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Wind and solar energy are crucial for meeting the growing energy demand and mitigating the impact of climate change, and their sources show a climate‐dependence. Here, based on the outputs from two regional climate models (RCMs) driven by three global climate models within the Coordinated Regional Climate Downscaling Experiments‐East Asia (CORDEX‐EA‐II), the effects of future climate change on wind power density (WPD) and photovoltaic power potential (PVP) in China under the Representative Concentration Pathway (RCP) 2.6 scenario and RCP8.5 scenario are comprehensively investigated, and the sources of uncertainty are also quantified. Results show that all RCMs can reproduce the observed WPD and PVP in China by employing the Quantile Delta Mapping method for wind speed simulations. For the future projections, the annual averages of WPD and PVP in China tend to decrease by −11.67% to −1.7% and −4.6% to −1.12%, respectively, with more significant reduction under the RCP8.5 than under the RCP2.6. Note that uncertainties exist among the RCMs' simulations in terms of future projections and long‐term trends. Further analysis reveals that the uncertainty in both WPD and PVP projections are primarily driven by the internal variability in most sub‐regions except the Tibetan Plateau, where GCM uncertainty and emission scenarios play the dominant role in the uncertainty of WPD and PVP projection, respectively. This study highlights the potential benefits of controlling greenhouse gas emissions in enhancing and stabilizing renewable energy in China. Plain Language Summary: China has set an ambitious goal of achieving carbon neutrality by 2060, which requires an accelerated energy transition toward wind power and photovoltaic. However, the sources of wind and solar energy are sensitive to weather and climate, and future potential of wind and solar energy in China under a warming climate remains uncertain, posing a significant concern for the decision‐makers. Based on high‐resolution multiple regional climate models (RCMs) projections, we found that the annual average of wind power density (WPD) and photovoltaic power potential (PVP) are expected to decrease in the future by approximately −11.67% to −1.7% and −4.6% to −1.12%, respectively. There are notable uncertainties among the multi‐RCM projections, and the internal variability is the main source of uncertainty for most sub‐regions except the Tibetan Plateau, where GCM uncertainty and emission scenarios play the dominant role in the uncertainty of WPD and PVP projection, respectively. Our results highlight the advantages of reducing greenhouse gas emissions to enhance and stabilize renewable energy sources in China. Key Points: The annual average wind and solar energy in China are expected to decrease in the future based on a multi‐RCM ensembleThe internal variability plays a dominated role in the uncertainty of WPD and PVP projections in most sub‐regionsThe uncertainty in WPD and PVP projections over the TP is driven primarily by the different GCMs and emission scenarios, respectively [ABSTRACT FROM AUTHOR]

Wind energy has grown rapidly in recent years as a measure to control carbon emissions and mitigate climate change. Extreme wind can damage wind turbines, cause losses to wind power plants, limit economic benefits of wind energy facilities, and disrupt regional grid balance. Therefore, an accurate assessment of extreme wind speeds at wind turbine hub height and their spatiotemporal variation under climate change is critical for the planning of wind energy and for guaranteeing regional energy security. In this study, the 100‐m extreme wind speeds in China are estimated using an empirical downscaling and Bayesian model averaging ensemble method with the latest ERA5 reanalysis and 20 global climate models (GCMs) from the Coupled Model Intercomparison Project phase 6 (CMIP6). Two shared socioeconomic pathways (SSP), that is, SSP2‐4.5 and SSP5‐8.5, are considered to account for the uncertainty in anthropogenic emissions. According to the results, the highest extreme wind speeds are primarily found in Inner Mongolia, northeast China, western Tibet, and the eastern coastal region. Extreme wind speeds in central and southeastern China are projected to increase by approximately 2% in the middle (2031–2060) and the end (2071–2100) of the 21st century relative to the baseline period (1985–2014). Summer extreme wind speeds in northwestern Tibet are expected to increase by more than 9% at the end of the century. The findings of this study indicate that it is important to take the present and projected changes in local wind extremes into account when choosing locations for wind power plants and wind energy installations. Plain Language Summary: Wind energy is one of the key renewable energy sources and has been widely developed worldwide to mitigate global warming. Extreme wind can damage wind turbines and jeopardize the reliability of wind power generation, disturbing the stability of regional power supplies. Therefore, understanding the spatial and temporal variability of extreme wind speeds at wind turbine hub height under climate change is essential for the development of local wind power. This study examines the spatial and temporal patterns of 100‐m extreme wind speeds in China using empirical downscaling and Bayesian model averaging ensemble methods driven by several global climate models and reanalysis data. The results show that Inner Mongolia, northeastern China, western Tibet, and eastern coastal areas are the areas in China where high extreme wind speeds are most prevalent. This spatial pattern will typically persist in the future, but extreme wind speeds in Tibet, Qinghai, and Sichuan will drop dramatically by the end of the 21st century. Extreme wind speeds are considerably higher in winter and autumn than in the other two seasons. Local governments should take measures to prevent regional energy disparities due to the deterioration of wind turbines because of the anticipated wind extremes under climate change. Key Points: An empirical downscaling Bayesian ensemble projection model is constructed to investigate extreme wind speed variationsExtreme wind speeds in central and southeastern China are projected to increase in future periodsExtreme wind speeds are considerably higher in winter and autumn than in the other two seasons [ABSTRACT FROM AUTHOR]

Sustainable energy sources, such as wind energy, are pivotal in driving our energy landscape towards a more environmentally conscious and responsible future. Wind power, as an exemplar of clean and renewable energy solutions, adeptly harnesses the kinetic energy of the wind to generate electricity. While wind energy significantly contributes to our sustainability objectives, the quality of the energy it produces is equally essential. A critical challenge in this context is harmonic distortion, which manifests as unwanted fluctuations in the frequency and amplitude of electrical waveforms. Effectively mitigating these distortions within wind energy systems is vital to maintaining the stability and reliability of power grids, guaranteeing that the electricity supplied adheres to high-quality standards. The objective of this study was to conduct a technological prospection focused on the contemporary scenario of genetic algorithm applications in addressing harmonic variations. This investigation unearthed a total of 634 relevant documents. The findings suggest that the utilization of genetic algorithms for enhancing energy quality is a relatively recent but promising field. The State Grid Corp of China emerged as the principal contributor, with ten noteworthy inventors identified. Remarkably, both China and the United States lead in patent filings. The insights gleaned from these documents underscore the potential for further exploration and the synergistic application of these techniques. These collaborative efforts have the potential to yield processes and devices that offer significant economic and environmental advantages for the energy industry, solidifying our commitment to a cleaner and more sustainable energy future. [ABSTRACT FROM AUTHOR]

This study examines the impact of the COVID-19 pandemic on wind and green energy, with a focus on controlling the rising cost of wind energy using the levelized cost of energy (LCOE) method. The objectives include exploring green financial policies to mitigate the pandemic's effects and analyzing the cost of wind energy in China before and after the outbreak. The findings reveal a decrease in wind energy costs and increased consumption during the COVID-19 crisis, attributed to the role of green financing. The study emphasizes the importance of wind energy growth for achieving CO2 reduction goals and highlights the need for green finance options to mitigate the economic impact of the pandemic on wind energy production. The research also suggests implications for stakeholders, such as promoting green financing and wind energy resource development. Furthermore, the study emphasizes the significance of collaboration, policy reforms, geographical spillover effects, and environmental regulations in establishing a sustainable green financial system and enhancing green innovation. Future research directions include exploring data-driven approaches, addressing endogeneity concerns, and validating findings through enhanced indices and data collection. [ABSTRACT FROM AUTHOR]

Innovative activities are increasingly decentralized and globally dispersed, which provides new upgrading opportunities for emerging market firms. However, little is known about how latecomer firms (re-)organize their research and development (R&D) over time as conditions for upgrading change. This paper systematically maps the R&D networks of China's lead firms in the wind turbine industry. The empirical findings reveal that latecomer firms not only exploit but increasingly co-create vanguard knowledge in global R&D networks through organizational diversification. Taking an evolutionary perspective, the paper extends our understanding of the changing nature of upgrading mechanisms and provides new insights into the reorganization of innovation processes in an era of technological change. [ABSTRACT FROM AUTHOR]

