Wind Farm Development Research Articles

Spatial planning offshore wind energy farms in California for mediating fisheries and wildlife conservation impacts

Achieving a blue economy will require reconciling the value of emerging ocean uses with their impacts on the seascape and sectors with historical access to marine resources and areas. To meet this challenge, we developed an analytical framework for conducting marine spatial planning through tradeoff analysis, and applied it to prospective offshore wind energy development in the ∼974 km2 Morro Bay, California, USA Wind Energy Area (WEA). We generated spatial data layers estimating MW power production and impacts on fisheries value and marine wildlife conservation (seabird and cetacean populations) from wind farm development. We then quantified each sector's response to plans of development across the WEA and inside three leases recently acquired by the energy industry for prospective development. Finally, we integrated the sector response data into an analytical framework for mitigating sector tradeoffs with novel spatial planning solutions (maps of wind farm size, location, and configuration) that optimally maximize value to the emergent energy sector (MW power) while minimizing impacts to historical (fisheries and wildlife) sectors. We found that western sites in the WEA had the highest potential power production concurrent with the lowest impact on the historical sectors, revealing the eastern lease to be less efficient at optimally balancing the sector's objectives relative to the development of the central or western leases or the optimal spatial plans identified in the tradeoff analysis. Within a lease, tradeoff analysis found spatial planning able to generate out-sized savings in fisheries value with only modest losses in MW power – for example, by avoiding development in just 5% of the eastern lease to preserve nearly half its fisheries value and still generate 95% its total power potential. Small-scale development opportunities (e.g., a pilot project) with significant power potential and no fisheries impact were also identified, in this case by placing turbines in an area in the western lease with no fisheries value and high power production potential. These plans would also have a relatively low impact on the wildlife conservation sectors, due to decreases in vulnerability levels of both seabird and cetacean populations to turbines going from east to west across the WEA. Our results can inform site evaluation and permitting processes for wind energy development in the Morro Bay WEA. We also expect the tradeoff analysis framework we developed to provide a simple and actionable analytical tool for supporting marine spatial planning of offshore wind energy and other emerging blue economy activities from a balanced perspective that values emerging uses of marine resources alongside existing socio-economic and conservation interests.

Acceptance of offshore wind farm in Southwest Sardinia in Italy. Do regional energy policies matter?

PurposeThe social acceptance of wind energy is increasingly conditioning the Italian Government and regions to authorize the construction of these plants. The proposal of offshore wind farm in the south-west of Sardinia has raised many perplexities both from the marine environmental point of view and from concern about increasing the electricity production in a region that already exports electricity to the peninsula. The purpose of this study is to evaluate what are the factors that most affect the coastal residents’ acceptability of an offshore wind farm.Design/methodology/approachThe data is based on a Sardinia-wide in-person survey with about 512 participants in the period between May and June 2023. Respondents were selected randomly from five different locations in Carloforte region. Multiple regression analysis investigates the factors that influenced acceptability to construction and development of offshore wind power plant. Four independent variables were selected in the regression models such as (i) interest towards wind energy, (ii) attitude towards renewable energy production, (iii) perceived regional energy policy and (iv) attitude towards offshore wind farm. The dependent variables are the rates of coastal local residents’ acceptance of offshore wind farm for respondents in five coastal towns.FindingsFishermen and summer tourism operators’ respondents expressed significant reluctance to accept the new project because they perceive a strong risk of environmental impact on both tuna fishing and marine recreational activities in the vicinity. The distance between the turbines at sea and local residents along the coast and surrounding small islands does not have an influence to accept wind energy. The energy policy of the Region of Sardinia, which is perceived by respondents as an expansive energy policy in the production of electricity that exceeds their needs, not being accompanied by a scenario of reducing that from fossil fuels, is the variable that led about 70% of respondents to express a negative opinion towards this project.Research limitations/implicationsA potential problem associated with the survey is that local response produced sample selection bias. The proportion of respondents with secondary education and no school certificate is quite high. It is possible that respondents who provided valid questionnaires were more low-educated and therefore, the sample may be biased towards lower-educated people. While this does not invalidate the results of this study, it is important to note that the sample was on average less educated than the overall population in Italy. In addition, people with lower incomes were more likely to complete the questionnaires; the results are more representative of a portion of the population with incomes below the national average.Practical implicationsResults showed how coastal communities are in favour of wind energy within their island, but not within a marine protected area that is considered unique for its beauty and marine ecosystem. Wind energy developers should, before submitting a proposal for an offshore wind farm project, consider among other things the long approval time and understand the activities carried out by local communities and their attachment to those places. Politicians and developers should develop a coherent energy transition policy based on a long-term vision of zero emissions, because according to the findings of this study, it was the regional energy policy that is the most controversial reason for residents to reject the project.Originality/valueThe contribution of this study is to fill a part of the research gap linking to ongoing energy transitions. In particular, this study analysed for the first time in Italy the acceptability level of an offshore renewable energy project considering the environmental implications and risks in the fragile marine ecosystem of Carloforte waters. This study also made it possible to analyse the impact of the regional energy policy on the acceptability of residents towards the development of offshore wind farms, which is scarcely discussed in the literature.

