Historic drivers of onshore wind power siting and inevitable future trade-offs

Abstract

The required acceleration of onshore wind deployment requires the consideration of both economic and social criteria. With a spatially explicit analysis of the validated European turbine stock, we show that historical siting focused on cost-effectiveness of turbines and minimization of local disamenities, resulting in substantial regional inequalities. A multi-criteria turbine allocation approach demonstrates in 180 different scenarios that strong trade-offs have to be made in the future expansion by 2050. The sites of additional onshore wind turbines can be associated with up to 43% lower costs on average, up to 42% higher regional equality, or up to 93% less affected population than at existing turbine locations. Depending on the capacity generation target, repowering decisions and spatial scale for siting, the mean costs increase by at least 18% if the affected population is minimized – even more so if regional equality is maximized. Meaningful regulations that compensate the affected regions for neglecting one of the criteria are urgently needed.

How spatial policies can leverage energy transitions − Finding Pareto-optimal solutions for wind turbine locations with evolutionary multi-objective optimization

“…From a local perspective, clustered WT allocation solution could be perceived as inequitable, forcing the affected regions to bear all the negative impacts of the national wind energy strategy (Jørgensen et al, 2020). On the other hand, clustered solutions tend to have lower operational costs compared to evenly distributed allocations (Weinand et al, 2022). The optimization results show that restricting certain areas for WT development might influence the general distribution of WT significantly.…”

“…On the one hand spatial clustering of WT, which we integrated as an optimization target, might be beneficial regarding cost-efficiency and cumulative visual impact. On the other hand the spatial patterns derived when including the clustering objective, lack distributional justice or regional equality and can trigger local oppositions (Weinand et al, 2022). The multi-objective optimization approach employed in this study does not lead to the formulation of wind energy strategies but it helps identify spatial patterns and effects that support the development of such strategies.…”

“…However, achieving this goal is a major challenge, as it has been shown that the allocation of renewable energy systems involves sustainability trade-offs between energy production, ecological and social objectives (Lehmann et al, 2021). For instance in Europe, the wind turbines (WT) have been placed in the most cost effective locations in the past, but there is significant potential for affecting less people in future allocation scenarios (Weinand et al, 2022). Therefore, energy resource assessments (McKenna et al, 2022) and coordinated allocation strategies for various renewable energy systems are critical to reach climate targets and secure social welfare (Drechsler et al, 2017).…”

Spatial Trade-Offs in National Land-Based Wind Power Production in Times of Biodiversity and Climate Crises

“…Research on spatial trade-offs in wind power deployment has to date focused primarily on the negative effects for nearby residents (Zerrahn 2017;Mattmann et al 2016;Wen et al 2018;Weinand et al 2022), and only a few such studies have, to our knowledge, incorporated some measure of (primarily local) environmental and disamenity costs in energy system models (e.g., Lehmann et al 2021a, b;Drechsler et al 2017;Grimsrud et al 2021;Salomon et al 2020). However, with the latest assessment of the Intergovernmental Panel on Biodiversity and Ecosystem Services (IPBES 2019) and the Dasgupta review (Dasgupta 2021), it has become increasingly clear that the degradation and loss of nature and biodiversity may be just as serious as the climate crisis and that the two effects are mutually reinforcing.…”

“…The paper also contributes to a wider and more heterogeneous literature on trade-offs in renewable energy deployment that investigates a range of different impacts (such as on land use and landscapes, scenics, biodiversity, equality, etc.) on different geographical levels by means of different methods (such as multicriteria analysis) (e.g., Lehman et al 2021b;Mckenna et al 2021Mckenna et al , 2022Sasse and Trutnevyte 2020;Tafarte and Lehman 2021;Weinand et al 2022).…”