European Union Allowances Research Articles

Forecasting interval carbon price through a multi-scale interval-valued decomposition ensemble approach

This paper proposes a novel Multi-scale Interval-valued Decomposition Ensemble (MIDE) framework for forecasting European Union Allowance (EUA) carbon futures prices, which integrates Noise-assisted Multivariate Empirical Mode Decomposition (NAMEMD), Interval-valued Vector Auto-Regressive (IVAR) model, Interval Event Analysis (IEA) method, and Interval Multi-Layer Perceptron (IMLP). First, the original interval-valued carbon prices with other interval-valued control variables are decomposed and integrated into high, medium, and low-frequency components by NAMEMD. Second, IVAR is used to investigate the dynamics of the interval-valued vector system in low-frequency components, while IMLP is employed to characterize the high-frequency components. Besides, the interval event analysis investigates typical events that significantly impact carbon prices in the medium-frequency component. Furthermore, empirical findings indicate that our proposed MIDE learning approach significantly outperforms some other benchmark models in out-of-sample forecasting.

MFTM-Informer: A multi-step prediction model based on multivariate fuzzy trend matching and Informer

Multi-step forecasting is a critical process in various fields, such as disaster warning and financial analysis. Nevertheless, achieving precise multi-step forecasting is challenging due to the intricate nature of the factors influencing the time series, most of which are highly nonlinear and nonstationary. In this paper, a multi-step forecasting model named MFTM-Informer, employing a multiple input multiple output strategy for multivariate trend matching is proposed. The dependent variable and the influencing factors are initially decomposed using multivariate variational modal decomposition to minimize noise. Afterwards, the decomposed data are reconstructed into multivariate trends and fluctuations using sample entropy, enabling the development of tailored forecasting strategies based on data characteristics. A multivariate trend is predicted using an enhanced pattern matching model, while the high-frequency fluctuation is modelled using Informer. Finally, the outcomes are combined to generate multi-step predictions. To validate the performance of the proposed model, we observed its performance on three real-world datasets, including Brent crude oil prices, European Union Allowance future prices, and Standard & Poor’s 500 index. Results indicate that the model surpasses all the benchmark models in terms of multiple evaluation metrics and forecast ranges, highlighting its effectiveness and robustness in multi-step forecasting.

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.

Financial stability and European union allowances: a tale of jumps and regime shifts

ABSTRACT Using an event-study methodology, we investigate the impact of European Union Allowances (EUA) on measures of systemic risks for financial institutions. We analyse daily data on all banks listed on European exchanges. We find statistically significant abnormal changes in systemic risk measures around days when prices of allowances either jumped or shifted regimes. The paper also documents reduced systemic risk around positive jumps and regime shifts of allowances and increased systemic risk throughout negative jumps and regime shifts. Our contribution to the literature consists in producing evidence that financial stability supports the European carbon market initiative to mitigate climate change. These results have significant implications for policymakers and financial institutions.

The asymmetric nexus between the cryptocurrency market and the carbon market: Evidence from the quantile-on-quantile method

We employ a novel framework to measure the asymmetric nexus between the cryptocurrency market and the carbon futures market based on different market conditions. Specifically, we use a quantile-on-quantile regression (QQR) approach to explore the correlation between cryptocurrencies (Bitcoin, Ethereum, and Ripple) and European Union Allowance (EUA) futures. We find that there is an asymmetric relationship between markets that is affected by different cryptocurrencies and market conditions. Overall, Bitcoin or Ethereum are positively correlated with the carbon market, while the results of Ripple are more complex. Under certain conditions, EUA futures can be a better hedge against cryptocurrency risk.

Model-driven multimodal LSTM-CNN for unbiased structural forecasting of European Union allowances open-high-low-close price

European Union allowances (EUAs), the “currency in circulation” of the EU Emissions Trading Scheme (ETS), have spawned a great deal of speculative trading. This study proposes a model-driven long short-term memory network (LSTM)-convolutional neural network (CNN) hybrid model that integrates numerical data features and candlestick features to achieve accurate and unbiased structural prediction of EUA futures open-high-low-close (OHLC) prices during the four phases of the EU ETS. During EU ETS Phase IV, the out-of-sample prediction outcomes of the LSTM-CNN model exhibited a mean absolute percentage error (MAPE) of 0.942%, a mean absolute error (MAE) of 0.877, a root mean squared error (RMSE) of 1.157, a goodness-of-fit (R2) of 0.953, an accuracy ratio (AR) of 0.544, and a forecast correct rate of ups and downs (UP) of 0.579. In comparison to Naive methods, vector autoregression (VAR) combined with vector error correction model (VECM), multiple linear regression (MLR), partial least squares (PLS), support vector regression (SVR), and standalone LSTM, the LSTM-CNN approach demonstrated a notable enhancement in the average MAPE across the four stages of the EU ETS—specifically, an improvement of 21.66%, 43.15%, 15.73%, 15.72%, 10.45%, and 5.91%, respectively. Drawing from the unbiased structural forecasts of OHLC data, this study proposes fruitful intraday trading strategies that attain substantive investment returns in the realm of EUA futures trading. The multimodal forecasting methodology and intraday trading strategies advanced in this study hold considerable promise within the domain of energy finance.

