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In the last two decades, the tourism and energy sectors have grown rapidly and boosted economic growth, but it is inevitable that these sectors will cause environmental changes. So far, attempts have been made to determine the impact of the tourism and energy sectors on environmental degradation by examining pollution indicators such as CO 2 emissions and ecological footprint. However, these indicators neglect the supply side of the environment. In this context, this paper, for the first time, examines the influence of tourism, income, and energy consumption on the load capacity factor that results from dividing biocapacity by ecological footprint. Thus, the study aims to conduct a comprehensive sustainability analysis for Turkey by assessing the environmental quality on the supply and demand side. For this purpose, the study employs the novel dynamic Autoregressive-Distributed Lag (ARDL) simulations for the period 1965–2017, and the results indicate that tourist arrivals, energy consumption, and economic growth have a negative long run effect on the load capacity factor. Among these factors, only economic growth exerts a significant impact on the load capacity factor in both the short and long run. In the long run, the negative environmental effect of economic growth is less than in the short run. Therefore, the environmental Kuznets curve hypothesis is valid for Turkey. Based on the results, some policy recommendations are proposed to help Turkey improve its environmental quality.

Due to the commissioning of floating wind units, the latest technological developments, significant growth, and improvements in turbines, developments in offshore wind power capacity are estimated to increase faster than in the last two decades. The total installed offshore wind power capacity, which is currently 35 GW, is predicted to be approximately 382 GW by 2030 and approximately 2002 GW by 2050. For this reason, attempts are proposed to lower levelized cost of electricity (LCOE) for offshore wind power generation more than for other energy sources. In this study firstly, the global growth in the nominal capacity and size of offshore wind turbines over the last twenty years is examined. Then, the effects of this increase in nominal capacity and size on the LCOE, total installation cost (TIC), and turbine capacity factor are investigated. In parallel with this development, the changes in distance to shore and water depth for installation offshore wind power plants are reviewed according to the years. In addition, the effects of this global growth on wind farm capacity, turbine-specific power capacity, number of turbines per GW, and area needed per GW are investigated and discussed in detail.

The objective of this work is to understand the fluctuating nature of wind speed characteristics on different time scales and to find the long-term annual trends of wind speed at different locations in South Africa. The hourly average mean wind speed values over a period of 20 years are used to achieve the set objective. Wind speed frequency, directional availability of maximum mean wind speed, total energy, annual energy yield and plant capacity factors are determined for seven locations situated both inland and along the coast of South Africa. The highest mean wind speed (6.01 m/s) is obtained in Port Elizabeth and the lowest mean wind speed (3.86 m/s) is obtained in Bloemfontein. Wind speed increased with increasing latitudes at coastal sites (Cape Town, Durban, East London and Port Elizabeth), while the reverse trend was observed at inland locations (Bloemfontein, Johannesburg and Pretoria). Noticeable annual changes and relative wind speed values are found at coastal locations compared to inland sites. The energy pattern factor, also known as the cube factor, varied between a minimum of 1.489 in Pretoria and a maximum of 1.858 in Cape Town. Higher energy pattern factor (EPF) values correspond to sites with fair to good wind power potential. Finally, Cape Town, East London and Port Elizabeth are found to be good sites for wind power deployments based on the wind speed and power characteristics presented in this study.

The environmental degradation in the Middle East and North Africa (MENA) region leads to significant challenges regarding economic sustainability and the attainment of sustainable development goals (SDGs). The extensive use of fossil fuels in the region, as well as rapid urbanization and economic growth, has led to significant carbon emissions, together with unprecedented ecological footprints compromising environmental sustainability. The study aims to elucidate the influence exerted by technological innovation, trade openness, and natural resources on environmental sustainability in Turkey and Egypt for the period 1990–2022. In assessing the empirical relations, the study employed the Fourier function incorporate estimation techniques, that is, Fourier ADF for unit root test, Fourier ARDL, and Fourier NARDL for long-run and short-run elasticities of technological innovation (TI), trade openness (TO,) and natural resources rent (NRR) on load capacity factor (LCF) and inverted LCF (ILCF); finally, the directional causality evaluate through Fourier TY causality test. The results revealed that both Turkey and Egypt have severe environmental problems due to their high carbon emissions and ecological footprints. Technological change and international trade separately negatively affect environmental sustainability; however, these negative impacts have mixed character. On the one hand, technology can improve efficiency and reduce ecological footprints by obviating the use of high-impact processes or allowing cleaner production systems. In the same vein, trade openness helps transfer green technologies more quickly, but it can also lead to unsustainable resource extraction and pollution. The findings of the paper propose that in order to move forward, Turkey and Egypt need strategic policy shifts to ensure environmental sustainability, including transitioning towards renewable energy from fossil fuels while bolstering their capacity for energy efficiency. Policymakers must balance economic development with environmental conservation to reduce the harmful effects of climate degradation and help safeguard continued economic survival in the face of increasing climatic instability. This research helps to inform policy and investment decisions about how the SDGs can be achieved and how they are relevant for sustainable development in the MENA region.

