Data-driven landscape scenicness mapping for continental-scale onshore wind resource assessment

Abstract

Visual impacts on scenic landscapes dominate public opposition to onshore wind turbines. Yet wind resource assessments often overlook landscape scenicness due to limited data availability. This study introduces a scalable machine learning framework for generating continental scenicness layers, trained on crowdsourced scenicness ratings from Great Britain and achieving high predictive performance. The resulting scenicness maps are integrated into an onshore wind resource assessment under three landscape preservation scenarios across 29 European countries. We show that prioritizing scenic landscapes in planning can reduce wind generation potential in certain countries by over 60%. However, it only modestly affects the continental median levelized costs of electricity (57 €/MWh and 54 €/MWh under low and high preservation scenarios), while substantially increasing regional costs in scenic mountainous regions such as the Alps and Norway. These findings demonstrate how data-driven approaches can enable socially aware and large-scale energy system planning.