Sequential management of energy and low-carbon portfolios

This study explores the ability of clean energy and European Union Allowance (EUA) assets to diminish portfolio risk when mixed with unclean energy assets. We use a family of Asymmetric Dynamic Conditional Correlation-Generalized AutoRegressive Conditional Heteroskedastic (ADCC-GARCH) models and provide a flexible and adaptive estimation and model selection framework based on a sequential strategy with differently sized estimation and validation windows, as well as different model update frequencies. Through this procedure, we obtain accurate estimations of the conditional covariance matrices of day-to-day asset returns and build adequate optimal minimum variance portfolios. The analyzed period (Jan. 2010–May. 2022) includes the latest crisis episodes (Sovereign debt crisis, Brexit, COVID-19, and the Russian–Ukrainian war). Our findings show that since the 2015 Paris Agreement (the only exception being the pandemic period), investing in clean energy companies and EUAs is an attractive investment in terms of return-risk. These results should provide investors with more incentives to decarbonize their portfolios.

Spillover in higher‐order moments across carbon and energy markets: A portfolio view

AbstractMotivated by the occurrence of extreme events and nonnormality of returns, we examine the spillovers among the conditional volatility, skewness and (excess) kurtosis of European Union allowances (EUA), Brent oil, natural gas, coal, electricity and clean energy markets. The jointly estimated spillover index in the system of the three higher‐order moments is notably high, exceeding the spillover index estimated for each individual moment separately. This suggests that spillovers across moments in the carbon‐energy system are important for the sake of completeness of the spillover analysis, and should not be ignored. The performance of the portfolio improves after considering higher‐order moments.

Forecasting the volatility of European Union allowance futures with macroeconomic variables using the GJR-GARCH-MIDAS model

Forecasting the volatility of European Union allowance futures with macroeconomic variables using the GJR-GARCH-MIDAS model

Potential application of cement kiln dust in carbon capture, utilisation, and storage technology

The cement industry is a significant source of CO2 emissions. The implementation of carbon capture, utilisation, and storage (CCUS) technology is one measure aimed at limiting these emissions. This study analyses the mineral carbonation process using CO2 and flue gas as the last stage of the CCUS process applied to cement kiln dust. Moreover, this study provides directions for utilising the products of the process. The results suggest that mineral sequestration using the cement kiln dust can reduce CO2 emissions by 0.42 % at a plant at a cost of EUR 80 per European Union Allowance; this approach can save approximately 41,000 EUR/year. Mineral carbonation can be used as a form of recovery, because the waste products from cement plants can be used in the implementation of CCUS technology, rendering them economically viable.

The Empirical Analysis of the Effects of the COVID-19 Pandemic Crisis on the South Korea and Europe Carbon Market

This study examines the impact of the COVID-19 pandemic on carbon credit prices in South Korea and Europe, aiming to analyze the implications for the South Korea and Europe carbon markets. The empirical analysis was based on the data of the Korean carbon credit and European carbon markets, which accounts for more than 80% of the global carbon credit market. The study is based on the daily Korean Allowance Unit (KAU) data from January 2015, when the Emissions Trading System (ETS) was first implemented, to December 2022, when the restrictions eased. For Europe, this study employed the European Union allowance (EUA) market data for the same period, using the EUA futures data, which expires in December every year and exhibit considerably higher liquidity than the EUA spot price. The results indicated that no significant Granger causality between the Volatility Index (VIX), EUA, and KAU during 2015 to 2019, before COVID-19 was identified. However, for 2020 to 2022 (the COVID-19 outbreak period), the null hypothesis of no Granger causality between the VIX and EUA at the 5% confidence level was rejected. These findings suggests that the economic crises and the consequent changes in the demand and supply of carbon emissions can affect the carbon credit price.

Exploring the influence of the geopolitical risks on the natural resource price volatility and correlation: Evidence from DCC-MIDAS-X model

This paper uses the expanded dynamic conditional correlation mixed data sampling (DCC-MIDAS-X) model to examine the impact of low-frequency geopolitical risks (GPR) on high-frequency volatility and the correlation of natural resource prices. The empirical results show that GPR and its sub-indices can significantly affect the volatility and correlation of natural resources. The increase in Geopolitical Threats (GPRT) significantly affects the long-run volatility of European Union Allowance (EUA) and Brent crude oil prices. Geopolitical Acts (GPRA) mainly affect the long-run volatility of natural gas prices. In addition, the long-run correlation coefficient, long-run correlation coefficient indicates a competitive relationship between natural resources. Finally, increased GPR and GPRT can significantly reduce the long-run correlation between natural resources. Based on its conclusions, this research makes policy suggestions for businesses and the government. Investors should effectively identify different types of geopolitical risks and their influences to reduce losses and optimize investments. Governments could adjust their policies appropriately to avoid economic shocks due to natural resource volatility caused by geopolitical risks and ensure a stable supply of natural resources.