Sailing through end-of-life challenges: A comprehensive review for offshore wind

Over the past thirty years, European offshore wind farm development surged, yet end-of-life and decommissioning considerations were overshadowed by initial climate and energy security objectives during design and construction. As the first major projects near their final decade, numerous unanswered questions persist. Through a comprehensive literature review, this study identifies, maps, and evaluates challenges across technical, economic, environmental, social, and policy dimensions spanning five end-of-life phases: planning, dismantling, transport and logistics, waste management, and site recovery. Examining 42 publications reveals 46 distinct challenges affecting stakeholders such as the end-of-life supply chain, policy makers, and society. While 33 % of the challenges manifested in the technical dimension, 48 % of the challenges covered the planning phase. Notably, the economic challenge of vessel cost and availability was raised most often. Less-explored challenges underscore the importance of consideration before the end-of-life phase intensifies. The study illustrates the complex interconnection of numerous end-of-life challenges across phases, dimensions, and disciplines, emphasizing the imperative of addressing bottlenecks in a comprehensive and integrated manner. The results of this study help steering future research, while also improving awareness of challenges for stakeholders, emphasizing the need for collaborative efforts between governmental bodies and industry stakeholders to address imminent challenges through transparent guidelines, data exchange, and circular design principles. The novelty of this study lies in its holistic, multidisciplinary approach, systematic framework for identifying challenges, and critical perspective unveiling interconnectedness.

Whose Expertise and Whose Knowledge Matter? Influencing Wind Power Decisions in Norway

ABSTRACT Norway is committed to promoting energy from renewable sources, but wind power installations are often sited in areas where the environment remains relatively untouched. This has generated a debate about the acceptability of such installations in the country. Host municipalities in Norway hold an unofficial veto power over the outcomes of proposals for wind power developments, so the ways that local attitudes and beliefs are influenced could be crucial to the outcome of licensing decisions. This research explores how elected officials come to hear about proposed wind farm developments, whose knowledge and expertise they trust on the issue – as well as whose is discounted – and how this impacts the eventual perceptions that they adopt. We investigate the persuasive influence of narrative messaging on both supportive and oppositional stakeholders in a land-based wind power licensing process in Norway.

