Carbon price drivers: Phase I versus Phase II equilibrium?

Cretì, Anna; Jouvet, Pierre‐André; Mignon, Valérie

doi:10.1016/j.eneco.2011.11.001

Cited by 244 publications

(141 citation statements)

References 38 publications

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“…While Bredin and Muckley (2011) confirm that a long-term relationship between the electricity price and the EUA price established during Phase II 9 , this result should be interpreted with caution because their analysis is not restricted to a regional electricity market and might therefore not be an indication for a long-term relationship in the EU ETS. This caveat does not apply for the study of Creti et al (2012). While Rickels et al (2007) and Hintermann (2010) do not find any cointegration between EUA prices and fuel prices, Creti et al (2007) show that, in Phase II, such a stable long-term relationship between the price series emerges.…”

Section: Empirical Evidencementioning

confidence: 95%

“…8 Therefore, different results potentially stem from differing explanatory variables and there is only limited information about the actual fuel prices relevant for a power utility in its decision about fuel switching. Consequently, while analyzing the fuel switching price effect implicitly by including the gas and coal price, or explicitly by calculating the price like it is done for example by Alberola et al (2008) or Creti et al (2012), it may be the case that this fuel price only applies to certain areas of Europe. Not surprisingly, market observers and researchers like CDC climate research or Point Carbon publish different fuel switching prices, for e.g.…”

Section: Empirical Evidencementioning

confidence: 99%

“…looking at coal prices. Most studies on the determinants of the EUA price rely on a coal price closely related on the Argus/McCloskey's Coal Price API2 CIF ARA 11 (e.g., Mansanet-Bataller et al 2007;Hintermann 2010;Bredin and Muckley 2011;Creti et al 2012), which is often regarded as the primary reference coal price for northwest Europe. 12 But the EU ETS countries import coal from all over the world and it is likely that prices for each single trade differ significantly.…”

Section: Fuel Switching: Coal and Gas Pricesmentioning

confidence: 99%

“…In Phase II the market was expected to gain more maturity so that price dynamics can be explained to a larger extent by fundamental factors. Indeed, Bredin and Muckley (2011), and Creti et al (2012) provide first evidence that a stable long-term relationship between market fundamentals like coal, gas, oil prices and the EUA price is establishing.…”

Section: Introductionmentioning

confidence: 99%

“…Recent studies extending into Phase II use prices of EUA futures from the ICE (e.g., Bredin and Muckley 2011;Conrad et al 2012;Creti et al 2012). ICE price data are also used in regular market reports like the Carbon Market Daily by Point Carbon (Allan et al 2012) or the Tendances Carbone monthly bulletin on the European Carbon Market (Stephan 2012).…”

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confidence: 99%

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Explaining European Emission Allowance Price Dynamics: Evidence from Phase II

Rickels

Görlich

Peterson

2015

German Economic Review

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In this paper we empirically investigate potential determinants of allowance (EUA) price dynamics in the European Union Emission Trading Scheme (EU ETS) during Phase II. In contrast to previous papers, we analyze a significantly longer time series, place particular emphasis on the importance of price variable selection, and include an extensive data of renewable energy feed-in in Europe. We show (i) that results are extremely sensitive to choosing different price series of potential determinants, such as coal and gas prices, (ii) that EUA price dynamics are only marginally influenced by renewable energy provision in Europe, and iii) that EUA prices currently do not reflect marginal abatement costs across Europe. We conclude that the expectation of a more mature allowance market in Phase II cannot be confirmed. In this paper we empirically investigate potential determinants of allowance (EUA) price dynamics in the European Union Emission Trading Scheme (EU ETS) during Phase II. In contrast to previous papers, we analyze a significantly longer time series, place particular emphasis on the importance of price variable selection, and include an extensive data of renewable energy feed-in in Europe. We show (i) that results are extremely sensitive to choosing different price series of potential determinants, such as coal and gas prices, (ii) that EUA price dynamics are only marginally influenced by renewable energy provision in Europe, and iii) that EUA prices currently do not reflect marginal abatement costs across Europe. We conclude that the expectation of a more mature allowance market in Phase II cannot be confirmed.

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Section: Empirical Evidencementioning

confidence: 95%

Section: Empirical Evidencementioning

confidence: 99%

Section: Fuel Switching: Coal and Gas Pricesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Explaining European Emission Allowance Price Dynamics: Evidence from Phase II

Rickels

Görlich

Peterson

2015

German Economic Review

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Emissions Markets and Products

Chesney¹,

Taschini²,

Gheyssens³

2014

Handbook of Multi‐Commodity Markets and Products

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Forecasting carbon dioxide emission price using a novel mode decomposition machine learning hybrid model of CEEMDAN‐LSTM

Yun

Huang

et al. 2022

Energy Science & Engineering

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Global carbon dioxide emissions have become a great threat to economic sustainability and human health. The carbon market is recognized as the most promising mean to curb carbon emissions, furthermore, carbon price forecasting will promote the role of the carbon market in emissions reduction and achieve reduction targets at lower economic costs for emission entities. However, there are still some technical problems in carbon price prediction, such as mode mixing and larger reconstruction error for the traditional empirical mode decomposition-type models. Therefore, the innovation of this paper is constructing a novel carbon price prediction model of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)-long short-term memory (LSTM), that combines the advantages of CEEMDAN in decomposing the multiscale time-frequency carbon price signals and the LSTM model in fitting the financial signals. The results show the proposed CEEMDAN-LSTM model has significant accuracy in predicting the complex carbon price signals. The prediction error and expectation indicators of root mean square error, mean absolute error, mean absolute percentage error, and direction accuracy are 0.638342, 0.448695, 0.015666, and 0.687631, respectively, which is better than other benchmark models. Further evidence convince that the short-term forecasting performance is superior to the long-term and medium-term performance. That evidence concludes that the proposed model is a reliable method to reveal the carbon price-driving mechanism from the point of multiscale time-frequency characteristics. Particularly, short-term forecasting is more accurate and can provide a valuable technical reference for reduction entities and green financial companies to judge the market situation and formulate quantitative transactions.

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Carbon price drivers: Phase I versus Phase II equilibrium?

Cited by 244 publications

References 38 publications

Explaining European Emission Allowance Price Dynamics: Evidence from Phase II

Explaining European Emission Allowance Price Dynamics: Evidence from Phase II

Emissions Markets and Products

Forecasting carbon dioxide emission price using a novel mode decomposition machine learning hybrid model of CEEMDAN‐LSTM

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