Parkes, O; (2015) Defining a comprehensive methodology for sustainability assessment of mega-event projects. Doctoral thesis , UCL (University College London).

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Abstract

Mega-event projects such as the Olympic Games or FIFA World Cup are unique large-scale projects that involve complex planning process, vast array of stakeholders and substantial capital investment. They attract global media attention and tourism to a host city. However, the success of a mega-event is not measured only in terms of its organisation and staging. It is crucial to create a sustainable positive post-event legacy because this is where the most of the long-term impacts will occur. Planning of such projects is a complicated process that requires consideration of multiple economic, environmental and social aspects and the trade-offs between them. The main objective of this work is to develop a comprehensive framework that can assist decision makers with assessment of the alternative site design scenarios in order to identify the optimum solution. A case study based on the London Olympic Park is applied to test the feasibility of the proposed framework. Stakeholders’ engagement in a mega-event project planning is a prerequisite for its success. This work demonstrates how a multi-criteria decision analysis (MCDA) tool can be applied to analyse and quantify the views of different stakeholder groups and identify the design features which are considered the most important by the majority of stakeholders. The environmental assessment framework includes a combination of computational models which evaluate and optimise the total emissions resulting from the transportation, materials, water and energy use, and a series of life cycle assessment (LCA) models which estimate environmental burdens resulting from municipal solid waste (MSW) treatment systems. The results of the assessment provide valuable information for the decision makers in terms of the amount of materials and energy used and related environmental burdens for each scenario. Optimisation models can determine ‘the optimum’ solution for each scenario which can serve as a performance benchmark during the planning process.

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