China Wind Mover Equipment: Financial Expense: Year to Date data was reported at 0.342 RMB bn in Oct 2015. This records an increase from the previous number of 0.318 RMB bn for Sep 2015. China Wind Mover Equipment: Financial Expense: Year to Date data is updated monthly, averaging 0.110 RMB bn from Jan 2012 (Median) to Oct 2015, with 46 observations. The data reached an all-time high of 0.362 RMB bn in Dec 2014 and a record low of 0.021 RMB bn in Feb 2013. China Wind Mover Equipment: Financial Expense: Year to Date data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BHW: Boiler and Prime Mover Equipment: Wind Mover Equipment.

Abstract The world is experiencing a deep climate change caused by the predatory activity of man, due to the long time economic activities practiced without concerns on the environmental impacts. However, nowadays there is the necessity to expand the current economy without compromising the necessary resources for the survival of the future generations. The intensive use of alternative energy configures a contribution to this new development path. In another perspective, the main technical analysis of economic and financial feasibility is widely known; however, the application of techniques that consider the risk is not so usual. Thus, this current work addresses whether it is possible to identify the economic feasibility of a potential wind power generation plant in different Brazilian locations considering the risk of applying Monte Carlo simulation and Beta distribution techniques. In order to answer this question tests were performed in four different locations in Brazil, utilizing the Internal Rate of Return (IRR) method with the mentioned techniques to consider the risk in investment analysis. The results showed the sensitivity of the wind project to the financing costs, regardless the region studied.

China Wind Mover Equipment: YoY: Financial Expense: Interest Expense: Year to Date data was reported at -29.381 % in Oct 2015. This records an increase from the previous number of -34.881 % for Sep 2015. China Wind Mover Equipment: YoY: Financial Expense: Interest Expense: Year to Date data is updated monthly, averaging 9.334 % from Jan 2012 (Median) to Oct 2015, with 46 observations. The data reached an all-time high of 73.875 % in Apr 2012 and a record low of -58.285 % in Feb 2015. China Wind Mover Equipment: YoY: Financial Expense: Interest Expense: Year to Date data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BHW: Boiler and Prime Mover Equipment: Wind Mover Equipment.

China’s export benefits from the significant fiscal stimulus in the United States. This paper analyzes the global spillover effect of the American economy on China’s macro-economy using the Markov Chain Monte Carlo (MCMC)-Gibbs sampling approach, with the goal of improving the ability of China’s financial system to protect against foreign threats. This paper examines the theories of the consequences of uncertainty on macroeconomics first. Then, using medium-sized economic and financial data, the uncertainty index of the American and Chinese economies is built. In order to complete the test and analysis of the dynamic relationship between American economic uncertainty and China’s macro-economy, a Time Varying Parameter-Stochastic Volatility-Vector Autoregression (TVP- VAR) model with random volatility is constructed. The model is estimated using the Gibbs sampling method based on MCMC. For the empirical analysis, samples of China’s and the United States’ economic data from January 2001 to January 2022 were taken from the WIND database and the FRED database, respectively. The data reveal that there are typically fewer than 5 erroneous components in the most estimated parameters of the MCMC model, which suggests that the model’s sampling results are good. China’s pricing level reacted to the consequences of the unpredictability of the American economy by steadily declining, reaching its lowest point during the financial crisis in 2009, and then gradually diminishing. After 2012, the greatest probability density range of 68% is extremely wide and contains 0, indicating that the impact of economic uncertainty in the United States on China’s pricing level is no longer significant. China should therefore focus on creating a community of destiny by working with nations that have economic cooperation to lower systemic financial risks and guarantee the stability of the capital market.

The dataset covers 2006 counties in China and ranges from 2009 to 2015.It includes land finance data at county level, efficiency of public service supply, economic bias of public service supply, household registration difference of public service supply, financial decentralization data, agricultural development level data, financial development level data, foreign direct investment data and industrial structure data.