Austria is committed to the net-zero climate goal along with the European Union. This requires all sectors to be decarbonized. Hereby, hydrogen plays a vital role as stated in the national hydrogen strategy. A report commissioned by the Austrian government predicts a minimum hydrogen demand of 16 TWh per year in Austria in 2040. Besides hydrogen imports, domestic production can ensure supply. Hence, this study analyses the levelized cost of hydrogen for an off-grid production plant including a proton exchange membrane electrolyzer, wind power and solar photovoltaics in Austria. In the first step, the capacity factors of the renewable electricity sources are determined by conducting a geographic information system analysis. Secondly, the levelized cost of electricity for wind power and solarphotovoltaics plants in Austria is calculated. Thirdly, the most cost-efficient portfolio of wind power and solar photovoltaics plants is determined using electricity generation profiles with a 10-min granularity. The modelled system variants differ among location, capacity factors of the renewable electricity sources and the full load hours of the electrolyzer. Finally, selected variables are tested for their sensitivities. With the applied model, the hydrogen production cost for decentralized production plants can be calculated for any specific location. The levelized cost of hydrogen estimates range from 3.08 EUR/kg to 13.12 EUR/kg of hydrogen, whereas it was found that the costs are most sensitive to the capacity factors of the renewable electricity sources and the full load hours of the electrolyzer. The novelty of the paper stems from the model applied that calculates the levelized cost of renewable hydrogen in an off-grid hydrogen production system. The model finds a cost-efficient portfolio of directly coupled wind power and solar photovoltaics systems for 80 different variants in an Austria-specific context.

In the 2030 Agenda for Sustainable Development, adopted by the United Nations (UN) member states in 2015, half of the target period has been exceeded. However, China, whose energy consumption relies heavily on fossil resources, remains at the top of the list of global polluters. Therefore, investigating the environmental impacts of energy types is essential to China’s path towards Sustainable Development Goals (SDG)-7 and SDG-13. Based on this motivation, the paper offers new insights into the energy-environment literature for China with wavelet coherence analysis (WCA). This approach can investigate the relationship between variables in a periodic manner based on the frequency behavior of the models. The paper separately analyzes the effects of primary energy consumption (PEC), fossil energy consumption (FEC), renewable energy consumption (REC), nuclear energy consumption (NEC), GDP, and population (POP) on three different environmental indicators in China. Using two environmental pollution indicators (carbon emission (CO 2 ) and ecological footprint (EF)) and one environmental quality indicator (load capacity factor (LCF)), the paper allows for comparison and robustness checks on the environmental impacts of energy indicators. Empirical findings reveal the following: (i) Except for REC and POP in the CO 2 model, the variables in all three models largely move together during the period under observation; (ii) variables other than POP have consistent coefficient signs; (iii) PEC, FEC, NEC, and GDP increase CO 2 and EF while decreasing LCF; (iv) the effect of NEC on LCF is more obvious until 2000; (v) unlike the others, REC affects CO 2 and EF negatively and LCF positively; (vi) there is bidirectional causality between PEC and environmental indicators but not for REC; (vii) the causality relations of other variables with environmental indicators differ in terms of model, time, and direction of causality. In light of the findings, the paper highlights that only the REC improves environmental quality in China. Other energy indicators contribute to environmental degradation. China, whose ecological deficit has increased dramatically in recent years, urgently needs to reduce its dependence on fossil energy sources by accelerating investments in REC. Governments should also review nuclear energy policies, which are expected to help achieve carbon neutrality.

