Conference Agenda

Wind turbines are essential for decarbonization but often face local resistance due to noise, visual disruption, and landscape change. These burdens are spatially concentrated and materialize in reduced property values, yet existing compensation policies rarely align with where these losses occur. Here, we assess how well different financial compensation designs match the local disamenities of wind energy. Using over 680,000 housing listings in Germany, we estimate distance-sensitive property value losses from turbine exposure using causal forests and extrapolate these effects nationally via spatial smoothing. We then simulate eight compensation schemes, defined by their assessment basis (e.g., generated power, number of houses) and apportionment rule (by land area or house count). We find that compensation performance varies widely by design: to offset half of the disamenities, the least efficient Power-Area scheme requires 118% more funding than the most precise alternative, House-House. Schemes tend to overcompensate rural municipalities and undercompensate larger cities. Current designs, like Germany’s Power-Area policy, cover less than 10% of disamenities. Linking payments to housing patterns, rather than turbine output, yields significantly better alignment. Our framework offers a scalable tool to improve the targeting precision of compensation schemes in wind deployment.