Explainability in wind farm planning: A machine learning framework for automatic site selection of wind farms

The process of wind farm site selection involves complex considerations spanning environmental, economic, and social factors, often handled through multi-criteria decision making (MCDM) methods. However, MCDM approaches face several challenges. To address these, integrating machine learning (ML) techniques offers promise. Nevertheless, existing studies have yet to employ a standalone ML-based approach for this purpose. This study seeks to pioneer an explainable ML-based framework for automated wind farm site selection. The framework incorporates feature selection techniques, seven ML algorithms, statistical tests, and a local explainability method. The experimental area was chosen as Balıkesir province, which hosts 13% of Türkiye's installed wind energy capacity. According to the prediction performances of the ML models, Extreme Gradient Boosting-XGBoost has the highest accuracy (0.9607), followed by Light Gradient Boosting Machine-LightGBM (0.9580), Random Forest-RF (0.9518), Histogram-based Gradient Boosting-HGB (0.9387), Classification and Regression Trees-CART (0.8946), Logistic Regression-LR (0.8856), and Naive Bayes-NB (0.8456). Additionally, McNemar’s tests revealed statistically significant differences among ML models. Based on the explanations provided by SHapley Additive exPlanations, wind speed, distance to transmission lines, distance to protected zones, and elevation were the top contributing criteria. Moreover, analysis revealed regions with elevated wind speeds, higher elevations, and closer proximity to transmission lines and protected areas as favorable for wind farm installation. The study's outcomes indicate strong agreement (97%) between current wind turbine locations and those identified as suitable by ML models. Within Balıkesir province, our research has identified a significant number of highly suitable areas where wind turbines have not yet been installed. These findings underscore the critical importance of these locations for future investments in wind farm development. The proposed framework could be highly efficient for wind farm siting studies in provinces that share similar characteristics with the study area used in this study offering a robust tool for future wind farm siting studies. Finally, the study contributes to achieving UN SDG 7 by employing ML to refine wind farm site selection, thereby facilitating the transition towards sustainable and universally accessible energy.

Pyrolysis-recycling Utilization Properties Waste Wind Turbine Composite Blades

As a clean and environmentally friendly energy, wind energy is developing rapidly. With the development and construction of wind farms, wind turbines have reached their service life in succession and entered the stage of decommissioning. In this paper, the typical waste glass fiber reinforced epoxy resin composites wind turbine blades are studied and discussed, and the mechanical tensile properties and surface micro properties are analyzed. The tensile strength of blade materials pyrolysis residue was measured by universal testing machine, and the pyrolysis experimental residue of blade materials were characterized by SEM, the change law of micro morphology of blade samples in pyrolysis process were analyzed. The results show that when the pyrolysis temperature is 350.0 ℃, the loss rate of elastic modulus of pyrolysis residue of blade materials at this temperature is low, and the pyrolysis residue with good elastic modulus and tensile strength can be recovered.

Technical-economic evaluation of a 94.5 MW wind power plant at different elevation heights. A case study in Peru's countryside

The scarce promotion of wind energy generation in Peru leads to using fossil fuels and polluting the environment with greenhouse effect gasses. Furthermore, the countryside at height provides some of the areas with great wind resource potential and it has not been exploited yet. This study aims to examine five cases in the Peruvian countryside located at heights ranging from 2400 to 4200 m above sea level to explore the effect of elevation height in energy production and economic indicators. Wind data from the Global Wind Atlas and POWER were used for each area, and a comparative analysis was conducted to assess the accuracy of these datasets. For the economic analysis, break-even Levelized Cost of Energy and Net Present Value were calculated. The findings indicate that the Levelized Cost of Energy was the lowest, ranging between 35 and 39 $/MWh, for elevations between 3000 and 4200 m above sea level. Conversely, the highest costs, ranging from 50 to 79 $/MWh, were observed at lower elevations. This suggests that these high-altitude provinces offer significant wind energy potential, making them suitable for wind farm development.

