European Union Allowance Prices Research Articles

Carbon emission price point-interval forecasting based on multivariate variational mode decomposition and attention-LSTM model

Accurate forecasting of carbon emission prices is a crucial yet challenging task, given the complexity of the trading market and the variability of influencing factors. To comprehensively address factors related to carbon prices while considering interval forecasts associated with uncertainty, this study introduces a novel multistep ahead point-interval forecasting framework for carbon emission prices. This framework integrates multifactor selection, multivariate decomposition and reconstruction, an intelligent point and interval forecasting network, and an evaluation system. First, influencing factors are selected using a recursive feature elimination algorithm based on the extreme gradient boosting estimator. Second, original carbon prices and related influencing factors are decomposed into a set of multimode components (MMCs) using multivariate variational mode decomposition and sample entropy reconstruction techniques. Third, each row of MMCs is predicted using a long short-term memory model with an attention mechanism, and final point predictions are produced through simple addition. Finally, interval predictions are derived using enhanced kernel density estimation (KDE), which can expand the upper and lower bounds of the prediction interval. The stability and robustness have been tested in European Union Allowance (EUA) price forecasting. The results show that the proposed model is superior to other benchmark models, with MAPEs of 0.83%, 3.54%, and 3.82% in 1-step, 15-step, and 30-step ahead forecasting, respectively. Additionally, the proposed enhanced KDE demonstrates excellent performance; in 1-step ahead forecasting, the F-value is 1.86, 1.82, and 1.75 at the 80%, 90%, and 95% confidence levels, respectively. This proves that the proposed framework can effectively improve the point forecasting performance of EUA prices and quantify the prediction uncertainty.

The relationship between the contaminating industries and the European carbon price, machine learning approach

This study explores the bidirectional relationship between the industrial stock returns of greenhouse gas (GHG) emitting industries in the European Union (EU) and the European Union Allowance (EUA) price for the period of 2015–2022 by applying novel supervised machine learning (ML) models combined with a granger vector auto regression (VAR) test. Research findings present bidirectional causality and factor significance scores for each industry index in addition to performance metrics of the applied ML models. Results identify the chemicals, renewable energy, meat, electricity, iron&steel, transportation, construction, manufacturing, and cement industries as the most significant factors in explaining the EUA price volatility, while the Extra Trees (ET), K-Nearest Neighbor (KNN), Recurrent Neural Networks (RNN), and Random Forest (RF) models are determined to be the top performers. From the political perspective, research outputs conclude that the current carbon price is below the marginal decarbonization cost and fails to stimulate the technological decarbonization investments for the majority of the contaminating industries of the EU.

Identifying the determinants of European carbon allowances prices: A novel robust partial least squares method for open-high-low-close data

This study investigates whether energy prices and macroeconomic indicators are determinants of the European Union Allowance (EUA) prices and how their relationships changed in the Phase III of the European Union Emission Trading Schema (EU ETS). An innovative application of the partial least squares (PLS) regression model for open-high-low-close (OHLC) data is proposed and implemented. The OHLC prices of EUA, pivotal energy prices, and macroeconomic indicators spanning the period from 2013 to 2020 are considered in the models. Based on the average results from 2015 to 2020, the energy prices of Brent crude oil, gasoline, and diesel provide stronger explanatory power for EUA prices than macroeconomic indicators, including the European Industrial Development Index, STOXX 600, STOXX 50, CAC 40, FTSE 100, and DAX 30. However, the price of coal does not appear to exert a significant influence on the pricing of EUA throughout the investigated period. The correlation between EUA prices and macroeconomic indicators appears to strengthen over time, while the interpretation degree of energy prices on EUA prices decreases. EUA increasingly manifests its financial attributes in later Phase III of the EU ETS. Based on the regression results, investment suggestions are provided for quota traders to hedge portfolios.

