Plug-in Electric Vehicles (PEVs) are considered one solution to reducing GHG emissions from private transport. Additionally, PEV adopters often have free access to public charging facilities. Through a pattern analysis, this study identifies five distinct clusters of daily PEV charging profiles observed at the public charging stations. Empirically observed patterns indicate a significant amount of operational inefficiency, where 54% of the total parking duration PEVs do not consume electricity, preventing other users from charging. This study identifies the opportunity cost in terms of GHG emissions savings if gasoline vehicles are replaced with potential PEV adopters. The time spent in parking without charging by current PEV users can be used by these potential PEV users to charge their PEVs and replace the use of gasoline. The results suggest that reducing inefficient station use leads to significant reductions in emissions. Overall, there is significant variability in outcomes depending on the specific cluster membership.

No abstract

The capacity to utilize geographic information is a critical element of disaster risk management. Although access to and use of geographic information system (GIS) technology continues to grow, there remain significant gaps in approaches used by disaster risk management stakeholders to understand geographic information needs, sources, and information flow-ultimately limiting the efficacy of management efforts. To address this problem, we introduce the concept of geographic information capacity (GIC) to measure and analyze the ability of stakeholders to understand, access, and work with geographic information for disaster risk management. We propose a framework for assessing GIC, the GIC Profile, which we situate within a review of disaster risk managementrelevant frameworks. We evaluate the GIC Profile using two case study countries at the first (sub-national) geoadministrative boundary level. Chi-square analyses suggest GIC across equivalent regional units within each country is relatively uniform, and that this uniformity is comparable between nations despite significant difference in overall capacity. Contributions of the GIC Profile to disaster risk management research are twofold. First, this is a first attempt to develop a profile based on key indicators for quantifying GIC highlights critical areas for capacity improvement, allowing decision makers to identify and prioritize pathways to strengthen disaster risk management programs. Through this initial effort, a decision tool has been developed which may enhance decisions on how to utilize GIS in support of disaster risk management. This tool is iterative and can be updated as new events occur to maximize GIS benefits, ultimately reducing disaster risks and their potential consequences.

No abstract

No abstract