Forecasting the Future Sustainability of Technology Choices: Qualitative Predictive Validity of Models as a Complement to Quantitative Uncertainty

Abstract

To support product and technology choices toward a more sustainable future, diverse assessment methods are used, involving life cycle assessment (LCA). This raises the question of their predictive validity. Whereas, many studies focus on quantitative uncertainty, here the main aim is to address the complementary qualitative aspect of the LCA-related model variants. To that end, we first specify three general influential aspects: (1) future conditions, (2) needed predictivity, and (3) mechanism coverage. These have been translated into a more concrete checklist for qualitative predictive validity. Second, we categorized the model variants into a limited number of basic model types, based on five predefined modeling characteristics. These model types show increasingly complex steps for investigating the future, illustrated with energy systems for transport. Different answers to the same questions may result. With increasing model complexity, the relevant questions may change from analysing specific products, to more general product systems, and next to product-technology domain systems. As a third step, the qualitative predictive validity of the nine modeling types is evaluated using the developed checklist. All have limited predictive validity, increasingly so for longer time horizons, as they lack most causal mechanisms, especially the institutional drivers for development and employment of technologies to emerge. Also, the future is only partially determined. For supporting choices, the conclusion is that the comparative analysis regarding long-term also broader product-technology systems has limited predictive validity. As a solution, conditional statements may show directions for explorative analysis resulting in highly tentative advice on potentially attractive directions.