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With its European Green Deal, the European Union (EU) aims to substantially increase renewable electricity generation and decrease greenhouse gas emissions in the electricity sector by 2035. European countries independently set these targets, but might not account for cross-border infrastructure interdependencies that may facilitate or impede their implementation. This study quantifies regional interdependencies among 36 European countries aggregated to eight regions by applying electricity sector modeling, Modeling to Generate Alternatives, tree-based ensemble method, and scenario discovery. Cost-effectively reaching EU targets in 2035 allows for flexibility in uptake of electricity system infrastructure across European regions, with few exceptions. Reaching EU targets with low costs requires high renewable capacities in Central and Nordic Europe and France, steep reductions of fossil fuel capacities in Central and Eastern Europe, keeping existing nuclear capacities in France, and high storage capacities in Alpine and Central Europe. Regional targets are most influential for increasing renewable capacities, while EU targets are most influential for decreasing greenhouse gas emissions. If EU targets are met, reaching regional targets of Central Europe and keeping nuclear capacities in France substantially reduce infrastructure requirements in Eastern Europe, highlighting the importance of regional cooperation for electricity system planning and policy. • EU targets allows for flexibility in new electricity infrastructure in Europe • Renewables in Central and Nordic Europe are cost-effective to reach EU targets • Regional interdependencies highlight the importance of cooperation across Europe [ABSTRACT FROM AUTHOR]

Spatially-disaggregated projections of new solar photovoltaic (PV) installations are essential for planning electricity grids and managing the electricity system at large scale. Such projections at sub-national level can be obtained by statistical models or by electricity system optimization models, but there is barely any study that compares the performances of these approaches. This study aims to compare methods for projecting PV installations at a level of 143 districts in Switzerland, using a simple extrapolation method (as a benchmark of the common practice today), a multiple linear regression model, two spatial regression models, and a spatially-explicit optimization model (EXPANSE) with various features to account for policy. The performance of different approaches is evaluated retrospectively for 2012–2020, using multiple accuracy indicators. The evaluation results show that statistical regression models, which account for socio-demographic and techno-economic characteristics as predictors of future PV growth, overall perform better than simple extrapolation or optimization. Although commonly used, extrapolation has the highest variability in accuracy, indicating the least robust performance. The optimization model tends to underestimate PV installations in its least-cost scenarios, if the role of policy is not considered. Incorporating solar PV policies and renewable electricity generation targets increases the overall accuracy of the optimization model at a national level, but not necessarily at a spatially-explicit level. We thus conclude that statistical models are preferred over extrapolation or optimization models for projecting future PV installations at a sub-national scale. • Statistical and optimization models for projecting spatial PV growth are compared. • Out-of-sample testing in 143 Swiss districts in 2012–2020 is used. • Statistical and optimization models perform better than simple extrapolation. • Optimization with policy is more accurate at a national, but not sub-national level. • Statistical models are preferred for projecting PV installations at a sub-national scale. [ABSTRACT FROM AUTHOR]