The data of land finance is from the Ministry of Land and Resources. It is also supplemented by the websites of the prefecture-level municipal land and resources bureaus, the provincial statistical yearbooks and Wind database. The data of public service variables and control variables mainly come from the Economic Statistical Yearbook of China County and City (2010-2016), Statistical Yearbook of China’s Counties (2010-2016), County-level statistical bulletin (2010-2016), Statistical Yearbook of China's Provinces (2010-2016), Statistical Yearbook of Prefecture-level Cities (2010-2016), Wind database, etc.

This data set is mainly suitable for studying the development of land finance in the China's counties, the changing trend of peri-urbanization, and the research on the public services supply in China's counties.

A-share listed companies of the Shanghai and Shenzhen Stock Exchanges from 2008 to 2023 were selected to comprise the research sample. Considering the particularity of the industry characteristics, the financial industry, special treatment companies, and missing data of variables were eliminated. Moreover, the final research sample contained 14,048 firm-year observations. All continuous variables were winsorized at the 1 and 99% levels. Financial and tax risk data were obtained from the China Stock Market and Accounting Research and Wind databases. The data of enterprise digital transformation were obtained using Python technology through text analysis.

This data was prepared as input for the Selkie GIS-TE tool. This GIS tool aids site selection, logistics optimization and financial analysis of wave or tidal farms in the Irish and Welsh maritime areas. Read more here: https://www.selkie-project.eu/selkie-tools-gis-technoeconomic-model/

This research was funded by the Science Foundation Ireland (SFI) through MaREI, the SFI Research Centre for Energy, Climate and the Marine and by the Sustainable Energy Authority of Ireland (SEAI). Support was also received from the European Union's European Regional Development Fund through the Ireland Wales Cooperation Programme as part of the Selkie project.

File Formats

Results are presented in three file formats:

tif Can be imported into a GIS software (such as ARC GIS) csv Human-readable text format, which can also be opened in Excel png Image files that can be viewed in standard desktop software and give a spatial view of results

Input Data

All calculations use open-source data from the Copernicus store and the open-source software Python. The Python xarray library is used to read the data.

Hourly Data from 2000 to 2019

• Wind - Copernicus ERA5 dataset 17 by 27.5 km grid
  10m wind speed

• Wave - Copernicus Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis dataset 3 by 5 km grid

Accessibility

The maximum limits for Hs and wind speed are applied when mapping the accessibility of a site.
The Accessibility layer shows the percentage of time the Hs (Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis) and wind speed (ERA5) are below these limits for the month.

Input data is 20 years of hourly wave and wind data from 2000 to 2019, partitioned by month. At each timestep, the accessibility of the site was determined by checking if
the Hs and wind speed were below their respective limits. The percentage accessibility is the number of hours within limits divided by the total number of hours for the month.

Environmental data is from the Copernicus data store (https://cds.climate.copernicus.eu/). Wave hourly data is from the 'Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis' dataset.
Wind hourly data is from the ERA 5 dataset.

Availability

A device's availability to produce electricity depends on the device's reliability and the time to repair any failures. The repair time depends on weather
windows and other logistical factors (for example, the availability of repair vessels and personnel.). A 2013 study by O'Connor et al. determined the
relationship between the accessibility and availability of a wave energy device. The resulting graph (see Fig. 1 of their paper) shows the correlation between accessibility at Hs of 2m and wind speed of 15.0m/s and availability. This graph is used to calculate the availability layer from the accessibility layer.

The input value, accessibility, measures how accessible a site is for installation or operation and maintenance activities. It is the percentage time the
environmental conditions, i.e. the Hs (Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis) and wind speed (ERA5), are below operational limits.
Input data is 20 years of hourly wave and wind data from 2000 to 2019, partitioned by month. At each timestep, the accessibility of the site was determined
by checking if the Hs and wind speed were below their respective limits. The percentage accessibility is the number of hours within limits divided by the total
number of hours for the month. Once the accessibility was known, the percentage availability was calculated using the O'Connor et al. graph of the relationship between the two. A mature technology reliability was assumed.

