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Although the transition to energy supply through renewables (RE) is, in general, politically accepted in Germany, its progress is slowed by conflicting interests, primarily nature conservation and protesting residents. This study aims to find ways to solve these conflicts in Germany. To this end, the researchers developed a geospatial model that calculates RE potentials and vulnerabilities of nature and humans. Both data input and some evaluation standards are variables in the model. The outcomes are compared to an estimated total energy demand in 2050. Two ambitious scenarios ("no regret" and "compromise") show that a maximum of 4% of the German territory is available to meet the energy demand. This demand can be met using PV in urban areas and wind in rural landscapes without significantly impairing nature's and people's wellbeing. Solar parks and other potentials not considered in the model are treated as a reserve, which can be included if the energy targets are not met under the assumed scenario conditions. Such reserves also provide flexibility for co-determination in public participation.

Rivers and floodplains provide many regulating, provisioning and cultural ecosystem services (ES) such as flood risk regulation, crop production or recreation. Intensive use of resources such as hydropower production, construction of detention basins and intensive agriculture substantially change ecosystems and may affect their capacity to provide ES. Legal frameworks such as the European Water Framework Directive, Bird and Habitats Directive and Floods Directive already address various uses and interests. However, management is still sectoral and often potential synergies or trade‐offs between sectors are not considered. The ES concept could support a joint and holistic evaluation of impacts and proactively suggest advantageous options. The river ecosystem service index (RESI) method evaluates the capacity of floodplains to provide ES by using a standardized five‐point scale for 1 km‐floodplain segments based on available spatial data. This scaling allows consistent scoring of all ES and their integration into a single index. The aim of this article is to assess ES impacts of different flood prevention scenarios on a 75 km section of the Danube river corridor in Germany. The RESI method was applied to evaluate scenario effects on 13 ES with the standardized five‐point scale. Synergies and trade‐offs were identified as well as ES bundles and dependencies on land use and connectivity. The ratio of actual and former floodplain has the strongest influence on the total ES provision: the higher the percentage and area of an active floodplain, the higher the sum of ES. The RESI method proved useful to support decision‐making in regional planning.

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A key challenge of environmental planning is to craft recommendations for future sustainable spatial development amid ubiquitous uncertainties. This paper aims to explore how different data uncertainties, usually unknown to the planner, may influence environmental planning recommendations. We apply a case study-based approach, in which we provide three illustrative examples of how data with different kinds and levels of uncertainty affect environmental assessments and, by that, the decision-support provided by environmental planning. The cases stem from different spatial levels in Germany and consider ‘Regional soil-based climate change mitigation’ in the region of Hannover, ‘State-wide habitat conservation siting’ in the federal state of Saxony-Anhalt, and ‘National renewable energy planning’. Based on the three examples, we discuss implications for planning practice and derive recommendations for further research. The three cases studies illustrate the substantial effects of data uncertainty on environmental assessments and planning recommendations derived from those results. We identify four problem constellations of dealing with data uncertainty in environmental planning that relate to the severeness of uncertainty impacts, the responsibility of the decision-maker, and the kinds of impacts that wrong decisions may have. We close with recommendations for further research, among others to develop robust and pragmatic methods for identifying the uncertainty levels in environmental data and assessment results.