Carbon conundrums: Geopolitical clashes and market mayhem in the race for sustainability

This research presents an in-depth investigation into the dynamic correlation between geopolitical conflicts and carbon markets utilizing the Time-Varying Parameter Vector Autoregression (TVP-VAR) technique. The analysis focuses on the interconnectedness between the Geopolitical Risk Index Daily (GPRD) and vital carbon pricing instruments, specifically the Intercontinental Exchange Endex European Union Allowance (ECEFDC), KraneShares California Carbon Allowance Strat ETF (KCCAK), Shanghai Environment and Energy Exchange China Emission Allowances Online Transactions (SAXCEA), and S&P Global Ex-Japan LargeMidCap Carbon Efficient Index (SPGJ). The daily fluctuations were traced from May 2021 to July 2023. The analysis is divided into short- and long-term connectedness, with particular emphasis on the impact of the Russia-Ukraine conflict on the GPRD's spillover on carbon markets. The short-term connectedness (1–5 days) between GPRD and ECEFDC shows variability, fluctuating between 10% and 40%. Conversely, long-term connectedness exhibited a significant increase during the conflict, peaking at approximately 34% by mid-2022. The analysis of the Total Dynamic Connectedness (TCI) between the GPRD and the KCCAK indicates comparable magnitudes, although with minor initial discrepancies. The short-term connectedness of GPRD and KCCAK decreases from its peak of approximately 10% to approximately 1%. Conversely, long-term connectedness varies between approximately 32% and 2% from May 2022 onwards. The long-term connectedness between GPRD and SAXCEA revealed variable patterns, peaking at around 18% at the beginning of the sample period and rapidly reducing to around 1% within two months. The analysis of the connectedness between GPRD and the SPG) identifies intense fluctuations in both TCI and long-term connectedness. After an initial increase and decrease, these patterns rebound and experience another increase. This research provides significant insights into the complex dynamics of geopolitical conflicts and carbon markets, particularly the impact of the Russia-Ukraine conflict on carbon market behavior.

The heterogeneous role of economic and financial uncertainty in green bond market efficiency

Purpose This paper aims to explore the quantile-specific short- and long-term effects of economic policy uncertainty (EPU) on the efficiency of the green bond market. Design/methodology/approach This study examines the long-term cointegration relationship and the short-term fluctuation relationship of EPU, WTI crude oil price (WTI) and European Union Allowances price (EUA) with the green bond market efficiency (GBE) using the quantile autoregressive distributed lag method. Additionally, the authors analyze the differences before and after the Covid-19 pandemic. Findings EPU has a significant positive impact on the GBE before the outbreak. However, during the crisis period, the impact of EPU and WTI was greatly weakened, whereas the impact of EUA was strengthened. Practical implications This paper demonstrates the dynamics of GBE and its influencing factors under different periods. The findings provide insights for market participants and policymakers to gain a clearer understanding of the green bond market. Originality/value This paper extends the study of green bonds by quantifying the GBE and elucidating the nonlinear relationship between efficiency and independent variables at different quantiles over different periods.

Enhancing Carbon Capture and Storage Deployment in the EU: A Sectoral Analysis of a Ton-Based Incentive Strategy

The EU considers carbon capture and storage (CCS) technology as an option for achieving climate goals, but its cost remains appreciable. Therefore, the purpose of this research was to investigate the implementation of a ton-based incentive system for CCS in the EU using Croatia as an example based on an analysis of the existing legislative framework in the EU and relevant tax credit provisions in the USA. A novel methodology for the design of the incentive system is presented in the form of partial allocation of the state’s auction revenues from the EU emissions trading system (ETS) into the CCS fund for five years. The CCS fund assets then incentivize the capture site for 10 years. The incentives are determined for each emitter in cement, electricity, paper and pulp, glass, oil refining, and petrochemical sectors based on varying European Union allowance (EUA) prices, CCS fund sizes, and CO2 emission scenarios. In addition to designing the methodology, a novel method for forecasting CO2 emissions is applied using geometric Brownian motion. The calculated incentives are categorized as underperforming, optimal, or overperforming, with upper and lower limits set to 80 and 10 EUR/t. The results are optimistic, since all sectors can be efficiently incentivized within the defined boundaries, meaning that the incentive system can be applied to all member states. The contracting of the incentives is proposed through carbon contracts for difference to avoid irregularities. Also, regulatory amendments are proposed so that emitters with emissions higher than 100 kt would have to consider CCS. Finally, the contributions are presented by proving the feasibility of the incentive system together with demonstrating its applicability to all member states.