Comprehensive onshore wind energy assessment in Malawi based on the WRF downscaling with ERA5 reanalysis data, optimal site selection, and energy production

Malawi, despite its minimal CO2 emissions, faces huge climate impacts and a low electrification rate. Recognizing these challenges, this study presents a comprehensive and nationwide assessment of Malawi's wind energy potential, leveraging the high-resolution capabilities of the Weather Research and Forecasting (WRF) model. Unlike most site-specific studies, this study covers the entire country, factoring in different turbine hub heights. Through sensitivity analysis, the optimal configuration for the WRF model in Malawi was determined and ERA5 reanalysis data was used for initial and boundary conditions. The study's findings were validated using MERRA-2 reanalysis and actual observational datasets. A combination of GIS and the fuzzy analytic hierarchy process (AHP) was employed, integrating key factors like land use and topographical complexity to determine optimal wind farm sites. Notably, the wind suitability map uncovered significant potential for wind farm development. Furthermore, in the evaluation of a 50 MW wind farm, 80 m hub heights consistently outperformed 100 m elevations, both technically and economically. An 80 m wind farm at Site A can potentially power up to 32,931 urban and 84,475 rural households. Economically, 80 m hub heights showcased lower levelized cost of electricity (LCOE) values and faster payback periods than 100 m ones. Furthermore, these proposed wind projects can markedly reduce CO2 emissions, positioning Malawi in tandem with global sustainability aspirations. Overall, this study provides a key foundation for Malawi's renewable energy strategies, highlighting the potential for improved electrification and better climate change mitigation outcomes.

Energy Islands in Greece: Are they Feasible in Aegean Sea?

The future development of energy islands in North Sea is going to assist the further growth of offshore wind farms in a territory characterized by rich wind energy resources. The development of offshore wind farms in Aegean Sea, which is characterized by high mean annual wind velocities, is in the initial phase while it is foreseen that by 2050 the electricity generated by offshore wind farms will have a high share in the energy mix in Greece. The future interconnection of the electric grids of the small islands in Aegean Sea with the grid of continental Greece is going to trigger the development of new renewable energy installations in these islands reducing the current use of oil-based fuels in them. The development of offshore wind farms in Aegean Sea could be combined with green hydrogen production, when it is profitable, while it could be also used in the future for electricity generation from sea waves. The future development of physical energy islands in the Archipelago combined with the development of offshore wind farms and the interconnection of the small islands' grids could promote the further development of the rich solar and wind energy resources in Aegean Sea assisting the de-carbonization of the Greek power system. The current work could be useful to policy makers, to local and regional authorities as well as to energy companies which are willing to invest in renewable energy systems in Aegean Sea.

Equivalent biodiversity area: A novel metric for No Net Loss success in Brazil’s changing biomes

This study presents a new method to incorporate the No Net Loss (NNL) principle within corporate Environmental, Social, and Governance (ESG) frameworks. This principle aims to ensure that biodiversity losses from human activities are fully offset. In this context, we tackle two main challenges: managing epistemic uncertainties in environmental modeling and accurately assessing compensatory areas needed to replace lost habitats. Focusing on Brazil’s diverse biomes, which are undergoing rapid changes, we highlight the role of expert opinion surveys in addressing the uncertainties of the InVEST Habitat Quality, a model that simulates changes in landscape integrity under different land use scenarios. Our analysis across three of Brazil’s regions – Caatinga Semi-arid, Cerrado Savanna, and Atlantic Forest – leverages open-source data to reveal substantial habitat losses due to activities like wind farm development, mining, and intensive agriculture, leading to a widespread decline in habitat quality. We introduce the Equivalent Biodiversity Area (EBA) metric to support NNL and Net Gain of Biodiversity efforts, measured in hectares. Findings show a reduction in EBA across all studied areas, highlighting the need for effective compensation strategies. Such strategies should merge Legal Reserves and ecological restoration into ESG policies, encourage landholder collaboration, and align with larger environmental efforts, such as watershed revitalization and Biodiversity Credits markets.