As the power generation mode with the lowest carbon emissions, wind power generation plays an indispensable role in achieving the goal of carbon neutralization. To optimize the wind power density (WPD), forecasting is crucial to improve wind power utilization and power system stability. However, because near-surface wind is characterized by notable randomness, diversity, intermittence, and uncontrollability, accurately forecasting the WPD on wind farms remains a challenging task. In this study, we attempted to improve the WPD forecast in the middle- and high-latitude regions of China (wind energy resources are abundant there) by selecting the relatively optimal planetary boundary layer (PBL) scheme, as the PBL processes exert notable effects on the near-surface wind field directly. Based on a whole month in the summer (July 2021), seven PBL schemes were compared quantitatively by using the Weather Research and Forecasting (WRF) model for a total of 70 runs (for each run, the forecast period was 3 days). The results show that no PBL schemes could always show the best performance in forecasting all variables, and the forecast accuracy showed a notable dependence on the evolution of the weather systems. Among the seven PBL schemes, the Medium-Range Forecast (MRF) scheme showed the overall best performance in forecasting the 100 m wind speed, sea level pressure, and 2 m temperature, which ensured that it had the highest forecast skill for the WPD in the middle- and high-latitude regions of China. Further analyses indicate that the background conditions were also well forecasted by the MRF scheme (ranked first or second). This was a crucial reason why the WPD forecast was the best for the MRF scheme. [ABSTRACT FROM AUTHOR]

This work uses the previously developed mixed distribution, however, with added a parameter and function. We introduce the improved mixed distribution model as an alternative wind speed distribution, which combines three distributions. Four extreme value distributions are considered to model mixed distribution, improved mixed distribution, and estimate wind speed. The efficiency of univariate, mixed, and improved mixed distributions on actual wind speed data from China's diverse regions is estimated. Using the minimal standard error of fit (SEF) test, 65.5% of samples are better suitable for the improved mixed distribution. The best model's return period is also calculated. Moreover, the addition of another parameter and function in mixed distributions has improved the results. This statistical analysis concluded that improved mixed distributions are more suitable and accurate for assessing wind speed data. [ABSTRACT FROM AUTHOR]

This research presents a comprehensive approach to improving the accuracy of wind turbine power curve (WTPC) modeling. The WTPC is a critical tool for monitoring wind turbine performance and estimating wind power potential, but current models have limited ability to capture the complex relationship between wind speed and power output. To address these limitations, this study implements a dual-pronged refinement of WTPC modeling. First, an innovative data preprocessing technique is introduced, using a 97 % confidence interval around a KNN-estimated WTPC constructed using the Laplace distribution to meticulously eliminate prominent outliers. Second, a novel WTPC modeling approach based on quantile regression is adopted, accounting for the asymmetric error characteristics in the loss function. Four distinct quantile regression models are developed, including three tree-based algorithms - decision trees, random forests, and gradient boosting - and a deep learning-based quantile regression neural network. Comparative analysis against ten established parametric and nonparametric techniques confirms the superiority of the proposed models, with the decision tree quantile regression model achieving the lowest validation errors. The proposed techniques are validated on two real-world datasets from operational wind turbines in Turkey and China, demonstrating significant improvements in WTPC modeling accuracy compared to conventional methods. Overall, this study successfully presents a comprehensive modeling approach that addresses outliers and leverages quantile regression to significantly enhance WTPC accuracy. • Proposes innovative data pre-processing to eliminate outliers for enhanced WTPC modeling accuracy. • Leverages quantile regression's asymmetric loss function to handle non-Gaussian errors from outliers. • Develops multiple quantile regression models including tree-based and neural network architectures. • Demonstrates significant WTPC modeling improvements on real wind turbine data compared to benchmarks. [ABSTRACT FROM AUTHOR]

While many developed countries in the world set 2050, Türkiye has determined 2053 as the year of net zero emissions target. Increase in the usage of renewable energy sources such as wind for the energy production is one of the main methods to achieve this goal. Among the renewable energy sources used for electricity production, wind energy has been the most increasing energy source in Türkiye and around the world in recent years. Today, more than 100 countries in the world generate electricity from wind energy. China, the United States of America and Germany are the top 3 countries with the highest installed capacity of wind energy for a long time. Based on 2022 data, Türkiye ranks 7th in Europe and 12th in the world with respect to the installed wind power plant capacity. Among the renewable energy sources, wind energy in Türkiye has the largest installed power and electricity production after hydroelectricity. In this study, the potential and installed capacity of the wind energy for the different countries in the world and Türkiye are analysed, the shares of the wind energy in current energy production and its contributions to the total energy consumption has been examined. The conditions that will enable the world including Türkiye to meet the energy consumption completely from renewable energy sources and the possible contribution of wind energy to all renewable resources have been estimated. [ABSTRACT FROM AUTHOR]

The development and utilization of renewable energy (RE) is crucial for achieving the sustainable development goals (SDGs). The northwest China, endowed with abundant RE sources such as wind and solar power, accounts for over 70% of the country's total resources. The assessment and utilization of RE in this region has become a critical means to achieve the SDGs, particularly SDG7. However, lack of knowledge regarding the RE potential poses a barrier to achieving high-quality energy development. Thus, through a Geographical Information System (GIS) based multi-criteria analysis, we assess the solar and wind energy potential in northwest China, quantitatively examine the energy potential and its contribution towards achieving the SDGs. Our results show that a substantial portion of RE can be harnessed in northwest China, with wind energy generation reaching up to 9.84PWh/km2/yr at 110m and 12.43 PWh/km2/yr at 140m. Concurrently, solar energy can contribute up to 15.16 PWh/km2/yr. Xinjiang province has the highest RE potential for it contains a large share of suitable area with good resource quality. The findings illustrate the contribution of northwest China towards achieving SDGs and facilitate the formulation of more targeted resource policies. • Responding to the imperatives of carbon and haze reduction with renewable energy development under the framework of the SGDs. • Geographic and techno-economic quantification of Northwest China's solar and wind potential from a regional provincial perspective. • Wind energy generation in northwest China can reach up to 9.84PWh/km2/yr and solar energy contributes up to 15.16 PWh/km2/yr. • With RPS, the energy potential of the Northwest China is capable of facilitating the achievement of SDG7 and carbon neutrality vision. [ABSTRACT FROM AUTHOR]

China's coastal region is the major geographical unit for the future development of China's industrial sector. The transformation of basic structure to high-class development in China's coastal places is a significant tool for promoting the changes related to quality, power and efficiency in regional economic development. In the 21st century, environmental and energy issues have increased worldwide, and challenges related to environmental pollution, energy crises, and ecological imbalances have emerged. To climate change and energy utilization, the sustainable progress of clean energy is the new route of future energy development. Based on China's non-polluting energy growth process in the last ten years, this article explores China's clean/green energy policies and economic growth development plans. Clean energy utilization is crucial for sustainable development in the context of high-quality economic growth and climate change. However, the monetary evolution and carbon emission are not investigated whole from the clean energy aspects. Using Wind energy sources as the acceptable variable, this paper employs threshold regression and impulse functions to assess the energy consumption and economic growth on carbon emission in 30 Chinese provinces over the 2000 to 2020 period. The Deep Belief Network (DBN) model predicts wind energy utilization and efficiency. The results show that economic development and carbon emissions are connected. Further, growth influences promote the offset of carbon emissions. Green innovation alters the nexus of carbon emissions, and China's economy reduces carbon usage. It provides the decision-making policies for clean energy development. • China's Coastal region is the major geographical unit for the future development of China's industrial. • In high-quality economic growth and climate change, clean energy use is vital for sustainable development. • The utilization of wind energy and it's efficiency is predicted using Deep Belief Network (DBN) model. • Empirical results link economic growth with carbon emissions, showing growth promotes emissions offset. • Green innovation changes the economic growth-carbon link, aiding China's carbon reduction efforts. [ABSTRACT FROM AUTHOR]

