The intern will be undertaking research with Carbon Credit Corp. (CCC), a designer of green strategies for public companies, to gain an understanding of the Canadian Carbon Market, as it evolves, with specific attention on carbon sequestration in tilled land in the Canadian Prairies. CCC is uniquely positioned in this eco-market to determine how carbon sequestration will be treated as a viable carbon credit resource.
The focus of this project with NyFound Energy Services Inc., a developer of mid-sized clean heat, power, and energy utilities in western North America, revolves around the use of wood waste produced by the local sawmills in Merritt to produce electricity and the potential to also capture waste heat to supply dryers used at the sawmills, community buildings, greenhouses, and/or a wood pellet manufacturing plant. The use of wood waste would displace some or all of the natural gas currently used for drying purposes in the dry kilns and for provision of space heating.
The purpose of this project is to study the policies used to relocate ambulances in the City of Edmonton. The main goal of this internship is to improve the performance while minimizing the effects on ambulance crews’ workload. With this purpose in mind, the first step is to put in place a software simulation as a test bed to evaluate new solutions proposed (with the added advantage of being useful to evaluate changes in other areas). The second step will be to develop new models useful to analyze and improve on the ambulance relocation policies in use.
The intern will study possible enhancements to insolvency prediction models of the partner company. Her study will extend the previous models by combining macroeconomic market information and firm-specific information to identify firms facing possible financial trouble. The results of the study will provide helpful information for the company’s surveillance efforts to prevent insolvency or reduce the costs of insolvency for PACICC.
In many customer surveys and database marketing applications such as segmentation, profiling and predicting consumers’ choices, the joint distributions of covariates of interests are required. However, for variety of reasons, such as protection of clients’ confidentiality, the data are often available only in marginal frequency distribution format. This makes it difficult to use the covariates in a meaningful way where joint distributions are required for analysis decision making.