Geese migrating over the Pacific Ocean select altitudes coinciding with offshore wind turbine blades

Abstract Renewable energy facilities are a key part of mitigating climate change, but can pose threats to wild birds and bats, most often through collisions with infrastructure. Understanding collision risk and the factors affecting it can help minimize impacts on wild populations. For wind turbines, flight altitude is a major factor influencing collision risk, and altitude‐selection analyses can evaluate when and why animals fly at certain altitudes under certain conditions. We used GPS tags to track Pacific Flyway geese (Pacific greater white‐fronted goose, tule greater white‐fronted goose and lesser snow goose) on transoceanic migrations between Alaska and the Pacific Coast of the contiguous United States, an area where offshore windfarm development is beginning. We evaluated how geographic and environmental covariates affected (1) whether birds were at rest on the water versus in flight (binomial model) and (2) altitude selection when in flight (similar to a step‐selection framework). We then used a Monte Carlo simulation to predict the probability of flying at each altitude under various conditions, considering both the fly/rest decision and altitude selection. In both spring and fall, geese showed strong selection for altitudes within the expected rotor‐swept zone (20–200 m asl), with 56% of locations expected to be within the rotor‐swept zone under mean daylight conditions and 28% at night. This indicates a high possibility that migrating geese may be at risk of collision when passing through windfarms. Although there was some variation across subspecies, geese were most likely to be within the rotor‐swept zone with little wind or light tailwinds, low clouds, little to no precipitation and moderate to cool air temperatures. Geese were unlikely to be in the rotor‐swept zone at night, when most individuals were at rest on the water. Synthesis and applications. These results could be used to inform windfarm management, including decisions to shut down turbines when collision risk is high. The altitude‐selection framework we demonstrate could facilitate further study of other bird species to develop a holistic view of how windfarms in this area could affect the migratory bird community as a whole.

Downscaling and Wind Resource Assessment of Climatic Wind Speed Data Based on Deep Learning: A Case Study of the Tengger Desert Wind Farm

Analyzing historical and reanalysis datasets for wind energy climatic characteristics offers crucial insights for wind farms and short-term electricity generation forecasting. However, large-scale wind farms in Chinese deserts, the Gobi, and barren areas often lack sufficient wind measurement data, leading to challenges in assessing long-term power generation revenue and introducing uncertainty. This study focuses on the Tengger Desert as the study area, processes the Coupled Model Intercomparison Project Phase 6 (CMIP6) data, and analyzes and compares wind energy’s future characteristics utilizing a developed deep learning (DL) downscaling algorithm. The findings indicate that (1) the Convolutional Neural Network (CNN) downscaling model, with the Weather Research and Forecasting Model (WRF) numerical simulation results as the targets, exhibits spatial distribution consistency with WRF simulation results in the experimental area. (2) Through testing and validation with three practical wind measurements, the annual average wind speed error is below 4%. (3) In the mid-term future (~2050), the average wind speed in the experimental area remains stable with a multi-year average of approximately 7.00 m·s−1. The overall wind speed distribution range is significant, meeting the requirements for wind farm development.

Metocean conditions at two Norwegian sites for development of offshore wind farms

This paper examines metocean data from NORA3, a state-of-the-art wind and wave hindcast dataset for Northern Europe. Two offshore Norwegian areas, Utsira Nord (UN) and Sørlige Nordsjø II (SN2), are investigated. Both areas offer significant potential for the offshore wind sector. UN is situated in deep-sea water, suitable for floating offshore wind turbines. In contrast, SN2 lies in intermediate waters and ranks among the North Sea’s most promising regions allocated for offshore wind. Data from NORA3, originally on a 3-km resolution grid, are resampled into unstructured grids spanning from 1982 to 2022. This refined dataset offers a climatology time scale with superior spatial and temporal resolution compared to most other hindcast and reanalysis databases. The study examines mean wind speed and direction across seven levels, ranging from 10 m to 750 m above the surface. Analyses of extreme wind and wave conditions have been conducted. Results reveal that UN experiences higher extreme wave heights than SN2 while the extreme wind speeds may be substantially larger at SN2 than UN. Moreover, this study establishes joint distribution models that encompass several parameters, including mean wind speed, significant wave height, wave spectral peak period, and direction difference between wind and waves. Thus, this metocean data is valuable for designing and analyzing floating wind farms over their lifecycles.