Impact of Changes in Membership on Prices of a Unified Carbon Market: Case Study of the European Union Emissions Trading System

Using the event study methodology, the paper studies the effects of 22 key events in countries’ process of entering and exiting the European Union on returns of European Union Allowance (EUA) future prices in the EU Emissions Trading System (EU ETS). The events include 17 entry events concerning the signing of relevant agreements, becoming a candidate or potential candidate country, the process of a negotiation and formally entering the EU, and five exit events including the process of Brexit and the suspension of Iceland. The results show that two entry events involving Albania and Ukraine, respectively, have a significant positive impact, and five entry events have a significant negative impact. Among the exit events, the announcement of the Brexit referendum results causes significant negative market reaction. Most events regarding small carbon emitters entering the EU lead to negative cumulative abnormal returns (CAR) of EUA prices, and a significant negative correlation between the countries’ annual average carbon emissions and CAR is found, while the change of national allocation plans does not affect the market reaction notably. In the process of establishing a unified carbon market, regulators should carry out appropriate policy arrangements of emission allowances allocation when new members join, in order to guide market expectations and enhance market stability.

Climate uncertainty and carbon emissions prices: The relative roles of transition and physical climate risks

This study examines the role of climate uncertainty on carbon emissions price dynamics using novel measures of uncertainty that capture transitional and physical climate risks. Applying a multivariate stochastic volatility model to daily European Union Allowance prices, we show that climate uncertainty indeed serves as a significant driver of price fluctuations in emissions prices with physical climate risks associated with uncertainty surrounding natural hazards playing a more dominant role over policy uncertainty in recent years. While our findings highlight the growing role of public concern over global warming and climate hazards than policy aspects as a driver of pricing dynamics in the emissions market, our findings present an interesting opening for hedging strategies towards attaining decarbonization goals in investment positions.

The dynamic causality in sporadic bursts between CO2 emission allowance prices and clean energy index.

This study examines the dynamic causality between the carbon emission market and the clean energy market, using an information flow-based, quantitative Liang causality analysis which is firmly grounded on physics and derived from first principles. The dynamic causal relationships between European Union Allowance (EUA) prices and clean energy index allow us to explore whether the causality in return or in variance from CO2 emission allowances to the clean energy index is time-varying. The results show that the causal relationships in return and in variance between EUA and Clean Energy Index (CEI) are drastically time-varying. For the causality in return, a significant unidirectional long-term and stable causality from CEI to EUA is identified after March 2020. For that in variance, a bidirectional causality is found after March 2020, but values after 2020 are opposite to those in return. It seems when fluctuations in the clean energy market are low, the clean energy market has a weak causal effect on the carbon emission market but when volatility in the clean energy market is increasing, causalities between the two markets are significantly strengthened. These results obtained through this rigorous causality analysis can serve as a reference for academics, market participants, and policymakers to understand the underlying links between EUA prices and clean energy index.

Improving Artificial Intelligence Forecasting Models Performance with Data Preprocessing: European Union Allowance Prices Case Study

European Union Allowances (EUAs) are rights to emit CO2 that may be sold or bought by enterprises. They were originally created to try to reduce greenhouse gas emissions, although they have become assets that may be used by financial intermediaries to seek for new business opportunities. Therefore, forecasting the time evolution of their price is very important for agents involved in their selling or buying. Neural Networks, an artificial intelligence paradigm, have been proved to be accurate and reliable tools for time series forecasting, and have been widely used to predict economic and energetic variables; two of them are used in this work, the Multilayer Preceptron (MLP) and the Long Short-Term Memories (LSTM), along with another artificial intelligence algorithm (XGBoost). They are combined with two preprocessing tools, decomposition of the time series into its trend and fluctuation and decomposition into Intrinsic Mode Functions (IMF) by the Empirical Mode Decomposition (EMD). The price prediction is obtained by adding those from each subseries. These two tools are combined with the three forecasting tools to provide 20 future predictions of EUA prices. The best results are provided by MLP-EMD, which is able to achieve a Mean Absolute Percentage Error (MAPE) of 2.91% for the first predicted datum and 5.65% for the twentieth, with a mean value of 4.44%.

