Socially optimal replacement of conventional with electric vehicles for the US household fleet

In this study, a framework is proposed for minimizing the societal cost of replacing gas-powered household passenger cars with battery electric ones (BEVs). The societal cost consists of operational costs of heterogeneous driving patterns' cars, government investments for charging deployment, and monetized environmental externalities. The optimization framework determines the timeframe needed for conventional vehicles to be replaced with BEVs. It also determines the BEVs driving range during the planning timeframe, as well as the density of public chargers deployed on a linear transportation network over time. We leverage data sets that represent US household driving patterns, as well as the automobile and the energy markets, to apply the model. Results indicate that it takes 8 years for 80% of our conventional vehicle sample to be replaced with electric vehicles, under the base case scenario. The socially optimal all-electric driving range is 204 miles, with chargers placed every 172 miles on a linear corridor. All public chargers should be deployed at the beginning of the planning horizon to achieve greater savings over the years. Sensitivity analysis reveals that the timeframe for the socially optimal conversion of 80% of the sample varies from 6 to 12 years. The optimal decision variables are sensitive to battery pack and vehicle body cost, gasoline cost, the discount rate, and conventional vehicles' fuel economy. Faster conventional vehicle replacement is achieved when the gasoline cost increases, electricity cost decreases, and battery packs become cheaper over the years.     

Sprint＇s Data Control

It＇s been more than a decade since AI Gore bragged about inventing the ＂information superhighway,＂ and in that time the lanes havegotten crowded. Thanks to the advances in broadband and mobile technology, consumers have almost any fact or figure available at their fingertips--so much so, in fact, that wading through the deluge of data can be daunting.     

Transitions: Taking complexity seriously

The aim of this paper is to make the case for a systematic engagement of transition studies with complexity theory and research. We argue that transition research is important for the understanding and development of possible sustainable future pathways. However, there are several controversies in transition research, including, the role of agency in transitions; the relationships between levels (niche, regime and landscapes); the origin of transitions; the identification of a transition, including starting and ending point of transition processes; and the paradox of replicability and scaling up of independent experiments. We argue that transition research, and future studies can benefit from taking complexity theory seriously. In the paper, we elaborate four insights from complexity studies that can move the research agenda of transition studies forward: empirically investigating the degree of complexity of a system; investigating complexity at the level of the policy-making system; longitudinal and retrospective research designs for the identification of transitions; methodological tools which accommodate complexity, such as agent-based modeling and ARIMA modeling. Further, we suggest how these can improve our knowledge of transitions towards sustainable future.     

A secondary market metaphor for content delivery networks

A Secondary Market (SM) mechanism is used to allow the exchange of unused resources between Origin Servers (content-generating entities) that claim and reserve resources from Content Delivery Networks (CDNs). Using a Predictive Reservation Scheme, network and disk resources are being monitored through well-established techniques (Kernel Regression Estimators) in a given time frame. We show that the SM mechanism significantly improves the efficiency and robustness of our Predictive Reservation Scheme by allowing fast exchange of unused resources between the Origin Servers (CDN Clients), either by implementing socially optimal practices or by allowing automatic electronic auctions at the end of day (EOD) or at shorter time intervals. Therefore, all the involved players (including the CDN itself) benefit from the rationalized use of CDN resources.