[Display omitted] • A liner positive correlation is revealed between wind farm distance and ecological impacts. • The ecological obstacle points increased 350 % after construction of wind farm. • The proportions of forest around weak points raised from 38 % (50 m range) to 49 % (150 m range). • Protection, restoration, and compensation strategies are proposed according to recession degrees. The wind energy industry is developing rapidly in China with a maximum capacity worldwide. Meanwhile, the stability of the ecological network is challenged by extensive renewable energy expansion. There are still research deficiencies in wind farm ecological impacts at project scale from an ecological restoration perspective. It is of great necessity to monitor the ecological impacts with time going and explore ecological restoration strategies for wind power development. The research case of Zhongying Wind Farm is selected based on investigation and interviews regarding its ecological impact in the prior study. It employs a comparative methodology by comparing the ecological quality before and after the construction of Zhongying Wind Farm. Raw data from Landsat 8, the Digital Elevation Model (DEM), the Normalized Difference Vegetation Index (NDVI), and wind turbine statistics originating from open data platforms are analyzed and visually represented with ArcGIS. This paper establishes an ecological network based on morphological spatial pattern analysis (MSPA) and the Minimum Cumulative Resistance (MCR) model to quantify wind farms' impacts on ecological corridors. The circuit theory and Pajek software are used to identify and categorize the ecological nodes into weak and obstacle points. The results reveal that ecological impacts on ecological corridors decay with distance, reflecting changes in length, connection strength and cumulative values of corridors. While the impacts on ecological nodes depend on nodes' attributes: the number of weak points increased by 18.5 %, and obstacle points increased by 350 %, with their locations shifting from the edges of urban construction sites to the edges of wind farm. Differentiated strategies are proposed according to the impact magnitude of each ecological corridor and node: such as dynamic inspection mechanism, construction prohibition for protection; corridor structure and function recovering, stepping stones addition for restoration; buffer belts, warning radar, and monetary schemes for compensation. This research fills the gap of ecological impact assessment under the challenges of wind energy expansion. It helps to balance wind energy development and ecological restoration at the regional level. [ABSTRACT FROM AUTHOR]

As the world is moving toward greener forms of energy, to mitigate the effects of global warming due to greenhouse gas emissions, wind energy has risen as the most invested-in renewable energy. China, as the largest consumer of world energy, has started investing heavily in wind energy resources. Most of the wind farms in China are located in Northern China, and they possess the disadvantage of being far away from the energy load. To mitigate this, recently, offshore wind farms are being proposed and invested in. As an initial step in the wind farm setting, a thorough knowledge of the wind energy potential of the candidate region is required. Here, we conduct numerical experiments with Weather Research and Forecasting (WRF) model forced by analysis (NCEP-FNL) and reanalysis (ERA-Interim and NCEP-CFSv2) to find the best choice in terms of initial and boundary data for downscale in the South China Sea. The simulations are validated by observation and several analyses. Specific locations along China's coast are analyzed and validated for their wind speed, surface temperature, and energy production. The analysis shows that the model forced with ERA-Interim data provides the best simulation of surface wind speed characteristics in the South China Sea, yet the other models are not too far behind. Moreover, the analysis indicates that the Taiwan Strait along the coastal regions of China is an excellent region to set up wind farms due to possessing the highest wind speeds along the coast. [ABSTRACT FROM AUTHOR]

Offshore wind power is an important renewable energy source and plays an essential role in optimizing the energy structure worldwide. Simultaneously, offshore wind turbine (OWT) selection is a complicated process since it concerning various variables and optimization scenarios. In this paper, a novel fuzzy Bayesian network-based model for multiple-attribute decision-making (MADM) is proposed. First of all, a three-layer decision-making framework for OWT selection is established through systematically combing previous studies, expert knowledge, and the principal component analysis (PCA) results by treating the wind turbine parameters, wind turbine economy, wind turbine reliability, and navigation safety as the attributes, and the corresponding 11 influencing factors are identified and quantified. Moreover, a triangular fuzzy number is introduced to fuzzify each influencing factor, and the belief degree for different linguistic variables corresponding to the specific influencing factor is employed in the fuzzy IF-THEN rule system. Then, the belief rule base is transformed into the Bayesian network as the conditional probability tables (CPTs), which can directly express the influence relationship of various factors and realize the integration of various influence factors to obtain the optimal scheme. Finally, the proposed model is validated by taking a case study in busy waterways in the Eastern China Sea as an example. This research provides an intuitive, feasible, and practical way for OWT selection. • A novel fuzzy Bayesian network-based MADM model for OTW selection in busy waterways is proposed. • A three-layer decision-making framework is established for the OWT selection. • Comprehensively consider each influencing factor's qualitative and quantitative characteristics. • Model can not only conduct the uncertainty caused by ambiguity but also intuitively achieve optimal reasoning and evaluation. [ABSTRACT FROM AUTHOR]

The aim of this paper is to drive the discourse towards the increasing shift to renewables, especially offshore wind energy generation, in the emerging international energy order. The Indian Ocean Region (IOR), despite its increasing contribution to onshore wind energy generation and impending policies on offshore wind energy, is reluctant to invest in the latter. Hence, this paper highlights four important aspects that challenge IOR's offshore wind energy development: Indian Ocean's strategic location, environment impacts, blue economy and maritime terrorism. In the background of the geopolitical rivalry existing in the Indian Ocean Region (IOR), with the increasing presence of China and the USA in the Indian Ocean, this paper aims to study if these geopolitical challenges are hindering offshore wind energy generation in IOR. The key findings of the paper include the necessity of addressing the geopolitical rivalry in IOR as an important hindrance in huge investments needed in OWE farms, so that a regional cooperative mechanism is arrived at especially from the point of view of policies towards OWE generation. [ABSTRACT FROM AUTHOR]

China, India, and the USA are the world's biggest energy consumers and CO 2 emitters. Being the leading contributors to climate change, these economies are also at the core of environmental solutions. This paper investigates the causal relationship among solar and wind energy production, coal consumption, economic growth, and CO 2 emissions for these three countries. To do so, we use an advanced methodology in Machine Learning to verify the predictive causal linkages among variables. The Causal Direction from Dependency (D2C) algorithm set CO 2 emissions as the target variable. The obtained results were disaggregated and estimated in a supervised prediction model. The findings, confirmed by three different Machine Learning procedures, showed an interesting output. While a reduction in overall carbon emissions is predicted in China and the US (resulting from the intensive use of renewable sources of energy), India displays critical predictions of a rise in CO 2 emissions. This indicates that curbing CO 2 emissions cannot be achieved without conducting a comprehensive shift from fossil to renewable resources, although China and the U.S. present a more promising path to sustainability than India. Being an emerging renewable energy leader, India should further enhance the use of low-carbon sources in its power supply and limit its dependence on coal. • Renewable energies, coal consumption, economic growth, and CO 2 we examine. • We investigate the causal predicted relationship on China, India, and the USA. • An advanced methodology in Machine Learning is used. • India has a prediction of an increase in emissions of CO 2. • China and the U.S. present a more promising path to sustainability than India. [ABSTRACT FROM AUTHOR]

Paper aims: This paper presents the results of technological analysis on wind energy based on patent documents classified with code F03D - "Wind Motors.". Originality: Despite several studies on wind energy, this paper follows a different approach, presenting an overview that brings together technical and market information through social network analysis methods. Research method: The study covered data from 55,223 documents filed in the principal patent offices of the world, obtained from the Derwent Innovations Index. The publications were organized and analyzed through correlation and technology transfer networks. Main findings: General Electric (GE) and European companies have dominated the wind energy industry. However, in the last years, companies from emerging countries are also penetrating and becoming major players. Emphasis can be given to China, which has a deposit pattern detached from the rest of the world. Implications for theory and practice: This study indicates that these companies became important turbine manufacturers and started to invest in developing new technologies and seek patent protection. Companies from emerging countries are new entrants, and their performance is still restricted to few markets compared to traditional companies. [ABSTRACT FROM AUTHOR]