The Probability of Ship Collision during the Fully Submerged Towing Process of Floating Offshore Wind Turbines

As global warming intensifies, the development of offshore wind farms is swiftly progressing, especially deep-water Floating Offshore Wind Turbines (FOWTs) capable of energy capture in deep-sea regions, which have emerged as a focal point of both academic and industrial interest. Although numerous researchers have conducted comprehensive and multifaceted studies on various components of wind turbines, less attention has been paid to the operational stage responses of FOWTs to wind, waves, and currents and the reliability of their structural components. This study primarily employs a theoretical analysis to establish mathematical models under a series of reasonable assumptions, examining the possibility of collisions between FOWT transport fleets and other vessels in the passage area during the towing process. Using the model, this paper takes the Wanning Floating Offshore Wind Farm (FOWF) project, which is scheduled to be deployed in the South China Sea, as its research object and calculates the probability of collisions between FOWTs and other vessels in three months from the pier near Wanning, Hainan, to a predetermined position 22 km away. The findings of the analysis indicate that the mathematical model developed in this study integrates the quantities and velocities of navigational vessels within the target maritime area as well as the speeds, routes, and schedules of the FOWT transport fleet. By employing statistical techniques and geometric calculations, the model can determine the frequency of collisions between various types of vessels and the FOWT transport fleet during the transportation period. This has substantial relevance for future risk assessments and disaster prevention and mitigation measures in the context of FOWT transportation.

Wind farms increase land surface temperature and reduce vegetation productivity in the Inner Mongolia

Wind power has been developing rapidly as a key measure to mitigate human-driven global warming. The understanding of the development and impacts of wind farms on local climate and vegetation is of great importance for their rational use but is still limited. In this study, we combined remote sensing and on-site investigations to identify wind farm locations in Inner Mongolia and performed landscape pattern analyses using Fragstats. We explored the impacts of wind farms on land surface temperature (LST) and vegetation net primary productivity (NPP) between 1990 and 2020 by contrasting these metrics in wind farms with those in non-wind farm areas. The results showed that the area of wind farms increased rapidly from 1.2 km2 in 1990 to 10,755 km2 in 2020. Spatially, wind farms are mainly clustered in three aggregation areas in the center. Further, wind farms increased nighttime LST, with a mean value of 0.23 °C, but had minor impacts on the daytime LST. Moreover, wind farms caused a decline in NPP, especially over forest areas, with an average reduction of 12.37 GC/m². Given the impact of wind farms on LST and NPP, we suggest that the development of wind farms should fully consider their direct and potential impacts. This study provides scientific guidance on the spatial pattern of future wind farms.

Analysis of NREL-5MW wind turbine wake under varied incoming turbulence conditions

The offshore environment is an ideal site for wind farm development as it offers abundant wind energy resources. Investigating offshore wind turbine wake under different turbulence conditions is essential for enhancing energy generation efficiency and promoting sustainable development. This paper utilizes a synthetic turbulence method called Discretizing and Synthesizing Random Flow Generation (DSRFG) to generate four categories of turbulence environments based on International Electrotechnical Commission design requirements for offshore wind turbines. A standalone NREL offshore 5-MW baseline wind turbine is then simulated using the actuator line method coupled with large eddy simulation. Proper Orthogonal Decomposition is employed to analyze the wake characteristics under different incoming turbulence conditions. For the overall wake behaviors, the flow mode, in which some tip vortices flow back towards the hub, become more significant with higher incoming turbulence intensity. Those vortices disturb the turbine and result in unfavorable effects. Meanwhile, when the detailed characteristics of the wake are studied, each primary mode experiences an increase in the number of small-scale vortices with higher incoming turbulence intensity. It results from the interaction between the wake and outer flow, which accelerates wake recovery and benefits the downstream turbine.

