Predicting European carbon emission price movements

Hong, Ki-Hoon; Jung, Hojin; Park, Minjae

doi:10.1080/17583004.2016.1275813

Cited by 32 publications

(14 citation statements)

References 36 publications

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“…Benjaafar et al [19] presented a method for the operational management subject to the restricting situation for carbon emission. Hong et al [20] developed a regression model for predicting the movement of carbon price. An and Lee [21] suggested methods for allocating the carbon emissions to each entity and then reduced total carbon emission using a newsvendor model among non-cooperative entities under a Stackelberg game scheme.…”

Section: Carbon Cap and Tradementioning

confidence: 99%

Operational Decision Model with Carbon Cap Allocation and Carbon Trading Price

Lee

2019

Journal of Open Innovation: Technology, Market, and Complexity

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This paper considers a carbon emission cap and trade market, where the carbon emission cap for each entity (either government or firm) is allocated first and then the carbon trading price is decided interdependently in the carbon trading market among the non-cooperative entities which make their production decision. We assume that there are n entities emitting carbon during the production process. After allocating the carbon (emission) cap for each participating entity in the carbon cap and trade market, each participant makes a production decision using the Newsvendor model given carbon trading price determined in the carbon trading market and trades some amount of its carbon emission, if its carbon emission is below or above its own carbon cap. Here, the carbon trading price depends on how carbon caps over the entities are allocated, since the carbon trading price is determined through the carbon (emission) trading market, which considers total amount of carbon emission being equal to total carbon caps over entities and some fraction of total carbon emission should be from each entity participating in the carbon cap and trade market. Thus, we can see the interdependency among the production decision, carbon cap and carbon trading price. We model this as a non-cooperative Stackelberg game in which carbon cap for each entity is allocated in the first stage and each entity’s production quantity is decided in the second stage considering the carbon trading price determined in the carbon trading market. First, we show the monotonic property of the carbon trading price and each entity’s production over the carbon cap allocation. In addition, we show that there exists an optimality condition for the carbon cap allocation. Using this optimality condition, we provide various results for carbon cap and trade market.

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Section: Carbon Cap and Tradementioning

confidence: 99%

Operational Decision Model with Carbon Cap Allocation and Carbon Trading Price

Lee

2019

Journal of Open Innovation: Technology, Market, and Complexity

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“…Böhringer and Lange [21] use a simple multi-period partial equilibrium model to derive optimal schemes for the free allocation of emission allowances in a dynamic context considering emissions-allocation rule which allows for updating of the basis of allocation over time. Hong et al [22] develop a predictive regression model of carbon pricing movements with past returns of various commodities and financial products. Additionally, Kim et al [23] investigate a dynamic programming model to make joint pricing and inventory replenishment decisions in orer to optimize the firm's profit assuming that customers are loss averse and the firm is risk averse.…”

Section: Literature Reviewmentioning

confidence: 99%

A Newsboy Model with Quick Response under Sustainable Carbon Cap-N-Trade

Lee

Park

2018

Sustainability

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In this study, we consider a carbon emission cap-and-trade system in which the policymaker decides the cap for carbon emissions for each company and also has the power to regulate the carbon price in the carbon trading market for the purpose of minimizing total carbon emissions. We assume that there are n companies regulated in terms of carbon emissions by the policymaker, each of which emits carbon when producing its own product. After learning the carbon cap and carbon price regulated by the policymaker, each company makes simultaneous pricing and production decisions using the quick response strategy, and can trade some of its carbon emissions in the carbon market at the carbon price set by the policymaker, if the carbon emissions are below the cap. We model this non-cooperative game between the policymaker and companies as a Stackelberg game in which the policymaker is the leader and the companies are the followers. We show that there exists an equilibrium for the policymaker's carbon pricing decisions and each company's production and pricing decisions. From this equilibrium, we derive a carbon cap for the company at which the amount of traded carbon emissions is zero. This implies that some company's production and pricing decisions, even under carbon emission restrictions, will be equal to those without the carbon emission restrictions. Also, we find that companies participating in the carbon cap-and-trade system would reduce their carbon emissions through reduced production, but can have a chance to improve profit through control of the product's selling price.

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“…In comparison with phase 1, they find more asymmetric dependences of the impact of energy in phases 2 and 3, and that the EUA-energy dependence is stronger than it has been in the past. Hong et al [15] employ various statistical methods to predict European carbon price movements, and find that Brent crude futures (oil) and natural gas are statistically significant in terms of forecasting CO 2 price movements, having a positive and a negative relationship with it, respectively.…”

Section: Literature Reviewmentioning

confidence: 99%

The Price Determinants of the EU Allowance in the EU Emissions Trading Scheme

2018

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The Kyoto Protocol came into effect in 1997 to curb greenhouse gas (GHG) emissions and to address the problem of climate change. The Protocol includes a market-based mechanism designed to offset GHG emissions, called the emissions trading scheme (ETS), allowing companies to “trade” their shortage or surplus allowance. This study examines the determinants of the EU allowance (EUA) price in Phase 3 of the EU ETS (2013–2017). First, the causality between the EUA price and other variables is determined using a Granger causality test. Second, the correlation between the EUA price and each variable is measured using a VECM estimation and an impulse response function. Finally, the relative effect of each variable on the EUA price is determined using a forecast error variance decomposition. The results show that the EUA price has a causal effect on the prices of electricity and natural gas. Second, all variables, except the minimum temperature, show a positive relationship with the EUA price. Furthermore, when unexpected shocks occur, the EUA price shows the highest response to its past price, followed by the electricity price. Third, the past EUA price has the most influence on the EUA price, followed by the coal price.

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Predicting European carbon emission price movements

Cited by 32 publications

References 36 publications

Operational Decision Model with Carbon Cap Allocation and Carbon Trading Price

Operational Decision Model with Carbon Cap Allocation and Carbon Trading Price

A Newsboy Model with Quick Response under Sustainable Carbon Cap-N-Trade

The Price Determinants of the EU Allowance in the EU Emissions Trading Scheme

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