Significant water pollution caused by flooding due to heavy precipitation and extreme weather events has become a considerable problem in urbanized areas such as in Northern New Jersey. These cities experience heavy downpour-related contamination and water pollution when stormwater and untreated sewage are diverted through combined sewer overflow drainage systems to adjacent water bodies. Green infrastructure has proven a successful intervention method for mitigating these unintended environmental consequences. However, while the effects of CSOs and the ability of GI to reduce them are well documented, there has been considerably less study addressing public preferences and willingness to pay for GI-based solutions. As such, this study seeks to understand these facets of GI management in urbanized areas of New Jersey, focusing on Newark, Paterson, and Elizabeth townships. A discrete choice experiment method was used to analyze the willingness of residents to pay for additional CSO infrastructure through the installation of GI options such as bioretention gardens, rain barrels, and green roofs. Furthermore, study identified attributes such as secondary benefits, proximity, and water retention that respondents found the most utility in when choosing GI stormwater management interventions. We found that several attributes, including improved air quality ($58.60), increased water supply ($49.71), and closer proximity ($110.01-$125.97) had the highest utility and similarly were associated with a higher willingness to pay than other tested attributes. These findings are important in assessing the overall attitude toward these fixtures, and may be critical in crafting local policy and development, especially to address environmental equity.
Bioenergy has been globally recognized as one of the sustainable alternatives to fossil fuels. An assured supply of biomass feedstocks is a crucial bottleneck for the bioenergy industry emanating from uncertainties in land-use changes and future prices. Analytical approaches deriving from geographical information systems (GIS)-based analysis, mathematical modeling, optimization analyses, and empirical techniques have been widely used to evaluate the potential for bioenergy feedstock. In this study, we propose a three-phase methodology integrating fuzzy logic, network optimization, and ecosystem services assessment to estimate potential bioenergy supply. The fuzzy logic analysis uses multiple spatial criteria to identify suitable biomass cultivating regions. We extract spatial information based on favorable conditions and potential constraints, such as developed urban areas and croplands. Further, the network analysis uses the road network and existing biorefineries to evaluate feedstock production locations. Our analysis extends previous studies by incorporating biodiversity and ecologically sensitive areas into the analysis, as well as incorporating ecosystem service benefits as an additional driver for adoption, ensuring that biomass cultivation will minimize the negative consequences of large-scale land-use change. We apply the concept of assessing the potential for switchgrass-based bioenergy in Missouri to the proposed methodology.
This study analyzed the perceptions of four stakeholder groups (forest landowners, private forest consultants, forest management researchers or educators, and federal or state agency foresters), regarding their management practices and preferred geographic growing conditions of loblolly pine in Virginia by combining AHP (analytical hierarchy process) and regression modeling. By ranking the importance of different geographical conditions for managing loblolly pine, we aimed to identify ways to support loblolly growth as a potential feedstock for biofuel generation. We achieved this through collecting survey responses from 43 stakeholders during the 2019 Virginia Forestry Summit. The results showed that the landowner, researcher/educator, and federal/state agency stakeholder groups all indicated that proximity to a mill was the most important criteria, whereas the consultant stakeholder group indicated that proximity to a road was the most important criteria. All the stakeholder groups indicated that distance from protected land was the least important criteria, followed by proximity to a water body and flat land. The regression model revealed that acres of land managed and loblolly rotation age were correlated to the weight given to the distance to a mill criterion, where increased acreage and increased rotation age were associated with an increased prioritization of proximity to a mill. Distance from protected land, the lowest-ranking criteria, was shown to have an association with the level of experience with loblolly, where more experience was associated with a lower prioritization of proximity from protected land. A contingency analysis of the self-identified level of experience with loblolly in each stakeholder group revealed that federal/state agency foresters had the most experience, followed by consultants, landowners, and researchers/educators. The research supports the importance of understanding the variation of perceptions between and within stakeholder groups in order to develop the necessary infrastructural and policy support for the sustainable development of bioenergy.
Pests and disease have become an increasingly common issue as globalized trade brings non-native species into unfamiliar systems. Emerald ash borer (Agrilus planipennis), is an Asiatic species of boring beetle currently devastating the native population of ash (Fraxinus) trees in the northern forests of the United States, with 85 million trees having already succumbed across much of the Midwest. We have developed a reaction-diffusion partial differential equation model to predict the spread of emerald ash borer over a heterogeneous 2-D landscape, with the initial ash tree distribution given by data from the Forest Inventory and Analysis. As expected, the model predictions show that emerald ash borer consumes ash which causes the local ash population to decline, while emerald ash borer spreads outward to other areas. Once the local ash population begins to decline emerald ash borer also declines due to the loss of available habitat. Our model’s strength lies with its focus on the county scale and its linkage between emerald ash borer population growth and ash density. This enables one to make accurate predictions regarding emerald ash borer spread which allows one to consider various methods of control as well as to accurately study the economic effects of emerald ash borer spread.
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