Reflections on the Mechanisms to Protect Against Formation of Price Bubble in the EU ETS Market

Abstract The rapid increases of European Union Allowance (EUA) prices and very high market volatility, resulting mainly from the growing role of speculative entities, can contribute to forming a price bubble. This may cause the market instability and could have a implications on planning future reduction investments by European Union Emissions Trading Scheme (EU ETS) participants. That is why they need some kind of ‘safety valve’, an effective EU ETS instrument, which can be triggered when the situation requires it. The purpose of this paper is to examine whether the current legislative rules of the EU ETS protect against sudden EUA price fluctuation and the risk of formation of a price bubble. This paper tries to assess the potential EUA price bubble and to review of existing instruments within the EU ETS, analysing their efficiency using different assumptions and identify channels of possible other market instruments to efficiently prevent the carbon market instability caused by rising EUA prices and market speculation. We argue that the European Commission (EC) does not currently have an appropriate market instrument to respond to the EUA price fluctuation. Moreover, there are some legislative loopholes in the system, which may encourage market speculators to influence EUA prices, and there is need to introduce better market safeguards.

Short-term European Union Allowance price forecasting with artificial neural networks

The European Union Emissions Trading Scheme (EU ETS) was created to reduce greenhouse gas emissions. Companies producing carbon emissions have to manage associated cash flows by buying or selling carbon allowances. Moreover, future carbon prices could affect company decision making on decarbonization technology investments. In this paper, we forecasted short-term future carbon allowance prices using an artificial intelligence tool: a neural network. The resulting mean error was 1.7617 %. This is indicative of very good performance for a time series whose evolution is influenced by subjective economic and political decisions. The inclusion in the forecasting model of variables possibly directly related to the evolution of the price of CO2 emission allowances did not improve prediction accuracy. Therefore, we can assume that emission allowances evolve following a random path. The neural network provided reliable predictions which agents selling or buying allowances can use to make their decisions.

The Long-Term Trend Analysis and Scenario Simulation of the Carbon Price Based on the Energy-Economic Regulation

Purpose China’s national carbon market will be officially launched in 2020, when it will become the world’s largest carbon market. However, China’s carbon market is faced with various major challenges. One of the most important challenges is its impact on the social and economic development of arid and semi-arid regions. By simulating the carbon price trends under different economic development and energy consumption levels, this study aims to help the government can plan ahead to formulate various countermeasures to promote the integration of arid and semi-arid regions into the national carbon market. Design/methodology/approach To achieve this goal, this paper builds a back propagation neural network model, takes the third phase of the European Union Emissions Trading System (EU ETS) as the research object and uses the mean impact value method to screen out the important driving variables of European Union Allowance (EUA) price, including economic development (Stoxx600, Stoxx50, FTSE, CAC40 and DAX), black energy (coal and Brent), clean energy (gas, PV Crystalox Solar and Nordex) and carbon price alternatives Certification Emission Reduction (CER). Finally, this paper sets up six scenarios by combining the above variables to simulate the impact of different economic development and energy consumption levels on carbon price trends. Findings Under the control of the unchanged CER price level, economic development, black energy and clean energy development will all have a certain impact on the EUA price trends. When economic development, black energy consumption and clean energy development are on the rise, the EUA price level will increase. When the three types of variables show a downward trend, except for the sluggish development of clean energy, which will cause the EUA price to rise sharply, the EUA price trend will also decline accordingly in the remaining scenarios. Originality/value On the one hand, this paper incorporates driving factors of carbon price into the construction of carbon price prediction system, which not only has higher prediction accuracy but also can simulate the long-term price trend. On the other hand, this paper uses scenario simulation to show the size, direction and duration of the impact of economic development, black energy consumption and clean energy development on carbon prices in a more intuitive way.

