Introducing Cross-impact Scenario Policy Analysis (CRISPA): building scenarios and policy mixes based on logical consistency

Uncertainty is a fundamental challenge for public decision-making in domains requiring long-term investments, such as transport. Scenario planning is increasingly used to explore a wide range of possible futures. Scenarios typically combine several uncertain contextual factors, each of which may develop in different ways. A key difficulty, however, is that the number of possible scenarios grows exponentially with each additional factor. Methods such as Cross-Impact Balance analysis (CIB) address this by reducing the scenario space to a manageable size through filtering based on internal consistency, without requiring extensive data or advanced modelling. To date, CIB has been applied primarily to scenarios composed of external (uncontrolled) factors. The question is how to combine external factors with internal factors (i.e. policy measures) within a single analytical framework, so as to identify policy mixes that are robust across multiple scenarios. To address this, we present Cross-Impact Scenario-Policy Analysis (CRISPA), a dependency-based extension of consistency-oriented scenario analysis. Rather than scoring interrelations as promoting or restrictive impacts, CRISPA models them as logical dependencies, enabling policy mixes and contextual uncertainties to be evaluated in terms of mutual compatibility and contradiction. With the example of a metro construction project in Brussels, Belgium, we demonstrate how CRISPA can be used to evaluate (1) the consistency of scenarios, (2) consistency of policy mixes, (3) fitness of policy mixes under a single scenario or their robustness across multiple scenarios. CRISPA integrates external uncertainties and policy measures in one framework CRISPA represents interrelations through logical dependencies rather than impacts The method identifies internally consistent scenarios and policy mixes It evaluates whether policy mixes are scenario-fit or robust across futures A Brussels metro case illustrates the method in a transport planning context