Error correlation in input-output frameworks

Summary

Today, many frameworks for environmental and sustainability assessment utilise input-output techniques (Forssell and Polenske 1998). These techniques are based on input-output analysis, a discipline founded by Nobel Prize Laureate Wassily Leontief in the 1940s (Leontief 1936). Since its invention, numerous analysts in academia, industry and government alike, use input-output analysis for economic and environmental studies (Foran, Lenzen et al. 2005; Foran, Lenzen et al. 2005). More than 100 countries worldwide regularly publish input-output tables, according to guidelines governed by the (United Nations Department for Economic and Social Affairs Statistics Division 1999).

More recently, users of environmental assessments are increasingly asking for uncertainty appraisals to be provided along with main findings. In any quantitative study, this requires uncertainty techniques to be applied.

There are a number of examples that demonstrate state-of-the-art uncertainty analysis, however these are still the exception. The rationale of this project is to contribute to the advancement of uncertainty calculus in environmental analysis.

Supervisor(s)

Professor Manfred Lenzen

Research Location

School of Physics

Program Type

Masters/PHD

Synopsis

The results of this project will be valuable, because they will cast light on the question whether traditional assumptions of uncorrelated, normally distributed raw data errors lead to uncertainty estimates of environmental assessment results that are too low, and hence provide decision-makers using these results with expectations that are too optimistic in terms of analytical reliability.

Skills required
• Good understanding of matrix algebra
• Understanding of basic statistics, such as distributions
• Programming in MatLab, FORTRAN or C
• Basic understanding of economics, or willingness to learn
• Good scientific writing style

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Keywords

physics, sustainability, input-output analysis, IOA, economic studies, environmental analysis, environmental studies, environmental assessment, error correlation

Opportunity ID

The opportunity ID for this research opportunity is: 1441

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