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The Biden Administration’s Justice40 Initiative states that 40% of federal investment in clean energy needs to benefit historically disadvantaged communities. This unprecedented policy has contributed to greater interest in how to measure environmental justice (EJ) and account for the disparate impacts that renewable energy development may have on different communities. This research uses spatial data to map and mea- sure EJ in the context of bioenergy development. A geographic information system (GIS) database was developed to compare data from Michigan’s MiEJScreen tool to the location of different types of wood energy technologies, including residential wood heating, commercial boilers, pellet manufacturing facili- ties, and biomass power stations. The results of statistical analyses using ArcGIS Pro found that as MiEJScreen values increased, the use of residential wood heat decreased. A hotspot analysis found that, in general, commercial boilers, pellet plants, and power stations were not clustered in areas with high MiEJScreen scores, although there were important exceptions. For all scales of wood energy technologies, rurality seemed to drive associations. Tools such as MiEJScreen can help to illuminate broader environ- mental and socioeconomic landscapes in which bioenergy technologies have been developed, but they may miss important nuances, particularly in rural communities. Implementing federal policies that aim at EJ requires accurate ways of mapping and measuring community dynamics and regional differences. This study uses the context of bioenergy development in Michigan to show how different analyses can be used, and some of the strengths and limitations of the existing tools and approaches. 

Recent publications have described the ability of citizen scientists to conduct unoccupied aerial system (UAS) flights to collect data for coastal management. Ground control points (GCPs) can be collected to georeference these data, however collecting ground control points require expensive surveying equipment not accessible to citizen scientists. Instead, existing infrastructure can be used as naturally occurring ground control points (NGCPs), although availably of naturally occurring ground control point placement on such infrastructure differs from published best practices of ground control point placement. This study therefore evaluates the achievable accuracy of sites georeferenced with naturally occurring ground control points through an analysis of 20 diverse coastal sites. At most sites naturally occurring ground control points produced horizontal and vertical root mean square errors (RMSE) less than 0.060 m which are similar to those obtained using traditional ground control points. To support future unoccupied aerial system citizen science coastal monitoring programs, an assessment to determine the optimal naturally occurring ground control point quantity and distribution was conducted for six coastal sites. Results revealed that generally at least seven naturally occurring ground control points collected in the broadest distribution across the site will result in a horizontal and vertical root mean square errors less than 0.030 m and 0.075 m respectively. However, the relationship between these placement characteristics and root mean square errors was poor, indicating that georeferencing accuracy using naturally occurring ground control points cannot be optimized solely through ideal quantity and distribution. The results of these studies highlight the value of naturally occurring ground control points to support unoccupied aerial system citizen science coastal monitoring programs, however they also indicate a need for an initial accuracy assessment of sites surveyed with naturally occurring ground control points at the onset of such programs. 

Globally, coastal communities are impacted by hazards including storm events, rising water levels, and associated coastal erosion. These hazards destroy homes and infrastructure causing human and financial risks for communities. At the same time, the economic and governance capacity of these communities varies widely, impacting their ability to plan and adapt to hazards. In order to identify locations vulnerable to coastal hazards, knowledge of the physical coastal changes must be integrated with the socio-economic profiles of communities. To do this, we couple information about coastal erosion rates and economic data in communities along the Great Lakes to develop a typology that summarizes physical and economic vulnerability to coastal erosion. This typology classifies communities into one of four categories: (1) High physical and economic vulnerability to coastal erosion, (2) High physical but low economic vulnerability to coastal erosion, (3) Low physical and low economic vulnerability to coastal erosion, and (4) High economic but low physical vulnerability to coastal erosion. An analysis of this typology over three time periods (2005–2010), (2010–2014), and (2014–2018) reveals the dynamic nature of vulnerability over this fourteen year time span. Given this complexity, it can be difficult for managers and decision-makers to decide where to direct limited resources for coastal protection. Our typology provides an analytical tool to proactively address this challenge. Further, it advances existing work on coastal change and associated vulnerability in three ways. One, it implements a regional, analytical approach that moves beyond case study-oriented work and facilitates community analyses in a comparative context. Two, the typology provides an integrated assessment of vulnerability that considers economic vulnerability to coastal erosion, which is a contextual variable that compounds or helps mitigate vulnerability. Three, the typology facilitates community comparisons over time, which is important to identifying drivers of change in Great Lakes coastal communities over time and community efforts to mitigate and adapt to these hazards. 

Water insecurity may precipitate interpersonal conflict, although no studies to date have rigorously examined these relationships. We examined relationships between household demographics, water insecurity, regional conflict, and interpersonal conflict over water. Using survey data from eight sub-Saharan African countries, we found that interpersonal conflict within and outside the home is associated with multiple domains of water insecurity, particularly accessibility. Furthermore, we found that higher levels of remote violence and protests are associated with greater within household conflict, whereas riots and violent armed conflict are associated with greater conflict between neighbors. Our findings expand upon the current literature by examining factors affecting interpersonal conflict over water, which may become increasingly important as precipitation patterns and land temperatures change in this region.