Browsing by Author "Gamtessa, Samuel"

Now showing 1 - 4 of 4

Open Access
Energy Inefficiency of Canadian goods producing industries: Policy opportunities
(2017-02) Childs, Jason; Gamtessa, Samuel
Canadians face a daunting challenge. The Government of Canada has committed to reducing emissions of carbon dioxide (CO2) to 522.9 million tonnes by 2030, a 32 per cent reduction from current levels. In 2014 Canada emitted 7681 million tonnes of CO2, which means Canadians will be required to reduce emissions by 245.1 tonnes to meet this objective. Given the magnitude of the challenge, it's critical to recognize the reality Canada faces. There are really only two ways Canadians can meet the national target - by reducing energy inefficiency, or by reducing their material standard of living. In this Policy Brief, we explore the potential for reducing CO2 emissions by eliminating inefficiency.
Open Access
Predictive Modelling of Extreme Values in Unbalanced Panel Data
(Faculty of Graduate Studies and Research, University of Regina, 2019-06) Liu, Xiaohua; Bae, Taehan; Volodin, Andrei; Gamtessa, Samuel
This thesis aims at predictive modelling of a two-sided, heavy-tailed data under a mixture model setting. A robust regression method is used to fit the main body, while the peaks-over-threshold method is employed to select the tails or the extreme events. Based on the extreme value theory, the tails are modelled with Pareto or exponential distributions. For the estimation of the tail distributions, the Bayesian maximum a posterior estimation (MAP) with conjugate priors is used to smooth the maximum likelihood estimates (MLEs). With regard to each of the two tail, the MAP approach leads to two optimization problems for the estimation of tail parameters: the tail decay rate and the tail quantile level. This filter tuning process provides stability and efficiency in computation and prediction. Several constrained, non-convex optimization problems have been converted to unconstrained, convex problems by quadratic approximation and variable changes. Newton’s iteration method is employed to solve the optimization problems numerically. This formulated methodology is applied to a large, multi-period, unbalanced data set of daily returns of global stocks, containing nearly 120,000 records. The out-of-sample prediction results show the out-performance of the smoothed estimates over the regular MLEs.
Open Access
Risks Governance of Innovative Power Generation Technologies in Saskatchewan: Pathways to a Sustainable Energy Future
(Faculty of Graduate Studies and Research, University of Regina, 2020-03) Osazuwa-Peters, Mac Osa; Hurlbert, Margot; McNutt, Kathleen; Rayner, Jeremy; Gamtessa, Samuel; Bratt, Duane
Literature on socio-technical transitions acknowledge that negative risk perception is important to socio-technical transitions. However, beyond acknowledging risk as a potential barrier to the deployment of innovative technologies, this study points out that the acceptance or rejection of innovative technologies due to their associated risks can be a predictor of the socio-technical transition pathway that will be followed. This dissertation uses socio-technical transitions in the electric power generation sector of Saskatchewan as a case study. It finds that existing literature in the field of energy systems transitions fail to fully map the relationship between risk analysis and socio-technical transition pathways. This, the dissertation argues could be a function of the multidimensional and multidisciplinary nature of the concept of risk. This dissertation applies the risk governance framework to understand the multidimensional nature of risk and then map the findings on the multilevel perspective through which it showed how the outcome of citizen’s risk analysis may result in several transition pathways. The analysis in this dissertation is based on data collected from six citizen’s juries held in Saskatchewan in 2017. Through the discussions from these citizen’s jury sessions, this study identified how citizens apply heuristic devices as they analyse their perceived risks in two baseload power generation sources, carbon capture and storage (CCS) and small modular nuclear reactors (SMRs). This dissertation finds that in Saskatchewan, familiarity, experiential knowledge of a technology based on a history of usage and consumption, rather than cost or technical risk, are the strongest factors influencing people’s perception and attitude toward the risk in innovative technologies in the energy sector. These factors are most potent when CCS and SMRs are compared directly. But when compared as part of a portfolio of options including renewable resources such as solar and wind, citizens seem to balance the risks they associate with SMRs with the gains from having renewable resources as part of the grid. Hence, there is a chance that Saskatchewan can advance its energy system transition through either a reproduction, substitution, or a transformation pathway. Clearly, this study not only maps the way risk analysis influence transition pathways, it also provides insight into the difference in the tools experts deploy in analysing risks compared to those that unspecialized citizens use. Through the use of expert witnesses in the citizen’s jury sessions, this dissertation challenges the knowledge deficit model of citizen engagement as it revealed that citizens are more likely to develop confirmation biases when they are exposed to new information that deviates from their previous understanding of a phenomenon based on their lived experience, especially when they do not have full trust in the information sources.
Open Access
Stochastic Modelling of Heavy-Tailed Precipitations in Canadian Prairies
(Faculty of Graduate Studies and Research, University of Regina, 2020-12) Mazjini, Maral; Bae, Taehan; Deng, Dianliang; Volodin, Andrei; Gamtessa, Samuel; Zhao, Yang; Feng, Cindy Xin
The statistical modelling of extreme precipitation structures is essential in many aspects such as assessing and managing risks resulting from the occurrence of such extreme events for agricultural purposes, in particular. Typically, daily precipitation time series with many zero (on dry days) and positive (on wet days) observations exhibits characteristics such as heavy-tailedness and volatility clustering (i.e., some periods of high and some periods of low volatility) which make it challenging to develop an effective model for both the theoretical and observations viewpoints. The main goal of this study is to introduce a model capable of describing structure of precipitation data and apply it to a historical data set from twelve stations in Canadian prairies where precipitation is a crucial factor in agriculture. In this thesis, the three main important characteristic of a precipitation based data set, as mentioned above, have been described through a dynamic mixture model. Firstly, in order to study the full range of precipitation measurements, we have assigned probabilities to zero and positive observations. Then, positive observations have been assumed to be drawn from a generalized Gaussian crack (GGCR) distribution which has exibility to fit heavy-tailed observations. In addition, a specification of a GARCH type model for the scale parameter of the GGCR distribution has been considered to capture the time-varying volatility. Meanwhile, for a model fitting method, the maximum likelihood estimation has been utilized with the profile log-likelihood algorithm. Furthermore, in order to con- firm the performance of the proposed model, simulation studies are performed. For the application purpose, the dynamic mixture model has been fitted to the Canadian prairies precipitation data set and the dependence structure of the residuals has been studied through Archimedean copulas.