Because of the increase in the nation's need for wind energy, the impacts of climate change on wind energy have been investigated. In addition to long‐term changes, wind energy also shows robust interannual variations, but little effort has been devoted to understanding the underlying mechanisms. In this study, the impact of El Niño on the summer mean wind power density (WPD) over China is investigated. The abilities of five sets of reanalysis data in measuring the interannual variability of the WPD over China are assessed. Encouraging results are seen for all reanalysis datasets, with the MERRA and ERA‐Interim datasets showing the best performance. The relationship between El Niño and the following summer WPD is identified over China. During El Niño decaying year summers, the WPD over south of the Yangtze River valley increases, whereas the WPD over north of the Yangtze River valley decreases. The WPD changes are dominated by an anomalous anticyclone located in the northwestern Pacific. The anticyclone leads to strong southerly winds in southern China and thereby enhances the WPD. In regions north of the Yangtze River valley, the low surface pressure gradient causes a reduction in wind speed and thereby a weak WPD. Because the year‐by‐year variation in El Niño‐Southern Oscillation (ENSO) is highly predictable, our results shed light on the seasonal prediction of wind power over China. [ABSTRACT FROM AUTHOR]

This study conducts the mid-long term predictions and assesses the multi-year average offshore wind energy in the China seas for the next five years based on CMIP5 wind data, in hope of providing reference for the long term prediction of wind energy. Results show that (1) large areas with multi-year average wind power densities (WPD) greater than 350 W/m² are mainly found throughout the Ryukyu Islands and the Taiwan and Luzon Straits in the southeastern area of the Indo-China Peninsula. (2) From 2015-2019, the WPD in the Bohai Sea, Taiwan Strait and Beibu Gulf will be lower than the multi-year average level and thus will be similar to the multi-year average in the Yellow Sea and Gulf of Thailand but greater than the multi-year average in the East China Sea and South China Sea. (3) The predicted occurrences of exploitable wind speeds of the East China Sea and South China Sea are promising for the next five years, exceeding 60%. A value above 40% is predicted for the Yellow Sea, and a lower value is anticipated for the Bohai Sea. (4) The predicted occurrences of WPD > 50 W/m² are more promising for the next five years. [ABSTRACT FROM AUTHOR]

A novel project solution for large-scale hydrogen application is proposed utilizing surplus wind and solar generated electricity for hydrogen generation and NG pipeline transportation for hydrogen-natural gas mixtures (called HCNG). This application can practically solve urgent issues of large-scale surplus wind and solar generated electricity and increasing NG demand in China. Economic evaluation is performed in terms of electricity and equipment capacity estimation, cost estimation, sensitivity analysis, profitability analysis and parametric study. Equipment expenses are dominant in the construction period, especially those of the electrolysers. Electricity cost and transportation cost are the main annual operating costs and greatly influence the HCNG and pure hydrogen costs. The project proves to be feasible through the profitability analysis. The main influence items are tested individually to guarantee project profitability within 22 years. The project can reduce 388.40 M Nm3 CO 2 emissions and increase 2998.52 M$ incomes for solar and wind power stations. • Proposed a novel solution for solving the unbalanced energy distribution in China. • Investigated the sensitivity and profitability of cost parameters and profit targets. • Distinguished influence items and obtained limit values of key parameters. • Identified positive economic benefits, energy optimization and emission reduction. [ABSTRACT FROM AUTHOR]

[Display omitted] • A methodology is developed to predict potential land suitability of wind energy. • Explainable machine learning method is applied to examine the marginal effects. • Capacity factor and average wind speed are the most important decision criteria. • High-resolution gridded probability prediction maps were produced. • 14–29 % of total land area were identified as emerging priority development zones. Spatial siting decisions for wind energy utilization are known to be greatly influenced by complex drivers, such as climate, geography, and socio-economics. However, traditional multi-criteria decision making methods (MCDM) inevitably subject to subjectivity and uncertainties in weight determination of assessment index, which may lead into the mismatch between real-world practices and evaluation outcomes. Here, we proposed a novel data-driven machine learning-based modeling framework to predict potential land suitability of large-scale wind energy development by combining the national level inventory of existing wind farms and various spatial decision criteria. Three major wind energy application economies (China, USA, and EU) were selected as the case study. Four advanced machine learning algorithms, Deep Neural Network (DNN), Random Forest (RF), XGBoost and LightGBM, were adopted to build the prediction model. Thirty conditioning factors were chosen as informative reflecting biophysical, geographical, socio-economic, landscape composition and configuration properties for modelling optimized wind farms site. Further, three model interpretation techniques including variable importance, partial dependence plots, as well as Shapley additive explanation (SHAP), were used to discern the contribution of individual variables and explain their nonlinear driving effects on the wind energy utilization. Results showed that RF models achieved the highest predictive accuracy (AUC > 0.91) among the other algorithms for the testing dataset. Local wind energy resource endowments including capacity factor (CF) and average wind speed (WS) are the most important decision-making criteria of wind farms siting for all three economics, which confirmed that a resource-oriented layout pattern was currently followed by wind energy development. Furthermore, China has a relatively relaxed standards of wind farms location selection among three economies (such as the threshold of CF close to 0.23 and threshold of WS close to 6.5 w/s). According to high-resolution gridded probability prediction mapping derived from optimized RF model, we identified high priority development zones occupied about 14–29 % of total land area in each economy, with estimated technical potential of 5028 TWh/year for China, 2297 TWh/year for USA, 1114 TWh/year for EU, respectively. An ensemble learning framework provides valuable insights and useful practical tools for optimizing renewable energy capacity expansion planning, informing policy development, and promoting the long-term sustainability of wind energy development. [ABSTRACT FROM AUTHOR]

Hydrogen produced from renewables-fuelled electrolysis has the potential to provide deep decarbonisation for sectors that are the most difficult to decarbonise, but the intermittency of renewable sources may result in low electrolyser utilisation and, consequently, high costs. This study determines the greenhouse gas (GHG) emissions and levelised cost of hydrogen (LCOH) associated with blending wind electricity with the grid to increase electrolyser utilisation. A novel cost model was developed to determine the production performance, costs and embodied CO 2 emissions of an electrolyser with hybrid fuel supply (wind and grid) considering dynamic load constraints of alkaline (ALK) and Proton exchange membrane (PEM) electrolysers and using high-resolution historical weather data to determine wind production profiles in two case study regions: northern (Bayannur) and southern (Pingtan) China. The results show that using 52% of grid electricity in an ALK electrolyser in Bayannur reduces LCOH by 11%, increasing hydrogen production by 74% but with 17.4 kg CO 2 / kg H 2 generated in CO 2 emissions. When a carbon price is accounted for, the optimal electrolyser utilisation is reduced to 40%, with the LCE 44% reduced. Emissions of 9.8 kg CO 2 /kg H 2 are lower than for stream methane reforming with carbon capture. In Southern China (Pingtan), LCOH is higher than Bayanur because of the high electricity cost. The economic benefits of increasing grid power depend primarily on the total CAPEX cost, grid power cost, and carbon tax. By altering the carbon tax, the optimal blend of wind/ grid differs. The grid contribution to the optimal blend diminishes as a carbon tax approaches 35 USD/ t CO 2. • This paper analyses the value of adding grid electricity to a pure wind-powered H 2 production plant. • The LCOH and CO 2 life-cycle emissions are obtained for Alkaline and PEM electrolysers. • We are introducing the novel ICOH metric to optimise the trade-off of LCOH and CO 2 emission on H 2 production. • Using only 52% of grid electricity in an ALK electrolyser, H 2 production can increase by 75%. [ABSTRACT FROM AUTHOR]