Currently, more than 90% of the electricity produced in the Kingdom of Saudi Arabia originates from fossil fuels. Under the Vision 2030 initiative, the Kingdom aims to derive 50% of its energy from renewable sources by 2030. This study presents a comprehensive evaluation and ranking of renewable energy technologies for a selection of cities across the country using an integrated methodology that combines the Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The focus is on four renewable sources, namely Photovoltaic (PV), Concentrated Solar Power (CSP), Wind Energy, and Fuel Cell, each with a capacity of 10 MW. The performance of these renewable sources is assessed in five different cities of Ar’Ar, Ha’il, Riyadh, Najran, and Al Baha. The evaluation considers multiple criteria that encompass climate, environment, and social aspects, providing a holistic assessment of the alternatives. The AHP method is employed to determine the relative weights of the criteria, ensuring consistency in the pairwise comparisons. The TOPSIS method is then applied to rank the alternatives based on their performance scores. The results highlight the preferences and relative performance of the different renewable energy technologies across the considered cities. Fuel Cell technology emerges as the most favorable option in all the cities with a score higher than 0.68, demonstrating superior capacity factor (92.4%), minimal environmental impact, and reliable power generation but with a negative net present value (NPV) of − 12.22 M$. Wind Energy and CSP technologies followed in ranking, indicating their competitiveness and suitability as renewable energy options by producing in excess of 25,000 MWh/year. PV technology demonstrates competitiveness across all cities with the lowest levelized cost of electricity (LCOE) (4.33 C/kWh) and quickest payback period (12.4 years). The findings of this study provide valuable insights for decision-makers and stakeholders involved in the selection of appropriate renewable energy technologies. The rankings serve as a valuable tool in informing decision-making processes and facilitating the transition to sustainable energy systems.

Wind turbine technology has advanced significantly during the past 10 years all around the world. To raise the turbine capacity factor, developers are building bigger, more dependable wind turbines with bigger hub heights and rotor diameters. Long-bladed, large-rotor, tall-tower, and low-specific power wind turbines with higher capacity factors (CFs) developed in this direction may become more important in the future energy systems since they can generate electricity in more economical conditions. The increase in nameplate capacity, hub height, and rotor diameter of onshore wind turbines around the world as well as in Turkey is, therefore, examined in this study. The impacts of this development in wind turbine size on the levelized cost of electricity, total installed cost, and CF are analyzed. Moreover, the effects of this expansion on turbine-specific power capacity, wind farm capacity, and the number of wind turbines per GW are thoroughly evaluated. To promote future advancements in large-scale wind turbine technology, these inspection results can be used to evaluate the technical and financial viability of turbines. They can also serve as a useful data source and a substantial advantage in the construction and development of new wind energy facilities.

The capacity factor of a power plant is the ratio of generation over its potential generation. It is an important measure to describe wind and solar resources. However, the fluctuating nature of renewable power generation makes it difficult to integrate all generation at times. Whenever generation exceeds the load, curtailment or storage of energy is required. With increasing renewable shares in the power system, the level of curtailment will further increase. In this work, the influence of the curtailment on the capacity factors for a highly renewable German power system is studied. An effective capacity factor is introduced, and the implications for the distribution of renewable power plants are discussed. Three years of highly-resolved weather data were used to model wind and solar power generation. Together with historical load data and a transmission model, a possible future German power system was simulated. It is shown that effective capacity factors for unlimited transmission are strongly reduced by up to 60% (wind) and 70% (photovoltaics) and therefore of limited value in a highly renewable power system. Furthermore, the results demonstrate that wind power benefits more strongly from a reinforced transmission grid than photovoltaics (PV) does.

This communication discusses the two parameters recently emerged as key performance indicators of wind energy facilities, the mean capacity factor over a year, and the standard deviation of the capacity factor from a high-frequency sampling of 1 min or less (the annual mean does not change if the sampling interval of the statistical population is every month or every minute; the standard deviation does). Both parameters impact the levelised cost of electricity. They permit to quantify the energy production by the specific facility. They also permit to attribute grid energy storage costs to the specific facility. This manuscript shows that the annual mean capacity factors of wind energy facilities are not improving in the U.S. with the year of completion. The average of the annual mean capacity factors of every facility operational in a given year is about 33% now in 2019 as it was in 2012. Additional to the flat trend in the average of the annual mean capacity factor, their variability is large at the individual facility level. With 5 min sampling frequency, the standard deviation of the capacity factor of an individual wind energy facility is about that same magnitude of the mean. This translates into a coefficient of variability—the ratio of the standard deviation to the mean—approaching unity. At the grid level, the variability of all the wind energy supply is still large and necessitates significant energy storage.