The price of emissions allowances (EUA) traded on the European Union's Emissions Trading Scheme (ETS) exceed 100 euros per metric ton of CO₂ for the first time in February 2023. Although average annual EUA prices have increased significantly since the 2018 reform of the EU-ETS, they fell ** percent year-on-year in 2024 to ** euros. What is the EU-ETS? The EU-ETS became the world’s first carbon market in 2005. The scheme was introduced as a way of limiting GHG emissions from polluting installations by putting a price on carbon, thus incentivizing entities to reduce their emissions. A fixed number of emissions allowances are put on the market each year, which can be traded between companies. The number of available allowances is reduced each year. The EU-ETS is now in its fourth phase (2021 to 2030). Carbon price comparisons The EU ETS has one of the highest average annual carbon prices worldwide, averaging ** U.S. dollars as of April 2025. In comparison, prices for UK ETS carbon credits averaged 57 U.S. dollars during same period, while those under the Regional Greenhouse Gas Initiative (RGGI) in the United States averaged just ** U.S. dollars.

Prices for EU Carbon Permits including live quotes, historical charts and news. EU Carbon Permits was last updated by Trading Economics this September 6 of 2025.

The average closing spot price of European Emission Allowances (EUAs) has increased notably since reforms were made to the EU ETS in 2018. In 2022, the average closing spot price of CO₂ EUAs increased by roughly ** percent to **** euros per metric ton of CO₂.

The average annual price of European Union Emissions Trading System (EU ETS) allowances fell ** percent year-on-year in 2024, to ** euros. Still, EU ETS carbon allowances are forecast to rise to almost *** euros by the end of the decade. Each EU ETS emissions allowance (EUA) gives the holder the right to emit one metric ton of carbon dioxide equivalent.

An accurate prediction of carbon pricing is essential in carbon emission management, and also provides an important role for governments to formulate corresponding policies. However, due to the inherent complexity and dynamics of carbon price sequence, the effectiveness of different decomposition algorithms for carbon price remains to be tested. In addition, existing studies lack a systematic framework to explore the organic integration of external factors and secondary decomposition technology, and the feature processing of complex external factors still needs to be improved. In order to overcome the shortcomings of existing research, This paper presents a Variational Modal Decomposition(VMD) algorithm and a Complete Ensemble Empirical Mode Decomposition with Adaptive Second decomposition technology of Noise(CEEMDAN) decomposition algorithm, and extract the features of external factors by Extreme Gradient Boosting (XGBoost) algorithm. The HI-VMD-PE-CEEMDAN-XGBoost-Transformer model for predicting carbon price is constructed by the combined Transformer algorithm. Specifically, first, we use Hampel identifer(HI) to detect and rectify the anomalies in the original sequence. After applying Variational Mode Decomposition(VMD) decomposition algorithm, Permutation Entropy(PE) is utilized to reassemble the decomposed component. Quadratic Decomposition is performed by Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN) algorithm. Then, the XGBoost algorithm is employed to extract features of external factors and screen key factors as predictive input variables. Finally, Transformer, which has stronger capability of large-scale data parallel processing, is selected as the prediction model to achieve a more scientific and effective carbon price prediction. The empirical analysis results based on EU carbon market data verify the validity and superiority of the proposed model in different forecasting scenarios.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Is LON:EUA Stock Expected to Go Up? (Stock Forecast)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Eurasia Mining (EUA): A Mining Empire in the Making?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Graph and download economic data for Import Price Index by Origin (NAICS): All Industries for European Union (EECTOT) from Dec 1990 to Jul 2025 about imports, commodities, price index, indexes, price, and USA.

Corn fell to 397.01 USd/BU on September 5, 2025, down 0.69% from the previous day. Over the past month, Corn's price has risen 4.55%, but it is still 2.27% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Corn - values, historical data, forecasts and news - updated on September of 2025.

Supplementary raw data on EUA prices, electricity prices and electricity consumption for EU countries.

This dataset contains USA Daily RBOB Regular Gasoline Spot Price form 2003. Data from US Energy Information Administration. Follow datasource.kapsarc.org for timely data to advance energy economics research.Notes:RBOB: "Reformulated Gasoline Blendstock for Oxygenate Blending" is motor gasoline blending components intended for blending with oxygenates to produce finished reformulated gasoline.Regular Gasoline: Gasoline having an antiknock index (average of the research octane rating and the motor octane number) greater than or equal to 85 and less than 88. Note: Octane requirements may vary by altitude. Los Angeles Reformulated RBOB Regular Gasoline Spot Price (Dollars per Gallon)

Banana price (USA) in , July, 2025 For that commodity indicator, we provide data from January 1960 to July 2025. The average value during that period was 0.55 USD per kilogram with a minimum of 0.11 USD per kilogram in January 1968 and a maximum of 1.68 USD per kilogram in December 2022. | TheGlobalEconomy.com

In August 2022, the activated carbon price per ton stood at $4.8K, which is down by -3.8% against the previous month.

Dataset

This dataset was created by Omkar Suryawanshi

Released under CC0: Public Domain

Contents

Explore the Redfin USA Properties Dataset, available in CSV format. This extensive dataset provides valuable insights into the U.S. real estate market, including detailed property listings, prices, property types, and more across various states and cities. Perfect for those looking to conduct in-depth market analysis, real estate investment research, or financial forecasting.

Key Features:

• Comprehensive Property Data: Includes essential details such as listing prices, property types, square footage, and the number of bedrooms and bathrooms.
• Geographic Coverage: Encompasses a wide range of U.S. states and cities, providing a broad view of the national real estate market.
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Download the Redfin USA Properties Dataset to access essential information on the U.S. housing market, ideal for professionals in real estate, finance, and data analytics. Unlock key insights to make informed decisions in a dynamic market environment.

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Cosan USA stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

Graph and download economic data for Index of Spot Market Prices of 13 Raw Industrial Commodities for United States (M0401BUSM350NNBR) from Jul 1946 to Jun 1968 about commodities, industry, price index, indexes, price, and USA.

Sugar prices, USA in , June, 2025 For that commodity indicator, we provide data from January 1960 to June 2025. The average value during that period was 0.45 USD per kilogram with a minimum of 0.12 USD per kilogram in January 1960 and a maximum of 1.26 USD per kilogram in November 1974. | TheGlobalEconomy.com

As of June 20, 2024, copper futures contracts to be settled in July 2029 were trading on U.S. markets at around *** U.S. dollars per pound. This is higher than the price of **** U.S. dollars per pound for contracts to be settled in January 2024, indicating that copper traders expect the price of copper to fluctuate. Copper futures are contracts that effectively lock in a price for an amount of copper to be purchased at a time in the future, which can then be traded on markets. Futures markets therefore provide an indicator of how investors think a commodities market will develop in the future.