This paper presents a comparative in-depth investigation of the energy standings, opportunities and the barriers that occur in the expansion of renewable energy in the Asia's two fastest growing economies, i.e. India and China. These two countries contain almost 36% of the world's population and constitute the fastest emerging hub for trade and industries in the energy sector in which different types of energy resources are sought to meet the energy demands. Non-conventional sources of energy are the inevitable alternative for the achievement of economic improvement, ecological balance, nature friendly environment and sustainable development. It is recommended to eliminate the obstacles to achieving the estimated energy targets of the 13th financial year plan (FYP) of both nations and stimulate the enhancement of the rate of green energy resources exploitation. [ABSTRACT FROM AUTHOR]

Abstract Wind energy, acknowledged as a promising form of renewable energy and the fastest-growing clean method for electricity generation, has attracted considerable attention from many scientists and researchers in recent decades. However, wind energy forecasting is still a challenging task owing to its inherent features of non-linearity and randomness. Therefore, this study develops a hybrid wind energy forecasting and analysis system including a deterministic forecasting module and an uncertainty analysis module to mitigate the challenges in existing studies. In particular, these challenges are as follows: (1) It is difficult to guarantee that the data characteristics underlying the time series are effectively extracted; (2) in the modeling of each subseries, i.e., when the original data is decomposed into some time series, forecasting accuracy and stability are not simultaneously considered, and thus, they are not properly modeled; and (3) the best function to perform a deterministic forecasting and uncertainty analysis based on the forecaster of each subseries is unknown. The developed hybrid system consists of three steps: First, data preprocessing is conducted to capture and mine the main feature of the wind energy time series and weaken the noises' negative effects; second, multi-objective optimization is proposed to achieve the forecasting of each subseries with improvements in accuracy and stability; finally, search for the best function, which obtains the deterministic forecasting and uncertainty analysis results using an optimized extreme learning machine based on different modeling objectives, is conducted. Experimental simulations are performed using data from three sites in a real wind farm, which indicate that the developed system has a better performance in engineering applications than that of other methods. Furthermore, this system could not only be used as an effective tool for wind energy deterministic forecasting and uncertainty analysis, but also for other engineering application areas in the future. Highlights • A hybrid forecasting and analysis system is developed for wind energy. • Effectiveness and applicability are satisfactorily validated in a real wind farm. • A new integration method is developed to obtain the final forecasting results. • Focus is placed on both deterministic forecasting and uncertainty analysis. • Suggestions for potential future research on EMD family based methods are provided. [ABSTRACT FROM AUTHOR]

By adopting the constant market share model and revealed comparative advantage approach, this paper measured the long-term trends of the comparative advantages of China's wind energy products, based on the UN Comtrade data of 2007–2016 from the Belt and Road perspective. The results indicate that: (1) the international competitiveness of wind energy products in China is constantly increasing, and the competition effect has become the dominant factor for export growth; (2) China's exports of wind energy products to the Belt and Road countries have been continuously rising, especially in the ASEAN and South Asian markets; (3) the export structure of China's wind energy products has been optimized with a limited degree; (4) China's wind energy products have a small market share in the markets where the import demand growth increases rapidly; and (5) the comparative advantages of China's wind energy products have been on the rise in the past decade, yet the overall comparative advantages are still weak. Finally, relevant policy recommendations are proposed accordingly based on the above conclusions. [ABSTRACT FROM AUTHOR]

Offshore wind resource assessment seems to be urgently needed due to the rapid development of offshore wind energy in the coming decades. Technical potential of offshore wind energy over the sea area shallower than 250 m along China coast is investigated. To avoid erroneous estimation of wind power density, a statistical model considering sea state effect is proposed. Long-term CCMP wind field data are examined using that model to reduce uncertainties. Further, influential factors including wind power density, water depth, wind turbine size, wind farm layout and various spatial constraints are analyzed on the GIS platform. Technical potential under different scenarios are presented and discussed. It shows that wind resource at Taiwan Strait is particularly abundant, where wind power density at 70 m height can be above 900 W/m 2 . Technical potential is quite sensitive to the size of wind turbine. Taking the layout S1 (8 × 15 turbines in each farm, 8 rotor diameters apart between wind turbines, 20 km buffer region between neighboring farms) as an example: the total technical potential of the study area is 613 GW for rotor radius 60 m, and that for rotor radius 90 m is 1264 GW; the growth rates of technical potential with rotor radius is 19.3 GW/m roughly. Spatial constraints has significant impact on the region with water depth less than 50 m, where only 48.1% of area is available for developing wind energy and the technical potential there is about 23% of that of the study area. [ABSTRACT FROM AUTHOR]

The volatility and uncertainty of wind energy has brought great challenges to its accommodation in electric power systems. To determine a wind power trading strategy in the real-time power market, the accurate assessment of wind energy accommodation capability (WAC) is of great significance. However, most current studies investigate the wind energy accommodation potential from the perspective of dispatch, but rarely provide quantitative assessment results. Additionally, the non-anticipativity of decisions has not been considered in the assessment yet. This paper proposes a novel quantitative assessment method for WAC based on the multi-stage robust optimization (RO), which addresses the anticipativity issues in the traditional two-stage method while maintaining the decision-making process' robustness. In the assessment method, the operational risk is integrated via a tractable scheme to obtain dynamic admissible WAC boundaries. To obtain the optimal solution of the proposed multi-stage RO problem, a fast robust dual dynamic programming (FRDDP) algorithm is employed, where the adaptive techniques are developed to accelerate the computation. Numerical studies on the modified IEEE 14-Bus system and a real-world system in Zhejiang Province of China validate the effectiveness of the proposed assessment model and adaptive acceleration techniques. The simulation results demonstrate the presented method brings a 18.87% reduction in operational cost, and reduces 29.34% curtailment of wind energy. Compared with the original FRDDP, the adaptive technique significantly reduces the computational consumption by 73.34% on the real-world test system. • A novel assessment method for WAC is proposed based on the multi-stage robust optimization. • Tractable risk evaluation method is integrated in the multi-stage framework. • Adaptive techniques are designed for FRDDP method to accelerate the optimal solution. • The proposed method achieves both non-anticipative and robust assessment decisions. [ABSTRACT FROM AUTHOR]

Since the early stage of wind power development, the Chinese government has attached great importance to the nurturing of the domestic wind power industry and the establishment of a domestic supply chain. This paper examines the trajectory of China's policy pathway by linking the evolution of policy packages with the changing operation states of wind equipment manufacturers (mainly upstream and midstream). It is found that the government selectively combined quantity-oriented policies, quality-oriented, and cost-oriented policies at different historical stages and that a gradualist approach was adopted by strengthening quality and cost regulations in a piecemeal fashion. Though this gradualist approach might risk regulatory gaps, business disputes, and financial stress along the supply chain, it also provides some buffer time for manufacturers to adapt to new quality standards and cost expectations through technological improvement, optimization of production scale, and supply-chain management. The subtle balance between the strengths and weaknesses of this gradualist approach has been centered around the government's capacity to constantly assess the conditions and reactively respond to the needs of domestic enterprises. An in-depth analysis of these dynamic relations helps to better comprehend China's industrial policy pathway and appreciate both its past achievements and future challenges. [ABSTRACT FROM AUTHOR]

