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.

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.

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.

Accurate wind speed forecasting (WSF) not only ensures stable power system operation but also contributes to enhancing the competitiveness of wind power companies in the market. In this paper, a hybrid prediction model based on secondary decomposition algorithm (SDA) is proposed for WSF. First, wavelet transform (WT) is used to decompose the wind speed sequence into approximate and detailed components. Second, the obtained detailed components are further decomposed by symplectic geometry mode decomposition (SGMD). Then, the marine predators algorithm-optimized back-propagation neural network (BPNN) is used to predict the new subsequences. The case study was implemented on 4 datasets. The experimental results show that, first, the proposed hybrid model has the highest prediction accuracy and the best robustness among all the compared models in 1–4-step prediction. Second, the proposed hybrid decomposition strategy has significant utility in reducing the difficulty of WSF. After adding SDA, the average improvement levels of MAPE in 1–4-step prediction were 85.64%, 84.93%, 81.08% and 80.67%, respectively. Third, the re-decomposition of the details obtained by WT can improve the prediction accuracy. After the re-decomposition of the details obtained by WT, the proposed WT-SGMD-MPA-BP model leads to the average improvement percentages of 44.44%, 61.69%, 50.56% and 49.28% in RMSE compared with WT-MPA-BP model in various horizons. The proposed model provides valuable reference for WSF. In future work, the performance of the model for other nonlinear sequences is worth exploring.

People’s sentiments and perceptions of greenhouse gas emission and renewable energy are important information to understand their reaction to the planned mitigation policy. Therefore, this research analyzes people’s perceptions of greenhouse gas emissions and their preferences for renewable energy resources using a sample of Twitter data. We first identify themes of discussion using semantic text similarity and network analysis. Next, we measure people’s interest in renewable energy resources based on the mentioned rate in Twitter and search interest in Google trends. Then, we measure people’s sentiment toward these resources and compare the interest with sentiments to identify opportunities for policy improvement. The results indicate a minor influence of governmental assemblies on Twitter discourses compared to a very high influence of two renewable energy providers amounts to more than 40% of the tweeting activities related to renewable energy. The search interest analysis shows a slight shift in people’s interest in favor of renewable energy. The interest in geothermal energy is decreasing while interest in biomass energy is increasing. The sentiment analysis shows that biomass energy has the highest positive sentiments while solar and wind energy have higher interest. Solar and wind energy are found to be the two most promising sources for the future energy transition. Our study implies that governments should practice a higher influence on promoting awareness of the environment and converging between people’s interests and feasible energy solutions. We also advocate Twitter as a source for collecting real-time data about social preferences for environmental policy input.

Energy is the source of economic growth, and energy consumption indicates the country’s state of development. Energy engineering is a relatively new technical discipline. It is increasingly considered as a significant step in meeting carbon reduction targets, which can produce a variety of appealing outcomes that are useful to humanity’s evolution. Many countries have adopted national policies to decrease pollution by reducing fossil fuel use and increasing renewable energy usage by alleviating climate change (wind and solar, etc.). The ever-growing need for renewable sources has led to economic and technological problems, such as wind energy, essential for effective grid control, and the design of a wind project. Precise estimates offer network operators and power system designers vital information for the generation of an appropriate wind turbine and controlling demand and supply power. This work provides an in-depth study of the proliferation of artificial intelligence (AI) in the prediction of wind energy generation. The devices employed to calculate wind speed are examined and discussed, with a focus on studies recently published. This review’s findings show that AI is being employed in power wind energy measurement and forecasts. When compared to individual systems, the hybrid AI system gives more accurate findings. The discussion also found that correct handling and calibration of the anemometer can increase predicting accuracy. This conclusion suggests that increasing the accuracy of wind forecasting can be accomplished by lowering equipment errors that measure the meteorological parameter and mitigate carbon emission.

Slowing and reversing climate change and keeping energy prices at affordable levels are the main important achievements of the use of renewable energy. About 210% increase in energy consumption from 1990 to 2018, reduction in fossil fuel reserves, and high capacity of renewable energy in Arab countries encourage them to increase the use of renewable and sustainable energy sources as a key way to supply the energy in future and have a sustainable economy. There is no a comprehensive review study to focus on the capacity and strategies of renewable energy in Arab countries at the transnational level until 2030. To fill this gap, this article investigates the current and future capacities and strategies of renewable energy production by 22 Arab countries, which are the center of fossil energy production in the world, until 2030. Indeed, it provides a roadmap for advancement towards energy production from renewable sources in these countries. It is observed that Egypt and Morocco with an installed capacity of 5980 and 3447 MW, respectively, had the highest installed renewable energy capacity among the Arab countries in 2020. The results also showed that most ambitious goal is related to Djibouti, where it is targeted to supply 100% of energy from renewable resource by 2035. Finally, it should be mentioned that most Arab countries focus on solar and wind energy, and very little attention is paid to geothermal, biomass, and hydroelectric energy.

Pakistan is developing South Asian country which is currently considering alternative energy sources including coal, solar, compressed natural gas, and wind energy to cope with the worst energy crisis in its history. Moreover, the policy promotion of compressed natural gas especially in the transport sector has raised concerns about the demand management of natural gas to avoid future shortages and ensure sustainable use of this precious non-renewable source of energy. Against this background, this study aimed to forecast natural gas demand in Pakistan for the 2016–2030 period by applying relevant univariate time series econometric methods. Apart from forecasting the overall natural gas demand, the forecasting analysis is also conducted for natural gas demand in Pakistan’s total natural gas consumption and also for natural gas consumption across the household, industrial, commercial, transport, fertilizer production, power generation, and cement production sectors. Overall, the findings revealed that ARIMA is the appropriate model for forecasting gas consumption in Pakistan. Further, the growth of increase in the level of compressed natural gas consumption in the household sector is more as compared to all other sectors of the economy up to the year 2030. The key findings show that (a) natural gas consumption is likely to grow with time, (b) mixed projection trends are observed for the overall natural gas consumption and other sector-based natural gas consumption trends, and (c) the difference between natural gas consumption and production in Pakistan is likely to grow leading to 2030. As part of the policy recommendation in line with the findings, policymakers in Pakistan should increase the availability of natural gas, particularly in sectors where its consumption is likely to be declining. In addition, more proactive measures should be undertaken to explore the existing natural gas reserves in the long run while also importing natural gas from the neighboring nations in the short run. Furthermore, the government of Pakistan should seriously consider strategizing the development of the nation’s compressed natural gas sector.

Wind energy is one of the potential renewable energy sources being exploited around the globe today. Accurate prediction of wind speed is mandatory for precise estimation of wind power at a site. In this study, hybrid machine learning models have been deployed for short-term wind speed prediction. The twin support vector regression (TSVR), primal least squares twin support vector regression (PLSTSVR), iterative Lagrangian twin parametric insensitive support vector regression (ILTPISVR), extreme learning machine (ELM), random vector functional link (RVFL), and large-margin distribution machine-based regression (LDMR) models have been adopted in predicting the short-term wind speed collected from five stations named as Chennai, Coimbatore, Madurai, Salem, and Tirunelveli in Tamil Nadu, India. Further to check the applicability of the models, the performance of the models was compared based on various performance measures like RMSE, MAPE, SMAPE, MASE, SSE/SST, SSR/SST, and R². The results suggest that LDMR outperforms other models in terms of its prediction accuracy and ELM is computationally faster compared to other models.