Predicting soil cone index and assessing suitability for wind and solar farm development in using machine learning techniques

This study proposes a novel approach that combines machine learning models to predict soil compaction using the soil cone index values. The methodology incorporates support vector regression (SVR) to gather input data on key soil parameters, and the output data from SVR are used as inputs for additional machine learning techniques such as Gradient Boosting, Decision Tree, Artificial Neural Networks, and Adaptive Neuro-Fuzzy Inference System. Evaluation of Artificial Intelligent techniques shows that the XGBoost model outperforms others, exhibiting high accuracy and reliability with low mean square error and high correlation coefficient. The effectiveness of the XGBoost model has implications for soil management, agricultural productivity, and land suitability evaluations, particularly for renewable energy projects. By integrating advanced AI techniques, stakeholders can make informed decisions about land use planning, sustainable farming practices, and the feasibility of renewable energy installations. Overall, this research contributes to soil science by demonstrating the potential of AI techniques, specifically the XGBoost model, in accurately predicting soil compaction and supporting optimal soil management practices.

Wind Energy Assessment in Forested Regions Based on the Combination of WRF and LSTM-Attention Models

In recent years, the energy crisis has become increasingly severe, and global attention has shifted towards the development and utilization of wind energy. The establishment of wind farms is gradually expanding to encompass forested regions. This paper aims to create a Weather Research and Forecasting (WRF) model suitable for simulating wind fields in forested terrains, combined with a long short-term time (LSTM) neural network enhanced with attention mechanisms. The simulation focuses on capturing wind characteristics at various heights, short-term wind speed prediction, and wind energy assessment in forested areas. The low-altitude observational data are obtained from the flux tower within the study area, while high-altitude data are collected using mobile radar. The research findings indicate that the WRF simulations using the YSU boundary layer scheme and MM5 surface layer scheme are applicable to forested terrains. The LSTM model with attention mechanisms exhibits low prediction errors for short-term wind speeds at different heights. Furthermore, based on the WRF simulation results, a wind energy assessment is conducted for the study area, demonstrating abundant wind energy resources at the 150 m height in forested regions. This provides valuable support for the site selection in wind farm development.

Political expectations and electoral responses to wind farm development in Sweden

Wind energy expansion has influenced electoral behavior by decreasing support for incumbents, primarily explained by not-in-my-backyard (NIMBY) arguments. However, how does the establishment of wind farms shape electoral behavior when municipal politicians can veto such establishments? Analyzing Sweden, this study shows that voters respond not only by retrospectively evaluating past constructions but also by evaluating parties differently based on their expectations. Our results indicate that parties more likely to approve turbine constructions are punished more than those less expected to do so. Additionally, we find no evidence that the construction of wind turbines influences support for either Green or radical right-wing parties. In conclusion, this study show that the political repercussions of wind farm expansion are multifaceted, influenced by a combination of past actions, future expectations, and the ideological stances of political parties.

Using fisheries-dependent data to investigate landings of European lobster (Homarus gammarus) within an offshore wind farm

Abstract The need for alternative strategies to assist in the monitoring and sustainable management of fisheries’ resources is becoming increasingly important. In recent years, greater utilization of fishers’ knowledge has been advocated as a potentially valuable source of data that could be applied to fisheries management issues. In the current study, we addressed this by investigating the landing per unit effort (LPUE) of European lobster (Homarus gammarus) from within an offshore wind farm using a fisheries-dependent commercial fishing logbook. The logbook was provided by a single fisherman who targeted lobsters within the wind farm between 2015 and 2022 using single pots deployed in association with individual wind turbines. Generalized linear mixed modeling was used to investigate changes in LPUE over temporal scales and as the result of the presence of scour protection at turbine locations. LPUE was found to be significantly higher at turbine locations where scour protection was present compared to those turbines where it was not. Predictions from modeling suggested LPUE was nearly 1.5× greater at turbines where scour protection was present. Significant differences in mean monthly and yearly LPUE were detected with this variation likely to reflect seasonal changes in lobster activity and the effect of introducing fishing into a previously unfished area. This work highlights the potential for fishing logbooks to be applied in fisheries management. Our results also demonstrate potential fishing opportunities arising from the development of offshore wind farms and the potential for these opportunities to be enhanced.