Intermittent renewable energy sources have been transformed from supplementary energy to dominant incremental alternative energy sources in the context of carbon neutrality target worldwide. For coastal countries, it is urgent to integrate abundant offshore energy into regional onshore renewable power sources economically and reliably considering the scarcity of land resources and negative environmental impacts. However, limited knowledge on the complex spatiotemporally synergy relationships between energy potential (onshore and offshore) is a barrier in making reasonable policies to promote collaborative development of onshore and offshore energies. This study presents a spatiotemporally explicit modelling framework for assessing the synergy potential characteristics of onshore and offshore renewable energy sources. The framework firstly estimates the technical potential of solar PV and wind energy across the country by using 40 years of hourly meteorological reanalysis data (1980–2020), and then investigates the long-term changes trend, stability, and complementarity of solar-wind energy potential spatiotemporally at both onshore and offshore sub-regions. We find that a considerable amount of solar PV and wind energy sources could be harnessed in China, and onshore potential across the country (178 PWh/yr) is approximate 20 times higher than offshore energy potential (8.9 PWh/yr). Overall, onshore solar-wind energy potentials exhibit statistically insignificant but slightly increasing trend over the past 40 years, while offshore wind potentials observe an opposite change trend for solar PV and wind energy production. Complementarity assessment showed the northeast and offshore parts of China present high complementarity effects for hourly and daily power production of solar-wind systems. Furthermore, inter-regional combination between offshore and onshore northwest regions also exhibits promising synergy effect for renewable energy power output. The results on resources dynamic and complementarity highlight the advantage of interregional resources cooperative configuration and electric power grid interconnections. The analysis performed in this paper could provide a global picture for the integration planning and collaborative development strategies formulation of on-/offshore renewable energy across the country. • Synergy potential of onshore and offshore renewable energy was evaluated. • Trend, stability, and complementarity of solar and wind potential were examined. • Onshore energy potential in China is 20 times higher than offshore potential. • Synergy development strategies of onshore and offshore energy were proposed. [ABSTRACT FROM AUTHOR]

Wind energy patents are conventionally defined using Cooperative Patent Classification (CPC) and International Patent Classification (IPC) codes that represent wind motors (F03D) and wind energy (Y02E 10/70). This study examines whether these codes sufficiently represent the wind energy patent domain. Using a combination of keywords and classification codes identified through expert input and manual review, we construct an expanded domain with a 7.5% increase in patent count, a 6% increase in recall, and only a 1% decrease in precision for wind energy patents. We also found that the conventional domain is not unbiased; it underrepresents patenting from China and patents published since 2010. This is important because an improved patent domain allows for a holistic patent data set, which is necessary to complete a thorough analysis of the wind industry. Through this wind energy application, we extend our methodology to form a generalized patent search process that can be used to target technology domains within a patent data set. • Our methodology improves patent retrieval with keywords and codes identified through expert input and manual review. • Improved wind energy domain observed 7.5% increase in patent count, 6% increase in recall, and only 1% decrease in precision. • Patents retrieved using conventional codes for wind energy underrepresent patents from China and published since 2010. • Methodology for wind energy application can be generalized for patent searching to target other technology domains. [ABSTRACT FROM AUTHOR]

An accurate assessment of wind and solar resources is important for China's future transition to clean energy and the achievement of its carbon-neutrality goals. Based on climate data, geographical information, and the latest technical and economic information, this work estimates the total technical and economic potential of wind and solar power in China and their distribution by province. The main conclusions drawn by analyzing the spatial distribution and temporal evolution of the potential of wind and solar energy are as follows. (1) Both wind and solar energy have sufficient technical potential to support China's vision of carbon neutrality by 2060. (2) The potential is unevenly distributed spatially and is more concentrated in western and northern China, away from demand centers, with Xinjiang and Inner Mongolia accounting for >76% of the total technical potential of onshore wind and solar energy. The offshore wind potential is fairly equally distributed in coastal provinces. (3) In the near-to-medium term (5–10 years), fully utilized local wind and solar resources could help to meet the requirements of the non-hydro Renewable Portfolio Standard (RPS), and could significantly ease the urgency of building long-distance cross-province power transmission lines. For provinces with below-average RPS targets, the suggested non-hydro RPS targets could be further enhanced. (4) In the long term, the construction of adequate west-east and south-north transmission lines will remain essential to the construction of a national carbon-neutral power system. • This is the first study to assess the wind and solar power potential in a unified manner at provincial level in China. • China has sufficient renewable power potential to support its carbon neutrality vision, but unevenly distributed spatially. • The RPS targets in certain provinces could be further enhanced with the adequate local wind and solar economic potential. [ABSTRACT FROM AUTHOR]

In a globalized world, emerging economies are faced with competition from technologically leading countries, which poses particular challenges for development. The Technological Innovation Systems (TIS) approach is employed to compare the innovation systems for wind energy in China and Brazil. In both countries, wind energy has recently gained in importance for the national energy system and TIS have emerged to support this development. Researchers interested in TIS formation in emerging economies have stressed the importance of international technology linkages, thus special attention is paid to such linkages between local and global actors as well as to the use of domestic versus foreign technology. Although both countries are latecomers in the industry, the way of balancing foreign and domestic technology and the resulting configuration of the TIS differs widely. In the case of China, the balance gradually shifted from foreign to domestic technology, supported by a fine-tuned and adaptive design of the relevant policy mix. In contrast, Brazil is highly reliant on the inflow of foreign technology, but was successful in attracting foreign direct investment and building a domestic supply chain. [ABSTRACT FROM AUTHOR]

The long-term wind and wave characteristics, and their associated stored power, are investigated in the South China Sea (SCS) from 1986 to 2015. The Weather Research and Forecasting model (WRF) and WAVEWATCH-III (WW III) are continuously performed to simulate a 30-year wind and wave hindcasts in the entire domain. Comparisons between the simulated and observed wind and wave data show good agreement under extreme typhoon conditions as well. The spatio-temporal patterns of annual, seasonal and monthly averaged wind fields and wind power density, significant wave heights, and wave potential are presented using the 30-year simulated results. Our results show that offshore winds and waves, with mean annual energy densities reaching up to 1100 W/m 2 and 65 kW/m, respectively, are relatively stronger than they are nearshore or inland. The most abundant power occurs in December and the least abundant appears in May. Furthermore, wind and wave energy roses of average power potential at 15 typical points across the SCS are calculated at length. The dominant directions for both the wind and waves are consistently NNE, NE, and ENE. Additionally, the wave energy is mainly the result of energy periods (between 6 and 11 s) and significant wave heights (between 1 and 6 m). [ABSTRACT FROM AUTHOR]

This paper advances our understanding of how technology upgrading in the Chinese wind turbine industry is linked to internationalisation of Danish component suppliers. In order to grasp the interlinkages and implications hereof, the paper combines perspectives of global value chains (GVC), national innovation systems (NIS) and firm-level capabilities. The paper employs a qualitative methodology, drawing on explorative case studies of component suppliers and their links to lead firms in the wind turbine industry. The findings of the paper highlight the new pattern of upgrading by upstream linkages, i.e. linking up with global suppliers in the wind turbine global value chain, and the new role of component suppliers as technology transmitters across national innovation systems into emerging markets. Conceptually, the paper contributes to understanding how technological catching up in value chains links to the intersection between national innovation systems, a process driven by global value chain dynamics.Cet article fait progresser notre compréhension de la façon dont la modernisation de la technologie dans l’industrie éolienne chinoise est liée à l’internationalisation des fournisseurs de composants danois. Afin d’en saisir les interdépendances et les implications, l’article combine les perspectives des chaînes de valeur mondiales (CVM), des systèmes d’innovation nationaux (SIN) et des capacités au niveau de l’entreprise. L’article utilise une méthodologie qualitative, s’appuyant sur des études de cas exploratoires de fournisseurs de composants et de leurs liens avec des entreprises de pointe dans l’industrie des éoliennes. Les conclusions de l’article mettent en avant la nouvelle tendance de modernisation grâce aux liens en amont, à savoir la connexion avec les fournisseurs mondiaux dans la chaîne de valeur mondiale des éoliennes et le nouveau rôle des fournisseurs de composants comme transmetteurs de technologie dans les systèmes nationaux d’innovation et jusque dans les marchés émergents. Sur le plan conceptuel, l’article contribue à comprendre comment le rattrapage technologique dans les chaînes de valeur est lié à l’intersection entre les systèmes d’innovation nationaux, un processus conduit par la dynamique de la chaîne de valeur mondiale. [ABSTRACT FROM AUTHOR]