Weather Window

The weather window availability is the percentage of possible x-duration windows where weather conditions (Hs, wind speed) are below maximum limits for the
given duration for the month.

The resolution of the wave dataset (0.05° × 0.05°) is higher than that of the wind dataset
(0.25° x 0.25°), so the nearest wind value is used for each wave data point. The weather window layer is at the resolution of the wave layer.

The first step in calculating the weather window for a particular set of inputs (Hs, wind speed and duration) is to calculate the accessibility at each timestep.
The accessibility is based on a simple boolean evaluation: are the wave and wind conditions within the required limits at the given timestep?

Once the time series of accessibility is calculated, the next step is to look for periods of sustained favourable environmental conditions, i.e. the weather
windows. Here all possible operating periods with a duration matching the required weather-window value are assessed to see if the weather conditions remain
suitable for the entire period. The percentage availability of the weather window is calculated based on the percentage of x-duration windows with suitable
weather conditions for their entire duration.The weather window availability can be considered as the probability of having the required weather window available
at any given point in the month.

Extreme Wind and Wave

The Extreme wave layers show the highest significant wave height expected to occur during the given return period. The Extreme wind layers show the highest wind speed expected to occur during the given return period.

To predict extreme values, we use Extreme Value Analysis (EVA). EVA focuses on the extreme part of the data and seeks to determine a model to fit this reduced
portion accurately. EVA consists of three main stages. The first stage is the selection of extreme values from a time series. The next step is to fit a model
that best approximates the selected extremes by determining the shape parameters for a suitable probability distribution. The model then predicts extreme values
for the selected return period. All calculations use the python pyextremes library. Two methods are used - Block Maxima and Peaks over threshold.

The Block Maxima methods selects the annual maxima and fits a GEVD probability distribution.

The peaks_over_threshold method has two variable calculation parameters. The first is the percentile above which values must be to be selected as extreme (0.9 or 0.998). The second input is the time difference between extreme values for them to be considered independent (3 days). A Generalised Pareto Distribution is fitted to the selected
extremes and used to calculate the extreme value for the selected return period.

This paper using panel data of 2008-2019 Shanghai and Shenzhen A-share listed companies as the research sample and employing the multiple regression method to tests the relationship between executive compensation incentives and R&D investment of listed companies in China, further investigates the path of the relationship between the two and the influence of government subsidy to the relationship. In this paper, the selected samples are excluded according to the following criteria: ①Companies with incomplete data on financial indicators and corporate governance indicators are excluded. ②Eliminate companies with negative asset-liability ratio or greater than 1. ③Exclude companies in the financial and insurance industry. ④Exclude listed companies less than 1 year. ⑤Exclude companies containing S, ST and *ST. ⑥Exclude the companies with extreme sample data. The risk-taking data involved in this paper came from the WIND database. Other data come from the CSMAR database.

This dataset highlights key financing terms for U.S. renewable energy projects that closed financing in Q3 2010. Information tracked includes debt interest rates, equity returns, financial structure applied, PPA duration, and other information.

NREL's Renewable Energy Finance Tracking Initiative (REFTI) tracks renewable energy project financing terms by technology and project size. The intelligence gathered is intended to reveal industry trends and to inform input assumptions for models.

Product Market size is rising upward in the past few years And it is estimated that the market will grow significantly in the forecasted period

There is a dataset including climate physical risk (CPR), climate transition risk (CTR), climate policy uncertainty (CPU), and systemic financial risks (SFRs) indices,where CPR10, CTR10 and CPU10 represent these indicators are constructed by ten newspapers, and CPR3, CTR3 and CPU3 represent these indicators are constructed by three newspapers. Systemic financial risks (SFRs) indicators can be constructed using the approach in our article. These indicators in this dataset are calculated by the author and are not raw data. Since copyright issues with raw data, we do not provide it here. If necessary, you can use the Wind and Wisers Information Portal database to download.

Wind Turbine Condition Monitoring System Market size was valued at USD 384.12 Million in 2024 and is projected to reach USD 597.23 Million by 2031, growing at a CAGR of 8.14% from 2024 to 2031.

