Ontario is Canada’s provincial leader in wind energy, with over 4000 MW of installed capacity supplying approximately five percent of the province’s electricity demand. Wind energy is now one of the fastest-growing sources of renewable power in Canada and many other countries. However, its possible negative impact on population health, as a new source of environmental noise, has raised concerns for people living in proximity to wind turbines (WTs). The aims of this study were to assess the effect of individual differences and annoyance on the self-reported general health and health-related quality of life (QOL) of nearby residents, using a pre- and post-exposure design. Prospective cohort data were collected before and after WT operations, from the individuals (n = 43) in Ontario, Canada. General health and QOL metrics were measured using standard scales, such as SF12, life satisfaction scales developed by Diener (SWLS) and the Canadian Community Health Survey (CCHS-SWL). The mean values for the Mental Component Score of SF12 (p = 0.002), SWLS (p < 0.001), and CCHS-SWL (p = 0.044) significantly worsened after WT operation for those participants who had a negative attitude to WTs, who voiced concerns about property devaluation, and/or who reported being visually or noise annoyed.

Wind energy has experienced a notable development during the last decades, driving new challenges for animal communities. Although bird collisions with wind turbines and spatial displacement due to disturbance have been widely described in the literature, other potential impacts remain unclear. In this study, we addressed the effect of turbine noise on the vocal behaviour of a threatened shrub-steppe passerine highly dependent on acoustic communication, the Dupont's lark Chersophilus duponti. Based on directional recordings of 49 calling and singing males exposed to a gradient of turbine noise level (from 15 up to 51 dBA), we tested for differences in signal diversity, redundancy, and complexity, as well as temporal and spectral characteristics of their vocalizations (particularly the characteristic whistle). Our results unveiled that Dupont's lark males varied the vocal structure when subject to turbine noise, by increasing the probability of emitting more complex whistles (with increased number of notes) and shifting the dominant note (emphasizing the longest and higher-pitched note). In addition, males increased duration and minimum frequency of specific notes of the whistle, while repertoire size and signal redundancy remain constant. To our knowledge, this is the first study reporting multiple and complex responses on the vocal repertoire of animals exposed to turbine noise and unveiling a shift of the dominant note in response to anthropogenic noise in general. These findings suggest that the Dupont's lark exhibits some level of phenotypic plasticity, which might enable the species to cope with noisy environments, although the vocal adjustments observed might have associated costs or alter the functionality of the signal. Future wind energy projects must include fine-scale noise assessments to quantify the consequences of chronic noise exposure.

Human activities at sea are still increasing. As biodiversity is a central topic in the management of our seas, it is important to understand how diversity responds to different disturbances related with physical impacts. We investigated the effects of three impacts, i.e. sand extraction, dredge disposal and offshore wind energy exploitation, on the soft-bottom macrobenthic assemblages in the Belgian part of the North Sea. We found similar diversity-disturbance responses, mainly related to the fact that different impacts caused similar environmental changes. We observed a sediment refinement which triggered a shift towards a heterogenic, dynamic (transitional) soft-bottom macrobenthic assemblage, with several species typically associated with muddy sands. This led to a local unexpected biodiversity increase in the impacted area. On a wider regional scale, the ever increasing human impacts might lead to a homogenization of the sediment, resulting in a more uniform, yet less diverse benthic ecosystem.

The North Atlantic right whale (Eubalaena glacialis) faces increasing pressure from commercial shipping traffic and proposed marine renewable energy developments. Drawing upon the successful Stellwagen Bank National Marine Sanctuary model, we propose a multi-stakeholder marine spatial planning process that considers both appropriate positioning of offshore wind farms and redefining commercial shipping lanes relative to whale migration routes: placement of wind turbines within certain right whale habitats may prove beneficial for the species. To that end, it may be advisable to initially relocate the shipping lanes for the benefit of the whales prior to selecting wind energy areas. The optimal end-state is the commercial viability of renewable energy, as well as a safe shipping infrastructure, with minimal risk of collision and exposure to shipping noise for the whales. This opportunity to manage impacts on right whales could serve as a model for other problematic interactions between marine life and commercial activities.

In response to the growing demand for the global energy supply chain, wind power has become an important research subject among studies in the advancement of renewable energy sources. The major concern is the stochastic volatility of weather conditions that hinder the development of wind power forecasting approaches. To address this issue, the current study proposes a weather prediction method divided into two models for wind speed and atmospheric system forecasting. First, the data-based model incorporated with wavelet transform and recurrent neural networks is employed to predict the wind speed. Second, the physics-informed echo state network was used to learn the chaotic behavior of the atmospheric system. The findings were validated with a case study conducted on wind speed data from Turkmenistan. The results suggest the outperformance of physics-informed model for accurate and reliable forecasting analysis, which indicates the potential for implementation in wind energy analysis.

This study is hinged on analyzing factors such as agriculture and globalization (de jure trade and financial) that threaten a sustainable environment using two proxies of ecological footprint: carbon and noncarbon ecological footprint in the Philippines while controlling for the influence of fossil to GDP, economic growth, urban population, and financial development using the autoregressive distributed lag (ARDL) framework. The result provides evidence of long‐run stable state among the variables. The result validates inverted U‐shaped pattern of EKC involving relationship between agricultural development and ecological footprint for the Philippines indicating that initially, ecological footprint increases as the agriculture develops and then declines as the agriculture matures to generate efficiency and low carbon. In addition, this study explores elasticities of the variables using ARDL, FMOLS, DOLS, and CCR procedure and found that de jure financial globalization exerts positive influence on ecological footprint in the long run. De jure trade globalization is found to be negative and significant in the long run. It is also found that agricultural level operates below the threshold level required to maximize the growth benefits of agricultural system towards mitigating environmental sustainability. Further empirical result shows a positive relationship between economic growth, fossil fuel, urban-population growth, and ecological footprint, and negative insignificant relationship between credit to private sector and ecological footprint. The government should optimize the use of agricultural land through well-articulated economic integration strategy fashioned to pave way for cleaner and low-carbon technologies sources like solar, geothermal, biomass, biogas, tidal power, photovoltaic, and wind energy in the agricultural production to avoid further deterioration of the environment.

