Disasters exact a heavy toll globally. However, the degree to which we can accurately quantify their impact, in particular mortality, remains challenging. It is critical to ensure that disaster data reliably reflects the scale, type, and distribution of disaster impacts given the role of data in: (1) risk assessments; (2) developing disaster risk management programs; (3) determining the resources for response to emergencies; (4) the types of action undertaken in planning for prevention and preparedness; and (5) identifying research gaps. The Sendai Framework for Disaster Risk Reduction 2015-2030s seven global disaster-impact reduction targets represent the first international attempt to systematically measure the effectiveness of disaster-impact reduction as a means of better informing policy with evidence. Target A of the Sendai Framework aims to ''substantially reduce global disaster mortality by 2030, aiming to lower the average per 100,000 global mortality rate in the decade 2020-2030 compared to the period 2005-2015.'' This article provides an overview of the complexities associated with defining, reporting, and interpreting disaster mortality data used for gauging success in meeting Target A, acknowledging different challenges for different types of hazard events and subsequent disasters. It concludes with suggestions of how to address these challenges to inform the public health utility of monitoring through the Sendai Framework.
Investment in conservation and ecological restoration depends on various socioeconomic factors and the social license for these activities. Our study demonstrates a method for targeting management of ecosystem services based on social values, identified by respondents through a collection of social survey data. We applied the Social Values for Ecosystem Services (SolVES) geographic information systems (GIS)-based tool in the Sonoita Creek watershed, Arizona, to map social values across the watershed. The survey focused on how respondents engage with the landscape, including through their ranking of 12 social values (eg, recreational, economic, or aesthetic value) and their placement of points on a map to identify their associations with the landscape. Additional information was elicited regarding how respondents engaged with water and various land uses, as well as their familiarity with restoration terminology. Results show how respondents perceive benefits from the natural environment. Specifically, maps of social values on the landscape show high social value along streamlines. Life-sustaining services, biological diversity, and aesthetics were the respondents’ highest rated social values. Land surrounding National Forest and private lands had lower values than conservation-based and state-owned areas, which we associate with landscape features. Results can inform watershed management by allowing managers to consider social values when prioritizing restoration or conservation investments.
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