Wind Turbine Condition Monitoring System Market Drivers

Increasing Deployment of Wind Energy: As the world seeks to transition towards cleaner and more sustainable energy sources, the deployment of wind energy continues to rise. Governments, corporations, and utilities are investing in wind power projects to reduce carbon emissions and diversify their energy portfolios. The growing number of wind turbines in operation creates a significant demand for condition monitoring systems to ensure the reliability and optimal performance of these assets.

Need for Predictive Maintenance: Wind turbines operate in harsh environmental conditions and are subject to wear and tear over time. Unplanned downtime due to equipment failure can result in substantial financial losses for wind farm operators. Condition monitoring systems enable predictive maintenance by continuously monitoring the health of critical components such as bearings, gearboxes, and blades. Proactive maintenance based on real-time data helps prevent costly breakdowns and extends the lifespan of wind turbines.

Technological Advancements in Sensors and Data Analytics: The advancement of sensor technology, IoT (Internet of Things) connectivity, and data analytics has revolutionized condition monitoring in the wind energy sector. Sophisticated sensors installed on wind turbines collect a wealth of operational data, including vibration levels, temperature, oil condition, and structural integrity. Machine learning algorithms and predictive analytics processes this data to detect early signs of equipment degradation or potential failures, enabling operators to take preemptive action.

The goal of the South West Indian Ocean Risk Assessment and Financing Initiative (SWIO RAFI) is to improve the resiliency and capacity of the island states through the creation of disaster risk financing strategies. A key component of this effort involves the quantification of site specific risk from the perils of flood, earthquakes, and tropical cyclones as well as their secondary hazards of storm surge and tsunamis.

Regional hazard intensity calculations were applied to 10,000 years of Stochastic catalogs derived from the historical records to produce hazard intensity profiles at mean return periods of 25, 50, 100, 250, 500 and 1,000 years. All datasets are at their original resolution (0.00083) except for Madagascar (0.0032) which was resampled to reduce file sizes.

This data set was produced with financial support from the European Union in the framework of the ACP-EU Natural Disaster Risk Reduction Program, managed by the Global Facility for Disaster Reduction and Recovery (GFDRR).

The goal of the South West Indian Ocean Risk Assessment and Financing Initiative (SWIO RAFI) is to improve the resiliency and capacity of the island states through the creation of disaster risk financing strategies. A key component of this effort involves the quantification of site specific risk from the perils of flood, earthquakes, and tropical cyclones as well as their secondary hazards of storm surge and tsunamis.

Regional hazard intensity calculations were applied to 10,000 years of Stochastic catalogs derived from the historical records to produce hazard intensity profiles at mean return periods of 25, 50, 100, 250, 500 and 1,000 years. All datasets are at their original resolution (0.00083) except for Madagascar (0.0032) which was resampled to reduce file sizes.

This data set was produced with financial support from the European Union in the framework of the ACP-EU Natural Disaster Risk Reduction Program, managed by the Global Facility for Disaster Reduction and Recovery (GFDRR).

The goal of the South West Indian Ocean Risk Assessment and Financing Initiative (SWIO RAFI) is to improve the resiliency and capacity of the island states through the creation of disaster risk financing strategies. A key component of this effort involves the quantification of site specific risk from the perils of flood, earthquakes, and tropical cyclones as well as their secondary hazards of storm surge and tsunamis.

Regional hazard intensity calculations were applied to 10,000 years of Stochastic catalogs derived from the historical records to produce hazard intensity profiles at mean return periods of 25, 50, 100, 250, 500 and 1,000 years. All datasets are at their original resolution (0.00083) except for Madagascar (0.0032) which was resampled to reduce file sizes.

This data set was produced with financial support from the European Union in the framework of the ACP-EU Natural Disaster Risk Reduction Program, managed by the Global Facility for Disaster Reduction and Recovery (GFDRR).

