Jacquetta Lee
University of Surrey
Dr Lee has a MEng in Mechanical Engineering and Materials and holds a PhD in Environmental Systems Analysis from Cranfield University. Prior to joining the CES at the University of Surrey, she worked for Rolls-Royce plc in their Environmental Strategy Department, specialising in Life Cycle Assessment and Design for Environment. On joining academia in 2003, she spent two years as a Leverhulme Special Research Fellow, investigating the potential for television to influence environmental behaviour of consumers. Dr Lee has a holistic approach to sustainability systems analysis, incorporating environmental and social aspects from both academic and industrial perspectives. She has over 25 years of experience across a diverse range of industrial sectors including aerospace, electronics, construction, agriculture , fast moving consumer goods market, automotive, nano-technology, architecture, and nuclear energy. Her current areas of research interest include the operationalisation of Absolute Sustainability through: Business appropriate mid-point indicators for Life Cycle Sustainability Analysis, The development of Stock and Flow (Performance Economy) diagrams for decision making in business Social life cycle assessment approaches She is leading research into reducing uncertainty in early design decision making within aeropsace, and improving resource efficiency in the electronics industry. As Director of the Practitioner Doctorate in Sustainability Programme and Associate Professor (Senior Lecturer) in the Centre for Environment and Sustainability at the University of Surrey, Dr Lee is responsible for engaging major industry leaders and high calibre postgraduate researchers to work collaboratively on specific research briefs designed to resolve current sustainability issues within industry. This innovative programme offers a unparalleled opportunity, uniting academia and industry to develop solutions that will have enduring value for individual organisations, industry and governance. She also currently holds the post of Executive Secretary for the ISIE.
Uncertainty is an unavoidable issue in most data-based studies (e.g., life cycle assessment, risk assessment), since they all require amounts of data of good quality or well-known modelling mechanism. As decisions making are... [ view full abstract ]
Uncertainty is an unavoidable issue in most data-based studies (e.g., life cycle assessment, risk assessment), since they all require amounts of data of good quality or well-known modelling mechanism. As decisions making are often constraint by lack of information and ignoring such problem may introduce costly error or bias, so an appropriate management of unknown information helps us to improve the credibility of decisions under uncertainty. Probabilistic methods are mainstream way to deal with data related uncertainty, while alternative methods (e.g., interval or fuzzy interval, evidence theory) have also been developed, since the former still requires information to determine the probability distribution, which may not be available. Therefore, our previous study developed a general decision tree to help choosing appropriate methods to describe the real state of knowledge. This study aims to apply the proposed methodology to deal with uncertainties in an industrial case study for Eco-design, and discuss how uncertain information is described for decision making support.
In this study, an eco-audit model is built to estimate energy consumption and CO2 emissions during the whole life period of an electronic tablet. The selection of materials and manufacturing processes, as well as their associated data, are determined based on a tool for materials information management. Although this matrix-based model uses single value for input variables, they do give information about uncertainty as a range of value in our database. Therefore, we are going to make two scenarios to deal with uncertainties for illustrative purpose. In the first scenario (S1), we assume probability distribution function for each uncertain input variable as well as scenario uncertainty, even though such information may not be given. In the second scenario (S2), we assume that probability distribution is unknown for a part of uncertain input variables, but their intervals or set of intervals are given. So non-probabilistic method is applied according to our decision tree and a combined propagation method is performed to calculate the overall uncertainty in outputs.
As result, S2 generates graphically two bounds (lower and upper) of probability distributions, rather than a single one from S1 result. The width of two bounds indicates intuitionally potential space to reduce epistemic uncertainty by adding more information. The statistics of interest (e.g., mean, confidence interval) are formed as interval which indicates the unknown about the shape of distribution, while the classic probabilistic method does not consider this source of uncertainty, but ignore it by assumption. Therefore, S2 provides conservative, but more realistic information than that from S1. In conclusion, this decision tree offers several practical methods for the users to manage uncertainty given available information with different levels of precision. Moreover, it guides users to combine different sources of uncertainty in the same framework and evaluate them quantitatively. For example, scenario uncertainty is also integrated by probabilistic method, so that the “hot-spots” uncertainties can be identified via sensitivity analysis that will help decision makers to fill data gaps more efficiently. Although it covers commonly used methods, the decision tree is open for critiques and extensions.
