A major challenge for TEA and LCA studies is risk and uncertainty quantification. Industry employs economic risk, measured by indicators such as rate of return and net present value, to identify investment opportunities. Policy makers rely on LCA to estimate greenhouse gas emissions and resource use. Varying degrees of uncertainty underlie these measures. Therefore, research requires additional tools to understand the implications of these uncertainties.

Researchers employ an increasing number of techniques such as case studies, sensitivity analysis, and Monte Carlo simulations to improve uncertainty quantification in TEA and LCA studies. The need for uncer­tainty quantification is driven by uncertainties in model parameters, their interactions, and the outputs generated by these analyses.

Case studies are the most trivial approach to quantifying uncertainty and are not always recognized as such. However, careful selection of system scenarios can provide more than enough data to understand project risks. For example, case studies based on the extreme values of historical market prices for a given commodity could be enough to rule out a potential project. The drawback of case studies is that they provide minimal insight into the interactions between different model parameters.

Sensitivity analyses improve upon case studies by evaluating several points within a range of parameter values. Their key insight is the extent to which system outputs change based on different input assumptions. Sensitivity analyses that involve a large number of randomized model evaluations are known as Monte Carlo simulations. Monte Carlo simulations benefit from inexpensive computational resources that allow rapid model evaluations. Researchers employ Monte Carlo extensively in a wide range of fields to develop model probability distributions. Increasing model complexity has limited the use of this brute-force method because it would consume significant computational time and resources. Researchers continue to adopt powerful techniques to model, collect, and assess TEA data that are beyond the scope of this chapter.

These uncertainty quantification techniques help reduce risks from assumption bias and failure to consider adverse scenarios. However, they are not a substitute for robust models with sensible built-in assumptions.