Applying Artificial Neural Networks to Forecast European Union Allowance Prices: The Effect of Information from Pollutant-Related Sectors

In this paper, we forecast the price of CO2 emission allowances using an artificial intelligence tool: neural networks. We were able to provide confident predictions of several future prices by processing a set of past data. Different model structures were tested. The influence of subjective economic and political decisions on price evolution leads to complex behavior that is hard to forecast. We analyzed correlations with different economic variables related to the price of CO2 emission allowances and found the behavior of two to be similar: electricity prices and iron and steel prices. They, along with CO2 emission allowance prices, were included in the forecasting model in order to verify whether or not this improved forecasting accuracy. Only slight improvements were observed, which proved to be more significant when their respective time series trends or fluctuations were used instead of the original time series. These results show that there is some sort of link between the three variables, suggesting that the price of CO2 emission allowances is closely related to the time evolution of the price of electricity and that of iron and steel, which are very pollutant industrial sectors. This can be regarded as evidence that the CO2 market is working properly.

Return and volatility linkages between CO2 emission and clean energy stock prices

The emission of CO2 has emerged as one of the key factors behind the significant growth of clean energy sources that are less carbon-intensive than conventional energy bases. However, investigating the link between the carbon emission market and the market of clean energy stocks remains extremely understudied. Accordingly, this study examines daily return and volatility linkages between the European Union Allowance (EUA) prices and clean energy stock returns. Employing the bivariate VAR-GARCH approach, we document that variations in the EUA prices affect the renewable energy stock returns positively, though the association is usually found to be statistically insignificant. Our findings further indicate a significant volatility linkage between emission and European clean energy price indexes. Such findings, however, do not hold for the US market, suggesting that emission return and volatility shocks are country- or region-specific. Further results show that the average correlations between emission and stock prices is very low and hence portfolio diversification benefits seem to be attainable if investors hold assets in the EUA and clean energy equity markets.

How do Spanish polluting sectors' stock market returns react to European Union allowances prices? A panel data approach

This paper analyses whether and to what extent the EUA (European Union allowance) prices may be linked with Spanish polluting sectors' stock market returns.By utilizing a multifactor market model specification and panel data econometric approach, we test the if EUA price changes and stock returns of the Spanish sectors affected by EU ETS are correlated, the direction of this correlation and whether the carbon market effect is asymmetric and sector-specific. We use daily data for the full Phase II period and part of Phase III period of the EU ETS (from the 1st January 2008 to the 18th September 2015).A statistically significant positive and negative impact of EU ETS on stock market return is found for Phase II and III respectively. Additionally, EUA prices effect is shown to be sector-specific and there are different responses to increasing and decreasing carbon prices in sector-specific stock returns for both commitment periods.In contrast to others studies which refer to the first and/or earlier years of second EU ETS period and to a single sector (mainly power), our paper focuses on the second and the ongoing third period, and includes all polluting sectors encompassed under EU ETS.

Modeling and Explaining the Dynamics of European Union Allowance Prices at High-Frequency

In this paper we model the adjustment process of European Union Allowance (EUA) prices to the releases of announcements at high-frequency controlling for intraday periodicity, volatility clustering and volatility persistence. We find that the high-frequency EUA price dynamics are very well captured by a fractionally integrated asymmetric power GARCH process. The decisions of the European Commission on second National Allocation Plans have a strong and immediate impact on EUA prices. On the other hand, our results suggest that EUA prices are only weakly connected to indicators about the future economic development as well as the current economic activity.

Modeling and explaining the dynamics of European Union Allowance prices at high-frequency

In this paper we model the adjustment process of European Union Allowance (EUA) prices to the releases of announcements at high-frequency controlling for intraday periodicity, volatility clustering and volatility persistence. We find that the high-frequency EUA price dynamics are very well captured by a fractionally integrated asymmetric power GARCH process. The decisions of the European Commission on second National Allocation Plans have a strong and immediate impact on EUA prices. Further, EUA prices increase in response to better than expected news on the future economic development as well as the current economic activity in Germany and the U.S.

