The current decision-making processes involved in Environmental Impact Assessments (EIAs) in South Africa suffer from a lack of coherence and do not include evaluation of trade-offs between qualitative and quantitative impacts, as well as environmental, economic, and social dimensions. In addition, insufficient capacity and knowledge among authorities, a lack of objectivity among Environmental Assessment Practitioners (EAPs), and mediocre reports add to the problems associated with effective decision-making. This work presents a framework aimed at improving the effectiveness and objectivity of the decision-making process applied in South Africa’s EIAs in the energy sector. A number of decision-making models and tools are available to researchers and practitioners throughout the world that could potentially be applied in EIAs. Among these are Cost-Benefit Analysis (CBA), Rapid Impact Assessment Matrix (RIAM), and Multi Criteria Analysis (MCA). Each of the tools has its own advantages and disadvantages. With respect to the CBA, its biggest disadvantage is the fact that it requires conversion into monetary terms of all impacts, which is sometimes difficult to achieve. The RIAM, on the other hand, fails to provide a systematic approach to the ranking of alternatives. Both of these issues are addressed by the MCA tools. The MCA framework, furthermore, is universal, transparent, easy to replicate, and does not require a particularly large amount of labour and financial resources to complete. It is, however, subjective, but this shortcoming can be overcome by making the decision process more transparent. The framework proposed in this research paper is based on the Multi Criteria Analysis (MCA) technique that allows the identification of the proposed development's cumulative impact versus the current status of the environment. It then compares possible alternatives, where available, in order to identify the most optimal solution. The proposed solution takes into account the trade-offs between the different impact metrics. The research methodology followed in this paper comprised four steps, namely:<ul><li> Selection of case studies, </li><li> Information collection, </li><li> Framework application and testing and </li><li> Feedback. </li></ul> The development of the framework followed an eight-step approach that is generic for MCA and was tested on two case studies that have already gone through the Environmental Impact Assessment process, i.e. the Open Cycle Gas Turbine (OCGT) plant in the Western Cape and the Concentrating Solar Power (CSP) plant in the Northern Cape. The former was evaluated against the "no-go option", but included a decision tree comprised of impact areas, categories of impacts and dimensions (environmental, social, and economic). The latter included alternatives for four components of the project, but the decision tree comprised only of categories and dimensions. The effectiveness of the framework was verified by testing the results of the case studies against the recommendations proposed in the respective Environmental Impact Reports. In all cases, but one, the results of the framework correlated with the recommendations made by the Environmental Assessment Practitioners in the respective studies. In addition, a workshop with the decision-makers was held to obtain their viewpoints regarding the usefulness of the framework in their decision-making environment. These decision-makers supported the use of the framework in their environment as it offered an integrated and transparent approach to the evaluation of projects and alternatives. They emphasised, however, that the decision-making process was complex and the application of the framework alone would not be able to address all the challenges. The case studies demonstrated that the proposed framework could be successfully applied in the process of undertaking impact assessments in the energy sector. It can be used to determine the trade-offs between impacts and dimensions, while taking into consideration the opinions of specialists and decision-makers when assigning weights. The framework has the ability to clearly illustrate the benefit of introducing mitigation measures and it also indicates an alternative that produces the optimal cumulative impact. In conclusion, the work presented contributes to the new body of knowledge in the field of Environmental Impact Assessment in the energy sector as it will assist authorities in making objective and informed decisions, while ensuring greater transparency in the process. It also opens opportunities for conducting follow-on investigations, such the application of the framework in other sectors of the economy, undertaking a sensitivity analysis to compare the range of scores used in the evaluation of impacts, and investigating the possibility of acquiring input from Interested and Affected Parties (I&APs) and integrating those into the framework. / Dissertation (MSc)--University of Pretoria, 2011. / Graduate School of Technology Management (GSTM) / unrestricted

<p dir="ltr">Highway safety continues to pose a serious challenge to the social sustainability of transportation systems, and initiatives are being pursued at all levels of government to reduce the high fatality count of 42,000. At the same time, it is sought to ensure higher travel efficiency in order to increase economic productivity. The emergence of automated transportation provides great promise to mitigate these ills of the transportation sector that have persisted for so many decades. With regards to safety, such promise is rooted in the capability of autonomous vehicles to self-drive some or all of the time, thus reducing the impact of inherently errant human driving to which 95% of all crashes have been attributed. With regards to mobility, such promise is guided by the capability of the autonomous vehicle to carry out path planning, navigation, and vehicle controls in ways that are far more efficient than the human brain, thereby facilitating mobility and reducing congestion-related issues such as delay, emissions, driver frustration, and so on.</p><p dir="ltr">Unfortunately, the two key outcomes (safety and mobility) are reciprocal in the sense that navigation solutions that enhance safety generally tend to reduce mobility, and vice versa. As such, there is a need to assign values explicit to these performance criteria in order to develop balanced solutions for AV decisions. Most existing machine-learning-based path planning algorithms derive these weights using a learning approach. Unfortunately, the stability of these weights across time, individuals, and trip types, is not guaranteed. It is necessary to develop weights and processes that are trip situation-specific. Secondly, user trust in automation remains a key issue, given the relatively recent emergence of this technology and a few highly-publicized crashes, which has led to reservations among potential users.</p><p dir="ltr">To address these research questions, this thesis identifies various situational contexts of the problem, identifies the alternatives (the viable trajectories by fitting curves between the vehicle maneuver’s initial and final positions), develops the decision criteria (safety, mobility, comfort), carries out weighting of the criteria to reflect their relative significance, and scales the criteria to develop dimensionless equivalents of their raw values. Finally, a process for amalgamating the overall impacts of each driving decision alternative is developed based on the weighted and scaled criteria, to identify the best decision (optimal trajectory path). This multi-criteria decision making (MCDM) problem involves the collection of data through questionnaire surveys.</p><p dir="ltr">The weights obtained early in the MCDM process could be integrated into any one of two types of planning algorithms. First, they could be incorporated into interpolating curve-based planning algorithms, to identify the optimal trajectory based on human preferences. Additionally, they can be integrated into optimization-based planning algorithms to allocate weights to the various functions used.</p><p dir="ltr">Overall, this research aims to align the behavior of autonomous vehicles closely with human-driven vehicles, serving two primary purposes: first, facilitating their seamless coexistence on mixed-traffic roads and second, enhancing public acceptance of autonomous vehicles.</p>