerable locations to areas better suited to rising sea levels. A great deal of research has shown how social capital (the bonding, bridging, and linking connections to others) provides information on trustworthiness, facilitates collective action, and connects us to external resources during disasters and crises. We know far less about the relationship between social capital and adaptation behaviors in terms of the choices that people make to accommodate changing environmental conditions. A number of unanswered but critical questions remain: How precisely does social capital function in climate change adaptation? To what degree does strong bonding social capital substitute for successful adaptation behaviors for individuals or groups? Which combinations of social factors make coping, adapting, and transforming most likely? How can social capital help migrating populations maintain cultural identity under stress? How can local networks be integrated into higher-level policy interventions to improve adaptation? Which political and social networks contribute to transformative responses to climate change at local, regional, and international levels? This article serves as a comprehensive literature review, overview of empirical findings to date, and a research agenda for the future.
Protective policies have been unequally and inconsistently applied in the UnitedStates throughout the Covid-19 pandemic. This study investigates the relationship between state and local policies and Covid-19 deaths, combining three datasets: the Centers for Disease Control and Prevention's Social Vulnerability Index; local laws and regulations from the COVID Analysis and Mapping of Policies (AMP) database; and Covid-19 deaths by county reported by The New York Times. It examines, using propensity score matching, local policies and regulations as treatments during the crisis, and assesses how, inter alia, face mask requirements, gathering restrictions, stay-at-home orders, and social distancing mandates enacted at the county level altered Covid-19 deaths. The results indicate that the first three variables reduced average Covid-19 deaths in high-vulnerability communities. Despite clear gaps in federal policy guidance and coordinated policies, some efforts led by local and state governments promoted safer behaviour and lessened the impact of Covid-19 in communities, especially those with higher social vulnerability rates.
Hurricane Harvey was social media's first real stress test as a disaster response and recovery mechanism. A confluence of conditions makes it an ideal case study of social media's role in disaster recovery: the lack of a government-issued evacuation order, a call from government leadership for willing and able volunteers with a boat or high-water vehicle to perform life-saving rescues, and wide-spread adoption of social media platforms in the Houston area. While research on online social networks and disasters continues to grow, social scientists know little about how these online networks transform during a crisis and, further, how they drive disaster outcomes. With two original datasets, this study investigates how Houston's online social network transformed during Hurricane Harvey (2017), and the relationship between social media activity and post-Harvey recovery. The findings of a social network analysis (N= 2,387,610) and subsequent statistical analyses reveal the Houston-area online social network grew denser, clustered, and more efficient during the disaster. A spatial analysis and three separate regression models of activity before, during, and after Hurricane Harvey reveal that among 333 Nextdoor Neighborhoods, hyperlocal social media activity was a statistically significant predictor of the rate of rebuilding in these geographically based online communities. These findings suggest that policy and decision-makers should invest into online and offline hyperlocal social networks well before a disaster strikes, and leverage resources and legislation to maintain and strengthen the telecommunications and energy infrastructure that supports access to social media and telecommunications infrastructure during a time of crisis.
Over the past thirty years, disaster scholars have highlighted that communities with stronger social infrastructure—including social ties that enable trust, mutual aid, and collective action—tend to respond to and recover better from crises. However, comprehensive measurements of social capital across communities have been rare. This study adapts Kyne and Aldrich’s (Risk Hazards Crisis Public Policy11, 61–86, 2020) county-level social capital index to the census-tract level, generating social capital indices from 2011 to 2018 at the census-tract, zipcode, and county subdivision levels. To demonstrate their usefulness to disaster planners, public health experts, and local officials, we paired these with the CDC’s Social Vulnerability Index to predict the incidence of COVID-19 in case studies in Massachusetts, Wisconsin, Illinois, and New York City. We found that social capital predicted 41–49% of the variation in COVID-19 outbreaks, and up to 90% with controls in specific cases, highlighting its power as diagnostic and predictive tools for combating the spread of COVID.
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