Three Essays on Economic Development in the Natural Resources Sector

This thesis examines two ongoing development projects that received financial support from international development organizations, and an alternative mining tax proposed by the academia. Chapter 2 explores the impact of commoditization of coffee on its export price in Ethiopia. The first part of the chapter traces how the Ethiopian’s current coffee trade system and commoditization come to be. Using regression analysis, the second part tests and confirms the hypothesis that commoditization has led to a reduction in coffee export price. Chapter 3 conducts a cost-benefit analysis on a controversial, liquefied natural gas export project in Peru that sought to export one-third of the country’s proven natural gas reserves. While the country can receive royalty and corporate income tax in the short and medium term, these benefits are dwarfed by the future costs of paying for alternative energy after gas depletion. The conclusion is robust for a variety of future energy-price and energy-demand scenarios. Chapter 4 quantifies through simulation the economic distortions of two common mining taxes, the royalty and ad-valorem tax, vis-à-vis the resource rent tax. The latter is put forward as a better mining tax instrument on account of its non-distortionary nature. The rent tax, however, necessitates additional administrative burdens and induces tax-avoidance behavior, both leading to a net loss of tax revenue. By quantifying the distortions of royalty and the ad-valorem tax, one can establish the maximum loss that can be incurred by the rent tax. Simulation results indicate that the distortion of the ad-valorem tax is quite modest. If implemented, the rent tax is likely to result in a greater loss. While the subject matters may appear diverse, they are united by one theme. These initiatives were endorsed and supported by authorities and development agencies in the aim of furthering economic development and efficiency, but they are unlikely to fulfill the goal. Lessons for international development can be learnt from successful stories as well as from unsuccessful ones.

Robust scenario formulations for strategic supply chain optimization under uncertainty

Strategic supply chain optimization (SCO) problems are often modelled as a two-stage optimization problem, in which the first-stage variables represent decisions on the development of the supply chain and the second-stage variables represent decisions on the operations of the supply chain. When uncertainty is explicitly considered, the problem becomes an intractable infinite-dimensional optimization problem, which is usually solved approximately via a scenario or a robust approach. This paper proposes a novel synergy of the scenario and robust approaches for strategic SCO under uncertainty. Two formulations are developed, namely, naïve robust scenario formulation and affinely adjustable robust scenario formulation. It is shown that both formulations can be reformulated into tractable deterministic optimization problems if the uncertainty is bounded with the infinity-norm, and the uncertain equality constraints can be reformulated into deterministic constraints without assumption of the uncertainty region. Case studies of a classical farm planning problem and an energy and bioproduct SCO problem demonstrate the advantages of the proposed formulations over the classical scenario formulation. The proposed formulations not only can generate solutions with guaranteed feasibility or indicate infeasibility of a problem, but also can achieve optimal expected economic performance with smaller numbers of scenarios.

Digital geometric-sequence control technique for bidirectional dual active bridge DC-DC converters used in future electric vehicles

Bidirectional DC-DC converters are widely used in different applications such as energy storage systems, Electric Vehicles (EVs), UPS, etc. In particular, future EVs require bidirectional power flow in order to integrate energy storage units into smart grids. These bidirectional power converters provide Grid to Vehicle (V2G)/ Vehicle to Grid (G2V) power flow capability for future EVs. Generally, there are two control loops used for bidirectional DC-DC converters: The inner current loop and The outer loop. The control of DAB converters used in EVs are proved to be challenging due to the wide range of operating conditions and non-linear behavior of the converter. In this thesis, the precise mathematical model of the converter is derived and non-linear control schemes are proposed for the control system of bidirectional DC-DC converters based on the derived model. The proposed inner current control technique is developed based on a novel Geometric-Sequence Control (GSC) approach. The proposed control technique offers significantly improved performance as compared to one for conventional control approaches. The proposed technique utilizes a simple control algorithm which saves on the computational resources. Therefore, it has higher reliability, which is essential in this application. Although, the proposed control technique is based on the mathematical model of the converter, its robustness against parameter uncertainties is proven. Three different control modes for charging the traction batteries in EVs are investigated in this thesis: the voltage mode control, the current mode control, and the power mode control. The outer loop control is determined by each of the three control modes. The structure of the outer control loop provides the current reference for the inner current loop. Comprehensive computer simulations have been conducted in order to evaluate the performance of the proposed control methods. In addition, the proposed control have been verified on a 3.3 kW experimental prototype. Simulation and experimental results show the superior performance of the proposed control techniques over the conventional ones.

Anticipating climate-induced changes to forest cover in Ontario and the implications for future bioenergy development

Climate change is expected to have marked impacts on forest ecosystems. In Ontario forests, this includes changes in tree growth, stand composition and disturbance regimes, with expected impacts on many forest-dependent communities, the bioeconomy, and other environmental considerations. In response to climate change, renewable energy systems, such as forest bioenergy, are emerging as critical tools for carbon emissions reductions and climate change mitigation. However, these systems may also need to adapt to changing forest conditions. Therefore, the aim of this research was to estimate changes in forest growth and forest cover in response to anticipated climatic changes in the year 2100 in Ontario forests, to ultimately explore the sustainability of bioenergy in the future. Using the Haliburton Forest and Wildlife Reserve in Ontario as a case study, this research used a spatial climate analog approach to match modeled Haliburton temperature and precipitation (via Fourth Canadian Regional Climate Model) to regions currently exhibiting similar climate (climate analogs). From there, current forest cover and growth rates of core species in Haliburton were compared to forests plots in analog regions from the US Forest Service Forest Inventory and Analysis (FIA). This comparison used two different emission scenarios, corresponding to a high and a mid-range emission future. This research then explored how these changes in forests may influence bioenergy feasibility in the future. It examined possible volume availability and composition of bioenergy feedstock under future conditions. This research points to a potential decline of softwoods in the Haliburton region with a simultaneous expansion of pre-established hardwoods such as northern red oak and red maple, as well as a potential loss in sugar maple cover. From a bioenergy perspective, hardwood residues may be the most feasible feedstock in the future with minimal change in biomass availability for energy production; under these possible conditions, small scale combined heat and power (CHP) and residential pellet use may be the most viable and ecologically sustainable options. Ultimately, understanding the way in which forests may change is important in informing meaningful policy and management, allowing for improved forest bioenergy systems, now and in the future.