Wind resource in China is abundant due to its vast land mass and long coastline. Based on wind speed and direction records from wind measurement towers at six onshore sites with different geographical climate conditions in China, statistical assessment of wind characteristics and wind energy potential at height of 70 m corresponding to the hub heights of multi-megawatt wind turbines is presented and discussed in this paper. First of all, the Weibull distribution function is verified to be a reliable model for wind speed prediction and the moment method is proved to be an accurate approach for estimation of the Weibull parameters at all the sites. Moreover, the variations of mean wind speed, the Weibull parameters and wind power density at the six sites are investigated in terms of seasonal, monthly and diurnal time scales. Finally, annual energy outputs at the six sites are determined by taking a commercial wind turbine as an example to evaluate the economic feasibility of wind energy development. The results of this study indicate that the six sites in North China, Northeast China, Central China, coastal regions in East China and Southeast China are in rich wind energy areas which are suitable for wind energy utilization. In particular, among the six sites, Tongliao in North China is the most promising location for wind energy development with the maximum annual energy output of 10.33 GWh. The objective of this study is to provide useful information for wind energy development and an effective approach for wind energy potential assessment. [ABSTRACT FROM AUTHOR]

In this study, nearshore wind and wave climates and their potential as renewable energy sources are evaluated by means of buoy observational data for the Shenzhen coastal region. Six buoys were originally deployed in the region by the city local government of China in 2014, and are located in different areas of the study region, including Dapeng Bay, Daya Bay, Shenzhen Bay. The waters in these areas are relatively shallow, ranging in depth between about 3 and 22 m. The results show that during 2014–2016, annual mean wind speeds (at 2.5 m above the sea surface) in the region varied between 3.1 and 4.1 m s − 1 , leading to wind powers between 37 and 94 W m − 2 ; significant wave heights were mostly less than 1 m, while wave energy periods were mostly in the range 3 − 7 s. As a result, wave power was mostly less than 1.0 kW m − 1 . It is concluded that the potential of wave energy as a renewable resource at the buoy locations was very small. This may be due to the fact that, first, water depth is very shallow, and, secondly, the buoys are located in bays where the sea is somewhat semi-enclosed, all of which are not favorable for the development of wind waves. [ABSTRACT FROM AUTHOR]

This article argues that manufacturing policies of Chinese local governments have provided an important corrective to some of the weaknesses inherent in the central government's indigenous innovation framework, most importantly its inattention to the importance of advanced manufacturing capabilities for innovation. Based on an original dataset of over one hundred executive interviews conducted with 43 Chinese wind and solar firms, I identify both central government R&D funding and continued local government support for manufacturing as critical factors in enabling innovation among China's renewable energy firms. In particular, this article shows that firms have utilized a combination of both central and local government policies to establish unique engineering capabilities required for innovation in commercialization and scale-up to mass production. The findings suggest that continued local government support for the manufacturing economy has not undermined central government innovation policies, but has (1) broadened the range of resources available to entrepreneurial firms and (2) enabled new options for industrial upgrading that are outside the conceptualization of innovation underlying the central government's indigenous innovation framework. [ABSTRACT FROM PUBLISHER]

Background As a non-fossil technology, wind power has an enormous advantage over coal because of its role in climate change mitigation. Therefore, it is important to investigate how substituting wind power for coal-fired electricity will affect emission reductions, changes in radiative forcing and rising temperatures, particularly in the context of emission limits. Methods We developed an integrated methodology that includes two parts: an energy-economy-environmental (3E) integrated model and an emission-temperature response model. The former is used to simulate the dynamic relationships between economic output, wind energy and greenhouse gas (GHG) emissions; the latter is used to evaluate changes in radiative forcing and warming. Results Under the present development projection, wind energy cannot serve as a major force in curbing emissions, even under the strictest space-restraining scenario. China's temperature contribution to global warming will be up to 21.76% if warming is limited to 2 degrees. With the wind-for-coal power substitution, the corresponding contribution to global radiative forcing increase and temperature rise will decrease by up to 10% and 6.57%, respectively. Conclusions Substituting wind power for coal-fired electricity has positive effects on emission reductions and warming control. However, wind energy alone is insufficient for climate change mitigation. It forms an important component of the renewable energy portfolio used to combat global warming. [ABSTRACT FROM AUTHOR]

The rapid increase in world's installed wind energy capacity may have masked the power loss caused by declining global surface wind speed (termed 'global stilling'), particularly for China with huge wind energy investments. Here, we estimated the potential impact of global stilling on wind energy production in China, using data from 1226 meteorological stations between 1971 and 2015. We show that surface wind speeds have on average declined at a rate of 5.5% decade−1, while the corresponding wind power density dropped by 24.5% decade−1. Although the decline in wind speeds has slowed after 1991, the decline in wind power density did not show this slowdown. This was attributed to the absence of slowdown in the declining trend of strong winds. Compared with the wind speed levels in the 2000s, the mean capacity factor would have dropped from 20.7% in 2001 to 14.9% in 2015, with the largest absolute change in the "Three North" regions. Furthermore, wind electricity generation would have declined by 40 TWh/a using the installed capacity of 2015, representing a decrease of 16%. The correlation analysis indicates that the decreased wind speed is most likely caused by changes in the large-scale atmospheric circulation rather than increased surface roughness. • We quantified the influence of global stilling on wind energy in China. • The large-scale circulation variability is probably the main cause of wind stilling over China. • We proposed some strategies for wind energy to cope with climate system change. [ABSTRACT FROM AUTHOR]

ABSTRACT The mean climatology, seasonal and interannual variability and trend of wind speeds at the hub height (80 m) of modern wind turbines over China and its surrounding regions are revisited using 33-year (1979-2011) wind data from the Climate Forecast System Reanalysis ( CFSR) that has many improvements including higher spatial resolution over previous global reanalysis products. Mean 80-m wind speeds are consistently higher over China Seas and the ocean areas than over land, and inside China high winds are found in areas of Inner Mongolia and the Tibetan Plateau. There is a considerable seasonal variability that reflects primarily the influence of East and Southeast Asia Monsoon with generally higher speeds in winter followed by summer, and weaker winds in autumn, followed by spring. There is also a strong interannual variability, and regions of larger amplitude of variability coincide with regions of higher mean winds. A decreasing trend, dominated by a sharp decline beginning in 2005, is seen across China and the surrounding seas in summer and autumn, and the summer trends over land and over ocean appear to be related respectively to the Pacific Decadal Oscillation ( PDO) and the East Asian summer monsoon. Trends are not consistent across the region in spring and winter, however, with positive trends over some areas in northeastern and northwestern China, Mongolia and tropical oceans whereas negative trends in other regions. Nearly all areas of China experience mean annual 80-m wind speed less than 6.9 m s−1 (wind power classes of 1-2) except for some areas of Inner Mongolia where mean annual 80-m wind speeds exceed 6.9 m s−1 (Classes 3 or higher, suitable for wind energy development). China Seas and ocean areas generally fall in Class 3 or above, with the Taiwan and Luzon Straights reaching the highest Class 7. [ABSTRACT FROM AUTHOR]

China has adopted an ambitious plan for wind energy deployment. This paper uses the theory of the learning curve to investigate financing options to support grid parity for wind electricity. First, relying on a panel dataset consisting of information from 1207 wind projects in China’s thirty provinces over the period of 2004–2011, this study empirically estimates the learning rate of onshore wind technology to be around 4.4%. Given this low learning rate, achieving grid parity requires a policy of pricing carbon at 13 €/ton CO2e in order to increase the cost of coal-generated electricity. Alternatively, a learning rate of 8.9% would be necessary in the absence of a carbon price. Second, this study assesses the evolution of additional capital subsidies in a dynamic framework of technological learning. The implicit average CO2 abatement cost derived from this learning investment is estimated to be around 16 €/ton CO2e over the breakeven time period. The findings suggest that climate finance could be structured in a way to provide up-front financing to support this paradigm shift in energy transition. [ABSTRACT FROM AUTHOR]