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.

Economic and financial instruments of forest management in the Czech Republic

The forest bioeconomy becomes a feature of a climate-neutral economic system, while effective financial support is crucial for sustainable forest management. The main goal of this paper is to explain the impact of economic and financial instruments on the development of the forestry sector in the Czech Republic in the period 2000–2020. For research objectives, the methods of literature review, data analysis, correlation analysis, and regression analysis were used. Several models were established and tested. This paper presents the forest land model (FOLM) and wood biomass production model (WBIOM). In the monitored period, there was an increase in forest land in hectares in the Czech Republic, which was positively influenced by environmental investments in biodiversity and negatively by subsidies from the Rural Development Programme and the price of European Union Allowance. Based on the FOLM model results, 100 million CZK (4.07 million EUR) of environmental investments in biodiversity would contribute to an increase of 228 hectares of forest land. Concerning wood biomass production in cubic meters, it was influenced positively by the whole mixture of economic and financial instruments, such as emission trading, environmental taxation, financial contributions for forest management, state financial obligations, and subsidies. Based on the WBIOM model results, an increase in the price of an emission allowance by 100 CZK Mg–1 (approx. 4 EUR Mg–1) would increase wood biomass production by approximately 934,614 cubic meters. Generally, the economic and financial instruments in the Czech Republic have an environmental impact and can influence the forest bioeconomy, at least in the long-term period. Concerning the complex influence of the emission trading on the forestry sector in the Czech Republic, it is ambiguous—in the case of forest land rather negative, and in the case of wood biomass production positive. Therefore, focusing on the policy recommendations, we should underline economic and financial instruments connected with positive motivation in the forestry sector, such as grant schemas, subsidies, and investments in biodiversity.

Re-examining the efficiency of the EU carbon futures market in phase Ⅱ: price discovery and intertemporal arbitrage

Introduction: Carbon futures market plays an important role in energy conservation and emission reduction of energy-intensive firms, and its efficiency is largely reflected by the dynamic relationship between carbon spot and futures prices. Even though the EU carbon futures market has undergone three phases, its efficiency in Phase Ⅱ still contains much controversy.Methods: Based on the cost-of-carry model, we apply linear and nonlinear Granger causality test, vector error correction model and threshold vector error correction model to re-examine the efficiency of the carbon futures market in the second phase of the European Union Emissions Trading System from the perspectives of price discovery and intertemporal arbitrage.Results: i) the EU carbon futures market has the function of price discovery by virtue of the broad scope of participants and the consequent huge liquidity, suggesting that the carbon futures price can predict the carbon spot price; ii) the coefficient requirement of the cost-of-carry model is satisfied and the relationship between the European Union Allowance (EUA) futures and spot prices is consistent with the cost-of-carry model, which indicates that the former is an unbiased estimator of the latter and an equilibrium relationship exists between the two prices so that the carbon futures market is efficient in the long run; iii) however, the carbon futures market shows inefficiency in the short run since it has an error correction function only when the basis is less than −0.07 and the carbon futures price indicates a remarkably slow adjustment to the long-run equilibrium when there is deviation, which can be ascribed to the global financial crisis as well as its aftermath in 2008.Discussion: In the context of global warming and carbon neutrality, the results of this paper are of great significance to emitters, arbitrageurs, hedgers and policymakers in emerging and young carbon markets.

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.

Using EGARCH models to predict volatility in unconsolidated financial markets: the case of European carbon allowances

The growing interest and direct impact of carbon trading in the economy have drawn an increasing attention to the evolution of the price of CO2 allowances (European Union Allowances, EUAs) under the European Union Emissions Trading Scheme (EU ETS). As a novel financial market, the dynamic analysis of its volatility is essential for policymakers to assess market efficiency and for investors to carry out an adequate risk management on carbon emission rights. In this research, the main autoregressive conditional heteroskedasticity (ARCH) models were applied to evaluate and analyze the volatility of daily data of the European carbon future prices, focusing on the last finished phase of market operations (phase III, 2013–2020), which is structurally and significantly different from previous phases. Some empirical findings derive from the results obtained. First, the EGARCH (1,1) model exhibits a superior ability to describe the price volatility even using fewer parameters, partly because it allows to collect the sign of the changes produced over time. In this model, the Akaike information criterion (AIC) is lower than ARCH (4) and GARCH (1,1) models, and all its coefficients are significative (p < 0.02). Second, a sustained increase in prices is detected at the end of phase III, which makes it possible to foresee a stabilization path with higher prices for the first years of phase IV. These changes will motivate both companies and individual energy investors to be proactive in making decisions about the risk management on carbon allowances.