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This thesis has two major objectives. First, some of the key environmental problems, such as waste disposal, pollution and sand encroachment in Kuwait are analysed in the context of the decision making process of the country. On the end once presented it is suggested that decision making is responsible for many of the environmental problems that now exist. This analysis adopts the social science approach of analyzing decision making by investigating both political theories and decision making models and relating them to Kuwait. The second objective is to consider to what extent certain tools and methods of environmental management could be introduced in Kuwait to help resolve the environmental problems. Based on the evaluation of a number of approaches it is suggested that Environmental Impact Assessment (E.I.A.) is probably the most effective environmental management tool to help protect Kuwait's environment and achieve the concept of sustainable development. To be effective however it is aruged that E.I.A. should be applied not only at the project level but also in policy formulation. The case studies are then presented. One considers the application of E.I.A. at the policy level for solid waste management whilst the second describes a project E.I.A. approach for a proposed navigation channel. It is suggested in this thesis that economic growth on its own, should no longer be seen as the main indicator of development. Sustainable development with all that this encompasses including the linking of economic and environmental concerns should be seen as the new development objective. This specifies the need to maintain the quality of natural resources over time to meet the needs of present and future generations. E.I.A. should help to achieve this objective if applied to both projects and policies. This thesis concludes with certain recommendations to impose E.I.A. practice in Kuwait, based on the evidence presented.

Thesis (M.Com.(Taxation))--University of Pretoria, 2009. / Abstract in English and Afrikaans. Includes bibliographical references.

One of the major tools for assisting in the implementation of sustainable development is environmental assessment (EA). This thesis has sought to develop a model and associated techniques required to provide an effective and efficient EA of projects in the water environment. The challenge has been to integrate a number of disparate elements into a cohesive model that provides workable procedures and outputs. The conceptual elements of the EA process have included the needs of environmental ethics and values; the political decision-making processes; current legislation and policy; the communication of infonnation for a range internal and external stakeholders and decisionmakers; the links with technical and economic issues; and The Environmental Agency's project management systems.

In predicting environmental hazards or estimating the impact of human activities on the marine ecosystem, scientists have multiplied the need for sample analysis. The classical microscopic approach is time consuming and wastes the talent and intellectual abilities of trained specialists. Therefore, scientists developed an automated optical tool, called a Flow Cytometer (FC), to analyse samples quickly and in large quantities. The flow cytometer has successfully been applied to real phytoplankton studies. However, analysis of the data extracted from samples is still required. Artificial Neural Networks (ANNs) are one of the tools applied to FC data analysis. Despite several successful applications, ANNs have not been widely adopted by the marine biologist community, as they can not possible to change the number of species in the classification problem without retraining of the full system from scratch. Training is time consuming and requires expertise in ANNs. Moreover, most ANN paradigms cannot cope effectively with unknown data, such as data coming from new phytoplankton species or from species outside the scope of the studies. This project developed a new ANN technique based on a modular architecture that removes the need for retraining and allows unknowns to be detected and rejected. Furthermore, the Support Vector Machine architecture is applied in this domain for the first time and compared against another ANN paradigm called Radial Basis Function Networks. The results show that the modular architecture is able to effectively deal with new data which can be incorporated into the ANN architecture without fully retraining the system.

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