Through a study of China's wind turbine sector, this paper demonstrates how China liberally implements industrial policies and then removes them when the WTO disputes them. China's convenient compliance with the WTO rulings reflects Beijing's realpolitik navigation through the organization's dispute-resolution process, rather than socialization to international norms. [ABSTRACT FROM AUTHOR]

Governments across the globe are implementing targeted industrial policies to reap the socio-economic benefits of renewable power deployment for their local industries. Here, we analyse local content requirements, a specific type of industrial policy that incentivizes local manufacturing through requiring minimum shares of locally produced inputs in renewable energy projects. Local content requirements are one of the most prominent industrial policy tools, with more than 140 applications across different sectors since 2008, yet their impact on industrial development is not well established. We create a panel dataset from 1995 to 2017 of 124 countries, among which 17 introduced local content requirements in wind and solar PV, to analyze the policies' impact on exports. We employ synthetic control methods to create country-specific counterfactuals. For most countries, local content requirements have not led to a significant increase in exports of solar and wind energy components. The exceptions are China and Spain, which built significant export capacities in wind energy. A central reason for the limited impact of local content requirements is likely that countries target wind and solar components that are too far away from their existing industrial structures. We show that countries that have succeeded in breaking into solar or wind exports have already exhibited export capabilities in related industries before the introduction of local content requirements, such as electronics for solar PV. Policymakers should carefully consider the potential of existing industrial structures before introducing local content requirements. • Countries that introduced local content requirements have not developed competitive export sectors in solar and wind energy components, except for China and Spain. • Solar and wind energy technology exhibit high average complexity levels and the distance to current complexity levels of developing countries may explain their difficulties to develop competitive manufacturing in these sectors. • Large heterogeneities in product complexity at the component level offer diversification opportunities for economies with less technological capabilities. • Previous capacities in related industries, such as electronics in the case of solar PV or steel and machinery in wind energy are important for successful diversification. [ABSTRACT FROM AUTHOR]

The solar-wind hybrid renewable energy systems, including wind farm, photovoltaic (PV) plant, concentrated solar power (CSP) plant, electric heater, battery, and bidirectional inverter, are analyzed in 36 typical locations in China. The effects of wind and solar energy resources on power supply reliability and economy and the optimal installed capacities are analyzed. The regions in China recommended for the development of the systems are given. Results show that the levelized cost of energy (LCOE) of system in Huade can be as low as 0.1 $/kWh when 90% of annual load demand is met. The wind speed and solar irradiation have a major effect while the complementary characteristics of wind and solar energy have an auxiliary effect on power supply reliability and cost of the system. Compared with the system in Tongliao, the LCOE of system in Qiqihar with lower wind speed and solar irradiation intensity is reduced by 9.8% due to the better complementary characteristics of wind and solar energy. For systems in locations with different wind and solar energy resources, the wind farm or PV plant is still the technology with the greatest cost advantage but the worst power supply reliability. The electric heater with thermal energy storage and power cycle is an essential factor to greatly improve power supply reliability economically. Meanwhile, one of the solar field and battery should be integrated into the system according to the direct normal irradiance conditions. When high power supply reliability is pursued, the CSP plant is recommended to be introduced in most regions. Moreover, the commercial promotion of CSP plant brings the most significant economic benefits to the system. As the CSP plant cost is reduced by 30%, the LCOE of system is reduced by 20.4%. • Capacities of complex solar-wind systems are optimized in various locations of China. • Wind and solar energy intensity and complementarity affect system performance. • Electric heater with TES and power cycle can greatly reduce LPSP economically. • CSP plant is recommended to be introduced in most regions when low LPSP is pursued. [ABSTRACT FROM AUTHOR]

For several decades the leading wind energy nations were European, while China and India were mainly the recipients of technology transfer. This paper aims to explore the role technology transfer/cooperation from Europe played in shaping firm-level wind energy technologies in China and India and to discuss whether the recent technology cooperation between the Chinese, Indian, and European wind firms challenges the classical North–South technology cooperation paradigm. The research finds that firm-level technology transfer/cooperation shaped the leading wind energy technologies in China and to a lesser extent in India. The paper also finds that several trends towards South–North technology cooperation have been observed between China, India and Europe, such as South–North flows of capital, drivers for market access, and R&D leadership, while the origins of innovation (e.g. patents) seem to remain predominantly in the global North. The paper concludes that the technology cooperation between China, India, and Europe has become more multi-faceted and increasingly Southern-led. [ABSTRACT FROM AUTHOR]

China is the global leader in terms of installed wind capacity. Further, wind energy development is expected for the next years and decades to meet the continuously increasing electricity demand and the need of using clean domestic energy. Since 2009 China is divided into four geographical regions, each assigned with a different benchmark on-grid tariff. Moreover the existing infrastructures are not equally developed throughout the country, making investment decisions more complicated and risky. The scope of this paper is to apply an innovative methodology and evaluate the attractiveness of each region for wind energy development, by taking into consideration all relevant investment risks, such as wind potential, wind curtailment, access to the grid and macroeconomic parameters. To this purpose a Monte Carlo simulation approach, integrated into a typical financial model, is implemented in each of the four regions, performing many hundreds of iterations, each characterized by a randomly selected set of the examined uncertain parameters. This approach intends to provide information to private investors doing a first exploratory research in the huge country׳s area in order to decide whether and where to invest, as well as to policy makers to help them assess critical policy parameters and investigate different scenarios of wind energy development. The evaluation of the current framework for wind energy development in China verifies that the existing system of feed-in tariffs in China is very effective for the balanced deployment of wind energy in the whole country. However, it is shown that the risk of curtailment and grid accessibility may significantly reduce the potential profitability of wind energy investments in all four regions. Priority for development of infrastructures should be given in isolated northern windy areas with high-accumulation of wind farms. [ABSTRACT FROM AUTHOR]

This paper studies the 'catching-up' process of China's emerging wind turbine manufacturing ecosystem, and analyses the dynamics of the shifting collaborative innovation network for the period 1980-2011. We have focused on the leading wind turbine manufacturers [i.e., manufacturers of entire wind turbine system integrators (EWSIs)], as well as manufacturers of the turbines' sub-ecosystems (i.e., four key component provider manufacturers: blade, gearbox, generator and control system). A network analysis method was adopted to depict the evolutionary process of the wind turbine manufacturing ecosystem. In summary, this research explores: 1) the different technology accumulation/localisation patterns in four different phases of the manufacturing ecosystem, and the collaborative innovation network; 2) the different roles and their interactions in the wind turbine manufacturing ecosystem in China; 3) the driving forces of the evolution of the wind turbine manufacturing ecosystem. The findings aim to demonstrate the evolutionary trajectory of China in terms of the 'catching-up' of this industry, and contribute to theories of the business ecosystem lifecycle and complementary assets. [ABSTRACT FROM AUTHOR]

Abstract: Wind energy has been identified in China as an important alternative energy source to balance its energy mix. By the end of 2012, wind power (2%) has surpassed nuclear power to become China's 3rd largest energy resource of electricity, only behind thermal power and hydropower. The rapid growth of China wind power industry in recent years has made China become the biggest market in the world. Meanwhile, it has created a big market for educational institutions to provide wind energy related education and training. The main objective of this study is to review current wind energy education and training in Chinese universities and training centers. Most of wind energy courses are provided by public universities because they have been accredited by Ministry of Education of China to offer students different options, including full-time and part-time degrees in Bachelor, Master or Ph.D. On-the-job training also has tremendous demand from the professionals who prefer short-term courses or on-site courses. Generally, the development of wind energy education and training lags behind the growth of wind power industry. Our study highlights the major opportunities and future challenges in China wind energy education and training. [Copyright &y& Elsevier]