This study introduces SCAIRM, a Spatially explicit Cumulative Impact Assessment method specifically developed for application to guide Ecosystem-Based Management. The method incorporates relevant human activities, their pressures and their impacts on ecosystem components. Cumulative assessment methods usually apply categorical semi-quantitative scores based on expert judgement in order to be comprehensive, or alternatively, only cover a small subset of interactions using more sophisticated quantitative information. The SCAIRM approach aims to capture 'the best of both worlds': it builds on categorical risk-based approaches, but modified such that their outcome, i.e. Impact Risk from cumulative pressures, is conceptually identical to the outcome from the quantitative approaches. This allows harmonization of qualitative and quantitative approaches into a single method, that has the advantage that it is comprehensive and can be used in data-poor situations while allowing the use of available quantitative information in more data-rich situations. It results in an improved capacity to inform policy or provide operational-centric guidance required for the essentially sectoral management. The method was applied to the North Sea ecosystem as an illustrative example of the typical output of most Cumulative Impact Assessments: indicating the ecosystem components mostly under threat, and the main activities and their pressures responsible for it. To that end, SCAIRM provides an assessment outcome expressed in ecologically meaningful terms, i.e. potential change in state compared to an undisturbed situation, implying all receptors except the pelagic water column to be potentially “endangered” (more than 50% decrease) and requiring conservation efforts. A decision tree was developed to guide management towards an increased effectiveness showing that much of the Impact Risk (41%) is caused by few well-known stressors (7%) that are difficult to mitigate but a remaining 29% of the Impact Risk from many often lesser-known stressors (59%) where management is likely to perform better.