[ Derived from parent entry - See data hierarchy tab ]

Hindcast atmospheric simulation for the North Sea using COSMO6.0-CLMWF version driven with ERA5 reanalysis data and the wind farm parametrization from Fitch et al., 2012 (referenced by Elizalde, 2023) with wind turbines types (3.6, 5, 8, 10 and 15 MW rated capacity). Wind farm areas are activated in chronological order based on the year in which they became operational. The covered period is from 2008 to 2022 with hourly frequency output. The model uses a rotated grid with 356 x 396 grid points and a grid spacing of 0.02 degrees, the rotated North pole is located at 180 W, 30 N. We gratefully acknowledge financial support through the H2Mare PtX-Wind project with funds provided by the Federal Ministry of Education and Research (BMBF) under Grant No. 03HY302J.

The goal of the South West Indian Ocean Risk Assessment and Financing Initiative (SWIO RAFI) is to improve the resiliency and capacity of the island states through the creation of disaster risk financing strategies. A key component of this effort involves the quantification of site specific risk from the perils of flood, earthquakes, and tropical cyclones as well as their secondary hazards of storm surge and tsunamis.

Regional hazard intensity calculations were applied to 10,000 years of Stochastic catalogs derived from the historical records to produce hazard intensity profiles at mean return periods of 25, 50, 100, 250, 500 and 1,000 years. All datasets are at their original resolution (0.00083) except for Madagascar (0.0032) which was resampled to reduce file sizes.

This data set was produced with financial support from the European Union in the framework of the ACP-EU Natural Disaster Risk Reduction Program, managed by the Global Facility for Disaster Reduction and Recovery (GFDRR).

TU Global Wind Atlas: onshore and 30 km offshore wind climate dataset accounting for high resolution terrain effects.The Global Wind Atlas provides a high resolution wind climatology at 50, 100, 200m hub heights above the surface for the whole world (onshore and 30 km offshore). These layers have been produced using microscale modelling in the Wind Atlas Analysis and Application Program (WAsP) and capture small scale spatial variability of winds speeds due to high resolution orography (terrain elevation), surface roughness and surface roughness change effects. The layers shared through the IRENA Global Atlas are served at 1km spatial resolution. The full Atlas contains data at a higher spatial resolution of 250 m, some of the IRENA Global Atlas tools access this data for aggregated statistics.Original website:http://globalwindatlas.com/Data quality and validation:The layers have been produced by the Technical University of Denmark (DTU), Department of Wind Energy (DTU Wind Energy), using state-of-the art scientifically verified models and methods (Report accessible: http://globalwindatlas.com/). This data is classified as POLICY+BUSINESS, according to IRENA’s classification framework for solar and wind resource maps (http://www.irena.org/DocumentDownloads/Publications/Global%20Atlas_Data%20_Quality.pdf) - POLICY: The information provided is meant to inform high-level policy debate (identification of opportunity areas for further prospection, preliminary assessment of technical potentials), or to perform market screening (cross referencing the resource information with policy information). It is suitable for decision-making activities, excluding financial commitments. - +BUSINESS: the information provided is a sub-sample of a dataset of better spatial and/or temporal resolution than that available from the Global Atlas, and that of sufficient magnitude to initiate business-related activities, (e.g.,. kilometre (km) or less than a-kilometre, hourly data). Detailed information can be supplied by the owner of the data. - Detailed data quality information: http://globalatlas.irena.org/dqif/DQIF.aspx?datasetid=5039Terms of use:By using this dataset, the user accepts the following Terms and Conditions:- USE OF THE DATASET: Terms of use of the Global Wind Atlas: http://globalwindatlas.com/- USE OF THE IRENA GLOBAL ATLAS: Terms of use of the Global Atlas for Renewable Energy shown here: http://irena.masdar.ac.ae/clients/irena/legal.html

Explore the progression of average salaries for graduates in Department Of Wind And String Instruments from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Department Of Wind And String Instruments relative to other fields. This data is essential for students assessing the return on investment of their education in Department Of Wind And String Instruments, providing a clear picture of financial prospects post-graduation.