Policy incentives for carbon capture and storage technologies in Europe: A qualitative multi-criteria analysis

In this paper, we compare different policy incentives for overcoming investment uncertainties that are typical for low-carbon technologies prior to their commercialisation, some of which may be attributable to market failures. The paper focuses on the particular case of carbon capture and storage (CCS) technologies and conducts a qualitative multi-criteria analysis of different public policy support schemes for CCS demonstration to evaluate their suitability. The assessed schemes include mandatory CCS, emission performance standards and several different financial incentives (in addition to the European Union Emission Trading Scheme). Based on the available literature and on experience in the UK and Germany with promotion instruments for low-carbon technologies, the results of our analysis suggest that two alternative schemes, a CCS bonus incentive or a carbon dioxide (CO 2) price guarantee, perform best in comparison with the other assessed instruments. While they reduce the uncertainty of CCS investments in the face of low European Union Allowance prices, they also avoid significant adverse impacts on operational and investment decisions in electricity markets.

Predicting the Price of EU ETS Carbon Credits

The aim of this paper is to examine what drives the changes in price of carbon credits in the European Union's Emission Trading Scheme (EU ETS) and to make predictions based on these relationships. The study is based on dataset from the United Kingdom (UK) energy market and global equity indices. The large dataset is reduced in dimension using correlation analysis and predictions are then made by multiple linear regression. Certified emission reduction units (CERs) are shown to be the only same-day market relationship which provides useful predictions of European Union Allowance prices (EUAs). No significant correlation is found between EUAs and the UK power market and the theoretical price of carbon credits; switching price, is shown to be a poor indicator of the price of carbon credits.

An investigation into the evolved relationship between spot and futures in the European Union Emission Trading Scheme

This paper fills the gap in the literature that the functions of the European Union Allowance (EUA) futures have only been studied in the pilot phase of the European Union Emission Trading Scheme (EU ETS) and no market evolvement has been explored. The cost-of-carry pricing efficiency of the EUA futures and information spillover between the EUA spot market and futures market are examined in Phase I (2005–2007) and Phase II (2008–2012), respectively. First, it was found that only the December 2007 futures price was in long-run equilibrium with the spot price, but the relationship was not given by a cost-of-carry model. Second, the information spillover pattern also changed from Phase I to Phase II. The leading role of futures contract in price discovery can only be seen in Phase I, but not in Phase II. We conclude that, in the second phase of EU ETS, the market fundamentals are imposing stronger impacts on EUA price formation while the impact from speculation has weakened significantly.

Is There a Stochastic Trend in European Union Emission Trading Scheme Prices?

The European Union’s Emission Trading Scheme (ETS) is the key policy instrument of the European Commissions Climate Change Program aimed at reducing greenhouse gas emissions to eight percent below 1990 levels by 2012. The key asset traded under the scheme is the European Union Allowance (EUA). To further curtail greenhouse gas emissions the EU Allowance prices should follow a stationary path about a persistent upward trend. In this vein, this article finds evidence to the contrary indicating the presence of a stochastic trend (a unit root) in EU Allowance prices during the period from April 2005 through to July 2009. This finding is indicative of incoherence between the European Union ETS de jure policy, with regard to transiting to a low carbon economy, and EUA price behavior.

Is There a Stochastic Trend in European Union Emission Trading Scheme Prices?

The European Union’s Emission Trading Scheme (ETS) is the key policy instrument of the European Commissions Climate Change Program aimed at reducing greenhouse gas emissions to eight percent below 1990 levels by 2012. The key asset traded under the scheme is the European Union Allowance (EUA). To further curtail greenhouse gas emissions the EU Allowance prices should follow a stationary path about a persistent upward trend. In this vein, this article finds evidence to the contrary indicating the presence of a stochastic trend (a unit root) in EU Allowance prices during the period from April 2005 through to July 2009. This finding is indicative of an incoherence between the European Union ETS de jure policy, with regard to transiting to a low carbon economy, and EUA price behaviour.