Aiming at the uncertainty of wind power and the low accuracy of multi-step interval prediction, an ultra-short-term wind power multi-step interval prediction method based on complete ensemble empirical mode decomposition with adaptive noise-fuzzy information granulation (CEEMDAN-FIG) and convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) is proposed. Firstly, the CEEMDAN is used to decompose the wind power time series into several sub-components to reduce the non-stationary characteristics of the wind power time series. Then, different components are selected for FIG, and the maximum value sequence, average value sequence, minimum value sequence gotten from FIG, and the remaining components without FIG are combined with the wind speed data, wind direction data, and the temperature data. They all are input into the CNN-BiLSTM combined prediction model to obtain the initial wind power prediction interval. The prediction results of the maximum value sequence, the average value sequence, and the minimum value sequence are respectively superimposed on the prediction results of the remaining components to obtain the upper limit, point prediction, and lower limit of the initial prediction interval. Finally, the improved coverage width criterion is used as the objective function to optimize the interval, and the forecast interval of wind power under a given confidence level is generated. Taking the actual operating data of a certain unit of a wind farm as an example, the validity of the proposed model is verified.

In recent years, a series of environmental problems have come one after another under the use of traditional fossil energy, such as greenhouse effect, acid rain, haze and so on. In order to solve the environmental problems and achieve sustainable development, seeking alternative resources has become the direction of joint efforts of China and the world. As an important part of new energy, wind energy needs strong wind speed prediction support in terms of providing stable electric power. As a result, it is very important to improve the accuracy of wind speed prediction. In view of this, this paper proposes a signal processing method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) combined with singular value decomposition (SVD), and uses Elman neural network optimized by particle swarm optimization algorithm (PSO) and autoregressive integrated moving average model (ARIMA) to predict the intrinsic mode functions (IMFs). Firstly, CEEMDAN combined with SVD is used to decompose and denoise the data, and the weights and thresholds of Elman are optimized by PSO. Finally, the optimized Elman and ARIMA are used to respectively predict the processed wind speed data components, and then the final prediction results are obtained. The final prediction results show that the proposed model can improve the effect of wind speed prediction, reduce the prediction error, and provide strong support for the stable operation of wind farms and the grid connection of power plants.

Energy security and environmental measurements are incomplete without renewable energy; therefore, there is a dire need to explore new energy sources. Hence, this study aimed to measure the wind power potential to generate renewable hydrogen (H₂), including its production and supply cost. This study used first-order engineering model and net present value to measure the levelized cost of wind-generated renewable hydrogen by using the data source of the Pakistan Meteorological Department and State Bank of Pakistan. Results showed that the use of surplus wind and renewable hydrogen energy for green economic production is suggested as an innovative project option for large-scale hydrogen use. The key annual running expenses for hydrogen are electricity and storage costs, which have a significant impact on the costs of renewable hydrogen. The results also indicated that the project can potentially cut carbon dioxide (CO₂) pollution by 139 million metric tons and raise revenue for wind power plants by US$2998.52 million. The renewable electrolyzer plants avoided CO₂ at a rate of US$24.9–36.9/ton under baseload service, relative to US$44.3/ton for the benchmark. However, in the more practical mid-load situation, these plants have significant benefits. Further, the wind-generated renewable hydrogen delivers 6–11% larger annual rate of return than the standard CO₂ catch plant due to their capacity to remain running and supply hydrogen to the consumer through periods of plentiful wind and heat. Also, the measured levelized output cost of hydrogen (LCOH) was US$6.22/kgH₂, and for the PEC system, it was US$8.43/kgH₂. Finally, it is a mutually agreed consensus among environmental scientists that the integration of renewable energy is the way forward to increase energy security and environmental performance by ensuring uninterrupted clean and green energy. This application has the potential to address Pakistan’s urgent issues of large-scale surplus wind- and solar-generated energy, as well as rising energy demand.

Energy is an essential parameter for the economic growth and sustainable development of any country. Due to the rapid increase in energy demand, depletion of fossil fuels and environmental concerns, many developing and developed countries are moving towards alternative renewable resources such as solar energy, wind energy and biomass. Wind energy as a renewable energy source is gaining a lot of significant attention. Wind energy is a sustainable solution to produce energy having potential benefits such as clean source, reduced toxic gases emission and environmental friendly protocol for operation. Pakistan is among the top countries facing the worst energy crisis due to different political and financial issues. Pakistan is blessed with a huge potential of wind energy having all the basic requirements such as windy regions and good wind speed for harnessing energy. Pakistan can utilize the potential of wind energy to reduce the problem of energy outrage in the country and also take steps towards green economy from conventional fuel economy. This critical review highlights the current status, potential and the steps taken in the past and present to overcome the energy shortage in Pakistan by employing wind energy. Outlook on wind speed data, deployment of wind energy, environmental effect of wind energy and its barriers in the adoption are discussed with recommendations and suggestions to utilize this clean energy in an effective way. Graphical abstract